From c61fa719c34116639de7f8837fa30c8e99d7281a Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sun, 15 Dec 2024 21:49:37 +0300
Subject: [PATCH 001/175] rqvae & k-core research

---
 .gitignore                                    |   2 +
 configs/train/sasrec_train_config.json        |   9 +-
 notebooks/AmazonBeautyDatasetStatistics.ipynb | 111 ++--
 src/main.ipynb                                | 588 ++++++++++++++++++
 src/main.py                                   |  46 ++
 src/rqvae.py                                  | 128 ++++
 todo.txt                                      |   7 +
 7 files changed, 830 insertions(+), 61 deletions(-)
 create mode 100644 src/main.ipynb
 create mode 100644 src/main.py
 create mode 100644 src/rqvae.py
 create mode 100644 todo.txt

diff --git a/.gitignore b/.gitignore
index 964fa39a..8b8e6f07 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,5 @@
 __pycache__
 data/*
 tensorboard_logs/*
+.venv
+papers
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 1336b91c..32fd1db1 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -2,7 +2,7 @@
   "experiment_name": "sasrec_test",
   "best_metric": "eval/ndcg@20",
   "dataset": {
-    "type": "sequence",
+    "type": "scientific",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
@@ -60,10 +60,9 @@
     "type": "composite",
     "losses": [
       {
-        "type": "sasrec",
-        "positive_prefix": "positive_embeddings",
-        "negative_prefix": "negative_embeddings",
-        "representation_prefix": "current_embeddings",
+        "type": "bpr",
+        "positive_prefix": "positive_scores",
+        "negative_prefix": "negative_scores",
         "output_prefix": "downstream_loss"
       }
     ],
diff --git a/notebooks/AmazonBeautyDatasetStatistics.ipynb b/notebooks/AmazonBeautyDatasetStatistics.ipynb
index a1a2c3d2..b59e40ac 100644
--- a/notebooks/AmazonBeautyDatasetStatistics.ipynb
+++ b/notebooks/AmazonBeautyDatasetStatistics.ipynb
@@ -174,21 +174,10 @@
    "execution_count": 6,
    "id": "f3182b59",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 1210271, (1210271,))"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df.user_id = pd.factorize(df.user_id)[0] + 1\n",
-    "df.user_id.min(), df.user_id.max(), df.user_id.unique().shape"
+    "# df.user_id = pd.factorize(df.user_id)[0] + 1\n",
+    "# df.user_id.min(), df.user_id.max(), df.user_id.unique().shape"
    ]
   },
   {
@@ -196,21 +185,10 @@
    "execution_count": 7,
    "id": "7225ddeb",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 249274, (249274,))"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df.item_id = pd.factorize(df.item_id)[0] + 1\n",
-    "df.item_id.min(), df.item_id.max(), df.item_id.unique().shape"
+    "# df.item_id = pd.factorize(df.item_id)[0] + 1\n",
+    "# df.item_id.min(), df.item_id.max(), df.item_id.unique().shape"
    ]
   },
   {
@@ -249,36 +227,36 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>A39HTATAQ9V7YF</td>\n",
+       "      <td>0205616461</td>\n",
        "      <td>5.0</td>\n",
        "      <td>1369699200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>A3JM6GV9MNOF9X</td>\n",
+       "      <td>0558925278</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1355443200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>A1Z513UWSAAO0F</td>\n",
+       "      <td>0558925278</td>\n",
        "      <td>5.0</td>\n",
        "      <td>1404691200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
+       "      <td>A1WMRR494NWEWV</td>\n",
+       "      <td>0733001998</td>\n",
        "      <td>4.0</td>\n",
        "      <td>1382572800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
+       "      <td>A3IAAVS479H7M7</td>\n",
+       "      <td>0737104473</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1274227200</td>\n",
        "    </tr>\n",
@@ -287,12 +265,12 @@
        "</div>"
       ],
       "text/plain": [
-       "   user_id  item_id  rating   timestamp\n",
-       "0        1        1     5.0  1369699200\n",
-       "1        2        2     3.0  1355443200\n",
-       "2        3        2     5.0  1404691200\n",
-       "3        4        3     4.0  1382572800\n",
-       "4        5        4     1.0  1274227200"
+       "          user_id     item_id  rating   timestamp\n",
+       "0  A39HTATAQ9V7YF  0205616461     5.0  1369699200\n",
+       "1  A3JM6GV9MNOF9X  0558925278     3.0  1355443200\n",
+       "2  A1Z513UWSAAO0F  0558925278     5.0  1404691200\n",
+       "3  A1WMRR494NWEWV  0733001998     4.0  1382572800\n",
+       "4  A3IAAVS479H7M7  0737104473     1.0  1274227200"
       ]
      },
      "execution_count": 8,
@@ -314,7 +292,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023070it [01:12, 28030.70it/s]"
+      "100%|██████████| 2023070/2023070 [01:14<00:00, 27172.73it/s]"
      ]
     },
     {
@@ -335,10 +313,10 @@
    "source": [
     "data = []\n",
     "\n",
-    "for _, row in tqdm(df.iterrows()):\n",
+    "for _, row in tqdm(df.iterrows(), total=len(df)):\n",
     "    data.append({\n",
-    "        'user_id': int(row.user_id),\n",
-    "        'item_id': int(row.item_id),\n",
+    "        'user_id': row.user_id,\n",
+    "        'item_id': row.item_id,\n",
     "        'timestamp': int(row.timestamp)\n",
     "    })\n",
     "\n",
@@ -355,7 +333,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████| 2023070/2023070 [00:04<00:00, 421809.17it/s]\n"
+      "100%|██████████| 2023070/2023070 [00:05<00:00, 375198.84it/s]\n"
      ]
     },
     {
@@ -432,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 18,
    "id": "dcc9f464",
    "metadata": {},
    "outputs": [
@@ -440,8 +418,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████| 1210271/1210271 [00:01<00:00, 840307.33it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████| 249274/249274 [00:00<00:00, 571256.27it/s]\n"
+      "100%|██████████| 1210271/1210271 [00:01<00:00, 738179.23it/s]\n",
+      "100%|██████████| 249274/249274 [00:00<00:00, 486768.45it/s]\n"
      ]
     }
    ],
@@ -494,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "id": "16df848f",
    "metadata": {},
    "outputs": [
@@ -520,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "id": "90f7ccc1",
    "metadata": {},
    "outputs": [],
@@ -532,11 +510,32 @@
     "        ]))\n",
     "        f.write('\\n')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "393b8b57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pickle import dump\n",
+    "\n",
+    "\n",
+    "dump(set(item_mapping.keys()), open('../data/Beauty/item_mapping.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2eb5b656",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -550,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/src/main.ipynb b/src/main.ipynb
new file mode 100644
index 00000000..996961e5
--- /dev/null
+++ b/src/main.ipynb
@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import json\n",
+    "import gzip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse(path):\n",
+    "    g = gzip.open(path, \"rb\")\n",
+    "    for l in g:\n",
+    "        yield eval(l)\n",
+    "\n",
+    "\n",
+    "def getDF(path):\n",
+    "    i = 0\n",
+    "    df = {}\n",
+    "    for d in parse(path):\n",
+    "        df[i] = d\n",
+    "        i += 1\n",
+    "    return pd.DataFrame.from_dict(df, orient=\"index\")\n",
+    "\n",
+    "\n",
+    "df = getDF(\"../data/meta_Beauty.json.gz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of unique items: 12101\n",
+      "Number of unique users: 22363\n"
+     ]
+    }
+   ],
+   "source": [
+    "file_name = \"../data/reviews_Beauty_5.json\"\n",
+    "\n",
+    "unique_items = set()\n",
+    "unique_users = set()\n",
+    "\n",
+    "with open(file_name, \"r\") as file:\n",
+    "    for line in file:\n",
+    "        review = json.loads(line.strip())\n",
+    "        unique_items.add(review[\"asin\"])\n",
+    "        unique_users.add(review[\"reviewerID\"])\n",
+    "\n",
+    "print(f\"Number of unique items: {len(unique_items)}\")\n",
+    "print(f\"Number of unique users: {len(unique_users)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12101"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[df['asin'].isin(unique_items)]\n",
+    "len(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
+    "import torch\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model_name = \"google-t5/t5-small\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess(row: pd.Series):\n",
+    "    row = row.fillna('empty') # unknown?\n",
+    "    # remove column description / title / cat?\n",
+    "    return f\"Description: {row['description']}. Title: {row['title']}. Categories: {', '.join(row['categories'][0])}\"\n",
+    "\n",
+    "\n",
+    "df[\"combined_text\"] = df.apply(preprocess, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_text(text):\n",
+    "    enc = tokenizer(text, return_tensors=\"pt\", truncation=True).to(device)\n",
+    "\n",
+    "    output = model.encoder(\n",
+    "        input_ids=enc[\"input_ids\"],\n",
+    "        attention_mask=enc[\"attention_mask\"],\n",
+    "        return_dict=True,\n",
+    "    )\n",
+    "\n",
+    "    embeddings = output.last_hidden_state.mean(\n",
+    "        dim=1\n",
+    "    ).squeeze()  # mean over all tokens (mb CLS?)\n",
+    "\n",
+    "    return embeddings.cpu().detach()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 12101/12101 [01:00<00:00, 199.89it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "tqdm.pandas()\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    df[\"embeddings\"] = df[\"combined_text\"].progress_apply(encode_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>asin</th>\n",
+       "      <th>description</th>\n",
+       "      <th>title</th>\n",
+       "      <th>imUrl</th>\n",
+       "      <th>salesRank</th>\n",
+       "      <th>categories</th>\n",
+       "      <th>price</th>\n",
+       "      <th>related</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>combined_text</th>\n",
+       "      <th>embeddings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>7806397051</td>\n",
+       "      <td>An extensive range of 15 multiple vibrant long...</td>\n",
+       "      <td>WAWO 15 Color Professionl Makeup Eyeshadow Cam...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/41Rn18Oe...</td>\n",
+       "      <td>{'Beauty': 10486}</td>\n",
+       "      <td>[[Beauty, Makeup, Face, Concealers &amp; Neutraliz...</td>\n",
+       "      <td>5.04</td>\n",
+       "      <td>{'also_bought': ['B00KR26VFE', 'B00E7LQHZ0', '...</td>\n",
+       "      <td>COKA</td>\n",
+       "      <td>Description: An extensive range of 15 multiple...</td>\n",
+       "      <td>[tensor(-0.0002), tensor(0.0026), tensor(0.008...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>9759091062</td>\n",
+       "      <td>Xtreme Brite  Brightening gel is a highly conc...</td>\n",
+       "      <td>Xtreme Brite Brightening Gel 1oz.</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/41QWW9v1...</td>\n",
+       "      <td>{'Beauty': 52254}</td>\n",
+       "      <td>[[Beauty, Hair Care, Styling Products, Creams,...</td>\n",
+       "      <td>19.99</td>\n",
+       "      <td>{'also_bought': ['B0054GLD1U', 'B003BRZCUC', '...</td>\n",
+       "      <td>Xtreme Brite</td>\n",
+       "      <td>Description: Xtreme Brite  Brightening gel is ...</td>\n",
+       "      <td>[tensor(0.0054), tensor(0.0238), tensor(-0.015...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>9788072216</td>\n",
+       "      <td>Prada Candy By Prada Eau De Parfum Spray 1.7 O...</td>\n",
+       "      <td>Prada Candy By Prada Eau De Parfum Spray 1.7 O...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/51iT2k6L...</td>\n",
+       "      <td>{'Beauty': 78916}</td>\n",
+       "      <td>[[Beauty, Fragrance, Women's, Eau de Parfum]]</td>\n",
+       "      <td>65.86</td>\n",
+       "      <td>{'also_bought': ['B006C5OHSI', 'B006P14842', '...</td>\n",
+       "      <td>Prada</td>\n",
+       "      <td>Description: Prada Candy By Prada Eau De Parfu...</td>\n",
+       "      <td>[tensor(-0.0383), tensor(0.0212), tensor(-0.01...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>555</th>\n",
+       "      <td>9790790961</td>\n",
+       "      <td>Versace Bright Crystal Perfume for Women 3 oz ...</td>\n",
+       "      <td>Versace Bright Crystal Eau de Toilette Spray f...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/418LYGLE...</td>\n",
+       "      <td>{'Beauty': 764}</td>\n",
+       "      <td>[[Beauty, Fragrance, Women's, Eau de Toilette]]</td>\n",
+       "      <td>52.33</td>\n",
+       "      <td>{'also_bought': ['B007P7OPQQ', 'B0017JT658', '...</td>\n",
+       "      <td>Versace</td>\n",
+       "      <td>Description: Versace Bright Crystal Perfume fo...</td>\n",
+       "      <td>[tensor(0.0284), tensor(0.0173), tensor(0.0334...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>9790794231</td>\n",
+       "      <td>STELLA For Women By STELLA MCCARTNEY 1.7 oz ED...</td>\n",
+       "      <td>Stella McCartney Stella</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/31L2n60J...</td>\n",
+       "      <td>{'Beauty': 142503}</td>\n",
+       "      <td>[[Beauty, Fragrance, Women's, Eau de Parfum]]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{'also_bought': ['B0019M21OQ', 'B000E7YM8K', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Description: STELLA For Women By STELLA MCCART...</td>\n",
+       "      <td>[tensor(0.0138), tensor(0.0021), tensor(0.0366...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           asin                                        description  \\\n",
+       "115  7806397051  An extensive range of 15 multiple vibrant long...   \n",
+       "179  9759091062  Xtreme Brite  Brightening gel is a highly conc...   \n",
+       "192  9788072216  Prada Candy By Prada Eau De Parfum Spray 1.7 O...   \n",
+       "555  9790790961  Versace Bright Crystal Perfume for Women 3 oz ...   \n",
+       "587  9790794231  STELLA For Women By STELLA MCCARTNEY 1.7 oz ED...   \n",
+       "\n",
+       "                                                 title  \\\n",
+       "115  WAWO 15 Color Professionl Makeup Eyeshadow Cam...   \n",
+       "179                  Xtreme Brite Brightening Gel 1oz.   \n",
+       "192  Prada Candy By Prada Eau De Parfum Spray 1.7 O...   \n",
+       "555  Versace Bright Crystal Eau de Toilette Spray f...   \n",
+       "587                            Stella McCartney Stella   \n",
+       "\n",
+       "                                                 imUrl           salesRank  \\\n",
+       "115  http://ecx.images-amazon.com/images/I/41Rn18Oe...   {'Beauty': 10486}   \n",
+       "179  http://ecx.images-amazon.com/images/I/41QWW9v1...   {'Beauty': 52254}   \n",
+       "192  http://ecx.images-amazon.com/images/I/51iT2k6L...   {'Beauty': 78916}   \n",
+       "555  http://ecx.images-amazon.com/images/I/418LYGLE...     {'Beauty': 764}   \n",
+       "587  http://ecx.images-amazon.com/images/I/31L2n60J...  {'Beauty': 142503}   \n",
+       "\n",
+       "                                            categories  price  \\\n",
+       "115  [[Beauty, Makeup, Face, Concealers & Neutraliz...   5.04   \n",
+       "179  [[Beauty, Hair Care, Styling Products, Creams,...  19.99   \n",
+       "192      [[Beauty, Fragrance, Women's, Eau de Parfum]]  65.86   \n",
+       "555    [[Beauty, Fragrance, Women's, Eau de Toilette]]  52.33   \n",
+       "587      [[Beauty, Fragrance, Women's, Eau de Parfum]]    NaN   \n",
+       "\n",
+       "                                               related         brand  \\\n",
+       "115  {'also_bought': ['B00KR26VFE', 'B00E7LQHZ0', '...          COKA   \n",
+       "179  {'also_bought': ['B0054GLD1U', 'B003BRZCUC', '...  Xtreme Brite   \n",
+       "192  {'also_bought': ['B006C5OHSI', 'B006P14842', '...         Prada   \n",
+       "555  {'also_bought': ['B007P7OPQQ', 'B0017JT658', '...       Versace   \n",
+       "587  {'also_bought': ['B0019M21OQ', 'B000E7YM8K', '...           NaN   \n",
+       "\n",
+       "                                         combined_text  \\\n",
+       "115  Description: An extensive range of 15 multiple...   \n",
+       "179  Description: Xtreme Brite  Brightening gel is ...   \n",
+       "192  Description: Prada Candy By Prada Eau De Parfu...   \n",
+       "555  Description: Versace Bright Crystal Perfume fo...   \n",
+       "587  Description: STELLA For Women By STELLA MCCART...   \n",
+       "\n",
+       "                                            embeddings  \n",
+       "115  [tensor(-0.0002), tensor(0.0026), tensor(0.008...  \n",
+       "179  [tensor(0.0054), tensor(0.0238), tensor(-0.015...  \n",
+       "192  [tensor(-0.0383), tensor(0.0212), tensor(-0.01...  \n",
+       "555  [tensor(0.0284), tensor(0.0173), tensor(0.0334...  \n",
+       "587  [tensor(0.0138), tensor(0.0021), tensor(0.0366...  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embs = torch.stack(df[\"embeddings\"].tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([12101, 512])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'loss': tensor(0.0125, device='cuda:0', grad_fn=<MeanBackward0>),\n",
+       " 'recon_loss': tensor(0.0051, device='cuda:0'),\n",
+       " 'rqvae_loss': tensor(0.0074, device='cuda:0'),\n",
+       " 'unique/0': 31,\n",
+       " 'unique/1': 79,\n",
+       " 'unique/2': 46,\n",
+       " 'unique/3': 102}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "from rqvae import RQVAE\n",
+    "\n",
+    "\n",
+    "rqvae = RQVAE(\n",
+    "    input_dim=embs.shape[1],\n",
+    "    hidden_dim=128,\n",
+    "    beta=0.25,\n",
+    "    codebook_sizes=[256] * 4,\n",
+    "    should_init_codebooks=False,\n",
+    "    should_reinit_unused_clusters=False,\n",
+    ").to(device)\n",
+    "\n",
+    "\n",
+    "embs = {\"embedding\": embs.to(device)}\n",
+    "\n",
+    "rqvae.forward(embs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_cb_tuples(embeddings):\n",
+    "    ind_lists = []\n",
+    "    for cb in rqvae.codebooks:\n",
+    "        dist = torch.cdist(rqvae.encoder(embeddings), cb)\n",
+    "        ind_lists.append(dist.argmin(dim=-1).cpu().numpy())\n",
+    "\n",
+    "    return zip(*ind_lists)\n",
+    "\n",
+    "\n",
+    "def search_similar_items(items_with_tuples, clust2search):\n",
+    "    random.shuffle(items_with_tuples)\n",
+    "    cnt = 0\n",
+    "    similars = []\n",
+    "    for item, clust_tuple in items_with_tuples:\n",
+    "        if clust_tuple[: len(clust2search)] == clust2search:\n",
+    "            similars.append((item, clust_tuple))\n",
+    "            cnt += 1\n",
+    "        if cnt >= 5:\n",
+    "            return similars\n",
+    "    return similars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cb_tuples = get_cb_tuples(embs[\"embedding\"])\n",
+    "items_with_tuples = list(zip(df[\"title\"], cb_tuples))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "104\n",
+      "item='Vasanti Cosmetics Brighten Up! Enzymatic Face Rejuvenator with Microderm Exfoliating Crystals - Treats Dull, Uneven Skintone' clust_tuple=(104, 249, 227, 132)\n",
+      "item='TEI SPA Oxyderm High Frequency Ozone Facial Tool' clust_tuple=(104, 249, 227, 136)\n",
+      "item='Jan Marini Benzoyl Peroxide 2.5%-8 oz' clust_tuple=(104, 142, 227, 132)\n",
+      "111\n",
+      "item='Babyliss Pro BABNT5548 2000 Watt Ionic Nano Titanium with Integrated Ion Generator Hair Dryer' clust_tuple=(111, 132, 227, 136)\n",
+      "112\n",
+      "item='10 Pcs Wearable Nail Soaker Acrylic Polish Remover Tool' clust_tuple=(112, 249, 227, 215)\n",
+      "item='IBD Just Gel JUPITER BLUE Soak Off Blue Green Nail Polish UV Manicure .5oz Salon' clust_tuple=(112, 249, 227, 136)\n",
+      "item='Ladies Beauty Box 6 Wheels Combo Set Nail Art Nailart Manicure Rhinestones Glitter Tips Deco + 2x Dotting Pen + Glue' clust_tuple=(112, 58, 227, 215)\n",
+      "item='Nail Soakers - 10pcs' clust_tuple=(112, 249, 227, 136)\n",
+      "item='IBD Just Gel GERBER DAISY Soak Off Pink Nail Polish UV Manicure Pedi .5 oz Salon' clust_tuple=(112, 249, 227, 136)\n",
+      "115\n",
+      "item='Nail Station' clust_tuple=(115, 249, 227, 136)\n",
+      "item=\"L'Oreal Paris EverStrong Thickening Shampoo, 8.5 Fluid Ounce\" clust_tuple=(115, 58, 227, 161)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(100, 120):\n",
+    "    sim = search_similar_items(items_with_tuples, (i,))\n",
+    "    if len(sim) == 0:\n",
+    "        continue\n",
+    "    print(i)\n",
+    "    for item, clust_tuple in sim:\n",
+    "        print(f\"{item=} {clust_tuple=}\")\n",
+    "        \n",
+    "# TODO fix collisisons (remainder = last embedding, auto-increment 4th id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1 2 3 0\n",
+    "# 1 2 3 1\n",
+    "# 4 5 6 0/2\n",
+    "# 4 5 6 1/3\n",
+    "\n",
+    "# Research last index aggregation\n",
+    "\n",
+    "# 1) last index = KMeans(last residuals, n=|last codebook|) - collision\n",
+    "# 2) auto increment last index (check paper)\n",
+    "# 3) decoder\n",
+    "# 4) [(1 2 3), (1 2 3)] single item -> ok\n",
+    "# 4.1) several -> get embeddings -> score. softmax(collisions), torch.logsoftmax(logits) -> score -> argmax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pos emb for item & codebook (000 111 222) - item\n",
+    "# codebook (012 012 012)\n",
+    "# splitting item ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(df, \"../data/df_with_embs.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 361M\n",
+      "-rw-r--r-- 1 peter peter  53M дек 15 16:23 df_with_embs.pt\n",
+      "-rw-r--r-- 1 peter peter 154K дек 12 23:55 item_mapping.pkl\n",
+      "-rw-r--r-- 1 peter peter  95M дек 11 22:19 meta_Beauty.json.gz\n",
+      "-rw-r--r-- 1 peter peter  79M дек 12 23:37 ratings_Beauty.csv\n",
+      "-rw-r--r-- 1 peter peter 135M дек 15 15:33 reviews_Beauty_5.json\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -lh ../data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/main.py b/src/main.py
new file mode 100644
index 00000000..21d47550
--- /dev/null
+++ b/src/main.py
@@ -0,0 +1,46 @@
+import torch
+import typing
+import random
+import os
+
+from rqvae import RQVAE
+
+device = torch.device("cuda")
+
+
+def get_cb_tuples(embeddings):
+    ind_lists = []
+    for cb in rqvae.codebooks:
+        dist = torch.cdist(rqvae.encoder(embeddings), cb)
+        ind_lists.append(dist.argmin(dim=-1).cpu().numpy())
+
+    return zip(*ind_lists)
+
+
+def search_similar_items(items_with_tuples):
+    random.shuffle(items_with_tuples)
+    clust2search = (585,)
+    cnt = 0
+    for item, clust_tuple in items_with_tuples:
+        if clust_tuple[: len(clust2search)] == clust2search:
+            print(item, clust_tuple)
+            cnt += 1
+        if cnt >= 5:
+            break
+
+
+# TODO: add T5 sentence construction from huggingface
+
+
+embs = {"embedding": []}
+
+rqvae = RQVAE(
+    input_dim=200,
+    hidden_dim=128,
+    beta=0.25,
+    codebook_sizes=[256] * 4,
+    should_init_codebooks=False,
+    should_reinit_unused_clusters=False,
+).to(device)
+
+rqvae.forward(embs)
diff --git a/src/rqvae.py b/src/rqvae.py
new file mode 100644
index 00000000..fb57a610
--- /dev/null
+++ b/src/rqvae.py
@@ -0,0 +1,128 @@
+import torch
+
+from sklearn.cluster import KMeans
+
+class RQVAE(torch.nn.Module):
+    def __init__(
+            self,
+            input_dim: int,
+            hidden_dim: int,
+            beta: float,
+            codebook_sizes: list[int],
+            should_init_codebooks=False,
+            should_reinit_unused_clusters=False
+        ):
+        super().__init__()
+
+        # In original paper it is set to 0.25
+        self.register_buffer('beta', torch.tensor(beta))  
+
+        # Kmeans initialization
+        self.should_init_codebooks = should_init_codebooks
+
+        # Trick with re-initing empty clusters
+        self.should_reinit_unused_clusters = should_reinit_unused_clusters 
+
+        self.mse_loss = torch.nn.MSELoss()
+
+        # Enc and dec are mirrored copies of each other
+        self.encoder = self.make_encoding_tower(input_dim, hidden_dim)
+        self.decoder = self.make_encoding_tower(hidden_dim, input_dim)
+
+        # Default initialization of codebook
+        self.codebooks = torch.nn.ParameterList()
+        for codebook_size in codebook_sizes:
+            cb = torch.FloatTensor(codebook_size, hidden_dim)
+            with torch.no_grad():
+                torch.nn.init.trunc_normal_(cb, std=0.02, a=-2 * 0.02, b=2 * 0.02)
+            self.codebooks.append(cb)
+
+    def make_encoding_tower(self, d1: int, d2: int):
+        return torch.nn.Linear(d1, d2, bias=False)
+    
+    # Get closest index for given embedding
+    @staticmethod
+    def get_codebook_indices(remainder, codebook):
+        dist = torch.cdist(remainder, codebook)
+        return dist.argmin(dim=-1)
+
+    # Recursive k-means initialization
+    @staticmethod
+    def kmeans(embeddings, num_clusters, num_steps=300):
+        # Just dummy kmeans implementation to get some better initial point
+        embeddings = torch.nn.functional.normalize(embeddings, dim=-1) # ???
+        closest_cluster = torch.randint(0, num_clusters, (embeddings.shape[0], ), device=embeddings.device)
+        cluster_centers = torch.zeros((num_clusters, embeddings.shape[1]), device=embeddings.device)
+        for clust_ind in range(num_clusters):
+            print(clust_ind, (closest_cluster == clust_ind).sum())
+            cluster_centers[clust_ind] = embeddings[closest_cluster == clust_ind].mean(dim=0)
+
+        for iter in range(num_steps):
+            dist = torch.cdist(embeddings, cluster_centers)
+            closest_cluster = dist.argmin(dim=-1)
+            print('Kmeans iter:', iter, closest_cluster.shape)
+            for clust_ind in range(num_clusters):
+                if clust_ind == 0:
+                    print(clust_ind, (closest_cluster == clust_ind).sum())
+                cluster_centers[clust_ind] = embeddings[closest_cluster == clust_ind].mean(dim=0)
+
+        return cluster_centers
+
+    def init_codebooks(self, embeddings):
+        with torch.no_grad():
+            remainder = self.encoder(embeddings)
+            for codebook in self.codebooks:
+                codebook.data = self.kmeans(embeddings=remainder, num_clusters=codebook.shape[0])
+                codebook_indices = self.get_codebook_indices(remainder, codebook)
+                codebook_vectors = codebook[codebook_indices]
+                remainder = remainder - codebook_vectors
+
+    @staticmethod
+    def reinit_unused_clusters(remainder, codebook, codebook_indices):
+        with torch.no_grad():
+            is_used = torch.full((codebook.shape[0], ), False, device=codebook.device)
+            unique_indices = codebook_indices.unique()
+            is_used[unique_indices] = True
+            rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(), ))
+            codebook[~is_used] = remainder[rand_input]
+
+    def forward(self, inputs):
+        embeddings = inputs['embedding']
+        if self.should_init_codebooks:
+            self.init_codebooks(embeddings)
+            self.should_init_codebooks = False
+        latent_vector = self.encoder(embeddings)
+
+        latent_restored = 0
+        rqvae_loss = 0
+        num_unique_clusters = []
+        remainder = latent_vector
+        for codebook in self.codebooks:
+            codebook_indices = self.get_codebook_indices(remainder, codebook)
+            codebook_vectors = codebook[codebook_indices]
+
+            if self.should_reinit_unused_clusters:
+                self.reinit_unused_clusters(remainder, codebook, codebook_indices)
+
+            num_unique_clusters.append(codebook_indices.unique().shape[0])
+            rqvae_loss += self.beta * self.mse_loss(remainder, codebook_vectors.detach())
+            rqvae_loss += self.mse_loss(codebook_vectors, remainder.detach())
+
+            latent_restored = latent_restored + codebook_vectors
+            remainder = remainder - codebook_vectors
+
+        # Here we cast recon loss to latent vector
+        latent_restored = latent_vector + (latent_restored - latent_vector).detach()
+        embeddings_restored = self.decoder(latent_restored)
+        recon_loss = self.mse_loss(embeddings_restored, embeddings)
+        loss = (recon_loss + rqvae_loss).mean()
+
+        return {
+            'loss': loss,
+            'recon_loss': recon_loss.mean().detach(),
+            'rqvae_loss': rqvae_loss.mean().detach(),
+            **{
+                f'unique/{i}': cnt
+                for i, cnt in enumerate(num_unique_clusters)
+            }
+        }
\ No newline at end of file
diff --git a/todo.txt b/todo.txt
new file mode 100644
index 00000000..1b0975f2
--- /dev/null
+++ b/todo.txt
@@ -0,0 +1,7 @@
+1) no biases on leave one out strategy (обрезаем по строго временному порогу)
+2) data: https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html#:~:text=image%20features-,Beauty,-reviews%20(2%2C023%2C070%20reviews
+
+
+user_id & cb_ids -> repr
+last 'seq' prediction
+dataloader (semantic ids lens)
\ No newline at end of file

From 173944c3729a1bd75f489dbfc3ce0c921e0c8c30 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sun, 15 Dec 2024 22:00:34 +0300
Subject: [PATCH 002/175] setup python env for dev

---
 src/main.ipynb | 308 ++++---------------------------------------------
 1 file changed, 21 insertions(+), 287 deletions(-)

diff --git a/src/main.ipynb b/src/main.ipynb
index 996961e5..1f212eed 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -19,8 +19,8 @@
    "source": [
     "def parse(path):\n",
     "    g = gzip.open(path, \"rb\")\n",
-    "    for l in g:\n",
-    "        yield eval(l)\n",
+    "    for line in g:\n",
+    "        yield eval(line)\n",
     "\n",
     "\n",
     "def getDF(path):\n",
@@ -37,18 +37,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of unique items: 12101\n",
-      "Number of unique users: 22363\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "file_name = \"../data/reviews_Beauty_5.json\"\n",
     "\n",
@@ -67,22 +58,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12101"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "df = df[df['asin'].isin(unique_items)]\n",
+    "df = df[df[\"asin\"].isin(unique_items)]\n",
     "len(df)"
    ]
   },
@@ -109,7 +89,7 @@
    "outputs": [],
    "source": [
     "def preprocess(row: pd.Series):\n",
-    "    row = row.fillna('empty') # unknown?\n",
+    "    row = row.fillna(\"unknown\")  # empty?\n",
     "    # remove column description / title / cat?\n",
     "    return f\"Description: {row['description']}. Title: {row['title']}. Categories: {', '.join(row['categories'][0])}\"\n",
     "\n",
@@ -141,17 +121,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 12101/12101 [01:00<00:00, 199.89it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from tqdm import tqdm\n",
     "\n",
@@ -163,174 +135,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>asin</th>\n",
-       "      <th>description</th>\n",
-       "      <th>title</th>\n",
-       "      <th>imUrl</th>\n",
-       "      <th>salesRank</th>\n",
-       "      <th>categories</th>\n",
-       "      <th>price</th>\n",
-       "      <th>related</th>\n",
-       "      <th>brand</th>\n",
-       "      <th>combined_text</th>\n",
-       "      <th>embeddings</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>115</th>\n",
-       "      <td>7806397051</td>\n",
-       "      <td>An extensive range of 15 multiple vibrant long...</td>\n",
-       "      <td>WAWO 15 Color Professionl Makeup Eyeshadow Cam...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/41Rn18Oe...</td>\n",
-       "      <td>{'Beauty': 10486}</td>\n",
-       "      <td>[[Beauty, Makeup, Face, Concealers &amp; Neutraliz...</td>\n",
-       "      <td>5.04</td>\n",
-       "      <td>{'also_bought': ['B00KR26VFE', 'B00E7LQHZ0', '...</td>\n",
-       "      <td>COKA</td>\n",
-       "      <td>Description: An extensive range of 15 multiple...</td>\n",
-       "      <td>[tensor(-0.0002), tensor(0.0026), tensor(0.008...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>179</th>\n",
-       "      <td>9759091062</td>\n",
-       "      <td>Xtreme Brite  Brightening gel is a highly conc...</td>\n",
-       "      <td>Xtreme Brite Brightening Gel 1oz.</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/41QWW9v1...</td>\n",
-       "      <td>{'Beauty': 52254}</td>\n",
-       "      <td>[[Beauty, Hair Care, Styling Products, Creams,...</td>\n",
-       "      <td>19.99</td>\n",
-       "      <td>{'also_bought': ['B0054GLD1U', 'B003BRZCUC', '...</td>\n",
-       "      <td>Xtreme Brite</td>\n",
-       "      <td>Description: Xtreme Brite  Brightening gel is ...</td>\n",
-       "      <td>[tensor(0.0054), tensor(0.0238), tensor(-0.015...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>192</th>\n",
-       "      <td>9788072216</td>\n",
-       "      <td>Prada Candy By Prada Eau De Parfum Spray 1.7 O...</td>\n",
-       "      <td>Prada Candy By Prada Eau De Parfum Spray 1.7 O...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/51iT2k6L...</td>\n",
-       "      <td>{'Beauty': 78916}</td>\n",
-       "      <td>[[Beauty, Fragrance, Women's, Eau de Parfum]]</td>\n",
-       "      <td>65.86</td>\n",
-       "      <td>{'also_bought': ['B006C5OHSI', 'B006P14842', '...</td>\n",
-       "      <td>Prada</td>\n",
-       "      <td>Description: Prada Candy By Prada Eau De Parfu...</td>\n",
-       "      <td>[tensor(-0.0383), tensor(0.0212), tensor(-0.01...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>555</th>\n",
-       "      <td>9790790961</td>\n",
-       "      <td>Versace Bright Crystal Perfume for Women 3 oz ...</td>\n",
-       "      <td>Versace Bright Crystal Eau de Toilette Spray f...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/418LYGLE...</td>\n",
-       "      <td>{'Beauty': 764}</td>\n",
-       "      <td>[[Beauty, Fragrance, Women's, Eau de Toilette]]</td>\n",
-       "      <td>52.33</td>\n",
-       "      <td>{'also_bought': ['B007P7OPQQ', 'B0017JT658', '...</td>\n",
-       "      <td>Versace</td>\n",
-       "      <td>Description: Versace Bright Crystal Perfume fo...</td>\n",
-       "      <td>[tensor(0.0284), tensor(0.0173), tensor(0.0334...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>587</th>\n",
-       "      <td>9790794231</td>\n",
-       "      <td>STELLA For Women By STELLA MCCARTNEY 1.7 oz ED...</td>\n",
-       "      <td>Stella McCartney Stella</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/31L2n60J...</td>\n",
-       "      <td>{'Beauty': 142503}</td>\n",
-       "      <td>[[Beauty, Fragrance, Women's, Eau de Parfum]]</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>{'also_bought': ['B0019M21OQ', 'B000E7YM8K', '...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Description: STELLA For Women By STELLA MCCART...</td>\n",
-       "      <td>[tensor(0.0138), tensor(0.0021), tensor(0.0366...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           asin                                        description  \\\n",
-       "115  7806397051  An extensive range of 15 multiple vibrant long...   \n",
-       "179  9759091062  Xtreme Brite  Brightening gel is a highly conc...   \n",
-       "192  9788072216  Prada Candy By Prada Eau De Parfum Spray 1.7 O...   \n",
-       "555  9790790961  Versace Bright Crystal Perfume for Women 3 oz ...   \n",
-       "587  9790794231  STELLA For Women By STELLA MCCARTNEY 1.7 oz ED...   \n",
-       "\n",
-       "                                                 title  \\\n",
-       "115  WAWO 15 Color Professionl Makeup Eyeshadow Cam...   \n",
-       "179                  Xtreme Brite Brightening Gel 1oz.   \n",
-       "192  Prada Candy By Prada Eau De Parfum Spray 1.7 O...   \n",
-       "555  Versace Bright Crystal Eau de Toilette Spray f...   \n",
-       "587                            Stella McCartney Stella   \n",
-       "\n",
-       "                                                 imUrl           salesRank  \\\n",
-       "115  http://ecx.images-amazon.com/images/I/41Rn18Oe...   {'Beauty': 10486}   \n",
-       "179  http://ecx.images-amazon.com/images/I/41QWW9v1...   {'Beauty': 52254}   \n",
-       "192  http://ecx.images-amazon.com/images/I/51iT2k6L...   {'Beauty': 78916}   \n",
-       "555  http://ecx.images-amazon.com/images/I/418LYGLE...     {'Beauty': 764}   \n",
-       "587  http://ecx.images-amazon.com/images/I/31L2n60J...  {'Beauty': 142503}   \n",
-       "\n",
-       "                                            categories  price  \\\n",
-       "115  [[Beauty, Makeup, Face, Concealers & Neutraliz...   5.04   \n",
-       "179  [[Beauty, Hair Care, Styling Products, Creams,...  19.99   \n",
-       "192      [[Beauty, Fragrance, Women's, Eau de Parfum]]  65.86   \n",
-       "555    [[Beauty, Fragrance, Women's, Eau de Toilette]]  52.33   \n",
-       "587      [[Beauty, Fragrance, Women's, Eau de Parfum]]    NaN   \n",
-       "\n",
-       "                                               related         brand  \\\n",
-       "115  {'also_bought': ['B00KR26VFE', 'B00E7LQHZ0', '...          COKA   \n",
-       "179  {'also_bought': ['B0054GLD1U', 'B003BRZCUC', '...  Xtreme Brite   \n",
-       "192  {'also_bought': ['B006C5OHSI', 'B006P14842', '...         Prada   \n",
-       "555  {'also_bought': ['B007P7OPQQ', 'B0017JT658', '...       Versace   \n",
-       "587  {'also_bought': ['B0019M21OQ', 'B000E7YM8K', '...           NaN   \n",
-       "\n",
-       "                                         combined_text  \\\n",
-       "115  Description: An extensive range of 15 multiple...   \n",
-       "179  Description: Xtreme Brite  Brightening gel is ...   \n",
-       "192  Description: Prada Candy By Prada Eau De Parfu...   \n",
-       "555  Description: Versace Bright Crystal Perfume fo...   \n",
-       "587  Description: STELLA For Women By STELLA MCCART...   \n",
-       "\n",
-       "                                            embeddings  \n",
-       "115  [tensor(-0.0002), tensor(0.0026), tensor(0.008...  \n",
-       "179  [tensor(0.0054), tensor(0.0238), tensor(-0.015...  \n",
-       "192  [tensor(-0.0383), tensor(0.0212), tensor(-0.01...  \n",
-       "555  [tensor(0.0284), tensor(0.0173), tensor(0.0334...  \n",
-       "587  [tensor(0.0138), tensor(0.0021), tensor(0.0366...  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
@@ -346,46 +153,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([12101, 512])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "embs.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'loss': tensor(0.0125, device='cuda:0', grad_fn=<MeanBackward0>),\n",
-       " 'recon_loss': tensor(0.0051, device='cuda:0'),\n",
-       " 'rqvae_loss': tensor(0.0074, device='cuda:0'),\n",
-       " 'unique/0': 31,\n",
-       " 'unique/1': 79,\n",
-       " 'unique/2': 46,\n",
-       " 'unique/3': 102}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import random\n",
     "\n",
@@ -449,29 +228,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "104\n",
-      "item='Vasanti Cosmetics Brighten Up! Enzymatic Face Rejuvenator with Microderm Exfoliating Crystals - Treats Dull, Uneven Skintone' clust_tuple=(104, 249, 227, 132)\n",
-      "item='TEI SPA Oxyderm High Frequency Ozone Facial Tool' clust_tuple=(104, 249, 227, 136)\n",
-      "item='Jan Marini Benzoyl Peroxide 2.5%-8 oz' clust_tuple=(104, 142, 227, 132)\n",
-      "111\n",
-      "item='Babyliss Pro BABNT5548 2000 Watt Ionic Nano Titanium with Integrated Ion Generator Hair Dryer' clust_tuple=(111, 132, 227, 136)\n",
-      "112\n",
-      "item='10 Pcs Wearable Nail Soaker Acrylic Polish Remover Tool' clust_tuple=(112, 249, 227, 215)\n",
-      "item='IBD Just Gel JUPITER BLUE Soak Off Blue Green Nail Polish UV Manicure .5oz Salon' clust_tuple=(112, 249, 227, 136)\n",
-      "item='Ladies Beauty Box 6 Wheels Combo Set Nail Art Nailart Manicure Rhinestones Glitter Tips Deco + 2x Dotting Pen + Glue' clust_tuple=(112, 58, 227, 215)\n",
-      "item='Nail Soakers - 10pcs' clust_tuple=(112, 249, 227, 136)\n",
-      "item='IBD Just Gel GERBER DAISY Soak Off Pink Nail Polish UV Manicure Pedi .5 oz Salon' clust_tuple=(112, 249, 227, 136)\n",
-      "115\n",
-      "item='Nail Station' clust_tuple=(115, 249, 227, 136)\n",
-      "item=\"L'Oreal Paris EverStrong Thickening Shampoo, 8.5 Fluid Ounce\" clust_tuple=(115, 58, 227, 161)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for i in range(100, 120):\n",
     "    sim = search_similar_items(items_with_tuples, (i,))\n",
@@ -480,7 +237,7 @@
     "    print(i)\n",
     "    for item, clust_tuple in sim:\n",
     "        print(f\"{item=} {clust_tuple=}\")\n",
-    "        \n",
+    "\n",
     "# TODO fix collisisons (remainder = last embedding, auto-increment 4th id)"
    ]
   },
@@ -526,32 +283,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total 361M\n",
-      "-rw-r--r-- 1 peter peter  53M дек 15 16:23 df_with_embs.pt\n",
-      "-rw-r--r-- 1 peter peter 154K дек 12 23:55 item_mapping.pkl\n",
-      "-rw-r--r-- 1 peter peter  95M дек 11 22:19 meta_Beauty.json.gz\n",
-      "-rw-r--r-- 1 peter peter  79M дек 12 23:37 ratings_Beauty.csv\n",
-      "-rw-r--r-- 1 peter peter 135M дек 15 15:33 reviews_Beauty_5.json\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
-      "To disable this warning, you can either:\n",
-      "\t- Avoid using `tokenizers` before the fork if possible\n",
-      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!ls -lh ../data"
    ]
@@ -580,7 +314,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,

From 7e11d9a864f9c4e16cf40553d7acbf563d6f91c9 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Wed, 18 Dec 2024 20:26:03 +0300
Subject: [PATCH 003/175] use faiss as init codebook

---
 src/main.ipynb    | 332 +++++++++++++++++++++++++---------------------
 src/rqvae.py      |  83 +++++-------
 src/rqvae_data.py |  79 +++++++++++
 3 files changed, 296 insertions(+), 198 deletions(-)
 create mode 100644 src/rqvae_data.py

diff --git a/src/main.ipynb b/src/main.ipynb
index 1f212eed..7203227e 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -4,147 +4,27 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import json\n",
-    "import gzip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse(path):\n",
-    "    g = gzip.open(path, \"rb\")\n",
-    "    for line in g:\n",
-    "        yield eval(line)\n",
-    "\n",
-    "\n",
-    "def getDF(path):\n",
-    "    i = 0\n",
-    "    df = {}\n",
-    "    for d in parse(path):\n",
-    "        df[i] = d\n",
-    "        i += 1\n",
-    "    return pd.DataFrame.from_dict(df, orient=\"index\")\n",
-    "\n",
-    "\n",
-    "df = getDF(\"../data/meta_Beauty.json.gz\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file_name = \"../data/reviews_Beauty_5.json\"\n",
-    "\n",
-    "unique_items = set()\n",
-    "unique_users = set()\n",
-    "\n",
-    "with open(file_name, \"r\") as file:\n",
-    "    for line in file:\n",
-    "        review = json.loads(line.strip())\n",
-    "        unique_items.add(review[\"asin\"])\n",
-    "        unique_users.add(review[\"reviewerID\"])\n",
-    "\n",
-    "print(f\"Number of unique items: {len(unique_items)}\")\n",
-    "print(f\"Number of unique users: {len(unique_users)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df[df[\"asin\"].isin(unique_items)]\n",
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/peter/university/diploma/GSRec/src/rqvae_data.py:77: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  df = torch.load(\"../data/df_with_embs.pt\")\n"
+     ]
+    }
+   ],
    "source": [
-    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
     "import torch\n",
     "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "model_name = \"google-t5/t5-small\"\n",
-    "\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def preprocess(row: pd.Series):\n",
-    "    row = row.fillna(\"unknown\")  # empty?\n",
-    "    # remove column description / title / cat?\n",
-    "    return f\"Description: {row['description']}. Title: {row['title']}. Categories: {', '.join(row['categories'][0])}\"\n",
-    "\n",
-    "\n",
-    "df[\"combined_text\"] = df.apply(preprocess, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def encode_text(text):\n",
-    "    enc = tokenizer(text, return_tensors=\"pt\", truncation=True).to(device)\n",
-    "\n",
-    "    output = model.encoder(\n",
-    "        input_ids=enc[\"input_ids\"],\n",
-    "        attention_mask=enc[\"attention_mask\"],\n",
-    "        return_dict=True,\n",
-    "    )\n",
-    "\n",
-    "    embeddings = output.last_hidden_state.mean(\n",
-    "        dim=1\n",
-    "    ).squeeze()  # mean over all tokens (mb CLS?)\n",
-    "\n",
-    "    return embeddings.cpu().detach()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "tqdm.pandas()\n",
+    "from rqvae_data import get_data\n",
     "\n",
-    "with torch.no_grad():\n",
-    "    df[\"embeddings\"] = df[\"combined_text\"].progress_apply(encode_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.head()"
+    "df = get_data()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,42 +33,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([12101, 512])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "embs.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4/4 [00:08<00:00,  2.07s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'loss': tensor(0.0057, device='cuda:0', grad_fn=<MeanBackward0>),\n",
+       " 'recon_loss': tensor(0.0052, device='cuda:0'),\n",
+       " 'rqvae_loss': tensor(0.0005, device='cuda:0'),\n",
+       " 'unique/0': 256,\n",
+       " 'unique/1': 256,\n",
+       " 'unique/2': 256,\n",
+       " 'unique/3': 256}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import random\n",
     "\n",
     "from rqvae import RQVAE\n",
     "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
     "\n",
     "rqvae = RQVAE(\n",
     "    input_dim=embs.shape[1],\n",
     "    hidden_dim=128,\n",
     "    beta=0.25,\n",
     "    codebook_sizes=[256] * 4,\n",
-    "    should_init_codebooks=False,\n",
+    "    should_init_codebooks=True,\n",
     "    should_reinit_unused_clusters=False,\n",
     ").to(device)\n",
     "\n",
     "\n",
-    "embs = {\"embedding\": embs.to(device)}\n",
+    "embs_dict = {\"embedding\": embs.to(device)}\n",
     "\n",
-    "rqvae.forward(embs)"
+    "rqvae.forward(embs_dict)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,7 +118,7 @@
     "    return zip(*ind_lists)\n",
     "\n",
     "\n",
-    "def search_similar_items(items_with_tuples, clust2search):\n",
+    "def search_similar_items(items_with_tuples, clust2search, max_cnt=5):\n",
     "    random.shuffle(items_with_tuples)\n",
     "    cnt = 0\n",
     "    similars = []\n",
@@ -209,29 +126,146 @@
     "        if clust_tuple[: len(clust2search)] == clust2search:\n",
     "            similars.append((item, clust_tuple))\n",
     "            cnt += 1\n",
-    "        if cnt >= 5:\n",
+    "        if cnt >= max_cnt:\n",
     "            return similars\n",
     "    return similars"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cb_tuples = get_cb_tuples(embs[\"embedding\"])\n",
+    "cb_tuples = get_cb_tuples(embs_dict[\"embedding\"])\n",
     "items_with_tuples = list(zip(df[\"title\"], cb_tuples))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "220\n",
+      "item='Fairy Dust by Paris Hilton for Women - 3.4 Ounce EDP Spray' clust_tuple=(220, 212, 67, 88)\n",
+      "item='D &amp; G Light Blue By Dolce &amp; Gabbana For Men Eau De Toilette Spray, 4.2-Ounces' clust_tuple=(220, 212, 25, 49)\n",
+      "item='Halle Pure Orchid by Halle Berry Eau De Parfum Spray for Women, 1 Ounce' clust_tuple=(220, 212, 25, 88)\n",
+      "item='Armani Code By Giorgio Armani For Men. Eau De Toilette Spray 1.7 Ounces' clust_tuple=(220, 212, 25, 88)\n",
+      "item='Taj Sunset by Escada for Women, Eau de Toilette Spray, 3.4 Ounce' clust_tuple=(220, 212, 25, 200)\n",
+      "item='Tom Ford Black Orchid By Tom Ford For Women. Eau De Parfum Spray 3.4-Ounces' clust_tuple=(220, 212, 67, 88)\n",
+      "item='Beyonce Heat Rush by Beyonce, 3.4 Ounce' clust_tuple=(220, 62, 67, 200)\n",
+      "item='In Control Curious by Britney Spears for Women, Eau De Parfum Spray, 1.7 Ounce' clust_tuple=(220, 212, 25, 49)\n",
+      "item='Lacoste Style In Play By Lacoste For Men. Eau De Toilette Spray 1.6 Ounces' clust_tuple=(220, 212, 25, 88)\n",
+      "item='Very Irresistible Sensual By Givenchy For Women. Eau De Parfum Spray 1.7 Ounces' clust_tuple=(220, 212, 25, 88)\n",
+      "221\n",
+      "item='HD High-Definition Super Palettes (Super Palette- Warm)' clust_tuple=(221, 196, 121, 88)\n",
+      "item='Coastal Scents Go Makeup Palette, Moscow, 0.28 Oz' clust_tuple=(221, 62, 121, 10)\n",
+      "item='Coastal Scents Go Makeup Palette, Cairo, 0.28 Ounce' clust_tuple=(221, 196, 121, 10)\n",
+      "item='Coastal Scents Go Makeup Palette, London, 0.28 Oz' clust_tuple=(221, 196, 8, 10)\n",
+      "item='TEMPTU AIRbrush Makeup System 2.0 and Signature Starter Kit, Fair' clust_tuple=(221, 214, 195, 10)\n",
+      "item='Urban Decay Deluxe Shadow Box' clust_tuple=(221, 214, 121, 10)\n",
+      "item='Coastal Scents 88  Palette, Ultra Shimmer' clust_tuple=(221, 209, 195, 208)\n",
+      "item='Coastal Scents Go Makeup Palette, Sydney, 0.28 Ounce' clust_tuple=(221, 196, 121, 10)\n",
+      "item='NICKA K LIPSTICK WITH VITAMIN E MOODY BLACK #992' clust_tuple=(221, 196, 205, 163)\n",
+      "item='SHANY Glamour Girl Makeup Kit - 48 Eyeshadow / 4 Blush /2 Powder' clust_tuple=(221, 196, 121, 163)\n",
+      "222\n",
+      "item='Maybelline New York Instant Age Rewind Eraser Treatment Makeup, Medium Beige 300, 0.68 Fluid Ounce' clust_tuple=(222, 114, 196, 88)\n",
+      "item='Garnier  Ultra-Lift 2-in-1 Wrinkle Reducer Serum and Moisturizer for Wrinkles and Firming, 1.7 Fluid Ounce' clust_tuple=(222, 214, 25, 88)\n",
+      "item='Hydrating with Peptides - A Renewal Complex - 1oz/30ml - A Professionally Formulated Combination of Unique Patented Peptides ArgirelineTM, Matrixyl&reg; 3000, and Syn&reg;-coll, Synergistically Work to Reduce Fine Lines and Wrinkles' clust_tuple=(222, 214, 25, 88)\n",
+      "item='Prevage MD Anti-Aging Treatment 30ml 1 Fluid Ounce' clust_tuple=(222, 196, 25, 88)\n",
+      "item=\"L'Oreal Paris Visible Lift Serum Absolute Advanced Age-Reversing Makeup, Nude Beige, 1.0 Ounces\" clust_tuple=(222, 196, 25, 88)\n",
+      "item='Keys Solar Rx Broad Spectrum SPF 30 Sunblock 3.4oz lotion by Keys Care' clust_tuple=(222, 112, 196, 88)\n",
+      "item='Eclos Restorative Eye Cream, 0.5-Ounce' clust_tuple=(222, 62, 25, 88)\n",
+      "item='New Mary Kay TimeWise Repair Volu-Firm 5 Product Set Adv Skin Care Full Size (Large)' clust_tuple=(222, 62, 25, 88)\n",
+      "item='LifeCell All In One Anti-Aging Treatment 2.54 oz' clust_tuple=(222, 62, 8, 88)\n",
+      "item='Bare Escentuals Active Cell Renewal Night Serum 1oz./30ml' clust_tuple=(222, 7, 25, 88)\n",
+      "223\n",
+      "item=\"OPI Euro Centrale Collection 2013, You're Such A Budapest\" clust_tuple=(223, 116, 110, 163)\n",
+      "item='Opi San Francisco Collection Fall &amp; Winter 2013 Dinng Al Frisco' clust_tuple=(223, 116, 110, 49)\n",
+      "item='OPI The Amazing New Spider-Man Collection, My Boyfriend Scales Walls' clust_tuple=(223, 116, 110, 208)\n",
+      "item='Gelish - The Shadows Collection - The Perfect Silhouette # 01460' clust_tuple=(223, 116, 25, 208)\n",
+      "item='Gelish Holiday Collection - Just What I Wanted #01551 - 0.5oz' clust_tuple=(223, 116, 25, 208)\n",
+      "item='OPI Sheer Tints, Sheer Mini Hint of Tints' clust_tuple=(223, 110, 110, 208)\n",
+      "item='Cover Girl Invisible Cream Concealer, Tawny 185 -1 Ea' clust_tuple=(223, 116, 25, 208)\n",
+      "item='China Glaze Velvet Bow 1017/80517' clust_tuple=(223, 116, 25, 208)\n",
+      "item='Revlon Luxurious Color Satin Eye Shadow, Nude Slip, 0.08 Ounce' clust_tuple=(223, 116, 25, 208)\n",
+      "item=\"Harmony Gelish I'm Brighter Than You - #01559\" clust_tuple=(223, 110, 25, 49)\n",
+      "224\n",
+      "item='Paris Hilton Tease Women Eau De Parfum Spray, 1 Ounce' clust_tuple=(224, 214, 224, 163)\n",
+      "item='Pure Dkny Eau De Parfum Spray by Donna Karan, 3.4 Ounce' clust_tuple=(224, 110, 8, 163)\n",
+      "item='MARC JACOBS DAISY by Marc Jacobs EDT SPRAY 3.4 OZ' clust_tuple=(224, 214, 110, 10)\n",
+      "item='Fresh Eau de Parfum, Sugar Lemon, 1 oz' clust_tuple=(224, 110, 110, 163)\n",
+      "item='Calvin Klein One Shock for Him Men Eau De Toilette Spray, 3.4 Ounce' clust_tuple=(224, 110, 229, 163)\n",
+      "item='DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ' clust_tuple=(224, 110, 8, 163)\n",
+      "item='Guess By Parlux Fragrances For Women. Eau De Parfum Spray 2.5 Oz' clust_tuple=(224, 62, 8, 163)\n",
+      "item='Jimmy Choo Flash Eau de Parfum Spray for Women, 2 Ounce' clust_tuple=(224, 110, 25, 163)\n",
+      "item='Diesel Fuel For Life by Diesel For Men. Eau De Toilette Spray 1.7-Ounces' clust_tuple=(224, 110, 8, 163)\n",
+      "item='Millesime Imperial By Creed For Men. Millesime Spray 4.0-Ounce Bottle' clust_tuple=(224, 110, 8, 163)\n",
+      "225\n",
+      "item='TIGI Bed Head Manipulator Styling Cream 2.0 oz.' clust_tuple=(225, 110, 110, 208)\n",
+      "item='DEVA CURL Flexible Hold Hair Spray 10oz' clust_tuple=(225, 224, 8, 200)\n",
+      "item=\"Givenchy Phenomen'Eyes Mascara 1 Phenomen'Black 0.24 oz\" clust_tuple=(225, 214, 110, 208)\n",
+      "item='Macadamia Natural Oil Oil Infused Brush Set' clust_tuple=(225, 196, 25, 208)\n",
+      "item='Neutrogena Clean Replenishing Conditioner, 10.1 Ounce' clust_tuple=(225, 110, 25, 10)\n",
+      "item='Pump for 16 oz. Jar' clust_tuple=(225, 110, 110, 215)\n",
+      "item='China Glaze: Pink Voltage, 0.5 oz' clust_tuple=(225, 110, 151, 49)\n",
+      "item='S-Curl Activator and Moisturizer 32 oz' clust_tuple=(225, 110, 252, 200)\n",
+      "item='Jan Marini Antioxidant Daily Face Protectant SPF 30, Tinted, 2 oz' clust_tuple=(225, 110, 25, 188)\n",
+      "item='Olay Regenerist Micro Sculpting Cream - 1.7 oz' clust_tuple=(225, 110, 25, 163)\n",
+      "226\n",
+      "item='SODIAL(TM) Stainless Steel Extension Eyelash Applicator Tool Fish Tail Clip' clust_tuple=(226, 155, 67, 200)\n",
+      "item='E.l.f. Eyelash Curler' clust_tuple=(226, 209, 252, 88)\n",
+      "item='Red Cherry #138 False Eyelashes (Pack of 6 Pairs)' clust_tuple=(226, 226, 195, 200)\n",
+      "item='Ardell Fashion Lashes Pair - 113 (Pack of 4)' clust_tuple=(226, 155, 25, 208)\n",
+      "item='Makeup Geek - Pointed Crease Brush' clust_tuple=(226, 212, 252, 239)\n",
+      "item='10 Pairs of Reusable Thick False Eyelashes' clust_tuple=(226, 209, 252, 208)\n",
+      "item='Ardell Fashion Lashes Pair - 105 (Pack of 4)' clust_tuple=(226, 155, 25, 208)\n",
+      "item=\"Winstonia's 50 Pairs False Eyelashes Fake Lashes Bundle Set w/ Adhesive - Natural, Thick, Criss-Cross Designs for Day &amp; Night\" clust_tuple=(226, 155, 25, 200)\n",
+      "item='Ardell Fashion Lashes - 120 Black Demi' clust_tuple=(226, 155, 25, 208)\n",
+      "item='10 Pairs of Long Black False Eyelashes Eye Lashes Makeup, Professional, Thin and Wispy' clust_tuple=(226, 209, 252, 200)\n",
+      "227\n",
+      "item='Joico K-Pak Intense Hydrator Treatment, 8.5 Ounce' clust_tuple=(227, 227, 25, 88)\n",
+      "item='Elasta Qp Soy Oyl UltraHydration Deep Conditioner, 32 oz' clust_tuple=(227, 4, 25, 163)\n",
+      "item='Biolage by Matrix Ultra-Hydrating Conditioning Balm 16.9 Ounces' clust_tuple=(227, 110, 25, 215)\n",
+      "item='Beautiful Collection Semi-Permanent Haircolor 175W Wine Brown' clust_tuple=(227, 62, 25, 200)\n",
+      "item='DIANE Imported Pure Bristle Professional Military Hair Brush (Model: 8114)' clust_tuple=(227, 110, 25, 255)\n",
+      "item='Kerastase Nutritive Bain Oleo-Relax Smoothing Shampoo For Dry and Rebellious Hair, 8.5 Ounce' clust_tuple=(227, 4, 25, 255)\n",
+      "item='Color Brilliance Permanent Creme Hair Color' clust_tuple=(227, 110, 8, 255)\n",
+      "item='Herbal Essesnces None of Your Frizzness Smoothing Conditioner By Clairol, 10.1 Ounce' clust_tuple=(227, 155, 25, 208)\n",
+      "item='Seche Vite Top Coat Professional Kit Size 4 oz' clust_tuple=(227, 110, 25, 200)\n",
+      "item='Doo Gro Stimulating Growth Oil' clust_tuple=(227, 110, 25, 163)\n",
+      "228\n",
+      "item='Clinique Even Better Eyes Dark Circle Corrector for Unisex, All Skin Types, 0.34 Ounce' clust_tuple=(228, 110, 25, 208)\n",
+      "item='Premier Dead Sea Eye Care Set(eye Serum + Eye Cream)' clust_tuple=(228, 20, 67, 77)\n",
+      "item='Avon Anew Genics Eye Treatment 0.5 oz' clust_tuple=(228, 110, 25, 239)\n",
+      "item='Image Skin Care Ageless Total Skin Bleaching Serum 1 oz' clust_tuple=(228, 110, 8, 239)\n",
+      "item=\"L'Oreal Infallible Eyeshadow, Gold Imperial 407\" clust_tuple=(228, 110, 8, 163)\n",
+      "item='vbeaut&eacute; Eye Never Nourishing DNA Repair Eye Cr&egrave;me, 0.5 Ounce' clust_tuple=(228, 203, 203, 239)\n",
+      "item='Loreal Limited Edition Infallible Eyeshadow - 607 Blinged &amp; Brilliant' clust_tuple=(228, 110, 8, 208)\n",
+      "item='Mary Kay&reg; Ultimate MascaraTM: Black, 0.28 oz' clust_tuple=(228, 110, 8, 163)\n",
+      "item='Embryolisse Lait Cr&egrave;me Concentr&eacute; 75 Ml(concentrated Creamy Lotion) 75 Ml' clust_tuple=(228, 110, 203, 239)\n",
+      "item=\"Skin Genesis Daily Eye Treatment Eye Serum Women Treatment by L'Oreal, 0.5 Ounce\" clust_tuple=(228, 110, 8, 208)\n",
+      "229\n",
+      "item='Debra Lynn Professional Plastic Cuticle Pusher' clust_tuple=(229, 110, 25, 239)\n",
+      "item='1800pcs Nail Art Rhinestones Round 1.5mm' clust_tuple=(229, 116, 110, 8)\n",
+      "item='OPI Nail Lacquer - Russian Navy Suede - 0.5 oz' clust_tuple=(229, 116, 25, 88)\n",
+      "item='gelish a petal for your thoughts 463 2013' clust_tuple=(229, 116, 110, 255)\n",
+      "item='144pcs Fimo Slice Lovely Animal Nail Art Decoration' clust_tuple=(229, 116, 194, 163)\n",
+      "item='Kleancolor Neon Night Life mini collection' clust_tuple=(229, 116, 25, 239)\n",
+      "item='Seche Nail Lacquer Lumiere' clust_tuple=(229, 116, 194, 255)\n",
+      "item='OPI RapiDry Spray Nail Polish Dryer' clust_tuple=(229, 110, 25, 255)\n",
+      "item='Essie Nail Color trophy wife 774' clust_tuple=(229, 116, 25, 239)\n",
+      "item='Orly Nail Bonder Nail Treatment-0.6 oz' clust_tuple=(229, 116, 229, 255)\n"
+     ]
+    }
+   ],
    "source": [
-    "for i in range(100, 120):\n",
-    "    sim = search_similar_items(items_with_tuples, (i,))\n",
+    "for i in range(220, 230):\n",
+    "    sim = search_similar_items(items_with_tuples, (i,), 10)\n",
     "    if len(sim) == 0:\n",
     "        continue\n",
     "    print(i)\n",
diff --git a/src/rqvae.py b/src/rqvae.py
index fb57a610..9f98b99f 100644
--- a/src/rqvae.py
+++ b/src/rqvae.py
@@ -1,27 +1,29 @@
 import torch
 
-from sklearn.cluster import KMeans
+from tqdm import tqdm
+import faiss
+
 
 class RQVAE(torch.nn.Module):
     def __init__(
-            self,
-            input_dim: int,
-            hidden_dim: int,
-            beta: float,
-            codebook_sizes: list[int],
-            should_init_codebooks=False,
-            should_reinit_unused_clusters=False
-        ):
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        beta: float,
+        codebook_sizes: list[int],
+        should_init_codebooks=False,
+        should_reinit_unused_clusters=False,
+    ):
         super().__init__()
 
         # In original paper it is set to 0.25
-        self.register_buffer('beta', torch.tensor(beta))  
+        self.register_buffer("beta", torch.tensor(beta))
 
         # Kmeans initialization
         self.should_init_codebooks = should_init_codebooks
 
         # Trick with re-initing empty clusters
-        self.should_reinit_unused_clusters = should_reinit_unused_clusters 
+        self.should_reinit_unused_clusters = should_reinit_unused_clusters
 
         self.mse_loss = torch.nn.MSELoss()
 
@@ -39,40 +41,24 @@ def __init__(
 
     def make_encoding_tower(self, d1: int, d2: int):
         return torch.nn.Linear(d1, d2, bias=False)
-    
-    # Get closest index for given embedding
+
     @staticmethod
     def get_codebook_indices(remainder, codebook):
         dist = torch.cdist(remainder, codebook)
         return dist.argmin(dim=-1)
 
-    # Recursive k-means initialization
-    @staticmethod
-    def kmeans(embeddings, num_clusters, num_steps=300):
-        # Just dummy kmeans implementation to get some better initial point
-        embeddings = torch.nn.functional.normalize(embeddings, dim=-1) # ???
-        closest_cluster = torch.randint(0, num_clusters, (embeddings.shape[0], ), device=embeddings.device)
-        cluster_centers = torch.zeros((num_clusters, embeddings.shape[1]), device=embeddings.device)
-        for clust_ind in range(num_clusters):
-            print(clust_ind, (closest_cluster == clust_ind).sum())
-            cluster_centers[clust_ind] = embeddings[closest_cluster == clust_ind].mean(dim=0)
-
-        for iter in range(num_steps):
-            dist = torch.cdist(embeddings, cluster_centers)
-            closest_cluster = dist.argmin(dim=-1)
-            print('Kmeans iter:', iter, closest_cluster.shape)
-            for clust_ind in range(num_clusters):
-                if clust_ind == 0:
-                    print(clust_ind, (closest_cluster == clust_ind).sum())
-                cluster_centers[clust_ind] = embeddings[closest_cluster == clust_ind].mean(dim=0)
-
-        return cluster_centers
-
     def init_codebooks(self, embeddings):
         with torch.no_grad():
             remainder = self.encoder(embeddings)
-            for codebook in self.codebooks:
-                codebook.data = self.kmeans(embeddings=remainder, num_clusters=codebook.shape[0])
+            for codebook in tqdm(self.codebooks):
+                embeddings_np = remainder.cpu().numpy()
+                n_clusters = codebook.shape[0]
+
+                kmeans = faiss.Kmeans(d=embeddings_np.shape[1], k=n_clusters, niter=100)
+                kmeans.train(embeddings_np)
+
+                codebook.data = torch.from_numpy(kmeans.centroids).to(codebook.device)
+
                 codebook_indices = self.get_codebook_indices(remainder, codebook)
                 codebook_vectors = codebook[codebook_indices]
                 remainder = remainder - codebook_vectors
@@ -80,14 +66,14 @@ def init_codebooks(self, embeddings):
     @staticmethod
     def reinit_unused_clusters(remainder, codebook, codebook_indices):
         with torch.no_grad():
-            is_used = torch.full((codebook.shape[0], ), False, device=codebook.device)
+            is_used = torch.full((codebook.shape[0],), False, device=codebook.device)
             unique_indices = codebook_indices.unique()
             is_used[unique_indices] = True
-            rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(), ))
+            rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(),))
             codebook[~is_used] = remainder[rand_input]
 
     def forward(self, inputs):
-        embeddings = inputs['embedding']
+        embeddings = inputs["embedding"]
         if self.should_init_codebooks:
             self.init_codebooks(embeddings)
             self.should_init_codebooks = False
@@ -105,7 +91,9 @@ def forward(self, inputs):
                 self.reinit_unused_clusters(remainder, codebook, codebook_indices)
 
             num_unique_clusters.append(codebook_indices.unique().shape[0])
-            rqvae_loss += self.beta * self.mse_loss(remainder, codebook_vectors.detach())
+            rqvae_loss += self.beta * self.mse_loss(
+                remainder, codebook_vectors.detach()
+            )
             rqvae_loss += self.mse_loss(codebook_vectors, remainder.detach())
 
             latent_restored = latent_restored + codebook_vectors
@@ -118,11 +106,8 @@ def forward(self, inputs):
         loss = (recon_loss + rqvae_loss).mean()
 
         return {
-            'loss': loss,
-            'recon_loss': recon_loss.mean().detach(),
-            'rqvae_loss': rqvae_loss.mean().detach(),
-            **{
-                f'unique/{i}': cnt
-                for i, cnt in enumerate(num_unique_clusters)
-            }
-        }
\ No newline at end of file
+            "loss": loss,
+            "recon_loss": recon_loss.mean().detach(),
+            "rqvae_loss": rqvae_loss.mean().detach(),
+            **{f"unique/{i}": cnt for i, cnt in enumerate(num_unique_clusters)},
+        }
diff --git a/src/rqvae_data.py b/src/rqvae_data.py
new file mode 100644
index 00000000..28b8f799
--- /dev/null
+++ b/src/rqvae_data.py
@@ -0,0 +1,79 @@
+import pandas as pd
+import json
+import gzip
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+from tqdm import tqdm
+
+tqdm.pandas()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model_name = "google-t5/t5-small"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
+
+
+def parse(path):
+    g = gzip.open(path, "rb")
+    for line in g:
+        yield eval(line)
+
+
+def getDF(path):
+    i = 0
+    df = {}
+    for d in parse(path):
+        df[i] = d
+        i += 1
+    return pd.DataFrame.from_dict(df, orient="index")
+
+
+def encode_text(text):
+    enc = tokenizer(text, return_tensors="pt", truncation=True).to(device)
+
+    output = model.encoder(
+        input_ids=enc["input_ids"],
+        attention_mask=enc["attention_mask"],
+        return_dict=True,
+    )
+
+    embeddings = output.last_hidden_state.mean(
+        dim=1
+    ).squeeze()  # mean over all tokens (mb CLS?)
+
+    return embeddings.cpu().detach()
+
+
+def preprocess(row: pd.Series):
+    row = row.fillna("unknown")  # empty?
+    # remove column description / title / cat?
+    return f"Description: {row['description']}. Title: {row['title']}. Categories: {', '.join(row['categories'][0])}"
+
+
+def get_data(cached=True):
+    if not cached:
+        df = getDF("../data/meta_Beauty.json.gz")
+
+        file_name = "../data/reviews_Beauty_5.json"
+
+        unique_items = set()
+        unique_users = set()
+
+        with open(file_name, "r") as file:
+            for line in file:
+                review = json.loads(line.strip())
+                unique_items.add(review["asin"])
+                unique_users.add(review["reviewerID"])
+
+        df = df[df["asin"].isin(unique_items)]
+
+        df["combined_text"] = df.apply(preprocess, axis=1)
+
+        with torch.no_grad():
+            df["embeddings"] = df["combined_text"].progress_apply(encode_text)
+    else:
+        df = torch.load("../data/df_with_embs.pt")
+        
+    return df

From d1c2a9d97f8612c94a06f87efafcce607a0b8877 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Wed, 18 Dec 2024 21:40:49 +0300
Subject: [PATCH 004/175] use conda env

---
 src/main.ipynb    | 318 ++++++++++++++++++----------------------------
 src/rqvae.py      |   9 +-
 src/rqvae_data.py |  27 +++-
 todo.txt          |  23 ++++
 4 files changed, 180 insertions(+), 197 deletions(-)

diff --git a/src/main.ipynb b/src/main.ipynb
index 7203227e..f63aae02 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -4,16 +4,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/peter/university/diploma/GSRec/src/rqvae_data.py:77: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "  df = torch.load(\"../data/df_with_embs.pt\")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "\n",
@@ -60,7 +51,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:08<00:00,  2.07s/it]\n"
+      "100%|██████████| 4/4 [00:07<00:00,  2.00s/it]\n"
      ]
     },
     {
@@ -81,8 +72,6 @@
     }
    ],
    "source": [
-    "import random\n",
-    "\n",
     "from rqvae import RQVAE\n",
     "\n",
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
@@ -109,190 +98,146 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_cb_tuples(embeddings):\n",
-    "    ind_lists = []\n",
-    "    for cb in rqvae.codebooks:\n",
-    "        dist = torch.cdist(rqvae.encoder(embeddings), cb)\n",
-    "        ind_lists.append(dist.argmin(dim=-1).cpu().numpy())\n",
-    "\n",
-    "    return zip(*ind_lists)\n",
+    "from rqvae_data import get_cb_tuples\n",
     "\n",
     "\n",
-    "def search_similar_items(items_with_tuples, clust2search, max_cnt=5):\n",
-    "    random.shuffle(items_with_tuples)\n",
-    "    cnt = 0\n",
-    "    similars = []\n",
-    "    for item, clust_tuple in items_with_tuples:\n",
-    "        if clust_tuple[: len(clust2search)] == clust2search:\n",
-    "            similars.append((item, clust_tuple))\n",
-    "            cnt += 1\n",
-    "        if cnt >= max_cnt:\n",
-    "            return similars\n",
-    "    return similars"
+    "cb_tuples = get_cb_tuples(rqvae, embs_dict[\"embedding\"])\n",
+    "items_with_tuples = list(zip(df[\"title\"].fillna(\"unknown\"), cb_tuples))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "cb_tuples = get_cb_tuples(embs_dict[\"embedding\"])\n",
-    "items_with_tuples = list(zip(df[\"title\"], cb_tuples))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "220\n",
-      "item='Fairy Dust by Paris Hilton for Women - 3.4 Ounce EDP Spray' clust_tuple=(220, 212, 67, 88)\n",
-      "item='D &amp; G Light Blue By Dolce &amp; Gabbana For Men Eau De Toilette Spray, 4.2-Ounces' clust_tuple=(220, 212, 25, 49)\n",
-      "item='Halle Pure Orchid by Halle Berry Eau De Parfum Spray for Women, 1 Ounce' clust_tuple=(220, 212, 25, 88)\n",
-      "item='Armani Code By Giorgio Armani For Men. Eau De Toilette Spray 1.7 Ounces' clust_tuple=(220, 212, 25, 88)\n",
-      "item='Taj Sunset by Escada for Women, Eau de Toilette Spray, 3.4 Ounce' clust_tuple=(220, 212, 25, 200)\n",
-      "item='Tom Ford Black Orchid By Tom Ford For Women. Eau De Parfum Spray 3.4-Ounces' clust_tuple=(220, 212, 67, 88)\n",
-      "item='Beyonce Heat Rush by Beyonce, 3.4 Ounce' clust_tuple=(220, 62, 67, 200)\n",
-      "item='In Control Curious by Britney Spears for Women, Eau De Parfum Spray, 1.7 Ounce' clust_tuple=(220, 212, 25, 49)\n",
-      "item='Lacoste Style In Play By Lacoste For Men. Eau De Toilette Spray 1.6 Ounces' clust_tuple=(220, 212, 25, 88)\n",
-      "item='Very Irresistible Sensual By Givenchy For Women. Eau De Parfum Spray 1.7 Ounces' clust_tuple=(220, 212, 25, 88)\n",
-      "221\n",
-      "item='HD High-Definition Super Palettes (Super Palette- Warm)' clust_tuple=(221, 196, 121, 88)\n",
-      "item='Coastal Scents Go Makeup Palette, Moscow, 0.28 Oz' clust_tuple=(221, 62, 121, 10)\n",
-      "item='Coastal Scents Go Makeup Palette, Cairo, 0.28 Ounce' clust_tuple=(221, 196, 121, 10)\n",
-      "item='Coastal Scents Go Makeup Palette, London, 0.28 Oz' clust_tuple=(221, 196, 8, 10)\n",
-      "item='TEMPTU AIRbrush Makeup System 2.0 and Signature Starter Kit, Fair' clust_tuple=(221, 214, 195, 10)\n",
-      "item='Urban Decay Deluxe Shadow Box' clust_tuple=(221, 214, 121, 10)\n",
-      "item='Coastal Scents 88  Palette, Ultra Shimmer' clust_tuple=(221, 209, 195, 208)\n",
-      "item='Coastal Scents Go Makeup Palette, Sydney, 0.28 Ounce' clust_tuple=(221, 196, 121, 10)\n",
-      "item='NICKA K LIPSTICK WITH VITAMIN E MOODY BLACK #992' clust_tuple=(221, 196, 205, 163)\n",
-      "item='SHANY Glamour Girl Makeup Kit - 48 Eyeshadow / 4 Blush /2 Powder' clust_tuple=(221, 196, 121, 163)\n",
-      "222\n",
-      "item='Maybelline New York Instant Age Rewind Eraser Treatment Makeup, Medium Beige 300, 0.68 Fluid Ounce' clust_tuple=(222, 114, 196, 88)\n",
-      "item='Garnier  Ultra-Lift 2-in-1 Wrinkle Reducer Serum and Moisturizer for Wrinkles and Firming, 1.7 Fluid Ounce' clust_tuple=(222, 214, 25, 88)\n",
-      "item='Hydrating with Peptides - A Renewal Complex - 1oz/30ml - A Professionally Formulated Combination of Unique Patented Peptides ArgirelineTM, Matrixyl&reg; 3000, and Syn&reg;-coll, Synergistically Work to Reduce Fine Lines and Wrinkles' clust_tuple=(222, 214, 25, 88)\n",
-      "item='Prevage MD Anti-Aging Treatment 30ml 1 Fluid Ounce' clust_tuple=(222, 196, 25, 88)\n",
-      "item=\"L'Oreal Paris Visible Lift Serum Absolute Advanced Age-Reversing Makeup, Nude Beige, 1.0 Ounces\" clust_tuple=(222, 196, 25, 88)\n",
-      "item='Keys Solar Rx Broad Spectrum SPF 30 Sunblock 3.4oz lotion by Keys Care' clust_tuple=(222, 112, 196, 88)\n",
-      "item='Eclos Restorative Eye Cream, 0.5-Ounce' clust_tuple=(222, 62, 25, 88)\n",
-      "item='New Mary Kay TimeWise Repair Volu-Firm 5 Product Set Adv Skin Care Full Size (Large)' clust_tuple=(222, 62, 25, 88)\n",
-      "item='LifeCell All In One Anti-Aging Treatment 2.54 oz' clust_tuple=(222, 62, 8, 88)\n",
-      "item='Bare Escentuals Active Cell Renewal Night Serum 1oz./30ml' clust_tuple=(222, 7, 25, 88)\n",
-      "223\n",
-      "item=\"OPI Euro Centrale Collection 2013, You're Such A Budapest\" clust_tuple=(223, 116, 110, 163)\n",
-      "item='Opi San Francisco Collection Fall &amp; Winter 2013 Dinng Al Frisco' clust_tuple=(223, 116, 110, 49)\n",
-      "item='OPI The Amazing New Spider-Man Collection, My Boyfriend Scales Walls' clust_tuple=(223, 116, 110, 208)\n",
-      "item='Gelish - The Shadows Collection - The Perfect Silhouette # 01460' clust_tuple=(223, 116, 25, 208)\n",
-      "item='Gelish Holiday Collection - Just What I Wanted #01551 - 0.5oz' clust_tuple=(223, 116, 25, 208)\n",
-      "item='OPI Sheer Tints, Sheer Mini Hint of Tints' clust_tuple=(223, 110, 110, 208)\n",
-      "item='Cover Girl Invisible Cream Concealer, Tawny 185 -1 Ea' clust_tuple=(223, 116, 25, 208)\n",
-      "item='China Glaze Velvet Bow 1017/80517' clust_tuple=(223, 116, 25, 208)\n",
-      "item='Revlon Luxurious Color Satin Eye Shadow, Nude Slip, 0.08 Ounce' clust_tuple=(223, 116, 25, 208)\n",
-      "item=\"Harmony Gelish I'm Brighter Than You - #01559\" clust_tuple=(223, 110, 25, 49)\n",
-      "224\n",
-      "item='Paris Hilton Tease Women Eau De Parfum Spray, 1 Ounce' clust_tuple=(224, 214, 224, 163)\n",
-      "item='Pure Dkny Eau De Parfum Spray by Donna Karan, 3.4 Ounce' clust_tuple=(224, 110, 8, 163)\n",
-      "item='MARC JACOBS DAISY by Marc Jacobs EDT SPRAY 3.4 OZ' clust_tuple=(224, 214, 110, 10)\n",
-      "item='Fresh Eau de Parfum, Sugar Lemon, 1 oz' clust_tuple=(224, 110, 110, 163)\n",
-      "item='Calvin Klein One Shock for Him Men Eau De Toilette Spray, 3.4 Ounce' clust_tuple=(224, 110, 229, 163)\n",
-      "item='DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ' clust_tuple=(224, 110, 8, 163)\n",
-      "item='Guess By Parlux Fragrances For Women. Eau De Parfum Spray 2.5 Oz' clust_tuple=(224, 62, 8, 163)\n",
-      "item='Jimmy Choo Flash Eau de Parfum Spray for Women, 2 Ounce' clust_tuple=(224, 110, 25, 163)\n",
-      "item='Diesel Fuel For Life by Diesel For Men. Eau De Toilette Spray 1.7-Ounces' clust_tuple=(224, 110, 8, 163)\n",
-      "item='Millesime Imperial By Creed For Men. Millesime Spray 4.0-Ounce Bottle' clust_tuple=(224, 110, 8, 163)\n",
-      "225\n",
-      "item='TIGI Bed Head Manipulator Styling Cream 2.0 oz.' clust_tuple=(225, 110, 110, 208)\n",
-      "item='DEVA CURL Flexible Hold Hair Spray 10oz' clust_tuple=(225, 224, 8, 200)\n",
-      "item=\"Givenchy Phenomen'Eyes Mascara 1 Phenomen'Black 0.24 oz\" clust_tuple=(225, 214, 110, 208)\n",
-      "item='Macadamia Natural Oil Oil Infused Brush Set' clust_tuple=(225, 196, 25, 208)\n",
-      "item='Neutrogena Clean Replenishing Conditioner, 10.1 Ounce' clust_tuple=(225, 110, 25, 10)\n",
-      "item='Pump for 16 oz. Jar' clust_tuple=(225, 110, 110, 215)\n",
-      "item='China Glaze: Pink Voltage, 0.5 oz' clust_tuple=(225, 110, 151, 49)\n",
-      "item='S-Curl Activator and Moisturizer 32 oz' clust_tuple=(225, 110, 252, 200)\n",
-      "item='Jan Marini Antioxidant Daily Face Protectant SPF 30, Tinted, 2 oz' clust_tuple=(225, 110, 25, 188)\n",
-      "item='Olay Regenerist Micro Sculpting Cream - 1.7 oz' clust_tuple=(225, 110, 25, 163)\n",
-      "226\n",
-      "item='SODIAL(TM) Stainless Steel Extension Eyelash Applicator Tool Fish Tail Clip' clust_tuple=(226, 155, 67, 200)\n",
-      "item='E.l.f. Eyelash Curler' clust_tuple=(226, 209, 252, 88)\n",
-      "item='Red Cherry #138 False Eyelashes (Pack of 6 Pairs)' clust_tuple=(226, 226, 195, 200)\n",
-      "item='Ardell Fashion Lashes Pair - 113 (Pack of 4)' clust_tuple=(226, 155, 25, 208)\n",
-      "item='Makeup Geek - Pointed Crease Brush' clust_tuple=(226, 212, 252, 239)\n",
-      "item='10 Pairs of Reusable Thick False Eyelashes' clust_tuple=(226, 209, 252, 208)\n",
-      "item='Ardell Fashion Lashes Pair - 105 (Pack of 4)' clust_tuple=(226, 155, 25, 208)\n",
-      "item=\"Winstonia's 50 Pairs False Eyelashes Fake Lashes Bundle Set w/ Adhesive - Natural, Thick, Criss-Cross Designs for Day &amp; Night\" clust_tuple=(226, 155, 25, 200)\n",
-      "item='Ardell Fashion Lashes - 120 Black Demi' clust_tuple=(226, 155, 25, 208)\n",
-      "item='10 Pairs of Long Black False Eyelashes Eye Lashes Makeup, Professional, Thin and Wispy' clust_tuple=(226, 209, 252, 200)\n",
-      "227\n",
-      "item='Joico K-Pak Intense Hydrator Treatment, 8.5 Ounce' clust_tuple=(227, 227, 25, 88)\n",
-      "item='Elasta Qp Soy Oyl UltraHydration Deep Conditioner, 32 oz' clust_tuple=(227, 4, 25, 163)\n",
-      "item='Biolage by Matrix Ultra-Hydrating Conditioning Balm 16.9 Ounces' clust_tuple=(227, 110, 25, 215)\n",
-      "item='Beautiful Collection Semi-Permanent Haircolor 175W Wine Brown' clust_tuple=(227, 62, 25, 200)\n",
-      "item='DIANE Imported Pure Bristle Professional Military Hair Brush (Model: 8114)' clust_tuple=(227, 110, 25, 255)\n",
-      "item='Kerastase Nutritive Bain Oleo-Relax Smoothing Shampoo For Dry and Rebellious Hair, 8.5 Ounce' clust_tuple=(227, 4, 25, 255)\n",
-      "item='Color Brilliance Permanent Creme Hair Color' clust_tuple=(227, 110, 8, 255)\n",
-      "item='Herbal Essesnces None of Your Frizzness Smoothing Conditioner By Clairol, 10.1 Ounce' clust_tuple=(227, 155, 25, 208)\n",
-      "item='Seche Vite Top Coat Professional Kit Size 4 oz' clust_tuple=(227, 110, 25, 200)\n",
-      "item='Doo Gro Stimulating Growth Oil' clust_tuple=(227, 110, 25, 163)\n",
-      "228\n",
-      "item='Clinique Even Better Eyes Dark Circle Corrector for Unisex, All Skin Types, 0.34 Ounce' clust_tuple=(228, 110, 25, 208)\n",
-      "item='Premier Dead Sea Eye Care Set(eye Serum + Eye Cream)' clust_tuple=(228, 20, 67, 77)\n",
-      "item='Avon Anew Genics Eye Treatment 0.5 oz' clust_tuple=(228, 110, 25, 239)\n",
-      "item='Image Skin Care Ageless Total Skin Bleaching Serum 1 oz' clust_tuple=(228, 110, 8, 239)\n",
-      "item=\"L'Oreal Infallible Eyeshadow, Gold Imperial 407\" clust_tuple=(228, 110, 8, 163)\n",
-      "item='vbeaut&eacute; Eye Never Nourishing DNA Repair Eye Cr&egrave;me, 0.5 Ounce' clust_tuple=(228, 203, 203, 239)\n",
-      "item='Loreal Limited Edition Infallible Eyeshadow - 607 Blinged &amp; Brilliant' clust_tuple=(228, 110, 8, 208)\n",
-      "item='Mary Kay&reg; Ultimate MascaraTM: Black, 0.28 oz' clust_tuple=(228, 110, 8, 163)\n",
-      "item='Embryolisse Lait Cr&egrave;me Concentr&eacute; 75 Ml(concentrated Creamy Lotion) 75 Ml' clust_tuple=(228, 110, 203, 239)\n",
-      "item=\"Skin Genesis Daily Eye Treatment Eye Serum Women Treatment by L'Oreal, 0.5 Ounce\" clust_tuple=(228, 110, 8, 208)\n",
-      "229\n",
-      "item='Debra Lynn Professional Plastic Cuticle Pusher' clust_tuple=(229, 110, 25, 239)\n",
-      "item='1800pcs Nail Art Rhinestones Round 1.5mm' clust_tuple=(229, 116, 110, 8)\n",
-      "item='OPI Nail Lacquer - Russian Navy Suede - 0.5 oz' clust_tuple=(229, 116, 25, 88)\n",
-      "item='gelish a petal for your thoughts 463 2013' clust_tuple=(229, 116, 110, 255)\n",
-      "item='144pcs Fimo Slice Lovely Animal Nail Art Decoration' clust_tuple=(229, 116, 194, 163)\n",
-      "item='Kleancolor Neon Night Life mini collection' clust_tuple=(229, 116, 25, 239)\n",
-      "item='Seche Nail Lacquer Lumiere' clust_tuple=(229, 116, 194, 255)\n",
-      "item='OPI RapiDry Spray Nail Polish Dryer' clust_tuple=(229, 110, 25, 255)\n",
-      "item='Essie Nail Color trophy wife 774' clust_tuple=(229, 116, 25, 239)\n",
-      "item='Orly Nail Bonder Nail Treatment-0.6 oz' clust_tuple=(229, 116, 229, 255)\n"
+      "100\n",
+      "item='Axe Anti-dandruff Styling Cream, 3.2 Ounce' clust_tuple=(100, 224, 126, 160)\n",
+      "item='Enjoy Texture Cream, 8.8-Ounce (Packaging may Vary)' clust_tuple=(100, 199, 72, 78)\n",
+      "item='Dove Hair Styling Oxygen Moisture Leave In Foam, 5.1 Ounce' clust_tuple=(100, 224, 126, 109)\n",
+      "item='Suave Professionals Natural Infusion Seaweed and Lotus Blossom Leave-in Foam, 5 Ounce' clust_tuple=(100, 224, 126, 109)\n",
+      "item='Motions Naturally You, Deep Conditioning Masque, 8 Ounce' clust_tuple=(100, 224, 126, 160)\n",
+      "item='Axe Styling Spiked Up Look Gel, 6 Ounce' clust_tuple=(100, 224, 126, 160)\n",
+      "item='Just For Me Texture Softener' clust_tuple=(100, 190, 126, 160)\n",
+      "item='Axe Styling Messy Look Matte Gel, 6 Ounce' clust_tuple=(100, 199, 126, 160)\n",
+      "item='Motions At Home Oil Moisturizer Hair Lotion, 12-Ounce Bottles (Pack of 6)' clust_tuple=(100, 199, 126, 160)\n",
+      "item='Redken Hair Cleansing Cream Shampoo for All Hair Types, 10.1-Ounces' clust_tuple=(100, 199, 72, 195)\n",
+      "101\n",
+      "item='Alba Botanica Even Advanced Eye Makeup Remover, 4 Ounce Bottle' clust_tuple=(101, 45, 141, 78)\n",
+      "item='Alba Botanica Smooth Sugar Cane Hawaiian Body Polish, 10 Ounce Tub' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Deep Moisturizing Kukui Nut Hawaiian Body Cream, 6.5 Ounce Jar' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Revitalizing Sea Salt Hawaiian Body Scrub, 14.5 Ounce Tub' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Refining Aloe &amp; Green Tea Hawaiian Oil Free Moisturizer, 3 Ounce Tubs' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Even Advanced Sea Kelp Facial Toner, 6 Ounce Bottles' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Aloe Vera Sunblock SPF 30, 4 Ounce' clust_tuple=(101, 45, 141, 78)\n",
+      "item='Alba Botanica Soothing Jasmine &amp; Vitamin E Hawaiian Moisture Cream, 3 Ounce Tub' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Maximum Very Emollient Body Lotion, 12 Ounce Bottle' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Original Very Emollient Body Lotion, 32 Ounce Bottle' clust_tuple=(101, 45, 146, 78)\n",
+      "102\n",
+      "item='Mary Kay Mineral Powder Foundation Beige 2' clust_tuple=(102, 199, 141, 78)\n",
+      "item='Revlon Colorstay Aquatm Mineral Finishing Powder (Project Mermaid), Tranlscuent, 0.35 Ounce' clust_tuple=(102, 199, 141, 78)\n",
+      "item='Iman Cosmetics Second To None Stick Foundation, Clay 2' clust_tuple=(102, 229, 141, 78)\n",
+      "item='Maybelline Mineral Power Finishing Veil Bronzing Powder' clust_tuple=(102, 199, 141, 78)\n",
+      "item='Neutrogena Mineral Sheers Powder Foundation, Natural Ivory 20, 0.34 Ounce' clust_tuple=(102, 229, 141, 78)\n",
+      "item='Studio Tone Correcting Powder' clust_tuple=(102, 199, 141, 78)\n",
+      "item='Neutrogena Healthy Skin Blends, Pure 22, 0.02 Ounce' clust_tuple=(102, 199, 141, 78)\n",
+      "item='Physicians Formula Organic Wear 100% Natural Loose Powder, Translucent Light Organics, 0.77-Ounces' clust_tuple=(102, 229, 141, 78)\n",
+      "item='Revlon Colorstay Aquatm Mineral Finishing Powder (Project Mermaid), Translucent Light, 0.35 Ounce' clust_tuple=(102, 199, 141, 78)\n",
+      "item='Physicians Formula Mineral Wear Talc-Free Loose Powder, Translucent Medium, 0.49 Ounce' clust_tuple=(102, 229, 141, 78)\n",
+      "103\n",
+      "item='Goody Mosaic Perfect Wrap Curved Bobby Pins - 18 pk.' clust_tuple=(103, 53, 126, 75)\n",
+      "item='Rhinestones Crystal Wedding Bridal Pageant Princess Tiara Crown 3150' clust_tuple=(103, 199, 126, 78)\n",
+      "item='American Crew American Crew Fiber Fiber - 3 oz' clust_tuple=(103, 45, 72, 78)\n",
+      "item='Kingsley Shave Soap Bowl with Lid Dark Wood' clust_tuple=(103, 199, 126, 78)\n",
+      "item='Goody Comfort-flex Updo Barrette 1 Pc Colors May Vary - 2 Packs' clust_tuple=(103, 53, 126, 78)\n",
+      "item='BONAMART &reg; Lovely Vintage Jewelry Crystal Peacock Hair Clip' clust_tuple=(103, 191, 126, 78)\n",
+      "item='Goody Ouchless Scrunchie, Chenille and Cotton, 5 Count' clust_tuple=(103, 199, 126, 78)\n",
+      "item='Mixed Chicks Paddle Brush' clust_tuple=(103, 199, 91, 78)\n",
+      "item='Tangle Teezer Original Pink' clust_tuple=(103, 191, 126, 78)\n",
+      "item='Conair Bun Maker Set' clust_tuple=(103, 199, 126, 78)\n",
+      "104\n",
+      "item='Wet Ones Sensitive Skin Hand and Face Wipes Singles, 24-Count (Pack of 5)' clust_tuple=(104, 199, 126, 212)\n",
+      "item='RoC BRILLIANCE Night Recharging Moisturizer, Activating Serum &amp; Recharging Creme' clust_tuple=(104, 229, 127, 78)\n",
+      "item='derma e - Pycnogenol &amp; Hyaluronic Acid Eye Creme, .5 oz cream [Misc.]' clust_tuple=(104, 45, 127, 78)\n",
+      "item='Gratiae Lifting Facial Serum' clust_tuple=(104, 199, 126, 78)\n",
+      "item='Rogaine Regular Strength for Women Triple Pack' clust_tuple=(104, 191, 64, 195)\n",
+      "item='Slimquick Pure Weight Loss Extra Strength, 60 count' clust_tuple=(104, 191, 64, 109)\n",
+      "item='Organix, Theraneem Skin Lotion, 8 Ounce' clust_tuple=(104, 229, 126, 78)\n",
+      "item='Ambi Skincare Fade Cream, Oily Skin, 2 oz (57 g)' clust_tuple=(104, 199, 91, 78)\n",
+      "item='Nerium Ad - Age Defying Night Cream (30ml) One Bottle' clust_tuple=(104, 174, 72, 78)\n",
+      "item='Eucerin Redness Relief Soothing Cleanser, 6.8-Ounce Tubes (Pack of 3)' clust_tuple=(104, 229, 126, 224)\n",
+      "105\n",
+      "item='Jerdon JP910NB 6-Inch Tabletop Two-Sided Swivel Vanity Mirror with 10x Magnification, 11-Inch Height, Nickel Finish' clust_tuple=(105, 199, 126, 254)\n",
+      "item='4 Dozen (48) Long Pink Perm Rods' clust_tuple=(105, 224, 64, 212)\n",
+      "item='Mebco Tortoise Shower Detangler' clust_tuple=(105, 199, 126, 78)\n",
+      "item='WEN Cleansing Creme Shower Comb NEW By Chaz Dean' clust_tuple=(105, 224, 126, 75)\n",
+      "item='Phillips Light Touch 6 Hair Brush' clust_tuple=(105, 211, 126, 75)\n",
+      "item='Mason Pearson Detangling Comb' clust_tuple=(105, 53, 126, 75)\n",
+      "item='DEVA CURL Flexible Hold Hair Spray 10oz' clust_tuple=(105, 191, 126, 148)\n",
+      "item='Conair Mega Self Holding Rollers, 9 Count' clust_tuple=(105, 199, 41, 160)\n",
+      "item='Remington H-1015 Ceramic Compact, Large and Medium Roller' clust_tuple=(105, 224, 44, 155)\n",
+      "item='Simply Beautiful Tangle Teaser Brush - Professional Detangling Hairbrush - Pink, Black, Purple, Blue or Green (Black)' clust_tuple=(105, 199, 126, 195)\n",
+      "106\n",
+      "item='32 PCS Makeup Brush Set + Black Pouch Bag' clust_tuple=(106, 199, 141, 78)\n",
+      "item='Bare Escentuals Refillable Mirror Compact Lavender' clust_tuple=(106, 199, 126, 78)\n",
+      "item='SHANY Cosmetics Professional Cotton Makeup Apron with Makeup Artist Brush Belt, Light Weight, 8 Ounce' clust_tuple=(106, 199, 126, 78)\n",
+      "item='SuperNail Brush Cleaner - 2oz / 59ml' clust_tuple=(106, 199, 141, 78)\n",
+      "item='e.l.f. Cosmetics Eye Shadow Brush' clust_tuple=(106, 199, 141, 78)\n",
+      "item='Neewer&reg; Fashion 16 Pcs Pro Purple Makeup Eye Shadow Brush Cosmetic Set Kit' clust_tuple=(106, 199, 141, 78)\n",
+      "item='Shiseido Perfect Foundation Brush (Boxed)' clust_tuple=(106, 199, 72, 78)\n",
+      "item='FantaSea Cosmetic Blending Sponge' clust_tuple=(106, 199, 126, 78)\n",
+      "item='elf Makeup Mist and Set, Clear, 2.02 Ounce and Stipple Brush' clust_tuple=(106, 199, 141, 78)\n",
+      "item='Seki Edge Folding Lash Pin Comb' clust_tuple=(106, 199, 126, 78)\n",
+      "107\n",
+      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Hipster Collection, &quot;Hippie Chick&quot;' clust_tuple=(107, 229, 126, 78)\n",
+      "item='Home Pedicure PediNova III - Electric Manicure Kit' clust_tuple=(107, 229, 127, 78)\n",
+      "item='Garnier Fructis Style Smoothing Milk, Strong, 5.1 Ounce Bottle' clust_tuple=(107, 229, 126, 78)\n",
+      "item='AquaBella Exfoliating Bath Cloth (Twin Pack) (colors will vary)' clust_tuple=(107, 45, 126, 78)\n",
+      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Fashionista Collection, &quot;diva&quot;' clust_tuple=(107, 229, 126, 78)\n",
+      "item='My Konjac Sponge All Natural Fiber Wave Body Sponge' clust_tuple=(107, 45, 126, 78)\n",
+      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Hipster Collection, &quot;Tough Love&quot;' clust_tuple=(107, 229, 126, 78)\n",
+      "item='Super Solano Professional Hair Dryer - Black' clust_tuple=(107, 229, 141, 78)\n",
+      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Hipster Collection, &quot;Flower Power&quot;' clust_tuple=(107, 45, 126, 78)\n",
+      "item='EcoTools Ecopouf Bath Sponge, Assorted Colors (Pack of 6)' clust_tuple=(107, 199, 126, 78)\n",
+      "108\n",
+      "item='Deodorant Rosemary Mint 2.50 Ounces' clust_tuple=(108, 84, 64, 78)\n",
+      "item='LOREAL Collagen Filler Eye Treatment' clust_tuple=(108, 199, 91, 78)\n",
+      "item=\"Dr. King's Natural Medicine Anxiety and Nervousness, 2 Fluid Ounce\" clust_tuple=(108, 38, 64, 78)\n",
+      "item='NaturalCare Homeopathic Ultra Vein-Gard Leg Therapy Cream, 2.25-Ounce Package' clust_tuple=(108, 199, 41, 78)\n",
+      "item='Bumble and Bumble Leave in Conditioner (8 Ounces)' clust_tuple=(108, 199, 64, 224)\n",
+      "item='Avene Redness relief soothing cream SPF25, 1.35-Ounce Package' clust_tuple=(108, 190, 64, 224)\n",
+      "item=\"Burt's Bees Acne Targeted Spot Treatment, 0.26 Ounce Bottle\" clust_tuple=(108, 199, 41, 78)\n",
+      "item='Nizoral Anti-Dandruff Shampoo, 4 oz.' clust_tuple=(108, 211, 64, 160)\n",
+      "item=\"Dr. King's Natural Medicine Gout Symptom Formula, 2 Fluid Ounce\" clust_tuple=(108, 199, 41, 224)\n",
+      "item='Mary Kay Blemish Control Toner: Formula 3' clust_tuple=(108, 199, 64, 224)\n",
+      "109\n",
+      "item='ELMA&amp;SANA Golden Argan Oil &reg; 100 Cold Pressed Virgin Organic Certified By Ecocert -1oz(30ml)' clust_tuple=(109, 84, 141, 78)\n",
+      "item='Skinceuticals  Daily Moisturize Pore-minimizing Moisturizer For Normal Or Oily Skin, 2-Ounce Jar' clust_tuple=(109, 199, 141, 78)\n",
+      "item='Deep Sea Cosmetics Sensual Dead Sea Body Salt Scrub milk and honey scent' clust_tuple=(109, 84, 146, 78)\n",
+      "item='Shampoo Green Apple and Ginger 8 Ounces' clust_tuple=(109, 84, 72, 78)\n",
+      "item='Desert Essence Thoroughly Clean Face Wash - Original -- 8.5 fl oz' clust_tuple=(109, 84, 146, 78)\n",
+      "item='Natural Clear Skin Cleansing Sponge' clust_tuple=(109, 84, 141, 78)\n",
+      "item='ELMA&amp;SANA&reg; Golden Argan Oil 100% Pure Cold Pressed Virgin Organic Certified By Ecocert -4oz(120ml)' clust_tuple=(109, 84, 146, 78)\n",
+      "item='Body Lotion, Coconut &amp; Papaya - 13oz' clust_tuple=(109, 84, 146, 78)\n",
+      "item='Eminence Wild Plum Eye Cream, 1.05 Ounce' clust_tuple=(109, 84, 72, 78)\n",
+      "item='Desert Essence Blemish Touch Concealer Light .33 oz' clust_tuple=(109, 199, 141, 78)\n"
      ]
     }
    ],
    "source": [
-    "for i in range(220, 230):\n",
+    "from rqvae_data import search_similar_items\n",
+    "\n",
+    "\n",
+    "for i in range(100, 110):\n",
     "    sim = search_similar_items(items_with_tuples, (i,), 10)\n",
     "    if len(sim) == 0:\n",
     "        continue\n",
     "    print(i)\n",
     "    for item, clust_tuple in sim:\n",
-    "        print(f\"{item=} {clust_tuple=}\")\n",
-    "\n",
-    "# TODO fix collisisons (remainder = last embedding, auto-increment 4th id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1 2 3 0\n",
-    "# 1 2 3 1\n",
-    "# 4 5 6 0/2\n",
-    "# 4 5 6 1/3\n",
-    "\n",
-    "# Research last index aggregation\n",
-    "\n",
-    "# 1) last index = KMeans(last residuals, n=|last codebook|) - collision\n",
-    "# 2) auto increment last index (check paper)\n",
-    "# 3) decoder\n",
-    "# 4) [(1 2 3), (1 2 3)] single item -> ok\n",
-    "# 4.1) several -> get embeddings -> score. softmax(collisions), torch.logsoftmax(logits) -> score -> argmax"
+    "        print(f\"{item=} {clust_tuple=}\")"
    ]
   },
   {
@@ -300,29 +245,14 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# pos emb for item & codebook (000 111 222) - item\n",
-    "# codebook (012 012 012)\n",
-    "# splitting item ?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.save(df, \"../data/df_with_embs.pt\")"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "!ls -lh ../data"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -334,7 +264,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "gsrec",
    "language": "python",
    "name": "python3"
   },
@@ -348,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,
diff --git a/src/rqvae.py b/src/rqvae.py
index 9f98b99f..be10ecee 100644
--- a/src/rqvae.py
+++ b/src/rqvae.py
@@ -17,7 +17,7 @@ def __init__(
         super().__init__()
 
         # In original paper it is set to 0.25
-        self.register_buffer("beta", torch.tensor(beta))
+        self.beta = beta
 
         # Kmeans initialization
         self.should_init_codebooks = should_init_codebooks
@@ -54,7 +54,12 @@ def init_codebooks(self, embeddings):
                 embeddings_np = remainder.cpu().numpy()
                 n_clusters = codebook.shape[0]
 
-                kmeans = faiss.Kmeans(d=embeddings_np.shape[1], k=n_clusters, niter=100)
+                kmeans = faiss.Kmeans(
+                    d=embeddings_np.shape[1],
+                    k=n_clusters,
+                    niter=1000,
+                    gpu=torch.cuda.is_available(),
+                )
                 kmeans.train(embeddings_np)
 
                 codebook.data = torch.from_numpy(kmeans.centroids).to(codebook.device)
diff --git a/src/rqvae_data.py b/src/rqvae_data.py
index 28b8f799..b31122da 100644
--- a/src/rqvae_data.py
+++ b/src/rqvae_data.py
@@ -3,6 +3,7 @@
 import gzip
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
+import random
 
 from tqdm import tqdm
 
@@ -74,6 +75,30 @@ def get_data(cached=True):
         with torch.no_grad():
             df["embeddings"] = df["combined_text"].progress_apply(encode_text)
     else:
-        df = torch.load("../data/df_with_embs.pt")
+        df = torch.load("../data/df_with_embs.pt", weights_only=False)
         
     return df
+
+def get_cb_tuples(rqvae, embeddings):
+    ind_lists = []
+    for cb in rqvae.codebooks:
+        dist = torch.cdist(rqvae.encoder(embeddings), cb)
+        ind_lists.append(dist.argmin(dim=-1).cpu().numpy())
+
+    return zip(*ind_lists)
+
+
+def search_similar_items(items_with_tuples, clust2search, max_cnt=5):
+    random.shuffle(items_with_tuples)
+    cnt = 0
+    similars = []
+    for item, clust_tuple in items_with_tuples:
+        if clust_tuple[: len(clust2search)] == clust2search:
+            similars.append((item, clust_tuple))
+            cnt += 1
+        if cnt >= max_cnt:
+            return similars
+    return similars
+
+
+
diff --git a/todo.txt b/todo.txt
index 1b0975f2..ca4034bd 100644
--- a/todo.txt
+++ b/todo.txt
@@ -2,6 +2,29 @@
 2) data: https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html#:~:text=image%20features-,Beauty,-reviews%20(2%2C023%2C070%20reviews
 
 
+
+# На чем обучать? То есть на каких данных запускать backward pass?
+# train model
+
+# TODO fix collisisons (remainder = last embedding, auto-increment 4th id)
+
+# 1 2 3 0
+# 1 2 3 1
+# 4 5 6 0/2
+# 4 5 6 1/3
+
+# Research last index aggregation
+
+# 1) last index = KMeans(last residuals, n=|last codebook|) - collision
+# 2) auto increment last index (check paper)
+# 3) decoder
+# 4) [(1 2 3), (1 2 3)] single item -> ok
+# 4.1) several -> get embeddings -> score. softmax(collisions), torch.logsoftmax(logits) -> score -> argmax
+
+# pos emb for item & codebook (000 111 222) - item
+# codebook (012 012 012)
+# splitting item ?
+
 user_id & cb_ids -> repr
 last 'seq' prediction
 dataloader (semantic ids lens)
\ No newline at end of file

From 900d647f88ad00307e69f03ae3890617f8e7466c Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Wed, 18 Dec 2024 22:02:00 +0300
Subject: [PATCH 005/175] handle collisions

---
 src/collisions.py |  14 +++
 src/main.ipynb    | 276 +++++++++++++++++++++++-----------------------
 src/rqvae_data.py |   4 +-
 3 files changed, 157 insertions(+), 137 deletions(-)
 create mode 100644 src/collisions.py

diff --git a/src/collisions.py b/src/collisions.py
new file mode 100644
index 00000000..263f76d7
--- /dev/null
+++ b/src/collisions.py
@@ -0,0 +1,14 @@
+def dedup(data):
+    count_dict = {}
+
+    result = []
+    for item in data:
+        code = item[2]
+        if code not in count_dict:
+            count_dict[code] = 0
+        unique_index = count_dict[code]
+        count_dict[code] += 1
+        new_last_element = (*code, unique_index)
+        result.append((item[0], item[1], new_last_element))
+
+    return result
diff --git a/src/main.ipynb b/src/main.ipynb
index f63aae02..e942991b 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -26,39 +26,19 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([12101, 512])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embs.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:07<00:00,  2.00s/it]\n"
+      "100%|██████████| 4/4 [00:07<00:00,  1.95s/it]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'loss': tensor(0.0057, device='cuda:0', grad_fn=<MeanBackward0>),\n",
-       " 'recon_loss': tensor(0.0052, device='cuda:0'),\n",
+       "{'loss': tensor(0.0056, device='cuda:0', grad_fn=<MeanBackward0>),\n",
+       " 'recon_loss': tensor(0.0051, device='cuda:0'),\n",
        " 'rqvae_loss': tensor(0.0005, device='cuda:0'),\n",
        " 'unique/0': 256,\n",
        " 'unique/1': 256,\n",
@@ -66,7 +46,7 @@
        " 'unique/3': 256}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94,20 +74,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from collisions import dedup\n",
     "from rqvae_data import get_cb_tuples\n",
     "\n",
     "\n",
-    "cb_tuples = get_cb_tuples(rqvae, embs_dict[\"embedding\"])\n",
-    "items_with_tuples = list(zip(df[\"title\"].fillna(\"unknown\"), cb_tuples))"
+    "cb_tuples = list(get_cb_tuples(rqvae, embs_dict[\"embedding\"]))\n",
+    "items_with_tuples = list(zip(df[\"asin\"], df[\"title\"].fillna(\"unknown\"), cb_tuples))\n",
+    "items_with_tuples = dedup(items_with_tuples)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -115,115 +97,108 @@
      "output_type": "stream",
      "text": [
       "100\n",
-      "item='Axe Anti-dandruff Styling Cream, 3.2 Ounce' clust_tuple=(100, 224, 126, 160)\n",
-      "item='Enjoy Texture Cream, 8.8-Ounce (Packaging may Vary)' clust_tuple=(100, 199, 72, 78)\n",
-      "item='Dove Hair Styling Oxygen Moisture Leave In Foam, 5.1 Ounce' clust_tuple=(100, 224, 126, 109)\n",
-      "item='Suave Professionals Natural Infusion Seaweed and Lotus Blossom Leave-in Foam, 5 Ounce' clust_tuple=(100, 224, 126, 109)\n",
-      "item='Motions Naturally You, Deep Conditioning Masque, 8 Ounce' clust_tuple=(100, 224, 126, 160)\n",
-      "item='Axe Styling Spiked Up Look Gel, 6 Ounce' clust_tuple=(100, 224, 126, 160)\n",
-      "item='Just For Me Texture Softener' clust_tuple=(100, 190, 126, 160)\n",
-      "item='Axe Styling Messy Look Matte Gel, 6 Ounce' clust_tuple=(100, 199, 126, 160)\n",
-      "item='Motions At Home Oil Moisturizer Hair Lotion, 12-Ounce Bottles (Pack of 6)' clust_tuple=(100, 199, 126, 160)\n",
-      "item='Redken Hair Cleansing Cream Shampoo for All Hair Types, 10.1-Ounces' clust_tuple=(100, 199, 72, 195)\n",
+      "item='Simple Protecting Light Moisturizer Spf 15, 4.2 Ounce' clust_tuple=(100, 43, 110, 111, 20)\n",
+      "item=\"POND'S Dry Skin Cream, 10.1-oz. (Pack of 3)\" clust_tuple=(100, 144, 110, 111, 0)\n",
+      "item='Kiss My Face Natural Mineral Lotion Sunscreen SPF 40 with Hydresia, 3 Fluid Ounce' clust_tuple=(100, 43, 110, 111, 16)\n",
+      "item=\"Pond's Luminous Moisture Day SPF 15 Lotion, 1.7 Ounce\" clust_tuple=(100, 43, 101, 111, 6)\n",
+      "item='St. Ives Facial Moisturizer, Timeless Skin Collagen Elastin, 10oz' clust_tuple=(100, 43, 110, 121, 2)\n",
+      "item='Clarins Cleansing Milk - Oily to Combination Skin, 7-Ounces Box' clust_tuple=(100, 43, 15, 111, 0)\n",
+      "item=\"POND'S Rejuveness Anti-Wrinkle Cream, 7-oz.\" clust_tuple=(100, 43, 110, 111, 9)\n",
+      "item='Olay Total Effects Blemish Control Salicylic Acid Acne Cleanser 5.0 Fl Oz (Pack of 3)' clust_tuple=(100, 144, 110, 111, 1)\n",
+      "item='Hydroxatone 90 Second Wrinkle Reducer' clust_tuple=(100, 43, 110, 31, 0)\n",
+      "item='Collagen Essence Full Face Mask 10 Pieces' clust_tuple=(100, 144, 88, 111, 0)\n",
       "101\n",
-      "item='Alba Botanica Even Advanced Eye Makeup Remover, 4 Ounce Bottle' clust_tuple=(101, 45, 141, 78)\n",
-      "item='Alba Botanica Smooth Sugar Cane Hawaiian Body Polish, 10 Ounce Tub' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Botanica Deep Moisturizing Kukui Nut Hawaiian Body Cream, 6.5 Ounce Jar' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Botanica Revitalizing Sea Salt Hawaiian Body Scrub, 14.5 Ounce Tub' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Botanica Refining Aloe &amp; Green Tea Hawaiian Oil Free Moisturizer, 3 Ounce Tubs' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Botanica Even Advanced Sea Kelp Facial Toner, 6 Ounce Bottles' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Aloe Vera Sunblock SPF 30, 4 Ounce' clust_tuple=(101, 45, 141, 78)\n",
-      "item='Alba Botanica Soothing Jasmine &amp; Vitamin E Hawaiian Moisture Cream, 3 Ounce Tub' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Botanica Maximum Very Emollient Body Lotion, 12 Ounce Bottle' clust_tuple=(101, 45, 146, 78)\n",
-      "item='Alba Botanica Original Very Emollient Body Lotion, 32 Ounce Bottle' clust_tuple=(101, 45, 146, 78)\n",
+      "item='Alba Botanica Fragrance Free SPF 30 Mineral Very Emollient Sunscreen, 4 Ounce Tube' clust_tuple=(101, 141, 88, 111, 10)\n",
+      "item='Alba Aloe Vera Sunblock SPF 30, 4 Ounce' clust_tuple=(101, 141, 88, 111, 6)\n",
+      "item='Alba Botanica Deep Moisturizing Kukui Nut Hawaiian Body Cream, 6.5 Ounce Jar' clust_tuple=(101, 209, 88, 111, 2)\n",
+      "item='Alba Botanica Sparkling Mint Very Emollient Bath &amp; Shower Gel, 32 Ounce Bottle' clust_tuple=(101, 209, 88, 111, 3)\n",
+      "item='Alba Botanica Leave-In Conditioner, 7 Ounce Tubes' clust_tuple=(101, 141, 88, 111, 7)\n",
+      "item='Alba Botanica Very Emollient Sunless Tanner, 4 Ounce Tube' clust_tuple=(101, 141, 88, 111, 14)\n",
+      "item='Alba Botanica Rejuvenating Papaya Mango Hawaiian Body Cream, 6.5 Ounce Jar' clust_tuple=(101, 141, 88, 111, 13)\n",
+      "item='Alba Botanica Pure Lavender SPF 45 Very Emollient Sunscreen, 4 Ounce Tubes (Pack of 2)' clust_tuple=(101, 141, 88, 111, 16)\n",
+      "item='Alba Botanica Even Advanced Sea Mineral Cleansing Gel, 6 Ounce Bottles' clust_tuple=(101, 141, 88, 111, 4)\n",
+      "item='Own Products Rejuvenating Facial Cleanser' clust_tuple=(101, 43, 110, 111, 1)\n",
       "102\n",
-      "item='Mary Kay Mineral Powder Foundation Beige 2' clust_tuple=(102, 199, 141, 78)\n",
-      "item='Revlon Colorstay Aquatm Mineral Finishing Powder (Project Mermaid), Tranlscuent, 0.35 Ounce' clust_tuple=(102, 199, 141, 78)\n",
-      "item='Iman Cosmetics Second To None Stick Foundation, Clay 2' clust_tuple=(102, 229, 141, 78)\n",
-      "item='Maybelline Mineral Power Finishing Veil Bronzing Powder' clust_tuple=(102, 199, 141, 78)\n",
-      "item='Neutrogena Mineral Sheers Powder Foundation, Natural Ivory 20, 0.34 Ounce' clust_tuple=(102, 229, 141, 78)\n",
-      "item='Studio Tone Correcting Powder' clust_tuple=(102, 199, 141, 78)\n",
-      "item='Neutrogena Healthy Skin Blends, Pure 22, 0.02 Ounce' clust_tuple=(102, 199, 141, 78)\n",
-      "item='Physicians Formula Organic Wear 100% Natural Loose Powder, Translucent Light Organics, 0.77-Ounces' clust_tuple=(102, 229, 141, 78)\n",
-      "item='Revlon Colorstay Aquatm Mineral Finishing Powder (Project Mermaid), Translucent Light, 0.35 Ounce' clust_tuple=(102, 199, 141, 78)\n",
-      "item='Physicians Formula Mineral Wear Talc-Free Loose Powder, Translucent Medium, 0.49 Ounce' clust_tuple=(102, 229, 141, 78)\n",
+      "item='Finulite Cellulite Smoothing Massage Mitt' clust_tuple=(102, 43, 88, 72, 0)\n",
+      "item='Neutrogena Healthy Skin Liquid Makeup, Classic Ivory 10, 1 Ounce' clust_tuple=(102, 208, 110, 111, 1)\n",
+      "item='Miracle Skin Transformer Face - SPF 20 - Translucent' clust_tuple=(102, 43, 80, 111, 0)\n",
+      "item='Physicians Formula Super BB All-in-1 Beauty Balm Cream Light Medium -- 1.2 fl oz' clust_tuple=(102, 184, 110, 111, 0)\n",
+      "item='Mary Kay Mineral Powder Foundation Beige 2' clust_tuple=(102, 209, 80, 111, 0)\n",
+      "item='Deep Sea Cosmetics Sensual Dead Sea Body Salt Scrub milk and honey scent' clust_tuple=(102, 43, 88, 111, 2)\n",
+      "item='Garnier Skin Renew Miracle Skin Perfector Bb Cream, Combination To Oily Skin, Light/Medium, 2 Fluid Ounce' clust_tuple=(102, 152, 110, 111, 1)\n",
+      "item='Studio Tone Correcting Powder' clust_tuple=(102, 7, 110, 31, 0)\n",
+      "item='Garnier  Skin Renew Miracle Skin Perfector B.B. Cream, Medium and Deep, 2.5 Fluid Ounce' clust_tuple=(102, 43, 88, 111, 5)\n",
+      "item='Maybelline Mineral Power Finishing Veil Bronzing Powder' clust_tuple=(102, 208, 110, 111, 0)\n",
       "103\n",
-      "item='Goody Mosaic Perfect Wrap Curved Bobby Pins - 18 pk.' clust_tuple=(103, 53, 126, 75)\n",
-      "item='Rhinestones Crystal Wedding Bridal Pageant Princess Tiara Crown 3150' clust_tuple=(103, 199, 126, 78)\n",
-      "item='American Crew American Crew Fiber Fiber - 3 oz' clust_tuple=(103, 45, 72, 78)\n",
-      "item='Kingsley Shave Soap Bowl with Lid Dark Wood' clust_tuple=(103, 199, 126, 78)\n",
-      "item='Goody Comfort-flex Updo Barrette 1 Pc Colors May Vary - 2 Packs' clust_tuple=(103, 53, 126, 78)\n",
-      "item='BONAMART &reg; Lovely Vintage Jewelry Crystal Peacock Hair Clip' clust_tuple=(103, 191, 126, 78)\n",
-      "item='Goody Ouchless Scrunchie, Chenille and Cotton, 5 Count' clust_tuple=(103, 199, 126, 78)\n",
-      "item='Mixed Chicks Paddle Brush' clust_tuple=(103, 199, 91, 78)\n",
-      "item='Tangle Teezer Original Pink' clust_tuple=(103, 191, 126, 78)\n",
-      "item='Conair Bun Maker Set' clust_tuple=(103, 199, 126, 78)\n",
+      "item='Spornette No.316 Deville Hair Brush' clust_tuple=(103, 43, 107, 111, 1)\n",
+      "item='Diane 2&quot; Bobby Pins, Black, 1lb Box' clust_tuple=(103, 43, 110, 31, 5)\n",
+      "item='Spornette German Porcupine Oval Cushion Brush' clust_tuple=(103, 152, 88, 72, 0)\n",
+      "item='Diane 3&quot; Hair Pins, Black, 32/card' clust_tuple=(103, 43, 107, 14, 1)\n",
+      "item='Spornette Large Smooth Operator, 4 Ounce' clust_tuple=(103, 66, 97, 111, 0)\n",
+      "item='10 Pcs Black Plastic Single Prong DIY Hairstyle Alligator Hair Clip 3.1&quot; Long' clust_tuple=(103, 152, 97, 176, 0)\n",
+      "item='Diane Double Prong Clip 1.75 Inches, 80 Clips' clust_tuple=(103, 43, 88, 72, 2)\n",
+      "item='Aquasentials Long Handle Bath Brush (Clear Handle)' clust_tuple=(103, 152, 16, 31, 0)\n",
+      "item='The Wet Comb Detangling Hair Comb - Metallics Collection (Colors May Vary)' clust_tuple=(103, 190, 88, 111, 0)\n",
+      "item='Sexy Hair Hard Up Gel, Packaging May Vary, 16.9-Ounce Pump Bottle' clust_tuple=(103, 43, 88, 111, 0)\n",
       "104\n",
-      "item='Wet Ones Sensitive Skin Hand and Face Wipes Singles, 24-Count (Pack of 5)' clust_tuple=(104, 199, 126, 212)\n",
-      "item='RoC BRILLIANCE Night Recharging Moisturizer, Activating Serum &amp; Recharging Creme' clust_tuple=(104, 229, 127, 78)\n",
-      "item='derma e - Pycnogenol &amp; Hyaluronic Acid Eye Creme, .5 oz cream [Misc.]' clust_tuple=(104, 45, 127, 78)\n",
-      "item='Gratiae Lifting Facial Serum' clust_tuple=(104, 199, 126, 78)\n",
-      "item='Rogaine Regular Strength for Women Triple Pack' clust_tuple=(104, 191, 64, 195)\n",
-      "item='Slimquick Pure Weight Loss Extra Strength, 60 count' clust_tuple=(104, 191, 64, 109)\n",
-      "item='Organix, Theraneem Skin Lotion, 8 Ounce' clust_tuple=(104, 229, 126, 78)\n",
-      "item='Ambi Skincare Fade Cream, Oily Skin, 2 oz (57 g)' clust_tuple=(104, 199, 91, 78)\n",
-      "item='Nerium Ad - Age Defying Night Cream (30ml) One Bottle' clust_tuple=(104, 174, 72, 78)\n",
-      "item='Eucerin Redness Relief Soothing Cleanser, 6.8-Ounce Tubes (Pack of 3)' clust_tuple=(104, 229, 126, 224)\n",
+      "item='Fair &amp; White Exclusive Gel Creme Whitenizer #08003' clust_tuple=(104, 43, 178, 111, 0)\n",
+      "item='RoC BRILLIANCE Eye Beautifier  .3 fl. oz.' clust_tuple=(104, 60, 230, 111, 0)\n",
+      "item='RoC BRILLIANCE Night Recharging Moisturizer, Activating Serum &amp; Recharging Creme' clust_tuple=(104, 43, 110, 111, 0)\n",
       "105\n",
-      "item='Jerdon JP910NB 6-Inch Tabletop Two-Sided Swivel Vanity Mirror with 10x Magnification, 11-Inch Height, Nickel Finish' clust_tuple=(105, 199, 126, 254)\n",
-      "item='4 Dozen (48) Long Pink Perm Rods' clust_tuple=(105, 224, 64, 212)\n",
-      "item='Mebco Tortoise Shower Detangler' clust_tuple=(105, 199, 126, 78)\n",
-      "item='WEN Cleansing Creme Shower Comb NEW By Chaz Dean' clust_tuple=(105, 224, 126, 75)\n",
-      "item='Phillips Light Touch 6 Hair Brush' clust_tuple=(105, 211, 126, 75)\n",
-      "item='Mason Pearson Detangling Comb' clust_tuple=(105, 53, 126, 75)\n",
-      "item='DEVA CURL Flexible Hold Hair Spray 10oz' clust_tuple=(105, 191, 126, 148)\n",
-      "item='Conair Mega Self Holding Rollers, 9 Count' clust_tuple=(105, 199, 41, 160)\n",
-      "item='Remington H-1015 Ceramic Compact, Large and Medium Roller' clust_tuple=(105, 224, 44, 155)\n",
-      "item='Simply Beautiful Tangle Teaser Brush - Professional Detangling Hairbrush - Pink, Black, Purple, Blue or Green (Black)' clust_tuple=(105, 199, 126, 195)\n",
+      "item=\"Wee Ones Baby Girls' Knot Genie Teeny Genie-Peaceful Pink\" clust_tuple=(105, 43, 170, 31, 0)\n",
+      "item='Remington Wet 2 Straight 2&quot; Wide Plate Wet/Dry Ceramic Hair Straightening Iron with Tourmaline' clust_tuple=(105, 43, 110, 176, 0)\n",
+      "item='Remington S-9951 Frizz Therapy, Humidity Resistant Ceramic Flat Hairstyling Iron, 1 Inch' clust_tuple=(105, 43, 110, 176, 2)\n",
+      "item='John Frieda Sleek Finish 1 Inch  Flat Iron' clust_tuple=(105, 43, 170, 31, 3)\n",
+      "item='Quick Beader for loading beads on hair' clust_tuple=(105, 43, 170, 31, 2)\n",
+      "item='Remington B90P Pearl Ceramic Paddle Hair Brush with Real Crushed Pearls' clust_tuple=(105, 105, 107, 31, 0)\n",
+      "item='Infiniti Professional Nano Tourmaline Ceramic Curling Iron, 1 1/2-Inch' clust_tuple=(105, 43, 107, 176, 1)\n",
+      "item='Remington S6500 Sleek and Curl Ceramic Pearl Multi-styler Hair Straightener, 1 Inch' clust_tuple=(105, 43, 110, 176, 5)\n",
+      "item='BaByliss PRO Nano Titanium Mini Straightening Iron (1 inch)' clust_tuple=(105, 208, 68, 31, 0)\n",
+      "item='Remington Ci96z1 Silk Ceramic Elliptical Waving Wand' clust_tuple=(105, 208, 170, 31, 2)\n",
       "106\n",
-      "item='32 PCS Makeup Brush Set + Black Pouch Bag' clust_tuple=(106, 199, 141, 78)\n",
-      "item='Bare Escentuals Refillable Mirror Compact Lavender' clust_tuple=(106, 199, 126, 78)\n",
-      "item='SHANY Cosmetics Professional Cotton Makeup Apron with Makeup Artist Brush Belt, Light Weight, 8 Ounce' clust_tuple=(106, 199, 126, 78)\n",
-      "item='SuperNail Brush Cleaner - 2oz / 59ml' clust_tuple=(106, 199, 141, 78)\n",
-      "item='e.l.f. Cosmetics Eye Shadow Brush' clust_tuple=(106, 199, 141, 78)\n",
-      "item='Neewer&reg; Fashion 16 Pcs Pro Purple Makeup Eye Shadow Brush Cosmetic Set Kit' clust_tuple=(106, 199, 141, 78)\n",
-      "item='Shiseido Perfect Foundation Brush (Boxed)' clust_tuple=(106, 199, 72, 78)\n",
-      "item='FantaSea Cosmetic Blending Sponge' clust_tuple=(106, 199, 126, 78)\n",
-      "item='elf Makeup Mist and Set, Clear, 2.02 Ounce and Stipple Brush' clust_tuple=(106, 199, 141, 78)\n",
-      "item='Seki Edge Folding Lash Pin Comb' clust_tuple=(106, 199, 126, 78)\n",
+      "item='50 New Empty Clear Plastic Cosmetic Containers 5 Gram Size Pot Jars Eyshadow Container Lot' clust_tuple=(106, 112, 88, 72, 0)\n",
+      "item='Swisspers Cotton Rounds, 100 Count' clust_tuple=(106, 43, 110, 31, 7)\n",
+      "item='Lipstick - 10pc. Assorted Color Lipstick Set' clust_tuple=(106, 43, 110, 111, 7)\n",
+      "item='Clean &amp; Clear Instant Oil-Absorbing Sheets 50 sheets' clust_tuple=(106, 43, 110, 111, 0)\n",
+      "item='Bare Escentuals Tapered Shadow Brush' clust_tuple=(106, 43, 110, 31, 2)\n",
+      "item='EZ Flow Grand Artist Oval 508 with Brush Cover' clust_tuple=(106, 43, 88, 111, 2)\n",
+      "item='Classic Cotton Balls Jumbo Size, 100 Count' clust_tuple=(106, 43, 88, 111, 0)\n",
+      "item='Sally Hansen Airbrush Leg Tan Glow,4.4 oz' clust_tuple=(106, 43, 110, 31, 1)\n",
+      "item='FantaSea Cosmetic Blending Sponge' clust_tuple=(106, 43, 110, 31, 6)\n",
+      "item='UBU Super Softy Extra Large and Soft Powder Brush' clust_tuple=(106, 43, 110, 31, 8)\n",
       "107\n",
-      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Hipster Collection, &quot;Hippie Chick&quot;' clust_tuple=(107, 229, 126, 78)\n",
-      "item='Home Pedicure PediNova III - Electric Manicure Kit' clust_tuple=(107, 229, 127, 78)\n",
-      "item='Garnier Fructis Style Smoothing Milk, Strong, 5.1 Ounce Bottle' clust_tuple=(107, 229, 126, 78)\n",
-      "item='AquaBella Exfoliating Bath Cloth (Twin Pack) (colors will vary)' clust_tuple=(107, 45, 126, 78)\n",
-      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Fashionista Collection, &quot;diva&quot;' clust_tuple=(107, 229, 126, 78)\n",
-      "item='My Konjac Sponge All Natural Fiber Wave Body Sponge' clust_tuple=(107, 45, 126, 78)\n",
-      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Hipster Collection, &quot;Tough Love&quot;' clust_tuple=(107, 229, 126, 78)\n",
-      "item='Super Solano Professional Hair Dryer - Black' clust_tuple=(107, 229, 141, 78)\n",
-      "item='Betty Dain Stylish Design Mold Resistant Shower Cap, The Hipster Collection, &quot;Flower Power&quot;' clust_tuple=(107, 45, 126, 78)\n",
-      "item='EcoTools Ecopouf Bath Sponge, Assorted Colors (Pack of 6)' clust_tuple=(107, 199, 126, 78)\n",
+      "item='Weleda Calendula Shampoo and Body Wash, 6.8-Ounce' clust_tuple=(107, 209, 16, 111, 0)\n",
+      "item='Africana - 6ml (.2 oz) Perfume Oil by Al-Rehab (Crown Perfumes)' clust_tuple=(107, 43, 110, 111, 13)\n",
+      "item=\"Burt's Bees Radiance Facial Cleanser, 6 Fluid Ounce\" clust_tuple=(107, 95, 110, 111, 0)\n",
+      "item='Kimora Lee Simmons Baby Phat Goddess Eau de Parfum Spray for Women, 3.4 Ounce' clust_tuple=(107, 43, 110, 31, 1)\n",
+      "item='White Full - 6ml (.2 oz) Perfume Oil by Al-Rehab (Crown Perfumes)' clust_tuple=(107, 43, 110, 31, 6)\n",
+      "item='With Love by Hilary Duff for Women 3.3 oz Eau de Parfum Spray' clust_tuple=(107, 43, 88, 111, 1)\n",
+      "item=\"Burt's Bees Orange Essence Facial Cleanser, 4.3 Ounces\" clust_tuple=(107, 43, 110, 111, 0)\n",
+      "item='White Musk - 6ml (.2 oz) Perfume Oil by Al-Rehab (Crown Perfumes)' clust_tuple=(107, 43, 110, 31, 9)\n",
+      "item='Betsey Johnson Too Eau de Parfum Spray, 3.4 Ounce' clust_tuple=(107, 43, 110, 31, 3)\n",
+      "item=\"L'Oreal Paris EverStrong Bodify Shampoo, 8.5-Fluid Ounce\" clust_tuple=(107, 36, 16, 111, 1)\n",
       "108\n",
-      "item='Deodorant Rosemary Mint 2.50 Ounces' clust_tuple=(108, 84, 64, 78)\n",
-      "item='LOREAL Collagen Filler Eye Treatment' clust_tuple=(108, 199, 91, 78)\n",
-      "item=\"Dr. King's Natural Medicine Anxiety and Nervousness, 2 Fluid Ounce\" clust_tuple=(108, 38, 64, 78)\n",
-      "item='NaturalCare Homeopathic Ultra Vein-Gard Leg Therapy Cream, 2.25-Ounce Package' clust_tuple=(108, 199, 41, 78)\n",
-      "item='Bumble and Bumble Leave in Conditioner (8 Ounces)' clust_tuple=(108, 199, 64, 224)\n",
-      "item='Avene Redness relief soothing cream SPF25, 1.35-Ounce Package' clust_tuple=(108, 190, 64, 224)\n",
-      "item=\"Burt's Bees Acne Targeted Spot Treatment, 0.26 Ounce Bottle\" clust_tuple=(108, 199, 41, 78)\n",
-      "item='Nizoral Anti-Dandruff Shampoo, 4 oz.' clust_tuple=(108, 211, 64, 160)\n",
-      "item=\"Dr. King's Natural Medicine Gout Symptom Formula, 2 Fluid Ounce\" clust_tuple=(108, 199, 41, 224)\n",
-      "item='Mary Kay Blemish Control Toner: Formula 3' clust_tuple=(108, 199, 64, 224)\n",
+      "item='Moroccan Mint Scented Anti Cellulite Cream with Indian Ginseng, Oregano, Horsetail, Juniper Berry, Coffee, Caffeine and More,By Diva Stuff' clust_tuple=(108, 101, 88, 111, 2)\n",
+      "item=\"Dr. King's Natural Medicine Gout Symptom Formula, 2 Fluid Ounce\" clust_tuple=(108, 190, 88, 108, 1)\n",
+      "item='Kiss My Face Face Factor Sun Screen for Face and Neck, SPF 30, 2 Ounce Tube' clust_tuple=(108, 43, 110, 121, 0)\n",
+      "item='Wild Growth Hair Care System' clust_tuple=(108, 43, 101, 111, 2)\n",
+      "item='GrandeLASH MD Eyelash Enhancer for Length, Fullness, and Darkness,2 ml' clust_tuple=(108, 43, 101, 111, 3)\n",
+      "item=\"Dr. King's Natural Medicine Sciatic Nerve Formula , 2 Fluid Ounce\" clust_tuple=(108, 190, 88, 108, 2)\n",
+      "item=\"Smiles' Prid Homeopathic Drawing Salve, 18 GM, 1 each\" clust_tuple=(108, 190, 88, 121, 0)\n",
+      "item='Mederma Cream with SPF 30, 20 Grams' clust_tuple=(108, 101, 88, 111, 1)\n",
+      "item=\"Dr. King's Natural Medicine Advanced Arnica , 2 Fluid Ounce\" clust_tuple=(108, 190, 101, 108, 0)\n",
+      "item='IQ Natural Microdermabrasion Exfoliate Crystals Aluminum Oxide 1.5oz' clust_tuple=(108, 101, 101, 121, 0)\n",
       "109\n",
-      "item='ELMA&amp;SANA Golden Argan Oil &reg; 100 Cold Pressed Virgin Organic Certified By Ecocert -1oz(30ml)' clust_tuple=(109, 84, 141, 78)\n",
-      "item='Skinceuticals  Daily Moisturize Pore-minimizing Moisturizer For Normal Or Oily Skin, 2-Ounce Jar' clust_tuple=(109, 199, 141, 78)\n",
-      "item='Deep Sea Cosmetics Sensual Dead Sea Body Salt Scrub milk and honey scent' clust_tuple=(109, 84, 146, 78)\n",
-      "item='Shampoo Green Apple and Ginger 8 Ounces' clust_tuple=(109, 84, 72, 78)\n",
-      "item='Desert Essence Thoroughly Clean Face Wash - Original -- 8.5 fl oz' clust_tuple=(109, 84, 146, 78)\n",
-      "item='Natural Clear Skin Cleansing Sponge' clust_tuple=(109, 84, 141, 78)\n",
-      "item='ELMA&amp;SANA&reg; Golden Argan Oil 100% Pure Cold Pressed Virgin Organic Certified By Ecocert -4oz(120ml)' clust_tuple=(109, 84, 146, 78)\n",
-      "item='Body Lotion, Coconut &amp; Papaya - 13oz' clust_tuple=(109, 84, 146, 78)\n",
-      "item='Eminence Wild Plum Eye Cream, 1.05 Ounce' clust_tuple=(109, 84, 72, 78)\n",
-      "item='Desert Essence Blemish Touch Concealer Light .33 oz' clust_tuple=(109, 199, 141, 78)\n"
+      "item='Moisturizer-Almond Aloe With SPF15 Earth Science 5 oz Cream' clust_tuple=(109, 209, 124, 20, 0)\n",
+      "item=\"Dr. Bronner's &amp; All-One Organic Lotion for Hands &amp; Body, Peppermint, 8-Ounce Pump Bottle\" clust_tuple=(109, 7, 110, 31, 0)\n",
+      "item='AUBREY Collagen Restorative Moisturizer 1.7 fl.oz' clust_tuple=(109, 7, 110, 111, 0)\n",
+      "item='Home Health Roll-On Deodorant Herbal Scent -- 3 fl oz' clust_tuple=(109, 152, 124, 111, 0)\n",
+      "item='Moisturizer-Daily Essential Jojoba/Aloe Desert Essence 4 oz Cream' clust_tuple=(109, 235, 110, 111, 0)\n",
+      "item='Reviva - Seaweed Soap, 4.5 oz bar soap' clust_tuple=(109, 60, 124, 111, 0)\n",
+      "item=\"Organic Peppermint Hair Conditioner and Style Creme Dr. Bronner's 6 oz Cream\" clust_tuple=(109, 209, 110, 111, 1)\n",
+      "item='Perfume-Bourbon Vanilla Ecco Bella Botanicals 1 oz Liquid' clust_tuple=(109, 235, 110, 111, 1)\n",
+      "item='Devoted Creations Forever Black 50XXX Tanning Lotion Instantly Dark Dramatic Bronzing Gelee 12.25 oz' clust_tuple=(109, 184, 110, 72, 0)\n",
+      "item='derma e Pycnognol Facial Toner, Fragrance Free, 6-Ounce Bottle' clust_tuple=(109, 152, 110, 185, 0)\n"
      ]
     }
    ],
@@ -236,23 +211,54 @@
     "    if len(sim) == 0:\n",
     "        continue\n",
     "    print(i)\n",
-    "    for item, clust_tuple in sim:\n",
+    "    for asin, item, clust_tuple in sim:\n",
     "        print(f\"{item=} {clust_tuple=}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "plt.hist(Counter(item[-1][:-1] for item in items_with_tuples).values(), bins=100)\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12101"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(item[-1] for item in items_with_tuples))"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/src/rqvae_data.py b/src/rqvae_data.py
index b31122da..19fb23d1 100644
--- a/src/rqvae_data.py
+++ b/src/rqvae_data.py
@@ -92,9 +92,9 @@ def search_similar_items(items_with_tuples, clust2search, max_cnt=5):
     random.shuffle(items_with_tuples)
     cnt = 0
     similars = []
-    for item, clust_tuple in items_with_tuples:
+    for asin, item, clust_tuple in items_with_tuples:
         if clust_tuple[: len(clust2search)] == clust2search:
-            similars.append((item, clust_tuple))
+            similars.append((asin, item, clust_tuple))
             cnt += 1
         if cnt >= max_cnt:
             return similars

From 7ff12a0ddfda1421581a6a053563610d1442c97f Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Wed, 18 Dec 2024 22:12:43 +0300
Subject: [PATCH 006/175] add review md

---
 review.md | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 todo.txt  | 30 ------------------------------
 2 files changed, 52 insertions(+), 30 deletions(-)
 create mode 100644 review.md
 delete mode 100644 todo.txt

diff --git a/review.md b/review.md
new file mode 100644
index 00000000..bfd3618d
--- /dev/null
+++ b/review.md
@@ -0,0 +1,52 @@
+# Review
+
+## Links
+
+- [dataset](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html)
+
+## Remarks
+
+- no biases on leave one out strategy (обрезаем по строго временному порогу)
+
+## Todo
+
+### Train
+
+- На чем обучать? То есть на каких данных запускать backward pass?
+- train model
+
+### Collisions
+
+- fix collisisons
+- last index = `KMeans(last residuals, n=|last codebook|)` - collision
+- remainder = last embedding
+- auto increment last index (check paper)
+- Research last index aggregation
+  
+#### possible collisions example
+
+- item1: 1 2 3 0
+- item2: 1 2 3 1
+- item3: 4 5 6 0/2
+- item4: 4 5 6 1/3
+
+### Retreive
+
+- single item -> ok
+- too many items -> get embeddings -> score. Softmax(collisions), torch.logsoftmax(logits) -> score -> argmax
+
+### Framework
+
+- positional emb for item & codebook
+- splitting item ?
+
+### positional embeddings example
+
+- (000 111 222) - item
+- (012 012 012) - codebook
+
+### Fixes in framework
+
+- user_id & codebook_ids -> repr ???
+- add last 'sequence' prediction, now only last item is supported
+- dataloader (semantic ids lens)
diff --git a/todo.txt b/todo.txt
deleted file mode 100644
index ca4034bd..00000000
--- a/todo.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-1) no biases on leave one out strategy (обрезаем по строго временному порогу)
-2) data: https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html#:~:text=image%20features-,Beauty,-reviews%20(2%2C023%2C070%20reviews
-
-
-
-# На чем обучать? То есть на каких данных запускать backward pass?
-# train model
-
-# TODO fix collisisons (remainder = last embedding, auto-increment 4th id)
-
-# 1 2 3 0
-# 1 2 3 1
-# 4 5 6 0/2
-# 4 5 6 1/3
-
-# Research last index aggregation
-
-# 1) last index = KMeans(last residuals, n=|last codebook|) - collision
-# 2) auto increment last index (check paper)
-# 3) decoder
-# 4) [(1 2 3), (1 2 3)] single item -> ok
-# 4.1) several -> get embeddings -> score. softmax(collisions), torch.logsoftmax(logits) -> score -> argmax
-
-# pos emb for item & codebook (000 111 222) - item
-# codebook (012 012 012)
-# splitting item ?
-
-user_id & cb_ids -> repr
-last 'seq' prediction
-dataloader (semantic ids lens)
\ No newline at end of file

From 7c827a3c12836a3824d246bfc2f0c80090e8613d Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 23 Dec 2024 00:14:26 +0300
Subject: [PATCH 007/175] add rqvae dataset

---
 configs/train/rqvae_train_config.json |  78 +++++++++++++
 modeling/dataset/base.py              |  68 +++++++++++
 modeling/dataset/samplers/__init__.py |   1 +
 modeling/dataset/samplers/identity.py |  35 ++++++
 modeling/train.py                     |   4 +
 src/main.ipynb                        | 161 ++++++++------------------
 src/rqvae.py                          |   2 +-
 src/train.py                          |  41 +++++++
 8 files changed, 274 insertions(+), 116 deletions(-)
 create mode 100644 configs/train/rqvae_train_config.json
 create mode 100644 modeling/dataset/samplers/identity.py
 create mode 100644 src/train.py

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
new file mode 100644
index 00000000..4bdd6c10
--- /dev/null
+++ b/configs/train/rqvae_train_config.json
@@ -0,0 +1,78 @@
+{
+  "experiment_name": "rqvae_beauty",
+  "best_metric": "eval/ndcg@20",
+  "dataset": {
+    "type": "rqvae",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "samplers": {
+      "type": "identity"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 128,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "rqvae",
+    "user_prefix": "user",
+    "sequence_prefix": "item",
+    "labels_prefix": "labels",
+    "candidate_prefix": "candidates",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.2,
+    "activation": "gelu",
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 1e-4
+    },
+    "clip_grad_threshold": 5.0
+  },
+  "loss": {
+    "type": "composite",
+    "losses": [
+      {
+        "type": "ce",
+        "predictions_prefix": "logits",
+        "labels_prefix": "labels",
+        "output_prefix": "downstream_loss",
+        "weight": 1.0
+      }
+    ],
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      }
+    ]
+  }
+}
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 9240cc2d..94b8884b 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -700,6 +700,74 @@ def meta(self):
             'num_items': self.num_items,
             'max_sequence_length': self.max_sequence_length
         }
+        
+class RqVaeDataset(BaseDataset, config_name='rqvae'):
+
+    def __init__(
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_items
+    ):
+        self._train_sampler = train_sampler
+        self._validation_sampler = validation_sampler
+        self._test_sampler = test_sampler
+        self._num_items = num_items
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
+        train_dataset, validation_dataset, test_dataset = [], [], []
+
+        dataset_path = os.path.join(data_dir_path, '{}.pt'.format('all_data'))
+        df = torch.load(dataset_path, weights_only=False)
+
+        for _idx, sample in df.iterrows():
+            train_dataset.append({
+                'item.id': sample['asin'],
+                'item.embed': sample["embeddings"]
+            })
+            
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+
+        train_sampler = TrainSampler.create_from_config(
+            config['samplers'],
+            dataset=train_dataset
+        )
+        validation_sampler = EvalSampler.create_from_config(
+            config['samplers'],
+            dataset=validation_dataset
+        )
+        test_sampler = EvalSampler.create_from_config(
+            config['samplers'],
+            dataset=test_dataset
+        )
+
+        return cls(
+            train_sampler=train_sampler,
+            validation_sampler=validation_sampler,
+            test_sampler=test_sampler,
+            num_items=len(df)
+        )
+
+    def get_samplers(self):
+        return self._train_sampler, self._validation_sampler, self._test_sampler
+
+    @property
+    def num_items(self):
+        return self._num_items
+
+    @property
+    def max_sequence_length(self):
+        return self._max_sequence_length
+
+    @property
+    def meta(self):
+        return {
+            'num_items': self.num_items
+        }
 
 
 class MultiDomainScientificDataset(ScientificDataset, config_name='multi_domain_scientific'):
diff --git a/modeling/dataset/samplers/__init__.py b/modeling/dataset/samplers/__init__.py
index c2bf473d..41773163 100644
--- a/modeling/dataset/samplers/__init__.py
+++ b/modeling/dataset/samplers/__init__.py
@@ -9,3 +9,4 @@
 from .mclsr import MCLSRTrainSampler, MCLSRPredictionEvalSampler
 from .pop import PopTrainSampler, PopEvalSampler
 from .s3rec import S3RecPretrainTrainSampler, S3RecPretrainEvalSampler
+from .identity import IdentityTrainSampler, IdentityEvalSampler
diff --git a/modeling/dataset/samplers/identity.py b/modeling/dataset/samplers/identity.py
new file mode 100644
index 00000000..ffe01e23
--- /dev/null
+++ b/modeling/dataset/samplers/identity.py
@@ -0,0 +1,35 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
+import copy
+
+
+class IdentityTrainSampler(TrainSampler, config_name='identity'):
+
+    def __init__(self, dataset):
+        super().__init__()
+        self._dataset = dataset
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            dataset=kwargs['dataset']
+        )
+
+    def __getitem__(self, index):
+        sample = copy.deepcopy(self._dataset[index])
+        return sample
+
+
+class IdentityEvalSampler(EvalSampler, config_name='identity'):
+    def __init__(self, dataset):
+        self._dataset = dataset
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            dataset=kwargs['dataset']
+        )
+    
+    def __getitem__(self, index):
+        sample = copy.deepcopy(self._dataset[index])
+        return sample
\ No newline at end of file
diff --git a/modeling/train.py b/modeling/train.py
index b15672bf..0675d42a 100644
--- a/modeling/train.py
+++ b/modeling/train.py
@@ -96,6 +96,10 @@ def main():
         dataset=test_sampler,
         **dataset.meta
     )
+    
+    print(len(train_dataloader))
+    
+    exit(0)
 
     model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
     if 'checkpoint' in config:
diff --git a/src/main.ipynb b/src/main.ipynb
index e942991b..61c464a8 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -31,15 +31,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:07<00:00,  1.95s/it]\n"
+      "100%|██████████| 4/4 [00:05<00:00,  1.38s/it]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'loss': tensor(0.0056, device='cuda:0', grad_fn=<MeanBackward0>),\n",
-       " 'recon_loss': tensor(0.0051, device='cuda:0'),\n",
-       " 'rqvae_loss': tensor(0.0005, device='cuda:0'),\n",
+       "{'loss': tensor(0.0057, grad_fn=<MeanBackward0>),\n",
+       " 'recon_loss': tensor(0.0052),\n",
+       " 'rqvae_loss': tensor(0.0005),\n",
        " 'unique/0': 256,\n",
        " 'unique/1': 256,\n",
        " 'unique/2': 256,\n",
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,109 +96,39 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "100\n",
-      "item='Simple Protecting Light Moisturizer Spf 15, 4.2 Ounce' clust_tuple=(100, 43, 110, 111, 20)\n",
-      "item=\"POND'S Dry Skin Cream, 10.1-oz. (Pack of 3)\" clust_tuple=(100, 144, 110, 111, 0)\n",
-      "item='Kiss My Face Natural Mineral Lotion Sunscreen SPF 40 with Hydresia, 3 Fluid Ounce' clust_tuple=(100, 43, 110, 111, 16)\n",
-      "item=\"Pond's Luminous Moisture Day SPF 15 Lotion, 1.7 Ounce\" clust_tuple=(100, 43, 101, 111, 6)\n",
-      "item='St. Ives Facial Moisturizer, Timeless Skin Collagen Elastin, 10oz' clust_tuple=(100, 43, 110, 121, 2)\n",
-      "item='Clarins Cleansing Milk - Oily to Combination Skin, 7-Ounces Box' clust_tuple=(100, 43, 15, 111, 0)\n",
-      "item=\"POND'S Rejuveness Anti-Wrinkle Cream, 7-oz.\" clust_tuple=(100, 43, 110, 111, 9)\n",
-      "item='Olay Total Effects Blemish Control Salicylic Acid Acne Cleanser 5.0 Fl Oz (Pack of 3)' clust_tuple=(100, 144, 110, 111, 1)\n",
-      "item='Hydroxatone 90 Second Wrinkle Reducer' clust_tuple=(100, 43, 110, 31, 0)\n",
-      "item='Collagen Essence Full Face Mask 10 Pieces' clust_tuple=(100, 144, 88, 111, 0)\n",
-      "101\n",
-      "item='Alba Botanica Fragrance Free SPF 30 Mineral Very Emollient Sunscreen, 4 Ounce Tube' clust_tuple=(101, 141, 88, 111, 10)\n",
-      "item='Alba Aloe Vera Sunblock SPF 30, 4 Ounce' clust_tuple=(101, 141, 88, 111, 6)\n",
-      "item='Alba Botanica Deep Moisturizing Kukui Nut Hawaiian Body Cream, 6.5 Ounce Jar' clust_tuple=(101, 209, 88, 111, 2)\n",
-      "item='Alba Botanica Sparkling Mint Very Emollient Bath &amp; Shower Gel, 32 Ounce Bottle' clust_tuple=(101, 209, 88, 111, 3)\n",
-      "item='Alba Botanica Leave-In Conditioner, 7 Ounce Tubes' clust_tuple=(101, 141, 88, 111, 7)\n",
-      "item='Alba Botanica Very Emollient Sunless Tanner, 4 Ounce Tube' clust_tuple=(101, 141, 88, 111, 14)\n",
-      "item='Alba Botanica Rejuvenating Papaya Mango Hawaiian Body Cream, 6.5 Ounce Jar' clust_tuple=(101, 141, 88, 111, 13)\n",
-      "item='Alba Botanica Pure Lavender SPF 45 Very Emollient Sunscreen, 4 Ounce Tubes (Pack of 2)' clust_tuple=(101, 141, 88, 111, 16)\n",
-      "item='Alba Botanica Even Advanced Sea Mineral Cleansing Gel, 6 Ounce Bottles' clust_tuple=(101, 141, 88, 111, 4)\n",
-      "item='Own Products Rejuvenating Facial Cleanser' clust_tuple=(101, 43, 110, 111, 1)\n",
-      "102\n",
-      "item='Finulite Cellulite Smoothing Massage Mitt' clust_tuple=(102, 43, 88, 72, 0)\n",
-      "item='Neutrogena Healthy Skin Liquid Makeup, Classic Ivory 10, 1 Ounce' clust_tuple=(102, 208, 110, 111, 1)\n",
-      "item='Miracle Skin Transformer Face - SPF 20 - Translucent' clust_tuple=(102, 43, 80, 111, 0)\n",
-      "item='Physicians Formula Super BB All-in-1 Beauty Balm Cream Light Medium -- 1.2 fl oz' clust_tuple=(102, 184, 110, 111, 0)\n",
-      "item='Mary Kay Mineral Powder Foundation Beige 2' clust_tuple=(102, 209, 80, 111, 0)\n",
-      "item='Deep Sea Cosmetics Sensual Dead Sea Body Salt Scrub milk and honey scent' clust_tuple=(102, 43, 88, 111, 2)\n",
-      "item='Garnier Skin Renew Miracle Skin Perfector Bb Cream, Combination To Oily Skin, Light/Medium, 2 Fluid Ounce' clust_tuple=(102, 152, 110, 111, 1)\n",
-      "item='Studio Tone Correcting Powder' clust_tuple=(102, 7, 110, 31, 0)\n",
-      "item='Garnier  Skin Renew Miracle Skin Perfector B.B. Cream, Medium and Deep, 2.5 Fluid Ounce' clust_tuple=(102, 43, 88, 111, 5)\n",
-      "item='Maybelline Mineral Power Finishing Veil Bronzing Powder' clust_tuple=(102, 208, 110, 111, 0)\n",
-      "103\n",
-      "item='Spornette No.316 Deville Hair Brush' clust_tuple=(103, 43, 107, 111, 1)\n",
-      "item='Diane 2&quot; Bobby Pins, Black, 1lb Box' clust_tuple=(103, 43, 110, 31, 5)\n",
-      "item='Spornette German Porcupine Oval Cushion Brush' clust_tuple=(103, 152, 88, 72, 0)\n",
-      "item='Diane 3&quot; Hair Pins, Black, 32/card' clust_tuple=(103, 43, 107, 14, 1)\n",
-      "item='Spornette Large Smooth Operator, 4 Ounce' clust_tuple=(103, 66, 97, 111, 0)\n",
-      "item='10 Pcs Black Plastic Single Prong DIY Hairstyle Alligator Hair Clip 3.1&quot; Long' clust_tuple=(103, 152, 97, 176, 0)\n",
-      "item='Diane Double Prong Clip 1.75 Inches, 80 Clips' clust_tuple=(103, 43, 88, 72, 2)\n",
-      "item='Aquasentials Long Handle Bath Brush (Clear Handle)' clust_tuple=(103, 152, 16, 31, 0)\n",
-      "item='The Wet Comb Detangling Hair Comb - Metallics Collection (Colors May Vary)' clust_tuple=(103, 190, 88, 111, 0)\n",
-      "item='Sexy Hair Hard Up Gel, Packaging May Vary, 16.9-Ounce Pump Bottle' clust_tuple=(103, 43, 88, 111, 0)\n",
-      "104\n",
-      "item='Fair &amp; White Exclusive Gel Creme Whitenizer #08003' clust_tuple=(104, 43, 178, 111, 0)\n",
-      "item='RoC BRILLIANCE Eye Beautifier  .3 fl. oz.' clust_tuple=(104, 60, 230, 111, 0)\n",
-      "item='RoC BRILLIANCE Night Recharging Moisturizer, Activating Serum &amp; Recharging Creme' clust_tuple=(104, 43, 110, 111, 0)\n",
-      "105\n",
-      "item=\"Wee Ones Baby Girls' Knot Genie Teeny Genie-Peaceful Pink\" clust_tuple=(105, 43, 170, 31, 0)\n",
-      "item='Remington Wet 2 Straight 2&quot; Wide Plate Wet/Dry Ceramic Hair Straightening Iron with Tourmaline' clust_tuple=(105, 43, 110, 176, 0)\n",
-      "item='Remington S-9951 Frizz Therapy, Humidity Resistant Ceramic Flat Hairstyling Iron, 1 Inch' clust_tuple=(105, 43, 110, 176, 2)\n",
-      "item='John Frieda Sleek Finish 1 Inch  Flat Iron' clust_tuple=(105, 43, 170, 31, 3)\n",
-      "item='Quick Beader for loading beads on hair' clust_tuple=(105, 43, 170, 31, 2)\n",
-      "item='Remington B90P Pearl Ceramic Paddle Hair Brush with Real Crushed Pearls' clust_tuple=(105, 105, 107, 31, 0)\n",
-      "item='Infiniti Professional Nano Tourmaline Ceramic Curling Iron, 1 1/2-Inch' clust_tuple=(105, 43, 107, 176, 1)\n",
-      "item='Remington S6500 Sleek and Curl Ceramic Pearl Multi-styler Hair Straightener, 1 Inch' clust_tuple=(105, 43, 110, 176, 5)\n",
-      "item='BaByliss PRO Nano Titanium Mini Straightening Iron (1 inch)' clust_tuple=(105, 208, 68, 31, 0)\n",
-      "item='Remington Ci96z1 Silk Ceramic Elliptical Waving Wand' clust_tuple=(105, 208, 170, 31, 2)\n",
-      "106\n",
-      "item='50 New Empty Clear Plastic Cosmetic Containers 5 Gram Size Pot Jars Eyshadow Container Lot' clust_tuple=(106, 112, 88, 72, 0)\n",
-      "item='Swisspers Cotton Rounds, 100 Count' clust_tuple=(106, 43, 110, 31, 7)\n",
-      "item='Lipstick - 10pc. Assorted Color Lipstick Set' clust_tuple=(106, 43, 110, 111, 7)\n",
-      "item='Clean &amp; Clear Instant Oil-Absorbing Sheets 50 sheets' clust_tuple=(106, 43, 110, 111, 0)\n",
-      "item='Bare Escentuals Tapered Shadow Brush' clust_tuple=(106, 43, 110, 31, 2)\n",
-      "item='EZ Flow Grand Artist Oval 508 with Brush Cover' clust_tuple=(106, 43, 88, 111, 2)\n",
-      "item='Classic Cotton Balls Jumbo Size, 100 Count' clust_tuple=(106, 43, 88, 111, 0)\n",
-      "item='Sally Hansen Airbrush Leg Tan Glow,4.4 oz' clust_tuple=(106, 43, 110, 31, 1)\n",
-      "item='FantaSea Cosmetic Blending Sponge' clust_tuple=(106, 43, 110, 31, 6)\n",
-      "item='UBU Super Softy Extra Large and Soft Powder Brush' clust_tuple=(106, 43, 110, 31, 8)\n",
-      "107\n",
-      "item='Weleda Calendula Shampoo and Body Wash, 6.8-Ounce' clust_tuple=(107, 209, 16, 111, 0)\n",
-      "item='Africana - 6ml (.2 oz) Perfume Oil by Al-Rehab (Crown Perfumes)' clust_tuple=(107, 43, 110, 111, 13)\n",
-      "item=\"Burt's Bees Radiance Facial Cleanser, 6 Fluid Ounce\" clust_tuple=(107, 95, 110, 111, 0)\n",
-      "item='Kimora Lee Simmons Baby Phat Goddess Eau de Parfum Spray for Women, 3.4 Ounce' clust_tuple=(107, 43, 110, 31, 1)\n",
-      "item='White Full - 6ml (.2 oz) Perfume Oil by Al-Rehab (Crown Perfumes)' clust_tuple=(107, 43, 110, 31, 6)\n",
-      "item='With Love by Hilary Duff for Women 3.3 oz Eau de Parfum Spray' clust_tuple=(107, 43, 88, 111, 1)\n",
-      "item=\"Burt's Bees Orange Essence Facial Cleanser, 4.3 Ounces\" clust_tuple=(107, 43, 110, 111, 0)\n",
-      "item='White Musk - 6ml (.2 oz) Perfume Oil by Al-Rehab (Crown Perfumes)' clust_tuple=(107, 43, 110, 31, 9)\n",
-      "item='Betsey Johnson Too Eau de Parfum Spray, 3.4 Ounce' clust_tuple=(107, 43, 110, 31, 3)\n",
-      "item=\"L'Oreal Paris EverStrong Bodify Shampoo, 8.5-Fluid Ounce\" clust_tuple=(107, 36, 16, 111, 1)\n",
-      "108\n",
-      "item='Moroccan Mint Scented Anti Cellulite Cream with Indian Ginseng, Oregano, Horsetail, Juniper Berry, Coffee, Caffeine and More,By Diva Stuff' clust_tuple=(108, 101, 88, 111, 2)\n",
-      "item=\"Dr. King's Natural Medicine Gout Symptom Formula, 2 Fluid Ounce\" clust_tuple=(108, 190, 88, 108, 1)\n",
-      "item='Kiss My Face Face Factor Sun Screen for Face and Neck, SPF 30, 2 Ounce Tube' clust_tuple=(108, 43, 110, 121, 0)\n",
-      "item='Wild Growth Hair Care System' clust_tuple=(108, 43, 101, 111, 2)\n",
-      "item='GrandeLASH MD Eyelash Enhancer for Length, Fullness, and Darkness,2 ml' clust_tuple=(108, 43, 101, 111, 3)\n",
-      "item=\"Dr. King's Natural Medicine Sciatic Nerve Formula , 2 Fluid Ounce\" clust_tuple=(108, 190, 88, 108, 2)\n",
-      "item=\"Smiles' Prid Homeopathic Drawing Salve, 18 GM, 1 each\" clust_tuple=(108, 190, 88, 121, 0)\n",
-      "item='Mederma Cream with SPF 30, 20 Grams' clust_tuple=(108, 101, 88, 111, 1)\n",
-      "item=\"Dr. King's Natural Medicine Advanced Arnica , 2 Fluid Ounce\" clust_tuple=(108, 190, 101, 108, 0)\n",
-      "item='IQ Natural Microdermabrasion Exfoliate Crystals Aluminum Oxide 1.5oz' clust_tuple=(108, 101, 101, 121, 0)\n",
-      "109\n",
-      "item='Moisturizer-Almond Aloe With SPF15 Earth Science 5 oz Cream' clust_tuple=(109, 209, 124, 20, 0)\n",
-      "item=\"Dr. Bronner's &amp; All-One Organic Lotion for Hands &amp; Body, Peppermint, 8-Ounce Pump Bottle\" clust_tuple=(109, 7, 110, 31, 0)\n",
-      "item='AUBREY Collagen Restorative Moisturizer 1.7 fl.oz' clust_tuple=(109, 7, 110, 111, 0)\n",
-      "item='Home Health Roll-On Deodorant Herbal Scent -- 3 fl oz' clust_tuple=(109, 152, 124, 111, 0)\n",
-      "item='Moisturizer-Daily Essential Jojoba/Aloe Desert Essence 4 oz Cream' clust_tuple=(109, 235, 110, 111, 0)\n",
-      "item='Reviva - Seaweed Soap, 4.5 oz bar soap' clust_tuple=(109, 60, 124, 111, 0)\n",
-      "item=\"Organic Peppermint Hair Conditioner and Style Creme Dr. Bronner's 6 oz Cream\" clust_tuple=(109, 209, 110, 111, 1)\n",
-      "item='Perfume-Bourbon Vanilla Ecco Bella Botanicals 1 oz Liquid' clust_tuple=(109, 235, 110, 111, 1)\n",
-      "item='Devoted Creations Forever Black 50XXX Tanning Lotion Instantly Dark Dramatic Bronzing Gelee 12.25 oz' clust_tuple=(109, 184, 110, 72, 0)\n",
-      "item='derma e Pycnognol Facial Toner, Fragrance Free, 6-Ounce Bottle' clust_tuple=(109, 152, 110, 185, 0)\n"
+      "230\n",
+      "item='Nicole by OPI Nail Lacquer, Make Mine Lime, 0.5 Fluid Ounce' clust_tuple=(230, 230, 151, 95, 0)\n",
+      "231\n",
+      "item='Bundle Monster 5 Nail Art Nailart Manicure Wheels w/ 3D Designs Glitters Rhinestones Beads - total over 7000pc' clust_tuple=(231, 62, 31, 169, 0)\n",
+      "item='Bundle Monster 26pc Nail Art Image Manicure Stamping Plates-2013 CYO Collection' clust_tuple=(231, 62, 171, 126, 0)\n",
+      "item='MASH Set of 25 Nail Art Nailart Polish Stamp Stamping Manicure Image Plates Accessories Set Kit' clust_tuple=(231, 62, 101, 169, 1)\n",
+      "item='PUEEN 2013 Nail Art Stamp Collection Set 24E - LOVE ELEMENTS - NEW Unique Set of 24 Nailart Polish Stamping Manicure Image Plates Accessories Kit (Totaling 144 Images) with BONUS Storage Case' clust_tuple=(231, 60, 171, 95, 2)\n",
+      "item='Bundle Monster Nail Art Nailart Polish Stamp Stamping Manicure Image Plates Accessories Set Kit 25pc' clust_tuple=(231, 62, 171, 169, 2)\n",
+      "item='Konad Stamping Nail Art Set Care Ca' clust_tuple=(231, 62, 171, 95, 0)\n",
+      "item='Nail Art Plates Bundle - 40 PACK , Nail Polish Stamping Manicure Plates, 40 Different Plates A total of over 400 designs' clust_tuple=(231, 62, 101, 95, 1)\n",
+      "item='CICI&amp;SISI Nail Art Stamp Collection Set Jumbo 2 - Set of 6 JUMBO Nailart Polish Stamping Manicure Image Plates Accessories Kit (Totaling 216 Images) All New Designs with FREE STAMPER &amp; SCRAPER TOOLS SET PROMOTIONAL OFFER' clust_tuple=(231, 62, 171, 95, 7)\n",
+      "item='NAIL ART IMAGE PLATES POLISH STAMP STAMPING MIXED DESIGNS SET KIT 25pc' clust_tuple=(231, 62, 171, 95, 3)\n",
+      "232\n",
+      "item='KLEANCOLOR Nail Lacquer - Chunky Holo Black 236' clust_tuple=(232, 66, 118, 59, 0)\n",
+      "item='OPI Nail Lacquer, DS Reflection, 0.5-Fluid Ounce' clust_tuple=(232, 116, 151, 107, 0)\n",
+      "item='Revlon Sheer Nail Enamel, Sheer Rose 011' clust_tuple=(232, 33, 171, 180, 0)\n",
+      "item=\"OPI Nail Polish Nicki Minaj Collection - Did It On 'Em\" clust_tuple=(232, 62, 151, 95, 1)\n",
+      "item='Opi Nail Polish Nicki Minaj Save Me Nl N17 .5 Oz' clust_tuple=(232, 62, 151, 95, 0)\n",
+      "item='Sensationail Invincible Gel Polish 71587 Pink Chiffon' clust_tuple=(232, 116, 151, 198, 0)\n",
+      "item='Nabi Nail Polish Purple Jumbo Glitter 161 - 15mL' clust_tuple=(232, 66, 151, 180, 0)\n",
+      "item='Jade Is The New Black, NLH45, OPI, Hong Kong / Nail Polish / Lacquer / Enamel' clust_tuple=(232, 131, 171, 95, 0)\n",
+      "233\n",
+      "234\n",
+      "item='Orly Nail Lacquer, Terracotta, 0.6 Fluid Ounce' clust_tuple=(234, 62, 151, 169, 0)\n",
+      "item='LA Colors Nail Art Polish - 48 Colors! NEW!' clust_tuple=(234, 170, 171, 95, 0)\n",
+      "item='Del Sol - Color Changing Nail Polish - Reckless' clust_tuple=(234, 60, 171, 95, 0)\n",
+      "item='Kleancolor - Neon Brights - 6 Nail Lacquer Colors' clust_tuple=(234, 62, 151, 95, 0)\n",
+      "item='Kleancolor - 6 Awesome Nail Lacquers - Set 14' clust_tuple=(234, 62, 151, 95, 1)\n",
+      "235\n",
+      "236\n",
+      "237\n",
+      "238\n",
+      "239\n"
      ]
     }
    ],
@@ -206,23 +136,24 @@
     "from rqvae_data import search_similar_items\n",
     "\n",
     "\n",
-    "for i in range(100, 110):\n",
+    "for i in range(230, 240):\n",
     "    sim = search_similar_items(items_with_tuples, (i,), 10)\n",
     "    if len(sim) == 0:\n",
     "        continue\n",
     "    print(i)\n",
     "    for asin, item, clust_tuple in sim:\n",
-    "        print(f\"{item=} {clust_tuple=}\")"
+    "        if 'nail' in item.lower():\n",
+    "            print(f\"{item=} {clust_tuple=}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -242,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -251,7 +182,7 @@
        "12101"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -284,7 +215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/src/rqvae.py b/src/rqvae.py
index be10ecee..cfd4f902 100644
--- a/src/rqvae.py
+++ b/src/rqvae.py
@@ -57,7 +57,7 @@ def init_codebooks(self, embeddings):
                 kmeans = faiss.Kmeans(
                     d=embeddings_np.shape[1],
                     k=n_clusters,
-                    niter=1000,
+                    niter=100,
                     gpu=torch.cuda.is_available(),
                 )
                 kmeans.train(embeddings_np)
diff --git a/src/train.py b/src/train.py
new file mode 100644
index 00000000..46560dfe
--- /dev/null
+++ b/src/train.py
@@ -0,0 +1,41 @@
+def train_rqvae():
+    data = torch.randn(1000, 10)  # 1000 samples, 10 features
+    train_dataset = MyDataset(data)
+    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+
+    # Initialize model, optimizer, and loss function
+    model = RQVAE(input_dim=10, latent_dim=5)
+    optimizer = optim.Adam(model.parameters(), lr=1e-3)
+
+    # Training loop
+    epochs = 10
+    for epoch in range(epochs):
+        model.train()  # Set model to training mode
+        running_loss = 0.0
+        for batch_idx, batch_data in enumerate(train_loader):
+            batch_data = batch_data.float()  # Ensure the data is in float32 format
+            optimizer.zero_grad()  # Zero the gradients
+
+            # Forward pass
+            recon_batch, z = model(batch_data)
+
+            # Compute the loss (e.g., MSE loss for reconstruction + KL divergence for VAE)
+            # Replace with your actual loss calculation
+            reconstruction_loss = F.mse_loss(recon_batch, batch_data, reduction='sum')
+            # Example of a simple KL divergence term for VAE (replace with your method)
+            kl_loss = -0.5 * torch.sum(1 + z - z.pow(2) - z.exp())  # Example KL loss
+
+            # Total loss (can combine reconstruction and KL losses)
+            loss = reconstruction_loss + kl_loss
+
+            # Backpropagation
+            loss.backward()
+            optimizer.step()
+
+            running_loss += loss.item()
+
+        # Print statistics at the end of the epoch
+        avg_loss = running_loss / len(train_loader.dataset)
+        print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}")
+
+    print("Training finished!")
\ No newline at end of file

From 51410aeb690c13dd567de9c8159c50dbacb829fb Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 23 Dec 2024 14:45:28 +0300
Subject: [PATCH 008/175] rqvae training implemented

---
 .gitignore                              |   1 +
 configs/train/rqvae_train_config.json   |  42 +++----
 modeling/dataloader/batch_processors.py |   8 ++
 modeling/dataset/base.py                |   5 +-
 modeling/loss/base.py                   |  39 +++++++
 modeling/models/__init__.py             |   1 +
 modeling/models/base.py                 |   7 ++
 modeling/models/rqvae.py                | 148 ++++++++++++++++++++++++
 modeling/train.py                       |   4 -
 review.md                               |   4 +
 src/main.ipynb                          | 138 +++++++---------------
 11 files changed, 271 insertions(+), 126 deletions(-)
 create mode 100644 modeling/models/rqvae.py

diff --git a/.gitignore b/.gitignore
index 8b8e6f07..90acddb5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,4 @@ data/*
 tensorboard_logs/*
 .venv
 papers
+checkpoints/*
diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index 4bdd6c10..d5d25308 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "best_metric": "eval/ndcg@20",
+  "train_steps_num": 6000,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
@@ -14,7 +14,7 @@
       "type": "torch",
       "batch_size": 128,
       "batch_processor": {
-        "type": "basic"
+        "type": "embed"
       },
       "drop_last": false,
       "shuffle": true
@@ -23,7 +23,7 @@
       "type": "torch",
       "batch_size": 256,
       "batch_processor": {
-        "type": "basic"
+        "type": "embed"
       },
       "drop_last": false,
       "shuffle": false
@@ -31,17 +31,12 @@
   },
   "model": {
     "type": "rqvae",
-    "user_prefix": "user",
-    "sequence_prefix": "item",
-    "labels_prefix": "labels",
-    "candidate_prefix": "candidates",
-    "embedding_dim": 64,
-    "num_heads": 2,
-    "num_layers": 2,
-    "dim_feedforward": 256,
-    "dropout": 0.2,
-    "activation": "gelu",
-    "layer_norm_eps": 1e-9,
+    "input_dim": 512,
+    "hidden_dim": 128,
+    "n_iter": 100,
+    "codebook_sizes": [256, 256, 256, 256],
+    "should_init_codebooks": true,
+    "should_reinit_unused_clusters": false,
     "initializer_range": 0.02
   },
   "optimizer": {
@@ -50,19 +45,16 @@
       "type": "adam",
       "lr": 1e-4
     },
-    "clip_grad_threshold": 5.0
+    "clip_grad_threshold": 5.0,
+    "scheduler": {
+      "type": "step",
+      "step_size": 100, 
+      "gamma": 0.98
+    }
   },
   "loss": {
-    "type": "composite",
-    "losses": [
-      {
-        "type": "ce",
-        "predictions_prefix": "logits",
-        "labels_prefix": "labels",
-        "output_prefix": "downstream_loss",
-        "weight": 1.0
-      }
-    ],
+    "type": "rqvae_loss",
+    "beta": 0.25,
     "output_prefix": "loss"
   },
   "callback": {
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 436f98fe..9991a073 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -12,6 +12,14 @@ class IdentityBatchProcessor(BaseBatchProcessor, config_name='identity'):
 
     def __call__(self, batch):
         return torch.tensor(batch)
+    
+class EmbedBatchProcessor(BaseBatchProcessor, config_name='embed'):
+
+    def __call__(self, batch):
+        ids = torch.tensor([entry['item.id'] for entry in batch])
+        embeds = torch.stack([entry['item.embed'] for entry in batch])
+        
+        return {'ids': ids, 'embeddings': embeds}
 
 
 class BasicBatchProcessor(BaseBatchProcessor, config_name='basic'):
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 94b8884b..ca9e451c 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -725,7 +725,7 @@ def create_from_config(cls, config, **kwargs):
 
         for _idx, sample in df.iterrows():
             train_dataset.append({
-                'item.id': sample['asin'],
+                'item.id': sample['asin_numeric'],
                 'item.embed': sample["embeddings"]
             })
             
@@ -766,7 +766,8 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            'num_items': self.num_items
+            'num_items': self.num_items,
+            'train_sampler': self._train_sampler
         }
 
 
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index d6bc56aa..ee7c1879 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -104,6 +104,45 @@ def forward(self, inputs):
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
+    
+class RqVaeLoss(TorchLoss, config_name='rqvae_loss'):
+
+    def __init__(self, beta, output_prefix=None):
+        super().__init__()
+        self.beta = beta
+        self._output_prefix = output_prefix
+        
+        self._loss = nn.MSELoss()
+        
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        # 0.25 is default Beta in paper
+        return cls(
+            beta = config.get('beta', 0.25),
+            output_prefix = config['output_prefix'],
+        )
+                    
+    def forward(self, inputs):
+        embeddings = inputs["embeddings"]
+        embeddings_restored = inputs["embeddings_restored"]
+        remainders = inputs["remainders"]
+        codebooks_vectors = inputs["codebooks_vectors"]
+        
+        rqvae_loss = 0
+        
+        for remainder, codebook_vectors in zip(remainders, codebooks_vectors):
+            rqvae_loss += self.beta * self._loss(
+                remainder, codebook_vectors.detach()
+            )
+            rqvae_loss += self._loss(codebook_vectors, remainder.detach())
+            
+        recon_loss = self._loss(embeddings_restored, embeddings)
+        loss = (recon_loss + rqvae_loss).mean() # TODO mean?
+        
+        if self._output_prefix is not None:
+            inputs[self._output_prefix] = loss.cpu().item()
+        
+        return loss
 
 
 class BinaryCrossEntropyLoss(TorchLoss, config_name='bce'):
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index afed8466..e52aed9e 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -15,3 +15,4 @@
 from .sasrec import SasRecModel, SasRecInBatchModel
 from .sasrec_ce import SasRecCeModel
 from .s3rec import S3RecModel
+from .rqvae import RqVaeModel
diff --git a/modeling/models/base.py b/modeling/models/base.py
index e09a0eb4..28be6a98 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -27,6 +27,13 @@ def _init_weights(self, initializer_range):
                     )
             elif 'bias' in key:
                 nn.init.zeros_(value.data)
+            elif 'codebook' in key:
+                nn.init.trunc_normal_(
+                    value.data,
+                    std=initializer_range,
+                    a=-2 * initializer_range,
+                    b=2 * initializer_range
+                )
             else:
                 raise ValueError(f'Unknown transformer weight: {key}')
 
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
new file mode 100644
index 00000000..616ca023
--- /dev/null
+++ b/modeling/models/rqvae.py
@@ -0,0 +1,148 @@
+from models.base import TorchModel
+
+import torch
+import torch.nn as nn
+
+import torch
+
+from tqdm import tqdm
+import faiss
+
+class RqVaeModel(TorchModel, config_name='rqvae'):
+
+    def __init__(
+            self,
+            all_data,
+            input_dim: int,
+            hidden_dim: int,
+            n_iter: int,
+            codebook_sizes: list[int],
+            should_init_codebooks,
+            should_reinit_unused_clusters,
+            initializer_range
+    ):
+        super().__init__()
+
+        self.n_iter = n_iter
+
+        # Kmeans initialization
+        self.should_init_codebooks = should_init_codebooks
+
+        # Trick with re-initing empty clusters
+        self.should_reinit_unused_clusters = should_reinit_unused_clusters
+
+        # Enc and dec are mirrored copies of each other
+        self.encoder = self.make_encoding_tower(input_dim, hidden_dim)
+        self.decoder = self.make_encoding_tower(hidden_dim, input_dim)
+
+        # Default initialization of codebook
+        self.codebooks = torch.nn.ParameterList()
+        for codebook_size in codebook_sizes:
+            cb = torch.FloatTensor(codebook_size, hidden_dim)
+            self.codebooks.append(cb)
+         
+        self._init_weights(initializer_range)
+        
+        embeddings = torch.stack([entry['item.embed'] for entry in all_data._dataset])
+        
+        if self.should_init_codebooks:
+            self.init_codebooks(embeddings)
+            print('Codebooks initialized with Faiss Kmeans')
+            self.should_init_codebooks = False
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            all_data=kwargs['train_sampler'],
+            input_dim=config['input_dim'],
+            hidden_dim=config['hidden_dim'],
+            n_iter=config['n_iter'],
+            codebook_sizes=config['codebook_sizes'],
+            should_init_codebooks=config.get('should_init_codebooks', False),
+            should_reinit_unused_clusters=config.get('should_reinit_unused_clusters', False),
+            initializer_range=config.get('initializer_range', 0.02)
+        )
+
+    def make_encoding_tower(self, d1: int, d2: int):
+        return torch.nn.Linear(d1, d2, bias=False)
+
+    @staticmethod
+    def get_codebook_indices(remainder, codebook):
+        dist = torch.cdist(remainder, codebook)
+        return dist.argmin(dim=-1)
+
+    def init_codebooks(self, embeddings):
+        with torch.no_grad():
+            remainder = self.encoder(embeddings)
+            for codebook in self.codebooks:
+                embeddings_np = remainder.cpu().numpy()
+                n_clusters = codebook.shape[0]
+
+                kmeans = faiss.Kmeans(
+                    d=embeddings_np.shape[1],
+                    k=n_clusters,
+                    niter=self.n_iter,
+                    gpu=torch.cuda.is_available(),
+                )
+                kmeans.train(embeddings_np)
+
+                codebook.data = torch.from_numpy(kmeans.centroids).to(codebook.device)
+
+                codebook_indices = self.get_codebook_indices(remainder, codebook)
+                codebook_vectors = codebook[codebook_indices]
+                remainder = remainder - codebook_vectors
+
+    @staticmethod
+    def reinit_unused_clusters(remainder, codebook, codebook_indices):
+        with torch.no_grad():
+            is_used = torch.full((codebook.shape[0],), False, device=codebook.device)
+            unique_indices = codebook_indices.unique()
+            is_used[unique_indices] = True
+            rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(),))
+            codebook[~is_used] = remainder[rand_input]
+    
+    def train_pass(self, embeddings):
+        latent_vector = self.encoder(embeddings)
+
+        latent_restored = 0
+        
+        num_unique_clusters = []
+        remainder = latent_vector
+        
+        remainders = []
+        codebooks_vectors = []
+        
+        for codebook in self.codebooks:
+            remainders.append(remainder)
+            
+            codebook_indices = self.get_codebook_indices(remainder, codebook)
+            codebook_vectors = codebook[codebook_indices]
+
+            if self.should_reinit_unused_clusters:
+                self.reinit_unused_clusters(remainder, codebook, codebook_indices)
+
+            num_unique_clusters.append(codebook_indices.unique().shape[0])
+            
+            codebooks_vectors.append(codebook_vectors)
+
+            latent_restored = latent_restored + codebook_vectors
+            remainder = remainder - codebook_vectors
+
+        # Here we cast recon loss to latent vector
+        latent_restored = latent_vector + (latent_restored - latent_vector).detach()
+        embeddings_restored = self.decoder(latent_restored)
+
+        return {
+            "embeddings": embeddings,
+            "embeddings_restored": embeddings_restored,
+            "remainders": remainders,
+            "codebooks_vectors": codebooks_vectors
+        }
+
+    def forward(self, inputs):
+        embeddings = inputs["embeddings"]
+        
+        if self.training:  # training mode
+            return self.train_pass(embeddings)
+        else:  # eval mode
+            raise NotImplementedError("No eval mode for RqVae model!")
diff --git a/modeling/train.py b/modeling/train.py
index 0675d42a..b15672bf 100644
--- a/modeling/train.py
+++ b/modeling/train.py
@@ -96,10 +96,6 @@ def main():
         dataset=test_sampler,
         **dataset.meta
     )
-    
-    print(len(train_dataloader))
-    
-    exit(0)
 
     model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
     if 'checkpoint' in config:
diff --git a/review.md b/review.md
index bfd3618d..572accc1 100644
--- a/review.md
+++ b/review.md
@@ -1,5 +1,9 @@
 # Review
 
+## Todos
+
+- TODO backward on mean loss? in `RqVae`
+
 ## Links
 
 - [dataset](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html)
diff --git a/src/main.ipynb b/src/main.ipynb
index 61c464a8..f6d7b18b 100644
--- a/src/main.ipynb
+++ b/src/main.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,33 +24,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 4/4 [00:05<00:00,  1.38s/it]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'loss': tensor(0.0057, grad_fn=<MeanBackward0>),\n",
-       " 'recon_loss': tensor(0.0052),\n",
-       " 'rqvae_loss': tensor(0.0005),\n",
-       " 'unique/0': 256,\n",
-       " 'unique/1': 256,\n",
-       " 'unique/2': 256,\n",
-       " 'unique/3': 256}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "embs.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from rqvae import RQVAE\n",
     "\n",
@@ -89,49 +74,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "230\n",
-      "item='Nicole by OPI Nail Lacquer, Make Mine Lime, 0.5 Fluid Ounce' clust_tuple=(230, 230, 151, 95, 0)\n",
-      "231\n",
-      "item='Bundle Monster 5 Nail Art Nailart Manicure Wheels w/ 3D Designs Glitters Rhinestones Beads - total over 7000pc' clust_tuple=(231, 62, 31, 169, 0)\n",
-      "item='Bundle Monster 26pc Nail Art Image Manicure Stamping Plates-2013 CYO Collection' clust_tuple=(231, 62, 171, 126, 0)\n",
-      "item='MASH Set of 25 Nail Art Nailart Polish Stamp Stamping Manicure Image Plates Accessories Set Kit' clust_tuple=(231, 62, 101, 169, 1)\n",
-      "item='PUEEN 2013 Nail Art Stamp Collection Set 24E - LOVE ELEMENTS - NEW Unique Set of 24 Nailart Polish Stamping Manicure Image Plates Accessories Kit (Totaling 144 Images) with BONUS Storage Case' clust_tuple=(231, 60, 171, 95, 2)\n",
-      "item='Bundle Monster Nail Art Nailart Polish Stamp Stamping Manicure Image Plates Accessories Set Kit 25pc' clust_tuple=(231, 62, 171, 169, 2)\n",
-      "item='Konad Stamping Nail Art Set Care Ca' clust_tuple=(231, 62, 171, 95, 0)\n",
-      "item='Nail Art Plates Bundle - 40 PACK , Nail Polish Stamping Manicure Plates, 40 Different Plates A total of over 400 designs' clust_tuple=(231, 62, 101, 95, 1)\n",
-      "item='CICI&amp;SISI Nail Art Stamp Collection Set Jumbo 2 - Set of 6 JUMBO Nailart Polish Stamping Manicure Image Plates Accessories Kit (Totaling 216 Images) All New Designs with FREE STAMPER &amp; SCRAPER TOOLS SET PROMOTIONAL OFFER' clust_tuple=(231, 62, 171, 95, 7)\n",
-      "item='NAIL ART IMAGE PLATES POLISH STAMP STAMPING MIXED DESIGNS SET KIT 25pc' clust_tuple=(231, 62, 171, 95, 3)\n",
-      "232\n",
-      "item='KLEANCOLOR Nail Lacquer - Chunky Holo Black 236' clust_tuple=(232, 66, 118, 59, 0)\n",
-      "item='OPI Nail Lacquer, DS Reflection, 0.5-Fluid Ounce' clust_tuple=(232, 116, 151, 107, 0)\n",
-      "item='Revlon Sheer Nail Enamel, Sheer Rose 011' clust_tuple=(232, 33, 171, 180, 0)\n",
-      "item=\"OPI Nail Polish Nicki Minaj Collection - Did It On 'Em\" clust_tuple=(232, 62, 151, 95, 1)\n",
-      "item='Opi Nail Polish Nicki Minaj Save Me Nl N17 .5 Oz' clust_tuple=(232, 62, 151, 95, 0)\n",
-      "item='Sensationail Invincible Gel Polish 71587 Pink Chiffon' clust_tuple=(232, 116, 151, 198, 0)\n",
-      "item='Nabi Nail Polish Purple Jumbo Glitter 161 - 15mL' clust_tuple=(232, 66, 151, 180, 0)\n",
-      "item='Jade Is The New Black, NLH45, OPI, Hong Kong / Nail Polish / Lacquer / Enamel' clust_tuple=(232, 131, 171, 95, 0)\n",
-      "233\n",
-      "234\n",
-      "item='Orly Nail Lacquer, Terracotta, 0.6 Fluid Ounce' clust_tuple=(234, 62, 151, 169, 0)\n",
-      "item='LA Colors Nail Art Polish - 48 Colors! NEW!' clust_tuple=(234, 170, 171, 95, 0)\n",
-      "item='Del Sol - Color Changing Nail Polish - Reckless' clust_tuple=(234, 60, 171, 95, 0)\n",
-      "item='Kleancolor - Neon Brights - 6 Nail Lacquer Colors' clust_tuple=(234, 62, 151, 95, 0)\n",
-      "item='Kleancolor - 6 Awesome Nail Lacquers - Set 14' clust_tuple=(234, 62, 151, 95, 1)\n",
-      "235\n",
-      "236\n",
-      "237\n",
-      "238\n",
-      "239\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from rqvae_data import search_similar_items\n",
     "\n",
@@ -148,20 +93,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from collections import Counter\n",
     "import matplotlib.pyplot as plt\n",
@@ -173,24 +107,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12101"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "len(set(item[-1] for item in items_with_tuples))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "labels = df['asin']\n",
+    "\n",
+    "le = preprocessing.LabelEncoder()\n",
+    "targets = le.fit_transform(labels)\n",
+    "\n",
+    "df['asin_numeric'] = targets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(df, './all_data.pt')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 7e17fd6604498b5c3c95b4fe7de8d6dd115db544 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 23 Dec 2024 14:50:24 +0300
Subject: [PATCH 009/175] revert unneded changes

---
 configs/train/sasrec_train_config.json        |   9 +-
 notebooks/AmazonBeautyDatasetStatistics.ipynb | 111 ++++++++--------
 src/rqvae.py                                  | 118 ------------------
 src/train.py                                  |  41 ------
 4 files changed, 61 insertions(+), 218 deletions(-)
 delete mode 100644 src/rqvae.py
 delete mode 100644 src/train.py

diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 32fd1db1..1336b91c 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -2,7 +2,7 @@
   "experiment_name": "sasrec_test",
   "best_metric": "eval/ndcg@20",
   "dataset": {
-    "type": "scientific",
+    "type": "sequence",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
@@ -60,9 +60,10 @@
     "type": "composite",
     "losses": [
       {
-        "type": "bpr",
-        "positive_prefix": "positive_scores",
-        "negative_prefix": "negative_scores",
+        "type": "sasrec",
+        "positive_prefix": "positive_embeddings",
+        "negative_prefix": "negative_embeddings",
+        "representation_prefix": "current_embeddings",
         "output_prefix": "downstream_loss"
       }
     ],
diff --git a/notebooks/AmazonBeautyDatasetStatistics.ipynb b/notebooks/AmazonBeautyDatasetStatistics.ipynb
index b59e40ac..a1a2c3d2 100644
--- a/notebooks/AmazonBeautyDatasetStatistics.ipynb
+++ b/notebooks/AmazonBeautyDatasetStatistics.ipynb
@@ -174,10 +174,21 @@
    "execution_count": 6,
    "id": "f3182b59",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1210271, (1210271,))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# df.user_id = pd.factorize(df.user_id)[0] + 1\n",
-    "# df.user_id.min(), df.user_id.max(), df.user_id.unique().shape"
+    "df.user_id = pd.factorize(df.user_id)[0] + 1\n",
+    "df.user_id.min(), df.user_id.max(), df.user_id.unique().shape"
    ]
   },
   {
@@ -185,10 +196,21 @@
    "execution_count": 7,
    "id": "7225ddeb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 249274, (249274,))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# df.item_id = pd.factorize(df.item_id)[0] + 1\n",
-    "# df.item_id.min(), df.item_id.max(), df.item_id.unique().shape"
+    "df.item_id = pd.factorize(df.item_id)[0] + 1\n",
+    "df.item_id.min(), df.item_id.max(), df.item_id.unique().shape"
    ]
   },
   {
@@ -227,36 +249,36 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>A39HTATAQ9V7YF</td>\n",
-       "      <td>0205616461</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>5.0</td>\n",
        "      <td>1369699200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>A3JM6GV9MNOF9X</td>\n",
-       "      <td>0558925278</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1355443200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>A1Z513UWSAAO0F</td>\n",
-       "      <td>0558925278</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "      <td>5.0</td>\n",
        "      <td>1404691200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>A1WMRR494NWEWV</td>\n",
-       "      <td>0733001998</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
        "      <td>4.0</td>\n",
        "      <td>1382572800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>A3IAAVS479H7M7</td>\n",
-       "      <td>0737104473</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1274227200</td>\n",
        "    </tr>\n",
@@ -265,12 +287,12 @@
        "</div>"
       ],
       "text/plain": [
-       "          user_id     item_id  rating   timestamp\n",
-       "0  A39HTATAQ9V7YF  0205616461     5.0  1369699200\n",
-       "1  A3JM6GV9MNOF9X  0558925278     3.0  1355443200\n",
-       "2  A1Z513UWSAAO0F  0558925278     5.0  1404691200\n",
-       "3  A1WMRR494NWEWV  0733001998     4.0  1382572800\n",
-       "4  A3IAAVS479H7M7  0737104473     1.0  1274227200"
+       "   user_id  item_id  rating   timestamp\n",
+       "0        1        1     5.0  1369699200\n",
+       "1        2        2     3.0  1355443200\n",
+       "2        3        2     5.0  1404691200\n",
+       "3        4        3     4.0  1382572800\n",
+       "4        5        4     1.0  1274227200"
       ]
      },
      "execution_count": 8,
@@ -292,7 +314,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2023070/2023070 [01:14<00:00, 27172.73it/s]"
+      "2023070it [01:12, 28030.70it/s]"
      ]
     },
     {
@@ -313,10 +335,10 @@
    "source": [
     "data = []\n",
     "\n",
-    "for _, row in tqdm(df.iterrows(), total=len(df)):\n",
+    "for _, row in tqdm(df.iterrows()):\n",
     "    data.append({\n",
-    "        'user_id': row.user_id,\n",
-    "        'item_id': row.item_id,\n",
+    "        'user_id': int(row.user_id),\n",
+    "        'item_id': int(row.item_id),\n",
     "        'timestamp': int(row.timestamp)\n",
     "    })\n",
     "\n",
@@ -333,7 +355,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2023070/2023070 [00:05<00:00, 375198.84it/s]\n"
+      "100%|████████████████████████████████████████████████████████████████████| 2023070/2023070 [00:04<00:00, 421809.17it/s]\n"
      ]
     },
     {
@@ -410,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "id": "dcc9f464",
    "metadata": {},
    "outputs": [
@@ -418,8 +440,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 1210271/1210271 [00:01<00:00, 738179.23it/s]\n",
-      "100%|██████████| 249274/249274 [00:00<00:00, 486768.45it/s]\n"
+      "100%|████████████████████████████████████████████████████████████████████| 1210271/1210271 [00:01<00:00, 840307.33it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████| 249274/249274 [00:00<00:00, 571256.27it/s]\n"
      ]
     }
    ],
@@ -472,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "id": "16df848f",
    "metadata": {},
    "outputs": [
@@ -498,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "id": "90f7ccc1",
    "metadata": {},
    "outputs": [],
@@ -510,32 +532,11 @@
     "        ]))\n",
     "        f.write('\\n')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "393b8b57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pickle import dump\n",
-    "\n",
-    "\n",
-    "dump(set(item_mapping.keys()), open('../data/Beauty/item_mapping.pkl', 'wb'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2eb5b656",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -549,7 +550,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.9.17"
   }
  },
  "nbformat": 4,
diff --git a/src/rqvae.py b/src/rqvae.py
deleted file mode 100644
index cfd4f902..00000000
--- a/src/rqvae.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import torch
-
-from tqdm import tqdm
-import faiss
-
-
-class RQVAE(torch.nn.Module):
-    def __init__(
-        self,
-        input_dim: int,
-        hidden_dim: int,
-        beta: float,
-        codebook_sizes: list[int],
-        should_init_codebooks=False,
-        should_reinit_unused_clusters=False,
-    ):
-        super().__init__()
-
-        # In original paper it is set to 0.25
-        self.beta = beta
-
-        # Kmeans initialization
-        self.should_init_codebooks = should_init_codebooks
-
-        # Trick with re-initing empty clusters
-        self.should_reinit_unused_clusters = should_reinit_unused_clusters
-
-        self.mse_loss = torch.nn.MSELoss()
-
-        # Enc and dec are mirrored copies of each other
-        self.encoder = self.make_encoding_tower(input_dim, hidden_dim)
-        self.decoder = self.make_encoding_tower(hidden_dim, input_dim)
-
-        # Default initialization of codebook
-        self.codebooks = torch.nn.ParameterList()
-        for codebook_size in codebook_sizes:
-            cb = torch.FloatTensor(codebook_size, hidden_dim)
-            with torch.no_grad():
-                torch.nn.init.trunc_normal_(cb, std=0.02, a=-2 * 0.02, b=2 * 0.02)
-            self.codebooks.append(cb)
-
-    def make_encoding_tower(self, d1: int, d2: int):
-        return torch.nn.Linear(d1, d2, bias=False)
-
-    @staticmethod
-    def get_codebook_indices(remainder, codebook):
-        dist = torch.cdist(remainder, codebook)
-        return dist.argmin(dim=-1)
-
-    def init_codebooks(self, embeddings):
-        with torch.no_grad():
-            remainder = self.encoder(embeddings)
-            for codebook in tqdm(self.codebooks):
-                embeddings_np = remainder.cpu().numpy()
-                n_clusters = codebook.shape[0]
-
-                kmeans = faiss.Kmeans(
-                    d=embeddings_np.shape[1],
-                    k=n_clusters,
-                    niter=100,
-                    gpu=torch.cuda.is_available(),
-                )
-                kmeans.train(embeddings_np)
-
-                codebook.data = torch.from_numpy(kmeans.centroids).to(codebook.device)
-
-                codebook_indices = self.get_codebook_indices(remainder, codebook)
-                codebook_vectors = codebook[codebook_indices]
-                remainder = remainder - codebook_vectors
-
-    @staticmethod
-    def reinit_unused_clusters(remainder, codebook, codebook_indices):
-        with torch.no_grad():
-            is_used = torch.full((codebook.shape[0],), False, device=codebook.device)
-            unique_indices = codebook_indices.unique()
-            is_used[unique_indices] = True
-            rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(),))
-            codebook[~is_used] = remainder[rand_input]
-
-    def forward(self, inputs):
-        embeddings = inputs["embedding"]
-        if self.should_init_codebooks:
-            self.init_codebooks(embeddings)
-            self.should_init_codebooks = False
-        latent_vector = self.encoder(embeddings)
-
-        latent_restored = 0
-        rqvae_loss = 0
-        num_unique_clusters = []
-        remainder = latent_vector
-        for codebook in self.codebooks:
-            codebook_indices = self.get_codebook_indices(remainder, codebook)
-            codebook_vectors = codebook[codebook_indices]
-
-            if self.should_reinit_unused_clusters:
-                self.reinit_unused_clusters(remainder, codebook, codebook_indices)
-
-            num_unique_clusters.append(codebook_indices.unique().shape[0])
-            rqvae_loss += self.beta * self.mse_loss(
-                remainder, codebook_vectors.detach()
-            )
-            rqvae_loss += self.mse_loss(codebook_vectors, remainder.detach())
-
-            latent_restored = latent_restored + codebook_vectors
-            remainder = remainder - codebook_vectors
-
-        # Here we cast recon loss to latent vector
-        latent_restored = latent_vector + (latent_restored - latent_vector).detach()
-        embeddings_restored = self.decoder(latent_restored)
-        recon_loss = self.mse_loss(embeddings_restored, embeddings)
-        loss = (recon_loss + rqvae_loss).mean()
-
-        return {
-            "loss": loss,
-            "recon_loss": recon_loss.mean().detach(),
-            "rqvae_loss": rqvae_loss.mean().detach(),
-            **{f"unique/{i}": cnt for i, cnt in enumerate(num_unique_clusters)},
-        }
diff --git a/src/train.py b/src/train.py
deleted file mode 100644
index 46560dfe..00000000
--- a/src/train.py
+++ /dev/null
@@ -1,41 +0,0 @@
-def train_rqvae():
-    data = torch.randn(1000, 10)  # 1000 samples, 10 features
-    train_dataset = MyDataset(data)
-    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-
-    # Initialize model, optimizer, and loss function
-    model = RQVAE(input_dim=10, latent_dim=5)
-    optimizer = optim.Adam(model.parameters(), lr=1e-3)
-
-    # Training loop
-    epochs = 10
-    for epoch in range(epochs):
-        model.train()  # Set model to training mode
-        running_loss = 0.0
-        for batch_idx, batch_data in enumerate(train_loader):
-            batch_data = batch_data.float()  # Ensure the data is in float32 format
-            optimizer.zero_grad()  # Zero the gradients
-
-            # Forward pass
-            recon_batch, z = model(batch_data)
-
-            # Compute the loss (e.g., MSE loss for reconstruction + KL divergence for VAE)
-            # Replace with your actual loss calculation
-            reconstruction_loss = F.mse_loss(recon_batch, batch_data, reduction='sum')
-            # Example of a simple KL divergence term for VAE (replace with your method)
-            kl_loss = -0.5 * torch.sum(1 + z - z.pow(2) - z.exp())  # Example KL loss
-
-            # Total loss (can combine reconstruction and KL losses)
-            loss = reconstruction_loss + kl_loss
-
-            # Backpropagation
-            loss.backward()
-            optimizer.step()
-
-            running_loss += loss.item()
-
-        # Print statistics at the end of the epoch
-        avg_loss = running_loss / len(train_loader.dataset)
-        print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}")
-
-    print("Training finished!")
\ No newline at end of file

From 1186e4fbfeddcb21fd1a42c772c99f6f361021a7 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 23 Dec 2024 14:55:19 +0300
Subject: [PATCH 010/175] add eval pass for rqvae (from checkpoint)

---
 modeling/models/rqvae.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 616ca023..b105d9e3 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -138,6 +138,13 @@ def train_pass(self, embeddings):
             "remainders": remainders,
             "codebooks_vectors": codebooks_vectors
         }
+        
+    def eval_pass(self, embeddings):
+        ind_lists = []
+        for cb in self.codebooks:
+            dist = torch.cdist(self.encoder(embeddings), cb)
+            ind_lists.append(dist.argmin(dim=-1).cpu().numpy())
+        return zip(*ind_lists)
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
@@ -145,4 +152,4 @@ def forward(self, inputs):
         if self.training:  # training mode
             return self.train_pass(embeddings)
         else:  # eval mode
-            raise NotImplementedError("No eval mode for RqVae model!")
+            return self.eval_pass(embeddings)

From fba00ba567be8cab1ba1dc65e628c807485986eb Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 23 Dec 2024 21:48:49 +0300
Subject: [PATCH 011/175] e2e model

---
 configs/train/rqvae_train_config.json |    8 +-
 configs/train/tiger_train_config.json |   72 ++
 modeling/main.ipynb                   | 1270 +++++++++++++++++++++++++
 modeling/models/rqvae.py              |   12 +-
 modeling/models/tiger.py              |   96 ++
 modeling/rqvae/__init__.py            |    0
 {src => modeling/rqvae}/collisions.py |    0
 {src => modeling/rqvae}/rqvae_data.py |   10 +-
 review.md                             |    7 +-
 src/main.ipynb                        |  171 ----
 src/main.py                           |   46 -
 11 files changed, 1455 insertions(+), 237 deletions(-)
 create mode 100644 configs/train/tiger_train_config.json
 create mode 100644 modeling/main.ipynb
 create mode 100644 modeling/models/tiger.py
 create mode 100644 modeling/rqvae/__init__.py
 rename {src => modeling/rqvae}/collisions.py (100%)
 rename {src => modeling/rqvae}/rqvae_data.py (88%)
 delete mode 100644 src/main.ipynb
 delete mode 100644 src/main.py

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index d5d25308..e9f3fe2f 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "train_steps_num": 6000,
+  "train_steps_num": 2000,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
@@ -12,7 +12,7 @@
   "dataloader": {
     "train": {
       "type": "torch",
-      "batch_size": 128,
+      "batch_size": 256,
       "batch_processor": {
         "type": "embed"
       },
@@ -36,7 +36,7 @@
     "n_iter": 100,
     "codebook_sizes": [256, 256, 256, 256],
     "should_init_codebooks": true,
-    "should_reinit_unused_clusters": false,
+    "should_reinit_unused_clusters": true,
     "initializer_range": 0.02
   },
   "optimizer": {
@@ -49,7 +49,7 @@
     "scheduler": {
       "type": "step",
       "step_size": 100, 
-      "gamma": 0.98
+      "gamma": 0.96
     }
   },
   "loss": {
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
new file mode 100644
index 00000000..23e7ef6b
--- /dev/null
+++ b/configs/train/tiger_train_config.json
@@ -0,0 +1,72 @@
+{
+  "experiment_name": "tiger_beauty",
+  "train_steps_num": 5000,
+  "dataset": {
+    "type": "rqvae",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "samplers": {
+      "type": "identity"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 128,
+      "batch_processor": {
+        "type": "embed"
+      },
+      "drop_last": false,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "embed"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "emb_dim": 512,
+    "n_tokens": 256,
+    "n_codebooks": 4,
+    "nhead": 8,
+    "num_encoder_layers": 6,
+    "num_decoder_layers": 6,
+    "dim_feedforward": 2048,
+    "dropout": 0.1
+  },
+  "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+  "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 1e-4
+    },
+    "clip_grad_threshold": 5.0,
+    "scheduler": {
+      "type": "step",
+      "step_size": 100, 
+      "gamma": 0.98
+    }
+  },
+  "loss": {
+    "type": "rqvae_loss",
+    "beta": 0.25,
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      }
+    ]
+  }
+}
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
new file mode 100644
index 00000000..db47d562
--- /dev/null
+++ b/modeling/main.ipynb
@@ -0,0 +1,1270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "from rqvae.rqvae_data import get_data\n",
+    "\n",
+    "df = get_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embs = torch.stack(df[\"embeddings\"].tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "from utils import DEVICE\n",
+    "from models.base import BaseModel\n",
+    "\n",
+    "config = json.load(open(\"../configs/train/tiger_train_config.json\"))\n",
+    "\n",
+    "rqvae_train_config = json.load(open(config['rqvae_train_config_path']))\n",
+    "rq_vae_config = rqvae_train_config['model']\n",
+    "rq_vae_config['should_init_codebooks'] = False\n",
+    "\n",
+    "rqvae_model = BaseModel.create_from_config(rq_vae_config).to(DEVICE)\n",
+    "\n",
+    "rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))\n",
+    "rqvae_model.eval()\n",
+    "\n",
+    "ids = df.asin_numeric.tolist()\n",
+    "\n",
+    "embs_dict = {\"ids\": torch.tensor(ids).to(DEVICE), \"embeddings\": embs.to(DEVICE)}\n",
+    "\n",
+    "semantic_ids = list(rqvae_model.forward(embs_dict))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rqvae.collisions import dedup\n",
+    "\n",
+    "items_with_tuples = list(zip(df[\"asin\"], df[\"title\"].fillna(\"unknown\"), semantic_ids))\n",
+    "items_with_tuples = dedup(items_with_tuples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       " (168, 254, 243, 5),\n",
+       " (232, 254, 243, 6),\n",
+       " (109, 254, 243, 6),\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "semantic_ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "item='OPI Pink Shatter, Pink of Hearts 2011 - NLE58' clust_tuple=(0, 254, 243, 5, 112)\n",
+      "item='Laura Mercier Secret Concealer # 1 0.08oz' clust_tuple=(0, 254, 243, 5, 5)\n",
+      "item='Alterna Caviar Anti-Aging Replenishing Moisture Conditioner for Unisex, 8.5 Ounce' clust_tuple=(0, 254, 243, 5, 124)\n",
+      "item=\"L'oreal Professional Paris Absolut Repair Cellular Lactic Acid Shampoo, 8.45-Ounce Bottle\" clust_tuple=(0, 254, 243, 5, 23)\n",
+      "item='NEW! Style Edit Conceal Spray 2 oz. BLACK/DARK BROWN (Conceal your gray between color services)' clust_tuple=(0, 254, 243, 5, 143)\n",
+      "1\n",
+      "item='BabyGanics Fine and Handy Foaming Hand Soap, Apple, 250 Ml, 8.45-Ounce (Pack of 2), Packaging May Vary' clust_tuple=(1, 254, 243, 5, 49)\n",
+      "item='TRESemme Thermal Creations Heat Tamer Spray, 8 Ounce (Pack of 6)' clust_tuple=(1, 254, 243, 5, 27)\n",
+      "item='Ardell Brow and Lash Growth Accelerator, 0.25-Ounce (Pack of 3)' clust_tuple=(1, 254, 243, 5, 26)\n",
+      "item='Maybelline New York Lash Stiletto Ultimate Length Washable Mascara, Brownish Black 952, 0.22 Fluid Ounce' clust_tuple=(1, 254, 243, 5, 33)\n",
+      "item='Natural Beige Cleansing Facial Sponges - 12 Pack' clust_tuple=(1, 254, 243, 5, 51)\n",
+      "2\n",
+      "item='Sigma F86 - Tapered Kabuki TM' clust_tuple=(2, 254, 243, 5, 8)\n",
+      "item='Pantene Pro-V Ultimate 10 Bb Creme 5.1 Fl Oz' clust_tuple=(2, 254, 243, 5, 10)\n",
+      "item='Swisspers Cotton Rounds, 80-Count  (Pack of 6)' clust_tuple=(2, 254, 243, 5, 4)\n",
+      "item='Axe Personal 6 Piece Travel Kit Gift Set Shower Gel, Bodyspray, Deodorant, Shampoo, Conditioner, Styling Putty' clust_tuple=(2, 254, 139, 5, 4)\n",
+      "item='Professional Vitamin C Serum 20% + E + Hyaluronic Acid + Ferulic Acid for Face - Contains Highly Effective Antioxidants Making a Potent Facial Anti Aging Skin Care Product - Boosting Collagen Creation Smoothing Wrinkles and Fine Lines and Fading Age Spots' clust_tuple=(2, 254, 153, 5, 0)\n",
+      "3\n",
+      "item='Gena Healthy Hoof, 1 Ounce' clust_tuple=(3, 254, 243, 5, 19)\n",
+      "item='Revlon Colorsilk Color Burgundy 48' clust_tuple=(3, 254, 243, 5, 9)\n",
+      "item='Body Drench Quick Tan Instant Self Tanner Bronzing Spray, Medium/Dark, 6 Ounce' clust_tuple=(3, 254, 243, 5, 74)\n",
+      "item='John Frieda Radiant Red Colour Protecting Conditioner, 8.45 Ounces (Pack of 2)' clust_tuple=(3, 254, 243, 5, 68)\n",
+      "item='Almay Wake-up Hydrating Makeup, Ivory, 0.35-Ounce' clust_tuple=(3, 254, 243, 5, 59)\n",
+      "4\n",
+      "item='Cucumber Infused Facial Cleansing Wipes, 33 Count' clust_tuple=(4, 254, 243, 5, 8)\n",
+      "item='Touch Back by Colormark Temporary Hair Color Marker Rich Black' clust_tuple=(4, 254, 243, 5, 4)\n",
+      "item='Got2b Fat-tastic Thickening Plumping Mousse, 8.5-Ounce' clust_tuple=(4, 254, 243, 6, 3)\n",
+      "item='ECOCO Eco Style Gel, Blue, 32 Ounce' clust_tuple=(4, 254, 243, 5, 3)\n",
+      "item='Andis Tangerine Twist Nano Ceramic Flat Iron,1-Inch' clust_tuple=(4, 254, 243, 5, 7)\n",
+      "5\n",
+      "item='Essie Nail Polish Go Ginza #825' clust_tuple=(5, 254, 243, 5, 25)\n",
+      "item='O.P.I Gelcolor Collection Nail Gel Lacquer, Alpine Snow, 0.5 Fluid Ounce' clust_tuple=(5, 254, 243, 5, 18)\n",
+      "item=\"It's A 10 Miracle Moisture Shampoo, 10-Ounce Bottle\" clust_tuple=(5, 254, 243, 5, 5)\n",
+      "item='China Glaze Nail Polish, Urban-Night, 0.5 Ounce' clust_tuple=(5, 254, 243, 5, 15)\n",
+      "item='OPI: Lacquer M44 Anti-Bleak, 0.5 oz' clust_tuple=(5, 254, 243, 5, 27)\n",
+      "6\n",
+      "item='The Body Shop Vitamin E Face Mist, 3.3-Fluid Ounce' clust_tuple=(6, 254, 84, 6, 0)\n",
+      "item='Blinc Black Mascara - Two Full-Size Tubes' clust_tuple=(6, 254, 84, 6, 1)\n",
+      "item='Duo Lash Adhesive - Clear, 0.25-Ounce (Pack of 2)' clust_tuple=(6, 254, 84, 6, 3)\n",
+      "item='THERA PEARL Eye-ssential Mask' clust_tuple=(6, 254, 84, 6, 2)\n",
+      "item='Duo Lash Adhesive, Clear, 0.25 Ounce' clust_tuple=(6, 254, 84, 199, 0)\n",
+      "7\n",
+      "item='Suave Professionals Shampoo, Rosemary Mint for All Hair Types, 12.6 Ounce Bottles (Pack of 6)' clust_tuple=(7, 254, 12, 5, 17)\n",
+      "item='Pantene Pro-V Smooth Shampoo 12.6 Fl Oz (Pack of 6)' clust_tuple=(7, 254, 12, 5, 22)\n",
+      "item='Dove Damage Therapy Intensive Repair Daily Super Conditioner, 8 Ounce (Pack of 3)' clust_tuple=(7, 254, 12, 5, 18)\n",
+      "item='Suave Professionals Conditioner, Humectant - 12.6 Ounce' clust_tuple=(7, 254, 12, 5, 1)\n",
+      "item='Motions At Home Lavish Conditioning Shampoo, 13 Ounce Bottles (Pack of 6)' clust_tuple=(7, 254, 12, 5, 7)\n",
+      "8\n",
+      "item='Snooki Instant Sunless Self Tanning Body Bronzing Spray 7.5z' clust_tuple=(8, 254, 243, 6, 16)\n",
+      "item='Wet N Wild Lipstick Bare It All #902C' clust_tuple=(8, 254, 243, 6, 11)\n",
+      "item='KoKo du lait Daily Leave In Moisturizing Detangler For Wavy, Curly, Kinky, Relaxed &amp; Chemically Treated Hair' clust_tuple=(8, 254, 243, 6, 15)\n",
+      "item='Solia 1875W Thermal Ionic Hair Dryer' clust_tuple=(8, 254, 243, 6, 1)\n",
+      "item=\"L'Oreal EverPure UV Protect Spray 8.5 fl oz (250 ml)\" clust_tuple=(8, 254, 243, 6, 7)\n",
+      "9\n",
+      "item='Peter Thomas Roth Anti-Aging Cellular Eye Repair Gel .76 oz' clust_tuple=(9, 254, 243, 6, 2)\n",
+      "item='WELLA Brilliance Shampoo for Fine to Normal Colored Hair 10.1oz' clust_tuple=(9, 254, 243, 6, 11)\n",
+      "item='Watts Beauty Moisturizing Hyaluronic Acid Advanced Skin Gel, 2.0 Ounce' clust_tuple=(9, 254, 243, 6, 15)\n",
+      "item='WEN&reg; Fig Cleansing Conditioner 32oz' clust_tuple=(9, 254, 243, 6, 4)\n",
+      "item='Skinceuticals Blemish plus Age Defense Acne Treatment, 1 Fluid Ounce' clust_tuple=(9, 254, 243, 6, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from rqvae.rqvae_data import search_similar_items\n",
+    "\n",
+    "\n",
+    "for i in range(0, 10):\n",
+    "    sim = search_similar_items(items_with_tuples, (i,), 5)\n",
+    "    if len(sim) == 0:\n",
+    "        continue\n",
+    "    print(i)\n",
+    "    for asin, item, clust_tuple in sim:\n",
+    "        # if 'nail' in item.lower():\n",
+    "        print(f\"{item=} {clust_tuple=}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from collections import Counter\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "plt.hist(Counter(item[-1][:-1] for item in items_with_tuples).values(), bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12101"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(item[-1] for item in items_with_tuples))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from sklearn import preprocessing\n",
+    "\n",
+    "# labels = df['asin']\n",
+    "\n",
+    "# le = preprocessing.LabelEncoder()\n",
+    "# targets = le.fit_transform(labels)\n",
+    "\n",
+    "# df['asin_numeric'] = targets\n",
+    "\n",
+    "# torch.save(df, './all_data.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gsrec",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index b105d9e3..314df443 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,18 +1,16 @@
 from models.base import TorchModel
 
 import torch
-import torch.nn as nn
 
 import torch
 
-from tqdm import tqdm
 import faiss
 
 class RqVaeModel(TorchModel, config_name='rqvae'):
 
     def __init__(
             self,
-            all_data,
+            train_sampler,
             input_dim: int,
             hidden_dim: int,
             n_iter: int,
@@ -43,9 +41,11 @@ def __init__(
          
         self._init_weights(initializer_range)
         
-        embeddings = torch.stack([entry['item.embed'] for entry in all_data._dataset])
-        
         if self.should_init_codebooks:
+            if train_sampler is None:
+                raise AttributeError("Train sampler is None")
+            
+            embeddings = torch.stack([entry['item.embed'] for entry in train_sampler._dataset])
             self.init_codebooks(embeddings)
             print('Codebooks initialized with Faiss Kmeans')
             self.should_init_codebooks = False
@@ -53,7 +53,7 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            all_data=kwargs['train_sampler'],
+            train_sampler=kwargs.get('train_sampler'),
             input_dim=config['input_dim'],
             hidden_dim=config['hidden_dim'],
             n_iter=config['n_iter'],
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
new file mode 100644
index 00000000..e92df225
--- /dev/null
+++ b/modeling/models/tiger.py
@@ -0,0 +1,96 @@
+import json
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from modeling.utils import DEVICE
+from models.base import BaseModel, TorchModel
+
+
+class TigerModel(TorchModel, config_name='tiger'):
+    def __init__(
+        self, 
+        rqvae_encoder, 
+        emb_dim, 
+        n_tokens, 
+        n_codebooks, 
+        nhead, 
+        num_encoder_layers, 
+        num_decoder_layers, 
+        dim_feedforward, 
+        dropout
+    ):
+        super().__init__()
+        
+        self.rqvae_encoder = rqvae_encoder
+        self.emb_dim = emb_dim
+        self.n_tokens = n_tokens
+        
+        self.position_embeddings = nn.Embedding(n_codebooks, emb_dim)
+        self.item_embeddings = nn.Embedding(n_tokens, emb_dim)
+        
+        self.transformer = nn.Transformer(
+            d_model=emb_dim, 
+            nhead=nhead,
+            num_encoder_layers=num_encoder_layers,
+            num_decoder_layers=num_decoder_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout
+        )
+        
+        self.proj = nn.Linear(emb_dim, n_tokens)
+        
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_train_config = json.load(open(config['rqvae_train_config_path']))
+        
+        rqvae_model = BaseModel.create_from_config(rqvae_train_config['model']).to(DEVICE)
+        rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
+        rqvae_model.eval()
+
+        return cls(
+            rqvae_encoder=rqvae_model,
+            emb_dim=config['emb_dim'],
+            n_tokens=config['n_tokens'],
+            n_codebooks=config['n_codebooks'],
+            nhead=config['nhead'],
+            num_encoder_layers=config['num_encoder_layers'],
+            num_decoder_layers=config['num_decoder_layers'],
+            dim_feedforward=config['dim_feedforward'],
+            dropout=config['dropout']
+        )    
+    
+    def forward(self, user_item_history):
+        # Get item embeddings from RQVAE encoder
+        item_sequence = self.rqvae_encoder(user_item_history)
+        
+        # Convert item sequence to embeddings (embedding size is emb_dim)
+        item_embs = self.item_embeddings(item_sequence)
+        
+        # Add positional embeddings (positions are in the range [0, 3] for each tuple in the sequence)
+        positions = torch.arange(0, item_embs.size(1), device=item_embs.device).unsqueeze(0)
+        position_embs = self.position_embeddings(positions)
+        
+        # Add position embeddings to item embeddings
+        embeddings = item_embs + position_embs
+        
+        # Transformer expects the input to be in (seq_len, batch, embedding_dim) format
+        embeddings = embeddings.permute(1, 0, 2)  # Convert to (seq_len, batch, emb_dim)
+        
+        # Create the target sequence for the transformer decoder
+        # You can shift the sequence for training as needed (e.g., teacher forcing)
+        target = embeddings.clone()  # Use input embeddings as target for now
+        
+        # Pass through the transformer (using embeddings as both input and target)
+        transformer_output = self.transformer(embeddings, target)
+        
+        # Project the output back to token space (256 possible values for each codebook)
+        logits = self.proj(transformer_output)
+        
+        # Apply softmax to get probabilities (for cross-entropy loss)
+        return logits
+
+    def compute_loss(self, logits, target):
+        # Compute cross-entropy loss
+        loss = F.cross_entropy(logits.view(-1, self.n_tokens), target.view(-1))
+        return loss
diff --git a/modeling/rqvae/__init__.py b/modeling/rqvae/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/collisions.py b/modeling/rqvae/collisions.py
similarity index 100%
rename from src/collisions.py
rename to modeling/rqvae/collisions.py
diff --git a/src/rqvae_data.py b/modeling/rqvae/rqvae_data.py
similarity index 88%
rename from src/rqvae_data.py
rename to modeling/rqvae/rqvae_data.py
index 19fb23d1..170c4e18 100644
--- a/src/rqvae_data.py
+++ b/modeling/rqvae/rqvae_data.py
@@ -75,18 +75,10 @@ def get_data(cached=True):
         with torch.no_grad():
             df["embeddings"] = df["combined_text"].progress_apply(encode_text)
     else:
-        df = torch.load("../data/df_with_embs.pt", weights_only=False)
+        df = torch.load("../data/Beauty/all_data.pt", weights_only=False)
         
     return df
 
-def get_cb_tuples(rqvae, embeddings):
-    ind_lists = []
-    for cb in rqvae.codebooks:
-        dist = torch.cdist(rqvae.encoder(embeddings), cb)
-        ind_lists.append(dist.argmin(dim=-1).cpu().numpy())
-
-    return zip(*ind_lists)
-
 
 def search_similar_items(items_with_tuples, clust2search, max_cnt=5):
     random.shuffle(items_with_tuples)
diff --git a/review.md b/review.md
index 572accc1..45523828 100644
--- a/review.md
+++ b/review.md
@@ -2,7 +2,12 @@
 
 ## Todos
 
+- posterior collapse (как будто все сваливается в один индекс в кодбуке)
+- обязательно использование reinit unused clusters
+- в Amazon датасете пофиг на rating? получается учитываются только implicit действия?
+- TODO какой базовый класс использовать для e2e модели? (LastPred?)
 - TODO backward on mean loss? in `RqVae`
+- TODO имя для модели (tiger)
 
 ## Links
 
@@ -14,7 +19,7 @@
 
 ## Todo
 
-### Train
+### Train full encoder-decoder
 
 - На чем обучать? То есть на каких данных запускать backward pass?
 - train model
diff --git a/src/main.ipynb b/src/main.ipynb
deleted file mode 100644
index f6d7b18b..00000000
--- a/src/main.ipynb
+++ /dev/null
@@ -1,171 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "from rqvae_data import get_data\n",
-    "\n",
-    "df = get_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embs = torch.stack(df[\"embeddings\"].tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embs.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rqvae import RQVAE\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "\n",
-    "rqvae = RQVAE(\n",
-    "    input_dim=embs.shape[1],\n",
-    "    hidden_dim=128,\n",
-    "    beta=0.25,\n",
-    "    codebook_sizes=[256] * 4,\n",
-    "    should_init_codebooks=True,\n",
-    "    should_reinit_unused_clusters=False,\n",
-    ").to(device)\n",
-    "\n",
-    "\n",
-    "embs_dict = {\"embedding\": embs.to(device)}\n",
-    "\n",
-    "rqvae.forward(embs_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collisions import dedup\n",
-    "from rqvae_data import get_cb_tuples\n",
-    "\n",
-    "\n",
-    "cb_tuples = list(get_cb_tuples(rqvae, embs_dict[\"embedding\"]))\n",
-    "items_with_tuples = list(zip(df[\"asin\"], df[\"title\"].fillna(\"unknown\"), cb_tuples))\n",
-    "items_with_tuples = dedup(items_with_tuples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rqvae_data import search_similar_items\n",
-    "\n",
-    "\n",
-    "for i in range(230, 240):\n",
-    "    sim = search_similar_items(items_with_tuples, (i,), 10)\n",
-    "    if len(sim) == 0:\n",
-    "        continue\n",
-    "    print(i)\n",
-    "    for asin, item, clust_tuple in sim:\n",
-    "        if 'nail' in item.lower():\n",
-    "            print(f\"{item=} {clust_tuple=}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "plt.hist(Counter(item[-1][:-1] for item in items_with_tuples).values(), bins=100)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(set(item[-1] for item in items_with_tuples))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import preprocessing\n",
-    "\n",
-    "labels = df['asin']\n",
-    "\n",
-    "le = preprocessing.LabelEncoder()\n",
-    "targets = le.fit_transform(labels)\n",
-    "\n",
-    "df['asin_numeric'] = targets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.save(df, './all_data.pt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "gsrec",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/main.py b/src/main.py
deleted file mode 100644
index 21d47550..00000000
--- a/src/main.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import torch
-import typing
-import random
-import os
-
-from rqvae import RQVAE
-
-device = torch.device("cuda")
-
-
-def get_cb_tuples(embeddings):
-    ind_lists = []
-    for cb in rqvae.codebooks:
-        dist = torch.cdist(rqvae.encoder(embeddings), cb)
-        ind_lists.append(dist.argmin(dim=-1).cpu().numpy())
-
-    return zip(*ind_lists)
-
-
-def search_similar_items(items_with_tuples):
-    random.shuffle(items_with_tuples)
-    clust2search = (585,)
-    cnt = 0
-    for item, clust_tuple in items_with_tuples:
-        if clust_tuple[: len(clust2search)] == clust2search:
-            print(item, clust_tuple)
-            cnt += 1
-        if cnt >= 5:
-            break
-
-
-# TODO: add T5 sentence construction from huggingface
-
-
-embs = {"embedding": []}
-
-rqvae = RQVAE(
-    input_dim=200,
-    hidden_dim=128,
-    beta=0.25,
-    codebook_sizes=[256] * 4,
-    should_init_codebooks=False,
-    should_reinit_unused_clusters=False,
-).to(device)
-
-rqvae.forward(embs)

From 8ec8ea5553d46348a1743dca15f551b7aa530c14 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 23 Dec 2024 22:03:15 +0300
Subject: [PATCH 012/175] add todo

---
 modeling/models/tiger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index e92df225..98a852db 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -6,7 +6,7 @@
 from modeling.utils import DEVICE
 from models.base import BaseModel, TorchModel
 
-
+# TODO finish tiger model
 class TigerModel(TorchModel, config_name='tiger'):
     def __init__(
         self, 

From a65976701320044c32e913210ae43b23375b9d79 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Tue, 24 Dec 2024 14:09:06 +0300
Subject: [PATCH 013/175] fix collapse (correct infer)

---
 configs/train/rqvae_train_config.json |    2 +-
 modeling/main.ipynb                   | 1178 ++-----------------------
 modeling/models/rqvae.py              |    9 +-
 review.md                             |    4 +-
 4 files changed, 97 insertions(+), 1096 deletions(-)

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index e9f3fe2f..ba2cfb05 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "train_steps_num": 2000,
+  "train_steps_num": 1024,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index db47d562..7f6194b7 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -59,1032 +59,14 @@
     "from rqvae.collisions import dedup\n",
     "\n",
     "items_with_tuples = list(zip(df[\"asin\"], df[\"title\"].fillna(\"unknown\"), semantic_ids))\n",
-    "items_with_tuples = dedup(items_with_tuples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(86, 254, 243, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (224, 254, 243, 6),\n",
-       " (61, 254, 243, 5),\n",
-       " (80, 254, 243, 5),\n",
-       " (224, 254, 243, 5),\n",
-       " (141, 254, 84, 6),\n",
-       " (68, 254, 84, 6),\n",
-       " (1, 254, 243, 5),\n",
-       " (224, 254, 243, 6),\n",
-       " (74, 254, 84, 6),\n",
-       " (244, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (109, 254, 243, 6),\n",
-       " (127, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (19, 254, 243, 6),\n",
-       " (230, 254, 243, 5),\n",
-       " (184, 254, 12, 5),\n",
-       " (143, 254, 243, 6),\n",
-       " (25, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (199, 254, 243, 6),\n",
-       " (19, 254, 243, 6),\n",
-       " (230, 254, 243, 6),\n",
-       " (63, 254, 243, 6),\n",
-       " (150, 254, 243, 6),\n",
-       " (77, 254, 243, 5),\n",
-       " (144, 254, 243, 6),\n",
-       " (249, 254, 243, 5),\n",
-       " (135, 254, 243, 6),\n",
-       " (109, 254, 243, 6),\n",
-       " (223, 254, 243, 6),\n",
-       " (146, 254, 243, 6),\n",
-       " (242, 254, 243, 6),\n",
-       " (109, 254, 243, 6),\n",
-       " (19, 254, 243, 6),\n",
-       " (144, 254, 243, 6),\n",
-       " (63, 254, 243, 6),\n",
-       " (46, 254, 243, 6),\n",
-       " (175, 254, 243, 6),\n",
-       " (19, 254, 243, 6),\n",
-       " (109, 254, 243, 6),\n",
-       " (9, 254, 243, 6),\n",
-       " (68, 254, 243, 6),\n",
-       " (254, 254, 243, 6),\n",
-       " (122, 254, 12, 5),\n",
-       " (109, 254, 243, 6),\n",
-       " (3, 254, 243, 5),\n",
-       " (77, 254, 243, 5),\n",
-       " (140, 254, 243, 5),\n",
-       " (63, 254, 243, 6),\n",
-       " (63, 254, 243, 6),\n",
-       " (106, 254, 243, 5),\n",
-       " (176, 254, 12, 5),\n",
-       " (70, 254, 153, 5),\n",
-       " (166, 254, 243, 6),\n",
-       " (244, 254, 243, 5),\n",
-       " (62, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (226, 254, 243, 6),\n",
-       " (122, 254, 12, 5),\n",
-       " (57, 254, 243, 5),\n",
-       " (194, 254, 243, 6),\n",
-       " (62, 254, 243, 5),\n",
-       " (252, 254, 139, 199),\n",
-       " (226, 254, 243, 6),\n",
-       " (242, 254, 243, 6),\n",
-       " (161, 254, 139, 199),\n",
-       " (7, 254, 12, 5),\n",
-       " (51, 254, 12, 5),\n",
-       " (7, 254, 12, 5),\n",
-       " (184, 254, 12, 5),\n",
-       " (174, 254, 243, 6),\n",
-       " (224, 254, 243, 5),\n",
-       " (229, 254, 243, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (176, 254, 12, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (96, 254, 243, 5),\n",
-       " (117, 254, 243, 6),\n",
-       " (102, 254, 243, 5),\n",
-       " (206, 254, 139, 6),\n",
-       " (224, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (62, 254, 243, 5),\n",
-       " (232, 254, 243, 199),\n",
-       " (254, 254, 243, 6),\n",
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-       " (3, 254, 243, 5),\n",
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-       " (144, 254, 243, 6),\n",
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-       " (153, 254, 139, 199),\n",
-       " (161, 254, 139, 199),\n",
-       " (19, 254, 243, 6),\n",
-       " (153, 254, 139, 199),\n",
-       " (19, 254, 243, 6),\n",
-       " (242, 254, 243, 5),\n",
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-       " (242, 254, 243, 6),\n",
-       " (212, 254, 243, 6),\n",
-       " (165, 254, 243, 5),\n",
-       " (123, 254, 243, 5),\n",
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-       " (198, 254, 243, 5),\n",
-       " (144, 254, 243, 5),\n",
-       " (21, 254, 139, 199),\n",
-       " (117, 254, 243, 5),\n",
-       " (19, 254, 243, 6),\n",
-       " (212, 254, 243, 6),\n",
-       " (128, 254, 243, 5),\n",
-       " (242, 254, 243, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (85, 254, 243, 5),\n",
-       " (86, 254, 84, 6),\n",
-       " (86, 254, 84, 6),\n",
-       " (141, 254, 84, 6),\n",
-       " (91, 254, 139, 6),\n",
-       " (63, 254, 243, 6),\n",
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-       " (3, 254, 243, 5),\n",
-       " (86, 254, 84, 6),\n",
-       " (13, 254, 243, 5),\n",
-       " (1, 254, 139, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (199, 254, 84, 6),\n",
-       " (242, 254, 243, 6),\n",
-       " (137, 254, 243, 5),\n",
-       " (19, 254, 243, 6),\n",
-       " (63, 254, 243, 6),\n",
-       " (106, 254, 243, 5),\n",
-       " (56, 254, 84, 6),\n",
-       " (111, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (120, 254, 12, 5),\n",
-       " (58, 254, 139, 5),\n",
-       " (146, 254, 243, 6),\n",
-       " (41, 254, 243, 5),\n",
-       " (1, 254, 243, 5),\n",
-       " (163, 254, 243, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (105, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (105, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (132, 254, 243, 5),\n",
-       " (132, 254, 243, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (185, 254, 243, 159),\n",
-       " (185, 254, 136, 159),\n",
-       " (3, 254, 243, 5),\n",
-       " (137, 254, 243, 159),\n",
-       " (106, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (159, 254, 243, 159),\n",
-       " (0, 254, 243, 5),\n",
-       " (105, 254, 243, 5),\n",
-       " (122, 254, 243, 5),\n",
-       " (180, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (91, 254, 139, 5),\n",
-       " (109, 254, 243, 6),\n",
-       " (109, 254, 243, 6),\n",
-       " (255, 254, 243, 5),\n",
-       " (122, 254, 153, 5),\n",
-       " (191, 254, 84, 6),\n",
-       " (176, 254, 12, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (41, 254, 243, 5),\n",
-       " (199, 254, 243, 6),\n",
-       " (176, 254, 153, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (79, 254, 243, 5),\n",
-       " (70, 254, 243, 5),\n",
-       " (253, 254, 243, 6),\n",
-       " (239, 254, 243, 6),\n",
-       " (234, 254, 243, 6),\n",
-       " (111, 254, 243, 5),\n",
-       " (135, 254, 243, 6),\n",
-       " (88, 254, 243, 6),\n",
-       " (102, 254, 243, 5),\n",
-       " (128, 254, 243, 5),\n",
-       " (19, 254, 243, 6),\n",
-       " (168, 254, 243, 5),\n",
-       " (74, 254, 84, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (173, 254, 243, 5),\n",
-       " (199, 254, 243, 6),\n",
-       " (60, 254, 243, 5),\n",
-       " (218, 254, 12, 5),\n",
-       " (13, 254, 243, 5),\n",
-       " (13, 254, 139, 5),\n",
-       " (58, 254, 243, 5),\n",
-       " (13, 254, 139, 5),\n",
-       " (226, 254, 243, 6),\n",
-       " (85, 254, 243, 6),\n",
-       " (198, 254, 243, 5),\n",
-       " (242, 254, 243, 5),\n",
-       " (135, 254, 243, 6),\n",
-       " (191, 254, 243, 6),\n",
-       " (51, 254, 12, 5),\n",
-       " (193, 254, 243, 5),\n",
-       " (88, 254, 243, 6),\n",
-       " (156, 254, 243, 6),\n",
-       " (91, 254, 139, 5),\n",
-       " (143, 254, 243, 6),\n",
-       " (32, 254, 243, 5),\n",
-       " (13, 254, 243, 5),\n",
-       " (96, 254, 243, 6),\n",
-       " (244, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (86, 254, 243, 6),\n",
-       " (241, 254, 243, 6),\n",
-       " (46, 254, 243, 6),\n",
-       " (198, 254, 243, 5),\n",
-       " (221, 254, 243, 6),\n",
-       " (167, 254, 243, 5),\n",
-       " (214, 254, 243, 6),\n",
-       " (106, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (226, 254, 243, 6),\n",
-       " (131, 254, 243, 6),\n",
-       " (146, 254, 243, 6),\n",
-       " (163, 254, 243, 6),\n",
-       " (194, 254, 243, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (177, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (13, 254, 243, 5),\n",
-       " (61, 254, 243, 5),\n",
-       " (25, 254, 243, 5),\n",
-       " (85, 254, 243, 5),\n",
-       " (1, 254, 243, 5),\n",
-       " (135, 254, 243, 6),\n",
-       " (218, 254, 12, 5),\n",
-       " (161, 254, 139, 199),\n",
-       " (0, 254, 243, 5),\n",
-       " (14, 254, 243, 6),\n",
-       " (178, 254, 243, 5),\n",
-       " (222, 254, 243, 6),\n",
-       " (184, 254, 12, 5),\n",
-       " (178, 254, 243, 5),\n",
-       " (105, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (231, 254, 243, 5),\n",
-       " (132, 254, 243, 5),\n",
-       " (178, 254, 243, 5),\n",
-       " (128, 254, 243, 5),\n",
-       " (109, 254, 243, 6),\n",
-       " (146, 254, 243, 6),\n",
-       " (143, 254, 243, 6),\n",
-       " (213, 254, 243, 6),\n",
-       " (146, 254, 243, 6),\n",
-       " (146, 254, 243, 6),\n",
-       " (131, 254, 243, 6),\n",
-       " (111, 254, 243, 5),\n",
-       " (19, 254, 243, 6),\n",
-       " (78, 254, 139, 199),\n",
-       " (67, 254, 136, 50),\n",
-       " (78, 254, 139, 199),\n",
-       " (60, 254, 139, 6),\n",
-       " (78, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (155, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (18, 254, 139, 199),\n",
-       " (168, 254, 243, 5),\n",
-       " (143, 254, 243, 6),\n",
-       " (135, 254, 243, 5),\n",
-       " (78, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (78, 254, 139, 199),\n",
-       " (18, 254, 139, 199),\n",
-       " (78, 254, 139, 199),\n",
-       " (73, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (252, 254, 139, 199),\n",
-       " (139, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (106, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (18, 254, 139, 199),\n",
-       " (155, 254, 139, 199),\n",
-       " (18, 254, 139, 199),\n",
-       " (73, 254, 243, 5),\n",
-       " (252, 254, 139, 199),\n",
-       " (73, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (132, 254, 243, 5),\n",
-       " (70, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (122, 254, 153, 5),\n",
-       " (143, 254, 243, 6),\n",
-       " (193, 254, 243, 5),\n",
-       " (212, 254, 243, 6),\n",
-       " (176, 254, 12, 5),\n",
-       " (135, 254, 243, 6),\n",
-       " (61, 254, 243, 5),\n",
-       " (201, 254, 243, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (71, 254, 153, 5),\n",
-       " (128, 254, 243, 5),\n",
-       " (1, 254, 243, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (120, 254, 12, 5),\n",
-       " (137, 254, 243, 5),\n",
-       " (163, 254, 243, 6),\n",
-       " (242, 254, 243, 6),\n",
-       " (110, 254, 243, 6),\n",
-       " (0, 254, 243, 5),\n",
-       " (23, 254, 243, 6),\n",
-       " (63, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (21, 254, 139, 199),\n",
-       " (22, 254, 243, 5),\n",
-       " (70, 254, 243, 5),\n",
-       " (86, 254, 84, 6),\n",
-       " (178, 254, 243, 5),\n",
-       " (131, 254, 243, 5),\n",
-       " (63, 254, 243, 6),\n",
-       " (241, 254, 243, 6),\n",
-       " (122, 254, 12, 5),\n",
-       " (109, 254, 243, 6),\n",
-       " (60, 254, 139, 5),\n",
-       " (177, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (86, 254, 139, 6),\n",
-       " (135, 254, 243, 5),\n",
-       " (136, 254, 243, 6),\n",
-       " (61, 254, 243, 5),\n",
-       " (38, 254, 12, 5),\n",
-       " (60, 254, 243, 5),\n",
-       " (168, 254, 243, 5),\n",
-       " (74, 254, 84, 6),\n",
-       " (218, 254, 12, 5),\n",
-       " (186, 254, 243, 6),\n",
-       " (167, 254, 243, 5),\n",
-       " (148, 254, 243, 5),\n",
-       " (229, 254, 243, 6),\n",
-       " (135, 254, 243, 6),\n",
-       " (60, 254, 139, 5),\n",
-       " (40, 254, 243, 5),\n",
-       " (250, 254, 243, 6),\n",
-       " (144, 254, 243, 6),\n",
-       " (132, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (41, 254, 139, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (63, 254, 243, 6),\n",
-       " (193, 254, 243, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (218, 254, 12, 5),\n",
-       " (1, 254, 243, 5),\n",
-       " (160, 254, 243, 5),\n",
-       " (62, 254, 243, 6),\n",
-       " (128, 254, 243, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (226, 254, 243, 6),\n",
-       " (194, 254, 243, 6),\n",
-       " (62, 254, 243, 6),\n",
-       " (158, 254, 243, 50),\n",
-       " (40, 254, 243, 6),\n",
-       " (25, 254, 243, 5),\n",
-       " (242, 254, 243, 6),\n",
-       " (111, 254, 243, 5),\n",
-       " (247, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (168, 254, 243, 5),\n",
-       " (68, 254, 243, 6),\n",
-       " (146, 254, 243, 6),\n",
-       " (163, 254, 243, 6),\n",
-       " (143, 254, 243, 6),\n",
-       " (184, 254, 12, 5),\n",
-       " (97, 254, 243, 159),\n",
-       " (211, 254, 243, 5),\n",
-       " (167, 254, 243, 5),\n",
-       " (201, 254, 243, 5),\n",
-       " (91, 254, 139, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (63, 254, 243, 6),\n",
-       " (82, 254, 243, 5),\n",
-       " (178, 254, 243, 5),\n",
-       " (134, 254, 243, 5),\n",
-       " (86, 254, 84, 6),\n",
-       " (168, 254, 243, 5),\n",
-       " (148, 254, 243, 5),\n",
-       " (128, 254, 243, 5),\n",
-       " (122, 254, 12, 5),\n",
-       " (18, 254, 139, 199),\n",
-       " (111, 254, 243, 5),\n",
-       " (214, 254, 243, 5),\n",
-       " (13, 254, 243, 5),\n",
-       " (25, 254, 243, 5),\n",
-       " (230, 254, 243, 6),\n",
-       " (244, 254, 243, 5),\n",
-       " (140, 254, 243, 5),\n",
-       " (120, 254, 12, 5),\n",
-       " (216, 254, 243, 5),\n",
-       " (123, 254, 243, 6),\n",
-       " (241, 254, 243, 6),\n",
-       " (224, 254, 243, 5),\n",
-       " (63, 254, 243, 6),\n",
-       " (226, 254, 243, 6),\n",
-       " (96, 254, 243, 5),\n",
-       " (207, 254, 139, 6),\n",
-       " (86, 254, 84, 6),\n",
-       " (0, 254, 243, 5),\n",
-       " (57, 254, 243, 5),\n",
-       " (173, 254, 243, 5),\n",
-       " (173, 254, 243, 5),\n",
-       " (230, 254, 243, 5),\n",
-       " (160, 254, 243, 5),\n",
-       " (231, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (212, 254, 243, 6),\n",
-       " (211, 254, 243, 5),\n",
-       " (79, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (25, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (102, 254, 243, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (178, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (148, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (79, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (73, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (40, 254, 243, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (178, 254, 243, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (42, 254, 243, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (68, 254, 243, 6),\n",
-       " (79, 254, 243, 5),\n",
-       " (135, 254, 243, 6),\n",
-       " (44, 254, 243, 5),\n",
-       " (130, 254, 243, 6),\n",
-       " (68, 254, 243, 6),\n",
-       " (106, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (106, 254, 243, 5),\n",
-       " (122, 254, 243, 5),\n",
-       " (234, 254, 243, 6),\n",
-       " (25, 254, 243, 5),\n",
-       " (178, 254, 243, 5),\n",
-       " (44, 254, 243, 5),\n",
-       " (61, 254, 243, 5),\n",
-       " (61, 254, 243, 5),\n",
-       " (36, 254, 243, 6),\n",
-       " (213, 254, 243, 5),\n",
-       " (244, 254, 243, 5),\n",
-       " (252, 254, 139, 199),\n",
-       " (252, 254, 139, 199),\n",
-       " (244, 254, 243, 5),\n",
-       " (51, 254, 12, 5),\n",
-       " (0, 254, 243, 5),\n",
-       " (176, 254, 12, 5),\n",
-       " (111, 254, 243, 5),\n",
-       " (132, 254, 153, 5),\n",
-       " (230, 254, 243, 5),\n",
-       " (168, 254, 243, 5),\n",
-       " (232, 254, 243, 6),\n",
-       " (109, 254, 243, 6),\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "semantic_ids"
+    "items_with_tuples = dedup(items_with_tuples)\n",
+    "\n",
+    "assert len(df) == len(set(item[-1] for item in items_with_tuples))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1092,65 +74,33 @@
      "output_type": "stream",
      "text": [
       "0\n",
-      "item='OPI Pink Shatter, Pink of Hearts 2011 - NLE58' clust_tuple=(0, 254, 243, 5, 112)\n",
-      "item='Laura Mercier Secret Concealer # 1 0.08oz' clust_tuple=(0, 254, 243, 5, 5)\n",
-      "item='Alterna Caviar Anti-Aging Replenishing Moisture Conditioner for Unisex, 8.5 Ounce' clust_tuple=(0, 254, 243, 5, 124)\n",
-      "item=\"L'oreal Professional Paris Absolut Repair Cellular Lactic Acid Shampoo, 8.45-Ounce Bottle\" clust_tuple=(0, 254, 243, 5, 23)\n",
-      "item='NEW! Style Edit Conceal Spray 2 oz. BLACK/DARK BROWN (Conceal your gray between color services)' clust_tuple=(0, 254, 243, 5, 143)\n",
+      "item='Revlon Beyond Natural Smoothing Primer, Clear, 0.85 Ounce' clust_tuple=(0, 33, 46, 141, 0)\n",
+      "item='Color Club Magic Attraction 843 Nail Polish' clust_tuple=(0, 228, 117, 102, 0)\n",
+      "item='Color Club Wild at Heart 871 Nail Polish' clust_tuple=(0, 228, 83, 106, 0)\n",
       "1\n",
-      "item='BabyGanics Fine and Handy Foaming Hand Soap, Apple, 250 Ml, 8.45-Ounce (Pack of 2), Packaging May Vary' clust_tuple=(1, 254, 243, 5, 49)\n",
-      "item='TRESemme Thermal Creations Heat Tamer Spray, 8 Ounce (Pack of 6)' clust_tuple=(1, 254, 243, 5, 27)\n",
-      "item='Ardell Brow and Lash Growth Accelerator, 0.25-Ounce (Pack of 3)' clust_tuple=(1, 254, 243, 5, 26)\n",
-      "item='Maybelline New York Lash Stiletto Ultimate Length Washable Mascara, Brownish Black 952, 0.22 Fluid Ounce' clust_tuple=(1, 254, 243, 5, 33)\n",
-      "item='Natural Beige Cleansing Facial Sponges - 12 Pack' clust_tuple=(1, 254, 243, 5, 51)\n",
+      "item='Natural Beauty White / Brightening Essence Full Face Mask 10 Pcs' clust_tuple=(1, 210, 157, 13, 0)\n",
+      "item='OGX Conditioner, Nourishing Coconut Milk, 13oz' clust_tuple=(1, 50, 213, 95, 0)\n",
+      "item='BurnOut Eco-sensitive Zinc Oxide Sunscreen SPF 35 (3 oz)' clust_tuple=(1, 119, 46, 204, 0)\n",
+      "item='Kerastase Reflection Chroma Riche Luminous Softening Essence, 4.2 Ounce' clust_tuple=(1, 119, 3, 229, 0)\n",
+      "item='KINeSYS Performance Sunscreen, SPF 30, Spray, Mango Scent, 4-Ounce Bottles' clust_tuple=(1, 242, 54, 95, 0)\n",
       "2\n",
-      "item='Sigma F86 - Tapered Kabuki TM' clust_tuple=(2, 254, 243, 5, 8)\n",
-      "item='Pantene Pro-V Ultimate 10 Bb Creme 5.1 Fl Oz' clust_tuple=(2, 254, 243, 5, 10)\n",
-      "item='Swisspers Cotton Rounds, 80-Count  (Pack of 6)' clust_tuple=(2, 254, 243, 5, 4)\n",
-      "item='Axe Personal 6 Piece Travel Kit Gift Set Shower Gel, Bodyspray, Deodorant, Shampoo, Conditioner, Styling Putty' clust_tuple=(2, 254, 139, 5, 4)\n",
-      "item='Professional Vitamin C Serum 20% + E + Hyaluronic Acid + Ferulic Acid for Face - Contains Highly Effective Antioxidants Making a Potent Facial Anti Aging Skin Care Product - Boosting Collagen Creation Smoothing Wrinkles and Fine Lines and Fading Age Spots' clust_tuple=(2, 254, 153, 5, 0)\n",
+      "item='Pink Sugar by Aquolina for Women - 3.4 Ounce EDT Spray' clust_tuple=(2, 173, 246, 41, 0)\n",
+      "item='Guess By Parlux Fragrances For Men. Eau De Toilette Spray 2.5 Oz.' clust_tuple=(2, 104, 233, 139, 0)\n",
+      "item='Black Xs By Paco Rabanne For Men, Eau De Toilette Spray, 3.4-Ounce Bottle' clust_tuple=(2, 177, 233, 95, 0)\n",
+      "item='Incanto Shine By Salvatore Ferragamo For Women, Eau De Toilette Spray, 3.4-Ounce Bottle' clust_tuple=(2, 128, 185, 76, 0)\n",
+      "item='Versace Man Eau Fraiche By Gianni Versace For Men Edt Spray 3.4 Oz' clust_tuple=(2, 148, 81, 107, 0)\n",
       "3\n",
-      "item='Gena Healthy Hoof, 1 Ounce' clust_tuple=(3, 254, 243, 5, 19)\n",
-      "item='Revlon Colorsilk Color Burgundy 48' clust_tuple=(3, 254, 243, 5, 9)\n",
-      "item='Body Drench Quick Tan Instant Self Tanner Bronzing Spray, Medium/Dark, 6 Ounce' clust_tuple=(3, 254, 243, 5, 74)\n",
-      "item='John Frieda Radiant Red Colour Protecting Conditioner, 8.45 Ounces (Pack of 2)' clust_tuple=(3, 254, 243, 5, 68)\n",
-      "item='Almay Wake-up Hydrating Makeup, Ivory, 0.35-Ounce' clust_tuple=(3, 254, 243, 5, 59)\n",
+      "item='HDE&reg; Facial Pore Cleanser Cleaner Blackhead Acne Remover' clust_tuple=(3, 106, 111, 172, 0)\n",
+      "item=\"Best Anti Aging Cream Reduces Wrinkels in Women and Men - Clinical Strength Bio-Peptide Wrinkle Cream Reduces Deep Wrinkles, Smooths Fine Lines and &quot;Crow's Feet&quot; - Tighten, Rejuvinate, and Rebuild Youthful, Healthy Skin - Boost Collagen and Ultra-Moisturize with Peptides Made For Your Skin - Great For Face, Under Eyes and Decolletage. You Love It Or We Buy It Back No Hassle Money Back Guarantee. [Reduced Price For Summer! Take 65% Off Automatically at Checkout!]&#9733; 2oz Jar (60ml)\" clust_tuple=(3, 90, 239, 106, 0)\n",
+      "item='Raw African Black soap Imported From Ghana 4oz' clust_tuple=(3, 145, 185, 168, 0)\n",
+      "item='Skin Obsession 20% TCA Home Chemical Peel for face and body removes lines, sun damage and signs of aging' clust_tuple=(3, 207, 151, 96, 0)\n",
+      "item='Skin Obsession 25% TCA Chemical Peel for Home Use 1 fl oz (30 ml)' clust_tuple=(3, 207, 47, 44, 0)\n",
       "4\n",
-      "item='Cucumber Infused Facial Cleansing Wipes, 33 Count' clust_tuple=(4, 254, 243, 5, 8)\n",
-      "item='Touch Back by Colormark Temporary Hair Color Marker Rich Black' clust_tuple=(4, 254, 243, 5, 4)\n",
-      "item='Got2b Fat-tastic Thickening Plumping Mousse, 8.5-Ounce' clust_tuple=(4, 254, 243, 6, 3)\n",
-      "item='ECOCO Eco Style Gel, Blue, 32 Ounce' clust_tuple=(4, 254, 243, 5, 3)\n",
-      "item='Andis Tangerine Twist Nano Ceramic Flat Iron,1-Inch' clust_tuple=(4, 254, 243, 5, 7)\n",
-      "5\n",
-      "item='Essie Nail Polish Go Ginza #825' clust_tuple=(5, 254, 243, 5, 25)\n",
-      "item='O.P.I Gelcolor Collection Nail Gel Lacquer, Alpine Snow, 0.5 Fluid Ounce' clust_tuple=(5, 254, 243, 5, 18)\n",
-      "item=\"It's A 10 Miracle Moisture Shampoo, 10-Ounce Bottle\" clust_tuple=(5, 254, 243, 5, 5)\n",
-      "item='China Glaze Nail Polish, Urban-Night, 0.5 Ounce' clust_tuple=(5, 254, 243, 5, 15)\n",
-      "item='OPI: Lacquer M44 Anti-Bleak, 0.5 oz' clust_tuple=(5, 254, 243, 5, 27)\n",
-      "6\n",
-      "item='The Body Shop Vitamin E Face Mist, 3.3-Fluid Ounce' clust_tuple=(6, 254, 84, 6, 0)\n",
-      "item='Blinc Black Mascara - Two Full-Size Tubes' clust_tuple=(6, 254, 84, 6, 1)\n",
-      "item='Duo Lash Adhesive - Clear, 0.25-Ounce (Pack of 2)' clust_tuple=(6, 254, 84, 6, 3)\n",
-      "item='THERA PEARL Eye-ssential Mask' clust_tuple=(6, 254, 84, 6, 2)\n",
-      "item='Duo Lash Adhesive, Clear, 0.25 Ounce' clust_tuple=(6, 254, 84, 199, 0)\n",
-      "7\n",
-      "item='Suave Professionals Shampoo, Rosemary Mint for All Hair Types, 12.6 Ounce Bottles (Pack of 6)' clust_tuple=(7, 254, 12, 5, 17)\n",
-      "item='Pantene Pro-V Smooth Shampoo 12.6 Fl Oz (Pack of 6)' clust_tuple=(7, 254, 12, 5, 22)\n",
-      "item='Dove Damage Therapy Intensive Repair Daily Super Conditioner, 8 Ounce (Pack of 3)' clust_tuple=(7, 254, 12, 5, 18)\n",
-      "item='Suave Professionals Conditioner, Humectant - 12.6 Ounce' clust_tuple=(7, 254, 12, 5, 1)\n",
-      "item='Motions At Home Lavish Conditioning Shampoo, 13 Ounce Bottles (Pack of 6)' clust_tuple=(7, 254, 12, 5, 7)\n",
-      "8\n",
-      "item='Snooki Instant Sunless Self Tanning Body Bronzing Spray 7.5z' clust_tuple=(8, 254, 243, 6, 16)\n",
-      "item='Wet N Wild Lipstick Bare It All #902C' clust_tuple=(8, 254, 243, 6, 11)\n",
-      "item='KoKo du lait Daily Leave In Moisturizing Detangler For Wavy, Curly, Kinky, Relaxed &amp; Chemically Treated Hair' clust_tuple=(8, 254, 243, 6, 15)\n",
-      "item='Solia 1875W Thermal Ionic Hair Dryer' clust_tuple=(8, 254, 243, 6, 1)\n",
-      "item=\"L'Oreal EverPure UV Protect Spray 8.5 fl oz (250 ml)\" clust_tuple=(8, 254, 243, 6, 7)\n",
-      "9\n",
-      "item='Peter Thomas Roth Anti-Aging Cellular Eye Repair Gel .76 oz' clust_tuple=(9, 254, 243, 6, 2)\n",
-      "item='WELLA Brilliance Shampoo for Fine to Normal Colored Hair 10.1oz' clust_tuple=(9, 254, 243, 6, 11)\n",
-      "item='Watts Beauty Moisturizing Hyaluronic Acid Advanced Skin Gel, 2.0 Ounce' clust_tuple=(9, 254, 243, 6, 15)\n",
-      "item='WEN&reg; Fig Cleansing Conditioner 32oz' clust_tuple=(9, 254, 243, 6, 4)\n",
-      "item='Skinceuticals Blemish plus Age Defense Acne Treatment, 1 Fluid Ounce' clust_tuple=(9, 254, 243, 6, 13)\n"
+      "item='Philosophy When Hope is Not Enough Firming and Lifting Serum for Unisex, 1 Ounce' clust_tuple=(4, 247, 239, 194, 0)\n",
+      "item='DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ' clust_tuple=(4, 182, 111, 5, 0)\n",
+      "item='Victorinox By Swiss Army For Men 125 Years Eau-de-toilette Spray, 3.4-Ounce' clust_tuple=(4, 128, 239, 5, 0)\n",
+      "item='Givenchy Play for Men by Givenchy 3.3 oz 100 ml EDT Spray' clust_tuple=(4, 50, 19, 168, 0)\n",
+      "item='Jilbere Hot Air Brush' clust_tuple=(4, 15, 47, 194, 0)\n"
      ]
     }
    ],
@@ -1158,24 +108,24 @@
     "from rqvae.rqvae_data import search_similar_items\n",
     "\n",
     "\n",
-    "for i in range(0, 10):\n",
+    "for i in range(5):\n",
     "    sim = search_similar_items(items_with_tuples, (i,), 5)\n",
     "    if len(sim) == 0:\n",
     "        continue\n",
     "    print(i)\n",
     "    for asin, item, clust_tuple in sim:\n",
-    "        # if 'nail' in item.lower():\n",
+    "        # if 'shampoo' in item.lower():\n",
     "        print(f\"{item=} {clust_tuple=}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Oo+Tk5IsdFgAACEGdHnT+5V/+RYsXL9Ydd9yhIUOGyOfz6d5771VhYaEkyePxSFKrVZe6ujp3lcfj8aipqUl+v7/dmqNHj7Z6/2PHjrVaLTpryZIlCgQC7lZdXf31BgsAAHq0Tg86p06d0mWXBTcbHh7uPl6ekpIij8ej0tJS93hTU5PKyso0YsQISVJGRoYiIyODampqarRnzx63JisrS4FAQDt27HBrtm/frkAg4NacKzo6WvHx8UEbAACwV6d/RmfixIn6xS9+oSuvvFLf+c53tHv3bq1cuVI//elPJX313035+fkqKChQamqqUlNTVVBQoN69eysvL0+S5DiOpk2bpvnz56tv375KSEjQggULNGTIEI0ZM0aSNGjQII0fP17Tp0/X6tWrJUkzZsxQTk7ORT1xBQAA7NfpQefxxx/X/fffr1mzZqmurk5er1czZ87Uv//7v7s1Cxcu1OnTpzVr1iz5/X5lZmZq06ZNiouLc2tWrVqliIgITZo0SadPn9bo0aO1du1ahYeHuzXr1q3T3Llz3aezcnNzVVRU1NlDAgAAIarTv0cnlPA9Oq3xPToAgJ6uW79HBwAAoKcg6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGt1SdD59NNP9U//9E/q27evevfurb//+79XRUWFe9wYo6VLl8rr9SomJkajRo3S3r17g9pobGzUnDlz1K9fP8XGxio3N1eHDx8OqvH7/fL5fHIcR47jyOfz6fjx410xJAAAEII6Pej4/X7dcMMNioyM1O9//3t98MEHevTRR3X55Ze7NStWrNDKlStVVFSknTt3yuPxaOzYsWpoaHBr8vPzVVxcrPXr12vLli06ceKEcnJy1NLS4tbk5eWpsrJSJSUlKikpUWVlpXw+X2cPCQAAhKgwY4zpzAYXL16sP/zhD3r33XfbPG6MkdfrVX5+vhYtWiTpq9WbpKQkLV++XDNnzlQgEFD//v31/PPPa/LkyZKkI0eOKDk5WRs3btS4ceO0b98+DR48WOXl5crMzJQklZeXKysrS/v371daWtoF+1pfXy/HcRQIBBQfH99JP4H/76rFGzq9za72ybIJ3d0FAADa1ZH7d6ev6Lz++usaNmyYfvSjHykxMVHXXXednnnmGfd4VVWVamtrlZ2d7e6Ljo7WyJEjtXXrVklSRUWFmpubg2q8Xq/S09Pdmm3btslxHDfkSNLw4cPlOI5bAwAAvt06Peh8/PHHevLJJ5Wamqo33nhDd999t+bOnatf//rXkqTa2lpJUlJSUtB5SUlJ7rHa2lpFRUWpT58+7dYkJia2ev/ExES35lyNjY2qr68P2gAAgL0iOrvBM2fOaNiwYSooKJAkXXfdddq7d6+efPJJ3XnnnW5dWFhY0HnGmFb7znVuTVv17bVTWFioBx988KLHAgAAQlunr+gMGDBAgwcPDto3aNAgHTp0SJLk8XgkqdWqS11dnbvK4/F41NTUJL/f327N0aNHW73/sWPHWq0WnbVkyRIFAgF3q66uvoQRAgCAUNHpQeeGG27QgQMHgvZ9+OGHGjhwoCQpJSVFHo9HpaWl7vGmpiaVlZVpxIgRkqSMjAxFRkYG1dTU1GjPnj1uTVZWlgKBgHbs2OHWbN++XYFAwK05V3R0tOLj44M2AABgr07/r6t7771XI0aMUEFBgSZNmqQdO3bo6aef1tNPPy3pq/9uys/PV0FBgVJTU5WamqqCggL17t1beXl5kiTHcTRt2jTNnz9fffv2VUJCghYsWKAhQ4ZozJgxkr5aJRo/frymT5+u1atXS5JmzJihnJyci3riCgAA2K/Tg87111+v4uJiLVmyRA899JBSUlL02GOPacqUKW7NwoULdfr0ac2aNUt+v1+ZmZnatGmT4uLi3JpVq1YpIiJCkyZN0unTpzV69GitXbtW4eHhbs26des0d+5c9+ms3NxcFRUVdfaQAABAiOr079EJJXyPTmt8jw4AoKfr1u/RAQAA6CkIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYK0uDzqFhYUKCwtTfn6+u88Yo6VLl8rr9SomJkajRo3S3r17g85rbGzUnDlz1K9fP8XGxio3N1eHDx8OqvH7/fL5fHIcR47jyOfz6fjx4109JAAAECK6NOjs3LlTTz/9tP7u7/4uaP+KFSu0cuVKFRUVaefOnfJ4PBo7dqwaGhrcmvz8fBUXF2v9+vXasmWLTpw4oZycHLW0tLg1eXl5qqysVElJiUpKSlRZWSmfz9eVQwIAACGky4LOiRMnNGXKFD3zzDPq06ePu98Yo8cee0z33XefbrvtNqWnp+u5557TqVOn9OKLL0qSAoGA1qxZo0cffVRjxozRddddpxdeeEHvv/++3nzzTUnSvn37VFJSov/8z/9UVlaWsrKy9Mwzz+h3v/udDhw40FXDAgAAIaTLgs4999yjCRMmaMyYMUH7q6qqVFtbq+zsbHdfdHS0Ro4cqa1bt0qSKioq1NzcHFTj9XqVnp7u1mzbtk2O4ygzM9OtGT58uBzHcWvO1djYqPr6+qANAADYK6IrGl2/fr127dqlnTt3tjpWW1srSUpKSgran5SUpIMHD7o1UVFRQStBZ2vOnl9bW6vExMRW7ScmJro15yosLNSDDz7Y8QEBAICQ1OkrOtXV1fr5z3+uF154Qb169TpvXVhYWNBrY0yrfec6t6at+vbaWbJkiQKBgLtVV1e3+34AACC0dXrQqaioUF1dnTIyMhQREaGIiAiVlZXpl7/8pSIiItyVnHNXXerq6txjHo9HTU1N8vv97dYcPXq01fsfO3as1WrRWdHR0YqPjw/aAACAvTo96IwePVrvv/++Kisr3W3YsGGaMmWKKisrdfXVV8vj8ai0tNQ9p6mpSWVlZRoxYoQkKSMjQ5GRkUE1NTU12rNnj1uTlZWlQCCgHTt2uDXbt29XIBBwawAAwLdbp39GJy4uTunp6UH7YmNj1bdvX3d/fn6+CgoKlJqaqtTUVBUUFKh3797Ky8uTJDmOo2nTpmn+/Pnq27evEhIStGDBAg0ZMsT9cPOgQYM0fvx4TZ8+XatXr5YkzZgxQzk5OUpLS+vsYQEAgBDUJR9GvpCFCxfq9OnTmjVrlvx+vzIzM7Vp0ybFxcW5NatWrVJERIQmTZqk06dPa/To0Vq7dq3Cw8PdmnXr1mnu3Lnu01m5ubkqKir6xscDAAB6pjBjjOnuTnSX+vp6OY6jQCDQJZ/XuWrxhk5vs6t9smxCd3cBAIB2deT+ze+6AgAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAa3V60CksLNT111+vuLg4JSYm6tZbb9WBAweCaowxWrp0qbxer2JiYjRq1Cjt3bs3qKaxsVFz5sxRv379FBsbq9zcXB0+fDioxu/3y+fzyXEcOY4jn8+n48ePd/aQAABAiOr0oFNWVqZ77rlH5eXlKi0t1Zdffqns7GydPHnSrVmxYoVWrlypoqIi7dy5Ux6PR2PHjlVDQ4Nbk5+fr+LiYq1fv15btmzRiRMnlJOTo5aWFrcmLy9PlZWVKikpUUlJiSorK+Xz+Tp7SAAAIESFGWNMV77BsWPHlJiYqLKyMt18880yxsjr9So/P1+LFi2S9NXqTVJSkpYvX66ZM2cqEAiof//+ev755zV58mRJ0pEjR5ScnKyNGzdq3Lhx2rdvnwYPHqzy8nJlZmZKksrLy5WVlaX9+/crLS3tgn2rr6+X4zgKBAKKj4/v9LFftXhDp7fZ1T5ZNqG7uwAAQLs6cv/u8s/oBAIBSVJCQoIkqaqqSrW1tcrOznZroqOjNXLkSG3dulWSVFFRoebm5qAar9er9PR0t2bbtm1yHMcNOZI0fPhwOY7j1pyrsbFR9fX1QRsAALBXlwYdY4zmzZunG2+8Uenp6ZKk2tpaSVJSUlJQbVJSknustrZWUVFR6tOnT7s1iYmJrd4zMTHRrTlXYWGh+3kex3GUnJz89QYIAAB6tC4NOrNnz9af/vQnvfTSS62OhYWFBb02xrTad65za9qqb6+dJUuWKBAIuFt1dfXFDAMAAISoLgs6c+bM0euvv67NmzfriiuucPd7PB5JarXqUldX567yeDweNTU1ye/3t1tz9OjRVu977NixVqtFZ0VHRys+Pj5oAwAA9ur0oGOM0ezZs/Xqq6/qrbfeUkpKStDxlJQUeTwelZaWuvuamppUVlamESNGSJIyMjIUGRkZVFNTU6M9e/a4NVlZWQoEAtqxY4dbs337dgUCAbcGAAB8u0V0doP33HOPXnzxRf33f/+34uLi3JUbx3EUExOjsLAw5efnq6CgQKmpqUpNTVVBQYF69+6tvLw8t3batGmaP3+++vbtq4SEBC1YsEBDhgzRmDFjJEmDBg3S+PHjNX36dK1evVqSNGPGDOXk5FzUE1cAAMB+nR50nnzySUnSqFGjgvY/++yzuuuuuyRJCxcu1OnTpzVr1iz5/X5lZmZq06ZNiouLc+tXrVqliIgITZo0SadPn9bo0aO1du1ahYeHuzXr1q3T3Llz3aezcnNzVVRU1NlDAgAAIarLv0enJ+N7dFrje3QAAD1dj/oeHQAAgO5C0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYK+aDzxBNPKCUlRb169VJGRobefffd7u4SAADoIUI66Lz88svKz8/Xfffdp927d+umm27SLbfcokOHDnV31wAAQA8Q0kFn5cqVmjZtmn72s59p0KBBeuyxx5ScnKwnn3yyu7sGAAB6gIju7sClampqUkVFhRYvXhy0Pzs7W1u3bm3znMbGRjU2NrqvA4GAJKm+vr5L+nim8VSXtNuVrrz3N93dhQ7b8+C47u4CAOAbdPa+bYy5YG3IBp3PPvtMLS0tSkpKCtqflJSk2traNs8pLCzUgw8+2Gp/cnJyl/QR3wznse7uAQCgOzQ0NMhxnHZrQjbonBUWFhb02hjTat9ZS5Ys0bx589zXx48f18CBA3Xo0KEL/qBCUX19vZKTk1VdXa34+Pju7k6XsH2Mto9Psn+MjC/02T7GUByfMUYNDQ3yer0XrA3ZoNOvXz+Fh4e3Wr2pq6trtcpzVnR0tKKjo1vtdxwnZCb3UsTHx1s9Psn+Mdo+Psn+MTK+0Gf7GENtfBe7QBGyH0aOiopSRkaGSktLg/aXlpZqxIgR3dQrAADQk4Tsio4kzZs3Tz6fT8OGDVNWVpaefvppHTp0SHfffXd3dw0AAPQAIR10Jk+erM8//1wPPfSQampqlJ6ero0bN2rgwIEXdX50dLQeeOCBNv87ywa2j0+yf4y2j0+yf4yML/TZPkbbxxdmLubZLAAAgBAUsp/RAQAAuBCCDgAAsBZBBwAAWIugAwAArGV90HniiSeUkpKiXr16KSMjQ++++2679WVlZcrIyFCvXr109dVX66mnnvqGetoxhYWFuv766xUXF6fExETdeuutOnDgQLvnvP322woLC2u17d+//xvqdccsXbq0VV89Hk+754TK/EnSVVdd1eZ83HPPPW3Wh8L8vfPOO5o4caK8Xq/CwsL02muvBR03xmjp0qXyer2KiYnRqFGjtHfv3gu2+8orr2jw4MGKjo7W4MGDVVxc3EUjaF9742tubtaiRYs0ZMgQxcbGyuv16s4779SRI0fabXPt2rVtzusXX3zRxaNp7ULzd9ddd7Xq5/Dhwy/Ybk+ZP+nCY2xrLsLCwvTwww+ft82eNIcXc28I9euwo6wOOi+//LLy8/N13333affu3brpppt0yy236NChQ23WV1VV6Qc/+IFuuukm7d69W//6r/+quXPn6pVXXvmGe35hZWVluueee1ReXq7S0lJ9+eWXys7O1smTJy947oEDB1RTU+Nuqamp30CPL813vvOdoL6+//77560NpfmTpJ07dwaN7eyXX/7oRz9q97yePH8nT57U0KFDVVRU1ObxFStWaOXKlSoqKtLOnTvl8Xg0duxYNTQ0nLfNbdu2afLkyfL5fPrjH/8on8+nSZMmafv27V01jPNqb3ynTp3Srl27dP/992vXrl169dVX9eGHHyo3N/eC7cbHxwfNaU1NjXr16tUVQ2jXheZPksaPHx/Uz40bN7bbZk+aP+nCYzx3Hn71q18pLCxMt99+e7vt9pQ5vJh7Q6hfhx1mLPYP//AP5u677w7ad+2115rFixe3Wb9w4UJz7bXXBu2bOXOmGT58eJf1sbPU1dUZSaasrOy8NZs3bzaSjN/v/+Y69jU88MADZujQoRddH8rzZ4wxP//5z80111xjzpw50+bxUJs/Saa4uNh9febMGePxeMyyZcvcfV988YVxHMc89dRT521n0qRJZvz48UH7xo0bZ+64445O73NHnDu+tuzYscNIMgcPHjxvzbPPPmscx+ncznWCtsY3depU88Mf/rBD7fTU+TPm4ubwhz/8ofn+97/fbk1PnUNjWt8bbLsOL4a1KzpNTU2qqKhQdnZ20P7s7Gxt3bq1zXO2bdvWqn7cuHF677331Nzc3GV97QyBQECSlJCQcMHa6667TgMGDNDo0aO1efPmru7a1/LRRx/J6/UqJSVFd9xxhz7++OPz1oby/DU1NemFF17QT3/60/P+UtqzQmn+/lpVVZVqa2uD5ig6OlojR4487zUpnX9e2zunpwgEAgoLC9Pll1/ebt2JEyc0cOBAXXHFFcrJydHu3bu/mQ5egrfffluJiYn627/9W02fPl11dXXt1ofy/B09elQbNmzQtGnTLljbU+fw3HvDt/E6tDbofPbZZ2ppaWn1Cz6TkpJa/SLQs2pra9us//LLL/XZZ591WV+/LmOM5s2bpxtvvFHp6ennrRswYICefvppvfLKK3r11VeVlpam0aNH65133vkGe3vxMjMz9etf/1pvvPGGnnnmGdXW1mrEiBH6/PPP26wP1fmTpNdee03Hjx/XXXfddd6aUJu/c5297jpyTZ49r6Pn9ARffPGFFi9erLy8vHZ/UeK1116rtWvX6vXXX9dLL72kXr166YYbbtBHH330Dfb24txyyy1at26d3nrrLT366KPauXOnvv/976uxsfG854Tq/EnSc889p7i4ON12223t1vXUOWzr3vBtuw6lEP8VEBfj3H8dG2Pa/RdzW/Vt7e9JZs+erT/96U/asmVLu3VpaWlKS0tzX2dlZam6ulqPPPKIbr755q7uZofdcsst7p+HDBmirKwsXXPNNXruuec0b968Ns8JxfmTpDVr1uiWW26R1+s9b02ozd/5dPSavNRzulNzc7PuuOMOnTlzRk888US7tcOHDw/6QO8NN9yg7373u3r88cf1y1/+squ72iGTJ092/5yenq5hw4Zp4MCB2rBhQ7thINTm76xf/epXmjJlygU/a9NT57C9e8O34To8y9oVnX79+ik8PLxV2qyrq2uVSs/yeDxt1kdERKhv375d1tevY86cOXr99de1efNmXXHFFR0+f/jw4d3+r46LFRsbqyFDhpy3v6E4f5J08OBBvfnmm/rZz37W4XNDaf7OPjHXkWvy7HkdPac7NTc3a9KkSaqqqlJpaWm7qzltueyyy3T99deHxLwOGDBAAwcObLevoTZ/Z7377rs6cODAJV2XPWEOz3dv+LZch3/N2qATFRWljIwM90mWs0pLSzVixIg2z8nKympVv2nTJg0bNkyRkZFd1tdLYYzR7Nmz9eqrr+qtt95SSkrKJbWze/duDRgwoJN71zUaGxu1b9++8/Y3lObvrz377LNKTEzUhAkTOnxuKM1fSkqKPB5P0Bw1NTWprKzsvNekdP55be+c7nI25Hz00Ud68803LylgG2NUWVkZEvP6+eefq7q6ut2+htL8/bU1a9YoIyNDQ4cO7fC53TmHF7o3fBuuw1a64xPQ35T169ebyMhIs2bNGvPBBx+Y/Px8Exsbaz755BNjjDGLFy82Pp/Prf/4449N7969zb333ms++OADs2bNGhMZGWn+67/+q7uGcF7//M//bBzHMW+//bapqalxt1OnTrk1545v1apVpri42Hz44Ydmz549ZvHixUaSeeWVV7pjCBc0f/588/bbb5uPP/7YlJeXm5ycHBMXF2fF/J3V0tJirrzySrNo0aJWx0Jx/hoaGszu3bvN7t27jSSzcuVKs3v3bvepo2XLlhnHccyrr75q3n//ffPjH//YDBgwwNTX17tt+Hy+oCcj//CHP5jw8HCzbNkys2/fPrNs2TITERFhysvLe9T4mpubTW5urrniiitMZWVl0HXZ2Nh43vEtXbrUlJSUmP/93/81u3fvNj/5yU9MRESE2b59e48aX0NDg5k/f77ZunWrqaqqMps3bzZZWVnmb/7mb0Jm/oy58N9RY4wJBAKmd+/e5sknn2yzjZ48hxdzbwj167CjrA46xhjzH//xH2bgwIEmKirKfPe73w16/Hrq1Klm5MiRQfVvv/22ue6660xUVJS56qqrzvsXvbtJanN79tln3Zpzx7d8+XJzzTXXmF69epk+ffqYG2+80WzYsOGb7/xFmjx5shkwYICJjIw0Xq/X3HbbbWbv3r3u8VCev7PeeOMNI8kcOHCg1bFQnL+zj8Cfu02dOtUY89WjrQ888IDxeDwmOjra3Hzzzeb9998PamPkyJFu/Vm/+c1vTFpamomMjDTXXnttt4W79sZXVVV13uty8+bNbhvnji8/P99ceeWVJioqyvTv399kZ2ebrVu3fvODM+2P79SpUyY7O9v079/fREZGmiuvvNJMnTrVHDp0KKiNnjx/xlz476gxxqxevdrExMSY48ePt9lGT57Di7k3hPp12FFhxvzfpzUBAAAsY+1ndAAAAAg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALDW/wMhPwLu9rHL3wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1189,40 +139,88 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "\n",
-    "plt.hist(Counter(item[-1][:-1] for item in items_with_tuples).values(), bins=100)\n",
+    "plt.hist(Counter(item[-1][:-1] for item in items_with_tuples).values())\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # raw full ids\n",
+    "# full_duplicates = Counter(item[-1][:-1] for item in items_with_tuples).items()\n",
+    "# duplicated = [(semantic_id, amount) for (semantic_id, amount) in full_duplicates if amount > 1]\n",
+    "# duplicated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "12101"
+       "Counter({1: 11195, 2: 316, 3: 44, 4: 12, 5: 3, 6: 3, 9: 2, 7: 2, 8: 1, 21: 1})"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(set(item[-1] for item in items_with_tuples))"
+    "# collison counters - (how many item have same full semantic id): amount of such sets\n",
+    "vals = Counter(item[-1][:-1] for item in items_with_tuples).values()\n",
+    "Counter(vals)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({0: 11579,\n",
+       "         1: 384,\n",
+       "         2: 68,\n",
+       "         3: 24,\n",
+       "         4: 12,\n",
+       "         5: 9,\n",
+       "         6: 6,\n",
+       "         7: 4,\n",
+       "         8: 3,\n",
+       "         10: 1,\n",
+       "         11: 1,\n",
+       "         17: 1,\n",
+       "         12: 1,\n",
+       "         15: 1,\n",
+       "         19: 1,\n",
+       "         13: 1,\n",
+       "         14: 1,\n",
+       "         9: 1,\n",
+       "         18: 1,\n",
+       "         16: 1,\n",
+       "         20: 1})"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# dedup idxes\n",
+    "Counter(item[-1][4] for item in items_with_tuples)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 314df443..de4bbf58 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -141,9 +141,12 @@ def train_pass(self, embeddings):
         
     def eval_pass(self, embeddings):
         ind_lists = []
-        for cb in self.codebooks:
-            dist = torch.cdist(self.encoder(embeddings), cb)
-            ind_lists.append(dist.argmin(dim=-1).cpu().numpy())
+        remainder = self.encoder(embeddings)
+        for codebook in self.codebooks:
+            codebook_indices = self.get_codebook_indices(remainder, codebook)
+            codebook_vectors = codebook[codebook_indices]
+            ind_lists.append(codebook_indices.cpu().numpy())
+            remainder = remainder - codebook_vectors
         return zip(*ind_lists)
 
     def forward(self, inputs):
diff --git a/review.md b/review.md
index 45523828..5977c185 100644
--- a/review.md
+++ b/review.md
@@ -2,8 +2,8 @@
 
 ## Todos
 
-- posterior collapse (как будто все сваливается в один индекс в кодбуке)
-- обязательно использование reinit unused clusters
+- posterior collapse (как будто все сваливается в один индекс в кодбуке) (fixed eval code)
+- обязательно использование reinit unused clusters!
 - в Amazon датасете пофиг на rating? получается учитываются только implicit действия?
 - TODO какой базовый класс использовать для e2e модели? (LastPred?)
 - TODO backward on mean loss? in `RqVae`

From 2cf409ec108c12cd841588311511c8608464fc27 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Tue, 24 Dec 2024 23:56:19 +0300
Subject: [PATCH 014/175] add tiger draft, todospks, review marks

---
 configs/train/tiger_train_config.json   | 133 ++++++++++++++----
 modeling/dataloader/__init__.py         |   2 +-
 modeling/dataloader/batch_processors.py |  59 +++++++-
 modeling/loss/base.py                   |   2 +-
 modeling/main.ipynb                     | 175 +++++++++++-------------
 modeling/models/base.py                 |   4 +-
 modeling/models/bert4rec.py             |   2 +-
 modeling/models/tiger.py                |  32 ++---
 review.md                               |  13 +-
 9 files changed, 269 insertions(+), 153 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 23e7ef6b..4ed3bc0e 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,62 +1,77 @@
 {
-  "experiment_name": "tiger_beauty",
-  "train_steps_num": 5000,
+  "experiment_name": "tiger",
+  "best_metric": "eval/ndcg@20",
   "dataset": {
-    "type": "rqvae",
+    "type": "scientific",
     "path_to_data_dir": "../data",
     "name": "Beauty",
+    "max_sequence_length": 50,
     "samplers": {
-      "type": "identity"
+      "num_negatives_val": 100,
+      "type": "next_item_prediction",
+      "negative_sampler_type": "random"
     }
   },
   "dataloader": {
     "train": {
       "type": "torch",
-      "batch_size": 128,
+      "batch_size": 256,
       "batch_processor": {
-        "type": "embed"
+        "type": "rqvae",
+        "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+        "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+        "embs_extractor_path": "../data/Beauty/embs_extractor.pt"
       },
-      "drop_last": false,
+      "drop_last": true,
       "shuffle": true
     },
     "validation": {
       "type": "torch",
       "batch_size": 256,
       "batch_processor": {
-        "type": "embed"
+        "type": "rqvae",
+        "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+        "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+        "embs_extractor_path": "../data/Beauty/embs_extractor.pt"
       },
       "drop_last": false,
       "shuffle": false
     }
   },
   "model": {
-    "emb_dim": 512,
-    "n_tokens": 256,
-    "n_codebooks": 4,
-    "nhead": 8,
-    "num_encoder_layers": 6,
-    "num_decoder_layers": 6,
-    "dim_feedforward": 2048,
-    "dropout": 0.1
+    "type": "sasrec",
+    "sequence_prefix": "item",
+    "positive_prefix": "positive",
+    "negative_prefix": "negative",
+    "candidate_prefix": "candidates",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
+    "activation": "gelu",
+    "use_ce": true,
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
   },
-  "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
-  "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
   "optimizer": {
     "type": "basic",
     "optimizer": {
       "type": "adam",
-      "lr": 1e-4
+      "lr": 0.001
     },
-    "clip_grad_threshold": 5.0,
-    "scheduler": {
-      "type": "step",
-      "step_size": 100, 
-      "gamma": 0.98
-    }
+    "clip_grad_threshold": 5.0
   },
   "loss": {
-    "type": "rqvae_loss",
-    "beta": 0.25,
+    "type": "composite",
+    "losses": [
+      {
+        "type": "bpr",
+        "positive_prefix": "positive_scores",
+        "negative_prefix": "negative_scores",
+        "output_prefix": "downstream_loss"
+      }
+    ],
     "output_prefix": "loss"
   },
   "callback": {
@@ -66,6 +81,70 @@
         "type": "metric",
         "on_step": 1,
         "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 64,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          }
+        }
       }
     ]
   }
diff --git a/modeling/dataloader/__init__.py b/modeling/dataloader/__init__.py
index 63583bd1..1036b559 100644
--- a/modeling/dataloader/__init__.py
+++ b/modeling/dataloader/__init__.py
@@ -1,2 +1,2 @@
 from .base import BaseDataloader, SplitDataloader
-from .batch_processors import BaseBatchProcessor, IdentityBatchProcessor
+from .batch_processors import BaseBatchProcessor, IdentityBatchProcessor, RqVaeProcessor
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 9991a073..8429ec7a 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,5 +1,8 @@
+import json
 import torch
-from utils import MetaParent
+from models.base import BaseModel
+from utils import DEVICE, MetaParent
+import itertools
 
 
 class BaseBatchProcessor(metaclass=MetaParent):
@@ -20,6 +23,60 @@ def __call__(self, batch):
         embeds = torch.stack([entry['item.embed'] for entry in batch])
         
         return {'ids': ids, 'embeddings': embeds}
+    
+class RqVaeProcessor(BaseBatchProcessor, config_name='rqvae'):
+    def __init__(self, rqvae, embs_extractor):
+        self._rqvae = rqvae
+        self._embs_extractor = embs_extractor
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_train_config = json.load(open(config['rqvae_train_config_path']))
+        rqvae_train_config['model']['should_init_codebooks'] = False
+        
+        rqvae_model = BaseModel.create_from_config(rqvae_train_config['model']).to(DEVICE)
+        rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
+        rqvae_model.eval()
+        
+        embs_extractor = torch.load(config['embs_extractor_path'], weights_only=True)
+
+        return cls(rqvae_model, embs_extractor)
+    
+    def get_semantic_ids(self, item_ids):
+        embs = torch.stack([self._embs_extractor.get(item_id, torch.ones((512,))) for item_id in item_ids]) # TODOPK fix
+        semantic_ids = self._rqvae({"embeddings": embs})
+        return list(semantic_ids)
+
+    def __call__(self, batch):
+        processed_batch = {}
+
+        for key in batch[0].keys():
+            if key.endswith('.ids'):
+                prefix = key.split('.')[0]
+                assert '{}.length'.format(prefix) in batch[0]
+
+                processed_batch[f'{prefix}.ids'] = []
+                processed_batch[f'{prefix}.length'] = []
+                
+                # item_ids = list(itertools.chain(*semantic_ids))
+                # length = len(item_ids) # sample[f'{prefix}.length']
+
+                for sample in batch:
+                    item_ids = sample[f'{prefix}.ids']
+                    length = sample[f'{prefix}.length']
+                    
+                    if prefix != 'user':
+                        semantic_ids = self.get_semantic_ids(item_ids)
+                        item_ids = list(itertools.chain(*semantic_ids))
+                        length = len(semantic_ids)
+                    
+                    processed_batch[f'{prefix}.ids'].extend(item_ids)
+                    processed_batch[f'{prefix}.length'].append(length)
+                    
+        for part, values in processed_batch.items():
+            processed_batch[part] = torch.tensor(values, dtype=torch.long)
+
+        return processed_batch
 
 
 class BasicBatchProcessor(BaseBatchProcessor, config_name='basic'):
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index ee7c1879..65a145ea 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -137,7 +137,7 @@ def forward(self, inputs):
             rqvae_loss += self._loss(codebook_vectors, remainder.detach())
             
         recon_loss = self._loss(embeddings_restored, embeddings)
-        loss = (recon_loss + rqvae_loss).mean() # TODO mean?
+        loss = (recon_loss + rqvae_loss).mean() # TODOPK mean?
         
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index 7f6194b7..8fddbbd4 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,44 +66,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "item='Revlon Beyond Natural Smoothing Primer, Clear, 0.85 Ounce' clust_tuple=(0, 33, 46, 141, 0)\n",
-      "item='Color Club Magic Attraction 843 Nail Polish' clust_tuple=(0, 228, 117, 102, 0)\n",
-      "item='Color Club Wild at Heart 871 Nail Polish' clust_tuple=(0, 228, 83, 106, 0)\n",
-      "1\n",
-      "item='Natural Beauty White / Brightening Essence Full Face Mask 10 Pcs' clust_tuple=(1, 210, 157, 13, 0)\n",
-      "item='OGX Conditioner, Nourishing Coconut Milk, 13oz' clust_tuple=(1, 50, 213, 95, 0)\n",
-      "item='BurnOut Eco-sensitive Zinc Oxide Sunscreen SPF 35 (3 oz)' clust_tuple=(1, 119, 46, 204, 0)\n",
-      "item='Kerastase Reflection Chroma Riche Luminous Softening Essence, 4.2 Ounce' clust_tuple=(1, 119, 3, 229, 0)\n",
-      "item='KINeSYS Performance Sunscreen, SPF 30, Spray, Mango Scent, 4-Ounce Bottles' clust_tuple=(1, 242, 54, 95, 0)\n",
-      "2\n",
-      "item='Pink Sugar by Aquolina for Women - 3.4 Ounce EDT Spray' clust_tuple=(2, 173, 246, 41, 0)\n",
-      "item='Guess By Parlux Fragrances For Men. Eau De Toilette Spray 2.5 Oz.' clust_tuple=(2, 104, 233, 139, 0)\n",
-      "item='Black Xs By Paco Rabanne For Men, Eau De Toilette Spray, 3.4-Ounce Bottle' clust_tuple=(2, 177, 233, 95, 0)\n",
-      "item='Incanto Shine By Salvatore Ferragamo For Women, Eau De Toilette Spray, 3.4-Ounce Bottle' clust_tuple=(2, 128, 185, 76, 0)\n",
-      "item='Versace Man Eau Fraiche By Gianni Versace For Men Edt Spray 3.4 Oz' clust_tuple=(2, 148, 81, 107, 0)\n",
-      "3\n",
-      "item='HDE&reg; Facial Pore Cleanser Cleaner Blackhead Acne Remover' clust_tuple=(3, 106, 111, 172, 0)\n",
-      "item=\"Best Anti Aging Cream Reduces Wrinkels in Women and Men - Clinical Strength Bio-Peptide Wrinkle Cream Reduces Deep Wrinkles, Smooths Fine Lines and &quot;Crow's Feet&quot; - Tighten, Rejuvinate, and Rebuild Youthful, Healthy Skin - Boost Collagen and Ultra-Moisturize with Peptides Made For Your Skin - Great For Face, Under Eyes and Decolletage. You Love It Or We Buy It Back No Hassle Money Back Guarantee. [Reduced Price For Summer! Take 65% Off Automatically at Checkout!]&#9733; 2oz Jar (60ml)\" clust_tuple=(3, 90, 239, 106, 0)\n",
-      "item='Raw African Black soap Imported From Ghana 4oz' clust_tuple=(3, 145, 185, 168, 0)\n",
-      "item='Skin Obsession 20% TCA Home Chemical Peel for face and body removes lines, sun damage and signs of aging' clust_tuple=(3, 207, 151, 96, 0)\n",
-      "item='Skin Obsession 25% TCA Chemical Peel for Home Use 1 fl oz (30 ml)' clust_tuple=(3, 207, 47, 44, 0)\n",
-      "4\n",
-      "item='Philosophy When Hope is Not Enough Firming and Lifting Serum for Unisex, 1 Ounce' clust_tuple=(4, 247, 239, 194, 0)\n",
-      "item='DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ' clust_tuple=(4, 182, 111, 5, 0)\n",
-      "item='Victorinox By Swiss Army For Men 125 Years Eau-de-toilette Spray, 3.4-Ounce' clust_tuple=(4, 128, 239, 5, 0)\n",
-      "item='Givenchy Play for Men by Givenchy 3.3 oz 100 ml EDT Spray' clust_tuple=(4, 50, 19, 168, 0)\n",
-      "item='Jilbere Hot Air Brush' clust_tuple=(4, 15, 47, 194, 0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from rqvae.rqvae_data import search_similar_items\n",
     "\n",
@@ -120,20 +85,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from collections import Counter\n",
     "import matplotlib.pyplot as plt\n",
@@ -157,20 +111,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({1: 11195, 2: 316, 3: 44, 4: 12, 5: 3, 6: 3, 9: 2, 7: 2, 8: 1, 21: 1})"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# collison counters - (how many item have same full semantic id): amount of such sets\n",
     "vals = Counter(item[-1][:-1] for item in items_with_tuples).values()\n",
@@ -179,40 +122,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({0: 11579,\n",
-       "         1: 384,\n",
-       "         2: 68,\n",
-       "         3: 24,\n",
-       "         4: 12,\n",
-       "         5: 9,\n",
-       "         6: 6,\n",
-       "         7: 4,\n",
-       "         8: 3,\n",
-       "         10: 1,\n",
-       "         11: 1,\n",
-       "         17: 1,\n",
-       "         12: 1,\n",
-       "         15: 1,\n",
-       "         19: 1,\n",
-       "         13: 1,\n",
-       "         14: 1,\n",
-       "         9: 1,\n",
-       "         18: 1,\n",
-       "         16: 1,\n",
-       "         20: 1})"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# dedup idxes\n",
     "Counter(item[-1][4] for item in items_with_tuples)"
@@ -236,6 +148,75 @@
     "# torch.save(df, './all_data.pt')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from utils import create_masked_tensor\n",
+    "\n",
+    "\n",
+    "embeddings = torch.rand((11, 2))\n",
+    "print(embeddings)\n",
+    "\n",
+    "lengths = torch.tensor([3, 1, 2, 5])\n",
+    "\n",
+    "padded_embeddings, mask = create_masked_tensor(embeddings, lengths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "padded_embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "mapping = pd.read_csv(\"../data/Beauty/ratings_Beauty_full.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "deduped_mapping = mapping.drop_duplicates(subset=['item_id', 'raw_item_id'])\n",
+    "\n",
+    "semantic_ids_extractor = {}\n",
+    "for idx, row in pd.merge(deduped_mapping, df, 'inner', left_on='raw_item_id', right_on='asin').iterrows():\n",
+    "    semantic_ids_extractor[row['item_id']] = row['embeddings']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(semantic_ids_extractor, \"../data/Beauty/embs_extractor.pt\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 28be6a98..07c04fef 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -109,7 +109,7 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = torch.arange(
+        positions = torch.arange( # TODOPK why inverted? (not 0...n)
             start=seq_len - 1, end=-1, step=-1, device=mask.device
         )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
@@ -135,7 +135,7 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
             embeddings = torch.cat((cls_token_expanded, embeddings), dim=1)
             mask = torch.cat((torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask), dim=1)
 
-        if self._is_causal:
+        if self._is_causal: # TODOPK causal?
             causal_mask = torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)  # (seq_len, seq_len)
             embeddings = self._encoder(
                 src=embeddings,
diff --git a/modeling/models/bert4rec.py b/modeling/models/bert4rec.py
index 40f1d331..273e1da7 100644
--- a/modeling/models/bert4rec.py
+++ b/modeling/models/bert4rec.py
@@ -81,7 +81,7 @@ def forward(self, inputs):
         if self.training:  # training mode
             all_sample_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_batch_events)
             embeddings = embeddings[mask]  # (all_batch_events, num_items)
-            labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events)
+            labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events) # TODOPK change that it accepts semantic ids (what is nonzero)
 
             needed_logits = embeddings[labels_mask]  # (non_zero_events, num_items)
             needed_labels = all_sample_labels[labels_mask]  # (non_zero_events)
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 98a852db..2cef46d1 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -3,26 +3,23 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from modeling.utils import DEVICE
-from models.base import BaseModel, TorchModel
+from models.base import TorchModel
 
-# TODO finish tiger model
+# TODOPK finish tiger model
 class TigerModel(TorchModel, config_name='tiger'):
     def __init__(
-        self, 
-        rqvae_encoder, 
-        emb_dim, 
-        n_tokens, 
-        n_codebooks, 
-        nhead, 
-        num_encoder_layers, 
-        num_decoder_layers, 
-        dim_feedforward, 
+        self,
+        emb_dim,
+        n_tokens,
+        n_codebooks,
+        nhead,
+        num_encoder_layers,
+        num_decoder_layers,
+        dim_feedforward,
         dropout
     ):
         super().__init__()
         
-        self.rqvae_encoder = rqvae_encoder
         self.emb_dim = emb_dim
         self.n_tokens = n_tokens
         
@@ -30,7 +27,7 @@ def __init__(
         self.item_embeddings = nn.Embedding(n_tokens, emb_dim)
         
         self.transformer = nn.Transformer(
-            d_model=emb_dim, 
+            d_model=emb_dim,
             nhead=nhead,
             num_encoder_layers=num_encoder_layers,
             num_decoder_layers=num_decoder_layers,
@@ -42,14 +39,7 @@ def __init__(
         
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        rqvae_train_config = json.load(open(config['rqvae_train_config_path']))
-        
-        rqvae_model = BaseModel.create_from_config(rqvae_train_config['model']).to(DEVICE)
-        rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
-        rqvae_model.eval()
-
         return cls(
-            rqvae_encoder=rqvae_model,
             emb_dim=config['emb_dim'],
             n_tokens=config['n_tokens'],
             n_codebooks=config['n_codebooks'],
diff --git a/review.md b/review.md
index 5977c185..4010105a 100644
--- a/review.md
+++ b/review.md
@@ -2,11 +2,20 @@
 
 ## Todos
 
+- check TODOPK
+- у нас авторегрессионный next item prediciton? то есть:
+
+1) items (10)
+2) semantic_ids (40)
+3) prediciting 11th item (next 4 semantic ids)
+4) if single - ok
+5) if several?
+6) if nothing?
+
 - posterior collapse (как будто все сваливается в один индекс в кодбуке) (fixed eval code)
-- обязательно использование reinit unused clusters!
+- обязательно использование reinit unused clusters! (mark)
 - в Amazon датасете пофиг на rating? получается учитываются только implicit действия?
 - TODO какой базовый класс использовать для e2e модели? (LastPred?)
-- TODO backward on mean loss? in `RqVae`
 - TODO имя для модели (tiger)
 
 ## Links

From f63d617e995bbb887d84f3f975958aa6ea5fd7fc Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 25 Dec 2024 00:26:11 +0300
Subject: [PATCH 015/175] fix item_id mapping

---
 configs/train/tiger_train_config.json         |   1 +
 modeling/dataloader/batch_processors.py       |   2 +-
 modeling/main.ipynb                           |  40 +-----
 notebooks/AmazonBeautyDatasetStatistics.ipynb | 121 ++++++++++++------
 4 files changed, 90 insertions(+), 74 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 4ed3bc0e..ca17cb44 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,5 +1,6 @@
 {
   "experiment_name": "tiger",
+  "train_steps_num": 1024,
   "best_metric": "eval/ndcg@20",
   "dataset": {
     "type": "scientific",
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 8429ec7a..e9f64e50 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -43,7 +43,7 @@ def create_from_config(cls, config, **kwargs):
         return cls(rqvae_model, embs_extractor)
     
     def get_semantic_ids(self, item_ids):
-        embs = torch.stack([self._embs_extractor.get(item_id, torch.ones((512,))) for item_id in item_ids]) # TODOPK fix
+        embs = torch.stack([self._embs_extractor[item_id] for item_id in item_ids])
         semantic_ids = self._rqvae({"embeddings": embs})
         return list(semantic_ids)
 
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index 8fddbbd4..e03fc168 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -148,6 +148,13 @@
     "# torch.save(df, './all_data.pt')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -184,39 +191,6 @@
     "mask"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "mapping = pd.read_csv(\"../data/Beauty/ratings_Beauty_full.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "deduped_mapping = mapping.drop_duplicates(subset=['item_id', 'raw_item_id'])\n",
-    "\n",
-    "semantic_ids_extractor = {}\n",
-    "for idx, row in pd.merge(deduped_mapping, df, 'inner', left_on='raw_item_id', right_on='asin').iterrows():\n",
-    "    semantic_ids_extractor[row['item_id']] = row['embeddings']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.save(semantic_ids_extractor, \"../data/Beauty/embs_extractor.pt\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/AmazonBeautyDatasetStatistics.ipynb b/notebooks/AmazonBeautyDatasetStatistics.ipynb
index a1a2c3d2..11fd2925 100644
--- a/notebooks/AmazonBeautyDatasetStatistics.ipynb
+++ b/notebooks/AmazonBeautyDatasetStatistics.ipynb
@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "path_to_df = '../data/Beauty/ratings_Beauty.csv'\n",
-    "df = pd.read_csv(path_to_df, names=['user_id', 'item_id', 'rating', 'timestamp'])"
+    "df = pd.read_csv(path_to_df, names=['raw_user_id', 'raw_item_id', 'rating', 'timestamp'])"
    ]
   },
   {
@@ -59,8 +59,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>user_id</th>\n",
-       "      <th>item_id</th>\n",
+       "      <th>raw_user_id</th>\n",
+       "      <th>raw_item_id</th>\n",
        "      <th>rating</th>\n",
        "      <th>timestamp</th>\n",
        "    </tr>\n",
@@ -106,7 +106,7 @@
        "</div>"
       ],
       "text/plain": [
-       "          user_id     item_id  rating   timestamp\n",
+       "      raw_user_id raw_item_id  rating   timestamp\n",
        "0  A39HTATAQ9V7YF  0205616461     5.0  1369699200\n",
        "1  A3JM6GV9MNOF9X  0558925278     3.0  1355443200\n",
        "2  A1Z513UWSAAO0F  0558925278     5.0  1404691200\n",
@@ -132,10 +132,10 @@
     {
      "data": {
       "text/plain": [
-       "user_id      0\n",
-       "item_id      0\n",
-       "rating       0\n",
-       "timestamp    0\n",
+       "raw_user_id    0\n",
+       "raw_item_id    0\n",
+       "rating         0\n",
+       "timestamp      0\n",
        "dtype: int64"
       ]
      },
@@ -166,7 +166,7 @@
     }
    ],
    "source": [
-    "df.user_id.max(), df.user_id.unique().shape"
+    "df.raw_user_id.max(), df.raw_user_id.unique().shape"
    ]
   },
   {
@@ -187,7 +187,7 @@
     }
    ],
    "source": [
-    "df.user_id = pd.factorize(df.user_id)[0] + 1\n",
+    "df['user_id'] = pd.factorize(df.raw_user_id)[0] + 1\n",
     "df.user_id.min(), df.user_id.max(), df.user_id.unique().shape"
    ]
   },
@@ -209,7 +209,7 @@
     }
    ],
    "source": [
-    "df.item_id = pd.factorize(df.item_id)[0] + 1\n",
+    "df['item_id'] = pd.factorize(df.raw_item_id)[0] + 1\n",
     "df.item_id.min(), df.item_id.max(), df.item_id.unique().shape"
    ]
   },
@@ -240,59 +240,71 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>user_id</th>\n",
-       "      <th>item_id</th>\n",
+       "      <th>raw_user_id</th>\n",
+       "      <th>raw_item_id</th>\n",
        "      <th>rating</th>\n",
        "      <th>timestamp</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>item_id</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>A39HTATAQ9V7YF</td>\n",
+       "      <td>0205616461</td>\n",
        "      <td>5.0</td>\n",
        "      <td>1369699200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>A3JM6GV9MNOF9X</td>\n",
+       "      <td>0558925278</td>\n",
        "      <td>3.0</td>\n",
        "      <td>1355443200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
+       "      <td>A1Z513UWSAAO0F</td>\n",
+       "      <td>0558925278</td>\n",
        "      <td>5.0</td>\n",
        "      <td>1404691200</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>3</td>\n",
+       "      <td>A1WMRR494NWEWV</td>\n",
+       "      <td>0733001998</td>\n",
        "      <td>4.0</td>\n",
        "      <td>1382572800</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
+       "      <td>A3IAAVS479H7M7</td>\n",
+       "      <td>0737104473</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1274227200</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   user_id  item_id  rating   timestamp\n",
-       "0        1        1     5.0  1369699200\n",
-       "1        2        2     3.0  1355443200\n",
-       "2        3        2     5.0  1404691200\n",
-       "3        4        3     4.0  1382572800\n",
-       "4        5        4     1.0  1274227200"
+       "      raw_user_id raw_item_id  rating   timestamp  user_id  item_id\n",
+       "0  A39HTATAQ9V7YF  0205616461     5.0  1369699200        1        1\n",
+       "1  A3JM6GV9MNOF9X  0558925278     3.0  1355443200        2        2\n",
+       "2  A1Z513UWSAAO0F  0558925278     5.0  1404691200        3        2\n",
+       "3  A1WMRR494NWEWV  0733001998     4.0  1382572800        4        3\n",
+       "4  A3IAAVS479H7M7  0737104473     1.0  1274227200        5        4"
       ]
      },
      "execution_count": 8,
@@ -306,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "5701edda",
    "metadata": {},
    "outputs": [
@@ -314,7 +326,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2023070it [01:12, 28030.70it/s]"
+      "2023070it [02:18, 14586.26it/s]"
      ]
     },
     {
@@ -347,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "c8f9aabc",
    "metadata": {},
    "outputs": [
@@ -355,7 +367,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████| 2023070/2023070 [00:04<00:00, 421809.17it/s]\n"
+      "100%|██████████| 2023070/2023070 [00:07<00:00, 275645.12it/s]\n"
      ]
     },
     {
@@ -432,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "dcc9f464",
    "metadata": {},
    "outputs": [
@@ -440,8 +452,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████| 1210271/1210271 [00:01<00:00, 840307.33it/s]\n",
-      "100%|██████████████████████████████████████████████████████████████████████| 249274/249274 [00:00<00:00, 571256.27it/s]\n"
+      "100%|██████████| 1210271/1210271 [00:01<00:00, 814485.15it/s] \n",
+      "100%|██████████| 249274/249274 [00:00<00:00, 538388.70it/s]\n"
      ]
     }
    ],
@@ -494,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "16df848f",
    "metadata": {},
    "outputs": [
@@ -520,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "90f7ccc1",
    "metadata": {},
    "outputs": [],
@@ -532,11 +544,40 @@
     "        ]))\n",
     "        f.write('\\n')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "83cb36e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "\n",
+    "deduped_mapping = df.drop_duplicates(subset=['item_id', 'raw_item_id'])\n",
+    "\n",
+    "embs = torch.load('../data/df_with_embs.pt')\n",
+    "\n",
+    "semantic_ids_extractor = {}\n",
+    "for idx, row in pd.merge(deduped_mapping, embs, 'inner', left_on='raw_item_id', right_on='asin').iterrows():\n",
+    "    semantic_ids_extractor[item_mapping[row['item_id']]] = row['embeddings']\n",
+    "    \n",
+    "torch.save(semantic_ids_extractor, '../data/Beauty/id_to_emb.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39c64b10",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "gsrec",
    "language": "python",
    "name": "python3"
   },
@@ -550,7 +591,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

From 55fc204feb925070ae1e23e58458ba73a04b873f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 25 Dec 2024 00:54:17 +0300
Subject: [PATCH 016/175] use idx instead of mapping in dataset

---
 modeling/dataset/base.py                      |  6 ++---
 notebooks/AmazonBeautyDatasetStatistics.ipynb | 22 ++++++++++++++-----
 2 files changed, 19 insertions(+), 9 deletions(-)

diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index ca9e451c..8fa712fc 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -720,12 +720,12 @@ def create_from_config(cls, config, **kwargs):
         data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, '{}.pt'.format('all_data'))
+        dataset_path = os.path.join(data_dir_path, '{}.pt'.format('data_full'))
         df = torch.load(dataset_path, weights_only=False)
 
-        for _idx, sample in df.iterrows():
+        for idx, sample in df.iterrows():
             train_dataset.append({
-                'item.id': sample['asin_numeric'],
+                'item.id': idx,
                 'item.embed': sample["embeddings"]
             })
             
diff --git a/notebooks/AmazonBeautyDatasetStatistics.ipynb b/notebooks/AmazonBeautyDatasetStatistics.ipynb
index 11fd2925..c613e17d 100644
--- a/notebooks/AmazonBeautyDatasetStatistics.ipynb
+++ b/notebooks/AmazonBeautyDatasetStatistics.ipynb
@@ -547,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 52,
    "id": "83cb36e1",
    "metadata": {},
    "outputs": [],
@@ -559,17 +559,27 @@
     "\n",
     "embs = torch.load('../data/df_with_embs.pt')\n",
     "\n",
-    "semantic_ids_extractor = {}\n",
-    "for idx, row in pd.merge(deduped_mapping, embs, 'inner', left_on='raw_item_id', right_on='asin').iterrows():\n",
-    "    semantic_ids_extractor[item_mapping[row['item_id']]] = row['embeddings']\n",
+    "merged = pd.merge(deduped_mapping, embs, 'inner', left_on='raw_item_id', right_on='asin')\n",
+    "merged['item_id'] = merged['item_id'].map(lambda x: item_mapping[x])\n",
     "    \n",
-    "torch.save(semantic_ids_extractor, '../data/Beauty/id_to_emb.pt')"
+    "assert len(merged) == len(merged.item_id.unique())\n",
+    "merged = merged.set_index('item_id')\n",
+    "\n",
+    "torch.save(merged, '../data/Beauty/data_full.pt')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "39c64b10",
+   "id": "670cfd1f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "587feb1f",
    "metadata": {},
    "outputs": [],
    "source": []

From f401727c9db99d979b7ae6c670ba291c281ba0ed Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 25 Dec 2024 02:05:28 +0300
Subject: [PATCH 017/175] fix tiger & get autoregression

---
 configs/train/tiger_train_config.json   |   6 +-
 image.png                               | Bin 0 -> 191122 bytes
 modeling/dataloader/batch_processors.py |   6 +-
 modeling/dataset/base.py                | 121 +++++++++++++++++
 modeling/models/__init__.py             |   1 +
 modeling/models/sasrec.py               |   2 +-
 modeling/models/tiger.py                | 171 +++++++++++++-----------
 review.md                               |   3 +
 8 files changed, 227 insertions(+), 83 deletions(-)
 create mode 100644 image.png

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index ca17cb44..d2fb5a97 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -3,7 +3,7 @@
   "train_steps_num": 1024,
   "best_metric": "eval/ndcg@20",
   "dataset": {
-    "type": "scientific",
+    "type": "rqvae_scientific",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
@@ -21,7 +21,7 @@
         "type": "rqvae",
         "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
         "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-        "embs_extractor_path": "../data/Beauty/embs_extractor.pt"
+        "embs_extractor_path": "../data/Beauty/data_full.pt"
       },
       "drop_last": true,
       "shuffle": true
@@ -33,7 +33,7 @@
         "type": "rqvae",
         "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
         "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-        "embs_extractor_path": "../data/Beauty/embs_extractor.pt"
+        "embs_extractor_path": "../data/Beauty/data_full.pt"
       },
       "drop_last": false,
       "shuffle": false
diff --git a/image.png b/image.png
new file mode 100644
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diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index e9f64e50..ddf6c92b 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -38,12 +38,12 @@ def create_from_config(cls, config, **kwargs):
         rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
         rqvae_model.eval()
         
-        embs_extractor = torch.load(config['embs_extractor_path'], weights_only=True)
+        embs_extractor = torch.load(config['embs_extractor_path'])
 
         return cls(rqvae_model, embs_extractor)
     
     def get_semantic_ids(self, item_ids):
-        embs = torch.stack([self._embs_extractor[item_id] for item_id in item_ids])
+        embs = torch.stack([self._embs_extractor.loc[item_id]['embeddings'] for item_id in item_ids])
         semantic_ids = self._rqvae({"embeddings": embs})
         return list(semantic_ids)
 
@@ -68,7 +68,7 @@ def __call__(self, batch):
                     if prefix != 'user':
                         semantic_ids = self.get_semantic_ids(item_ids)
                         item_ids = list(itertools.chain(*semantic_ids))
-                        length = len(semantic_ids)
+                        length = len(item_ids)
                     
                     processed_batch[f'{prefix}.ids'].extend(item_ids)
                     processed_batch[f'{prefix}.length'].append(length)
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 8fa712fc..1eee92ff 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -701,6 +701,127 @@ def meta(self):
             'max_sequence_length': self.max_sequence_length
         }
         
+class RqvaeScientificDataset(BaseDataset, config_name='rqvae_scientific'):
+
+    def __init__(
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_users,
+            num_items,
+            max_sequence_length
+    ):
+        self._train_sampler = train_sampler
+        self._validation_sampler = validation_sampler
+        self._test_sampler = test_sampler
+        self._num_users = num_users
+        self._num_items = num_items
+        self._max_sequence_length = max_sequence_length
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
+        max_sequence_length = config['max_sequence_length']
+        max_user_idx, max_item_idx = 0, 0
+        train_dataset, validation_dataset, test_dataset = [], [], []
+
+        dataset_path = os.path.join(data_dir_path, '{}.txt'.format('all_data'))
+        with open(dataset_path, 'r') as f:
+            data = f.readlines()
+
+        for sample in data:
+            sample = sample.strip('\n').split(' ')
+            user_idx = int(sample[0])
+            item_ids = [int(item_id) for item_id in sample[1:]]
+
+            max_user_idx = max(max_user_idx, user_idx)
+            max_item_idx = max(max_item_idx, max(item_ids))
+
+            assert len(item_ids) >= 5
+
+            train_dataset.append({
+                'user.ids': [user_idx],
+                'user.length': 1,
+                'item.ids': item_ids[:-2][-max_sequence_length:],
+                'item.length': len(item_ids[:-2][-max_sequence_length:])
+            })
+            assert len(item_ids[:-2][-max_sequence_length:]) == len(set(item_ids[:-2][-max_sequence_length:]))
+            validation_dataset.append({
+                'user.ids': [user_idx],
+                'user.length': 1,
+                'item.ids': item_ids[:-1][-max_sequence_length:],
+                'item.length': len(item_ids[:-1][-max_sequence_length:])
+            })
+            assert len(item_ids[:-1][-max_sequence_length:]) == len(set(item_ids[:-1][-max_sequence_length:]))
+            test_dataset.append({
+                'user.ids': [user_idx],
+                'user.length': 1,
+                'item.ids': item_ids[-max_sequence_length:],
+                'item.length': len(item_ids[-max_sequence_length:])
+            })
+            assert len(item_ids[-max_sequence_length:]) == len(set(item_ids[-max_sequence_length:]))
+
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user idx: {}'.format(max_user_idx))
+        logger.info('Max item idx: {}'.format(max_item_idx))
+        logger.info('Max sequence length: {}'.format(max_sequence_length))
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (len(train_dataset) + len(test_dataset)) / max_user_idx / max_item_idx
+        ))
+
+        train_sampler = TrainSampler.create_from_config(
+            config['samplers'],
+            dataset=train_dataset,
+            num_users=max_user_idx,
+            num_items=max_item_idx
+        )
+        validation_sampler = EvalSampler.create_from_config(
+            config['samplers'],
+            dataset=validation_dataset,
+            num_users=max_user_idx,
+            num_items=max_item_idx
+        )
+        test_sampler = EvalSampler.create_from_config(
+            config['samplers'],
+            dataset=test_dataset,
+            num_users=max_user_idx,
+            num_items=max_item_idx
+        )
+
+        return cls(
+            train_sampler=train_sampler,
+            validation_sampler=validation_sampler,
+            test_sampler=test_sampler,
+            num_users=max_user_idx,
+            num_items=max_item_idx,
+            max_sequence_length=max_sequence_length
+        )
+
+    def get_samplers(self):
+        return self._train_sampler, self._validation_sampler, self._test_sampler
+
+    @property
+    def num_users(self):
+        return self._num_users
+
+    @property
+    def num_items(self):
+        return self._num_items
+
+    @property
+    def max_sequence_length(self):
+        return self._max_sequence_length
+
+    @property
+    def meta(self):
+        return {
+            'num_users': self.num_users,
+            'num_items': self.num_items * 4, # TODOPK
+            'max_sequence_length': self.max_sequence_length * 4 # TODOPK
+        }
+        
 class RqVaeDataset(BaseDataset, config_name='rqvae'):
 
     def __init__(
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index e52aed9e..3fc87499 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -16,3 +16,4 @@
 from .sasrec_ce import SasRecCeModel
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
+from .tiger import TigerModel
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 061ea1d0..ec5221dc 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -102,7 +102,7 @@ def forward(self, inputs):
                 k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
+            return indices.repeat(4, 2) # TODOPK
 
 
 class SasRecInBatchModel(SasRecModel, config_name='sasrec_in_batch'):
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 2cef46d1..dddaf8d3 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,86 +1,105 @@
-import json
+from models.base import SequentialTorchModel
+
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
 
-from models.base import TorchModel
 
-# TODOPK finish tiger model
-class TigerModel(TorchModel, config_name='tiger'):
+class TigerModel(SequentialTorchModel, config_name='tiger'):
+
     def __init__(
-        self,
-        emb_dim,
-        n_tokens,
-        n_codebooks,
-        nhead,
-        num_encoder_layers,
-        num_decoder_layers,
-        dim_feedforward,
-        dropout
+            self,
+            sequence_prefix,
+            positive_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
-        super().__init__()
-        
-        self.emb_dim = emb_dim
-        self.n_tokens = n_tokens
-        
-        self.position_embeddings = nn.Embedding(n_codebooks, emb_dim)
-        self.item_embeddings = nn.Embedding(n_tokens, emb_dim)
-        
-        self.transformer = nn.Transformer(
-            d_model=emb_dim,
-            nhead=nhead,
-            num_encoder_layers=num_encoder_layers,
-            num_decoder_layers=num_decoder_layers,
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
             dim_feedforward=dim_feedforward,
-            dropout=dropout
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True
         )
-        
-        self.proj = nn.Linear(emb_dim, n_tokens)
-        
+        self._sequence_prefix = sequence_prefix
+        self._positive_prefix = positive_prefix
+
+        self._init_weights(initializer_range)
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            emb_dim=config['emb_dim'],
-            n_tokens=config['n_tokens'],
-            n_codebooks=config['n_codebooks'],
-            nhead=config['nhead'],
-            num_encoder_layers=config['num_encoder_layers'],
-            num_decoder_layers=config['num_decoder_layers'],
-            dim_feedforward=config['dim_feedforward'],
-            dropout=config['dropout']
-        )    
-    
-    def forward(self, user_item_history):
-        # Get item embeddings from RQVAE encoder
-        item_sequence = self.rqvae_encoder(user_item_history)
-        
-        # Convert item sequence to embeddings (embedding size is emb_dim)
-        item_embs = self.item_embeddings(item_sequence)
-        
-        # Add positional embeddings (positions are in the range [0, 3] for each tuple in the sequence)
-        positions = torch.arange(0, item_embs.size(1), device=item_embs.device).unsqueeze(0)
-        position_embs = self.position_embeddings(positions)
-        
-        # Add position embeddings to item embeddings
-        embeddings = item_embs + position_embs
-        
-        # Transformer expects the input to be in (seq_len, batch, embedding_dim) format
-        embeddings = embeddings.permute(1, 0, 2)  # Convert to (seq_len, batch, emb_dim)
-        
-        # Create the target sequence for the transformer decoder
-        # You can shift the sequence for training as needed (e.g., teacher forcing)
-        target = embeddings.clone()  # Use input embeddings as target for now
-        
-        # Pass through the transformer (using embeddings as both input and target)
-        transformer_output = self.transformer(embeddings, target)
-        
-        # Project the output back to token space (256 possible values for each codebook)
-        logits = self.proj(transformer_output)
-        
-        # Apply softmax to get probabilities (for cross-entropy loss)
-        return logits
-
-    def compute_loss(self, logits, target):
-        # Compute cross-entropy loss
-        loss = F.cross_entropy(logits.view(-1, self.n_tokens), target.view(-1))
-        return loss
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
+        )
+
+    def forward(self, inputs):
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+
+        embeddings, mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        if self.training:  # training mode
+            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+
+            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            all_positive_sample_embeddings = self._item_embeddings(
+                all_positive_sample_events
+            )  # (all_batch_events, embedding_dim)
+
+            all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
+
+            all_scores = torch.einsum(
+                'ad,nd->an',
+                all_sample_embeddings,
+                all_embeddings
+            )  # (all_batch_events, num_items + 2)
+            positive_scores = torch.gather(
+                input=all_scores,
+                dim=1,
+                index=all_positive_sample_events[..., None]
+            )  # (all_batch_items, 1)
+
+            return {
+                'positive_scores': positive_scores,
+                'negative_scores': all_scores
+            }
+        else:  # eval mode
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+
+            # b - batch_size, n - num_candidates, d - embedding_dim
+            candidate_scores = torch.einsum(
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
+            )  # (batch_size, num_items + 2)
+            candidate_scores[:, 0] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
+
+            _, indices = torch.topk(
+                candidate_scores,
+                k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices.repeat(4, 1)
diff --git a/review.md b/review.md
index 4010105a..4516e347 100644
--- a/review.md
+++ b/review.md
@@ -18,6 +18,9 @@
 - TODO какой базовый класс использовать для e2e модели? (LastPred?)
 - TODO имя для модели (tiger)
 
+Почему одинаковые длины?
+![alt text](image.png)
+
 ## Links
 
 - [dataset](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html)

From 155b224893bb591566b550b21937f5a346f70b82 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 28 Dec 2024 20:51:18 +0300
Subject: [PATCH 018/175] update reviews

---
 configs/train/tiger_train_config.json |   5 ++--
 image.png                             | Bin 191122 -> 0 bytes
 modeling/loss/base.py                 |   3 ++-
 modeling/models/base.py               |   5 ++--
 modeling/models/bert4rec.py           |   2 +-
 modeling/models/sasrec.py             |   2 +-
 modeling/models/tiger.py              |   2 ++
 review.md                             |  32 ++++++++++++++++----------
 8 files changed, 32 insertions(+), 19 deletions(-)
 delete mode 100644 image.png

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index d2fb5a97..ac332d8a 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -9,7 +9,7 @@
     "max_sequence_length": 50,
     "samplers": {
       "num_negatives_val": 100,
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -40,8 +40,9 @@
     }
   },
   "model": {
-    "type": "sasrec",
+    "type": "tiger",
     "sequence_prefix": "item",
+    "predictions_prefix": "predictions",
     "positive_prefix": "positive",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
diff --git a/image.png b/image.png
deleted file mode 100644
index 8ed39e36f80ab091591aa525a09ec3e6a090640b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 65a145ea..11aa2a9b 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -137,7 +137,8 @@ def forward(self, inputs):
             rqvae_loss += self._loss(codebook_vectors, remainder.detach())
             
         recon_loss = self._loss(embeddings_restored, embeddings)
-        loss = (recon_loss + rqvae_loss).mean() # TODOPK mean?
+        # print(recon_loss.shape, rqvae_loss.shape) # TODOPK
+        loss = (recon_loss + rqvae_loss).mean(dim=0)
         
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 07c04fef..937f9fd5 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -109,7 +109,7 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = torch.arange( # TODOPK why inverted? (not 0...n)
+        positions = torch.arange( # TODOPK invert decoder (position.reverse)
             start=seq_len - 1, end=-1, step=-1, device=mask.device
         )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
@@ -123,6 +123,7 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
         embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        # embeddings = embeddings + codebook_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -135,7 +136,7 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
             embeddings = torch.cat((cls_token_expanded, embeddings), dim=1)
             mask = torch.cat((torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask), dim=1)
 
-        if self._is_causal: # TODOPK causal?
+        if self._is_causal:
             causal_mask = torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)  # (seq_len, seq_len)
             embeddings = self._encoder(
                 src=embeddings,
diff --git a/modeling/models/bert4rec.py b/modeling/models/bert4rec.py
index 273e1da7..40f1d331 100644
--- a/modeling/models/bert4rec.py
+++ b/modeling/models/bert4rec.py
@@ -81,7 +81,7 @@ def forward(self, inputs):
         if self.training:  # training mode
             all_sample_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_batch_events)
             embeddings = embeddings[mask]  # (all_batch_events, num_items)
-            labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events) # TODOPK change that it accepts semantic ids (what is nonzero)
+            labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events)
 
             needed_logits = embeddings[labels_mask]  # (non_zero_events, num_items)
             needed_labels = all_sample_labels[labels_mask]  # (non_zero_events)
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index ec5221dc..061ea1d0 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -102,7 +102,7 @@ def forward(self, inputs):
                 k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices.repeat(4, 2) # TODOPK
+            return indices
 
 
 class SasRecInBatchModel(SasRecModel, config_name='sasrec_in_batch'):
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index dddaf8d3..9e388281 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -53,6 +53,8 @@ def create_from_config(cls, config, **kwargs):
         )
 
     def forward(self, inputs):
+        print(inputs)
+        
         all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
         all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
diff --git a/review.md b/review.md
index 4516e347..379c4656 100644
--- a/review.md
+++ b/review.md
@@ -2,24 +2,32 @@
 
 ## Todos
 
-- check TODOPK
-- у нас авторегрессионный next item prediciton? то есть:
+- next_item_pred / last_item_pred (какие задачи учим и как именно) # can be both tasks
+- предсказываем item = предсказываем 4 semantic id? # yes
+- как составить датасет для обучения # (map item seq -> semantic id seq)
+- берем правдивые semantic id # yes
+
+- у нас авторегрессионный next item prediction? # no (teacher learning)
+
+то есть:
 
 1) items (10)
-2) semantic_ids (40)
-3) prediciting 11th item (next 4 semantic ids)
+2) semantic_ids (40) -> (1, 2, 3, 4)
+3) predicting 11th item (next 4 semantic ids)
 4) if single - ok
-5) if several?
-6) if nothing?
+5) if several? -> dedup # let length be 5 in rqvae, dedup -> 4 + closest by dist
+6) if nothing? take all by longest prefix (closest by L^2 / COS / dot)
+
+encoder -> (b_size x 40 x emb_dim)
+target  -> (b_size x 4) [(1, 2, 3, 4); (29, 6, 7, 4); ...]
+decoder: (bos, 1, 2, 3) -> (1, 2, 3, 4) # causal mask so (bos -> 1), (bos, 1 -> 2), ...
+           \___ learnable embed
 
 - posterior collapse (как будто все сваливается в один индекс в кодбуке) (fixed eval code)
 - обязательно использование reinit unused clusters! (mark)
-- в Amazon датасете пофиг на rating? получается учитываются только implicit действия?
-- TODO какой базовый класс использовать для e2e модели? (LastPred?)
-- TODO имя для модели (tiger)
-
-Почему одинаковые длины?
-![alt text](image.png)
+- в Amazon датасете пофиг на rating? получается учитываются только implicit действия? # байтовый датасет (любое взаимодействие)
+- TODO какой базовый класс использовать для seq2seq модели? (LastPred?) # use encoder from SequentialTorchModel
+- TODO имя для модели (tiger) # tmp
 
 ## Links
 

From 0ab69ddf970bec5976d994322db1f8487a4ca7a5 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 30 Dec 2024 15:01:34 +0300
Subject: [PATCH 019/175] add codebook embeddings

---
 configs/train/tiger_train_config.json |  1 +
 modeling/dataset/base.py              | 16 ++++++---
 modeling/models/base.py               | 50 ++++++++++++++++++---------
 modeling/models/tiger.py              | 39 ++++++++++++++++++---
 review.md                             |  2 ++
 5 files changed, 82 insertions(+), 26 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index ac332d8a..04c76b30 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -5,6 +5,7 @@
   "dataset": {
     "type": "rqvae_scientific",
     "path_to_data_dir": "../data",
+    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 1eee92ff..b7c8ef70 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -1,4 +1,5 @@
 from collections import defaultdict
+import json
 
 from tqdm import tqdm
 
@@ -710,7 +711,8 @@ def __init__(
             test_sampler,
             num_users,
             num_items,
-            max_sequence_length
+            max_sequence_length,
+            semantic_id_length
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -718,6 +720,7 @@ def __init__(
         self._num_users = num_users
         self._num_items = num_items
         self._max_sequence_length = max_sequence_length
+        self._semantic_id_length = semantic_id_length
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
@@ -789,6 +792,9 @@ def create_from_config(cls, config, **kwargs):
             num_users=max_user_idx,
             num_items=max_item_idx
         )
+        
+        rqvae_config = json.load(open(config['rqvae_train_config_path']))
+        semantic_id_length = len(rqvae_config['model']['codebook_sizes'])
 
         return cls(
             train_sampler=train_sampler,
@@ -796,7 +802,8 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_idx,
             num_items=max_item_idx,
-            max_sequence_length=max_sequence_length
+            max_sequence_length=max_sequence_length,
+            semantic_id_length=semantic_id_length
         )
 
     def get_samplers(self):
@@ -818,8 +825,9 @@ def max_sequence_length(self):
     def meta(self):
         return {
             'num_users': self.num_users,
-            'num_items': self.num_items * 4, # TODOPK
-            'max_sequence_length': self.max_sequence_length * 4 # TODOPK
+            'num_items': self.num_items * self._semantic_id_length,
+            'max_sequence_length': self.max_sequence_length * self._semantic_id_length,
+            'semantic_id_length': self._semantic_id_length
         }
         
 class RqVaeDataset(BaseDataset, config_name='rqvae'):
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 937f9fd5..ccd02ce7 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -80,7 +80,7 @@ def __init__(
             embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value # TODOPK
             embedding_dim=embedding_dim
         )
 
@@ -98,7 +98,7 @@ def __init__(
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
 
-    def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
+    def _apply_sequential_encoder(self, events, lengths, add_cls_token=False, add_codebook_embeddings=False):
         embeddings = self._item_embeddings(events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
@@ -108,22 +108,18 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
-
-        positions = torch.arange( # TODOPK invert decoder (position.reverse)
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
-        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
-
-        positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
-        position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=lengths
-        )  # (batch_size, seq_len, embedding_dim)
-        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
-
+        
+        position_embeddings = self._get_position_embeddings(
+            embeddings, lengths, mask, batch_size, seq_len
+        ) # (batch_size, seq_len, embedding_dim)
+        
+        if add_codebook_embeddings:
+            codebook_embeddings = self._get_codebook_embeddings(
+                embeddings, lengths, mask, batch_size, seq_len
+            ) # (batch_size, seq_len, embedding_dim)
+            embeddings = embeddings + codebook_embeddings
+        
         embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
-        # embeddings = embeddings + codebook_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -150,7 +146,27 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
             )  # (batch_size, seq_len, embedding_dim)
 
         return embeddings, mask
+    
+    def _get_codebook_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
+        raise NotImplementedError
 
+    def _get_position_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
+        positions = torch.arange( # TODOPK invert decoder (position.reverse)
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        
+        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
+
+        positions = positions[positions_mask]  # (all_batch_events)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings, _ = create_masked_tensor(
+            data=position_embeddings,
+            lengths=lengths
+        )  # (batch_size, seq_len, embedding_dim)
+        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
+        
+        return position_embeddings
+    
     @staticmethod
     def _add_cls_token(items, lengths, cls_token_id=0):
         num_items = items.shape[0]
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 9e388281..56a68a0a 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,6 +1,8 @@
+from utils import create_masked_tensor
 from models.base import SequentialTorchModel
 
 import torch
+from torch import nn
 
 
 class TigerModel(SequentialTorchModel, config_name='tiger'):
@@ -15,6 +17,7 @@ def __init__(
             num_heads,
             num_layers,
             dim_feedforward,
+            semantic_id_length,
             dropout=0.0,
             activation='relu',
             layer_norm_eps=1e-9,
@@ -34,6 +37,12 @@ def __init__(
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
+        self._semantic_id_length = semantic_id_length
+        
+        self._codebook_embeddings = nn.Embedding(
+            num_embeddings=semantic_id_length,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
+        )
 
         self._init_weights(initializer_range)
 
@@ -48,18 +57,17 @@ def create_from_config(cls, config, **kwargs):
             num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
             num_layers=config['num_layers'],
             dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            semantic_id_length=kwargs['semantic_id_length'],
             dropout=config.get('dropout', 0.0),
             initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        print(inputs)
-        
         all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
         all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
-
+        
         embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
+            all_sample_events, all_sample_lengths, add_codebook_embeddings=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         if self.training:  # training mode
@@ -104,4 +112,25 @@ def forward(self, inputs):
                 k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices.repeat(4, 1)
+            return indices.repeat(self._semantic_id_length, 1)
+        
+    def _get_codebook_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
+        positions = torch.arange( # TODOPK invert decoder (position.reverse)
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        
+        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
+
+        positions = positions[positions_mask]  # (all_batch_events)
+        
+        positions = positions.flip(-1) % self._semantic_id_length # (all_batch_events)
+        # flip so first item has (0, 1, 2, 3) not (3, 2, 1, 0) semantic_id embeddings
+        
+        position_embeddings = self._codebook_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings, _ = create_masked_tensor(
+            data=position_embeddings,
+            lengths=lengths
+        )  # (batch_size, seq_len, embedding_dim)
+        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
+        
+        return position_embeddings
diff --git a/review.md b/review.md
index 379c4656..242aaa95 100644
--- a/review.md
+++ b/review.md
@@ -2,6 +2,8 @@
 
 ## Todos
 
+- max_sequence_length (TODOPK), why +1? не смог найти где дописывается в батч сама длина
+
 - next_item_pred / last_item_pred (какие задачи учим и как именно) # can be both tasks
 - предсказываем item = предсказываем 4 semantic id? # yes
 - как составить датасет для обучения # (map item seq -> semantic id seq)

From e24e0a9f47f634f51879ed846289ea3595b92768 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 30 Dec 2024 22:13:41 +0300
Subject: [PATCH 020/175] draft seq2seq model run

---
 configs/train/tiger_train_config.json   |   16 +-
 modeling/dataloader/batch_processors.py |   14 +-
 modeling/dataset/base.py                |  140 +-
 modeling/main.ipynb                     | 2710 ++++++++++++++++++++++-
 modeling/models/tiger.py                |  189 +-
 modeling/rqvae/rqvae_data.py            |    2 +-
 modeling/trie.py                        |   41 +
 review.md                               |   12 +
 8 files changed, 2948 insertions(+), 176 deletions(-)
 create mode 100644 modeling/trie.py

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 04c76b30..17441ba3 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -42,11 +42,10 @@
   },
   "model": {
     "type": "tiger",
+    "trie": "../data/Beauty/trie.pkl",
     "sequence_prefix": "item",
-    "predictions_prefix": "predictions",
-    "positive_prefix": "positive",
-    "negative_prefix": "negative",
-    "candidate_prefix": "candidates",
+    "predictions_prefix": "logits",
+    "labels_prefix": "labels",
     "embedding_dim": 64,
     "num_heads": 2,
     "num_layers": 2,
@@ -69,10 +68,11 @@
     "type": "composite",
     "losses": [
       {
-        "type": "bpr",
-        "positive_prefix": "positive_scores",
-        "negative_prefix": "negative_scores",
-        "output_prefix": "downstream_loss"
+        "type": "ce",
+        "predictions_prefix": "logits",
+        "labels_prefix": "semantic.labels",
+        "output_prefix": "downstream_loss",
+        "weight": 1.0
       }
     ],
     "output_prefix": "loss"
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index ddf6c92b..2c3948a2 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -58,6 +58,9 @@ def __call__(self, batch):
                 processed_batch[f'{prefix}.ids'] = []
                 processed_batch[f'{prefix}.length'] = []
                 
+                processed_batch[f'semantic.{prefix}.ids'] = []
+                processed_batch[f'semantic.{prefix}.length'] = []
+                
                 # item_ids = list(itertools.chain(*semantic_ids))
                 # length = len(item_ids) # sample[f'{prefix}.length']
 
@@ -65,14 +68,15 @@ def __call__(self, batch):
                     item_ids = sample[f'{prefix}.ids']
                     length = sample[f'{prefix}.length']
                     
-                    if prefix != 'user':
-                        semantic_ids = self.get_semantic_ids(item_ids)
-                        item_ids = list(itertools.chain(*semantic_ids))
-                        length = len(item_ids)
-                    
                     processed_batch[f'{prefix}.ids'].extend(item_ids)
                     processed_batch[f'{prefix}.length'].append(length)
                     
+                    if prefix != 'user':
+                        semantic_ids = self.get_semantic_ids(item_ids)
+                        semantic_ids = list(itertools.chain(*semantic_ids))
+                        processed_batch[f'semantic.{prefix}.ids'].extend(semantic_ids)
+                        processed_batch[f'semantic.{prefix}.length'].append(len(semantic_ids))
+                    
         for part, values in processed_batch.items():
             processed_batch[part] = torch.tensor(values, dtype=torch.long)
 
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index b7c8ef70..ee81fe90 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -702,132 +702,60 @@ def meta(self):
             'max_sequence_length': self.max_sequence_length
         }
         
-class RqvaeScientificDataset(BaseDataset, config_name='rqvae_scientific'):
+class RqvaeScientificDataset(ScientificDataset, config_name='rqvae_scientific'):
 
     def __init__(
-            self,
-            train_sampler,
-            validation_sampler,
-            test_sampler,
-            num_users,
-            num_items,
-            max_sequence_length,
-            semantic_id_length
+        self,
+        train_sampler,
+        validation_sampler,
+        test_sampler,
+        num_users,
+        num_items,
+        max_sequence_length,
+        semantic_id_arr
     ):
-        self._train_sampler = train_sampler
-        self._validation_sampler = validation_sampler
-        self._test_sampler = test_sampler
-        self._num_users = num_users
-        self._num_items = num_items
-        self._max_sequence_length = max_sequence_length
-        self._semantic_id_length = semantic_id_length
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
-        max_sequence_length = config['max_sequence_length']
-        max_user_idx, max_item_idx = 0, 0
-        train_dataset, validation_dataset, test_dataset = [], [], []
-
-        dataset_path = os.path.join(data_dir_path, '{}.txt'.format('all_data'))
-        with open(dataset_path, 'r') as f:
-            data = f.readlines()
-
-        for sample in data:
-            sample = sample.strip('\n').split(' ')
-            user_idx = int(sample[0])
-            item_ids = [int(item_id) for item_id in sample[1:]]
-
-            max_user_idx = max(max_user_idx, user_idx)
-            max_item_idx = max(max_item_idx, max(item_ids))
-
-            assert len(item_ids) >= 5
-
-            train_dataset.append({
-                'user.ids': [user_idx],
-                'user.length': 1,
-                'item.ids': item_ids[:-2][-max_sequence_length:],
-                'item.length': len(item_ids[:-2][-max_sequence_length:])
-            })
-            assert len(item_ids[:-2][-max_sequence_length:]) == len(set(item_ids[:-2][-max_sequence_length:]))
-            validation_dataset.append({
-                'user.ids': [user_idx],
-                'user.length': 1,
-                'item.ids': item_ids[:-1][-max_sequence_length:],
-                'item.length': len(item_ids[:-1][-max_sequence_length:])
-            })
-            assert len(item_ids[:-1][-max_sequence_length:]) == len(set(item_ids[:-1][-max_sequence_length:]))
-            test_dataset.append({
-                'user.ids': [user_idx],
-                'user.length': 1,
-                'item.ids': item_ids[-max_sequence_length:],
-                'item.length': len(item_ids[-max_sequence_length:])
-            })
-            assert len(item_ids[-max_sequence_length:]) == len(set(item_ids[-max_sequence_length:]))
-
-        logger.info('Train dataset size: {}'.format(len(train_dataset)))
-        logger.info('Test dataset size: {}'.format(len(test_dataset)))
-        logger.info('Max user idx: {}'.format(max_user_idx))
-        logger.info('Max item idx: {}'.format(max_item_idx))
-        logger.info('Max sequence length: {}'.format(max_sequence_length))
-        logger.info('{} dataset sparsity: {}'.format(
-            config['name'], (len(train_dataset) + len(test_dataset)) / max_user_idx / max_item_idx
-        ))
-
-        train_sampler = TrainSampler.create_from_config(
-            config['samplers'],
-            dataset=train_dataset,
-            num_users=max_user_idx,
-            num_items=max_item_idx
-        )
-        validation_sampler = EvalSampler.create_from_config(
-            config['samplers'],
-            dataset=validation_dataset,
-            num_users=max_user_idx,
-            num_items=max_item_idx
-        )
-        test_sampler = EvalSampler.create_from_config(
-            config['samplers'],
-            dataset=test_dataset,
-            num_users=max_user_idx,
-            num_items=max_item_idx
-        )
-        
-        rqvae_config = json.load(open(config['rqvae_train_config_path']))
-        semantic_id_length = len(rqvae_config['model']['codebook_sizes'])
-
-        return cls(
+        super().__init__(
             train_sampler=train_sampler,
             validation_sampler=validation_sampler,
             test_sampler=test_sampler,
-            num_users=max_user_idx,
-            num_items=max_item_idx,
+            num_users=num_users,
+            num_items=num_items,
             max_sequence_length=max_sequence_length,
-            semantic_id_length=semantic_id_length
         )
+        self._semantic_id_arr = semantic_id_arr
 
-    def get_samplers(self):
-        return self._train_sampler, self._validation_sampler, self._test_sampler
-
-    @property
-    def num_users(self):
-        return self._num_users
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_config = json.load(open(config['rqvae_train_config_path']))
+        semantic_id_arr = rqvae_config['model']['codebook_sizes']
+        
+        scientific_instance = ScientificDataset.create_from_config(config, **kwargs)
+        
+        return cls(
+            train_sampler=scientific_instance._train_sampler,
+            validation_sampler=scientific_instance._validation_sampler,
+            test_sampler=scientific_instance._test_sampler,
+            num_users=scientific_instance.num_users,
+            num_items=scientific_instance.num_items,
+            max_sequence_length=scientific_instance.max_sequence_length,
+            semantic_id_arr=semantic_id_arr,
+        )
 
     @property
     def num_items(self):
-        return self._num_items
+        return self._semantic_id_arr[0] # TODOPK?
 
     @property
     def max_sequence_length(self):
-        return self._max_sequence_length
+        return self._max_sequence_length * len(self._semantic_id_arr)
 
     @property
     def meta(self):
         return {
             'num_users': self.num_users,
-            'num_items': self.num_items * self._semantic_id_length,
-            'max_sequence_length': self.max_sequence_length * self._semantic_id_length,
-            'semantic_id_length': self._semantic_id_length
+            'num_items': self.num_items,
+            'max_sequence_length': self.max_sequence_length,
+            'semantic_id_arr': self._semantic_id_arr
         }
         
 class RqVaeDataset(BaseDataset, config_name='rqvae'):
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index e03fc168..57fe551c 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,22 +34,2698 @@
     "\n",
     "config = json.load(open(\"../configs/train/tiger_train_config.json\"))\n",
     "\n",
-    "rqvae_train_config = json.load(open(config['rqvae_train_config_path']))\n",
+    "batch_proc_config = config['dataloader']['train']['batch_processor']\n",
+    "\n",
+    "rqvae_train_config = json.load(open(batch_proc_config['rqvae_train_config_path']))\n",
     "rq_vae_config = rqvae_train_config['model']\n",
     "rq_vae_config['should_init_codebooks'] = False\n",
     "\n",
     "rqvae_model = BaseModel.create_from_config(rq_vae_config).to(DEVICE)\n",
     "\n",
-    "rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))\n",
+    "rqvae_model.load_state_dict(torch.load(batch_proc_config['rqvae_checkpoint_path'], weights_only=True))\n",
     "rqvae_model.eval()\n",
     "\n",
-    "ids = df.asin_numeric.tolist()\n",
+    "ids = df.index.tolist()\n",
     "\n",
     "embs_dict = {\"ids\": torch.tensor(ids).to(DEVICE), \"embeddings\": embs.to(DEVICE)}\n",
     "\n",
     "semantic_ids = list(rqvae_model.forward(embs_dict))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('7806397051',\n",
+       "  'WAWO 15 Color Professionl Makeup Eyeshadow Camouflage Facial Concealer Neutral Palette',\n",
+       "  (13, 66, 216, 169, 0)),\n",
+       " ('9759091062', 'Xtreme Brite Brightening Gel 1oz.', (132, 120, 59, 169, 0)),\n",
+       " ('9788072216',\n",
+       "  'Prada Candy By Prada Eau De Parfum Spray 1.7 Oz For Women',\n",
+       "  (250, 134, 192, 152, 0)),\n",
+       " ('9790790961',\n",
+       "  'Versace Bright Crystal Eau de Toilette Spray for Women, 3 Ounce',\n",
+       "  (63, 173, 70, 37, 0)),\n",
+       " ('9790794231', 'Stella McCartney Stella', (58, 80, 40, 27, 0)),\n",
+       " ('B00004TMFE',\n",
+       "  'Avalon Biotin B-Complex Thickening Conditioner, 14 Ounce',\n",
+       "  (38, 178, 162, 34, 0)),\n",
+       " ('B00004TUBL',\n",
+       "  'Better Living Classic Two Chamber Dispenser, White',\n",
+       "  (226, 59, 24, 116, 0)),\n",
+       " ('B00004TUBV',\n",
+       "  'Better Living The Ulti-Mate Dispenser',\n",
+       "  (174, 189, 124, 99, 0)),\n",
+       " ('B00004U9UY',\n",
+       "  'Crabtree &amp; Evelyn 2792 Gardeners Hand Therapy (100ml, 3.4 oz)',\n",
+       "  (38, 145, 226, 206, 0)),\n",
+       " ('B00004U9V2',\n",
+       "  \"Crabtree &amp; Evelyn - Gardener's Ultra-Moisturising Hand Therapy Pump - 250g/8.8 OZ\",\n",
+       "  (216, 161, 162, 182, 0)),\n",
+       " ('B000050B6U', 'Instant Heat Curling Iron, 1-Inch', (174, 77, 140, 27, 0)),\n",
+       " ('B000052WY7',\n",
+       "  'Maybelline New York Expert Wear Brow &amp; Eyeliner, 151 Velvet Black , .011 oz (310 mg)',\n",
+       "  (228, 89, 201, 195, 0)),\n",
+       " ('B000052WYD',\n",
+       "  'Maybelline New York Cover Stick Concealer, Ivory, Light 2, 0.16 Ounce',\n",
+       "  (110, 131, 34, 13, 0)),\n",
+       " ('B000052WYL',\n",
+       "  'Maybelline New York Expert Eyes Moisturizing Eye Makeup Remover, 2.3 Fl. Oz.',\n",
+       "  (123, 131, 70, 37, 0)),\n",
+       " ('B000052WYN',\n",
+       "  'Maybelline New York Ultra-Liner Liquid Liner, Waterproof, Black 135L-01 , .25 fl oz (7.3 ml)',\n",
+       "  (3, 135, 44, 12, 0)),\n",
+       " ('B000052X9G',\n",
+       "  'Nullo Internal Deodorant, Coated Caplets, 135 Caplets',\n",
+       "  (71, 247, 246, 91, 0)),\n",
+       " ('B000052XIA',\n",
+       "  'Vagisil Anti-Itch Creme, Original Formula - 1 oz',\n",
+       "  (205, 173, 132, 171, 0)),\n",
+       " ('B000052XW5',\n",
+       "  'Pretty Feet &amp; Hands Rough Skin Remover-Exfoliant, 3-Ounce Bottles (Pack of 3)',\n",
+       "  (194, 47, 234, 60, 0)),\n",
+       " ('B000052XYQ', 'DHS Clear Shampoo 8 Fl Oz', (69, 8, 226, 103, 0)),\n",
+       " ('B000052XZP',\n",
+       "  'Neutrogena T-Gel Shampoo, Stubborn Itch Control, 4.4 Ounce',\n",
+       "  (70, 103, 201, 76, 0)),\n",
+       " ('B000052XZX',\n",
+       "  'Neutrogena T/Sal Shampoo, Scalp Build-up Control, 4.5 fl oz',\n",
+       "  (167, 173, 65, 103, 0)),\n",
+       " ('B000052Y25',\n",
+       "  'DHS Conditioning Rinse With Panthenol 8 fl oz',\n",
+       "  (44, 159, 254, 27, 0)),\n",
+       " ('B000052Y33',\n",
+       "  'Alberto VO5 Moisturizing Hot Oil Treatment, 0.5 Ounce, 2-Count Tubes (Pack of 6)',\n",
+       "  (13, 134, 153, 159, 0)),\n",
+       " ('B000052Y5F',\n",
+       "  'Konsyl Pharmaceuticals Psyllium Fiber 15.9 oz',\n",
+       "  (127, 173, 122, 236, 0)),\n",
+       " ('B000052Y6Z',\n",
+       "  'Williams Lectric Shave Electric Razor Pre-Shave, Original, 3 fl oz',\n",
+       "  (99, 105, 192, 202, 0)),\n",
+       " ('B000052YJC',\n",
+       "  'Buf-Puf Double-Sided Body Sponge - 1 ea',\n",
+       "  (230, 47, 162, 99, 0)),\n",
+       " ('B000052YJD',\n",
+       "  'Buf-Puf Reusable Facial Sponge, Extra Gentle, - 1 ea',\n",
+       "  (71, 173, 163, 68, 0)),\n",
+       " ('B000052YJH',\n",
+       "  'Buf-Puf Reusable Facial Sponge (Pack of 6)',\n",
+       "  (245, 173, 219, 34, 0)),\n",
+       " ('B000052YJM',\n",
+       "  'Clean &amp; Clear ESSENTIALS Dual Action Moisturizer, 4 Ounce',\n",
+       "  (154, 180, 29, 37, 0)),\n",
+       " ('B000052YJX',\n",
+       "  'Neutrogena OntheSpot Acne Treatment, Vanishing Formula, 0.75 Ounce',\n",
+       "  (93, 145, 227, 77, 0)),\n",
+       " ('B000052YKI',\n",
+       "  'Purpose Dual Treatment Moisture Lotion with SPF 15 4 fl oz (120 ml)',\n",
+       "  (196, 161, 250, 76, 0)),\n",
+       " ('B000052YKM',\n",
+       "  'Stri-Dex Medicated Pads, Maximum Strength, 90-Count Containers, (Pack of 3)',\n",
+       "  (6, 66, 67, 241, 0)),\n",
+       " ('B000052YKS',\n",
+       "  'Basis Cleaner Clean Face Wash, 6 Fluid Ounces',\n",
+       "  (40, 100, 67, 170, 0)),\n",
+       " ('B000052YKY',\n",
+       "  'Cetaphil Gentle Cleansing Bar, Antibacterial - 4.5 oz',\n",
+       "  (102, 59, 11, 219, 0)),\n",
+       " ('B000052YL1',\n",
+       "  'Cuticura Medicated Anti-Bacterial Bar Soap, Original Formula - 5.25 oz bar',\n",
+       "  (71, 167, 192, 202, 0)),\n",
+       " ('B000052YLX',\n",
+       "  'Purpose Gentle Cleansing Bar 6 oz (170 g)',\n",
+       "  (128, 80, 65, 77, 0)),\n",
+       " ('B000052YM0',\n",
+       "  'Fruit Of The Earth 100% Aloe Vera 6oz. Gel Tube',\n",
+       "  (117, 47, 162, 159, 0)),\n",
+       " ('B000052YM3',\n",
+       "  'Alpha Hydrox AHA Enhanced Cream 2 oz.',\n",
+       "  (82, 134, 216, 103, 0)),\n",
+       " ('B000052YM4',\n",
+       "  'Alpha Hydrox Foaming Face Wash -- 6 fl oz',\n",
+       "  (69, 47, 219, 103, 0)),\n",
+       " ('B000052YM7',\n",
+       "  'Alpha Hydrox Enhanced Lotion 10 percent Glycolic AHA -- 6 fl oz Anti-Wrinkle',\n",
+       "  (100, 46, 246, 53, 0)),\n",
+       " ('B000052YM8',\n",
+       "  'Alpha Hydrox Oil Free Treatment 10 Percent AHA 1.7 fl oz.',\n",
+       "  (205, 134, 147, 152, 0)),\n",
+       " ('B000052YMG', 'Aquanil Cleanser 16 fl oz.', (71, 47, 59, 143, 0)),\n",
+       " ('B000052YMQ',\n",
+       "  'Cetaphil Moisturizing Cream, Fragrance Free - 16 oz',\n",
+       "  (167, 134, 226, 37, 0)),\n",
+       " ('B000052YMR',\n",
+       "  'Cetaphil Moisturizing Cream, 3.0 - Ounces Tube (Pack of 3)',\n",
+       "  (2, 2, 226, 76, 0)),\n",
+       " ('B000052YMS',\n",
+       "  'Cetaphil Moisturizing Lotion, Fragrance Free - 16 fl oz',\n",
+       "  (56, 134, 122, 15, 0)),\n",
+       " ('B000052YMT',\n",
+       "  'Cetaphil Gentle Skin Cleanser, 4.0 -Ounce Bottles (Pack of 6)',\n",
+       "  (56, 21, 201, 160, 0)),\n",
+       " ('B000052YMV',\n",
+       "  'Cetaphil Gentle Skin Cleanser - 16 fl oz',\n",
+       "  (56, 96, 65, 15, 0)),\n",
+       " ('B000052YMX',\n",
+       "  'Complex 15 Therapeutic Moisturizing Face Cream - 2.5 Ounce',\n",
+       "  (56, 3, 59, 160, 0)),\n",
+       " ('B000052YN5',\n",
+       "  'DML Daily Facial Moisturizer, SPF 25 - 1.5 Oz',\n",
+       "  (71, 134, 208, 60, 0)),\n",
+       " ('B000052YN7', 'DML Moisturizing Lotion 16 oz.', (117, 134, 234, 103, 0)),\n",
+       " ('B000052YOL',\n",
+       "  'Neutrogena Healthy Skin Anti-Wrinkle Cream, SPF 15, 1.4 Ounce',\n",
+       "  (99, 120, 162, 231, 0)),\n",
+       " ('B000052YOR',\n",
+       "  'Neutrogena Body Oil, Light Sesame Formula, 8.5 Ounce',\n",
+       "  (139, 173, 122, 99, 0)),\n",
+       " ('B000052YOX',\n",
+       "  'Neutrogena Oil-Free Moisture, Sensitive Skin, 4 Ounce',\n",
+       "  (99, 202, 231, 169, 0)),\n",
+       " ('B000052YP4',\n",
+       "  'Neutrogena Light Night Cream, 2.25 Ounce',\n",
+       "  (99, 3, 27, 241, 0)),\n",
+       " ('B000052YP6',\n",
+       "  'Neutrogena Norwegian Formula Hand Cream, Fragrance-Free (2 Ounce)',\n",
+       "  (117, 47, 71, 91, 0)),\n",
+       " ('B000052YQ0',\n",
+       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 Normal 4 Fl Oz',\n",
+       "  (215, 202, 139, 15, 0)),\n",
+       " ('B000052YQ2',\n",
+       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 - Sensitive 4 Fl Oz',\n",
+       "  (93, 129, 216, 13, 0)),\n",
+       " ('B000052YQN', \"POND'S Cold Cream Cleanser, 3.5 oz.\", (70, 23, 65, 76, 0)),\n",
+       " ('B000052YQU',\n",
+       "  \"POND'S Dry Skin Cream Facial Moisturizer, 3.9-Ounce (Packaging May Vary)\",\n",
+       "  (99, 159, 67, 99, 0)),\n",
+       " ('B000052Z5B',\n",
+       "  'Coty Airspun Face Powder, Naturally Neutral, 2.3 oz',\n",
+       "  (201, 180, 201, 135, 0)),\n",
+       " ('B000052Z8D',\n",
+       "  'Max Factor Pan-Stik Ultra Creamy Makeup, Sun Tone 137 .5 oz (14 g)',\n",
+       "  (188, 122, 254, 37, 0)),\n",
+       " ('B000052ZB1',\n",
+       "  'Neutrogena Intensified Day Moisture, SPF 15, 2.25 Ounce',\n",
+       "  (44, 120, 234, 169, 0)),\n",
+       " ('B000052ZB2',\n",
+       "  'Neutrogena Extra Gentle Cleanser, 6.7 Ounce',\n",
+       "  (44, 115, 71, 195, 0)),\n",
+       " ('B000052ZB4',\n",
+       "  'Neutrogena Deep Clean Facial Cleanser, Normal to Oily Skin, 6.7 Ounce',\n",
+       "  (239, 211, 192, 170, 0)),\n",
+       " ('B000052ZBD',\n",
+       "  'Neutrogena Rainbath Gel, Original, 16  Ounce',\n",
+       "  (71, 64, 201, 114, 0)),\n",
+       " ('B000052ZBH',\n",
+       "  'Almay One Coat Nourishing Mascara, Thickening, Black 402, 0.4-Ounce Package',\n",
+       "  (13, 66, 27, 236, 0)),\n",
+       " ('B000052ZBP',\n",
+       "  'Almay Moisturizing Eye Makeup Remover Pads, 80-Pads',\n",
+       "  (194, 89, 27, 162, 0)),\n",
+       " ('B000052ZTY',\n",
+       "  'Hugo By Hugo Boss For Men. Eau De Toilette Spray 1.3 Ounces',\n",
+       "  (8, 55, 10, 163, 0)),\n",
+       " ('B00005302B',\n",
+       "  'Jergen Extra Moisturizing Liquid Hand Wash 7.5 Oz',\n",
+       "  (28, 100, 122, 91, 0)),\n",
+       " ('B00005304H', 'Q-tips Cotton Swabs, 500 Count', (172, 159, 155, 169, 0)),\n",
+       " ('B0000530ED',\n",
+       "  \"L'Oreal Paris Feria Hair Color, 36 Dark Burgundy Brown/Chocolate Cherry\",\n",
+       "  (52, 167, 122, 170, 0)),\n",
+       " ('B0000530EZ',\n",
+       "  'Aussie Hair Insurance Leave-In Conditioner 8 Fl Oz (Pack of 3)',\n",
+       "  (223, 103, 234, 116, 0)),\n",
+       " ('B0000530G6',\n",
+       "  'Suave Professionals Conditioner, Humectant - 12.6 Ounce',\n",
+       "  (223, 103, 192, 219, 0)),\n",
+       " ('B0000530LO',\n",
+       "  'Suave Naturals Shampoo, Daily Clarifying - 22.5oz.',\n",
+       "  (75, 247, 71, 60, 0)),\n",
+       " ('B0000530LP',\n",
+       "  'Suave Shampoo, Daily Clarifying - 12oz.',\n",
+       "  (41, 120, 234, 91, 0)),\n",
+       " ('B0000530LV',\n",
+       "  'Suave Naturals Shampoo, Sun Ripened Strawberry 22.5oz',\n",
+       "  (71, 145, 11, 202, 0)),\n",
+       " ('B0000530M0',\n",
+       "  'Suave Professionals Humectant Moisture Shampoo , 12.6 fl Ounce (373 ml)',\n",
+       "  (70, 4, 41, 77, 0)),\n",
+       " ('B0000530NA',\n",
+       "  'Olay Daily Care Refreshing Toner, 7.2-Fluid Ounce',\n",
+       "  (78, 134, 67, 195, 0)),\n",
+       " ('B0000530O9',\n",
+       "  'Olay Age Defying Classic Eye Gel 0.5 Oz',\n",
+       "  (204, 255, 27, 241, 0)),\n",
+       " ('B0000531GD',\n",
+       "  'Maybelline New York Shine Free Oil Control Pressed Powder, Ivory - 1 ea',\n",
+       "  (107, 68, 33, 104, 0)),\n",
+       " ('B0000531GV',\n",
+       "  'Maybelline New York Expert Eyes 100% Oil-Free Eye Makeup Remover, 2.3 Fl. Oz.',\n",
+       "  (127, 122, 11, 76, 0)),\n",
+       " ('B0000531II',\n",
+       "  'Maybelline New York Shine Free Oil Control Loose Powder, Light, 0.7 Ounce',\n",
+       "  (177, 122, 41, 76, 0)),\n",
+       " ('B0000531NU',\n",
+       "  \"Maybelline New York Volum' Express - Waterproof Mascara, Very Black - .34 fl oz\",\n",
+       "  (194, 159, 75, 76, 0)),\n",
+       " ('B0000531SW',\n",
+       "  \"L'Oreal Paris Visible Lift Line-Minimizing &amp; Tone-Enhancing Makeup, Soft Ivory, 1.25 Ounces\",\n",
+       "  (92, 96, 11, 27, 0)),\n",
+       " ('B0000531WZ',\n",
+       "  'Revlon Super Lustrous Creme Lipstick, Fire and Ice 720, 0.15 Ounce',\n",
+       "  (70, 122, 55, 170, 0)),\n",
+       " ('B00005321Z',\n",
+       "  \"Revlon Eterna '27' Moisture Cream with Progenitin, 2 Ounce\",\n",
+       "  (250, 234, 73, 37, 0)),\n",
+       " ('B0000532AN', 'Revlon Lash Curlers, 1 Count', (192, 161, 41, 194, 0)),\n",
+       " ('B0000532VT',\n",
+       "  'Neutrogena Clean Conditioner, Replenishing , 10.1 Fluid Ounce',\n",
+       "  (17, 202, 138, 169, 0)),\n",
+       " ('B0000532YN',\n",
+       "  'Kiss My Face Bar Soap, 8.0 oz, Pure Olive Oil. 1-Bar',\n",
+       "  (110, 2, 70, 104, 0)),\n",
+       " ('B00005333G',\n",
+       "  \"L'Oreal Paris Hydra-Renewal Continuous Moisture Cream, 1.7 Ounce\",\n",
+       "  (44, 8, 139, 37, 0)),\n",
+       " ('B00005333I',\n",
+       "  \"L'Oreal Paris Eye Defense, 0.5 Fluid Ounce\",\n",
+       "  (133, 211, 192, 15, 0)),\n",
+       " ('B0000533CC',\n",
+       "  'Instead 12 Hour Feminine Protection Cup 24 ea',\n",
+       "  (172, 129, 163, 169, 0)),\n",
+       " ('B0000534VO',\n",
+       "  'Clean &amp; Clear Continuous Control Acne Cleanser,  5 oz  (141g)',\n",
+       "  (44, 103, 24, 236, 0)),\n",
+       " ('B0000535CH',\n",
+       "  \"Nature's Cure Two-part Acne Treatment System for Women 60 Tablets 1 Oz Cream\",\n",
+       "  (12, 47, 132, 15, 0)),\n",
+       " ('B0000535OF',\n",
+       "  'Alpha Hydrox Night Replenishing Cream 2 oz.',\n",
+       "  (71, 180, 122, 206, 0)),\n",
+       " ('B0000535RA',\n",
+       "  'Neutrogena Pore Refining Toner, Alpha and Beta Hydroxy Formula, 8.5 Ounce',\n",
+       "  (44, 4, 10, 100, 0)),\n",
+       " ('B0000535RC',\n",
+       "  'NeutrogenaClear Pore Oil Eliminating Astringent, 8 Ounce',\n",
+       "  (133, 4, 65, 104, 0)),\n",
+       " ('B0000535RD',\n",
+       "  'Neutrogena Body Clear Body Wash for Clean, Clear Skin, 8.5 Ounce',\n",
+       "  (128, 120, 201, 77, 0)),\n",
+       " ('B0000535U2', 'Jason C-Effects Creme, 2 Ounces', (50, 196, 219, 202, 0)),\n",
+       " ('B0000535UA', 'Jason Shampoo, Tea Tree, 17.5 Ounce', (50, 115, 140, 169, 0)),\n",
+       " ('B0000535UM',\n",
+       "  'Jason Thin-To-Thick Extra Volume Shampoo, 8 Ounce',\n",
+       "  (172, 115, 140, 169, 0)),\n",
+       " ('B0000535UN',\n",
+       "  'Thin to Thick Extra Volume Conditioner 8 oz',\n",
+       "  (102, 66, 65, 15, 0)),\n",
+       " ('B0000535UT',\n",
+       "  'Jason Purifying Tea Tree Body Wash 30 fl oz',\n",
+       "  (71, 1, 162, 143, 0)),\n",
+       " ('B0000535UX',\n",
+       "  'Jason Pure Natural Hand Soap, Purifying Tea Tree, 16 Ounce',\n",
+       "  (172, 196, 140, 13, 0)),\n",
+       " ('B0000535VH',\n",
+       "  'Alpha Hydrox Optimum Series, Retinol Night ResQ, Anti-Wrinkle Firming Complex - 1.05 oz',\n",
+       "  (205, 159, 139, 103, 0)),\n",
+       " ('B000053676',\n",
+       "  'Avalon Organics Vitamin C Revitalizing Eye Creme, 1 Ounce Bottle',\n",
+       "  (71, 3, 41, 15, 0)),\n",
+       " ('B0000536A3',\n",
+       "  'Tommy Hilfiger Tommy Cologne Spray for Men - 3.4 Fluid Ounces',\n",
+       "  (182, 62, 201, 219, 0)),\n",
+       " ('B0000536EK',\n",
+       "  \"Mother's Special Blend All Natural Skin Toning Oil, 8-Ounce Bottle\",\n",
+       "  (102, 173, 70, 91, 0)),\n",
+       " ('B0000536EW',\n",
+       "  'Aveeno Anti-Itch Concentrated Lotion, 4 Ounce',\n",
+       "  (127, 62, 163, 159, 0)),\n",
+       " ('B0000536F0',\n",
+       "  'Aveeno Moisturizing Bar with Natural Colloidal Oatmeal for Dry Skin, Fragrance Free, 3.5 oz',\n",
+       "  (161, 96, 246, 103, 0)),\n",
+       " ('B0000536M2',\n",
+       "  \"Maybelline New York Full 'N Soft Mascara, Very Black, 0.28 Oz.\",\n",
+       "  (65, 66, 208, 160, 0)),\n",
+       " ('B0000536M3',\n",
+       "  \"Maybelline Full' N Soft Mascara: Brownish Black #302\",\n",
+       "  (28, 120, 59, 169, 0)),\n",
+       " ('B0000536P3',\n",
+       "  'Olay Total Effects 7-In-1 Anti-Aging Daily Moisturizer 1.7 Fl. Oz.',\n",
+       "  (167, 96, 11, 53, 0)),\n",
+       " ('B0000536P4',\n",
+       "  'Olay Total Effects Anti-Aging Fragrance Free Moisturizer 1.7 Fl Oz',\n",
+       "  (21, 96, 11, 53, 0)),\n",
+       " ('B00005375C',\n",
+       "  'Aveeno Daily Moisturizing Lotion, 8 Ounce',\n",
+       "  (239, 89, 192, 99, 0)),\n",
+       " ('B0000537NH',\n",
+       "  'Neutrogena Sensitive Skin Sunscreen Lotion, SPF 30, 4 Ounce',\n",
+       "  (132, 202, 246, 53, 0)),\n",
+       " ('B0000537SD', 'ZapZyt Acne Treatment Gel, 1 oz', (93, 173, 227, 217, 0)),\n",
+       " ('B000053L6W',\n",
+       "  'Clubman Pinaud Virgin Island Bay Rum, 12 Ounce',\n",
+       "  (94, 115, 201, 76, 0)),\n",
+       " ('B000056I89',\n",
+       "  'Cetaphil Daily Facial Cleanser, Normal to Oily Skin - 8 fl oz',\n",
+       "  (44, 4, 37, 34, 0)),\n",
+       " ('B000056W5Y',\n",
+       "  \"Palmer's Cocoa Butter Formula Massage Cream for Stretch Marks, 4.4 Ounce\",\n",
+       "  (127, 103, 59, 202, 0)),\n",
+       " ('B000056W74',\n",
+       "  \"Palmer's Cocoa Butter Formula Moisturizing Body Oil with Vitamin E -- 8.5 fl oz\",\n",
+       "  (196, 211, 139, 76, 0)),\n",
+       " ('B00005A43X',\n",
+       "  'Conair Supreme 2-In-1 Hot Air Styling Brush',\n",
+       "  (67, 149, 163, 27, 0)),\n",
+       " ('B00005A443',\n",
+       "  'Conair Double-Sided Battery-Operated Lighted Makeup Mirror, Polished Chrome Finish',\n",
+       "  (36, 4, 67, 219, 0)),\n",
+       " ('B00005A9WP',\n",
+       "  'Coanir Facial Sauna System with Timer',\n",
+       "  (174, 131, 27, 160, 0)),\n",
+       " ('B00005AVC7',\n",
+       "  'Clean &amp; Clear Instant Oil-Absorbing Sheets 50 sheets',\n",
+       "  (28, 131, 192, 219, 0)),\n",
+       " ('B00005B9FV',\n",
+       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Lotion SPF, 4 Ounce',\n",
+       "  (196, 245, 116, 170, 0)),\n",
+       " ('B00005CDSP',\n",
+       "  'Revlon Perfect Heat 1-1/2inch Curling Iron',\n",
+       "  (174, 66, 153, 100, 0)),\n",
+       " ('B00005IBW0',\n",
+       "  'St. Ives, Sensitive Skin Apricot Scrub, 6-Ounce (Pack of 6)',\n",
+       "  (70, 46, 118, 184, 0)),\n",
+       " ('B00005LBRT',\n",
+       "  'Avalon Organics Lavender Luminosity Daily Moisturizer, 2 Ounce',\n",
+       "  (104, 234, 67, 34, 0)),\n",
+       " ('B00005NAOD',\n",
+       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Creme, 1.7 Ounce Jar',\n",
+       "  (196, 245, 56, 152, 0)),\n",
+       " ('B00005NAOJ',\n",
+       "  'Cetaphil Gentle Cleansing Bar - 4.5 oz',\n",
+       "  (71, 47, 70, 143, 0)),\n",
+       " ('B00005NASR',\n",
+       "  'Ecco Bella Original Organic Water-Free Herbal Body Lotion, Vanilla, 8-Ounce Bottle',\n",
+       "  (107, 47, 162, 99, 0)),\n",
+       " ('B00005NFBD',\n",
+       "  'Freeman Facial Masque, Purifying - 6 fl oz',\n",
+       "  (93, 173, 254, 103, 0)),\n",
+       " ('B00005NFBJ',\n",
+       "  'Freeman Cucumber Facial Peel-Off Mask - 6 oz',\n",
+       "  (117, 135, 24, 91, 0)),\n",
+       " ('B00005O0MZ',\n",
+       "  'Conair 1875 Watt Ionic Conditioning Hair Dryer',\n",
+       "  (174, 149, 124, 60, 0)),\n",
+       " ('B00005R1H2',\n",
+       "  'Skin Success Eventone Fade Milk with Vitamin E and Alpha Hydroxy - 8.5 Fluid Ounces',\n",
+       "  (205, 173, 122, 160, 0)),\n",
+       " ('B00005R1H3',\n",
+       "  'Skin Success Eventone Fade Cream, For Oily Skin - 2.7 oz',\n",
+       "  (167, 173, 22, 236, 0)),\n",
+       " ('B00005REAQ',\n",
+       "  'Alba BotanicaTM Even Advanced Natural Moisturizer Sea Moss SPF 15 -- 2 fl oz',\n",
+       "  (154, 100, 27, 77, 0)),\n",
+       " ('B00005REAT',\n",
+       "  'Body Bath Honey Mango Alba Botanica 12 oz Liquid',\n",
+       "  (109, 4, 3, 116, 0)),\n",
+       " ('B00005S81T',\n",
+       "  'Skin Success Eventone Fade Cream, Regular - 2.7 oz',\n",
+       "  (167, 173, 11, 170, 0)),\n",
+       " ('B00005T83D',\n",
+       "  \"Palmer's Cocoa Butter Formula, Cream Soap Bar with Vitamin E, 3.5 oz - 1 ea\",\n",
+       "  (44, 173, 11, 77, 0)),\n",
+       " ('B00005TZU8',\n",
+       "  'DreamTime Inner Peace Eye Pillow, Lavender Velvet',\n",
+       "  (144, 3, 24, 206, 0)),\n",
+       " ('B00005UN90',\n",
+       "  'Suave Naturals Body Wash, Ocean Breeze - 12oz.',\n",
+       "  (70, 202, 139, 53, 0)),\n",
+       " ('B00005UQSP',\n",
+       "  'EO Shampoo for Fine/Oily Hair, Rosemary &amp; Mint, 8 fl oz (240 ml)',\n",
+       "  (173, 180, 22, 34, 0)),\n",
+       " ('B00005V5OU',\n",
+       "  'Neutrogena Healthy Defense Daily Moisturizer, SPF 30, Light Tint 1.7 Ounce',\n",
+       "  (44, 24, 33, 169, 0)),\n",
+       " ('B00005V5OW',\n",
+       "  'Maybelline Lash Discovery Washable Mascara, Very Black - .16 fl oz',\n",
+       "  (200, 255, 153, 195, 0)),\n",
+       " ('B00005V5P6',\n",
+       "  'Maybelline New York Purestay Powder &amp; Foundation SPF 15, Ivory - .34 oz',\n",
+       "  (221, 103, 254, 160, 0)),\n",
+       " ('B0000632EE',\n",
+       "  'Fallene Total Block UVA/UVB Complete Broad Spectrum Sun Protection, SPF 65 Clear, 2 fl Ounces (59 ml)',\n",
+       "  (52, 109, 122, 125, 0)),\n",
+       " ('B0000632EN',\n",
+       "  'Aveeno Positively Radiant Skin Brightening Daily Scrub, 5 Ounce',\n",
+       "  (40, 118, 226, 27, 0)),\n",
+       " ('B00006598X',\n",
+       "  'Aquis Microfiber Hair Towel, Lisse Crepe, Pink (19 x 39-Inches)',\n",
+       "  (28, 120, 11, 114, 0)),\n",
+       " ('B000065DJY',\n",
+       "  'Revlon Perfect Heat 1875W Shine Boosting Hair Dryer, RV484',\n",
+       "  (144, 66, 226, 99, 0)),\n",
+       " ('B000065DK4',\n",
+       "  'Vidal Sassoon VS184C Three-Barrel Waver',\n",
+       "  (4, 247, 67, 219, 0)),\n",
+       " ('B000066CKS',\n",
+       "  'Badger - Healing Balm For Hardworking Hands',\n",
+       "  (12, 96, 139, 206, 0)),\n",
+       " ('B0000682T7', 'Badger Balm, Sleep Balm - 2 oz', (120, 47, 67, 99, 0)),\n",
+       " ('B0000683O5',\n",
+       "  'CoverGirl Lipslicks Lip Gloss, Clear - .14 oz',\n",
+       "  (216, 96, 163, 116, 0)),\n",
+       " ('B000068U49',\n",
+       "  'Kingsley Shave Soap Bowl with Lid Dark Wood',\n",
+       "  (116, 103, 49, 34, 0)),\n",
+       " ('B00006FDU6',\n",
+       "  'Conair Straight Waves 3-in-1 Specialty Styler',\n",
+       "  (220, 161, 24, 34, 0)),\n",
+       " ('B00006FE30',\n",
+       "  'Neutrogena Clear Pore Cleanser/Mask, 4.2 Fluid Ounce (125 ml)',\n",
+       "  (99, 202, 22, 75, 0)),\n",
+       " ('B00006FRW7',\n",
+       "  'Aveeno Clear Complexion Daily Moisturizer, 4 Ounce',\n",
+       "  (239, 196, 24, 76, 0)),\n",
+       " ('B00006IGL3',\n",
+       "  'Kiss My Face Organics Break Out, Botanical Acne Gel, 1 fl oz',\n",
+       "  (133, 96, 192, 15, 0)),\n",
+       " ('B00006IGL8',\n",
+       "  'Kiss My Face Deep Cleansing Mask Pore Shrink , 2 Fluid Ounce',\n",
+       "  (233, 122, 41, 37, 0)),\n",
+       " ('B00006IV22',\n",
+       "  'Conair 1875 Watt Dual Voltage Folding Handle Hair Dryer',\n",
+       "  (148, 145, 71, 76, 0)),\n",
+       " ('B00006IV2E',\n",
+       "  'Conair BC40JBC Hot Brush, White, 0.75 Inch',\n",
+       "  (36, 202, 94, 170, 0)),\n",
+       " ('B00006IV2F', 'Instant Heat Hot Brush, 3/4-Inch', (30, 180, 163, 195, 0)),\n",
+       " ('B00006IV30', 'Conair Instant Heat Travel Hairsetter', (220, 96, 24, 37, 0)),\n",
+       " ('B00006JN4F',\n",
+       "  \"L'Oreal Paris Superior Preference Hair Color, LB-01 Extra Light Ash Blonde\",\n",
+       "  (52, 180, 122, 170, 0)),\n",
+       " ('B00006JT6O',\n",
+       "  'Olay Total Effects Anti-Aging Night Firming Treatment - 1.7 Fl Oz',\n",
+       "  (133, 180, 216, 75, 0)),\n",
+       " ('B00006K14U',\n",
+       "  'Nivea for Men Energy Face Scrub, 4.4 Ounce Tube',\n",
+       "  (70, 122, 122, 13, 0)),\n",
+       " ('B00006L9KV',\n",
+       "  'Thicker Fuller Hair Revitalizing Shampoo-12 oz',\n",
+       "  (144, 202, 37, 53, 0)),\n",
+       " ('B00006L9KW',\n",
+       "  'Thicker Fuller Hair Weightless Conditioner-12 oz',\n",
+       "  (71, 47, 226, 100, 0)),\n",
+       " ('B00007FCQ1',\n",
+       "  'Blue Lizard Australian Sunscreen, Regular, SPF 30+, 8.75-Ounce Bottle',\n",
+       "  (28, 178, 162, 37, 0)),\n",
+       " ('B00007KQF3',\n",
+       "  'Barielle Nail Strengthener Cream, 1 Ounce',\n",
+       "  (28, 255, 71, 184, 0)),\n",
+       " ('B00007M0CP',\n",
+       "  'Conair Xtreme Instant Heat Multisized Hot Rollers, Pink',\n",
+       "  (67, 68, 153, 152, 0)),\n",
+       " ('B000087L6T', 'Rose Milk Skin Care Lotion - 8 fl oz', (99, 96, 192, 170, 0)),\n",
+       " ('B000089SA4', 'Soap Glycerine Vitamin-E 4 Ounces', (17, 173, 246, 138, 0)),\n",
+       " ('B00008BNZ4',\n",
+       "  'Wahl 79900B Clip-N-Trim 23-Piece Complete Haircut Kit',\n",
+       "  (105, 115, 33, 182, 0)),\n",
+       " ('B00008CMOQ',\n",
+       "  'Queen Helene Mint Julep Masque, 8 Ounce',\n",
+       "  (12, 62, 122, 103, 0)),\n",
+       " ('B00008CMOR',\n",
+       "  'Queen Helene Original Formula Antioxidant Grape Seed Extract Peel Off Masque -- 6 oz',\n",
+       "  (167, 145, 41, 103, 0)),\n",
+       " ('B00008CMOS',\n",
+       "  'Queen Helene Masque, Mud Pack - 8 oz',\n",
+       "  (128, 52, 122, 184, 0)),\n",
+       " ('B00008DPP8',\n",
+       "  'Softsoap Naturals Moisturizing Liquid Hand Soap with Milk &amp; Honey, Pump - 7.5 fl oz',\n",
+       "  (132, 196, 67, 159, 0)),\n",
+       " ('B00008J2XQ',\n",
+       "  'Aveeno Active Naturals Skin Relief Hand Cream, 3.5 Ounce',\n",
+       "  (102, 8, 24, 159, 0)),\n",
+       " ('B00008KA7P',\n",
+       "  'No Rinse Shampoo - 16 floz (473.1 mL)',\n",
+       "  (99, 66, 201, 202, 0)),\n",
+       " ('B00008KA8D',\n",
+       "  'Revlon ColorStay Overtime Liquid Lipcolor, Keep Blushing',\n",
+       "  (120, 122, 70, 195, 0)),\n",
+       " ('B00008MNZH',\n",
+       "  'Eucerin Daily Protection Moisturizing Face Lotion, Broad Spectrum SPF 30',\n",
+       "  (99, 202, 67, 13, 0)),\n",
+       " ('B00008MOQE',\n",
+       "  'Maybelline Great Lash Mascara, Very Black -.43 Fluid Ounce',\n",
+       "  (194, 59, 82, 195, 0)),\n",
+       " ('B00008MOQJ',\n",
+       "  'Maybelline Great Lash Curved - Very Black',\n",
+       "  (80, 196, 59, 103, 0)),\n",
+       " ('B00008MOQN',\n",
+       "  'Maybelline Great Lash Waterproof Mascara, Very Black- .43 Fluid Ounce',\n",
+       "  (80, 255, 59, 231, 0)),\n",
+       " ('B00008O2X5',\n",
+       "  'Almay Anti-Perspirant &amp; Deodorant, Clear Gel, Fragrance Free - 2.25 oz',\n",
+       "  (102, 131, 11, 15, 0)),\n",
+       " ('B00008PC1O',\n",
+       "  'Sun-In Spray-In Hair Lightener, Original - 4.7 fl oz',\n",
+       "  (199, 202, 11, 38, 0)),\n",
+       " ('B00008ZPG9',\n",
+       "  'Olay Regenerist Deep Hydration Regenerating Cream Facial Moisturirzer 1.7 Fl Oz',\n",
+       "  (99, 17, 71, 195, 0)),\n",
+       " ('B00008ZPGA',\n",
+       "  'Olay Regenerist Regenerating Serum Moisturizer 1.7 Fl Oz',\n",
+       "  (99, 22, 65, 13, 0)),\n",
+       " ('B000092OX4',\n",
+       "  'Olay Regenerist Regenerating Lotion With Sunscreen Broad Spectrum SPF 15 2.5 Fl Oz',\n",
+       "  (252, 129, 227, 103, 0)),\n",
+       " ('B000093A0U',\n",
+       "  'Hugo By Hugo Boss For Women. Eau De Toilette Spray 1.3 Ounces',\n",
+       "  (31, 4, 27, 34, 0)),\n",
+       " ('B000093I60', 'No-Rinse Body Bath, 16 oz', (141, 161, 124, 195, 0)),\n",
+       " ('B000094Q6C',\n",
+       "  'Tournmaline Ceramic Curling Iron, 3/4-Inch',\n",
+       "  (45, 66, 24, 100, 0)),\n",
+       " ('B000094ZDX',\n",
+       "  'Conair 1600 Watt Folding Handle Hair Dryer',\n",
+       "  (195, 77, 33, 116, 0)),\n",
+       " ('B0000950LF',\n",
+       "  'Promensil Menopausal Relief Supplement Tablets, 60-Count Box',\n",
+       "  (12, 234, 226, 159, 0)),\n",
+       " ('B000095SDS', 'Scalpicin Max Dermatology', (98, 47, 174, 114, 0)),\n",
+       " ('B00009WO0W', \"Weleda Children's Tooth Gel, 1.7 oz\", (56, 77, 163, 27, 0)),\n",
+       " ('B0000AFUTL',\n",
+       "  'Jerdon JP7506CF 8-Inch Two-Sided Swivel Wall Mount Mirror with 5x Magnification, 13.5-Inch Extension, Chrome Finish',\n",
+       "  (36, 4, 163, 125, 0)),\n",
+       " ('B0000AJ3PT',\n",
+       "  'Aveeno Active Naturals Creamy Moisturizing Oil, 12 Ounce',\n",
+       "  (239, 152, 71, 34, 0)),\n",
+       " ('B0000AJ3PU',\n",
+       "  'Aveeno Active Naturals Stress Relief Moisturizing Lotion, 12 Ounce',\n",
+       "  (56, 47, 65, 170, 0)),\n",
+       " ('B0000AJ3Q3',\n",
+       "  \"L'Oreal Paris Double Extend Mascara, Blackest Black, 0.33 Ounces\",\n",
+       "  (192, 121, 122, 101, 0)),\n",
+       " ('B0000AN9L9',\n",
+       "  'Neutrogena Transparent Facial Bar Bonus Pack, Original Formula - 6 ea, 3.5 oz each, total 21 oz',\n",
+       "  (102, 59, 124, 236, 0)),\n",
+       " ('B0000AS54S',\n",
+       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 4 Ounce Tube',\n",
+       "  (128, 141, 139, 219, 0)),\n",
+       " ('B0000BV14G',\n",
+       "  'Cool Water By Davidoff Eau-de-toilette Spray, 1.7-Ounce',\n",
+       "  (119, 115, 163, 15, 0)),\n",
+       " ('B0000C0XL8',\n",
+       "  'Desert Essence Organic Jojoba Oil -- 4 fl oz',\n",
+       "  (69, 173, 216, 206, 0)),\n",
+       " ('B0000C4COV',\n",
+       "  'derma e Soothing Skin Treatment, Tea Tree &amp; E Antiseptic Cr&egrave;me, 4 oz (113 g)',\n",
+       "  (133, 21, 147, 206, 0)),\n",
+       " ('B0000CC64W',\n",
+       "  'Olay Regenerist Daily Regenerating Serum - Fragrance Free Moisturizer 1.7 Fl Oz',\n",
+       "  (99, 46, 162, 34, 0)),\n",
+       " ('B0000CDVN6', 'Conair Spiral Styler, 0.75 Inch', (17, 249, 155, 159, 0)),\n",
+       " ('B0000CNR0E',\n",
+       "  'Household Essentials Travel Tie Case, Black',\n",
+       "  (36, 96, 226, 152, 0)),\n",
+       " ('B0000CNR0J',\n",
+       "  'Household Essentials Grooming Travel Bag Organizer, Black',\n",
+       "  (36, 180, 24, 27, 0)),\n",
+       " ('B0000UJJH0',\n",
+       "  'Desert Essence Tea Tree Oil 100% Pure, 2-Ounce',\n",
+       "  (39, 135, 208, 77, 0)),\n",
+       " ('B0000UTUS8',\n",
+       "  'Aquis Microfiber Hair Turban, Lisse Crepe, Patented Design, White',\n",
+       "  (28, 77, 192, 91, 0)),\n",
+       " ('B0000UTUVU',\n",
+       "  \"Mrs. Meyer's Clean Day Dish Soap, Lavender, 16 Ounce Bottle\",\n",
+       "  (149, 211, 226, 91, 0)),\n",
+       " ('B0000UTUW4',\n",
+       "  \"Mrs. Meyer's Clean Day Liquid Hand Soap, Lavender, 12.5 Ounce Bottle\",\n",
+       "  (6, 21, 201, 160, 0)),\n",
+       " ('B0000VC1YC',\n",
+       "  'Neutrogena Body Oil, Light Sesame Formula, 32 Ounce',\n",
+       "  (94, 129, 59, 219, 0)),\n",
+       " ('B0000XMQTA',\n",
+       "  'Irene Gari Cover Your Grey for Women Touch Up Stick Medium Brown',\n",
+       "  (193, 242, 192, 169, 0)),\n",
+       " ('B0000Y3C3I', 'Afirm 3X', (21, 47, 163, 188, 0)),\n",
+       " ('B0000Y3COC', 'PanOxyl Bar 10% - 4 oz - 1 Bar', (93, 4, 216, 118, 0)),\n",
+       " ('B0000Y3CRY', 'Nizoral Anti-Dandruff Shampoo, 4 oz.', (92, 47, 201, 182, 0)),\n",
+       " ('B0000Y3CSI',\n",
+       "  'Neutrogena T/Gel Original Shampoo - 16 oz',\n",
+       "  (128, 145, 226, 103, 0)),\n",
+       " ('B0000Y3D4G',\n",
+       "  'Neutrogena Norwegian Formula Lip Moisturizer, SPF 15, 0.15 Ounce',\n",
+       "  (92, 96, 67, 37, 0)),\n",
+       " ('B0000Y3D4Q', 'Rezamid Acne Treatment Lotion, 2 oz.', (71, 173, 246, 15, 0)),\n",
+       " ('B0000Y3DAK', 'DHS Clear Shampoo 8 oz', (13, 3, 201, 114, 0)),\n",
+       " ('B0000Y3FQC', 'Diamancel Tough Buffer #11', (192, 151, 250, 152, 0)),\n",
+       " ('B0000Y3FRG', 'Obagi Nu-Derm Foaming Gel -- 6.7 fl oz', (102, 8, 17, 77, 0)),\n",
+       " ('B0000Y3GF2',\n",
+       "  'Free &amp; Clear Hairspray Firm Hold, 8 Ounce',\n",
+       "  (71, 122, 139, 91, 0)),\n",
+       " ('B0000Y3GIE',\n",
+       "  'Nailtiques Formula 2 Nail Growth Formula Treatments, 0.25 Ounce',\n",
+       "  (28, 134, 59, 170, 0)),\n",
+       " ('B0000Y3LAC',\n",
+       "  'Jobst It Stays Roll-on Body Adhesive 2 oz. (3 pack)',\n",
+       "  (92, 115, 70, 169, 0)),\n",
+       " ('B0000Y8H3S',\n",
+       "  'Rogaine for Men Hair Regrowth Treatment, Extra Strength Original Unscented, Set of 3, 2-Ounce Bottles',\n",
+       "  (214, 51, 59, 103, 0)),\n",
+       " ('B0000YUWY0',\n",
+       "  'jerome russell Hair Color Thickener for Thinning Hair, Black, 3.5 Ounce',\n",
+       "  (120, 66, 10, 100, 0)),\n",
+       " ('B0000YUX4O', 'Mebco Tortoise Shower Detangler', (241, 196, 201, 53, 0)),\n",
+       " ('B0000YUXGW', 'Mavala Scientifique (Nail Hardener)', (250, 3, 219, 38, 0)),\n",
+       " ('B0000YUXI0',\n",
+       "  'Mavala Stop - Helps Cure Nail Biting and Thumb Sucking, 0.3-Fluid Ounce',\n",
+       "  (228, 196, 162, 103, 0)),\n",
+       " ('B0000YV6BI', 'ApHogee Shampoo for Damaged Hair', (132, 202, 226, 60, 0)),\n",
+       " ('B0000YV6YK', \"One 'n Only Colorfix\", (194, 247, 218, 53, 0)),\n",
+       " ('B0000YV83O', 'Facial-Flex Facial Exercise System', (132, 173, 33, 159, 0)),\n",
+       " ('B0000ZHH5G',\n",
+       "  'Vanicream Moisturizing Skin Cream, 16 Ounces',\n",
+       "  (44, 134, 33, 103, 0)),\n",
+       " ('B0000ZHOEU',\n",
+       "  'Free &amp; Clear Liquid Cleanser, 8 Ounce',\n",
+       "  (117, 21, 201, 15, 0)),\n",
+       " ('B0000ZLEFU',\n",
+       "  'Auric Blends Perfume Oil, 0.33 oz - Coco Mango',\n",
+       "  (127, 21, 122, 15, 0)),\n",
+       " ('B0000ZLEX2',\n",
+       "  'Black Opium - Auric Blends Perfume Oils',\n",
+       "  (47, 96, 187, 15, 0)),\n",
+       " ('B00011JI88', 'Ardell Fashion Lashes - 120 Black Demi', (36, 21, 59, 75, 0)),\n",
+       " ('B00011JOQY', 'Cricket Static Free Fast Flo', (36, 202, 153, 34, 0)),\n",
+       " ('B00011JOR8',\n",
+       "  'Cricket Hair Brush Static Free, Mini Fast Flo, 1.28 Ounce',\n",
+       "  (97, 149, 226, 99, 0)),\n",
+       " ('B00011JTFA', 'Star Nail Pen-Style Polish Corrector', (42, 129, 70, 170, 0)),\n",
+       " ('B00011QTZI',\n",
+       "  'Cricket Technique Barrel Hair Brush, XX-large Round, 2 Inch',\n",
+       "  (36, 202, 162, 170, 0)),\n",
+       " ('B00011QUDE',\n",
+       "  'Helen of Troy 1514 Brush Iron, White, 1 1/2 Inches Barrel',\n",
+       "  (155, 21, 132, 220, 0)),\n",
+       " ('B00011QUKW',\n",
+       "  'Hot Tools HT1108 Professional Marcel Iron, 1 Inch',\n",
+       "  (187, 77, 24, 3, 0)),\n",
+       " ('B00011QVHO',\n",
+       "  'Spornette Porcupine Rounder Brush, 2-Inch Diameter',\n",
+       "  (195, 129, 140, 76, 0)),\n",
+       " ('B00011QVIS',\n",
+       "  'Spornette Prego Brush, 2-Inch Diameter',\n",
+       "  (230, 3, 218, 52, 0)),\n",
+       " ('B00011QVUG',\n",
+       "  'For Pro Stretch Terry Headband with Velcro 3in.',\n",
+       "  (34, 3, 59, 77, 0)),\n",
+       " ('B000127UUA',\n",
+       "  'Camille Beckman Glycerin Hand Therapy, White Lilac, 8 Ounce',\n",
+       "  (232, 161, 139, 91, 0)),\n",
+       " ('B000127UVY',\n",
+       "  'Camille Beckman Glycerin Hand Therapy, Glycerine Rosewater, 6 Ounce',\n",
+       "  (232, 103, 226, 219, 0)),\n",
+       " ('B000127VFE',\n",
+       "  'Camille Beckman Silky Body Moisturizing Cream, Glycerine Rosewater, 16 Ounce',\n",
+       "  (232, 103, 208, 219, 0)),\n",
+       " ('B00012NEYG', \"Burt's Bees Head to Toe Starter Kit\", (28, 59, 43, 91, 0)),\n",
+       " ('B00012NI7E',\n",
+       "  \"Burt's Bees Avocado Butter Pre-Shampoo Hair Treatment with Nettles and Rosemary - 4.34 fl oz\",\n",
+       "  (196, 122, 65, 15, 0)),\n",
+       " ('B00012NJZA',\n",
+       "  \"Burt's Bees Orange Essence Facial Cleanser, 4.3 Ounces\",\n",
+       "  (128, 47, 65, 103, 0)),\n",
+       " ('B00012NKGS',\n",
+       "  \"Burt's Bees: Citrus Facial Scrub, 2 oz\",\n",
+       "  (47, 21, 216, 103, 0)),\n",
+       " ('B000136P7S',\n",
+       "  'Miracle of Aloe Miracle Hand Repair Cream 8 Oz',\n",
+       "  (247, 21, 246, 76, 0)),\n",
+       " ('B00013TQRE',\n",
+       "  'Rogaine for Women Hair Regrowth Treatment 3- 2 ounce bottles',\n",
+       "  (255, 51, 59, 15, 0)),\n",
+       " ('B00013YYS0',\n",
+       "  \"Burt's Bees - Cuticle Creme Lemon Butter - 0.6 oz.\",\n",
+       "  (47, 173, 216, 34, 0)),\n",
+       " ('B00013YZ7U', \"Dr. Bronner's Almond Soap 32oz\", (191, 161, 67, 116, 0)),\n",
+       " ('B00013Z15A',\n",
+       "  \"Burt's Bees Day Creme Carrot Nutritive -- 2 fl oz\",\n",
+       "  (218, 24, 254, 34, 0)),\n",
+       " ('B000141O6E',\n",
+       "  'Redken Extreme Anti-Snap Leave-in Treatment, 8.5 Ounce',\n",
+       "  (47, 196, 75, 169, 0)),\n",
+       " ('B000142AZ8',\n",
+       "  'Bain de Soleil Mega Tan Sunscreen Lotion With Self Tanner, SPF 4 - 4 fl oz',\n",
+       "  (132, 196, 163, 170, 0)),\n",
+       " ('B000142C1A',\n",
+       "  'Essie Nail Polish Marshmallow, .5 Ounce',\n",
+       "  (250, 62, 254, 116, 0)),\n",
+       " ('B000142FVW',\n",
+       "  'Opi Nail Lacquer, Not So Bora Pink, 0.5 Fluid Ounce',\n",
+       "  (210, 77, 59, 198, 0)),\n",
+       " ('B000142JDG',\n",
+       "  'Roux Fanci-Full Mousse, 18 Spun Sand, 6 Fluid Ounce',\n",
+       "  (39, 8, 192, 34, 0)),\n",
+       " ('B000142KZ8',\n",
+       "  'Pretty Nails Instant Polish Remover Regular',\n",
+       "  (201, 202, 201, 60, 0)),\n",
+       " ('B000142MGA',\n",
+       "  'Fran Wilson Eye Tees Precision Makeup Applicators 80 Cotton Swabs',\n",
+       "  (11, 120, 11, 116, 0)),\n",
+       " ('B000142NDC',\n",
+       "  'Bigen Permanent Powder Hair Color, Dark Chestnut, .21 Ounce',\n",
+       "  (5, 46, 27, 103, 0)),\n",
+       " ('B000142OZY',\n",
+       "  'Aphogee Conditioner Pro-Vitamin Leave-In 8 oz.',\n",
+       "  (13, 3, 24, 53, 0)),\n",
+       " ('B000142P12',\n",
+       "  'ApHogee Keratin and Green Tea Restructurizer 8 fl. oz',\n",
+       "  (132, 47, 43, 169, 0)),\n",
+       " ('B000142PUS',\n",
+       "  'Clairol Professional Beautiful Collection Semi-permanent Hair Color, Light Reddish Brown',\n",
+       "  (102, 149, 216, 219, 0)),\n",
+       " ('B000142Q6G', 'Colora Henna Veg-Hair Chestnut 2oz', (29, 119, 122, 169, 0)),\n",
+       " ('B000142TU4',\n",
+       "  'Sally Hansen Polish Remover 8 oz. Strengthening',\n",
+       "  (112, 202, 24, 99, 0)),\n",
+       " ('B000142U9E',\n",
+       "  'Fantasia High Potency IC Hair Polisher Styling Gel, with Sparkle Lites, 16 oz.',\n",
+       "  (245, 66, 140, 206, 0)),\n",
+       " ('B000142ZFS', 'Super Nail Polish Thinner 4oz', (3, 135, 65, 76, 0)),\n",
+       " ('B0001431AG',\n",
+       "  'Fran Wilson Cake Eyeliner, Black, 0.09 Ounce',\n",
+       "  (241, 211, 201, 219, 0)),\n",
+       " ('B0001432S2',\n",
+       "  'OPI Natural Nail Strengthener Treatment, 0.5-Fluid Ounce',\n",
+       "  (201, 145, 59, 195, 0)),\n",
+       " ('B0001433OU',\n",
+       "  'OPI Nail Lacquer, Pirates of The Caribbean Collection, Stranger Tides, 0.5 Fluid Ounce',\n",
+       "  (112, 4, 40, 12, 0)),\n",
+       " ('B0001433VI',\n",
+       "  'OPI Start-to-finish Base Coat, Top Coat and Nail Strengthener, 0.5-Fluid Ounce',\n",
+       "  (86, 46, 219, 91, 0)),\n",
+       " ('B000143AY8', 'Fran Wilson Yellow Mood Matcher', (119, 89, 140, 169, 0)),\n",
+       " ('B000143CFA', 'Andrea Eyelash Adhesive: Clear', (132, 159, 254, 76, 0)),\n",
+       " ('B000143K2A',\n",
+       "  \"Mane 'n Tail Deep Moisturizing Conditioner\",\n",
+       "  (132, 145, 71, 77, 0)),\n",
+       " ('B000143O7G', 'Instant Peel Natural Dead Skin Remover', (120, 2, 3, 12, 0)),\n",
+       " ('B000146L62',\n",
+       "  'Cassia Clove 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
+       "  (252, 149, 226, 152, 0)),\n",
+       " ('B000146LKS',\n",
+       "  'Patchouli 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
+       "  (252, 149, 226, 152, 1)),\n",
+       " ('B00014D138',\n",
+       "  'Jason Pure Natural Moisturizing Creme, Anti-Aging Tea Time, 4 Ounce',\n",
+       "  (50, 196, 219, 99, 0)),\n",
+       " ('B00014D1OW', 'Lemon Clarifying Shampoo 11 Ounces', (93, 247, 138, 217, 0)),\n",
+       " ('B00014D322',\n",
+       "  'MillCreek - Biotin Shampoo, 16 fl oz gel',\n",
+       "  (56, 24, 124, 75, 0)),\n",
+       " ('B00014D5O8', \"Burt's Bees Hand Salve, 3 oz Tin\", (230, 3, 201, 114, 0)),\n",
+       " ('B00014DGCO',\n",
+       "  'Plantlife Lavender 100% Pure Essential Oil - Bulgarian - 10 ml',\n",
+       "  (252, 211, 226, 169, 0)),\n",
+       " ('B00014DIKE',\n",
+       "  \"Burt's Bees Therapeutic Bath Crystals, 1 Pound\",\n",
+       "  (71, 145, 41, 206, 0)),\n",
+       " ('B00014DKKC',\n",
+       "  \"Burt's Bees Lemon &amp; Vitamin E Bath &amp; Body Oil, 4 Fluid Ounce\",\n",
+       "  (117, 23, 65, 162, 0)),\n",
+       " ('B00014DM5K',\n",
+       "  'Facial Toner &amp; Freshener-Aloe Vera Earth Science 8 oz Liquid',\n",
+       "  (196, 173, 226, 159, 0)),\n",
+       " ('B00014DMQE',\n",
+       "  'Reviva Labs 10% Glycolic Acid Cream -- 1.5 oz',\n",
+       "  (47, 255, 27, 60, 0)),\n",
+       " ('B00014DMRI',\n",
+       "  'Aubrey Organics - Natural Mist Herbal Hairspray (Regular Hold), 8 fl oz spray',\n",
+       "  (71, 134, 162, 15, 0)),\n",
+       " ('B00014DMUA',\n",
+       "  'Moisturizer-Daily Essential Jojoba/Aloe Desert Essence 4 oz Cream',\n",
+       "  (196, 173, 59, 77, 0)),\n",
+       " ('B00014DTY4',\n",
+       "  'Nutribiotic Skin Ointment, 0.5 Ounce',\n",
+       "  (170, 109, 59, 219, 0)),\n",
+       " ('B00014DU6G',\n",
+       "  'Earth Science Fragrance Free Clarifying Facial Wash 8 oz',\n",
+       "  (247, 3, 219, 195, 0)),\n",
+       " ('B00014DZ2K', 'AmeriLeather Leather Toiletry Bag', (241, 62, 122, 15, 0)),\n",
+       " ('B00014EH2C',\n",
+       "  'Apricot Intensive Night Cream, 1.65oz',\n",
+       "  (204, 228, 153, 77, 0)),\n",
+       " ('B00014EKJC',\n",
+       "  \"Nature's Plus Natural Beauty Cleansing Bar -- 3 oz\",\n",
+       "  (21, 4, 10, 219, 0)),\n",
+       " ('B00014FTTM',\n",
+       "  'Reviva Labs DMAE Firming Fluid - 1 oz',\n",
+       "  (182, 80, 65, 170, 0)),\n",
+       " ('B00014GT78',\n",
+       "  'Dermablend Cover Creme - Chroma 1 - Rose Beige, 1 oz',\n",
+       "  (99, 66, 3, 195, 0)),\n",
+       " ('B00014GTWS',\n",
+       "  'Dermablend Leg and Body Cover Make-Up SPF 15, Medium, 3.4 Ounce',\n",
+       "  (71, 80, 27, 206, 0)),\n",
+       " ('B00014HF94',\n",
+       "  \"Vitamin E 14,000 IU Nature's Plus 0.5 oz Liquid\",\n",
+       "  (132, 108, 246, 53, 0)),\n",
+       " ('B00014JKG0',\n",
+       "  \"Nature's Gate Tea Tree Moisturizing Lotion for Irritated, Distressed Skin, 18-Ounce Pump Bottle\",\n",
+       "  (170, 115, 11, 159, 0)),\n",
+       " ('B00014JOHA',\n",
+       "  'Kiss My Face - Whenever Shampoo, 11 fl oz liquid',\n",
+       "  (26, 46, 118, 103, 0)),\n",
+       " ('B00014WW38',\n",
+       "  'Naturtint Permanent Hair Colorant Sandy Blonde',\n",
+       "  (166, 96, 234, 37, 0)),\n",
+       " ('B00015GWQU',\n",
+       "  'Fran Wilson Nail Tees Precision Makeup Applicators 120 Cotton Swabs',\n",
+       "  (11, 69, 153, 116, 0)),\n",
+       " ('B00015HBD8',\n",
+       "  'Phillips Light Touch 1 Cushion Hair Brush',\n",
+       "  (244, 159, 24, 99, 0)),\n",
+       " ('B00015HBEC',\n",
+       "  'Spornette Large Oval Nylon Bristle Cushion Brush',\n",
+       "  (105, 77, 141, 27, 0)),\n",
+       " ('B00015HBEM',\n",
+       "  'Spornette Double Stranded XL Nylon Rounder Brush, 2-Inch Diameter',\n",
+       "  (80, 159, 192, 27, 0)),\n",
+       " ('B00015XAQ0',\n",
+       "  'Avalon Organics Vitamin C Renewal Refreshing Cleansing Gel, 8.5 Ounce Bottle',\n",
+       "  (38, 178, 162, 34, 1)),\n",
+       " ('B00016QYOO',\n",
+       "  'Desert Essence Thoroughly Clean Face Wash - Original -- 8.5 fl oz',\n",
+       "  (239, 100, 153, 195, 0)),\n",
+       " ('B00016RIIA', 'Yerba Prima Tampico Skin Brush 1 Ea', (132, 196, 234, 53, 0)),\n",
+       " ('B00016WSIK',\n",
+       "  'Earth Therapeutics Body Sponge, Anti-Bacterial, 1 Count',\n",
+       "  (232, 59, 118, 27, 0)),\n",
+       " ('B00016WUEW',\n",
+       "  'Heritage Products Rosewater &amp; Glycerin, 4 Ounce',\n",
+       "  (141, 59, 162, 178, 0)),\n",
+       " ('B00016WULK',\n",
+       "  'Avalon Organics Vitamin C Renewal Moisture Plus Lotion SPF 15 -- 4 fl oz',\n",
+       "  (104, 234, 67, 34, 1)),\n",
+       " ('B00016WV6E', 'Henna Persia Light Brown 4 Ounces', (13, 96, 37, 100, 0)),\n",
+       " ('B00016WW8Q', \"Dickinson's Witch Hazel 16 oz\", (1, 96, 41, 99, 0)),\n",
+       " ('B00016WXEY',\n",
+       "  'LILY OF THE DESERT, Aloe Vera Gelly - 12 oz',\n",
+       "  (154, 134, 11, 77, 0)),\n",
+       " ('B00016X06O',\n",
+       "  'Herbatint Permanent Herbal Haircolor Gel, Golden Chestnut 4D, 4.56-Ounce',\n",
+       "  (71, 21, 246, 206, 0)),\n",
+       " ('B00016X3G6',\n",
+       "  'Herbal Salt Scrub Lavender 20.50 Ounces',\n",
+       "  (231, 173, 226, 159, 0)),\n",
+       " ('B00016X3L6',\n",
+       "  'GIOVANNI COSMETICS, Nutrafix Hair Reconstructor - 8.5 oz',\n",
+       "  (71, 159, 162, 202, 0)),\n",
+       " ('B00016X604',\n",
+       "  \"Smiles' Prid Homeopathic Drawing Salve, 18 GM, 1 each\",\n",
+       "  (123, 161, 162, 160, 0)),\n",
+       " ('B00016X68Q',\n",
+       "  'J.R. Liggett Bar Shampoo, Original Formula, 3.5 Ounce',\n",
+       "  (28, 236, 201, 91, 0)),\n",
+       " ('B00016XJ4M',\n",
+       "  'Thayers - Rose Petal Witch Hazel with Aloe Vera Alcohol-Free Toner - 12 oz.',\n",
+       "  (128, 108, 162, 152, 0)),\n",
+       " ('B00016XJ7O',\n",
+       "  'Thayers Witch Hazel w/Aloe Vera Lemon 12 oz',\n",
+       "  (71, 135, 206, 77, 0)),\n",
+       " ('B00016XJ8S',\n",
+       "  'Arbordoun Abundantly Herbal Calendula Cream 4 Oz',\n",
+       "  (118, 47, 163, 100, 0)),\n",
+       " ('B000172R76',\n",
+       "  'Ace Teasing Tail Comb 8&quot; * Black',\n",
+       "  (155, 62, 254, 160, 0)),\n",
+       " ('B000177FXW',\n",
+       "  \"Bar Soap-Tea Tree Dr. Bronner's 5 oz Bar\",\n",
+       "  (71, 181, 252, 219, 0)),\n",
+       " ('B00017XWJ8',\n",
+       "  'Ralph Lauren Eau de Toilette Spray For Women, 1.7 Oz',\n",
+       "  (212, 47, 139, 38, 0)),\n",
+       " ('B000186X4I',\n",
+       "  'Shiseido Shiseido Cleansing Massage Brush',\n",
+       "  (91, 8, 33, 114, 0)),\n",
+       " ('B000195GAY',\n",
+       "  'Blue Grass By Elizabeth Arden Deodorant Cream, 1.5-Ounce',\n",
+       "  (191, 115, 226, 195, 0)),\n",
+       " ('B000196U04', 'Diamon Deb Nail File 8&quot;', (253, 24, 216, 114, 0)),\n",
+       " ('B0001AGMWE',\n",
+       "  'Miracell ProEar-for Itchy, Irritated Ears .5 OZ',\n",
+       "  (230, 62, 162, 170, 0)),\n",
+       " ('B0001AQCII',\n",
+       "  'China Glaze Nail Polish, Jetstream, 0.5 Fluid Ounce',\n",
+       "  (79, 196, 250, 21, 0)),\n",
+       " ('B0001B86HM',\n",
+       "  'Conair Pro Style 1875 Watt Hard Hat Hair Dryer',\n",
+       "  (28, 159, 24, 116, 0)),\n",
+       " ('B0001EKTZQ',\n",
+       "  'Dermalogica Multi-active Toner, 8.4 Fluid Ounce',\n",
+       "  (139, 180, 163, 15, 0)),\n",
+       " ('B0001EKWPI',\n",
+       "  'Vanicream Cleansing Bar, Fragrance Free, 3.9 Ounce Bars (Pack of 3)',\n",
+       "  (71, 134, 122, 169, 0)),\n",
+       " ('B0001EKYL0',\n",
+       "  'Exuviance Evening Restorative Complex, 1.75 Fluid Ounce',\n",
+       "  (133, 2, 59, 162, 0)),\n",
+       " ('B0001EL0CW',\n",
+       "  'Basis Cleans and Smoothes Sensitive Skin Bar',\n",
+       "  (170, 211, 67, 34, 0)),\n",
+       " ('B0001EL5R2', 'PCA Skin Acne Cream, 0.5 Ounce', (92, 202, 59, 77, 0)),\n",
+       " ('B0001EL658',\n",
+       "  'Bare Escentuals Maximum Coverage Concealer Brush',\n",
+       "  (149, 120, 153, 114, 0)),\n",
+       " ('B0001FF80G', 'Dopp Jumbo Kit w/ Zip Bottom', (34, 62, 67, 219, 0)),\n",
+       " ('B0001I8ZT4',\n",
+       "  'Mar-V-Cide Shampoo Brush &amp; Invigorator',\n",
+       "  (32, 161, 139, 116, 0)),\n",
+       " ('B0001LH6YG', 'My Spots Are Consealed Light Color', (194, 196, 70, 103, 0)),\n",
+       " ('B0001M3K4U',\n",
+       "  'Soft N Style Pro-Cap Professional Processing Caps, Extra Large, 30-count',\n",
+       "  (241, 196, 140, 37, 0)),\n",
+       " ('B0001M7D7A',\n",
+       "  'Marianna Single Prong Curl Clip 80 pcs',\n",
+       "  (225, 189, 43, 57, 0)),\n",
+       " ('B0001MC06Y',\n",
+       "  'Peter Thomas Roth Beta Hydroxy Acid 2% Acne Wash 8.5 fl oz',\n",
+       "  (133, 211, 218, 103, 0)),\n",
+       " ('B0001MROQA',\n",
+       "  'Hot Tools Big Bumper Spring Curling Iron, 1-1/2&quot;',\n",
+       "  (153, 66, 254, 100, 0)),\n",
+       " ('B0001TJX2Q',\n",
+       "  'Home Health Castor Oil, Cold Pressed and Cold Processed, 32-Ounce',\n",
+       "  (102, 211, 234, 220, 0)),\n",
+       " ('B0001TMDF0',\n",
+       "  'Heritage Store Rose Petals Rosewater 8 oz',\n",
+       "  (246, 236, 201, 114, 0)),\n",
+       " ('B0001TO3NA', \"Grandpa's Pine Tar Soap Medium Size\", (216, 21, 227, 27, 0)),\n",
+       " ('B0001TODNK',\n",
+       "  'Earth Therapeutics Hydro Exfoliating Towel, 1 each',\n",
+       "  (120, 96, 254, 152, 0)),\n",
+       " ('B0001TOH8G',\n",
+       "  'Exfoliating Hydro Gloves-Natural Earth Therapeutics 1 Set',\n",
+       "  (132, 173, 140, 169, 0)),\n",
+       " ('B0001TQ5MW', 'Margarite Zinc Cream -- 1 oz', (102, 255, 33, 53, 0)),\n",
+       " ('B0001TQ9WI',\n",
+       "  'Mountain Ocean Skin Trip Moisturizer, Coconut, 8-Ounce',\n",
+       "  (13, 55, 201, 114, 0)),\n",
+       " ('B0001TSJPI',\n",
+       "  'Bar Soap - Sandalwood, 6 Units / 2.6 oz',\n",
+       "  (234, 247, 218, 12, 0)),\n",
+       " ('B0001TSWWS',\n",
+       "  'Home Health Roll-On Deodorant Herbal Scent -- 3 fl oz',\n",
+       "  (71, 173, 219, 160, 0)),\n",
+       " ('B0001UWRCI',\n",
+       "  'Yu-Be Moisturizing Skin Cream 1.25 oz Tube',\n",
+       "  (252, 202, 163, 195, 0)),\n",
+       " ('B0001VKL7U', \"Burt's Bees Lip Shimmer, Rhubarb\", (128, 77, 37, 195, 0)),\n",
+       " ('B0001VOJWI',\n",
+       "  'Pheromone By Marilyn Miglin For Women. Eau De Parfum Spray 1.7 Oz / 50 Ml.',\n",
+       "  (47, 134, 65, 103, 0)),\n",
+       " ('B0001XKA38',\n",
+       "  'Roux Lash and Brow Tint, Brown, 40 Count',\n",
+       "  (120, 122, 139, 76, 0)),\n",
+       " ('B0001XLSQ6', 'Steam Facial By Kaz', (183, 4, 40, 12, 0)),\n",
+       " ('B0001XLSR0',\n",
+       "  'ROUX Tween Time Instant Haircolor Touch-Up Stick BLACK 1/3 oz/10g',\n",
+       "  (152, 47, 24, 159, 0)),\n",
+       " ('B0001XQNMK',\n",
+       "  'Roux Fanci Full Mousse #52 White Mink 6 oz',\n",
+       "  (194, 47, 122, 27, 0)),\n",
+       " ('B0001Y74VS',\n",
+       "  'Ouidad by Ouidad Ouidad Climate Control Heat and Humidity Gel for Unisex, 8.5 Ounce',\n",
+       "  (58, 68, 33, 169, 0)),\n",
+       " ('B0001Y74XG',\n",
+       "  'Ouidad Water Works Clarifying Shampoo, 8.5 Ounce',\n",
+       "  (212, 21, 246, 114, 0)),\n",
+       " ('B0001YOMD6',\n",
+       "  'Touch with Love Eau De Parfum Spray for Women by Fred Hayman, 1.7 Ounce',\n",
+       "  (175, 100, 234, 12, 0)),\n",
+       " ('B0001YXXUY',\n",
+       "  'Hot Tools HT1074 Anti-Static Ionic Hot Air Brush with Ion Technology, 1000 Watts, 1 1/2 Inches',\n",
+       "  (182, 159, 27, 76, 0)),\n",
+       " ('B0001ZA3Y2', 'U-Lactin Dry Skin Lotion - 16 oz.', (252, 115, 59, 77, 0)),\n",
+       " ('B0001ZA4CS',\n",
+       "  'NeoStrata Exuviance CoverBlend Multi Function Concealer SPF15 - Sand, .5 fl oz',\n",
+       "  (132, 115, 33, 13, 0)),\n",
+       " ('B0001ZYL3Q',\n",
+       "  'TheraNeem Cream - Original Organix South 2 oz Cream \\nVanilla',\n",
+       "  (118, 66, 216, 103, 0)),\n",
+       " ('B0001ZYLAO',\n",
+       "  'Emerita Pro-gest Natural Balancing Cream, 2 Ounce',\n",
+       "  (177, 62, 122, 103, 0)),\n",
+       " ('B0001ZZH0M',\n",
+       "  'Naturally Fresh, Deodorant Crystal Spray Mist, 4 Oz&nbsp;',\n",
+       "  (71, 21, 227, 27, 0)),\n",
+       " ('B00020DY2O',\n",
+       "  'TheraNeem Gentle Therape Shampoo Organix South 12 oz Liquid',\n",
+       "  (44, 173, 197, 171, 0)),\n",
+       " ('B00020E1P8', 'Shiseido Shiseido Facial Cotton', (194, 103, 27, 160, 0)),\n",
+       " ('B00021AK4I',\n",
+       "  'Calvin Klein Ck One EDT Spray 1.7 oz for Unisex',\n",
+       "  (9, 62, 122, 195, 0)),\n",
+       " ('B00021AM9G',\n",
+       "  'Hanae Mori by Hanae Mori for Women - 3.4 Ounce EDT Spray',\n",
+       "  (31, 24, 27, 116, 0)),\n",
+       " ('B00021AQCY',\n",
+       "  'Fekkai Shea Butter Moisturizing Shampoo Hair Products 8 Fl Oz',\n",
+       "  (41, 100, 67, 206, 0)),\n",
+       " ('B00021AYF8',\n",
+       "  'Mustela Foam Shampoo for Newborns - 5.07 oz.',\n",
+       "  (128, 47, 65, 170, 0)),\n",
+       " ('B00021B8L2',\n",
+       "  'Philosophy Hope in a Jar Daily Moisturizer, Dry Skin, 2 Ounce',\n",
+       "  (127, 3, 98, 75, 0)),\n",
+       " ('B00021B95W',\n",
+       "  'Philosophy Coconut Frosting Shampoo/Shower Gel/Bubble Bath, 16 Ounces',\n",
+       "  (117, 3, 254, 114, 0)),\n",
+       " ('B00021BEXY', 'Blinc Kiss Me Mascara, Dark Brown', (210, 4, 40, 12, 0)),\n",
+       " ('B00021C1LI',\n",
+       "  'Philosophy Purity Made Simple One-Step Facial Cleanser, 16 Ounces',\n",
+       "  (107, 8, 219, 182, 0)),\n",
+       " ('B00021C49W',\n",
+       "  'Philosophy Amazing Grace Shower Gel, 16 Ounces',\n",
+       "  (221, 8, 70, 152, 0)),\n",
+       " ('B00021C5Y6',\n",
+       "  'STILL JENNIFER LOPEZ by Jennifer Lopez EAU DE PARFUM SPRAY 3.4 OZ',\n",
+       "  (178, 145, 122, 103, 0)),\n",
+       " ('B00021C9BA', 'NARS Cream Blush, Lokoum', (161, 129, 41, 170, 0)),\n",
+       " ('B00021CNCK',\n",
+       "  'CASHMERE MIST by Donna Karan EDT SPRAY 3.4 OZ',\n",
+       "  (76, 211, 122, 53, 0)),\n",
+       " ('B00021CSBG', 'NARS Duo Eyeshadow, All About Eve', (200, 196, 201, 195, 0)),\n",
+       " ('B00021CT10', 'NARS Matte Eyeshadow, Thunderball', (65, 66, 254, 170, 0)),\n",
+       " ('B00021CT56', 'NARS Shimmer Eyeshadow, Nepal', (65, 66, 254, 116, 0)),\n",
+       " ('B00021D5FY',\n",
+       "  'Desire by Alfred Dunhill for Men - 1.7 Ounce EDT Spray',\n",
+       "  (119, 96, 226, 116, 0)),\n",
+       " ('B00021D6JE',\n",
+       "  'FCUK by French Connection Eau De Toilette Spray 1.7 oz for Men',\n",
+       "  (59, 167, 122, 77, 0)),\n",
+       " ('B00021DDR4', 'NARS The Multiple, Maui', (65, 122, 153, 170, 0)),\n",
+       " ('B00021DDYW', 'NARS Sheer Lipstick, Barbarella', (3, 248, 201, 15, 0)),\n",
+       " ('B00021DE14', 'NARS Semi-Matte Lipstick, Morocco', (3, 248, 201, 15, 1)),\n",
+       " ('B00021DG80', 'NARS Lip Gloss, Turkish Delight', (79, 202, 201, 76, 0)),\n",
+       " ('B00021DJ32', 'NARS Blush, Taj Mahal', (107, 77, 140, 34, 0)),\n",
+       " ('B00021DPHW', 'NARS Bronzing Powder, Casino', (109, 129, 65, 241, 0)),\n",
+       " ('B00021DTEG',\n",
+       "  'Bare Escentuals bareMinerals Glimmer Celestine',\n",
+       "  (117, 59, 70, 91, 0)),\n",
+       " ('B00021DUM2',\n",
+       "  \"bareMinerals Blush - I'm Amused Rouge\",\n",
+       "  (194, 80, 201, 170, 0)),\n",
+       " ('B00021DVCQ',\n",
+       "  'BareMinerals Original Foundation Broad Spectrum SPF 15 8 g/0.28 Oz (Fair C10 8g/0.28 oz)',\n",
+       "  (128, 145, 201, 76, 0)),\n",
+       " ('B00021DZCC',\n",
+       "  'Philosophy Falling in Love Spray Fragrance, 2 Ounce',\n",
+       "  (221, 145, 139, 15, 0)),\n",
+       " ('B00021EA9Y',\n",
+       "  'Mustela Hydra-Bebe Body Lotion w/ Pump 10.14 US fl. oz',\n",
+       "  (63, 213, 41, 103, 0)),\n",
+       " ('B00021KD2C',\n",
+       "  'derma e Anti-Wrinkle Vitamin A Retinyl Palmitate Cr&egrave;me , 4-Ounce Jar',\n",
+       "  (56, 21, 59, 160, 0)),\n",
+       " ('B00021PDN6',\n",
+       "  'Cacharel Anais Anais By Cacharel - Eau De Toilette Spray 3.4 Oz, 3.4 oz',\n",
+       "  (63, 145, 55, 91, 0)),\n",
+       " ('B00021UR3C',\n",
+       "  'Green Tea by Elizabeth Arden, 1.5 Ounce',\n",
+       "  (229, 180, 82, 202, 0)),\n",
+       " ('B00021W26M',\n",
+       "  'Prestige Liquid Eyeliner, Legend, 0.1 Ounce',\n",
+       "  (222, 29, 227, 160, 0)),\n",
+       " ('B00021WZVE',\n",
+       "  'Christian Super Long Lash Mascara Waterproof Seven Oils Black',\n",
+       "  (132, 159, 163, 99, 0)),\n",
+       " ('B000225ZL0', 'Fresh Farmacy Cleanser by LUSH', (1, 134, 27, 37, 0)),\n",
+       " ('B000225ZOC', 'Karma Handmade Soap by LUSH', (120, 145, 138, 91, 0)),\n",
+       " ('B0002260DW', 'Sex Bomb Bath Bomb by LUSH', (119, 161, 162, 53, 0)),\n",
+       " ('B0002260L4', 'Big Blue Bath Bomb by LUSH', (141, 211, 65, 53, 0)),\n",
+       " ('B0002260N2', 'Karma Komba Solid Shampoo by LUSH', (232, 100, 254, 91, 0)),\n",
+       " ('B00022F1DW',\n",
+       "  'Organic Excellence  Mint Shampoo, 16 Ounce',\n",
+       "  (93, 103, 34, 195, 0)),\n",
+       " ('B00022WA9K', 'Revlon Shine Enchancing Hot Air Kit', (149, 66, 201, 114, 0)),\n",
+       " ('B00022WA9U',\n",
+       "  'Revlon RV261 20-Roller Ionic Professional Hairsetter, Purple',\n",
+       "  (135, 77, 219, 114, 0)),\n",
+       " ('B00023DIBI',\n",
+       "  'Cucina Coriander and Olive Tree 33.8 oz Purifying Hand Wash Refill',\n",
+       "  (120, 21, 153, 104, 0)),\n",
+       " ('B00023KDAW',\n",
+       "  'amazing grace | perfumed firming body emulsion | philosophy 8 oz.',\n",
+       "  (99, 196, 67, 241, 0)),\n",
+       " ('B000248HLI',\n",
+       "  'StriVectin-SD Intensive Repair for Existing Stretch Marks, 6 oz.',\n",
+       "  (252, 47, 227, 169, 0)),\n",
+       " ('B000260KIO',\n",
+       "  'Ouidad Botanical Boost Moisture Infusing and Refreshing Spray, 8.5 Ounce',\n",
+       "  (132, 10, 43, 169, 0)),\n",
+       " ('B0002716FO',\n",
+       "  'Ardell Duralash Flare Short Black (56 Lashes)',\n",
+       "  (200, 100, 33, 169, 0)),\n",
+       " ('B000271KR8',\n",
+       "  'Clairol Natural Instincts  02 Sahara Light Blonde 1 Kit  (Pack of 3) (packaging may vary)',\n",
+       "  (70, 161, 231, 38, 0)),\n",
+       " ('B000273PEO',\n",
+       "  'Happy By Clinique For Women. Eau De Parfum Spray 1.7 Ounces',\n",
+       "  (175, 100, 10, 75, 0)),\n",
+       " ('B000273RI8', 'M.D. Forte Facial Cream III', (56, 135, 216, 91, 0)),\n",
+       " ('B000277N7Y',\n",
+       "  'Happy By Clinique For Men. Cologne Spray 1.7 Oz.',\n",
+       "  (92, 196, 59, 91, 0)),\n",
+       " ('B00027C9CS',\n",
+       "  'Suave For Men, Deep Clean Peppermint, Shampoo12.6 fl Ounce 373 ml)',\n",
+       "  (117, 115, 153, 27, 0)),\n",
+       " ('B00027CDY2',\n",
+       "  'Neutrogena Triple Moisture Silk Touch Leave-In Cream, 6 Fluid Ounce',\n",
+       "  (173, 202, 163, 103, 0)),\n",
+       " ('B00027CDYM',\n",
+       "  'Neutrogena Clean Replenishing Deep Recovery Hair Mask, 6 oz',\n",
+       "  (144, 202, 37, 114, 0)),\n",
+       " ('B00027CGWQ',\n",
+       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 12 Ounce Jar',\n",
+       "  (128, 141, 139, 219, 1)),\n",
+       " ('B00027D0DU',\n",
+       "  'Neutrogena Cosmetics  Makeup Remover, 3.8 oz',\n",
+       "  (13, 8, 122, 206, 0)),\n",
+       " ('B00027D6SE',\n",
+       "  'Bye Bye Blemish Drying Lotion - 1 fl oz',\n",
+       "  (56, 96, 10, 75, 0)),\n",
+       " ('B00027D8IC', 'Duo Lash Adhesive, Clear, 0.25 Ounce', (23, 141, 41, 152, 0)),\n",
+       " ('B00027D98Q',\n",
+       "  'Ardell Brow &amp; Lash Growth Accelerator Treatment Gel 7ml/0.25oz',\n",
+       "  (71, 21, 70, 184, 0)),\n",
+       " ('B00027DDEQ',\n",
+       "  'Clean &amp; Clear Morning Glow Moisturizer, SPF 15, 4 Ounce',\n",
+       "  (78, 135, 216, 53, 0)),\n",
+       " ('B00027DDOQ',\n",
+       "  'Clean &amp; Clear Clear Advantage Acne Spot Treatment, 0.75 Ounce',\n",
+       "  (78, 135, 122, 60, 0)),\n",
+       " ('B00027DHYM',\n",
+       "  'Natures Cure Body Acne Treatment Spray 3.5 Oz',\n",
+       "  (144, 103, 124, 241, 0)),\n",
+       " ('B00027DMI8',\n",
+       "  'RoC Retinol Correxion Deep Wrinkle Night Cream, 1-Ounce',\n",
+       "  (12, 170, 59, 91, 0)),\n",
+       " ('B00027DMJW',\n",
+       "  'Roc Retinol Correxion Deep Wrinkle Daily Moisturizer, SPF 30, 1-ounce Tube',\n",
+       "  (93, 135, 75, 152, 0)),\n",
+       " ('B00027DMLK',\n",
+       "  'Frownies Corners Of Eyes And Mouth, 144 Patches',\n",
+       "  (228, 247, 227, 34, 0)),\n",
+       " ('B00027DMSI',\n",
+       "  'Frownies Forehead &amp; Between Eyes, 144 Patches',\n",
+       "  (29, 145, 162, 169, 0)),\n",
+       " ('B00027EG9C',\n",
+       "  'Neutrogena Fresh Foaming Cleanser, 6.7 Ounce',\n",
+       "  (144, 55, 3, 241, 0)),\n",
+       " ('B00027YZKM', 'Mirror* Hand* Large* Black', (55, 161, 153, 34, 0)),\n",
+       " ('B00028F0NM',\n",
+       "  'Alba Botanica Maximum Very Emollient Body Lotion, 12 Ounce Bottle',\n",
+       "  (45, 96, 65, 60, 0)),\n",
+       " ('B00028LN1K',\n",
+       "  \"GRANDPA'S BRANDS, Pine Tar Soap Bath Size - 4.25 oz\",\n",
+       "  (32, 161, 24, 220, 0)),\n",
+       " ('B00028LURW', 'Henna Red 4 Ounces', (45, 21, 216, 77, 0)),\n",
+       " ('B00028LV4Y',\n",
+       "  'Natural Missst Herbal Hairspray Mist-Super Hold Aubrey Organics 8 oz Spray',\n",
+       "  (71, 99, 162, 171, 0)),\n",
+       " ('B00028M3N2',\n",
+       "  'Now Foods Essential Oil, Lemon, 4 Fluid Ounce',\n",
+       "  (63, 59, 67, 202, 0)),\n",
+       " ('B00028MLG6',\n",
+       "  'NOW Foods Apricot Kernel Oil (Liquid), 16 oz',\n",
+       "  (252, 202, 3, 100, 0)),\n",
+       " ('B00028MLHA',\n",
+       "  'Cocoa Butter with jojoba oil - 100% Pure 6.5 fl.oz',\n",
+       "  (71, 80, 22, 170, 0)),\n",
+       " ('B00028O7ZO',\n",
+       "  \"Castile Liquid Soap-Lavender Dr. Bronner's 1 Gallon Liquid\",\n",
+       "  (105, 63, 233, 219, 0)),\n",
+       " ('B00028OC4K',\n",
+       "  'Throughly Clean Face Wash - Original - 8.5 oz. (250 ml) - Liquid',\n",
+       "  (71, 134, 246, 37, 0)),\n",
+       " ('B00028OPZ6',\n",
+       "  'GPB (Glycogen Protein Balance) Conditioner Jumbo Size - 16 oz - Liquid',\n",
+       "  (71, 134, 162, 91, 0)),\n",
+       " ('B00028OQZA',\n",
+       "  'Sparkling Glacier Therapy Complexion Mist Aubrey Organics 3.4 oz (100ml) Liquid',\n",
+       "  (71, 249, 122, 131, 0)),\n",
+       " ('B00028OSI0', 'Aloe Life Skin Gel and Herbs, 8 oz', (100, 122, 71, 160, 0)),\n",
+       " ('B00028OTAM',\n",
+       "  'Gabriel Cosmetics Inc. - Moisturizing Liquid Foundation Soft Beige 18 SPF - 1 oz.',\n",
+       "  (192, 121, 116, 103, 0)),\n",
+       " ('B00028OTNY',\n",
+       "  'Mandarin Magic Hair Gel Aubrey Organics 8 oz Liquid',\n",
+       "  (63, 134, 246, 171, 0)),\n",
+       " ('B00028OU5Q', 'Blue Green Algae Hair Mask 4 oz', (247, 134, 43, 236, 0)),\n",
+       " ('B00028PDWK',\n",
+       "  'Apricot Gentle Facial Scrub, 4 Ounce',\n",
+       "  (245, 103, 231, 12, 0)),\n",
+       " ('B00028PFF0', 'WITCH HAZEL PADS EA 1/60 PADS', (216, 161, 246, 104, 0)),\n",
+       " ('B00028PXAC', 'Viobin Wheat Germ Oil 16 oz', (132, 23, 163, 195, 0)),\n",
+       " ('B0002A65LQ',\n",
+       "  'Philosophy Help Me Retinol Night Treatment, 1.05 Ounce',\n",
+       "  (233, 159, 201, 206, 0)),\n",
+       " ('B0002B0R14',\n",
+       "  'H2Ocean 1.5 FL OZ Purified Ocean Salt Water Piercing Aftercare',\n",
+       "  (43, 46, 71, 99, 0)),\n",
+       " ('B0002BB3YO',\n",
+       "  'NaturalCare Homeopathic Ultra Vein-Gard Leg Therapy Cream, 2.25-Ounce Package',\n",
+       "  (63, 180, 219, 114, 0)),\n",
+       " ('B0002BPIZO',\n",
+       "  'Philosophy Pure Grace Shampoo, Bath &amp; Shower gel, 16 Ounces',\n",
+       "  (19, 134, 139, 91, 0)),\n",
+       " ('B0002CTSRM', 'Amber Butter', (119, 196, 153, 206, 0)),\n",
+       " ('B0002DLEJG',\n",
+       "  'Nailtek Foundation No.2 Ridge-Filling Nail Strengthener Base Coat, 0.5 Fluid Ounce',\n",
+       "  (65, 122, 163, 236, 0)),\n",
+       " ('B0002DNZAC', 'MAC Eye Shadow Frost Satin Taupe', (225, 189, 227, 195, 0)),\n",
+       " ('B0002DO1RI',\n",
+       "  'MAC Studio Fix Powder Plus Foundation NC35 for Women, 0.52 Ounce',\n",
+       "  (91, 149, 75, 91, 0)),\n",
+       " ('B0002DO1RS',\n",
+       "  'MAC Studio Fix Powder Plus Foundation NC45',\n",
+       "  (91, 149, 163, 135, 0)),\n",
+       " ('B0002DPG1I', 'Fat Girl Slim by Bliss', (63, 247, 231, 202, 0)),\n",
+       " ('B0002DTV0K',\n",
+       "  'Emu Oil Soap Bar (110g) Brand: SoapWorks',\n",
+       "  (32, 96, 67, 114, 0)),\n",
+       " ('B0002DUSR0',\n",
+       "  'PediFix Visco GEL Toe Spacers, Medium',\n",
+       "  (28, 228, 122, 160, 0)),\n",
+       " ('B0002DUSUW',\n",
+       "  'PediFix Double-Toe Straightener #P57',\n",
+       "  (80, 131, 234, 114, 0)),\n",
+       " ('B0002E4WOO',\n",
+       "  \"Miracle of Aloe Miracle Hand Repair Cream 8 Oz Relieve Dry, Cracked, Flacking Hands Immediately! Therapeutic Formula Contains 60% Ultra Aloe - The Purest Most Potent Form of Whole Leaf Aloe Vera Gel. Fast Acting Relief, Say Good Bye to Dry, Cracked Hands Now! Keep Your Hands Healthy &amp; Warm this Winter! Reduces Flaking and Redness, Use on Hands, Elbows and Knees, Exclusive Fast Acting Formula Penetrates Deep Into Damaged Skin Layers to Moisturize Where It's Needed Most. Leaves Hands Feeling Silky Smooth and Comfortable. Olay, Ponds, Vaseline, Nivea, Eucerin, Aveeno.\",\n",
+       "  (247, 21, 246, 100, 0)),\n",
+       " ('B0002EBI82', 'Toppik Hair Fattener, 4-Ounce', (62, 145, 138, 188, 0)),\n",
+       " ('B0002EZXUG',\n",
+       "  'Vaseline Intensive Care Moisturizing Bath Beads, Gentle Breeze, 24 Ounces',\n",
+       "  (44, 122, 139, 236, 0)),\n",
+       " ('B0002F1H6E',\n",
+       "  'Jergens All-Purpose Face Cream 425g/15oz',\n",
+       "  (117, 196, 74, 75, 0)),\n",
+       " ('B0002FBOLW',\n",
+       "  'Atopalm Intensive Moisturizing Cream, 3.4 fl. Ounce',\n",
+       "  (239, 152, 138, 53, 0)),\n",
+       " ('B0002FCCH2',\n",
+       "  'Suave Naturals Shampoo, Tropical Coconut  - 22.5oz.',\n",
+       "  (41, 120, 10, 53, 0)),\n",
+       " ('B0002FCD5I',\n",
+       "  'Suave Naturals Conditioner, Tropical Coconut - 22.5oz.',\n",
+       "  (75, 4, 208, 219, 0)),\n",
+       " ('B0002FCDHQ',\n",
+       "  'Suave For Kids Double Dutch Apple Detangler Spray Conditioner 10.5Ounces',\n",
+       "  (115, 255, 22, 152, 0)),\n",
+       " ('B0002G214U', 'Conair Soft Bonnet Hair Dryer', (98, 129, 219, 114, 0)),\n",
+       " ('B0002G21GI',\n",
+       "  'Ion Shine&reg; by Conair Instant Heat Compact Styling Setter',\n",
+       "  (220, 66, 70, 60, 0)),\n",
+       " ('B0002JG2NI',\n",
+       "  'Home Health Castor Oil Cold Pressed &amp; Cold Processed 8 fl. oz.',\n",
+       "  (128, 159, 3, 37, 0)),\n",
+       " ('B0002JGIZA',\n",
+       "  'Avalon Organics Night Cream Lavender Luminosity -- 2 oz',\n",
+       "  (93, 4, 227, 217, 0)),\n",
+       " ('B0002JGU30',\n",
+       "  'Avalon Organics Hand &amp; Body Lotion, Peppermint, 12 Ounce',\n",
+       "  (104, 234, 67, 170, 0)),\n",
+       " ('B0002JHI1I',\n",
+       "  'Avalon Organics CoQ10 Facial Cleansing Milk -- 8.5 fl oz',\n",
+       "  (218, 145, 43, 169, 0)),\n",
+       " ('B0002JIHT0',\n",
+       "  'Home Health Antidandruff Shampoo, Everclean, Unscented, 8-Ounce',\n",
+       "  (119, 196, 234, 202, 0)),\n",
+       " ('B0002JISX0',\n",
+       "  'Earth Science A-D-E Creamy Fruit Oil Cleanser 8 oz.',\n",
+       "  (117, 98, 231, 219, 0)),\n",
+       " ('B0002JIUDS',\n",
+       "  'Nutricology Ox Bile, 500 mg,  100 Vegetarian Capsules',\n",
+       "  (233, 128, 70, 219, 0)),\n",
+       " ('B0002JKPA4', 'Caruso C97958 ION Steam Hairsetter', (215, 66, 139, 116, 0)),\n",
+       " ('B0002JKQ8K',\n",
+       "  'Caruso Professional Molecular Steam Rollers with Shields, Small (6-Pack)',\n",
+       "  (80, 55, 226, 114, 0)),\n",
+       " ('B0002JMZ7A',\n",
+       "  'Yves Saint Laurent Volume Effet Faux Cils Luxurious Mascara for a False Lash Effect 1 High Density Black',\n",
+       "  (132, 164, 43, 13, 0)),\n",
+       " ('B0002JNRCC',\n",
+       "  'Redken All Soft Conditioner Unisex Conditioner, 8.5 Ounce',\n",
+       "  (99, 66, 24, 76, 0)),\n",
+       " ('B0002KS5VE',\n",
+       "  'Body Fantasies Body Spray for Women, Cotton Candy Fantasy Fragrance, 8 Ounce',\n",
+       "  (162, 255, 59, 219, 0)),\n",
+       " ('B0002L2JLU',\n",
+       "  'Topix Pharm SPF 40 Citrix Antioxidant Sunscreen, 3.5 Fluid Ounce',\n",
+       "  (44, 236, 234, 34, 0)),\n",
+       " ('B0002M9OYY',\n",
+       "  'Fake Bake Passion Fruit Body Polish 6 oz.',\n",
+       "  (47, 3, 162, 103, 0)),\n",
+       " ('B0002NIAC0',\n",
+       "  'Obagi C-Exfoliating Day Lotion 2 fl oz.',\n",
+       "  (93, 192, 254, 13, 0)),\n",
+       " ('B0002OCNMC',\n",
+       "  'Goat Milk Soap - Original (3 Pack) Canus Vermont 3 Bar Soap',\n",
+       "  (18, 135, 65, 15, 0)),\n",
+       " ('B0002OMOIA',\n",
+       "  'Brahmi Oil - Ayurvedic Hair Growth massage oil',\n",
+       "  (252, 24, 216, 152, 0)),\n",
+       " ('B0002PCH2M',\n",
+       "  'Focus 21 Sea Plasma All Purpose Skin and Hair Moisturizing Spray 12 oz',\n",
+       "  (44, 255, 219, 152, 0)),\n",
+       " ('B0002Q8W9I',\n",
+       "  'NEW cnd Nail Design Stickey Base Coat 0.33Oz',\n",
+       "  (116, 120, 33, 13, 0)),\n",
+       " ('B0002QB1SC',\n",
+       "  'Creative Nail Design Creative Nail Solar Oil, 0.5 Ounce',\n",
+       "  (94, 46, 67, 77, 0)),\n",
+       " ('B0002RI2PG',\n",
+       "  'Dermablend Loose Setting Powder Original, 1 Ounce',\n",
+       "  (194, 47, 65, 37, 0)),\n",
+       " ('B0002RPTX4',\n",
+       "  'Shampoo Scalp Massage Brush- 1 Brush',\n",
+       "  (48, 255, 153, 160, 0)),\n",
+       " ('B0002RYHTQ',\n",
+       "  'Spa Sister Womens Cotton Moisture Enhancing Overnight Gloves',\n",
+       "  (99, 29, 140, 99, 0)),\n",
+       " ('B0002SA9BU',\n",
+       "  'Best Hair Growth Vitamins NEW ADVANCED Hair Formula 37 Hair Vitamins for Faster Growing Healthy Hair 1 month Supply 60 Capsules',\n",
+       "  (12, 228, 24, 91, 0)),\n",
+       " ('B0002SGNOM',\n",
+       "  'California Tan Sunsets Tanning Eyewear 1-PR Random Picked Colors',\n",
+       "  (116, 25, 75, 13, 0)),\n",
+       " ('B0002SGSNI',\n",
+       "  'China Glaze Strong Adhesion Base Coat',\n",
+       "  (152, 159, 24, 220, 0)),\n",
+       " ('B0002SI7DW',\n",
+       "  'NAIL TEK Regular Size Crystal File with Free Blue Case',\n",
+       "  (113, 96, 140, 195, 0)),\n",
+       " ('B0002SISY0',\n",
+       "  'Spornette My Favorite Mini Nylon Tipped Paddle Brush',\n",
+       "  (155, 145, 254, 170, 0)),\n",
+       " ('B0002SQ6YO',\n",
+       "  'Sponge Company Cosmetic Silk Sponge 1',\n",
+       "  (155, 236, 122, 120, 0)),\n",
+       " ('B0002TSCZY',\n",
+       "  'Solia Tourmaline Ceramic Ion Flat Iron (1&quot;)',\n",
+       "  (181, 3, 65, 103, 0)),\n",
+       " ('B0002TSD08',\n",
+       "  'Solia Professional Ceramic Ion Flat Iron (1-3/4&quot;)Solia',\n",
+       "  (9, 77, 234, 169, 0)),\n",
+       " ('B0002UEDWE', 'Evian Facial Water Spray 1.7 Oz', (252, 196, 227, 34, 0)),\n",
+       " ('B0002VHBTU', 'Thermal Spa Hair Wrap - White', (4, 3, 139, 13, 0)),\n",
+       " ('B0002VJIH8',\n",
+       "  'Best Hair Vitamin to Grow Hair Faster with Fast Grow Ethnic Hair Growth Enhancer 90 capsules 30 Day Supply',\n",
+       "  (45, 8, 67, 169, 0)),\n",
+       " ('B0002VQ0WO', 'Hair Dryer Stand', (200, 149, 226, 104, 0)),\n",
+       " ('B0002WUHTU',\n",
+       "  'Nailtiques Nail Protein Formula 2 Plus',\n",
+       "  (194, 180, 163, 103, 0)),\n",
+       " ('B0002X4F0Q',\n",
+       "  'PCA Skin Blemish Control Bar (Phaze 32), 3.3 Ounce',\n",
+       "  (94, 255, 75, 202, 0)),\n",
+       " ('B0002X9038',\n",
+       "  'PCA Skin Pigment Gel (Phaze 13), 1 Fluid Ounce',\n",
+       "  (63, 202, 70, 103, 0)),\n",
+       " ('B0002XBTVO', 'PCA Skin Clearskin, 1.7 Ounce', (177, 134, 155, 103, 0)),\n",
+       " ('B0002XG9X2',\n",
+       "  'Panasonic EH2511A Pore Cleanser with Micro-fine Mist',\n",
+       "  (174, 196, 71, 60, 0)),\n",
+       " ('B0002XY3QC', 'duri Miracote .61oz', (79, 135, 65, 135, 0)),\n",
+       " ('B0002YE8F2',\n",
+       "  'Revlon RV2513 Color Swirl Detangling Comb, Colors may vary',\n",
+       "  (132, 183, 163, 15, 0)),\n",
+       " ('B0002YE8IO',\n",
+       "  'Revlon RV2641 Soft Touch Porcupine Cushion Brush, Colors May Vary',\n",
+       "  (132, 105, 140, 13, 0)),\n",
+       " ('B0002YE8V6',\n",
+       "  'Vidal Sassoon VS7963 Ionic Thermal All Purpose Brush',\n",
+       "  (47, 60, 163, 231, 0)),\n",
+       " ('B0002YE8WA',\n",
+       "  'Vidal Sassoon VS14434 No Headache Headbands (2 Headbands)',\n",
+       "  (113, 74, 112, 246, 0)),\n",
+       " ('B0002YFQ6W',\n",
+       "  'Cloud Star Corporation Buddy Wash Lavender &amp; Mint',\n",
+       "  (79, 196, 147, 152, 0)),\n",
+       " ('B0002YLY9K', 'Obagi Nu-Derm Toner 6.7 oz.', (132, 3, 139, 77, 0)),\n",
+       " ('B0002Z0R9C',\n",
+       "  'Spornette No.215 Super Looper Hair Brush',\n",
+       "  (200, 228, 208, 116, 0)),\n",
+       " ('B0002Z0U5I',\n",
+       "  'Helen of Troy Professional Tangle-Free Hot Air Brush Iron',\n",
+       "  (253, 162, 246, 99, 0)),\n",
+       " ('B0002Z0UA8',\n",
+       "  'FusionBeauty LipFusion Micro-Injected Collagen Lip Plump Color Shine, Summer',\n",
+       "  (250, 125, 122, 103, 0)),\n",
+       " ('B0002Z1JJ4', 'Acquarella Remover', (232, 145, 226, 220, 0)),\n",
+       " ('B0002Z1JK8', 'Acquarella Conditioner', (232, 80, 254, 194, 0)),\n",
+       " ('B0002Z81O0',\n",
+       "  'Peter Thomas Roth Max Complexion Correction Pads? (60 Pads)',\n",
+       "  (56, 89, 3, 12, 0)),\n",
+       " ('B0002Z8GEK', 'Gena Hoof Lacquer, 5 Ounce', (39, 96, 24, 170, 0)),\n",
+       " ('B0002Z8HAI',\n",
+       "  'Essie Nail Lacquer, Sugar Daddy, 0.5 Fluid Ounce',\n",
+       "  (152, 3, 139, 37, 0)),\n",
+       " ('B0002Z8IV6',\n",
+       "  'Ardell Hair Color Bottle, Unred, 0.25 Ounce',\n",
+       "  (177, 21, 33, 103, 0)),\n",
+       " ('B0002Z8LLI',\n",
+       "  'Sensories Smoother Passionflower and Aloe Leave In Texturizing Condition By Rusk, 33.8 Ounce',\n",
+       "  (85, 247, 71, 77, 0)),\n",
+       " ('B0002Z8MLC', 'ISO Bouncy Creme 8.5 oz.', (127, 141, 226, 206, 0)),\n",
+       " ('B0002Z8NJS',\n",
+       "  'Essie Nail Lacquer, Blanc, 0.5 Fluid Ounce',\n",
+       "  (152, 3, 162, 76, 0)),\n",
+       " ('B0002Z8OUG', 'Ruby Stone Nail File', (89, 211, 140, 206, 0)),\n",
+       " ('B0002Z8P1E', 'Quantum Classic Extra Body Acid Perm', (117, 173, 24, 91, 0)),\n",
+       " ('B0002Z8P8M', \"L'Oreal ColorZap Haircolor Remover\", (250, 21, 40, 53, 0)),\n",
+       " ('B0002Z8QG8',\n",
+       "  'Denman Styling Brush, Heavy Weight, 9-Row',\n",
+       "  (201, 122, 70, 160, 0)),\n",
+       " ('B0002Z8QHW', 'Creative Nail Design SolarOil .5oz', (141, 8, 163, 56, 0)),\n",
+       " ('B0002Z8RGC', 'Blax 4 mm Ponytail Holders - Black', (55, 79, 138, 169, 0)),\n",
+       " ('B0002Z8SE8',\n",
+       "  'Spilo: MISC Big Bondini Plus Brush-On Glue, 0.5 oz',\n",
+       "  (79, 161, 219, 133, 0)),\n",
+       " ('B0002Z8SZW',\n",
+       "  'Bain De Terre Serum Anti-Frizz Recovery Complex 1.7 oz.',\n",
+       "  (154, 202, 33, 195, 0)),\n",
+       " ('B0002Z90RM',\n",
+       "  'Rusk Thick Body and Texture Amplifier, 6 Ounce',\n",
+       "  (117, 100, 70, 114, 0)),\n",
+       " ('B0003009NK',\n",
+       "  'Hai Classic Convertible Ceramic Flat Iron, 1-1/4 Inch',\n",
+       "  (241, 196, 17, 34, 0)),\n",
+       " ('B000629ZHG', 'Tinted Moisturizer - Almond', (132, 103, 153, 169, 0)),\n",
+       " ('B00062A0JI',\n",
+       "  'Laura Mercier Secret Concealer # 1 0.08oz',\n",
+       "  (132, 62, 138, 169, 0)),\n",
+       " ('B00062ADJA',\n",
+       "  'Laura Mercier Loose Setting Powder - Ivory - 29g/1oz',\n",
+       "  (132, 159, 162, 34, 0)),\n",
+       " ('B0006433MM',\n",
+       "  \"18'' Detachable Long Handle Natural Boar Bristle Brush\",\n",
+       "  (28, 3, 201, 77, 0)),\n",
+       " ('B00064A5L4',\n",
+       "  'Be Delicious by Donna Karan Eau De Parfum Spray 1 oz for Women',\n",
+       "  (59, 202, 122, 15, 0)),\n",
+       " ('B00064H44K',\n",
+       "  'Vanderbilt by Gloria Vanderbilt for Women - 3.3 Ounce EDT Spray',\n",
+       "  (8, 120, 216, 27, 0)),\n",
+       " ('B00065AFFE',\n",
+       "  'Conair 1875 Watt Ergonomic Handle Hair Dryer',\n",
+       "  (226, 103, 254, 103, 0)),\n",
+       " ('B00066D2JE',\n",
+       "  'Giovanni Conditioner, Smooth as Silk for Damaged Hair, 8.5 fl oz Bottle',\n",
+       "  (13, 18, 27, 170, 0)),\n",
+       " ('B00066D4M4',\n",
+       "  'Giovanni Direct Leave-In Conditioner, Weightless moisture conditioner 8.5 fl oz Bottle',\n",
+       "  (173, 2, 40, 100, 0)),\n",
+       " ('B00066LD7C',\n",
+       "  'Xenna Curlaway Gradual Curl Relaxer Green Apple Fragrance -- 6 oz',\n",
+       "  (92, 173, 65, 53, 0)),\n",
+       " ('B00066YC34',\n",
+       "  'Conair Supreme Triple Curling Iron Pack  - 1/2 inch, 3/4 inch and 1 inch',\n",
+       "  (220, 122, 43, 206, 0)),\n",
+       " ('B000674XN2',\n",
+       "  'Laura Mercier Secret Brightener - Secret Brightener #1, .05 oz.',\n",
+       "  (132, 3, 24, 77, 0)),\n",
+       " ('B000674XNM',\n",
+       "  'Laura Mercier Secret Brightening Powder - Secret Brightening Powder #1, 0.14 oz.',\n",
+       "  (132, 46, 67, 169, 0)),\n",
+       " ('B000674Z8A',\n",
+       "  'Laura Mercier Accessories - Sponge, 4-pack',\n",
+       "  (155, 60, 82, 202, 0)),\n",
+       " ('B00067EJ4U', 'Laura Mercier Foundation Primer', (164, 248, 131, 236, 0)),\n",
+       " ('B00067YSLO',\n",
+       "  'Remington Wet 2 Straight 2&quot; Wide Plate Wet/Dry Ceramic Hair Straightening Iron with Tourmaline',\n",
+       "  (241, 40, 59, 169, 0)),\n",
+       " ('B00068A0AQ',\n",
+       "  'Straight Sexy Hair Smooth &amp; Seal Spray by Sexy Hair for Unisex - 8.1 Ounce Hairspray',\n",
+       "  (195, 3, 227, 114, 0)),\n",
+       " ('B00068AZ88',\n",
+       "  'CombaColor Quick Hair Color Applicator (Comb-a-Color)',\n",
+       "  (67, 4, 40, 12, 0)),\n",
+       " ('B00068CG3U', 'Philosophy The Microdelivery Peel', (235, 196, 254, 202, 0)),\n",
+       " ('B00069FJUG',\n",
+       "  'Philosophy Supernatural Airbrushed Canvas Powder, SPF 15, Bronze',\n",
+       "  (13, 122, 40, 38, 0)),\n",
+       " ('B0006B12KK', 'NARS Velvet Matte Lip Pencil, Bahama', (5, 87, 153, 167, 0)),\n",
+       " ('B0006B66D8',\n",
+       "  'Super Solano Professional Hair Dryer - Black',\n",
+       "  (80, 47, 24, 159, 0)),\n",
+       " ('B0006BDMIU', 'Caudalie Beauty Elixir 1 fl oz.', (44, 3, 27, 103, 0)),\n",
+       " ('B0006BDO5G',\n",
+       "  'Peter Thomas Roth Sulfur Cooling Masque 5 Ounce.',\n",
+       "  (102, 47, 33, 169, 0)),\n",
+       " ('B0006BDO5Q',\n",
+       "  'Peter Thomas Roth Glycolic Acid 10% Hydrating Gel, 2 Ounce',\n",
+       "  (235, 135, 153, 77, 0)),\n",
+       " ('B0006BDQDG',\n",
+       "  'Peter Thomas Roth AHA/BHA Acne Clearing Gel 2 fl oz',\n",
+       "  (56, 3, 71, 160, 0)),\n",
+       " ('B0006D3IAU', 'Cococare Coconut Oil, 9 Ounce', (161, 161, 71, 103, 0)),\n",
+       " ('B0006DS3KU',\n",
+       "  'Ojon Restorative Hair Treatment 4.6 oz (140 g)',\n",
+       "  (242, 47, 122, 103, 0)),\n",
+       " ('B0006FMK98',\n",
+       "  'Pharmaceutical Specialties Free and Clear Shampoo 12 oz.',\n",
+       "  (93, 103, 216, 103, 0)),\n",
+       " ('B0006FMKB6', 'Robathol Bath Oil - 16 oz', (102, 62, 192, 53, 0)),\n",
+       " ('B0006G2EX4', 'Obagi Foaming Gel Cleanser-6.7 oz', (144, 202, 24, 206, 0)),\n",
+       " ('B0006GBEYE',\n",
+       "  'Coretex SunX SPF30 Sunscreen with Towelettes - 25 Individual Foil Pouch with Towelettes/Box, PABA Free, Oil-free, Water and Sweat Resistant, UVA/UVB Protection',\n",
+       "  (226, 103, 33, 202, 0)),\n",
+       " ('B0006GZAKI',\n",
+       "  'Honeysuckle Rose Moisturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
+       "  (71, 47, 162, 171, 0)),\n",
+       " ('B0006GZBMA',\n",
+       "  'White Camellia Ultra-Smoothing Conditioner Aubrey Organics 11 oz Liquid',\n",
+       "  (71, 47, 162, 169, 0)),\n",
+       " ('B0006GZC4W',\n",
+       "  'Aubrey Organics - Island Natural Conditioner, 11 fl oz liquid',\n",
+       "  (71, 180, 162, 15, 0)),\n",
+       " ('B0006GZCVK',\n",
+       "  'Honeysuckle Rose Conditioner Aubrey Organics 11 oz Liquid',\n",
+       "  (201, 134, 59, 77, 0)),\n",
+       " ('B0006GZDE6',\n",
+       "  'Aubrey Organics green tea Clarifying Shampoo',\n",
+       "  (71, 134, 234, 159, 0)),\n",
+       " ('B0006GZDEQ',\n",
+       "  'White Camellia Ultra-Smoothing Shampoo 11oz',\n",
+       "  (71, 134, 246, 104, 0)),\n",
+       " ('B0006GZE0O',\n",
+       "  'Calaguala Texturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
+       "  (71, 134, 246, 152, 0)),\n",
+       " ('B0006HCE60',\n",
+       "  'Cococare 100% Vitamin E Oil, 1 Ounce',\n",
+       "  (133, 180, 201, 53, 0)),\n",
+       " ('B0006HRCL2', '4 Dozen (48) Long Pink Perm Rods', (241, 149, 67, 91, 0)),\n",
+       " ('B0006I9YK8', 'Baggallini Complete Cosmetic Bagg', (34, 62, 59, 77, 0)),\n",
+       " ('B0006IHDQ0',\n",
+       "  'Liz Claiborne Curve Crush EDC Spray 4.2 oz',\n",
+       "  (92, 173, 201, 76, 0)),\n",
+       " ('B0006II74C',\n",
+       "  \"Nature's Secret 5-Day Fast and Cleanse Kit\",\n",
+       "  (252, 247, 139, 34, 0)),\n",
+       " ('B0006IJA5C',\n",
+       "  'Jhirmack Silver Brightening Shampoo 20 Oz.',\n",
+       "  (63, 2, 140, 13, 0)),\n",
+       " ('B0006IXSG4',\n",
+       "  'John Varvatos by John Varvatos for Men - 7 ml EDT Splash',\n",
+       "  (121, 223, 122, 219, 0)),\n",
+       " ('B0006LNC2G',\n",
+       "  'MAC Eye Kohl Smolder Eye Liner for Women, 0.048 Ounce',\n",
+       "  (81, 115, 24, 195, 0)),\n",
+       " ('B0006LNJ56', 'MAC Satin Lipstick - Rebel', (81, 20, 24, 170, 0)),\n",
+       " ('B0006LNKXW',\n",
+       "  'MAC Tinted Lipglass -- Viva Glam V (Boxed) 4.8g/.17oz',\n",
+       "  (189, 60, 163, 202, 0)),\n",
+       " ('B0006LNKYG', 'MAC Matte Lipstick - Russian Red', (92, 129, 82, 170, 0)),\n",
+       " ('B0006LNMIU', 'MAC Powder Blush Fever', (152, 159, 163, 13, 0)),\n",
+       " ('B0006M5566', 'eb5 Facial Cream (4 Ounces)', (71, 129, 139, 114, 0)),\n",
+       " ('B0006M56BK',\n",
+       "  'Wilkinson Sword Double Edge single Razor Cartridge, 5 blades',\n",
+       "  (195, 211, 24, 77, 0)),\n",
+       " ('B0006MSVCG',\n",
+       "  'Burberry Brit ~ Women 3.3 oz / 100 ml Eau de Parfum Spray',\n",
+       "  (63, 236, 27, 219, 0)),\n",
+       " ('B0006N29AU',\n",
+       "  \"Dr. King's Natural Medicine Back, Muscle and Joint Relief, 2 Fluid Ounce\",\n",
+       "  (117, 59, 234, 53, 0)),\n",
+       " ('B0006NXBJ8', 'Mason Pearson Detangler', (89, 32, 71, 77, 0)),\n",
+       " ('B0006NY35E',\n",
+       "  'Pre de Provence Soap, Cucumber, 7 Ounce',\n",
+       "  (120, 149, 43, 76, 0)),\n",
+       " ('B0006NYCT6',\n",
+       "  'Algemarin Bubble Bath 750ml bubble bath by Algemarin',\n",
+       "  (47, 211, 254, 27, 0)),\n",
+       " ('B0006NYDOU',\n",
+       "  'Herbacin Kamille Hand Cream with Glycerine - 2.5 oz',\n",
+       "  (71, 173, 246, 131, 0)),\n",
+       " ('B0006O2IQ4',\n",
+       "  'Fragrances Of Ireland Inis The Energy Of The Sea Cologne Spray, 1.7 Fluid Ounce',\n",
+       "  (63, 115, 227, 13, 0)),\n",
+       " ('B0006O4MCM',\n",
+       "  'Suki - Concentrated Balancing Toner  4.0 fl oz',\n",
+       "  (102, 131, 216, 15, 0)),\n",
+       " ('B0006O9ZAG',\n",
+       "  'Palladio Herbal Dual Wet and Dry Foundation, Ivory Myrrh, 0.28 Ounce',\n",
+       "  (44, 103, 27, 236, 0)),\n",
+       " ('B0006OU06E', 'Semi Oval Wild Boar Hair Brush', (79, 161, 250, 99, 0)),\n",
+       " ('B0006OU078', 'Bass Brushes Facial Cleansing Brush', (195, 21, 33, 160, 0)),\n",
+       " ('B0006PJRP8', 'Zoya Nail Polish .5 oz. Lola', (194, 236, 65, 103, 0)),\n",
+       " ('B0006PJSES',\n",
+       "  'CND: Treatments/Prep Stickey Base Coat, 2.3 oz',\n",
+       "  (79, 122, 163, 123, 0)),\n",
+       " ('B0006PJSSO',\n",
+       "  'Essie Nail Lacquer, First Base Base Coat, 0.46 Fluid Ounce',\n",
+       "  (201, 21, 43, 195, 0)),\n",
+       " ('B0006PJTB0',\n",
+       "  'Seche Base Ridge Filling Base Coat .5 oz. [Health and Beauty]',\n",
+       "  (28, 96, 201, 219, 0)),\n",
+       " ('B0006PKGJY',\n",
+       "  'Alba Botanica Natural Even Advanced Daily Cream -- 2 oz',\n",
+       "  (94, 115, 70, 77, 0)),\n",
+       " ('B0006PLNSC', 'Zoya Nail Polish .50 fl oz Charisma', (230, 145, 201, 76, 0)),\n",
+       " ('B0006PLPE4', 'ZOYA Armor Topcoat/UV Block 0.5oz', (18, 21, 163, 76, 0)),\n",
+       " ('B0006PS3XU', 'Marianna Bobby Pins 1 lb. - Black', (200, 12, 124, 160, 0)),\n",
+       " ('B0006PS3Y4', 'Marianna Bobby Pins 1 lb. - Brown', (200, 12, 153, 195, 0)),\n",
+       " ('B0006PT6TU',\n",
+       "  'City Lips Advanced in NUDE York Lip Plumper',\n",
+       "  (99, 79, 24, 219, 0)),\n",
+       " ('B0006Q00IK',\n",
+       "  'For Pro Cozie Liners Hand or Foot 100-ct.',\n",
+       "  (155, 115, 22, 76, 0)),\n",
+       " ('B0006Q00ZI', 'Gena Healthy Hoof Cream, 4 Ounce', (201, 3, 65, 206, 0)),\n",
+       " ('B0006Q0102', 'Gena Nail Brite with Brush, 4 Ounce', (180, 180, 71, 170, 0)),\n",
+       " ('B0006Q01EI', 'Hot Sock Diffuser', (80, 80, 192, 103, 0)),\n",
+       " ('B0006Q01WK', 'King Dy-zoff Pads 80-ct.', (67, 89, 27, 77, 0)),\n",
+       " ('B0006Q0HEC',\n",
+       "  'Wen by Chaz Dean Tea Tree Cleansing Conditioner 16 oz.',\n",
+       "  (24, 245, 11, 72, 0)),\n",
+       " ('B0006Q23H6',\n",
+       "  'John Masters Organics Evening Primrose Shampoo for Dry Hair 8 fl oz / 236 ml',\n",
+       "  (44, 66, 139, 100, 0)),\n",
+       " ('B0006Q3NTS',\n",
+       "  'WEN by Chaz Dean Fig Cleansing Conditioner 16 oz',\n",
+       "  (24, 245, 65, 100, 0)),\n",
+       " ('B0006Q3O3S',\n",
+       "  'WEN&reg; Sweet Almond Mint Styling Creme 6oz',\n",
+       "  (120, 96, 124, 100, 0)),\n",
+       " ('B0006Q3P50',\n",
+       "  'WEN&reg; Sweet Almond Mint Texture Balm 3oz',\n",
+       "  (79, 161, 192, 21, 0)),\n",
+       " ('B0006SFXXK',\n",
+       "  'John Masters Organics - Detangler Citrus &amp; Neroli - 8 oz.',\n",
+       "  (12, 2, 246, 114, 0)),\n",
+       " ('B0006SFXYY',\n",
+       "  'John Masters Organics John Masters Organics Deep Scalp Follicle Treatment &amp; Volumizer for Thinning Hair 4.2 fl oz - 4.2 fl oz',\n",
+       "  (128, 47, 139, 76, 0)),\n",
+       " ('B0006U81GY', 'Cricket Ultra Clean UC130', (92, 80, 24, 236, 0)),\n",
+       " ('B0006V7T9I',\n",
+       "  'Bee Bar Lotion by Honey House Naturals - Vanilla Scent - Long Lasting Lotion Bar Moisturizes and Leaves Skin Smelling Fresh - 2 ounces',\n",
+       "  (232, 66, 226, 220, 0)),\n",
+       " ('B0006ZEVU4',\n",
+       "  'Paul Mitchell Tea Tree Special Shampoo Unisex, 16.9 Ounce',\n",
+       "  (109, 202, 71, 77, 0)),\n",
+       " ('B0006ZHCK0', \"Murphy's Oil Soap, 32-Ounce\", (228, 129, 139, 34, 0)),\n",
+       " ('B0006ZHK7A',\n",
+       "  'Pureology Hydrate Shampoo, 8.5 Ounce',\n",
+       "  (224, 202, 201, 15, 0)),\n",
+       " ('B00070E8IS',\n",
+       "  'Elchim Professional 2001 2000 Watt Classic Hair Dryer (Colors May Vary)',\n",
+       "  (201, 66, 153, 77, 0)),\n",
+       " ('B00070QF20',\n",
+       "  'AcneFree Clear Skin System, 3-Step Kit (Purifying Cleanser, Renewing Toner, Repair Lotion)',\n",
+       "  (21, 105, 24, 99, 0)),\n",
+       " ('B00074XC68',\n",
+       "  'Helen of Troy Mini 1/2 Inch Professional Curling Iron with Ergonomic Handle for Short Hair, Bangs &amp; Wisps (Model: 1503n)',\n",
+       "  (16, 96, 246, 169, 0)),\n",
+       " ('B0007506U2',\n",
+       "  'TIGI Catwalk Curl Collection Curlesque Curls Rock Amplifier, 5.07 Ounce, Packaging May Vary',\n",
+       "  (111, 96, 246, 182, 0)),\n",
+       " ('B00076X0BS',\n",
+       "  'Alba Botanica Revitalizing Green Tea Hawaiian Eye Gel, 1 Ounce Bottles',\n",
+       "  (45, 96, 201, 103, 0)),\n",
+       " ('B0007CIEBI',\n",
+       "  'Colorescience Pro Sunforgettable SPF 30 Brush- Fair 0.21 Oz',\n",
+       "  (109, 135, 254, 77, 0)),\n",
+       " ('B0007CWVLM',\n",
+       "  'Tweezerman  Spirit 2000 Styling Shears',\n",
+       "  (189, 185, 140, 116, 0)),\n",
+       " ('B0007CXWQU',\n",
+       "  'Aveda Blue Malva Conditioner, 8.5-Ounce Tube',\n",
+       "  (194, 96, 139, 170, 0)),\n",
+       " ('B0007CXWRE',\n",
+       "  'Aveda Brilliant Anti-Humectant Pomade, 2.6 Ounces',\n",
+       "  (47, 47, 254, 162, 0)),\n",
+       " ('B0007CXWTW', 'Aveda Clove Conditioner 8.5 Ounces', (126, 29, 138, 38, 0)),\n",
+       " ('B0007CXWUG', 'Aveda Confixor Liquid Gel, 8.5 Ounces', (24, 96, 216, 15, 0)),\n",
+       " ('B0007CXWWY',\n",
+       "  'Aveda Madder Root Conditioner 8.5 Ounces',\n",
+       "  (98, 46, 71, 42, 0)),\n",
+       " ('B0007CXWXI', 'Aveda Phomollient 6.7 Ounces', (98, 46, 246, 38, 0)),\n",
+       " ('B0007CXX82',\n",
+       "  'Biosilk Silk Therapy Serum, Packaging May Vary, 5.64 Ounces',\n",
+       "  (170, 47, 122, 15, 0)),\n",
+       " ('B0007CXXDC',\n",
+       "  'Biolage by Matrix Color Care Conditioner 33.8 Ounces',\n",
+       "  (132, 103, 227, 77, 0)),\n",
+       " ('B0007CXXDM',\n",
+       "  'Biolage by Matrix Color Care Shampoo 33.8 Ounces',\n",
+       "  (132, 247, 138, 53, 0)),\n",
+       " ('B0007CXXE6',\n",
+       "  'Biolage by Matrix Conditioning Balm 16.9 Ounces',\n",
+       "  (24, 248, 65, 104, 0)),\n",
+       " ('B0007CXXGO',\n",
+       "  'Biolage by Matrix Hydratherapie Hydrating Shampoo 33.8 Ounces',\n",
+       "  (191, 115, 162, 103, 0)),\n",
+       " ('B0007CXXIC',\n",
+       "  'Biolage by Matrix Normalizing Shampoo 33.8 Ounces',\n",
+       "  (191, 247, 67, 202, 0)),\n",
+       " ('B0007CXXJQ',\n",
+       "  'Biolage by Matrix Ultra-Hydrating Conditioning Balm 16.9 Ounces',\n",
+       "  (47, 121, 71, 101, 0)),\n",
+       " ('B0007CXXVE',\n",
+       "  'Nexxus Humectress Ultimate Moisturizing Conditioner 33.8 Ounces',\n",
+       "  (171, 47, 163, 169, 0)),\n",
+       " ('B0007CXXYG',\n",
+       "  'Paul Mitchell Awapuhi Shampoo, 33.8-Ounce Bottle',\n",
+       "  (47, 3, 65, 231, 0)),\n",
+       " ('B0007CXY5E',\n",
+       "  'Paul Mitchell Shampoo  1   33.8 Ounces',\n",
+       "  (99, 105, 24, 27, 0)),\n",
+       " ('B0007CXY5O',\n",
+       "  'Paul Mitchell Shampoo  3   16.9 Ounces',\n",
+       "  (99, 145, 153, 38, 0)),\n",
+       " ('B0007CXYAE',\n",
+       "  'Paul Mitchell The Detangler 33.8 Ounces',\n",
+       "  (99, 29, 70, 170, 0)),\n",
+       " ('B0007CXYP4',\n",
+       "  'Sebastian Collection Potion 9   5.1 Ounces',\n",
+       "  (170, 66, 67, 91, 0)),\n",
+       " ('B0007D2F3K', 'Pedi,Quick Salon Pedicure Kit', (200, 180, 234, 116, 0)),\n",
+       " ('B0007DHMBK',\n",
+       "  'Henna Black Cream Surya Nature, Inc 2.31 oz Cream',\n",
+       "  (128, 180, 43, 219, 0)),\n",
+       " ('B0007DHMCO',\n",
+       "  'Henna Burgundy Cream Surya Nature, Inc 2.31 oz Cream',\n",
+       "  (128, 47, 226, 160, 0)),\n",
+       " ('B0007IFB2W',\n",
+       "  'Spornette 25 Wood Handle &quot;Porcupine&quot; Brush With Genuine Boar Bristle * Made In Germany',\n",
+       "  (60, 161, 65, 27, 0)),\n",
+       " ('B0007IQMVG',\n",
+       "  'Digestive Advantage Intensive Bowel Support, 96 Counts Capsules',\n",
+       "  (70, 202, 231, 163, 0)),\n",
+       " ('B0007LE3EG', 'Total Hair Makeover Kit 90001', (174, 122, 254, 236, 0)),\n",
+       " ('B0007NIRUU',\n",
+       "  'Nexxus Humectress Ultimate Moisturizing Conditioner, 13.5Ounce Bottles',\n",
+       "  (198, 3, 192, 170, 0)),\n",
+       " ('B0007R7ABS',\n",
+       "  'Kenra Volume Spray 25 Super Hold Finishing Spray (Aerosol) (10 oz.)',\n",
+       "  (201, 100, 163, 76, 0)),\n",
+       " ('B0007RBYA6',\n",
+       "  'Philosophy Microdelivery Peel Pads, 60 Count',\n",
+       "  (151, 47, 22, 34, 0)),\n",
+       " ('B0007V6PFQ',\n",
+       "  'Brush - Large Oval Cushion Wood Bristles Wood Handle Bass Brushes 1 Brush',\n",
+       "  (253, 159, 219, 37, 0)),\n",
+       " ('B0007V9API', 'Bella B Tummy Honey Cream - 4 oz', (133, 80, 27, 170, 0)),\n",
+       " ('B0007W1R58',\n",
+       "  'Olay Regenerist Night Recovery Cream 1.7 Oz',\n",
+       "  (41, 196, 162, 34, 0)),\n",
+       " ('B0007WL21M',\n",
+       "  'PETER THOMAS ROTH - Anti-Aging Cleansing Gel 8.5oz',\n",
+       "  (185, 4, 124, 38, 0)),\n",
+       " ('B0007WZ7YU',\n",
+       "  'Elchim 2001hp High Pressure 2000 Watt Hair Dryer, White',\n",
+       "  (15, 115, 139, 114, 0)),\n",
+       " ('B0007X74CW',\n",
+       "  'Redken Hair Cleansing Cream Shampoo for All Hair Types, 10.1-Ounces',\n",
+       "  (24, 120, 153, 114, 0)),\n",
+       " ('B0007X74E0',\n",
+       "  'Smooth Down Heat Glide Protective Smoother Unisex Smoother by Redken, 5 Ounce',\n",
+       "  (128, 149, 227, 60, 0)),\n",
+       " ('B0007X74IQ',\n",
+       "  'Extreme Cat Protein Treatment Unisex Treatment by Redken, 5 Ounce',\n",
+       "  (252, 122, 37, 34, 0)),\n",
+       " ('B0007XFB2W', 'Original Sprout Miracle Detangler', (252, 173, 138, 99, 0)),\n",
+       " ('B0007YJ5QY',\n",
+       "  'AMERICAN CREW Daily Moisturizing Shampoo, 8.4 Ounce',\n",
+       "  (110, 3, 162, 27, 0)),\n",
+       " ('B00080DK86',\n",
+       "  'Glycolix Elite Treatment Pads 20 Percent 60 count',\n",
+       "  (133, 21, 40, 103, 0)),\n",
+       " ('B00081J4P8',\n",
+       "  'NO-AD Sunblock Lotion, SPF 15, 16 Ounces',\n",
+       "  (104, 96, 10, 38, 0)),\n",
+       " ('B00082379Q', 'Orly Polish Thinner', (70, 149, 55, 219, 0)),\n",
+       " ('B0008394GA', 'NARS Lip Liner Pencil, Jungle Red', (79, 196, 65, 21, 0)),\n",
+       " ('B000851N9E',\n",
+       "  'BurnOut - Ocean Tested Physical Sunscreen 30 SPF - 3.4 oz.',\n",
+       "  (12, 47, 17, 53, 0)),\n",
+       " ('B00085DX0Q',\n",
+       "  \"Grandpa's Soap Company Wonder Pine Tar Shampoo, 8 Ounce\",\n",
+       "  (164, 46, 246, 103, 0)),\n",
+       " ('B0008ENT8I',\n",
+       "  'ProVersa JWM6CF Wall Caddy Hair Dryer with 2-Speed and 3-Heat Settings, 1600-Watts, White Finish',\n",
+       "  (174, 21, 140, 77, 0)),\n",
+       " ('B0008F6ZDI',\n",
+       "  'Philosophy The Present Clear Makeup, 2 Ounce',\n",
+       "  (136, 122, 240, 182, 0)),\n",
+       " ('B0008FUY0I',\n",
+       "  'Flowery Swedish Clover Foot File * #530',\n",
+       "  (241, 134, 139, 15, 0)),\n",
+       " ('B0008GIZKS',\n",
+       "  'Malibu C Hard Water Wellness Shampoo, 1 Bottle, 9 oz',\n",
+       "  (133, 59, 70, 100, 0)),\n",
+       " ('B0008IV7BU',\n",
+       "  'American Crew American Crew Fiber Fiber - 3 oz',\n",
+       "  (80, 62, 24, 77, 0)),\n",
+       " ('B00092M2VO',\n",
+       "  'Andis Professional 1875 Watt Ceramic Ionic Hair Dryer - Black Chrome (82005)',\n",
+       "  (243, 115, 219, 34, 0)),\n",
+       " ('B00092M2XC',\n",
+       "  'Hot Tools Professional 1069S 1600 Watt Professional Turbo Hair Dryer, Silver/Black',\n",
+       "  (3, 103, 153, 114, 0)),\n",
+       " ('B00092M2ZU',\n",
+       "  'Ceramic Tools CT2555 1&quot; Professional Ceramic Flat Iron',\n",
+       "  (187, 59, 219, 34, 0)),\n",
+       " ('B00092M36I',\n",
+       "  'Ceramic Tools CT155S 1-1/2&quot; Dual Voltage Professional Ceramic Spring Curling Iron',\n",
+       "  (30, 161, 163, 170, 0)),\n",
+       " ('B00092M386',\n",
+       "  'Belson Profiles Spa Single Hand Nail Dryer P1013',\n",
+       "  (181, 8, 70, 195, 0)),\n",
+       " ('B00094O6D4',\n",
+       "  'LaLicious Sugar Souffle Body Scrub 16 fl oz.',\n",
+       "  (2, 2, 147, 100, 0)),\n",
+       " ('B000960QJA',\n",
+       "  'Vaseline Aloe Fresh Hydrating Body Lotion 20.3Oz',\n",
+       "  (102, 8, 65, 34, 0)),\n",
+       " ('B000977Q8I',\n",
+       "  'Bobbi Brown Bobbi Brown Shimmer Brick',\n",
+       "  (149, 211, 3, 114, 0)),\n",
+       " ('B000980PGM',\n",
+       "  'Dermalogica Daily Microfoliant, 2.6-Ounce',\n",
+       "  (133, 3, 59, 104, 0)),\n",
+       " ('B000980PH6',\n",
+       "  'Dermalogica Oil Control Lotion (2 oz.)',\n",
+       "  (245, 134, 226, 103, 0)),\n",
+       " ('B0009953JA',\n",
+       "  'Jerdon JP7507NB 8-Inch Two-Sided Swivel Wall Mount Mirror with 7x Magnification, 13.5-Inch Extension, Nickel Beaded Finish',\n",
+       "  (215, 66, 140, 231, 0)),\n",
+       " ('B00099E8ZA',\n",
+       "  'Neutrogena Shampoo, Anti-Residue Formula, 6 Ounce',\n",
+       "  (41, 115, 122, 53, 0)),\n",
+       " ('B00099QZOW',\n",
+       "  'Colorful Neutral Protein Filler 1.2 oz.',\n",
+       "  (47, 196, 201, 160, 0)),\n",
+       " ('B00099Z2OQ',\n",
+       "  'Giovanni Hair Care - Direct Leave-In Conditioner, 8.5 fl oz liquid',\n",
+       "  (13, 161, 139, 159, 0)),\n",
+       " ('B0009BVK0E',\n",
+       "  'Organix South Maximum Strength Neem Soap Bar 4 oz',\n",
+       "  (71, 134, 246, 202, 0)),\n",
+       " ('B0009DT39W',\n",
+       "  'Panasonic EH2351AC Heated Eyelash Curling Wand',\n",
+       "  (200, 131, 24, 116, 0)),\n",
+       " ('B0009DVDTU',\n",
+       "  'Revision Teamine Eye Complex, 0.5 Ounce',\n",
+       "  (141, 247, 67, 157, 0)),\n",
+       " ('B0009DVMAU', 'Lafes Deodorant Spray with MSM, 8oz', (44, 62, 65, 76, 0)),\n",
+       " ('B0009EILHG',\n",
+       "  'Biore Pore Perfect Blemish Fighting Ice Cleanser, Cools &amp; Clears , 6.7 fl oz (198 ml)',\n",
+       "  (154, 173, 106, 103, 0)),\n",
+       " ('B0009EILI0',\n",
+       "  'Biore Deep Cleansing Pore Strips Combo 14 Ea',\n",
+       "  (238, 180, 226, 103, 0)),\n",
+       " ('B0009EILKS',\n",
+       "  'Biore  Deep Cleansing Pore Strips , 14 Nose Strips',\n",
+       "  (155, 180, 122, 103, 0)),\n",
+       " ('B0009EILVM',\n",
+       "  'Zia Natural Skincare Ultimate Body Firming Treatment, 7 fl oz',\n",
+       "  (233, 202, 70, 27, 0)),\n",
+       " ('B0009ET3SC',\n",
+       "  'Aquis Microfiber Body Towel, Lisse Crepe, White (29 x 55-Inches)',\n",
+       "  (28, 120, 65, 91, 0)),\n",
+       " ('B0009ET4VI',\n",
+       "  'Beauty without Cruelty Vitamin C, Vitality Serum, 1-Ounce',\n",
+       "  (167, 213, 132, 37, 0)),\n",
+       " ('B0009ET5H6',\n",
+       "  \"Burt's Bees Poison Ivy Soap, 2-Ounce\",\n",
+       "  (119, 161, 246, 159, 0)),\n",
+       " ('B0009ET5JE',\n",
+       "  \"Burt's Bees  Lip Shimmer, Champagne, 0.09 oz.\",\n",
+       "  (65, 122, 254, 77, 0)),\n",
+       " ('B0009ET5O4',\n",
+       "  \"Burt's Bees Healthy Treatment Repair Serum, 1 Fluid Ounce\",\n",
+       "  (252, 247, 139, 53, 0)),\n",
+       " ('B0009ET6BQ',\n",
+       "  'derma e Hyaluronic Acid Day Cr&egrave;me, 2-Ounces',\n",
+       "  (56, 202, 65, 60, 0)),\n",
+       " ('B0009EXM52',\n",
+       "  'OPI: Treatment &amp; Finish Natural Nail Base Coat, 0.5 oz',\n",
+       "  (192, 145, 227, 135, 0)),\n",
+       " ('B0009EXMB6',\n",
+       "  \"Palmer's Sunless Tanner &amp; Instant Bronzer SPF 15 5.25oz\",\n",
+       "  (71, 145, 98, 206, 0)),\n",
+       " ('B0009EXONC',\n",
+       "  'Big Sexy Hair Volumizing Hairspray, Spray &amp; Play, 10.0 oz (284 g)',\n",
+       "  (195, 59, 234, 53, 0)),\n",
+       " ('B0009EXOO6',\n",
+       "  'HEALTHY SEXY SOY TRI-WHEAT LEAVE-IN CONDITIONER 8.5 OZ -PACKAGING MAY VARY',\n",
+       "  (58, 185, 234, 159, 0)),\n",
+       " ('B0009EXOP0',\n",
+       "  'SHORT SEXY HAIR CONTOL MANIAC WAX 1.8 OZ-PACKAGING MAY VARY',\n",
+       "  (192, 145, 41, 13, 0)),\n",
+       " ('B0009EXOXW',\n",
+       "  'Source Naturals Skin Eternal Serum, 1.7  Ounce',\n",
+       "  (102, 211, 65, 152, 0)),\n",
+       " ('B0009F3M6A',\n",
+       "  'Eucerin Dry Skin Therapy Foot Creme, Plus Intensive Repair, 3-Ounce Tubes (Pack of 3)',\n",
+       "  (239, 161, 27, 99, 0)),\n",
+       " ('B0009F3NGE',\n",
+       "  \"L'Oreal Dermo-Expertise Sublime Bronze Self-Tanning Gelee, Medium-Natural , 5 fl oz (150 ml)\",\n",
+       "  (44, 159, 254, 37, 0)),\n",
+       " ('B0009F3NYQ',\n",
+       "  'Neutrogena MicroMist Tanning Sunless Spray, Deep, 5.3 Ounce',\n",
+       "  (13, 21, 70, 100, 0)),\n",
+       " ('B0009F3O18',\n",
+       "  'Neutrogena Makeup Remover Cleansing Towelettes, 25 Count',\n",
+       "  (201, 8, 227, 77, 0)),\n",
+       " ('B0009F3O8Q',\n",
+       "  \"Palmer's Cocoa Butter Formula with Vitamin E, 13.5 fl oz (400 ml)\",\n",
+       "  (231, 180, 163, 152, 0)),\n",
+       " ('B0009F3OOA',\n",
+       "  'Queen Helene Refreshing Natural Facial Scrub Mint Julep -- 6 oz',\n",
+       "  (102, 211, 254, 15, 0)),\n",
+       " ('B0009F3OR2',\n",
+       "  'Redken All Soft Heavy Cream, Avocado Oil , 8.5 fl oz (250 ml)',\n",
+       "  (127, 161, 122, 100, 0)),\n",
+       " ('B0009F3OS6',\n",
+       "  'Redken Extreme Anti-Snap, Lipids/Proteins , 8.5 fl oz (250 ml)',\n",
+       "  (117, 173, 59, 60, 0)),\n",
+       " ('B0009F3OX6', 'Revlon Cuticle Nippers, Half-Jaw', (155, 89, 10, 195, 0)),\n",
+       " ('B0009F3R7E', \"Burt's Bees Hand Repair Kit\", (149, 191, 162, 27, 0)),\n",
+       " ('B0009FHJOG',\n",
+       "  'Talika Lash Conditioning Cleanser - 4.06 oz',\n",
+       "  (245, 173, 27, 76, 0)),\n",
+       " ('B0009FHJRS',\n",
+       "  'Toppik Regular Dark Brown Hair Building Fibers, 0.42 Ounce',\n",
+       "  (70, 122, 163, 114, 0)),\n",
+       " ('B0009G05W8', 'Cricket Cool Down Iron Travel Case', (17, 199, 219, 171, 0)),\n",
+       " ('B0009GGY96',\n",
+       "  'AcneFree Acne and Blackhead Terminator (.75 Ounce Tube)',\n",
+       "  (47, 115, 11, 116, 0)),\n",
+       " ('B0009H04LO', 'NARS Bronzer Blush Duo, Orgasm/Laguna', (219, 3, 153, 60, 0)),\n",
+       " ('B0009HIFRY',\n",
+       "  'Pravana Intense Therapy Leave-In Treatment 10.1 oz',\n",
+       "  (132, 47, 227, 170, 0)),\n",
+       " ('B0009HJBPE',\n",
+       "  'Yves Saint Laurent TOUCHE ECLATRadiant Touch 2 Luminous Ivory',\n",
+       "  (144, 159, 216, 182, 0)),\n",
+       " ('B0009I4J4G',\n",
+       "  'Lubriderm Daily Moisture Lotion with Shea and Cocoa Butter, 16  Ounce',\n",
+       "  (132, 138, 162, 34, 0)),\n",
+       " ('B0009I4M02', 'TIGI Bed Head Small Talk, 8 Ounce', (169, 159, 24, 34, 0)),\n",
+       " ('B0009I4MCU',\n",
+       "  'Pureology Anti-Fade Complex Hydrate Condition, 8.5 Ounce',\n",
+       "  (231, 122, 124, 15, 0)),\n",
+       " ('B0009I4MFW', 'Pureology Pure Volume Shampoo 10.1 oz', (24, 21, 122, 30, 0)),\n",
+       " ('B0009I4MG6',\n",
+       "  'Pureology Safeguard Your Color Purify Shampoo, 10.1 Ounces',\n",
+       "  (170, 145, 70, 103, 0)),\n",
+       " ('B0009I4MGQ',\n",
+       "  'Pureology Anti-Fade Complex Pure Volume Condition, 8.5 Ounce Bottle (Packaging May Vary)',\n",
+       "  (117, 47, 201, 99, 0)),\n",
+       " ('B0009I4MKW', 'Nexxus shampoo therappe, 33.8oz', (183, 173, 227, 53, 0)),\n",
+       " ('B0009IMRLS',\n",
+       "  'Christian Dior Diorshow Waterproof Mascara, No. 090 Black, 0.38 Ounce',\n",
+       "  (132, 161, 140, 76, 0)),\n",
+       " ('B0009ION0G',\n",
+       "  'TIGI Catwalk Your Highness Root Boost Spray, 8.1 Ounce',\n",
+       "  (1, 66, 55, 76, 0)),\n",
+       " ('B0009J6ER0',\n",
+       "  'Grow Hair Faster with Grow Shampoo and Conditioner for Faster Growing Hair',\n",
+       "  (12, 134, 151, 103, 0)),\n",
+       " ('B0009JQFF6',\n",
+       "  '100% Organic West African Shea Butter 16 oz',\n",
+       "  (239, 196, 70, 202, 0)),\n",
+       " ('B0009M0C5W',\n",
+       "  'FusionBeauty LipFusion Xl 2x Micro-Injected Collagen Advanced Lip Plumping Therapy',\n",
+       "  (144, 211, 227, 13, 0)),\n",
+       " ('B0009MHJE4',\n",
+       "  'African Shea Butter Cream (100% Pure &amp; Raw, Gold) 8 Oz.',\n",
+       "  (26, 4, 40, 12, 0)),\n",
+       " ('B0009MHJS0',\n",
+       "  'Raw Unrefined Yellow Shea Butter FILTERED &amp; CREAMY 5 0z',\n",
+       "  (252, 89, 246, 53, 0)),\n",
+       " ('B0009MJE74',\n",
+       "  'Nubian Heritage - Body Wash African Black Soap - 13 oz.',\n",
+       "  (21, 145, 71, 15, 0)),\n",
+       " ('B0009ML5M6', 'African Shea Oil - 8 ozs.', (100, 115, 246, 169, 0)),\n",
+       " ('B0009MMK5M',\n",
+       "  'Egyptian Magic - All Purpose Skin Cream - 4 oz.',\n",
+       "  (214, 54, 138, 60, 0)),\n",
+       " ('B0009MSHR2',\n",
+       "  'Madina African Black Soap Cocoa Butter W/Vitamin E 3.5 oz',\n",
+       "  (127, 96, 246, 75, 0)),\n",
+       " ('B0009MYMJY',\n",
+       "  'Shalimar By Guerlain For Women. Eau De Parfum Spray 2.5 Oz.',\n",
+       "  (175, 100, 3, 152, 0)),\n",
+       " ('B0009N35D2',\n",
+       "  'Glow By Jennifer Lopez For Women. Eau De Toilette Spray 1 Ounces',\n",
+       "  (175, 89, 11, 75, 0)),\n",
+       " ('B0009N5EZY',\n",
+       "  'Kenneth Cole Reaction By Kenneth Cole Eau De Toilette Spray 3.4 Oz',\n",
+       "  (121, 134, 59, 77, 0)),\n",
+       " ('B0009OAFUM',\n",
+       "  'Remington HKVAC-2000 Precision Vacuum Haircut Kit',\n",
+       "  (220, 122, 219, 160, 0)),\n",
+       " ('B0009OAGRE',\n",
+       "  'Eternity by Calvin Klein for Women, Eau De Parfum, 3.4 Ounce',\n",
+       "  (175, 120, 98, 206, 0)),\n",
+       " ('B0009OAGSI',\n",
+       "  'Escape by Calvin Klein for Men, Eau De Toilette Spray, 3.4-Fluid Ounce (100 ml)',\n",
+       "  (47, 145, 65, 103, 0)),\n",
+       " ('B0009OAGT2',\n",
+       "  'Escape by Calvin Klein for Women, Eau De Parfum Spray, 1.7 Ounce',\n",
+       "  (175, 4, 22, 12, 0)),\n",
+       " ('B0009OAGWE',\n",
+       "  'Ralph Lauren Polo Sport Eau de Toilette Spray for Men, 4.2 Ounce',\n",
+       "  (8, 4, 10, 12, 0)),\n",
+       " ('B0009OAGXI',\n",
+       "  'Acqua Di Gio By Giorgio Armani For Men. Eau De Toilette Spray 1.7 Oz.',\n",
+       "  (93, 77, 192, 195, 0)),\n",
+       " ('B0009OAGZ6',\n",
+       "  'Burberry By Burberry For Men. Eau De Toilette Spray 3.3 Ounces',\n",
+       "  (8, 24, 43, 206, 0)),\n",
+       " ('B0009OAH0A',\n",
+       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 1.7 Oz.',\n",
+       "  (8, 4, 3, 34, 0)),\n",
+       " ('B0009OAHBE',\n",
+       "  'COOL WATER by Davidoff Cologne for Men (EDT SPRAY 1.35 OZ)',\n",
+       "  (132, 232, 41, 38, 0)),\n",
+       " ('B0009OAHC8',\n",
+       "  'Cool Water By Davidoff For Men. Eau De Toilette Spray 4.2 Ounces',\n",
+       "  (175, 4, 10, 12, 0)),\n",
+       " ('B0009OAHEQ',\n",
+       "  'Joop! By Joop! For Men. Eau De Toilette Spray 4.2 Ounces',\n",
+       "  (175, 89, 10, 75, 0)),\n",
+       " ('B0009OAHIM',\n",
+       "  'Dolce &amp; Gabbana By Dolce &amp; Gabbana For Men. Eau De Toilette Spray 4.2 Ounce',\n",
+       "  (8, 135, 118, 77, 0)),\n",
+       " ('B0009OAHIW',\n",
+       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 1.6 Ounces',\n",
+       "  (31, 21, 153, 53, 0)),\n",
+       " ('B0009OAHNM', 'Curve Men Cologne Spray, 4.2-Ounce', (245, 66, 70, 103, 0)),\n",
+       " ('B0009OAHOG',\n",
+       "  'Mambo By Liz Claiborne For Men. Cologne Spray 1.7 Ounces',\n",
+       "  (92, 100, 201, 27, 0)),\n",
+       " ('B0009OAHQ4',\n",
+       "  'Bora Bora by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
+       "  (92, 77, 201, 53, 0)),\n",
+       " ('B0009OAHQE',\n",
+       "  'Bora Bora by Liz Claiborne for Women, Eau De Parfum Spray, 3.4-Ounce',\n",
+       "  (8, 120, 216, 77, 0)),\n",
+       " ('B0009OAHQY',\n",
+       "  'Claiborne by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
+       "  (212, 173, 33, 77, 0)),\n",
+       " ('B0009OAHRI',\n",
+       "  'Lucky You By Lucky Brand For Women. Eau De Toilette Spray 3.4 Oz.',\n",
+       "  (237, 135, 22, 15, 0)),\n",
+       " ('B0009OAHRS',\n",
+       "  'Lucky You By Lucky Brand For Men. Cologne Spray 3.4 Oz.',\n",
+       "  (92, 105, 201, 103, 0)),\n",
+       " ('B0009OAHVO',\n",
+       "  \"L'eau D'issey (issey Miyake) by Issey Miyake for Men - EDT Spray\",\n",
+       "  (8, 80, 27, 104, 0)),\n",
+       " ('B0009OAHWI',\n",
+       "  'Lolita Lempicka By Lolita Lempicka For Women. Eau De Parfum Spray 3.4 Oz.',\n",
+       "  (175, 55, 27, 75, 0)),\n",
+       " ('B0009OAI18',\n",
+       "  'Sunflowers By Elizabeth Arden For Women. Eau De Toilette Spray 3.4 Oz.',\n",
+       "  (32, 47, 22, 77, 0)),\n",
+       " ('B0009OAI1S',\n",
+       "  'Red Door By Elizabeth Arden For Women. Eau De Toilette Spray 3.3 Ounces',\n",
+       "  (175, 89, 3, 75, 0)),\n",
+       " ('B0009OAI2C',\n",
+       "  'Arden Beauty By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
+       "  (8, 100, 10, 77, 0)),\n",
+       " ('B0009OAI3G',\n",
+       "  'Blue Grass By Elizabeth Arden For Women. Eau De Parfum Spray 1.7 Ounces',\n",
+       "  (8, 100, 37, 27, 0)),\n",
+       " ('B0009OAI40',\n",
+       "  'Green Tea By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
+       "  (175, 55, 3, 75, 0)),\n",
+       " ('B0009OAI7C',\n",
+       "  'Passion by Elizabeth Taylor for Men, Cologne Spray, 4-Ounce',\n",
+       "  (92, 236, 201, 91, 0)),\n",
+       " ('B0009OAI8G',\n",
+       "  'White Diamonds By Elizabeth Taylor For Women, Eau De Toilette Spray, 1.7 Ounces',\n",
+       "  (8, 55, 10, 75, 0)),\n",
+       " ('B0009OAIB8',\n",
+       "  'Black Pearls by Elizabeth Taylor for Women, Eau De Parfum Spray, 3.3-Ounce',\n",
+       "  (175, 120, 192, 104, 0)),\n",
+       " ('B0009OAIE0',\n",
+       "  'Pleasures by Estee Lauder for Women, Eau De Toilette Spray, 1.7 Ounce',\n",
+       "  (8, 120, 27, 75, 0)),\n",
+       " ('B0009OAIIG',\n",
+       "  'Gucci Rush By Gucci For Women. Eau De Toilette Spray 2.5 Ounces',\n",
+       "  (31, 24, 3, 75, 0)),\n",
+       " ('B0009OAIKY',\n",
+       "  'Halston 1-12 by Halston for Men, Cologne Spray, 4.2-Ounce',\n",
+       "  (92, 149, 201, 103, 0)),\n",
+       " ('B0009OAIQI',\n",
+       "  'Design by Paul Sebastian for Women, Fine Parfum Spray, 1.7-Ounce',\n",
+       "  (31, 120, 40, 75, 0)),\n",
+       " ('B0009OAISG',\n",
+       "  'True Love by Elizabeth Arden for Women - 1.7 Ounce EDT Spray',\n",
+       "  (32, 120, 82, 27, 0)),\n",
+       " ('B0009OMNGQ',\n",
+       "  'Awapuhi Moisture Mist Unisex Mist by Paul Mitchell, 16.9 Ounce',\n",
+       "  (237, 122, 162, 76, 0)),\n",
+       " ('B0009OXHC0',\n",
+       "  \"Nature's Blessings Hair Pomade 4 oz.\",\n",
+       "  (154, 210, 139, 15, 0)),\n",
+       " ('B0009P4PZC', 'Cococare Coconut Oil 100% Pure 4 Oz', (170, 47, 122, 38, 0)),\n",
+       " ('B0009PVV36',\n",
+       "  'Conair CD86SCS Instant Heat Iron and Straightener, 1.5 Inch',\n",
+       "  (241, 223, 246, 103, 0)),\n",
+       " ('B0009PVV40',\n",
+       "  'Conair BC171NCS Ceramic Ionic Hot Air Brush, Black, 1.25 Inch',\n",
+       "  (24, 66, 140, 170, 0)),\n",
+       " ('B0009PVV4A',\n",
+       "  'Infiniti Pro by Conair Professional 1 Inch  Tourmaline Ceramic Straightener',\n",
+       "  (70, 122, 163, 99, 0)),\n",
+       " ('B0009QJ3TE',\n",
+       "  'Model in a Bottle Sensitive Makeup Setting Spray - 1.7 oz',\n",
+       "  (13, 4, 10, 76, 0)),\n",
+       " ('B0009QZXP2',\n",
+       "  \"Africa's Best Organics Hair Mayonnaise, 15 oz\",\n",
+       "  (245, 4, 234, 37, 0)),\n",
+       " ('B0009QZXQ6',\n",
+       "  \"Africa's Best Organics Olive Oil Dry Hair and Scalp Therapy, 7.5 oz.\",\n",
+       "  (133, 96, 24, 77, 0)),\n",
+       " ('B0009QZYBU',\n",
+       "  'Soft Sheen Carson Care Free Curl Gold Instant Activator 16Oz/473Ml',\n",
+       "  (47, 138, 250, 169, 0)),\n",
+       " ('B0009QZYLU',\n",
+       "  'Clairol BW2 - 8 oz. Tub Powder Lightener',\n",
+       "  (91, 100, 201, 114, 0)),\n",
+       " ('B0009QZYN8',\n",
+       "  'CLAIROL Jazzing Gentle Temporary Semi Permanent Hair Color #58 RUBY RED',\n",
+       "  (63, 21, 71, 99, 0)),\n",
+       " ('B0009QZYO2', 'Clairol Pure White 30 Volume 16 oz.', (115, 236, 71, 27, 0)),\n",
+       " ('B0009QZZ4Q',\n",
+       "  'Demert Wig Luster Conditioner, 9.76 Ounce',\n",
+       "  (123, 149, 70, 91, 0)),\n",
+       " ('B0009R14NG',\n",
+       "  'Africas Best Herbal Gro Super 5.25oz Jar',\n",
+       "  (92, 202, 153, 15, 0)),\n",
+       " ('B0009R14Q8',\n",
+       "  \"Africa's Best Organincs Mayo Leave In Conditioner 6 oz.\",\n",
+       "  (92, 228, 59, 77, 0)),\n",
+       " ('B0009R14XQ',\n",
+       "  'Australian Gold SPF 15 Spray Gel with Bronzer, 8 Ounce',\n",
+       "  (136, 24, 10, 27, 0)),\n",
+       " ('B0009R16O8',\n",
+       "  \"Dr. Bronner's Magic Soaps: Liquid Castile Soap, Tea Tree 32 oz\",\n",
+       "  (194, 180, 153, 15, 0)),\n",
+       " ('B0009R33U8',\n",
+       "  'Aphogee Two-step Treatment Protein for Damaged Hair 16 oz.',\n",
+       "  (215, 134, 231, 100, 0)),\n",
+       " ('B0009R35GA',\n",
+       "  'Clairol Pure White 40 Volume 16 oz.',\n",
+       "  (115, 236, 122, 126, 0)),\n",
+       " ('B0009R5AYA',\n",
+       "  'Aussie Sydney Smooth 3 Minute Miracle Smoothing Treatment-8oz',\n",
+       "  (164, 187, 219, 160, 0)),\n",
+       " ('B0009R5B8A', 'Avon SSS Original OIL 5oz', (24, 234, 221, 37, 0)),\n",
+       " ('B0009R5CMK',\n",
+       "  'Doo Gro Medicated Hair Vitalizer Mega Thick Anti-Thinning Formula, 4 Ounce',\n",
+       "  (44, 66, 227, 60, 0)),\n",
+       " ('B0009RF9OG',\n",
+       "  'Rogaine for Women Hair Regrowth Treatment, 2 Ounce',\n",
+       "  (255, 51, 59, 15, 1)),\n",
+       " ('B0009RFAOK',\n",
+       "  'Clean &amp; Clear Deep Cleaning Astringent, Oil Fighting, 8 Ounce',\n",
+       "  (132, 62, 162, 77, 0)),\n",
+       " ('B0009RFAPY',\n",
+       "  'Purpose Gentle Cleansing Wash, 6-Ounce Pump Bottle',\n",
+       "  (194, 7, 40, 91, 0)),\n",
+       " ('B0009RFB76',\n",
+       "  'RoC Retinol Correxion Eye Cream, 0.5 Ounce',\n",
+       "  (12, 115, 208, 194, 0)),\n",
+       " ('B0009RMN0E',\n",
+       "  'Conair CS25WNCS Ultra Slim Ceramic Straightener, Red, 1 5/8 Inch',\n",
+       "  (182, 66, 201, 38, 0)),\n",
+       " ('B0009STDD8',\n",
+       "  \"Calvin Klein Women's Obsession Eau de Parfum Spray, 3.4 fl. oz.\",\n",
+       "  (175, 129, 153, 170, 0)),\n",
+       " ('B0009TNBVC',\n",
+       "  'Estee Lauder Double Wear Stay-In-Place Makeup SPF 10 18 Linen',\n",
+       "  (132, 159, 216, 13, 0)),\n",
+       " ('B0009V1YR8',\n",
+       "  'Farouk CHI 1 Inch Ceramic Flat Hairstyling Iron',\n",
+       "  (105, 62, 153, 100, 0)),\n",
+       " ('B0009V1YRS',\n",
+       "  'CHI Turbo Microchip Ceramic Hairstyling Iron 1&quot;',\n",
+       "  (241, 134, 122, 202, 0)),\n",
+       " ('B0009V1YS2',\n",
+       "  'Farouk CHI GF1539 Turbo Big 2 Inch Ceramic Flat Iron Hair Straightener',\n",
+       "  (67, 202, 226, 188, 0)),\n",
+       " ('B0009V2S8M',\n",
+       "  'Glo Minerals GloPressed Base Beige Dark 0.35oz',\n",
+       "  (132, 47, 162, 139, 0)),\n",
+       " ('B0009V8N4U',\n",
+       "  'Sun Laboratories Dark Sunsation Self Tanning Lotion - Very Dark 8 fl oz.',\n",
+       "  (118, 115, 70, 220, 0)),\n",
+       " ('B0009V8N5E',\n",
+       "  'Sun Self Tanning Lotion Ultra Dark Instant Tint - Dark 8oz/236ml',\n",
+       "  (62, 211, 71, 77, 0)),\n",
+       " ('B0009VD8YU',\n",
+       "  'AUBREY Collagen Restorative Moisturizer 1.7 fl.oz',\n",
+       "  (71, 202, 246, 219, 0)),\n",
+       " ('B0009VDBWY',\n",
+       "  'Sun Laboratories Ultra Dark Self Tanning Spray Can (6 oz)',\n",
+       "  (70, 115, 219, 60, 0)),\n",
+       " ('B0009VIJV2',\n",
+       "  'Floxite Fl-10h 10x Hand Held 2-sided Mirror with Stand, Clear',\n",
+       "  (200, 77, 33, 34, 0)),\n",
+       " ('B0009VNI2C',\n",
+       "  'Fruit Of The Earth Bogo Cream Aloe Vera 4oz. Jar',\n",
+       "  (127, 62, 234, 13, 0)),\n",
+       " ('B0009VNI40',\n",
+       "  'Fruit Of The Earth 100 % Aloe Vera Gel, 12 oz, 1-Pack',\n",
+       "  (117, 134, 163, 60, 0)),\n",
+       " ('B0009VQ8X8',\n",
+       "  'Got2b Glued Blasting Freeze Spray, 12 Ounce',\n",
+       "  (67, 120, 216, 100, 0)),\n",
+       " ('B0009WB49K',\n",
+       "  'Alterna Caviar Anti-Aging Working Hair Spray, 15.5 Ounce',\n",
+       "  (14, 21, 254, 103, 0)),\n",
+       " ('B0009WY4U6',\n",
+       "  'St. Ives Swiss Formula Makeup Remover &amp; Facial Cleanser, All Skin Types, 6 oz (170 g)',\n",
+       "  (245, 21, 219, 219, 0)),\n",
+       " ('B0009WY4UQ',\n",
+       "  'TRESemme Curl Enhancing Mousse, 10.5 oz',\n",
+       "  (71, 213, 43, 219, 0)),\n",
+       " ('B0009WY54G',\n",
+       "  'TRESemme Shampoo, 24 Hour Body, Healthy Volume, 32 Ounce',\n",
+       "  (183, 134, 67, 76, 0)),\n",
+       " ('B0009XH6SC',\n",
+       "  'Andis 1-Inch Ceramic Clamp Flat Iron (67095)',\n",
+       "  (174, 66, 208, 99, 0)),\n",
+       " ('B0009XH6TG',\n",
+       "  'Andis RC-2 Ionic1875W Ceramic Hair Dryer with Folding Handle and Retractable Cord (80020)',\n",
+       "  (241, 117, 208, 34, 0)),\n",
+       " ('B0009XH6UU',\n",
+       "  'Andis 40055 Pro Style 1600 Watt Hair Dryer - White',\n",
+       "  (80, 103, 219, 76, 0)),\n",
+       " ('B0009XH6V4',\n",
+       "  'Andis Micro Turbo 1600 Watt Dual Voltage Hair Dryer - White (33805)',\n",
+       "  (155, 145, 71, 53, 0)),\n",
+       " ('B0009Y6RHM',\n",
+       "  'Banana Boat Summer Color Self-Tanning Lotion - Deep Dark: 6 OZ',\n",
+       "  (199, 47, 27, 37, 0)),\n",
+       " ('B0009YDO32',\n",
+       "  'Zum Mist Aromatherapy Room and Body Spray Frankincense And Myrrh -- 4 fl oz',\n",
+       "  (114, 62, 59, 103, 0)),\n",
+       " ('B000A0ADT8',\n",
+       "  'Ole Henriksen Truth Serum Collagen Booster, 1.0 Fluid Ounce',\n",
+       "  (245, 134, 162, 170, 0)),\n",
+       " ('B000A0ADUW',\n",
+       "  'Ole Henriksen Blue Black Berry Enzyme Facial Mask, 3.5 Fluid Ounce',\n",
+       "  (123, 21, 122, 15, 0)),\n",
+       " ('B000A35L56',\n",
+       "  'Max Green Alchemy Scalp Rescue Shampoo 8.8 oz',\n",
+       "  (245, 24, 208, 99, 0)),\n",
+       " ('B000A3I2X4',\n",
+       "  'Revlon RV408 1875 Watt Full-Size Turbo Dryer, Black',\n",
+       "  (140, 159, 24, 34, 0)),\n",
+       " ('B000A3V1SW',\n",
+       "  \"Hask Placenta Henna 'n' Placenta for Extremely Damaged Hair 237ml/8oz\",\n",
+       "  (178, 60, 163, 171, 0)),\n",
+       " ('B000A3V1XC', 'Hollywood Beauty Tea Tree Oil 2 oz.', (56, 21, 65, 114, 0)),\n",
+       " ('B000A3V2PE',\n",
+       "  \"L'Oreal Excellence Hicolor Hilights Red 1.2 oz.\",\n",
+       "  (128, 141, 43, 76, 0)),\n",
+       " ('B000A3XI4M',\n",
+       "  'S-Curl Activator and Moisturizer 32 oz',\n",
+       "  (170, 141, 140, 60, 0)),\n",
+       " ('B000A3ZMLO', 'Hollywood Beauty Carrot Oil 8 oz.', (234, 255, 227, 60, 0)),\n",
+       " ('B000A3ZN7M',\n",
+       "  \"L'Oreal Oreor 30 Volume Creme Developer 16 oz.\",\n",
+       "  (47, 47, 201, 15, 0)),\n",
+       " ('B000A3ZN7W',\n",
+       "  'Loreal Oreor Creme 40 Volume Developer 16 Oz',\n",
+       "  (47, 149, 201, 103, 0)),\n",
+       " ('B000A408VC',\n",
+       "  'Sally Hansen Half Jaw Cuticle Nippers',\n",
+       "  (16, 96, 155, 103, 0)),\n",
+       " ('B000A4094I',\n",
+       "  'Liquid Glass Nail Laminate With Sunscreen 0.5oz',\n",
+       "  (138, 21, 33, 195, 0)),\n",
+       " ('B000A409J8',\n",
+       "  \"L'Oreal Quick Blue Powder Bleach 1 Lb\",\n",
+       "  (28, 135, 59, 170, 0)),\n",
+       " ('B000A7VRYG',\n",
+       "  'Ocusoft Lid Scrub Foaming Eyelid Cleanser (7.25 fl. oz.)',\n",
+       "  (24, 89, 201, 160, 0)),\n",
+       " ('B000AA2XQ4',\n",
+       "  'Rothco Compartment Travel Toiletry Bag',\n",
+       "  (89, 202, 153, 91, 0)),\n",
+       " ('B000AA5VD6',\n",
+       "  'Sally Hansen Insta, Dri Insta, Dri Anti, Chip Top Coat',\n",
+       "  (200, 196, 122, 195, 0)),\n",
+       " ('B000AA5VZ4', 'Super Nail Cuticle Oil 4 oz.', (194, 173, 153, 160, 0)),\n",
+       " ('B000AA5VZY', 'Super Nail 16 oz. Pure Acetone', (222, 135, 216, 100, 0)),\n",
+       " ('B000AA9H3Q',\n",
+       "  'Sally Hansen Natural Nail Growth Activator # 2741 0.45 oz.',\n",
+       "  (11, 167, 201, 34, 0)),\n",
+       " ('B000AA9HQ8',\n",
+       "  'Super Nail Cuticle Softener, 8 Ounce',\n",
+       "  (210, 80, 202, 100, 0)),\n",
+       " ('B000AAAVTU',\n",
+       "  'Sally Hansen No Chip Acrylic Top Coat, 0.45 Fluid Ounce',\n",
+       "  (13, 122, 70, 76, 0)),\n",
+       " ('B000AAAW9Y',\n",
+       "  \"Mane 'n Tail Moisture Enriched Hair Strengthener, Bonus, 6 oz.\",\n",
+       "  (102, 80, 11, 220, 0)),\n",
+       " ('B000AAAWAI',\n",
+       "  \"Straight Arrow Mane 'N Tail Herbal Gro Maximum 5.5oz\",\n",
+       "  (39, 129, 59, 114, 0)),\n",
+       " ('B000AAAWEO',\n",
+       "  'Sun In Hair Lightner Lemon 4.7 oz. Pump',\n",
+       "  (94, 202, 11, 34, 0)),\n",
+       " ('B000AAC8VO',\n",
+       "  'DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ',\n",
+       "  (164, 173, 43, 103, 0)),\n",
+       " ('B000AADENA',\n",
+       "  'Sally Hansen Cuticle Massage Cream -- 0.4 fl oz',\n",
+       "  (71, 8, 122, 236, 0)),\n",
+       " ('B000AADEP8',\n",
+       "  'Sally Hansen Double Duty Base and Top Coat, 0.45 Fluid Ounce',\n",
+       "  (28, 87, 138, 195, 0)),\n",
+       " ('B000AADF0M',\n",
+       "  'Sally Hansen Airbrush Face Tanner 1.8oz Spray [Health and Beauty]',\n",
+       "  (196, 164, 43, 171, 0)),\n",
+       " ('B000AADF20',\n",
+       "  'Sally Hansen Vitamin E Nail and Cuticle Oil, 0.45 Fluid Ounce',\n",
+       "  (128, 135, 27, 77, 0)),\n",
+       " ('B000AADG0G',\n",
+       "  'Village Naturals Bath Shoppe Lavender &amp; Chamomile Foaming Milk Bath 28 fl oz.(Pack of 1)',\n",
+       "  (178, 62, 43, 169, 0)),\n",
+       " ('B000AADG8I',\n",
+       "  'Wella Cc Liquid #0811/8N Light Blonde Haircolor',\n",
+       "  (201, 134, 59, 77, 1)),\n",
+       " ('B000ABOLZ4',\n",
+       "  'Kirks Original Coco Castile Soap, 24 Bars 1/2 Case',\n",
+       "  (144, 159, 33, 116, 0)),\n",
+       " ('B000ACAV8O', 'Henna Bright Red Pwd 4oz 4 Ounces', (45, 21, 216, 77, 1)),\n",
+       " ('B000ACB09I',\n",
+       "  \"Nature's Gate Organics C for Yourself, (1.7 fl oz) (50 ml)\",\n",
+       "  (100, 196, 162, 34, 0)),\n",
+       " ('B000ACB0C0',\n",
+       "  \"Nature's Gate Organics Oh What a Night Cream, (1 oz) (28 g)\",\n",
+       "  (145, 66, 24, 219, 0)),\n",
+       " ('B000ALBJ40',\n",
+       "  'Fekkai Glossing Hair Conditioner 2 Fl Oz',\n",
+       "  (41, 3, 153, 100, 0)),\n",
+       " ('B000ALBKGW',\n",
+       "  'Bliss Diamancel Diamond Nail File No.2, Medium',\n",
+       "  (9, 122, 163, 162, 0)),\n",
+       " ('B000ALBNGE',\n",
+       "  'Clinique Superfine Liner for Brows 01 Soft Blonde',\n",
+       "  (189, 46, 163, 99, 0)),\n",
+       " ('B000ALCJR6',\n",
+       "  'T3 Tourmaline 83808 Professional Featherweight Ceramic Ionic Hair Dryer',\n",
+       "  (105, 228, 139, 116, 0)),\n",
+       " ('B000ALDJ7A',\n",
+       "  'Diamancel Diamond Tough Foot Buffer No.11, Medium',\n",
+       "  (200, 120, 67, 83, 0)),\n",
+       " ('B000ALDK1A',\n",
+       "  'Fruit Of The Earth 100% Aloe Vera 24oz Gel Pump',\n",
+       "  (185, 62, 254, 160, 0)),\n",
+       " ('B000ALDLJG',\n",
+       "  'Clinique Quickliner for Eyes 07 Really Black',\n",
+       "  (18, 96, 201, 169, 0)),\n",
+       " ('B000ALFROS',\n",
+       "  'Fekkai Advanced Brilliant Glossing Cream 4 fl oz (113 g)',\n",
+       "  (128, 105, 201, 99, 0)),\n",
+       " ('B000ALFTUU',\n",
+       "  'Clinique 7 Day Scrub Cream Rinse-Off Formula 3.4 oz',\n",
+       "  (132, 173, 138, 169, 0)),\n",
+       " ('B000ALFTVO',\n",
+       "  'Clinique Take the Day off makeup remover 4.2 oz /125ml',\n",
+       "  (132, 149, 43, 76, 0)),\n",
+       " ('B000AM82US',\n",
+       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Towel, Pink (19 x 39-Inches)\",\n",
+       "  (80, 134, 139, 12, 0)),\n",
+       " ('B000AMA43Q',\n",
+       "  'Aquis Microfiber Hair Towel, Waffle, Linen (19 x 39-Inches)',\n",
+       "  (28, 149, 139, 91, 0)),\n",
+       " ('B000AMA4DQ',\n",
+       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Turban, Patented Design, White\",\n",
+       "  (210, 21, 162, 210, 0)),\n",
+       " ('B000AMBF5W',\n",
+       "  'Nivea Soft Refreshingly Soft Moisturizing Creme with Jojoba Oil &amp; Vitamin E, 6.8 Ounces',\n",
+       "  (92, 202, 59, 53, 0)),\n",
+       " ('B000AMHWCW',\n",
+       "  'Too Faced Cosmetics Bronzer, Snow Bunny, 0.28-Ounce',\n",
+       "  (188, 103, 254, 77, 0)),\n",
+       " ('B000AO2NXS',\n",
+       "  'Dove Body Wash with NutriumMoisture, Sensitive Skin Nourishing, 24 Ounce',\n",
+       "  (99, 255, 153, 53, 0)),\n",
+       " ('B000AQF50Y',\n",
+       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 3.3 Ounces',\n",
+       "  (31, 24, 27, 75, 0)),\n",
+       " ('B000AQI2EK',\n",
+       "  'Paul Mitchell Tea Tree Special Conditioner, 33.8 Ounce',\n",
+       "  (33, 196, 98, 76, 0)),\n",
+       " ('B000ARDBH2',\n",
+       "  'Guess Eau de Parfum Spray for Women, 2.5 Fluid Ounce',\n",
+       "  (141, 3, 201, 152, 0)),\n",
+       " ('B000ASDGK8',\n",
+       "  'BaByliss Pro BAB2000 Ceramix Xtreme  Dryer',\n",
+       "  (181, 202, 122, 195, 0)),\n",
+       " ('B000ASDX3S',\n",
+       "  'BaByliss Pro BAB2590 Porcelain Ceramic Straightening Iron with Removable Comb, 1.5 Inch',\n",
+       "  (132, 66, 246, 169, 0)),\n",
+       " ('B000AU15E0',\n",
+       "  'Blue Lizard Australian SUNSCREEN SPF 30+, Baby, SPF 30+, 8.75-Ounces',\n",
+       "  (128, 77, 43, 77, 0)),\n",
+       " ('B000AUTH0E',\n",
+       "  'Rusk Thermal Shine Spray Unisex, 4.4 Ounce',\n",
+       "  (71, 96, 138, 100, 0)),\n",
+       " ('B000AUTHEA',\n",
+       "  'Straight Sexy Hair Smooth &amp; Seal Aerated Anti-Frizz Spray (8.1 oz)',\n",
+       "  (154, 131, 139, 195, 0)),\n",
+       " ('B000AYKVOG', 'Dove Pink Beauty Bar, 8 Count', (115, 24, 208, 170, 0)),\n",
+       " ('B000B45BUE',\n",
+       "  'Silver Metallic Perfume Atomizer Spray 10 ML for purse or travel Refillable',\n",
+       "  (3, 236, 122, 202, 0)),\n",
+       " ('B000B5S2LS',\n",
+       "  'Focus 21 Sea Plasma Hair and Skin Moisturizer 32oz',\n",
+       "  (40, 100, 226, 76, 0)),\n",
+       " ('B000B5UPF4', 'OC Eight Professional Mattifying Gel', (232, 77, 226, 77, 0)),\n",
+       " ('B000B63Y1K',\n",
+       "  'Dermorganic Conditioning Shampoo, 12 Ounce',\n",
+       "  (63, 115, 11, 184, 0)),\n",
+       " ('B000B6VIPY',\n",
+       "  'Princereigns Shaving Gel Used to Remove Ingrown Hair and Razor Bumps',\n",
+       "  (28, 3, 59, 19, 0)),\n",
+       " ('B000B7VO66',\n",
+       "  'PCA Skin Eyexcellence (Phaze 12), 0.5 Ounce',\n",
+       "  (233, 21, 33, 15, 0)),\n",
+       " ('B000B82TD2',\n",
+       "  'NeoStrata Ultra Smoothing Cream AHA 10, 1.4 Ounce',\n",
+       "  (44, 115, 27, 195, 0)),\n",
+       " ('B000B8FW0Y', 'Lansky Dual Grit Sharpener', (220, 173, 71, 34, 0)),\n",
+       " ('B000B8VBJK',\n",
+       "  'HOT TOOLS 2108 Nano Ceramic Marcel Curling Iron, Black/Purple, 1 Inch',\n",
+       "  (67, 202, 226, 77, 0)),\n",
+       " ('B000B9J3GM', 'Mary Kay Indulge Soothing Eye Gel', (132, 211, 17, 13, 0)),\n",
+       " ('B000B9N0N4',\n",
+       "  'Mary Kay Blemish Control Toner: Formula 3',\n",
+       "  (78, 180, 139, 182, 0)),\n",
+       " ('B000B9W3CI',\n",
+       "  'White Plastic Jar with Dome Lid 4 Oz - 12 Per Bag',\n",
+       "  (241, 46, 3, 77, 0)),\n",
+       " ('B000B9W4MC',\n",
+       "  'White Plastic Jar with Flat Lid 8 Oz - 6 Per Bag',\n",
+       "  (241, 46, 234, 99, 0)),\n",
+       " ('B000BB9L0S',\n",
+       "  'Nailtiques Formula 2 Protein, .5 Ounce',\n",
+       "  (6, 159, 226, 91, 0)),\n",
+       " ('B000BBGP4I', 'Mason Pearson Detangling Comb', (210, 8, 33, 153, 0)),\n",
+       " ('B000BD0SEE',\n",
+       "  'MyChelle Fruit Enzyme Cleanser, 4.4 Ounce Bottle',\n",
+       "  (204, 8, 139, 91, 0)),\n",
+       " ('B000BFTAMS',\n",
+       "  'Ponds Caring Classic Extra, Rich Dry Skin Cream, 10.1 oz',\n",
+       "  (170, 4, 40, 12, 0)),\n",
+       " ('B000BGIYWY',\n",
+       "  \"Africa's Best Kids Organics Hair Lotion, Shea Butter Detangling Moisturizing 12 oz\",\n",
+       "  (177, 180, 162, 159, 0)),\n",
+       " ('B000BH92J2',\n",
+       "  'Shea Moisture Shea Butter Leave in Conditioner 8oz',\n",
+       "  (132, 234, 219, 34, 0)),\n",
+       " ('B000BIPJ20',\n",
+       "  'Perfekt Skin Perfection Gel Luminous 1 oz',\n",
+       "  (133, 196, 71, 236, 0)),\n",
+       " ('B000BIUGRI', 'Bumble and Bumble Prep (8 Ounces)', (117, 3, 140, 241, 0)),\n",
+       " ('B000BIUGSM',\n",
+       "  'Bumble and Bumble Styling Lotion (8 Ounces)',\n",
+       "  (3, 134, 153, 206, 0)),\n",
+       " ('B000BIUGUA',\n",
+       "  'Bumble and Bumble Sunday Shampoo (8 Ounces)',\n",
+       "  (10, 122, 153, 160, 0)),\n",
+       " ('B000BIUGUK',\n",
+       "  'Bumble and Bumble Thickening Conditioner (8 Ounces)',\n",
+       "  (123, 122, 153, 169, 0)),\n",
+       " ('B000BIUGV4',\n",
+       "  'Bumble and Bumble Thickening Hair Spray (8 Ounces)',\n",
+       "  (79, 145, 246, 241, 0)),\n",
+       " ('B000BIUGXM',\n",
+       "  'Bumble and Bumble Curl Conscious Defining Creme 8.5 oz',\n",
+       "  (117, 122, 208, 114, 0)),\n",
+       " ('B000BIVXZW',\n",
+       "  'Bumble and Bumble Classic Hairspray (10 Ounces)',\n",
+       "  (19, 62, 24, 160, 0)),\n",
+       " ('B000BIVY0G',\n",
+       "  'Bumble and Bumble  DeFRIZZ (4 Ounces)',\n",
+       "  (92, 250, 201, 220, 0)),\n",
+       " ('B000BIVY10',\n",
+       "  'Kerastase Nutritive Bain Satin 2 Complete Nutrition Shampoo For Dry and Sensitised Hair, 8.5 Oz.',\n",
+       "  (132, 7, 226, 15, 0)),\n",
+       " ('B000BIVY1U',\n",
+       "  'Bumble and Bumble Seaweed Conditioner (8 Ounces)',\n",
+       "  (177, 21, 10, 116, 0)),\n",
+       " ('B000BIVY24',\n",
+       "  'Bumble and Bumble Tonic Lotion, 8-Ounce Spray Bottle',\n",
+       "  (151, 21, 216, 152, 0)),\n",
+       " ('B000BIXP30',\n",
+       "  'Kerastase Resistance Bain Volumactive Volumizing Shampoo For Fine, Vulnerable Hair, 8.5 Ounce',\n",
+       "  (1, 180, 55, 91, 0)),\n",
+       " ('B000BIXP3K',\n",
+       "  'Bumble and Bumble Grooming Cream (5 Ounces)',\n",
+       "  (201, 247, 71, 91, 0)),\n",
+       " ('B000BIXP3U',\n",
+       "  'Bumble and Bumble Leave in Conditioner (8 Ounces)',\n",
+       "  (79, 206, 41, 37, 0)),\n",
+       " ('B000BIXP4O',\n",
+       "  'Bumble and Bumble Styling Creme, 8-Ounce Bottle',\n",
+       "  (3, 180, 216, 114, 0)),\n",
+       " ('B000BIXP5I',\n",
+       "  'Bumble and Bumble Surf Spray, 4-Ounce Bottle',\n",
+       "  (3, 3, 208, 75, 0)),\n",
+       " ('B000BIXP5S',\n",
+       "  'Kerastase Nutritive Oleo-Relax Serum, 4.2 Ounces',\n",
+       "  (99, 196, 67, 53, 0)),\n",
+       " ('B000BIXP62',\n",
+       "  'Kerastase Nutritive Bain Oleo-Relax Smoothing Shampoo For Dry and Rebellious Hair, 8.5 Ounce',\n",
+       "  (132, 115, 227, 34, 0)),\n",
+       " ('B000BIZSUS',\n",
+       "  'Bumble and Bumble Brilliantine (2 Ounces)',\n",
+       "  (3, 47, 192, 37, 0)),\n",
+       " ('B000BIZSV2',\n",
+       "  'Bumble and Bumble Deeep, 5-Ounce Tube',\n",
+       "  (177, 173, 208, 100, 0)),\n",
+       " ('B000BIZSYO',\n",
+       "  'Kerastase Resistance Bain De Force Fortifying Shampoo For Weakened to Fragile Hair, 8.5 Ounce',\n",
+       "  (132, 249, 71, 180, 0)),\n",
+       " ('B000BJ1CGQ', 'GiGi Mini Pro Waxing Kit', (113, 196, 201, 15, 0)),\n",
+       " ('B000BK1P9E',\n",
+       "  'Kerasilk Rich Care Instant Silk Fluid By Goldwell for Unisex, 4.2 Ounce',\n",
+       "  (47, 115, 22, 27, 0)),\n",
+       " ('B000BKLBES',\n",
+       "  'Marc Anthony Instantly Thick Hair Thickening Cream, 6 oz',\n",
+       "  (13, 159, 162, 103, 0)),\n",
+       " ('B000BKLCDS',\n",
+       "  'Super Nail Acetone Polish Remover, 16 Ounce',\n",
+       "  (3, 21, 153, 38, 0)),\n",
+       " ('B000BKXGXW',\n",
+       "  'NOW Foods -  Red Clay Powder Moroccan, 6 OZ.',\n",
+       "  (245, 103, 71, 159, 0)),\n",
+       " ('B000BNG4VU',\n",
+       "  'Coty Airspun Loose Powder, Translucent, 2.3 Ounce',\n",
+       "  (123, 238, 10, 12, 0)),\n",
+       " ('B000BP81IM', 'NARS Loose Powder, Eden', (173, 96, 71, 170, 0)),\n",
+       " ('B000BPBV2U',\n",
+       "  'Liquid Gold Brush-on Bonding Adhesive for Cold Fusion Hair Extensions and Braids - .5oz',\n",
+       "  (12, 149, 139, 202, 0)),\n",
+       " ('B000BPMB2Y',\n",
+       "  'Roux Lash &amp; Brow Tint, Black,40 Count',\n",
+       "  (92, 29, 70, 27, 0)),\n",
+       " ('B000BPQG6Q', 'Roux Tween Time Hair Crayon, Auburn', (3, 47, 219, 53, 0)),\n",
+       " ('B000BPT0TG',\n",
+       "  'Roux Fanci-full Rinse #49 Ultra White Minx',\n",
+       "  (238, 236, 162, 170, 0)),\n",
+       " ('B000BR50M0',\n",
+       "  'Symbiotics Colostrum Plus, 240 Capsules',\n",
+       "  (252, 47, 219, 34, 0)),\n",
+       " ('B000BR5B2Y',\n",
+       "  'Komenuka Bijin Japanese All-Natural Essence Whitening Cream with Rice Bran',\n",
+       "  (99, 149, 3, 169, 0)),\n",
+       " ('B000BR766S',\n",
+       "  'Komenuka Bijin Premium Hair Care Set: Moisturizing Hair Shampoo &amp; Hair Treatment / Conditioner',\n",
+       "  (132, 161, 226, 99, 0)),\n",
+       " ('B000BRGAOM',\n",
+       "  'Biosilk Therapy Shine On Spray, 5.30 Ounce',\n",
+       "  (132, 228, 70, 34, 0)),\n",
+       " ('B000BRO6O8',\n",
+       "  'Coty Airspun Loose Powder, Naturelle, 2.3 Ounce',\n",
+       "  (123, 4, 40, 12, 0)),\n",
+       " ('B000BRPMJG',\n",
+       "  'Sebamed moisturizing lotion, for sensitive skin, 6.8-Fluid Ounce',\n",
+       "  (63, 46, 139, 48, 0)),\n",
+       " ('B000BRSHXE',\n",
+       "  'Sebamed Moisturing Cream, Sensitive Skin, 2.6-Ounce',\n",
+       "  (78, 159, 122, 202, 0)),\n",
+       " ('B000BRY5UI',\n",
+       "  'Komenuka Bijin Facial Cleansing Powder from Natural Rice Bran - 30 Packs',\n",
+       "  (78, 180, 219, 77, 0)),\n",
+       " ('B000BRYMKG',\n",
+       "  'Hylexin Serious Dark Circles by Bremenn Research Labs .39 oz. 11.53 ml',\n",
+       "  (252, 247, 227, 103, 0)),\n",
+       " ('B000BTM2K6',\n",
+       "  'White Shoulders By Evyan For Women, Eau De Cologne Spray (4.5 Ounces)',\n",
+       "  (92, 180, 192, 91, 0)),\n",
+       " ('B000BTO6CI',\n",
+       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 3.4 Oz.',\n",
+       "  (8, 4, 3, 34, 1)),\n",
+       " ('B000BTO6EG',\n",
+       "  'PALOMA PICASSO For Women By PALOMA PICASSO Eau De Parfum Spray,3.4 Oz',\n",
+       "  (17, 24, 118, 77, 0)),\n",
+       " ('B000BTQRFM',\n",
+       "  'Paul Sebastian by Paul Sebastian for Men - 8 Ounce EDC De Luxe Splash',\n",
+       "  (177, 131, 226, 202, 0)),\n",
+       " ('B000BU7G1A',\n",
+       "  'ApHogee Intensive Two Minute Keratin Reconstructor',\n",
+       "  (47, 196, 75, 202, 0)),\n",
+       " ('B000BU7SMM', 'Aphogee Balancing Moisturizer 16 Oz', (41, 4, 40, 12, 0)),\n",
+       " ('B000BUFFPO', \"Burt's Bees Herbal Deodorant\", (70, 236, 122, 15, 0)),\n",
+       " ('B000BVCSP8',\n",
+       "  'Scruples High Definition Shape Spray, 10.6 Ounce',\n",
+       "  (117, 149, 254, 15, 0)),\n",
+       " ('B000BW4U58', 'Vitamin E Skin Oil 10000 IU. 4.6 Oz', (99, 202, 122, 37, 0)),\n",
+       " ('B000BX1Z00',\n",
+       "  'CHI Silk Infusion Leave-In Treatment, 12 Ounce',\n",
+       "  (39, 46, 43, 114, 0)),\n",
+       " ('B000BX5FS8',\n",
+       "  'CHI Straight Guard Smoothing Styling Cream 8.5 oz',\n",
+       "  (155, 145, 201, 152, 0)),\n",
+       " ('B000BY2N7S', 'NOW Foods Biotin 5000mcg, 120 Vcaps', (167, 54, 41, 246, 0)),\n",
+       " ('B000BYQBT4',\n",
+       "  'Obagi Nu Derm Exfoderm Skin Smoothing Lotion-2 oz',\n",
+       "  (196, 1, 163, 38, 0)),\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items_with_tuples"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -64,6 +2740,32 @@
     "assert len(df) == len(set(item[-1] for item in items_with_tuples))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trie import Trie\n",
+    "\n",
+    "_trie = Trie()\n",
+    "\n",
+    "for (id, tuple) in zip(df.index, semantic_ids):\n",
+    "    _trie.insert(tuple, id) # todo handle collisions, not overwrite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(\"../data/Beauty/trie.pkl\", 'wb') as f:\n",
+    "    pickle.dump(_trie, f)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 56a68a0a..1c1f106d 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,23 +1,26 @@
-from utils import create_masked_tensor
+from utils import DEVICE, create_masked_tensor, get_activation_function
 from models.base import SequentialTorchModel
-
+import pickle
 import torch
 from torch import nn
+import random
 
 
 class TigerModel(SequentialTorchModel, config_name='tiger'):
 
     def __init__(
             self,
+            trie,
             sequence_prefix,
-            positive_prefix,
+            pred_prefix,
+            labels_prefix,
             num_items,
             max_sequence_length,
             embedding_dim,
             num_heads,
             num_layers,
             dim_feedforward,
-            semantic_id_length,
+            semantic_id_arr,
             dropout=0.0,
             activation='relu',
             layer_norm_eps=1e-9,
@@ -35,12 +38,30 @@ def __init__(
             layer_norm_eps=layer_norm_eps,
             is_causal=True
         )
+        
+        transformer_decoder_layer = nn.TransformerDecoderLayer(
+            d_model=embedding_dim,
+            nhead=num_heads,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=get_activation_function(activation),
+            layer_norm_eps=layer_norm_eps,
+            batch_first=True
+        )
+        self._decoder = nn.TransformerDecoder(transformer_decoder_layer, num_layers)
+        self._trie = trie
+        
+        assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
+        self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
+        
         self._sequence_prefix = sequence_prefix
-        self._positive_prefix = positive_prefix
-        self._semantic_id_length = semantic_id_length
+        self._pred_prefix = pred_prefix
+        self._labels_prefix = labels_prefix
+        
+        self._semantic_id_arr = semantic_id_arr
         
         self._codebook_embeddings = nn.Embedding(
-            num_embeddings=semantic_id_length,  # in order to include `max_sequence_length` value
+            num_embeddings=len(semantic_id_arr),  # in order to include `max_sequence_length` value
             embedding_dim=embedding_dim
         )
 
@@ -48,71 +69,134 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
+        with open(config['trie'], 'rb') as f:
+            trie = pickle.load(f)
+    
         return cls(
+            trie=trie,
             sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
+            pred_prefix=config['predictions_prefix'],
+            labels_prefix=config['labels_prefix'],
             num_items=kwargs['num_items'],
             max_sequence_length=kwargs['max_sequence_length'],
             embedding_dim=config['embedding_dim'],
             num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
             num_layers=config['num_layers'],
             dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            semantic_id_length=kwargs['semantic_id_length'],
+            semantic_id_arr=kwargs['semantic_id_arr'],
             dropout=config.get('dropout', 0.0),
             initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs['semantic.{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['semantic.{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        
+        # TODOPK pass parameter as args (self._semantic_prefix)
         
+        label_events = inputs['semantic.{}.ids'.format(self._labels_prefix)]
+        label_lengths = inputs['semantic.{}.length'.format(self._labels_prefix)]
+    
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths, add_codebook_embeddings=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+        
+        decoder_outputs = self._apply_decoder(label_events, label_lengths, embeddings, mask)
+        
+        logits = self._projection(decoder_outputs)  # Shape: (batch_size, seq_len, _semantic_id_arr[0])
 
-        if self.training:  # training mode
-            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-
-            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
-            all_positive_sample_embeddings = self._item_embeddings(
-                all_positive_sample_events
-            )  # (all_batch_events, embedding_dim)
-
-            all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
-
-            all_scores = torch.einsum(
-                'ad,nd->an',
-                all_sample_embeddings,
-                all_embeddings
-            )  # (all_batch_events, num_items + 2)
-            positive_scores = torch.gather(
-                input=all_scores,
-                dim=1,
-                index=all_positive_sample_events[..., None]
-            )  # (all_batch_items, 1)
-
+        if self.training:
             return {
-                'positive_scores': positive_scores,
-                'negative_scores': all_scores
+                self._pred_prefix: logits
             }
-        else:  # eval mode
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
-
-            # b - batch_size, n - num_candidates, d - embedding_dim
-            candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
-            )  # (batch_size, num_items + 2)
-            candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
-
-            _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
-            )  # (batch_size, 20)
+        else:
+            preds = logits.argmax(dim=-1).view(len(all_sample_lengths), len(self._semantic_id_arr)) # Shape: (batch_size, seq_len)
+            ids = self._apply_trie(preds)
+            return torch.tensor(ids)
+    
+    def _apply_trie(self, preds): # TODOPK make this faster (how?)
+        native_repr = [tuple(row.tolist()) for row in preds]
+        ids = []
+        for semantic_id in native_repr:
+            cur_result = set()
+            prefixes = [
+                semantic_id[:i] for i in range(len(semantic_id), 0, -1)
+            ]
+            for prefix in prefixes:
+                prefix_ids = self._trie.search_prefix(prefix) # todo handle collisions (not overwrite)
+                for id in prefix_ids:
+                    cur_result.add(id)
+                    if len(cur_result) >= 20:
+                        break
+            cur_result = list(cur_result)
+            while len(cur_result) < 20:
+                cur_result.append(0) # solve empty event if shortest prefix
+            ids.append(cur_result)
+        return ids
+        
+    def _apply_decoder(self, label_events, label_lengths, encoder_embeddings, encoder_mask):
+        # делаем по аналогии с encoder'ом?
+        embeddings = self._item_embeddings(label_events)  # (batch_size * label_len, embedding_dim)
+
+        embeddings, mask = create_masked_tensor(
+            data=embeddings,
+            lengths=label_lengths
+        )  # (batch_size, label_len, embedding_dim), (batch_size, label_len)
+        
+        batch_size = mask.shape[0]
+        label_len = mask.shape[1]
+        
+        position_embeddings = self._get_position_embeddings(
+            embeddings, label_lengths, mask, batch_size, label_len
+        ) # (batch_size, label_len, embedding_dim)
+        codebook_embeddings = self._get_codebook_embeddings(
+            embeddings, label_lengths, mask, batch_size, label_len
+        ) # (batch_size, label_len, embedding_dim)
+       
+        embeddings = embeddings + codebook_embeddings
+        embeddings = embeddings + position_embeddings
+        
+        embeddings = self._layernorm(embeddings)
+        embeddings = self._dropout(embeddings)
+
+        embeddings[~mask] = 0
+
+        causal_mask = torch.tril(
+            torch.ones(label_len, label_len)
+        ).bool().to(DEVICE)  # (label_len, label_len)
+
+        decoder_outputs = self._decoder(
+            tgt=embeddings,
+            memory=encoder_embeddings,
+            tgt_mask=~causal_mask,
+            memory_key_padding_mask=~encoder_mask,
+            tgt_key_padding_mask=~mask
+        )  # (batch_size, label_len, embedding_dim)
+        
+        decoder_outputs = decoder_outputs.view(-1, self._embedding_dim)
+        
+        return decoder_outputs
+        
+    def _get_position_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
+        positions = torch.arange( # TODOPK invert decoder (position.reverse)
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        
+        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
-            return indices.repeat(self._semantic_id_length, 1)
+        positions = positions[positions_mask]  # (all_batch_events)
+        
+        positions = positions // len(self._semantic_id_arr)
+        # 5 5 5 5 4 4 4 4 3 3 3 3 ...
+        
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings, _ = create_masked_tensor(
+            data=position_embeddings,
+            lengths=lengths
+        )  # (batch_size, seq_len, embedding_dim)
+        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
+        
+        return position_embeddings
         
     def _get_codebook_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
         positions = torch.arange( # TODOPK invert decoder (position.reverse)
@@ -123,8 +207,9 @@ def _get_codebook_embeddings(self, embeddings, lengths, mask, batch_size, seq_le
 
         positions = positions[positions_mask]  # (all_batch_events)
         
-        positions = positions.flip(-1) % self._semantic_id_length # (all_batch_events)
+        positions = positions.flip(-1) % len(self._semantic_id_arr) # (all_batch_events)
         # flip so first item has (0, 1, 2, 3) not (3, 2, 1, 0) semantic_id embeddings
+        # 0 1 2 3 0 1 2 3 0 1 2 3
         
         position_embeddings = self._codebook_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
diff --git a/modeling/rqvae/rqvae_data.py b/modeling/rqvae/rqvae_data.py
index 170c4e18..47692cae 100644
--- a/modeling/rqvae/rqvae_data.py
+++ b/modeling/rqvae/rqvae_data.py
@@ -75,7 +75,7 @@ def get_data(cached=True):
         with torch.no_grad():
             df["embeddings"] = df["combined_text"].progress_apply(encode_text)
     else:
-        df = torch.load("../data/Beauty/all_data.pt", weights_only=False)
+        df = torch.load("../data/Beauty/data_full.pt", weights_only=False)
         
     return df
 
diff --git a/modeling/trie.py b/modeling/trie.py
new file mode 100644
index 00000000..1c98c362
--- /dev/null
+++ b/modeling/trie.py
@@ -0,0 +1,41 @@
+class TrieNode:
+    def __init__(self):
+        self.children = {}
+        self.id = None
+
+
+class Trie:
+    def __init__(self):
+        self.root = TrieNode()
+
+    def insert(self, tuple_key, id):
+        node = self.root
+        for number in tuple_key:
+            if number not in node.children:
+                node.children[number] = TrieNode()
+            node = node.children[number]
+        node.id = id
+
+    def search(self, tuple_key):
+        node = self.root
+        for number in tuple_key:
+            if number not in node.children:
+                return None
+            node = node.children[number]
+        return node.id
+
+    def _get_all_leaf_ids(self, node):
+        result = []
+        if node.id is not None:
+            result.append(node.id)
+        for child in node.children.values():
+            result.extend(self._get_all_leaf_ids(child))
+        return result
+
+    def search_prefix(self, prefix):
+        node = self.root
+        for number in prefix:
+            if number not in node.children:
+                return []
+            node = node.children[number]
+        return self._get_all_leaf_ids(node)
\ No newline at end of file
diff --git a/review.md b/review.md
index 242aaa95..f9534ce0 100644
--- a/review.md
+++ b/review.md
@@ -3,6 +3,18 @@
 ## Todos
 
 - max_sequence_length (TODOPK), why +1? не смог найти где дописывается в батч сама длина
+- positions = positions // self._semantic_id_length или reverse?
+как именно учитываем codebook_post & item_pos (тот же порядок или inverted)
+- как именно находим ближайшего при пересечении по embedding? (не понял о каком embedding речь)
+- почему
+
+```python
+candidate_scores = torch.einsum(
+    'bd,nd->bn',
+    predictions,
+    self._item_embeddings.weight
+)
+```
 
 - next_item_pred / last_item_pred (какие задачи учим и как именно) # can be both tasks
 - предсказываем item = предсказываем 4 semantic id? # yes

From 411e52f0a71ec36a1f82c07b44745511005df51a Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Tue, 31 Dec 2024 15:28:12 +0300
Subject: [PATCH 021/175] speed up semantic id fetching

---
 .gitignore                              |  1 +
 configs/train/tiger_train_config.json   |  8 ++++----
 modeling/dataloader/batch_processors.py | 22 +++++++++++++++-------
 modeling/loss/base.py                   |  1 -
 4 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/.gitignore b/.gitignore
index 90acddb5..7980d8a2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,4 @@ tensorboard_logs/*
 .venv
 papers
 checkpoints/*
+*.prof
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 17441ba3..bbb423d7 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "tiger",
-  "train_steps_num": 1024,
+  "train_steps_num": 50,
   "best_metric": "eval/ndcg@20",
   "dataset": {
     "type": "rqvae_scientific",
@@ -22,7 +22,7 @@
         "type": "rqvae",
         "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
         "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-        "embs_extractor_path": "../data/Beauty/data_full.pt"
+        "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt"
       },
       "drop_last": true,
       "shuffle": true
@@ -34,7 +34,7 @@
         "type": "rqvae",
         "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
         "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-        "embs_extractor_path": "../data/Beauty/data_full.pt"
+        "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt"
       },
       "drop_last": false,
       "shuffle": false
@@ -42,7 +42,7 @@
   },
   "model": {
     "type": "tiger",
-    "trie": "../data/Beauty/trie.pkl",
+    "trie": "../data/Beauty/rqvae/trie.pkl",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
     "labels_prefix": "labels",
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 2c3948a2..f1d17e0c 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -25,9 +25,8 @@ def __call__(self, batch):
         return {'ids': ids, 'embeddings': embeds}
     
 class RqVaeProcessor(BaseBatchProcessor, config_name='rqvae'):
-    def __init__(self, rqvae, embs_extractor):
-        self._rqvae = rqvae
-        self._embs_extractor = embs_extractor
+    def __init__(self, item_id_to_semantic_id):
+        self._item_id_to_semantic_id = item_id_to_semantic_id
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
@@ -39,13 +38,22 @@ def create_from_config(cls, config, **kwargs):
         rqvae_model.eval()
         
         embs_extractor = torch.load(config['embs_extractor_path'])
+        
+        item_ids = embs_extractor.index.tolist()
+        embeddings = torch.stack([emb for emb in embs_extractor['embeddings'].tolist()])
+        semantic_ids = list(rqvae_model({"embeddings": embeddings}))
+        
+        item_id_to_semantic_id = {
+            item_id: semantic_id for (item_id, semantic_id) in zip(item_ids, semantic_ids)
+        }
 
-        return cls(rqvae_model, embs_extractor)
+        return cls(item_id_to_semantic_id)
     
     def get_semantic_ids(self, item_ids):
-        embs = torch.stack([self._embs_extractor.loc[item_id]['embeddings'] for item_id in item_ids])
-        semantic_ids = self._rqvae({"embeddings": embs})
-        return list(semantic_ids)
+        semantic_ids = []
+        for item_id in item_ids:
+            semantic_ids.append(self._item_id_to_semantic_id[item_id])
+        return semantic_ids
 
     def __call__(self, batch):
         processed_batch = {}
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 11aa2a9b..c7cfb4ee 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -137,7 +137,6 @@ def forward(self, inputs):
             rqvae_loss += self._loss(codebook_vectors, remainder.detach())
             
         recon_loss = self._loss(embeddings_restored, embeddings)
-        # print(recon_loss.shape, rqvae_loss.shape) # TODOPK
         loss = (recon_loss + rqvae_loss).mean(dim=0)
         
         if self._output_prefix is not None:

From 28904f77298c38710de69bccf0fc1e9b13833be4 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Tue, 31 Dec 2024 15:55:40 +0300
Subject: [PATCH 022/175] fix infer (20 items) & logits

---
 modeling/models/tiger.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 1c1f106d..7078e1b0 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -101,18 +101,20 @@ def forward(self, inputs):
             all_sample_events, all_sample_lengths, add_codebook_embeddings=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
         
-        decoder_outputs = self._apply_decoder(label_events, label_lengths, embeddings, mask)
+        decoder_outputs = self._apply_decoder(label_events, label_lengths, embeddings, mask) # (batch_size, label_len, embedding_dim)
+        # todo pk correct place for projection? or view -> projection
+        logits = self._projection(decoder_outputs)  # (batch_size, seq_len, _semantic_id_arr[0])
+        
+        logits = logits.view(-1, self._semantic_id_arr[0]) # (batch_size * seq_len, _semantic_id_arr[0])
         
-        logits = self._projection(decoder_outputs)  # Shape: (batch_size, seq_len, _semantic_id_arr[0])
-
         if self.training:
             return {
                 self._pred_prefix: logits
             }
         else:
             preds = logits.argmax(dim=-1).view(len(all_sample_lengths), len(self._semantic_id_arr)) # Shape: (batch_size, seq_len)
-            ids = self._apply_trie(preds)
-            return torch.tensor(ids)
+            ids = torch.tensor(self._apply_trie(preds))
+            return ids
     
     def _apply_trie(self, preds): # TODOPK make this faster (how?)
         native_repr = [tuple(row.tolist()) for row in preds]
@@ -128,6 +130,9 @@ def _apply_trie(self, preds): # TODOPK make this faster (how?)
                     cur_result.add(id)
                     if len(cur_result) >= 20:
                         break
+                if len(cur_result) >= 20:
+                    break
+            
             cur_result = list(cur_result)
             while len(cur_result) < 20:
                 cur_result.append(0) # solve empty event if shortest prefix
@@ -173,8 +178,6 @@ def _apply_decoder(self, label_events, label_lengths, encoder_embeddings, encode
             tgt_key_padding_mask=~mask
         )  # (batch_size, label_len, embedding_dim)
         
-        decoder_outputs = decoder_outputs.view(-1, self._embedding_dim)
-        
         return decoder_outputs
         
     def _get_position_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):

From fa424289239aec0c10386eab078d0acc1b7b201e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Tue, 31 Dec 2024 16:28:15 +0300
Subject: [PATCH 023/175] tweak config

---
 configs/train/tiger_train_config.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index bbb423d7..0284c71a 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "tiger",
-  "train_steps_num": 50,
+  "train_epochs_num": 10,
   "best_metric": "eval/ndcg@20",
   "dataset": {
     "type": "rqvae_scientific",
@@ -119,7 +119,7 @@
       },
       {
         "type": "eval",
-        "on_step": 256,
+        "on_step": 64,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {

From 9a95b95fb3657ebcc30f3183310517a27d8d5a9e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 2 Jan 2025 00:10:41 +0300
Subject: [PATCH 024/175] clear outputs from main.ipynb

---
 modeling/main.ipynb | 2674 -------------------------------------------
 1 file changed, 2674 deletions(-)

diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index 57fe551c..0924a494 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -52,2680 +52,6 @@
     "semantic_ids = list(rqvae_model.forward(embs_dict))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('7806397051',\n",
-       "  'WAWO 15 Color Professionl Makeup Eyeshadow Camouflage Facial Concealer Neutral Palette',\n",
-       "  (13, 66, 216, 169, 0)),\n",
-       " ('9759091062', 'Xtreme Brite Brightening Gel 1oz.', (132, 120, 59, 169, 0)),\n",
-       " ('9788072216',\n",
-       "  'Prada Candy By Prada Eau De Parfum Spray 1.7 Oz For Women',\n",
-       "  (250, 134, 192, 152, 0)),\n",
-       " ('9790790961',\n",
-       "  'Versace Bright Crystal Eau de Toilette Spray for Women, 3 Ounce',\n",
-       "  (63, 173, 70, 37, 0)),\n",
-       " ('9790794231', 'Stella McCartney Stella', (58, 80, 40, 27, 0)),\n",
-       " ('B00004TMFE',\n",
-       "  'Avalon Biotin B-Complex Thickening Conditioner, 14 Ounce',\n",
-       "  (38, 178, 162, 34, 0)),\n",
-       " ('B00004TUBL',\n",
-       "  'Better Living Classic Two Chamber Dispenser, White',\n",
-       "  (226, 59, 24, 116, 0)),\n",
-       " ('B00004TUBV',\n",
-       "  'Better Living The Ulti-Mate Dispenser',\n",
-       "  (174, 189, 124, 99, 0)),\n",
-       " ('B00004U9UY',\n",
-       "  'Crabtree &amp; Evelyn 2792 Gardeners Hand Therapy (100ml, 3.4 oz)',\n",
-       "  (38, 145, 226, 206, 0)),\n",
-       " ('B00004U9V2',\n",
-       "  \"Crabtree &amp; Evelyn - Gardener's Ultra-Moisturising Hand Therapy Pump - 250g/8.8 OZ\",\n",
-       "  (216, 161, 162, 182, 0)),\n",
-       " ('B000050B6U', 'Instant Heat Curling Iron, 1-Inch', (174, 77, 140, 27, 0)),\n",
-       " ('B000052WY7',\n",
-       "  'Maybelline New York Expert Wear Brow &amp; Eyeliner, 151 Velvet Black , .011 oz (310 mg)',\n",
-       "  (228, 89, 201, 195, 0)),\n",
-       " ('B000052WYD',\n",
-       "  'Maybelline New York Cover Stick Concealer, Ivory, Light 2, 0.16 Ounce',\n",
-       "  (110, 131, 34, 13, 0)),\n",
-       " ('B000052WYL',\n",
-       "  'Maybelline New York Expert Eyes Moisturizing Eye Makeup Remover, 2.3 Fl. Oz.',\n",
-       "  (123, 131, 70, 37, 0)),\n",
-       " ('B000052WYN',\n",
-       "  'Maybelline New York Ultra-Liner Liquid Liner, Waterproof, Black 135L-01 , .25 fl oz (7.3 ml)',\n",
-       "  (3, 135, 44, 12, 0)),\n",
-       " ('B000052X9G',\n",
-       "  'Nullo Internal Deodorant, Coated Caplets, 135 Caplets',\n",
-       "  (71, 247, 246, 91, 0)),\n",
-       " ('B000052XIA',\n",
-       "  'Vagisil Anti-Itch Creme, Original Formula - 1 oz',\n",
-       "  (205, 173, 132, 171, 0)),\n",
-       " ('B000052XW5',\n",
-       "  'Pretty Feet &amp; Hands Rough Skin Remover-Exfoliant, 3-Ounce Bottles (Pack of 3)',\n",
-       "  (194, 47, 234, 60, 0)),\n",
-       " ('B000052XYQ', 'DHS Clear Shampoo 8 Fl Oz', (69, 8, 226, 103, 0)),\n",
-       " ('B000052XZP',\n",
-       "  'Neutrogena T-Gel Shampoo, Stubborn Itch Control, 4.4 Ounce',\n",
-       "  (70, 103, 201, 76, 0)),\n",
-       " ('B000052XZX',\n",
-       "  'Neutrogena T/Sal Shampoo, Scalp Build-up Control, 4.5 fl oz',\n",
-       "  (167, 173, 65, 103, 0)),\n",
-       " ('B000052Y25',\n",
-       "  'DHS Conditioning Rinse With Panthenol 8 fl oz',\n",
-       "  (44, 159, 254, 27, 0)),\n",
-       " ('B000052Y33',\n",
-       "  'Alberto VO5 Moisturizing Hot Oil Treatment, 0.5 Ounce, 2-Count Tubes (Pack of 6)',\n",
-       "  (13, 134, 153, 159, 0)),\n",
-       " ('B000052Y5F',\n",
-       "  'Konsyl Pharmaceuticals Psyllium Fiber 15.9 oz',\n",
-       "  (127, 173, 122, 236, 0)),\n",
-       " ('B000052Y6Z',\n",
-       "  'Williams Lectric Shave Electric Razor Pre-Shave, Original, 3 fl oz',\n",
-       "  (99, 105, 192, 202, 0)),\n",
-       " ('B000052YJC',\n",
-       "  'Buf-Puf Double-Sided Body Sponge - 1 ea',\n",
-       "  (230, 47, 162, 99, 0)),\n",
-       " ('B000052YJD',\n",
-       "  'Buf-Puf Reusable Facial Sponge, Extra Gentle, - 1 ea',\n",
-       "  (71, 173, 163, 68, 0)),\n",
-       " ('B000052YJH',\n",
-       "  'Buf-Puf Reusable Facial Sponge (Pack of 6)',\n",
-       "  (245, 173, 219, 34, 0)),\n",
-       " ('B000052YJM',\n",
-       "  'Clean &amp; Clear ESSENTIALS Dual Action Moisturizer, 4 Ounce',\n",
-       "  (154, 180, 29, 37, 0)),\n",
-       " ('B000052YJX',\n",
-       "  'Neutrogena OntheSpot Acne Treatment, Vanishing Formula, 0.75 Ounce',\n",
-       "  (93, 145, 227, 77, 0)),\n",
-       " ('B000052YKI',\n",
-       "  'Purpose Dual Treatment Moisture Lotion with SPF 15 4 fl oz (120 ml)',\n",
-       "  (196, 161, 250, 76, 0)),\n",
-       " ('B000052YKM',\n",
-       "  'Stri-Dex Medicated Pads, Maximum Strength, 90-Count Containers, (Pack of 3)',\n",
-       "  (6, 66, 67, 241, 0)),\n",
-       " ('B000052YKS',\n",
-       "  'Basis Cleaner Clean Face Wash, 6 Fluid Ounces',\n",
-       "  (40, 100, 67, 170, 0)),\n",
-       " ('B000052YKY',\n",
-       "  'Cetaphil Gentle Cleansing Bar, Antibacterial - 4.5 oz',\n",
-       "  (102, 59, 11, 219, 0)),\n",
-       " ('B000052YL1',\n",
-       "  'Cuticura Medicated Anti-Bacterial Bar Soap, Original Formula - 5.25 oz bar',\n",
-       "  (71, 167, 192, 202, 0)),\n",
-       " ('B000052YLX',\n",
-       "  'Purpose Gentle Cleansing Bar 6 oz (170 g)',\n",
-       "  (128, 80, 65, 77, 0)),\n",
-       " ('B000052YM0',\n",
-       "  'Fruit Of The Earth 100% Aloe Vera 6oz. Gel Tube',\n",
-       "  (117, 47, 162, 159, 0)),\n",
-       " ('B000052YM3',\n",
-       "  'Alpha Hydrox AHA Enhanced Cream 2 oz.',\n",
-       "  (82, 134, 216, 103, 0)),\n",
-       " ('B000052YM4',\n",
-       "  'Alpha Hydrox Foaming Face Wash -- 6 fl oz',\n",
-       "  (69, 47, 219, 103, 0)),\n",
-       " ('B000052YM7',\n",
-       "  'Alpha Hydrox Enhanced Lotion 10 percent Glycolic AHA -- 6 fl oz Anti-Wrinkle',\n",
-       "  (100, 46, 246, 53, 0)),\n",
-       " ('B000052YM8',\n",
-       "  'Alpha Hydrox Oil Free Treatment 10 Percent AHA 1.7 fl oz.',\n",
-       "  (205, 134, 147, 152, 0)),\n",
-       " ('B000052YMG', 'Aquanil Cleanser 16 fl oz.', (71, 47, 59, 143, 0)),\n",
-       " ('B000052YMQ',\n",
-       "  'Cetaphil Moisturizing Cream, Fragrance Free - 16 oz',\n",
-       "  (167, 134, 226, 37, 0)),\n",
-       " ('B000052YMR',\n",
-       "  'Cetaphil Moisturizing Cream, 3.0 - Ounces Tube (Pack of 3)',\n",
-       "  (2, 2, 226, 76, 0)),\n",
-       " ('B000052YMS',\n",
-       "  'Cetaphil Moisturizing Lotion, Fragrance Free - 16 fl oz',\n",
-       "  (56, 134, 122, 15, 0)),\n",
-       " ('B000052YMT',\n",
-       "  'Cetaphil Gentle Skin Cleanser, 4.0 -Ounce Bottles (Pack of 6)',\n",
-       "  (56, 21, 201, 160, 0)),\n",
-       " ('B000052YMV',\n",
-       "  'Cetaphil Gentle Skin Cleanser - 16 fl oz',\n",
-       "  (56, 96, 65, 15, 0)),\n",
-       " ('B000052YMX',\n",
-       "  'Complex 15 Therapeutic Moisturizing Face Cream - 2.5 Ounce',\n",
-       "  (56, 3, 59, 160, 0)),\n",
-       " ('B000052YN5',\n",
-       "  'DML Daily Facial Moisturizer, SPF 25 - 1.5 Oz',\n",
-       "  (71, 134, 208, 60, 0)),\n",
-       " ('B000052YN7', 'DML Moisturizing Lotion 16 oz.', (117, 134, 234, 103, 0)),\n",
-       " ('B000052YOL',\n",
-       "  'Neutrogena Healthy Skin Anti-Wrinkle Cream, SPF 15, 1.4 Ounce',\n",
-       "  (99, 120, 162, 231, 0)),\n",
-       " ('B000052YOR',\n",
-       "  'Neutrogena Body Oil, Light Sesame Formula, 8.5 Ounce',\n",
-       "  (139, 173, 122, 99, 0)),\n",
-       " ('B000052YOX',\n",
-       "  'Neutrogena Oil-Free Moisture, Sensitive Skin, 4 Ounce',\n",
-       "  (99, 202, 231, 169, 0)),\n",
-       " ('B000052YP4',\n",
-       "  'Neutrogena Light Night Cream, 2.25 Ounce',\n",
-       "  (99, 3, 27, 241, 0)),\n",
-       " ('B000052YP6',\n",
-       "  'Neutrogena Norwegian Formula Hand Cream, Fragrance-Free (2 Ounce)',\n",
-       "  (117, 47, 71, 91, 0)),\n",
-       " ('B000052YQ0',\n",
-       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 Normal 4 Fl Oz',\n",
-       "  (215, 202, 139, 15, 0)),\n",
-       " ('B000052YQ2',\n",
-       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 - Sensitive 4 Fl Oz',\n",
-       "  (93, 129, 216, 13, 0)),\n",
-       " ('B000052YQN', \"POND'S Cold Cream Cleanser, 3.5 oz.\", (70, 23, 65, 76, 0)),\n",
-       " ('B000052YQU',\n",
-       "  \"POND'S Dry Skin Cream Facial Moisturizer, 3.9-Ounce (Packaging May Vary)\",\n",
-       "  (99, 159, 67, 99, 0)),\n",
-       " ('B000052Z5B',\n",
-       "  'Coty Airspun Face Powder, Naturally Neutral, 2.3 oz',\n",
-       "  (201, 180, 201, 135, 0)),\n",
-       " ('B000052Z8D',\n",
-       "  'Max Factor Pan-Stik Ultra Creamy Makeup, Sun Tone 137 .5 oz (14 g)',\n",
-       "  (188, 122, 254, 37, 0)),\n",
-       " ('B000052ZB1',\n",
-       "  'Neutrogena Intensified Day Moisture, SPF 15, 2.25 Ounce',\n",
-       "  (44, 120, 234, 169, 0)),\n",
-       " ('B000052ZB2',\n",
-       "  'Neutrogena Extra Gentle Cleanser, 6.7 Ounce',\n",
-       "  (44, 115, 71, 195, 0)),\n",
-       " ('B000052ZB4',\n",
-       "  'Neutrogena Deep Clean Facial Cleanser, Normal to Oily Skin, 6.7 Ounce',\n",
-       "  (239, 211, 192, 170, 0)),\n",
-       " ('B000052ZBD',\n",
-       "  'Neutrogena Rainbath Gel, Original, 16  Ounce',\n",
-       "  (71, 64, 201, 114, 0)),\n",
-       " ('B000052ZBH',\n",
-       "  'Almay One Coat Nourishing Mascara, Thickening, Black 402, 0.4-Ounce Package',\n",
-       "  (13, 66, 27, 236, 0)),\n",
-       " ('B000052ZBP',\n",
-       "  'Almay Moisturizing Eye Makeup Remover Pads, 80-Pads',\n",
-       "  (194, 89, 27, 162, 0)),\n",
-       " ('B000052ZTY',\n",
-       "  'Hugo By Hugo Boss For Men. Eau De Toilette Spray 1.3 Ounces',\n",
-       "  (8, 55, 10, 163, 0)),\n",
-       " ('B00005302B',\n",
-       "  'Jergen Extra Moisturizing Liquid Hand Wash 7.5 Oz',\n",
-       "  (28, 100, 122, 91, 0)),\n",
-       " ('B00005304H', 'Q-tips Cotton Swabs, 500 Count', (172, 159, 155, 169, 0)),\n",
-       " ('B0000530ED',\n",
-       "  \"L'Oreal Paris Feria Hair Color, 36 Dark Burgundy Brown/Chocolate Cherry\",\n",
-       "  (52, 167, 122, 170, 0)),\n",
-       " ('B0000530EZ',\n",
-       "  'Aussie Hair Insurance Leave-In Conditioner 8 Fl Oz (Pack of 3)',\n",
-       "  (223, 103, 234, 116, 0)),\n",
-       " ('B0000530G6',\n",
-       "  'Suave Professionals Conditioner, Humectant - 12.6 Ounce',\n",
-       "  (223, 103, 192, 219, 0)),\n",
-       " ('B0000530LO',\n",
-       "  'Suave Naturals Shampoo, Daily Clarifying - 22.5oz.',\n",
-       "  (75, 247, 71, 60, 0)),\n",
-       " ('B0000530LP',\n",
-       "  'Suave Shampoo, Daily Clarifying - 12oz.',\n",
-       "  (41, 120, 234, 91, 0)),\n",
-       " ('B0000530LV',\n",
-       "  'Suave Naturals Shampoo, Sun Ripened Strawberry 22.5oz',\n",
-       "  (71, 145, 11, 202, 0)),\n",
-       " ('B0000530M0',\n",
-       "  'Suave Professionals Humectant Moisture Shampoo , 12.6 fl Ounce (373 ml)',\n",
-       "  (70, 4, 41, 77, 0)),\n",
-       " ('B0000530NA',\n",
-       "  'Olay Daily Care Refreshing Toner, 7.2-Fluid Ounce',\n",
-       "  (78, 134, 67, 195, 0)),\n",
-       " ('B0000530O9',\n",
-       "  'Olay Age Defying Classic Eye Gel 0.5 Oz',\n",
-       "  (204, 255, 27, 241, 0)),\n",
-       " ('B0000531GD',\n",
-       "  'Maybelline New York Shine Free Oil Control Pressed Powder, Ivory - 1 ea',\n",
-       "  (107, 68, 33, 104, 0)),\n",
-       " ('B0000531GV',\n",
-       "  'Maybelline New York Expert Eyes 100% Oil-Free Eye Makeup Remover, 2.3 Fl. Oz.',\n",
-       "  (127, 122, 11, 76, 0)),\n",
-       " ('B0000531II',\n",
-       "  'Maybelline New York Shine Free Oil Control Loose Powder, Light, 0.7 Ounce',\n",
-       "  (177, 122, 41, 76, 0)),\n",
-       " ('B0000531NU',\n",
-       "  \"Maybelline New York Volum' Express - Waterproof Mascara, Very Black - .34 fl oz\",\n",
-       "  (194, 159, 75, 76, 0)),\n",
-       " ('B0000531SW',\n",
-       "  \"L'Oreal Paris Visible Lift Line-Minimizing &amp; Tone-Enhancing Makeup, Soft Ivory, 1.25 Ounces\",\n",
-       "  (92, 96, 11, 27, 0)),\n",
-       " ('B0000531WZ',\n",
-       "  'Revlon Super Lustrous Creme Lipstick, Fire and Ice 720, 0.15 Ounce',\n",
-       "  (70, 122, 55, 170, 0)),\n",
-       " ('B00005321Z',\n",
-       "  \"Revlon Eterna '27' Moisture Cream with Progenitin, 2 Ounce\",\n",
-       "  (250, 234, 73, 37, 0)),\n",
-       " ('B0000532AN', 'Revlon Lash Curlers, 1 Count', (192, 161, 41, 194, 0)),\n",
-       " ('B0000532VT',\n",
-       "  'Neutrogena Clean Conditioner, Replenishing , 10.1 Fluid Ounce',\n",
-       "  (17, 202, 138, 169, 0)),\n",
-       " ('B0000532YN',\n",
-       "  'Kiss My Face Bar Soap, 8.0 oz, Pure Olive Oil. 1-Bar',\n",
-       "  (110, 2, 70, 104, 0)),\n",
-       " ('B00005333G',\n",
-       "  \"L'Oreal Paris Hydra-Renewal Continuous Moisture Cream, 1.7 Ounce\",\n",
-       "  (44, 8, 139, 37, 0)),\n",
-       " ('B00005333I',\n",
-       "  \"L'Oreal Paris Eye Defense, 0.5 Fluid Ounce\",\n",
-       "  (133, 211, 192, 15, 0)),\n",
-       " ('B0000533CC',\n",
-       "  'Instead 12 Hour Feminine Protection Cup 24 ea',\n",
-       "  (172, 129, 163, 169, 0)),\n",
-       " ('B0000534VO',\n",
-       "  'Clean &amp; Clear Continuous Control Acne Cleanser,  5 oz  (141g)',\n",
-       "  (44, 103, 24, 236, 0)),\n",
-       " ('B0000535CH',\n",
-       "  \"Nature's Cure Two-part Acne Treatment System for Women 60 Tablets 1 Oz Cream\",\n",
-       "  (12, 47, 132, 15, 0)),\n",
-       " ('B0000535OF',\n",
-       "  'Alpha Hydrox Night Replenishing Cream 2 oz.',\n",
-       "  (71, 180, 122, 206, 0)),\n",
-       " ('B0000535RA',\n",
-       "  'Neutrogena Pore Refining Toner, Alpha and Beta Hydroxy Formula, 8.5 Ounce',\n",
-       "  (44, 4, 10, 100, 0)),\n",
-       " ('B0000535RC',\n",
-       "  'NeutrogenaClear Pore Oil Eliminating Astringent, 8 Ounce',\n",
-       "  (133, 4, 65, 104, 0)),\n",
-       " ('B0000535RD',\n",
-       "  'Neutrogena Body Clear Body Wash for Clean, Clear Skin, 8.5 Ounce',\n",
-       "  (128, 120, 201, 77, 0)),\n",
-       " ('B0000535U2', 'Jason C-Effects Creme, 2 Ounces', (50, 196, 219, 202, 0)),\n",
-       " ('B0000535UA', 'Jason Shampoo, Tea Tree, 17.5 Ounce', (50, 115, 140, 169, 0)),\n",
-       " ('B0000535UM',\n",
-       "  'Jason Thin-To-Thick Extra Volume Shampoo, 8 Ounce',\n",
-       "  (172, 115, 140, 169, 0)),\n",
-       " ('B0000535UN',\n",
-       "  'Thin to Thick Extra Volume Conditioner 8 oz',\n",
-       "  (102, 66, 65, 15, 0)),\n",
-       " ('B0000535UT',\n",
-       "  'Jason Purifying Tea Tree Body Wash 30 fl oz',\n",
-       "  (71, 1, 162, 143, 0)),\n",
-       " ('B0000535UX',\n",
-       "  'Jason Pure Natural Hand Soap, Purifying Tea Tree, 16 Ounce',\n",
-       "  (172, 196, 140, 13, 0)),\n",
-       " ('B0000535VH',\n",
-       "  'Alpha Hydrox Optimum Series, Retinol Night ResQ, Anti-Wrinkle Firming Complex - 1.05 oz',\n",
-       "  (205, 159, 139, 103, 0)),\n",
-       " ('B000053676',\n",
-       "  'Avalon Organics Vitamin C Revitalizing Eye Creme, 1 Ounce Bottle',\n",
-       "  (71, 3, 41, 15, 0)),\n",
-       " ('B0000536A3',\n",
-       "  'Tommy Hilfiger Tommy Cologne Spray for Men - 3.4 Fluid Ounces',\n",
-       "  (182, 62, 201, 219, 0)),\n",
-       " ('B0000536EK',\n",
-       "  \"Mother's Special Blend All Natural Skin Toning Oil, 8-Ounce Bottle\",\n",
-       "  (102, 173, 70, 91, 0)),\n",
-       " ('B0000536EW',\n",
-       "  'Aveeno Anti-Itch Concentrated Lotion, 4 Ounce',\n",
-       "  (127, 62, 163, 159, 0)),\n",
-       " ('B0000536F0',\n",
-       "  'Aveeno Moisturizing Bar with Natural Colloidal Oatmeal for Dry Skin, Fragrance Free, 3.5 oz',\n",
-       "  (161, 96, 246, 103, 0)),\n",
-       " ('B0000536M2',\n",
-       "  \"Maybelline New York Full 'N Soft Mascara, Very Black, 0.28 Oz.\",\n",
-       "  (65, 66, 208, 160, 0)),\n",
-       " ('B0000536M3',\n",
-       "  \"Maybelline Full' N Soft Mascara: Brownish Black #302\",\n",
-       "  (28, 120, 59, 169, 0)),\n",
-       " ('B0000536P3',\n",
-       "  'Olay Total Effects 7-In-1 Anti-Aging Daily Moisturizer 1.7 Fl. Oz.',\n",
-       "  (167, 96, 11, 53, 0)),\n",
-       " ('B0000536P4',\n",
-       "  'Olay Total Effects Anti-Aging Fragrance Free Moisturizer 1.7 Fl Oz',\n",
-       "  (21, 96, 11, 53, 0)),\n",
-       " ('B00005375C',\n",
-       "  'Aveeno Daily Moisturizing Lotion, 8 Ounce',\n",
-       "  (239, 89, 192, 99, 0)),\n",
-       " ('B0000537NH',\n",
-       "  'Neutrogena Sensitive Skin Sunscreen Lotion, SPF 30, 4 Ounce',\n",
-       "  (132, 202, 246, 53, 0)),\n",
-       " ('B0000537SD', 'ZapZyt Acne Treatment Gel, 1 oz', (93, 173, 227, 217, 0)),\n",
-       " ('B000053L6W',\n",
-       "  'Clubman Pinaud Virgin Island Bay Rum, 12 Ounce',\n",
-       "  (94, 115, 201, 76, 0)),\n",
-       " ('B000056I89',\n",
-       "  'Cetaphil Daily Facial Cleanser, Normal to Oily Skin - 8 fl oz',\n",
-       "  (44, 4, 37, 34, 0)),\n",
-       " ('B000056W5Y',\n",
-       "  \"Palmer's Cocoa Butter Formula Massage Cream for Stretch Marks, 4.4 Ounce\",\n",
-       "  (127, 103, 59, 202, 0)),\n",
-       " ('B000056W74',\n",
-       "  \"Palmer's Cocoa Butter Formula Moisturizing Body Oil with Vitamin E -- 8.5 fl oz\",\n",
-       "  (196, 211, 139, 76, 0)),\n",
-       " ('B00005A43X',\n",
-       "  'Conair Supreme 2-In-1 Hot Air Styling Brush',\n",
-       "  (67, 149, 163, 27, 0)),\n",
-       " ('B00005A443',\n",
-       "  'Conair Double-Sided Battery-Operated Lighted Makeup Mirror, Polished Chrome Finish',\n",
-       "  (36, 4, 67, 219, 0)),\n",
-       " ('B00005A9WP',\n",
-       "  'Coanir Facial Sauna System with Timer',\n",
-       "  (174, 131, 27, 160, 0)),\n",
-       " ('B00005AVC7',\n",
-       "  'Clean &amp; Clear Instant Oil-Absorbing Sheets 50 sheets',\n",
-       "  (28, 131, 192, 219, 0)),\n",
-       " ('B00005B9FV',\n",
-       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Lotion SPF, 4 Ounce',\n",
-       "  (196, 245, 116, 170, 0)),\n",
-       " ('B00005CDSP',\n",
-       "  'Revlon Perfect Heat 1-1/2inch Curling Iron',\n",
-       "  (174, 66, 153, 100, 0)),\n",
-       " ('B00005IBW0',\n",
-       "  'St. Ives, Sensitive Skin Apricot Scrub, 6-Ounce (Pack of 6)',\n",
-       "  (70, 46, 118, 184, 0)),\n",
-       " ('B00005LBRT',\n",
-       "  'Avalon Organics Lavender Luminosity Daily Moisturizer, 2 Ounce',\n",
-       "  (104, 234, 67, 34, 0)),\n",
-       " ('B00005NAOD',\n",
-       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Creme, 1.7 Ounce Jar',\n",
-       "  (196, 245, 56, 152, 0)),\n",
-       " ('B00005NAOJ',\n",
-       "  'Cetaphil Gentle Cleansing Bar - 4.5 oz',\n",
-       "  (71, 47, 70, 143, 0)),\n",
-       " ('B00005NASR',\n",
-       "  'Ecco Bella Original Organic Water-Free Herbal Body Lotion, Vanilla, 8-Ounce Bottle',\n",
-       "  (107, 47, 162, 99, 0)),\n",
-       " ('B00005NFBD',\n",
-       "  'Freeman Facial Masque, Purifying - 6 fl oz',\n",
-       "  (93, 173, 254, 103, 0)),\n",
-       " ('B00005NFBJ',\n",
-       "  'Freeman Cucumber Facial Peel-Off Mask - 6 oz',\n",
-       "  (117, 135, 24, 91, 0)),\n",
-       " ('B00005O0MZ',\n",
-       "  'Conair 1875 Watt Ionic Conditioning Hair Dryer',\n",
-       "  (174, 149, 124, 60, 0)),\n",
-       " ('B00005R1H2',\n",
-       "  'Skin Success Eventone Fade Milk with Vitamin E and Alpha Hydroxy - 8.5 Fluid Ounces',\n",
-       "  (205, 173, 122, 160, 0)),\n",
-       " ('B00005R1H3',\n",
-       "  'Skin Success Eventone Fade Cream, For Oily Skin - 2.7 oz',\n",
-       "  (167, 173, 22, 236, 0)),\n",
-       " ('B00005REAQ',\n",
-       "  'Alba BotanicaTM Even Advanced Natural Moisturizer Sea Moss SPF 15 -- 2 fl oz',\n",
-       "  (154, 100, 27, 77, 0)),\n",
-       " ('B00005REAT',\n",
-       "  'Body Bath Honey Mango Alba Botanica 12 oz Liquid',\n",
-       "  (109, 4, 3, 116, 0)),\n",
-       " ('B00005S81T',\n",
-       "  'Skin Success Eventone Fade Cream, Regular - 2.7 oz',\n",
-       "  (167, 173, 11, 170, 0)),\n",
-       " ('B00005T83D',\n",
-       "  \"Palmer's Cocoa Butter Formula, Cream Soap Bar with Vitamin E, 3.5 oz - 1 ea\",\n",
-       "  (44, 173, 11, 77, 0)),\n",
-       " ('B00005TZU8',\n",
-       "  'DreamTime Inner Peace Eye Pillow, Lavender Velvet',\n",
-       "  (144, 3, 24, 206, 0)),\n",
-       " ('B00005UN90',\n",
-       "  'Suave Naturals Body Wash, Ocean Breeze - 12oz.',\n",
-       "  (70, 202, 139, 53, 0)),\n",
-       " ('B00005UQSP',\n",
-       "  'EO Shampoo for Fine/Oily Hair, Rosemary &amp; Mint, 8 fl oz (240 ml)',\n",
-       "  (173, 180, 22, 34, 0)),\n",
-       " ('B00005V5OU',\n",
-       "  'Neutrogena Healthy Defense Daily Moisturizer, SPF 30, Light Tint 1.7 Ounce',\n",
-       "  (44, 24, 33, 169, 0)),\n",
-       " ('B00005V5OW',\n",
-       "  'Maybelline Lash Discovery Washable Mascara, Very Black - .16 fl oz',\n",
-       "  (200, 255, 153, 195, 0)),\n",
-       " ('B00005V5P6',\n",
-       "  'Maybelline New York Purestay Powder &amp; Foundation SPF 15, Ivory - .34 oz',\n",
-       "  (221, 103, 254, 160, 0)),\n",
-       " ('B0000632EE',\n",
-       "  'Fallene Total Block UVA/UVB Complete Broad Spectrum Sun Protection, SPF 65 Clear, 2 fl Ounces (59 ml)',\n",
-       "  (52, 109, 122, 125, 0)),\n",
-       " ('B0000632EN',\n",
-       "  'Aveeno Positively Radiant Skin Brightening Daily Scrub, 5 Ounce',\n",
-       "  (40, 118, 226, 27, 0)),\n",
-       " ('B00006598X',\n",
-       "  'Aquis Microfiber Hair Towel, Lisse Crepe, Pink (19 x 39-Inches)',\n",
-       "  (28, 120, 11, 114, 0)),\n",
-       " ('B000065DJY',\n",
-       "  'Revlon Perfect Heat 1875W Shine Boosting Hair Dryer, RV484',\n",
-       "  (144, 66, 226, 99, 0)),\n",
-       " ('B000065DK4',\n",
-       "  'Vidal Sassoon VS184C Three-Barrel Waver',\n",
-       "  (4, 247, 67, 219, 0)),\n",
-       " ('B000066CKS',\n",
-       "  'Badger - Healing Balm For Hardworking Hands',\n",
-       "  (12, 96, 139, 206, 0)),\n",
-       " ('B0000682T7', 'Badger Balm, Sleep Balm - 2 oz', (120, 47, 67, 99, 0)),\n",
-       " ('B0000683O5',\n",
-       "  'CoverGirl Lipslicks Lip Gloss, Clear - .14 oz',\n",
-       "  (216, 96, 163, 116, 0)),\n",
-       " ('B000068U49',\n",
-       "  'Kingsley Shave Soap Bowl with Lid Dark Wood',\n",
-       "  (116, 103, 49, 34, 0)),\n",
-       " ('B00006FDU6',\n",
-       "  'Conair Straight Waves 3-in-1 Specialty Styler',\n",
-       "  (220, 161, 24, 34, 0)),\n",
-       " ('B00006FE30',\n",
-       "  'Neutrogena Clear Pore Cleanser/Mask, 4.2 Fluid Ounce (125 ml)',\n",
-       "  (99, 202, 22, 75, 0)),\n",
-       " ('B00006FRW7',\n",
-       "  'Aveeno Clear Complexion Daily Moisturizer, 4 Ounce',\n",
-       "  (239, 196, 24, 76, 0)),\n",
-       " ('B00006IGL3',\n",
-       "  'Kiss My Face Organics Break Out, Botanical Acne Gel, 1 fl oz',\n",
-       "  (133, 96, 192, 15, 0)),\n",
-       " ('B00006IGL8',\n",
-       "  'Kiss My Face Deep Cleansing Mask Pore Shrink , 2 Fluid Ounce',\n",
-       "  (233, 122, 41, 37, 0)),\n",
-       " ('B00006IV22',\n",
-       "  'Conair 1875 Watt Dual Voltage Folding Handle Hair Dryer',\n",
-       "  (148, 145, 71, 76, 0)),\n",
-       " ('B00006IV2E',\n",
-       "  'Conair BC40JBC Hot Brush, White, 0.75 Inch',\n",
-       "  (36, 202, 94, 170, 0)),\n",
-       " ('B00006IV2F', 'Instant Heat Hot Brush, 3/4-Inch', (30, 180, 163, 195, 0)),\n",
-       " ('B00006IV30', 'Conair Instant Heat Travel Hairsetter', (220, 96, 24, 37, 0)),\n",
-       " ('B00006JN4F',\n",
-       "  \"L'Oreal Paris Superior Preference Hair Color, LB-01 Extra Light Ash Blonde\",\n",
-       "  (52, 180, 122, 170, 0)),\n",
-       " ('B00006JT6O',\n",
-       "  'Olay Total Effects Anti-Aging Night Firming Treatment - 1.7 Fl Oz',\n",
-       "  (133, 180, 216, 75, 0)),\n",
-       " ('B00006K14U',\n",
-       "  'Nivea for Men Energy Face Scrub, 4.4 Ounce Tube',\n",
-       "  (70, 122, 122, 13, 0)),\n",
-       " ('B00006L9KV',\n",
-       "  'Thicker Fuller Hair Revitalizing Shampoo-12 oz',\n",
-       "  (144, 202, 37, 53, 0)),\n",
-       " ('B00006L9KW',\n",
-       "  'Thicker Fuller Hair Weightless Conditioner-12 oz',\n",
-       "  (71, 47, 226, 100, 0)),\n",
-       " ('B00007FCQ1',\n",
-       "  'Blue Lizard Australian Sunscreen, Regular, SPF 30+, 8.75-Ounce Bottle',\n",
-       "  (28, 178, 162, 37, 0)),\n",
-       " ('B00007KQF3',\n",
-       "  'Barielle Nail Strengthener Cream, 1 Ounce',\n",
-       "  (28, 255, 71, 184, 0)),\n",
-       " ('B00007M0CP',\n",
-       "  'Conair Xtreme Instant Heat Multisized Hot Rollers, Pink',\n",
-       "  (67, 68, 153, 152, 0)),\n",
-       " ('B000087L6T', 'Rose Milk Skin Care Lotion - 8 fl oz', (99, 96, 192, 170, 0)),\n",
-       " ('B000089SA4', 'Soap Glycerine Vitamin-E 4 Ounces', (17, 173, 246, 138, 0)),\n",
-       " ('B00008BNZ4',\n",
-       "  'Wahl 79900B Clip-N-Trim 23-Piece Complete Haircut Kit',\n",
-       "  (105, 115, 33, 182, 0)),\n",
-       " ('B00008CMOQ',\n",
-       "  'Queen Helene Mint Julep Masque, 8 Ounce',\n",
-       "  (12, 62, 122, 103, 0)),\n",
-       " ('B00008CMOR',\n",
-       "  'Queen Helene Original Formula Antioxidant Grape Seed Extract Peel Off Masque -- 6 oz',\n",
-       "  (167, 145, 41, 103, 0)),\n",
-       " ('B00008CMOS',\n",
-       "  'Queen Helene Masque, Mud Pack - 8 oz',\n",
-       "  (128, 52, 122, 184, 0)),\n",
-       " ('B00008DPP8',\n",
-       "  'Softsoap Naturals Moisturizing Liquid Hand Soap with Milk &amp; Honey, Pump - 7.5 fl oz',\n",
-       "  (132, 196, 67, 159, 0)),\n",
-       " ('B00008J2XQ',\n",
-       "  'Aveeno Active Naturals Skin Relief Hand Cream, 3.5 Ounce',\n",
-       "  (102, 8, 24, 159, 0)),\n",
-       " ('B00008KA7P',\n",
-       "  'No Rinse Shampoo - 16 floz (473.1 mL)',\n",
-       "  (99, 66, 201, 202, 0)),\n",
-       " ('B00008KA8D',\n",
-       "  'Revlon ColorStay Overtime Liquid Lipcolor, Keep Blushing',\n",
-       "  (120, 122, 70, 195, 0)),\n",
-       " ('B00008MNZH',\n",
-       "  'Eucerin Daily Protection Moisturizing Face Lotion, Broad Spectrum SPF 30',\n",
-       "  (99, 202, 67, 13, 0)),\n",
-       " ('B00008MOQE',\n",
-       "  'Maybelline Great Lash Mascara, Very Black -.43 Fluid Ounce',\n",
-       "  (194, 59, 82, 195, 0)),\n",
-       " ('B00008MOQJ',\n",
-       "  'Maybelline Great Lash Curved - Very Black',\n",
-       "  (80, 196, 59, 103, 0)),\n",
-       " ('B00008MOQN',\n",
-       "  'Maybelline Great Lash Waterproof Mascara, Very Black- .43 Fluid Ounce',\n",
-       "  (80, 255, 59, 231, 0)),\n",
-       " ('B00008O2X5',\n",
-       "  'Almay Anti-Perspirant &amp; Deodorant, Clear Gel, Fragrance Free - 2.25 oz',\n",
-       "  (102, 131, 11, 15, 0)),\n",
-       " ('B00008PC1O',\n",
-       "  'Sun-In Spray-In Hair Lightener, Original - 4.7 fl oz',\n",
-       "  (199, 202, 11, 38, 0)),\n",
-       " ('B00008ZPG9',\n",
-       "  'Olay Regenerist Deep Hydration Regenerating Cream Facial Moisturirzer 1.7 Fl Oz',\n",
-       "  (99, 17, 71, 195, 0)),\n",
-       " ('B00008ZPGA',\n",
-       "  'Olay Regenerist Regenerating Serum Moisturizer 1.7 Fl Oz',\n",
-       "  (99, 22, 65, 13, 0)),\n",
-       " ('B000092OX4',\n",
-       "  'Olay Regenerist Regenerating Lotion With Sunscreen Broad Spectrum SPF 15 2.5 Fl Oz',\n",
-       "  (252, 129, 227, 103, 0)),\n",
-       " ('B000093A0U',\n",
-       "  'Hugo By Hugo Boss For Women. Eau De Toilette Spray 1.3 Ounces',\n",
-       "  (31, 4, 27, 34, 0)),\n",
-       " ('B000093I60', 'No-Rinse Body Bath, 16 oz', (141, 161, 124, 195, 0)),\n",
-       " ('B000094Q6C',\n",
-       "  'Tournmaline Ceramic Curling Iron, 3/4-Inch',\n",
-       "  (45, 66, 24, 100, 0)),\n",
-       " ('B000094ZDX',\n",
-       "  'Conair 1600 Watt Folding Handle Hair Dryer',\n",
-       "  (195, 77, 33, 116, 0)),\n",
-       " ('B0000950LF',\n",
-       "  'Promensil Menopausal Relief Supplement Tablets, 60-Count Box',\n",
-       "  (12, 234, 226, 159, 0)),\n",
-       " ('B000095SDS', 'Scalpicin Max Dermatology', (98, 47, 174, 114, 0)),\n",
-       " ('B00009WO0W', \"Weleda Children's Tooth Gel, 1.7 oz\", (56, 77, 163, 27, 0)),\n",
-       " ('B0000AFUTL',\n",
-       "  'Jerdon JP7506CF 8-Inch Two-Sided Swivel Wall Mount Mirror with 5x Magnification, 13.5-Inch Extension, Chrome Finish',\n",
-       "  (36, 4, 163, 125, 0)),\n",
-       " ('B0000AJ3PT',\n",
-       "  'Aveeno Active Naturals Creamy Moisturizing Oil, 12 Ounce',\n",
-       "  (239, 152, 71, 34, 0)),\n",
-       " ('B0000AJ3PU',\n",
-       "  'Aveeno Active Naturals Stress Relief Moisturizing Lotion, 12 Ounce',\n",
-       "  (56, 47, 65, 170, 0)),\n",
-       " ('B0000AJ3Q3',\n",
-       "  \"L'Oreal Paris Double Extend Mascara, Blackest Black, 0.33 Ounces\",\n",
-       "  (192, 121, 122, 101, 0)),\n",
-       " ('B0000AN9L9',\n",
-       "  'Neutrogena Transparent Facial Bar Bonus Pack, Original Formula - 6 ea, 3.5 oz each, total 21 oz',\n",
-       "  (102, 59, 124, 236, 0)),\n",
-       " ('B0000AS54S',\n",
-       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 4 Ounce Tube',\n",
-       "  (128, 141, 139, 219, 0)),\n",
-       " ('B0000BV14G',\n",
-       "  'Cool Water By Davidoff Eau-de-toilette Spray, 1.7-Ounce',\n",
-       "  (119, 115, 163, 15, 0)),\n",
-       " ('B0000C0XL8',\n",
-       "  'Desert Essence Organic Jojoba Oil -- 4 fl oz',\n",
-       "  (69, 173, 216, 206, 0)),\n",
-       " ('B0000C4COV',\n",
-       "  'derma e Soothing Skin Treatment, Tea Tree &amp; E Antiseptic Cr&egrave;me, 4 oz (113 g)',\n",
-       "  (133, 21, 147, 206, 0)),\n",
-       " ('B0000CC64W',\n",
-       "  'Olay Regenerist Daily Regenerating Serum - Fragrance Free Moisturizer 1.7 Fl Oz',\n",
-       "  (99, 46, 162, 34, 0)),\n",
-       " ('B0000CDVN6', 'Conair Spiral Styler, 0.75 Inch', (17, 249, 155, 159, 0)),\n",
-       " ('B0000CNR0E',\n",
-       "  'Household Essentials Travel Tie Case, Black',\n",
-       "  (36, 96, 226, 152, 0)),\n",
-       " ('B0000CNR0J',\n",
-       "  'Household Essentials Grooming Travel Bag Organizer, Black',\n",
-       "  (36, 180, 24, 27, 0)),\n",
-       " ('B0000UJJH0',\n",
-       "  'Desert Essence Tea Tree Oil 100% Pure, 2-Ounce',\n",
-       "  (39, 135, 208, 77, 0)),\n",
-       " ('B0000UTUS8',\n",
-       "  'Aquis Microfiber Hair Turban, Lisse Crepe, Patented Design, White',\n",
-       "  (28, 77, 192, 91, 0)),\n",
-       " ('B0000UTUVU',\n",
-       "  \"Mrs. Meyer's Clean Day Dish Soap, Lavender, 16 Ounce Bottle\",\n",
-       "  (149, 211, 226, 91, 0)),\n",
-       " ('B0000UTUW4',\n",
-       "  \"Mrs. Meyer's Clean Day Liquid Hand Soap, Lavender, 12.5 Ounce Bottle\",\n",
-       "  (6, 21, 201, 160, 0)),\n",
-       " ('B0000VC1YC',\n",
-       "  'Neutrogena Body Oil, Light Sesame Formula, 32 Ounce',\n",
-       "  (94, 129, 59, 219, 0)),\n",
-       " ('B0000XMQTA',\n",
-       "  'Irene Gari Cover Your Grey for Women Touch Up Stick Medium Brown',\n",
-       "  (193, 242, 192, 169, 0)),\n",
-       " ('B0000Y3C3I', 'Afirm 3X', (21, 47, 163, 188, 0)),\n",
-       " ('B0000Y3COC', 'PanOxyl Bar 10% - 4 oz - 1 Bar', (93, 4, 216, 118, 0)),\n",
-       " ('B0000Y3CRY', 'Nizoral Anti-Dandruff Shampoo, 4 oz.', (92, 47, 201, 182, 0)),\n",
-       " ('B0000Y3CSI',\n",
-       "  'Neutrogena T/Gel Original Shampoo - 16 oz',\n",
-       "  (128, 145, 226, 103, 0)),\n",
-       " ('B0000Y3D4G',\n",
-       "  'Neutrogena Norwegian Formula Lip Moisturizer, SPF 15, 0.15 Ounce',\n",
-       "  (92, 96, 67, 37, 0)),\n",
-       " ('B0000Y3D4Q', 'Rezamid Acne Treatment Lotion, 2 oz.', (71, 173, 246, 15, 0)),\n",
-       " ('B0000Y3DAK', 'DHS Clear Shampoo 8 oz', (13, 3, 201, 114, 0)),\n",
-       " ('B0000Y3FQC', 'Diamancel Tough Buffer #11', (192, 151, 250, 152, 0)),\n",
-       " ('B0000Y3FRG', 'Obagi Nu-Derm Foaming Gel -- 6.7 fl oz', (102, 8, 17, 77, 0)),\n",
-       " ('B0000Y3GF2',\n",
-       "  'Free &amp; Clear Hairspray Firm Hold, 8 Ounce',\n",
-       "  (71, 122, 139, 91, 0)),\n",
-       " ('B0000Y3GIE',\n",
-       "  'Nailtiques Formula 2 Nail Growth Formula Treatments, 0.25 Ounce',\n",
-       "  (28, 134, 59, 170, 0)),\n",
-       " ('B0000Y3LAC',\n",
-       "  'Jobst It Stays Roll-on Body Adhesive 2 oz. (3 pack)',\n",
-       "  (92, 115, 70, 169, 0)),\n",
-       " ('B0000Y8H3S',\n",
-       "  'Rogaine for Men Hair Regrowth Treatment, Extra Strength Original Unscented, Set of 3, 2-Ounce Bottles',\n",
-       "  (214, 51, 59, 103, 0)),\n",
-       " ('B0000YUWY0',\n",
-       "  'jerome russell Hair Color Thickener for Thinning Hair, Black, 3.5 Ounce',\n",
-       "  (120, 66, 10, 100, 0)),\n",
-       " ('B0000YUX4O', 'Mebco Tortoise Shower Detangler', (241, 196, 201, 53, 0)),\n",
-       " ('B0000YUXGW', 'Mavala Scientifique (Nail Hardener)', (250, 3, 219, 38, 0)),\n",
-       " ('B0000YUXI0',\n",
-       "  'Mavala Stop - Helps Cure Nail Biting and Thumb Sucking, 0.3-Fluid Ounce',\n",
-       "  (228, 196, 162, 103, 0)),\n",
-       " ('B0000YV6BI', 'ApHogee Shampoo for Damaged Hair', (132, 202, 226, 60, 0)),\n",
-       " ('B0000YV6YK', \"One 'n Only Colorfix\", (194, 247, 218, 53, 0)),\n",
-       " ('B0000YV83O', 'Facial-Flex Facial Exercise System', (132, 173, 33, 159, 0)),\n",
-       " ('B0000ZHH5G',\n",
-       "  'Vanicream Moisturizing Skin Cream, 16 Ounces',\n",
-       "  (44, 134, 33, 103, 0)),\n",
-       " ('B0000ZHOEU',\n",
-       "  'Free &amp; Clear Liquid Cleanser, 8 Ounce',\n",
-       "  (117, 21, 201, 15, 0)),\n",
-       " ('B0000ZLEFU',\n",
-       "  'Auric Blends Perfume Oil, 0.33 oz - Coco Mango',\n",
-       "  (127, 21, 122, 15, 0)),\n",
-       " ('B0000ZLEX2',\n",
-       "  'Black Opium - Auric Blends Perfume Oils',\n",
-       "  (47, 96, 187, 15, 0)),\n",
-       " ('B00011JI88', 'Ardell Fashion Lashes - 120 Black Demi', (36, 21, 59, 75, 0)),\n",
-       " ('B00011JOQY', 'Cricket Static Free Fast Flo', (36, 202, 153, 34, 0)),\n",
-       " ('B00011JOR8',\n",
-       "  'Cricket Hair Brush Static Free, Mini Fast Flo, 1.28 Ounce',\n",
-       "  (97, 149, 226, 99, 0)),\n",
-       " ('B00011JTFA', 'Star Nail Pen-Style Polish Corrector', (42, 129, 70, 170, 0)),\n",
-       " ('B00011QTZI',\n",
-       "  'Cricket Technique Barrel Hair Brush, XX-large Round, 2 Inch',\n",
-       "  (36, 202, 162, 170, 0)),\n",
-       " ('B00011QUDE',\n",
-       "  'Helen of Troy 1514 Brush Iron, White, 1 1/2 Inches Barrel',\n",
-       "  (155, 21, 132, 220, 0)),\n",
-       " ('B00011QUKW',\n",
-       "  'Hot Tools HT1108 Professional Marcel Iron, 1 Inch',\n",
-       "  (187, 77, 24, 3, 0)),\n",
-       " ('B00011QVHO',\n",
-       "  'Spornette Porcupine Rounder Brush, 2-Inch Diameter',\n",
-       "  (195, 129, 140, 76, 0)),\n",
-       " ('B00011QVIS',\n",
-       "  'Spornette Prego Brush, 2-Inch Diameter',\n",
-       "  (230, 3, 218, 52, 0)),\n",
-       " ('B00011QVUG',\n",
-       "  'For Pro Stretch Terry Headband with Velcro 3in.',\n",
-       "  (34, 3, 59, 77, 0)),\n",
-       " ('B000127UUA',\n",
-       "  'Camille Beckman Glycerin Hand Therapy, White Lilac, 8 Ounce',\n",
-       "  (232, 161, 139, 91, 0)),\n",
-       " ('B000127UVY',\n",
-       "  'Camille Beckman Glycerin Hand Therapy, Glycerine Rosewater, 6 Ounce',\n",
-       "  (232, 103, 226, 219, 0)),\n",
-       " ('B000127VFE',\n",
-       "  'Camille Beckman Silky Body Moisturizing Cream, Glycerine Rosewater, 16 Ounce',\n",
-       "  (232, 103, 208, 219, 0)),\n",
-       " ('B00012NEYG', \"Burt's Bees Head to Toe Starter Kit\", (28, 59, 43, 91, 0)),\n",
-       " ('B00012NI7E',\n",
-       "  \"Burt's Bees Avocado Butter Pre-Shampoo Hair Treatment with Nettles and Rosemary - 4.34 fl oz\",\n",
-       "  (196, 122, 65, 15, 0)),\n",
-       " ('B00012NJZA',\n",
-       "  \"Burt's Bees Orange Essence Facial Cleanser, 4.3 Ounces\",\n",
-       "  (128, 47, 65, 103, 0)),\n",
-       " ('B00012NKGS',\n",
-       "  \"Burt's Bees: Citrus Facial Scrub, 2 oz\",\n",
-       "  (47, 21, 216, 103, 0)),\n",
-       " ('B000136P7S',\n",
-       "  'Miracle of Aloe Miracle Hand Repair Cream 8 Oz',\n",
-       "  (247, 21, 246, 76, 0)),\n",
-       " ('B00013TQRE',\n",
-       "  'Rogaine for Women Hair Regrowth Treatment 3- 2 ounce bottles',\n",
-       "  (255, 51, 59, 15, 0)),\n",
-       " ('B00013YYS0',\n",
-       "  \"Burt's Bees - Cuticle Creme Lemon Butter - 0.6 oz.\",\n",
-       "  (47, 173, 216, 34, 0)),\n",
-       " ('B00013YZ7U', \"Dr. Bronner's Almond Soap 32oz\", (191, 161, 67, 116, 0)),\n",
-       " ('B00013Z15A',\n",
-       "  \"Burt's Bees Day Creme Carrot Nutritive -- 2 fl oz\",\n",
-       "  (218, 24, 254, 34, 0)),\n",
-       " ('B000141O6E',\n",
-       "  'Redken Extreme Anti-Snap Leave-in Treatment, 8.5 Ounce',\n",
-       "  (47, 196, 75, 169, 0)),\n",
-       " ('B000142AZ8',\n",
-       "  'Bain de Soleil Mega Tan Sunscreen Lotion With Self Tanner, SPF 4 - 4 fl oz',\n",
-       "  (132, 196, 163, 170, 0)),\n",
-       " ('B000142C1A',\n",
-       "  'Essie Nail Polish Marshmallow, .5 Ounce',\n",
-       "  (250, 62, 254, 116, 0)),\n",
-       " ('B000142FVW',\n",
-       "  'Opi Nail Lacquer, Not So Bora Pink, 0.5 Fluid Ounce',\n",
-       "  (210, 77, 59, 198, 0)),\n",
-       " ('B000142JDG',\n",
-       "  'Roux Fanci-Full Mousse, 18 Spun Sand, 6 Fluid Ounce',\n",
-       "  (39, 8, 192, 34, 0)),\n",
-       " ('B000142KZ8',\n",
-       "  'Pretty Nails Instant Polish Remover Regular',\n",
-       "  (201, 202, 201, 60, 0)),\n",
-       " ('B000142MGA',\n",
-       "  'Fran Wilson Eye Tees Precision Makeup Applicators 80 Cotton Swabs',\n",
-       "  (11, 120, 11, 116, 0)),\n",
-       " ('B000142NDC',\n",
-       "  'Bigen Permanent Powder Hair Color, Dark Chestnut, .21 Ounce',\n",
-       "  (5, 46, 27, 103, 0)),\n",
-       " ('B000142OZY',\n",
-       "  'Aphogee Conditioner Pro-Vitamin Leave-In 8 oz.',\n",
-       "  (13, 3, 24, 53, 0)),\n",
-       " ('B000142P12',\n",
-       "  'ApHogee Keratin and Green Tea Restructurizer 8 fl. oz',\n",
-       "  (132, 47, 43, 169, 0)),\n",
-       " ('B000142PUS',\n",
-       "  'Clairol Professional Beautiful Collection Semi-permanent Hair Color, Light Reddish Brown',\n",
-       "  (102, 149, 216, 219, 0)),\n",
-       " ('B000142Q6G', 'Colora Henna Veg-Hair Chestnut 2oz', (29, 119, 122, 169, 0)),\n",
-       " ('B000142TU4',\n",
-       "  'Sally Hansen Polish Remover 8 oz. Strengthening',\n",
-       "  (112, 202, 24, 99, 0)),\n",
-       " ('B000142U9E',\n",
-       "  'Fantasia High Potency IC Hair Polisher Styling Gel, with Sparkle Lites, 16 oz.',\n",
-       "  (245, 66, 140, 206, 0)),\n",
-       " ('B000142ZFS', 'Super Nail Polish Thinner 4oz', (3, 135, 65, 76, 0)),\n",
-       " ('B0001431AG',\n",
-       "  'Fran Wilson Cake Eyeliner, Black, 0.09 Ounce',\n",
-       "  (241, 211, 201, 219, 0)),\n",
-       " ('B0001432S2',\n",
-       "  'OPI Natural Nail Strengthener Treatment, 0.5-Fluid Ounce',\n",
-       "  (201, 145, 59, 195, 0)),\n",
-       " ('B0001433OU',\n",
-       "  'OPI Nail Lacquer, Pirates of The Caribbean Collection, Stranger Tides, 0.5 Fluid Ounce',\n",
-       "  (112, 4, 40, 12, 0)),\n",
-       " ('B0001433VI',\n",
-       "  'OPI Start-to-finish Base Coat, Top Coat and Nail Strengthener, 0.5-Fluid Ounce',\n",
-       "  (86, 46, 219, 91, 0)),\n",
-       " ('B000143AY8', 'Fran Wilson Yellow Mood Matcher', (119, 89, 140, 169, 0)),\n",
-       " ('B000143CFA', 'Andrea Eyelash Adhesive: Clear', (132, 159, 254, 76, 0)),\n",
-       " ('B000143K2A',\n",
-       "  \"Mane 'n Tail Deep Moisturizing Conditioner\",\n",
-       "  (132, 145, 71, 77, 0)),\n",
-       " ('B000143O7G', 'Instant Peel Natural Dead Skin Remover', (120, 2, 3, 12, 0)),\n",
-       " ('B000146L62',\n",
-       "  'Cassia Clove 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
-       "  (252, 149, 226, 152, 0)),\n",
-       " ('B000146LKS',\n",
-       "  'Patchouli 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
-       "  (252, 149, 226, 152, 1)),\n",
-       " ('B00014D138',\n",
-       "  'Jason Pure Natural Moisturizing Creme, Anti-Aging Tea Time, 4 Ounce',\n",
-       "  (50, 196, 219, 99, 0)),\n",
-       " ('B00014D1OW', 'Lemon Clarifying Shampoo 11 Ounces', (93, 247, 138, 217, 0)),\n",
-       " ('B00014D322',\n",
-       "  'MillCreek - Biotin Shampoo, 16 fl oz gel',\n",
-       "  (56, 24, 124, 75, 0)),\n",
-       " ('B00014D5O8', \"Burt's Bees Hand Salve, 3 oz Tin\", (230, 3, 201, 114, 0)),\n",
-       " ('B00014DGCO',\n",
-       "  'Plantlife Lavender 100% Pure Essential Oil - Bulgarian - 10 ml',\n",
-       "  (252, 211, 226, 169, 0)),\n",
-       " ('B00014DIKE',\n",
-       "  \"Burt's Bees Therapeutic Bath Crystals, 1 Pound\",\n",
-       "  (71, 145, 41, 206, 0)),\n",
-       " ('B00014DKKC',\n",
-       "  \"Burt's Bees Lemon &amp; Vitamin E Bath &amp; Body Oil, 4 Fluid Ounce\",\n",
-       "  (117, 23, 65, 162, 0)),\n",
-       " ('B00014DM5K',\n",
-       "  'Facial Toner &amp; Freshener-Aloe Vera Earth Science 8 oz Liquid',\n",
-       "  (196, 173, 226, 159, 0)),\n",
-       " ('B00014DMQE',\n",
-       "  'Reviva Labs 10% Glycolic Acid Cream -- 1.5 oz',\n",
-       "  (47, 255, 27, 60, 0)),\n",
-       " ('B00014DMRI',\n",
-       "  'Aubrey Organics - Natural Mist Herbal Hairspray (Regular Hold), 8 fl oz spray',\n",
-       "  (71, 134, 162, 15, 0)),\n",
-       " ('B00014DMUA',\n",
-       "  'Moisturizer-Daily Essential Jojoba/Aloe Desert Essence 4 oz Cream',\n",
-       "  (196, 173, 59, 77, 0)),\n",
-       " ('B00014DTY4',\n",
-       "  'Nutribiotic Skin Ointment, 0.5 Ounce',\n",
-       "  (170, 109, 59, 219, 0)),\n",
-       " ('B00014DU6G',\n",
-       "  'Earth Science Fragrance Free Clarifying Facial Wash 8 oz',\n",
-       "  (247, 3, 219, 195, 0)),\n",
-       " ('B00014DZ2K', 'AmeriLeather Leather Toiletry Bag', (241, 62, 122, 15, 0)),\n",
-       " ('B00014EH2C',\n",
-       "  'Apricot Intensive Night Cream, 1.65oz',\n",
-       "  (204, 228, 153, 77, 0)),\n",
-       " ('B00014EKJC',\n",
-       "  \"Nature's Plus Natural Beauty Cleansing Bar -- 3 oz\",\n",
-       "  (21, 4, 10, 219, 0)),\n",
-       " ('B00014FTTM',\n",
-       "  'Reviva Labs DMAE Firming Fluid - 1 oz',\n",
-       "  (182, 80, 65, 170, 0)),\n",
-       " ('B00014GT78',\n",
-       "  'Dermablend Cover Creme - Chroma 1 - Rose Beige, 1 oz',\n",
-       "  (99, 66, 3, 195, 0)),\n",
-       " ('B00014GTWS',\n",
-       "  'Dermablend Leg and Body Cover Make-Up SPF 15, Medium, 3.4 Ounce',\n",
-       "  (71, 80, 27, 206, 0)),\n",
-       " ('B00014HF94',\n",
-       "  \"Vitamin E 14,000 IU Nature's Plus 0.5 oz Liquid\",\n",
-       "  (132, 108, 246, 53, 0)),\n",
-       " ('B00014JKG0',\n",
-       "  \"Nature's Gate Tea Tree Moisturizing Lotion for Irritated, Distressed Skin, 18-Ounce Pump Bottle\",\n",
-       "  (170, 115, 11, 159, 0)),\n",
-       " ('B00014JOHA',\n",
-       "  'Kiss My Face - Whenever Shampoo, 11 fl oz liquid',\n",
-       "  (26, 46, 118, 103, 0)),\n",
-       " ('B00014WW38',\n",
-       "  'Naturtint Permanent Hair Colorant Sandy Blonde',\n",
-       "  (166, 96, 234, 37, 0)),\n",
-       " ('B00015GWQU',\n",
-       "  'Fran Wilson Nail Tees Precision Makeup Applicators 120 Cotton Swabs',\n",
-       "  (11, 69, 153, 116, 0)),\n",
-       " ('B00015HBD8',\n",
-       "  'Phillips Light Touch 1 Cushion Hair Brush',\n",
-       "  (244, 159, 24, 99, 0)),\n",
-       " ('B00015HBEC',\n",
-       "  'Spornette Large Oval Nylon Bristle Cushion Brush',\n",
-       "  (105, 77, 141, 27, 0)),\n",
-       " ('B00015HBEM',\n",
-       "  'Spornette Double Stranded XL Nylon Rounder Brush, 2-Inch Diameter',\n",
-       "  (80, 159, 192, 27, 0)),\n",
-       " ('B00015XAQ0',\n",
-       "  'Avalon Organics Vitamin C Renewal Refreshing Cleansing Gel, 8.5 Ounce Bottle',\n",
-       "  (38, 178, 162, 34, 1)),\n",
-       " ('B00016QYOO',\n",
-       "  'Desert Essence Thoroughly Clean Face Wash - Original -- 8.5 fl oz',\n",
-       "  (239, 100, 153, 195, 0)),\n",
-       " ('B00016RIIA', 'Yerba Prima Tampico Skin Brush 1 Ea', (132, 196, 234, 53, 0)),\n",
-       " ('B00016WSIK',\n",
-       "  'Earth Therapeutics Body Sponge, Anti-Bacterial, 1 Count',\n",
-       "  (232, 59, 118, 27, 0)),\n",
-       " ('B00016WUEW',\n",
-       "  'Heritage Products Rosewater &amp; Glycerin, 4 Ounce',\n",
-       "  (141, 59, 162, 178, 0)),\n",
-       " ('B00016WULK',\n",
-       "  'Avalon Organics Vitamin C Renewal Moisture Plus Lotion SPF 15 -- 4 fl oz',\n",
-       "  (104, 234, 67, 34, 1)),\n",
-       " ('B00016WV6E', 'Henna Persia Light Brown 4 Ounces', (13, 96, 37, 100, 0)),\n",
-       " ('B00016WW8Q', \"Dickinson's Witch Hazel 16 oz\", (1, 96, 41, 99, 0)),\n",
-       " ('B00016WXEY',\n",
-       "  'LILY OF THE DESERT, Aloe Vera Gelly - 12 oz',\n",
-       "  (154, 134, 11, 77, 0)),\n",
-       " ('B00016X06O',\n",
-       "  'Herbatint Permanent Herbal Haircolor Gel, Golden Chestnut 4D, 4.56-Ounce',\n",
-       "  (71, 21, 246, 206, 0)),\n",
-       " ('B00016X3G6',\n",
-       "  'Herbal Salt Scrub Lavender 20.50 Ounces',\n",
-       "  (231, 173, 226, 159, 0)),\n",
-       " ('B00016X3L6',\n",
-       "  'GIOVANNI COSMETICS, Nutrafix Hair Reconstructor - 8.5 oz',\n",
-       "  (71, 159, 162, 202, 0)),\n",
-       " ('B00016X604',\n",
-       "  \"Smiles' Prid Homeopathic Drawing Salve, 18 GM, 1 each\",\n",
-       "  (123, 161, 162, 160, 0)),\n",
-       " ('B00016X68Q',\n",
-       "  'J.R. Liggett Bar Shampoo, Original Formula, 3.5 Ounce',\n",
-       "  (28, 236, 201, 91, 0)),\n",
-       " ('B00016XJ4M',\n",
-       "  'Thayers - Rose Petal Witch Hazel with Aloe Vera Alcohol-Free Toner - 12 oz.',\n",
-       "  (128, 108, 162, 152, 0)),\n",
-       " ('B00016XJ7O',\n",
-       "  'Thayers Witch Hazel w/Aloe Vera Lemon 12 oz',\n",
-       "  (71, 135, 206, 77, 0)),\n",
-       " ('B00016XJ8S',\n",
-       "  'Arbordoun Abundantly Herbal Calendula Cream 4 Oz',\n",
-       "  (118, 47, 163, 100, 0)),\n",
-       " ('B000172R76',\n",
-       "  'Ace Teasing Tail Comb 8&quot; * Black',\n",
-       "  (155, 62, 254, 160, 0)),\n",
-       " ('B000177FXW',\n",
-       "  \"Bar Soap-Tea Tree Dr. Bronner's 5 oz Bar\",\n",
-       "  (71, 181, 252, 219, 0)),\n",
-       " ('B00017XWJ8',\n",
-       "  'Ralph Lauren Eau de Toilette Spray For Women, 1.7 Oz',\n",
-       "  (212, 47, 139, 38, 0)),\n",
-       " ('B000186X4I',\n",
-       "  'Shiseido Shiseido Cleansing Massage Brush',\n",
-       "  (91, 8, 33, 114, 0)),\n",
-       " ('B000195GAY',\n",
-       "  'Blue Grass By Elizabeth Arden Deodorant Cream, 1.5-Ounce',\n",
-       "  (191, 115, 226, 195, 0)),\n",
-       " ('B000196U04', 'Diamon Deb Nail File 8&quot;', (253, 24, 216, 114, 0)),\n",
-       " ('B0001AGMWE',\n",
-       "  'Miracell ProEar-for Itchy, Irritated Ears .5 OZ',\n",
-       "  (230, 62, 162, 170, 0)),\n",
-       " ('B0001AQCII',\n",
-       "  'China Glaze Nail Polish, Jetstream, 0.5 Fluid Ounce',\n",
-       "  (79, 196, 250, 21, 0)),\n",
-       " ('B0001B86HM',\n",
-       "  'Conair Pro Style 1875 Watt Hard Hat Hair Dryer',\n",
-       "  (28, 159, 24, 116, 0)),\n",
-       " ('B0001EKTZQ',\n",
-       "  'Dermalogica Multi-active Toner, 8.4 Fluid Ounce',\n",
-       "  (139, 180, 163, 15, 0)),\n",
-       " ('B0001EKWPI',\n",
-       "  'Vanicream Cleansing Bar, Fragrance Free, 3.9 Ounce Bars (Pack of 3)',\n",
-       "  (71, 134, 122, 169, 0)),\n",
-       " ('B0001EKYL0',\n",
-       "  'Exuviance Evening Restorative Complex, 1.75 Fluid Ounce',\n",
-       "  (133, 2, 59, 162, 0)),\n",
-       " ('B0001EL0CW',\n",
-       "  'Basis Cleans and Smoothes Sensitive Skin Bar',\n",
-       "  (170, 211, 67, 34, 0)),\n",
-       " ('B0001EL5R2', 'PCA Skin Acne Cream, 0.5 Ounce', (92, 202, 59, 77, 0)),\n",
-       " ('B0001EL658',\n",
-       "  'Bare Escentuals Maximum Coverage Concealer Brush',\n",
-       "  (149, 120, 153, 114, 0)),\n",
-       " ('B0001FF80G', 'Dopp Jumbo Kit w/ Zip Bottom', (34, 62, 67, 219, 0)),\n",
-       " ('B0001I8ZT4',\n",
-       "  'Mar-V-Cide Shampoo Brush &amp; Invigorator',\n",
-       "  (32, 161, 139, 116, 0)),\n",
-       " ('B0001LH6YG', 'My Spots Are Consealed Light Color', (194, 196, 70, 103, 0)),\n",
-       " ('B0001M3K4U',\n",
-       "  'Soft N Style Pro-Cap Professional Processing Caps, Extra Large, 30-count',\n",
-       "  (241, 196, 140, 37, 0)),\n",
-       " ('B0001M7D7A',\n",
-       "  'Marianna Single Prong Curl Clip 80 pcs',\n",
-       "  (225, 189, 43, 57, 0)),\n",
-       " ('B0001MC06Y',\n",
-       "  'Peter Thomas Roth Beta Hydroxy Acid 2% Acne Wash 8.5 fl oz',\n",
-       "  (133, 211, 218, 103, 0)),\n",
-       " ('B0001MROQA',\n",
-       "  'Hot Tools Big Bumper Spring Curling Iron, 1-1/2&quot;',\n",
-       "  (153, 66, 254, 100, 0)),\n",
-       " ('B0001TJX2Q',\n",
-       "  'Home Health Castor Oil, Cold Pressed and Cold Processed, 32-Ounce',\n",
-       "  (102, 211, 234, 220, 0)),\n",
-       " ('B0001TMDF0',\n",
-       "  'Heritage Store Rose Petals Rosewater 8 oz',\n",
-       "  (246, 236, 201, 114, 0)),\n",
-       " ('B0001TO3NA', \"Grandpa's Pine Tar Soap Medium Size\", (216, 21, 227, 27, 0)),\n",
-       " ('B0001TODNK',\n",
-       "  'Earth Therapeutics Hydro Exfoliating Towel, 1 each',\n",
-       "  (120, 96, 254, 152, 0)),\n",
-       " ('B0001TOH8G',\n",
-       "  'Exfoliating Hydro Gloves-Natural Earth Therapeutics 1 Set',\n",
-       "  (132, 173, 140, 169, 0)),\n",
-       " ('B0001TQ5MW', 'Margarite Zinc Cream -- 1 oz', (102, 255, 33, 53, 0)),\n",
-       " ('B0001TQ9WI',\n",
-       "  'Mountain Ocean Skin Trip Moisturizer, Coconut, 8-Ounce',\n",
-       "  (13, 55, 201, 114, 0)),\n",
-       " ('B0001TSJPI',\n",
-       "  'Bar Soap - Sandalwood, 6 Units / 2.6 oz',\n",
-       "  (234, 247, 218, 12, 0)),\n",
-       " ('B0001TSWWS',\n",
-       "  'Home Health Roll-On Deodorant Herbal Scent -- 3 fl oz',\n",
-       "  (71, 173, 219, 160, 0)),\n",
-       " ('B0001UWRCI',\n",
-       "  'Yu-Be Moisturizing Skin Cream 1.25 oz Tube',\n",
-       "  (252, 202, 163, 195, 0)),\n",
-       " ('B0001VKL7U', \"Burt's Bees Lip Shimmer, Rhubarb\", (128, 77, 37, 195, 0)),\n",
-       " ('B0001VOJWI',\n",
-       "  'Pheromone By Marilyn Miglin For Women. Eau De Parfum Spray 1.7 Oz / 50 Ml.',\n",
-       "  (47, 134, 65, 103, 0)),\n",
-       " ('B0001XKA38',\n",
-       "  'Roux Lash and Brow Tint, Brown, 40 Count',\n",
-       "  (120, 122, 139, 76, 0)),\n",
-       " ('B0001XLSQ6', 'Steam Facial By Kaz', (183, 4, 40, 12, 0)),\n",
-       " ('B0001XLSR0',\n",
-       "  'ROUX Tween Time Instant Haircolor Touch-Up Stick BLACK 1/3 oz/10g',\n",
-       "  (152, 47, 24, 159, 0)),\n",
-       " ('B0001XQNMK',\n",
-       "  'Roux Fanci Full Mousse #52 White Mink 6 oz',\n",
-       "  (194, 47, 122, 27, 0)),\n",
-       " ('B0001Y74VS',\n",
-       "  'Ouidad by Ouidad Ouidad Climate Control Heat and Humidity Gel for Unisex, 8.5 Ounce',\n",
-       "  (58, 68, 33, 169, 0)),\n",
-       " ('B0001Y74XG',\n",
-       "  'Ouidad Water Works Clarifying Shampoo, 8.5 Ounce',\n",
-       "  (212, 21, 246, 114, 0)),\n",
-       " ('B0001YOMD6',\n",
-       "  'Touch with Love Eau De Parfum Spray for Women by Fred Hayman, 1.7 Ounce',\n",
-       "  (175, 100, 234, 12, 0)),\n",
-       " ('B0001YXXUY',\n",
-       "  'Hot Tools HT1074 Anti-Static Ionic Hot Air Brush with Ion Technology, 1000 Watts, 1 1/2 Inches',\n",
-       "  (182, 159, 27, 76, 0)),\n",
-       " ('B0001ZA3Y2', 'U-Lactin Dry Skin Lotion - 16 oz.', (252, 115, 59, 77, 0)),\n",
-       " ('B0001ZA4CS',\n",
-       "  'NeoStrata Exuviance CoverBlend Multi Function Concealer SPF15 - Sand, .5 fl oz',\n",
-       "  (132, 115, 33, 13, 0)),\n",
-       " ('B0001ZYL3Q',\n",
-       "  'TheraNeem Cream - Original Organix South 2 oz Cream \\nVanilla',\n",
-       "  (118, 66, 216, 103, 0)),\n",
-       " ('B0001ZYLAO',\n",
-       "  'Emerita Pro-gest Natural Balancing Cream, 2 Ounce',\n",
-       "  (177, 62, 122, 103, 0)),\n",
-       " ('B0001ZZH0M',\n",
-       "  'Naturally Fresh, Deodorant Crystal Spray Mist, 4 Oz&nbsp;',\n",
-       "  (71, 21, 227, 27, 0)),\n",
-       " ('B00020DY2O',\n",
-       "  'TheraNeem Gentle Therape Shampoo Organix South 12 oz Liquid',\n",
-       "  (44, 173, 197, 171, 0)),\n",
-       " ('B00020E1P8', 'Shiseido Shiseido Facial Cotton', (194, 103, 27, 160, 0)),\n",
-       " ('B00021AK4I',\n",
-       "  'Calvin Klein Ck One EDT Spray 1.7 oz for Unisex',\n",
-       "  (9, 62, 122, 195, 0)),\n",
-       " ('B00021AM9G',\n",
-       "  'Hanae Mori by Hanae Mori for Women - 3.4 Ounce EDT Spray',\n",
-       "  (31, 24, 27, 116, 0)),\n",
-       " ('B00021AQCY',\n",
-       "  'Fekkai Shea Butter Moisturizing Shampoo Hair Products 8 Fl Oz',\n",
-       "  (41, 100, 67, 206, 0)),\n",
-       " ('B00021AYF8',\n",
-       "  'Mustela Foam Shampoo for Newborns - 5.07 oz.',\n",
-       "  (128, 47, 65, 170, 0)),\n",
-       " ('B00021B8L2',\n",
-       "  'Philosophy Hope in a Jar Daily Moisturizer, Dry Skin, 2 Ounce',\n",
-       "  (127, 3, 98, 75, 0)),\n",
-       " ('B00021B95W',\n",
-       "  'Philosophy Coconut Frosting Shampoo/Shower Gel/Bubble Bath, 16 Ounces',\n",
-       "  (117, 3, 254, 114, 0)),\n",
-       " ('B00021BEXY', 'Blinc Kiss Me Mascara, Dark Brown', (210, 4, 40, 12, 0)),\n",
-       " ('B00021C1LI',\n",
-       "  'Philosophy Purity Made Simple One-Step Facial Cleanser, 16 Ounces',\n",
-       "  (107, 8, 219, 182, 0)),\n",
-       " ('B00021C49W',\n",
-       "  'Philosophy Amazing Grace Shower Gel, 16 Ounces',\n",
-       "  (221, 8, 70, 152, 0)),\n",
-       " ('B00021C5Y6',\n",
-       "  'STILL JENNIFER LOPEZ by Jennifer Lopez EAU DE PARFUM SPRAY 3.4 OZ',\n",
-       "  (178, 145, 122, 103, 0)),\n",
-       " ('B00021C9BA', 'NARS Cream Blush, Lokoum', (161, 129, 41, 170, 0)),\n",
-       " ('B00021CNCK',\n",
-       "  'CASHMERE MIST by Donna Karan EDT SPRAY 3.4 OZ',\n",
-       "  (76, 211, 122, 53, 0)),\n",
-       " ('B00021CSBG', 'NARS Duo Eyeshadow, All About Eve', (200, 196, 201, 195, 0)),\n",
-       " ('B00021CT10', 'NARS Matte Eyeshadow, Thunderball', (65, 66, 254, 170, 0)),\n",
-       " ('B00021CT56', 'NARS Shimmer Eyeshadow, Nepal', (65, 66, 254, 116, 0)),\n",
-       " ('B00021D5FY',\n",
-       "  'Desire by Alfred Dunhill for Men - 1.7 Ounce EDT Spray',\n",
-       "  (119, 96, 226, 116, 0)),\n",
-       " ('B00021D6JE',\n",
-       "  'FCUK by French Connection Eau De Toilette Spray 1.7 oz for Men',\n",
-       "  (59, 167, 122, 77, 0)),\n",
-       " ('B00021DDR4', 'NARS The Multiple, Maui', (65, 122, 153, 170, 0)),\n",
-       " ('B00021DDYW', 'NARS Sheer Lipstick, Barbarella', (3, 248, 201, 15, 0)),\n",
-       " ('B00021DE14', 'NARS Semi-Matte Lipstick, Morocco', (3, 248, 201, 15, 1)),\n",
-       " ('B00021DG80', 'NARS Lip Gloss, Turkish Delight', (79, 202, 201, 76, 0)),\n",
-       " ('B00021DJ32', 'NARS Blush, Taj Mahal', (107, 77, 140, 34, 0)),\n",
-       " ('B00021DPHW', 'NARS Bronzing Powder, Casino', (109, 129, 65, 241, 0)),\n",
-       " ('B00021DTEG',\n",
-       "  'Bare Escentuals bareMinerals Glimmer Celestine',\n",
-       "  (117, 59, 70, 91, 0)),\n",
-       " ('B00021DUM2',\n",
-       "  \"bareMinerals Blush - I'm Amused Rouge\",\n",
-       "  (194, 80, 201, 170, 0)),\n",
-       " ('B00021DVCQ',\n",
-       "  'BareMinerals Original Foundation Broad Spectrum SPF 15 8 g/0.28 Oz (Fair C10 8g/0.28 oz)',\n",
-       "  (128, 145, 201, 76, 0)),\n",
-       " ('B00021DZCC',\n",
-       "  'Philosophy Falling in Love Spray Fragrance, 2 Ounce',\n",
-       "  (221, 145, 139, 15, 0)),\n",
-       " ('B00021EA9Y',\n",
-       "  'Mustela Hydra-Bebe Body Lotion w/ Pump 10.14 US fl. oz',\n",
-       "  (63, 213, 41, 103, 0)),\n",
-       " ('B00021KD2C',\n",
-       "  'derma e Anti-Wrinkle Vitamin A Retinyl Palmitate Cr&egrave;me , 4-Ounce Jar',\n",
-       "  (56, 21, 59, 160, 0)),\n",
-       " ('B00021PDN6',\n",
-       "  'Cacharel Anais Anais By Cacharel - Eau De Toilette Spray 3.4 Oz, 3.4 oz',\n",
-       "  (63, 145, 55, 91, 0)),\n",
-       " ('B00021UR3C',\n",
-       "  'Green Tea by Elizabeth Arden, 1.5 Ounce',\n",
-       "  (229, 180, 82, 202, 0)),\n",
-       " ('B00021W26M',\n",
-       "  'Prestige Liquid Eyeliner, Legend, 0.1 Ounce',\n",
-       "  (222, 29, 227, 160, 0)),\n",
-       " ('B00021WZVE',\n",
-       "  'Christian Super Long Lash Mascara Waterproof Seven Oils Black',\n",
-       "  (132, 159, 163, 99, 0)),\n",
-       " ('B000225ZL0', 'Fresh Farmacy Cleanser by LUSH', (1, 134, 27, 37, 0)),\n",
-       " ('B000225ZOC', 'Karma Handmade Soap by LUSH', (120, 145, 138, 91, 0)),\n",
-       " ('B0002260DW', 'Sex Bomb Bath Bomb by LUSH', (119, 161, 162, 53, 0)),\n",
-       " ('B0002260L4', 'Big Blue Bath Bomb by LUSH', (141, 211, 65, 53, 0)),\n",
-       " ('B0002260N2', 'Karma Komba Solid Shampoo by LUSH', (232, 100, 254, 91, 0)),\n",
-       " ('B00022F1DW',\n",
-       "  'Organic Excellence  Mint Shampoo, 16 Ounce',\n",
-       "  (93, 103, 34, 195, 0)),\n",
-       " ('B00022WA9K', 'Revlon Shine Enchancing Hot Air Kit', (149, 66, 201, 114, 0)),\n",
-       " ('B00022WA9U',\n",
-       "  'Revlon RV261 20-Roller Ionic Professional Hairsetter, Purple',\n",
-       "  (135, 77, 219, 114, 0)),\n",
-       " ('B00023DIBI',\n",
-       "  'Cucina Coriander and Olive Tree 33.8 oz Purifying Hand Wash Refill',\n",
-       "  (120, 21, 153, 104, 0)),\n",
-       " ('B00023KDAW',\n",
-       "  'amazing grace | perfumed firming body emulsion | philosophy 8 oz.',\n",
-       "  (99, 196, 67, 241, 0)),\n",
-       " ('B000248HLI',\n",
-       "  'StriVectin-SD Intensive Repair for Existing Stretch Marks, 6 oz.',\n",
-       "  (252, 47, 227, 169, 0)),\n",
-       " ('B000260KIO',\n",
-       "  'Ouidad Botanical Boost Moisture Infusing and Refreshing Spray, 8.5 Ounce',\n",
-       "  (132, 10, 43, 169, 0)),\n",
-       " ('B0002716FO',\n",
-       "  'Ardell Duralash Flare Short Black (56 Lashes)',\n",
-       "  (200, 100, 33, 169, 0)),\n",
-       " ('B000271KR8',\n",
-       "  'Clairol Natural Instincts  02 Sahara Light Blonde 1 Kit  (Pack of 3) (packaging may vary)',\n",
-       "  (70, 161, 231, 38, 0)),\n",
-       " ('B000273PEO',\n",
-       "  'Happy By Clinique For Women. Eau De Parfum Spray 1.7 Ounces',\n",
-       "  (175, 100, 10, 75, 0)),\n",
-       " ('B000273RI8', 'M.D. Forte Facial Cream III', (56, 135, 216, 91, 0)),\n",
-       " ('B000277N7Y',\n",
-       "  'Happy By Clinique For Men. Cologne Spray 1.7 Oz.',\n",
-       "  (92, 196, 59, 91, 0)),\n",
-       " ('B00027C9CS',\n",
-       "  'Suave For Men, Deep Clean Peppermint, Shampoo12.6 fl Ounce 373 ml)',\n",
-       "  (117, 115, 153, 27, 0)),\n",
-       " ('B00027CDY2',\n",
-       "  'Neutrogena Triple Moisture Silk Touch Leave-In Cream, 6 Fluid Ounce',\n",
-       "  (173, 202, 163, 103, 0)),\n",
-       " ('B00027CDYM',\n",
-       "  'Neutrogena Clean Replenishing Deep Recovery Hair Mask, 6 oz',\n",
-       "  (144, 202, 37, 114, 0)),\n",
-       " ('B00027CGWQ',\n",
-       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 12 Ounce Jar',\n",
-       "  (128, 141, 139, 219, 1)),\n",
-       " ('B00027D0DU',\n",
-       "  'Neutrogena Cosmetics  Makeup Remover, 3.8 oz',\n",
-       "  (13, 8, 122, 206, 0)),\n",
-       " ('B00027D6SE',\n",
-       "  'Bye Bye Blemish Drying Lotion - 1 fl oz',\n",
-       "  (56, 96, 10, 75, 0)),\n",
-       " ('B00027D8IC', 'Duo Lash Adhesive, Clear, 0.25 Ounce', (23, 141, 41, 152, 0)),\n",
-       " ('B00027D98Q',\n",
-       "  'Ardell Brow &amp; Lash Growth Accelerator Treatment Gel 7ml/0.25oz',\n",
-       "  (71, 21, 70, 184, 0)),\n",
-       " ('B00027DDEQ',\n",
-       "  'Clean &amp; Clear Morning Glow Moisturizer, SPF 15, 4 Ounce',\n",
-       "  (78, 135, 216, 53, 0)),\n",
-       " ('B00027DDOQ',\n",
-       "  'Clean &amp; Clear Clear Advantage Acne Spot Treatment, 0.75 Ounce',\n",
-       "  (78, 135, 122, 60, 0)),\n",
-       " ('B00027DHYM',\n",
-       "  'Natures Cure Body Acne Treatment Spray 3.5 Oz',\n",
-       "  (144, 103, 124, 241, 0)),\n",
-       " ('B00027DMI8',\n",
-       "  'RoC Retinol Correxion Deep Wrinkle Night Cream, 1-Ounce',\n",
-       "  (12, 170, 59, 91, 0)),\n",
-       " ('B00027DMJW',\n",
-       "  'Roc Retinol Correxion Deep Wrinkle Daily Moisturizer, SPF 30, 1-ounce Tube',\n",
-       "  (93, 135, 75, 152, 0)),\n",
-       " ('B00027DMLK',\n",
-       "  'Frownies Corners Of Eyes And Mouth, 144 Patches',\n",
-       "  (228, 247, 227, 34, 0)),\n",
-       " ('B00027DMSI',\n",
-       "  'Frownies Forehead &amp; Between Eyes, 144 Patches',\n",
-       "  (29, 145, 162, 169, 0)),\n",
-       " ('B00027EG9C',\n",
-       "  'Neutrogena Fresh Foaming Cleanser, 6.7 Ounce',\n",
-       "  (144, 55, 3, 241, 0)),\n",
-       " ('B00027YZKM', 'Mirror* Hand* Large* Black', (55, 161, 153, 34, 0)),\n",
-       " ('B00028F0NM',\n",
-       "  'Alba Botanica Maximum Very Emollient Body Lotion, 12 Ounce Bottle',\n",
-       "  (45, 96, 65, 60, 0)),\n",
-       " ('B00028LN1K',\n",
-       "  \"GRANDPA'S BRANDS, Pine Tar Soap Bath Size - 4.25 oz\",\n",
-       "  (32, 161, 24, 220, 0)),\n",
-       " ('B00028LURW', 'Henna Red 4 Ounces', (45, 21, 216, 77, 0)),\n",
-       " ('B00028LV4Y',\n",
-       "  'Natural Missst Herbal Hairspray Mist-Super Hold Aubrey Organics 8 oz Spray',\n",
-       "  (71, 99, 162, 171, 0)),\n",
-       " ('B00028M3N2',\n",
-       "  'Now Foods Essential Oil, Lemon, 4 Fluid Ounce',\n",
-       "  (63, 59, 67, 202, 0)),\n",
-       " ('B00028MLG6',\n",
-       "  'NOW Foods Apricot Kernel Oil (Liquid), 16 oz',\n",
-       "  (252, 202, 3, 100, 0)),\n",
-       " ('B00028MLHA',\n",
-       "  'Cocoa Butter with jojoba oil - 100% Pure 6.5 fl.oz',\n",
-       "  (71, 80, 22, 170, 0)),\n",
-       " ('B00028O7ZO',\n",
-       "  \"Castile Liquid Soap-Lavender Dr. Bronner's 1 Gallon Liquid\",\n",
-       "  (105, 63, 233, 219, 0)),\n",
-       " ('B00028OC4K',\n",
-       "  'Throughly Clean Face Wash - Original - 8.5 oz. (250 ml) - Liquid',\n",
-       "  (71, 134, 246, 37, 0)),\n",
-       " ('B00028OPZ6',\n",
-       "  'GPB (Glycogen Protein Balance) Conditioner Jumbo Size - 16 oz - Liquid',\n",
-       "  (71, 134, 162, 91, 0)),\n",
-       " ('B00028OQZA',\n",
-       "  'Sparkling Glacier Therapy Complexion Mist Aubrey Organics 3.4 oz (100ml) Liquid',\n",
-       "  (71, 249, 122, 131, 0)),\n",
-       " ('B00028OSI0', 'Aloe Life Skin Gel and Herbs, 8 oz', (100, 122, 71, 160, 0)),\n",
-       " ('B00028OTAM',\n",
-       "  'Gabriel Cosmetics Inc. - Moisturizing Liquid Foundation Soft Beige 18 SPF - 1 oz.',\n",
-       "  (192, 121, 116, 103, 0)),\n",
-       " ('B00028OTNY',\n",
-       "  'Mandarin Magic Hair Gel Aubrey Organics 8 oz Liquid',\n",
-       "  (63, 134, 246, 171, 0)),\n",
-       " ('B00028OU5Q', 'Blue Green Algae Hair Mask 4 oz', (247, 134, 43, 236, 0)),\n",
-       " ('B00028PDWK',\n",
-       "  'Apricot Gentle Facial Scrub, 4 Ounce',\n",
-       "  (245, 103, 231, 12, 0)),\n",
-       " ('B00028PFF0', 'WITCH HAZEL PADS EA 1/60 PADS', (216, 161, 246, 104, 0)),\n",
-       " ('B00028PXAC', 'Viobin Wheat Germ Oil 16 oz', (132, 23, 163, 195, 0)),\n",
-       " ('B0002A65LQ',\n",
-       "  'Philosophy Help Me Retinol Night Treatment, 1.05 Ounce',\n",
-       "  (233, 159, 201, 206, 0)),\n",
-       " ('B0002B0R14',\n",
-       "  'H2Ocean 1.5 FL OZ Purified Ocean Salt Water Piercing Aftercare',\n",
-       "  (43, 46, 71, 99, 0)),\n",
-       " ('B0002BB3YO',\n",
-       "  'NaturalCare Homeopathic Ultra Vein-Gard Leg Therapy Cream, 2.25-Ounce Package',\n",
-       "  (63, 180, 219, 114, 0)),\n",
-       " ('B0002BPIZO',\n",
-       "  'Philosophy Pure Grace Shampoo, Bath &amp; Shower gel, 16 Ounces',\n",
-       "  (19, 134, 139, 91, 0)),\n",
-       " ('B0002CTSRM', 'Amber Butter', (119, 196, 153, 206, 0)),\n",
-       " ('B0002DLEJG',\n",
-       "  'Nailtek Foundation No.2 Ridge-Filling Nail Strengthener Base Coat, 0.5 Fluid Ounce',\n",
-       "  (65, 122, 163, 236, 0)),\n",
-       " ('B0002DNZAC', 'MAC Eye Shadow Frost Satin Taupe', (225, 189, 227, 195, 0)),\n",
-       " ('B0002DO1RI',\n",
-       "  'MAC Studio Fix Powder Plus Foundation NC35 for Women, 0.52 Ounce',\n",
-       "  (91, 149, 75, 91, 0)),\n",
-       " ('B0002DO1RS',\n",
-       "  'MAC Studio Fix Powder Plus Foundation NC45',\n",
-       "  (91, 149, 163, 135, 0)),\n",
-       " ('B0002DPG1I', 'Fat Girl Slim by Bliss', (63, 247, 231, 202, 0)),\n",
-       " ('B0002DTV0K',\n",
-       "  'Emu Oil Soap Bar (110g) Brand: SoapWorks',\n",
-       "  (32, 96, 67, 114, 0)),\n",
-       " ('B0002DUSR0',\n",
-       "  'PediFix Visco GEL Toe Spacers, Medium',\n",
-       "  (28, 228, 122, 160, 0)),\n",
-       " ('B0002DUSUW',\n",
-       "  'PediFix Double-Toe Straightener #P57',\n",
-       "  (80, 131, 234, 114, 0)),\n",
-       " ('B0002E4WOO',\n",
-       "  \"Miracle of Aloe Miracle Hand Repair Cream 8 Oz Relieve Dry, Cracked, Flacking Hands Immediately! Therapeutic Formula Contains 60% Ultra Aloe - The Purest Most Potent Form of Whole Leaf Aloe Vera Gel. Fast Acting Relief, Say Good Bye to Dry, Cracked Hands Now! Keep Your Hands Healthy &amp; Warm this Winter! Reduces Flaking and Redness, Use on Hands, Elbows and Knees, Exclusive Fast Acting Formula Penetrates Deep Into Damaged Skin Layers to Moisturize Where It's Needed Most. Leaves Hands Feeling Silky Smooth and Comfortable. Olay, Ponds, Vaseline, Nivea, Eucerin, Aveeno.\",\n",
-       "  (247, 21, 246, 100, 0)),\n",
-       " ('B0002EBI82', 'Toppik Hair Fattener, 4-Ounce', (62, 145, 138, 188, 0)),\n",
-       " ('B0002EZXUG',\n",
-       "  'Vaseline Intensive Care Moisturizing Bath Beads, Gentle Breeze, 24 Ounces',\n",
-       "  (44, 122, 139, 236, 0)),\n",
-       " ('B0002F1H6E',\n",
-       "  'Jergens All-Purpose Face Cream 425g/15oz',\n",
-       "  (117, 196, 74, 75, 0)),\n",
-       " ('B0002FBOLW',\n",
-       "  'Atopalm Intensive Moisturizing Cream, 3.4 fl. Ounce',\n",
-       "  (239, 152, 138, 53, 0)),\n",
-       " ('B0002FCCH2',\n",
-       "  'Suave Naturals Shampoo, Tropical Coconut  - 22.5oz.',\n",
-       "  (41, 120, 10, 53, 0)),\n",
-       " ('B0002FCD5I',\n",
-       "  'Suave Naturals Conditioner, Tropical Coconut - 22.5oz.',\n",
-       "  (75, 4, 208, 219, 0)),\n",
-       " ('B0002FCDHQ',\n",
-       "  'Suave For Kids Double Dutch Apple Detangler Spray Conditioner 10.5Ounces',\n",
-       "  (115, 255, 22, 152, 0)),\n",
-       " ('B0002G214U', 'Conair Soft Bonnet Hair Dryer', (98, 129, 219, 114, 0)),\n",
-       " ('B0002G21GI',\n",
-       "  'Ion Shine&reg; by Conair Instant Heat Compact Styling Setter',\n",
-       "  (220, 66, 70, 60, 0)),\n",
-       " ('B0002JG2NI',\n",
-       "  'Home Health Castor Oil Cold Pressed &amp; Cold Processed 8 fl. oz.',\n",
-       "  (128, 159, 3, 37, 0)),\n",
-       " ('B0002JGIZA',\n",
-       "  'Avalon Organics Night Cream Lavender Luminosity -- 2 oz',\n",
-       "  (93, 4, 227, 217, 0)),\n",
-       " ('B0002JGU30',\n",
-       "  'Avalon Organics Hand &amp; Body Lotion, Peppermint, 12 Ounce',\n",
-       "  (104, 234, 67, 170, 0)),\n",
-       " ('B0002JHI1I',\n",
-       "  'Avalon Organics CoQ10 Facial Cleansing Milk -- 8.5 fl oz',\n",
-       "  (218, 145, 43, 169, 0)),\n",
-       " ('B0002JIHT0',\n",
-       "  'Home Health Antidandruff Shampoo, Everclean, Unscented, 8-Ounce',\n",
-       "  (119, 196, 234, 202, 0)),\n",
-       " ('B0002JISX0',\n",
-       "  'Earth Science A-D-E Creamy Fruit Oil Cleanser 8 oz.',\n",
-       "  (117, 98, 231, 219, 0)),\n",
-       " ('B0002JIUDS',\n",
-       "  'Nutricology Ox Bile, 500 mg,  100 Vegetarian Capsules',\n",
-       "  (233, 128, 70, 219, 0)),\n",
-       " ('B0002JKPA4', 'Caruso C97958 ION Steam Hairsetter', (215, 66, 139, 116, 0)),\n",
-       " ('B0002JKQ8K',\n",
-       "  'Caruso Professional Molecular Steam Rollers with Shields, Small (6-Pack)',\n",
-       "  (80, 55, 226, 114, 0)),\n",
-       " ('B0002JMZ7A',\n",
-       "  'Yves Saint Laurent Volume Effet Faux Cils Luxurious Mascara for a False Lash Effect 1 High Density Black',\n",
-       "  (132, 164, 43, 13, 0)),\n",
-       " ('B0002JNRCC',\n",
-       "  'Redken All Soft Conditioner Unisex Conditioner, 8.5 Ounce',\n",
-       "  (99, 66, 24, 76, 0)),\n",
-       " ('B0002KS5VE',\n",
-       "  'Body Fantasies Body Spray for Women, Cotton Candy Fantasy Fragrance, 8 Ounce',\n",
-       "  (162, 255, 59, 219, 0)),\n",
-       " ('B0002L2JLU',\n",
-       "  'Topix Pharm SPF 40 Citrix Antioxidant Sunscreen, 3.5 Fluid Ounce',\n",
-       "  (44, 236, 234, 34, 0)),\n",
-       " ('B0002M9OYY',\n",
-       "  'Fake Bake Passion Fruit Body Polish 6 oz.',\n",
-       "  (47, 3, 162, 103, 0)),\n",
-       " ('B0002NIAC0',\n",
-       "  'Obagi C-Exfoliating Day Lotion 2 fl oz.',\n",
-       "  (93, 192, 254, 13, 0)),\n",
-       " ('B0002OCNMC',\n",
-       "  'Goat Milk Soap - Original (3 Pack) Canus Vermont 3 Bar Soap',\n",
-       "  (18, 135, 65, 15, 0)),\n",
-       " ('B0002OMOIA',\n",
-       "  'Brahmi Oil - Ayurvedic Hair Growth massage oil',\n",
-       "  (252, 24, 216, 152, 0)),\n",
-       " ('B0002PCH2M',\n",
-       "  'Focus 21 Sea Plasma All Purpose Skin and Hair Moisturizing Spray 12 oz',\n",
-       "  (44, 255, 219, 152, 0)),\n",
-       " ('B0002Q8W9I',\n",
-       "  'NEW cnd Nail Design Stickey Base Coat 0.33Oz',\n",
-       "  (116, 120, 33, 13, 0)),\n",
-       " ('B0002QB1SC',\n",
-       "  'Creative Nail Design Creative Nail Solar Oil, 0.5 Ounce',\n",
-       "  (94, 46, 67, 77, 0)),\n",
-       " ('B0002RI2PG',\n",
-       "  'Dermablend Loose Setting Powder Original, 1 Ounce',\n",
-       "  (194, 47, 65, 37, 0)),\n",
-       " ('B0002RPTX4',\n",
-       "  'Shampoo Scalp Massage Brush- 1 Brush',\n",
-       "  (48, 255, 153, 160, 0)),\n",
-       " ('B0002RYHTQ',\n",
-       "  'Spa Sister Womens Cotton Moisture Enhancing Overnight Gloves',\n",
-       "  (99, 29, 140, 99, 0)),\n",
-       " ('B0002SA9BU',\n",
-       "  'Best Hair Growth Vitamins NEW ADVANCED Hair Formula 37 Hair Vitamins for Faster Growing Healthy Hair 1 month Supply 60 Capsules',\n",
-       "  (12, 228, 24, 91, 0)),\n",
-       " ('B0002SGNOM',\n",
-       "  'California Tan Sunsets Tanning Eyewear 1-PR Random Picked Colors',\n",
-       "  (116, 25, 75, 13, 0)),\n",
-       " ('B0002SGSNI',\n",
-       "  'China Glaze Strong Adhesion Base Coat',\n",
-       "  (152, 159, 24, 220, 0)),\n",
-       " ('B0002SI7DW',\n",
-       "  'NAIL TEK Regular Size Crystal File with Free Blue Case',\n",
-       "  (113, 96, 140, 195, 0)),\n",
-       " ('B0002SISY0',\n",
-       "  'Spornette My Favorite Mini Nylon Tipped Paddle Brush',\n",
-       "  (155, 145, 254, 170, 0)),\n",
-       " ('B0002SQ6YO',\n",
-       "  'Sponge Company Cosmetic Silk Sponge 1',\n",
-       "  (155, 236, 122, 120, 0)),\n",
-       " ('B0002TSCZY',\n",
-       "  'Solia Tourmaline Ceramic Ion Flat Iron (1&quot;)',\n",
-       "  (181, 3, 65, 103, 0)),\n",
-       " ('B0002TSD08',\n",
-       "  'Solia Professional Ceramic Ion Flat Iron (1-3/4&quot;)Solia',\n",
-       "  (9, 77, 234, 169, 0)),\n",
-       " ('B0002UEDWE', 'Evian Facial Water Spray 1.7 Oz', (252, 196, 227, 34, 0)),\n",
-       " ('B0002VHBTU', 'Thermal Spa Hair Wrap - White', (4, 3, 139, 13, 0)),\n",
-       " ('B0002VJIH8',\n",
-       "  'Best Hair Vitamin to Grow Hair Faster with Fast Grow Ethnic Hair Growth Enhancer 90 capsules 30 Day Supply',\n",
-       "  (45, 8, 67, 169, 0)),\n",
-       " ('B0002VQ0WO', 'Hair Dryer Stand', (200, 149, 226, 104, 0)),\n",
-       " ('B0002WUHTU',\n",
-       "  'Nailtiques Nail Protein Formula 2 Plus',\n",
-       "  (194, 180, 163, 103, 0)),\n",
-       " ('B0002X4F0Q',\n",
-       "  'PCA Skin Blemish Control Bar (Phaze 32), 3.3 Ounce',\n",
-       "  (94, 255, 75, 202, 0)),\n",
-       " ('B0002X9038',\n",
-       "  'PCA Skin Pigment Gel (Phaze 13), 1 Fluid Ounce',\n",
-       "  (63, 202, 70, 103, 0)),\n",
-       " ('B0002XBTVO', 'PCA Skin Clearskin, 1.7 Ounce', (177, 134, 155, 103, 0)),\n",
-       " ('B0002XG9X2',\n",
-       "  'Panasonic EH2511A Pore Cleanser with Micro-fine Mist',\n",
-       "  (174, 196, 71, 60, 0)),\n",
-       " ('B0002XY3QC', 'duri Miracote .61oz', (79, 135, 65, 135, 0)),\n",
-       " ('B0002YE8F2',\n",
-       "  'Revlon RV2513 Color Swirl Detangling Comb, Colors may vary',\n",
-       "  (132, 183, 163, 15, 0)),\n",
-       " ('B0002YE8IO',\n",
-       "  'Revlon RV2641 Soft Touch Porcupine Cushion Brush, Colors May Vary',\n",
-       "  (132, 105, 140, 13, 0)),\n",
-       " ('B0002YE8V6',\n",
-       "  'Vidal Sassoon VS7963 Ionic Thermal All Purpose Brush',\n",
-       "  (47, 60, 163, 231, 0)),\n",
-       " ('B0002YE8WA',\n",
-       "  'Vidal Sassoon VS14434 No Headache Headbands (2 Headbands)',\n",
-       "  (113, 74, 112, 246, 0)),\n",
-       " ('B0002YFQ6W',\n",
-       "  'Cloud Star Corporation Buddy Wash Lavender &amp; Mint',\n",
-       "  (79, 196, 147, 152, 0)),\n",
-       " ('B0002YLY9K', 'Obagi Nu-Derm Toner 6.7 oz.', (132, 3, 139, 77, 0)),\n",
-       " ('B0002Z0R9C',\n",
-       "  'Spornette No.215 Super Looper Hair Brush',\n",
-       "  (200, 228, 208, 116, 0)),\n",
-       " ('B0002Z0U5I',\n",
-       "  'Helen of Troy Professional Tangle-Free Hot Air Brush Iron',\n",
-       "  (253, 162, 246, 99, 0)),\n",
-       " ('B0002Z0UA8',\n",
-       "  'FusionBeauty LipFusion Micro-Injected Collagen Lip Plump Color Shine, Summer',\n",
-       "  (250, 125, 122, 103, 0)),\n",
-       " ('B0002Z1JJ4', 'Acquarella Remover', (232, 145, 226, 220, 0)),\n",
-       " ('B0002Z1JK8', 'Acquarella Conditioner', (232, 80, 254, 194, 0)),\n",
-       " ('B0002Z81O0',\n",
-       "  'Peter Thomas Roth Max Complexion Correction Pads? (60 Pads)',\n",
-       "  (56, 89, 3, 12, 0)),\n",
-       " ('B0002Z8GEK', 'Gena Hoof Lacquer, 5 Ounce', (39, 96, 24, 170, 0)),\n",
-       " ('B0002Z8HAI',\n",
-       "  'Essie Nail Lacquer, Sugar Daddy, 0.5 Fluid Ounce',\n",
-       "  (152, 3, 139, 37, 0)),\n",
-       " ('B0002Z8IV6',\n",
-       "  'Ardell Hair Color Bottle, Unred, 0.25 Ounce',\n",
-       "  (177, 21, 33, 103, 0)),\n",
-       " ('B0002Z8LLI',\n",
-       "  'Sensories Smoother Passionflower and Aloe Leave In Texturizing Condition By Rusk, 33.8 Ounce',\n",
-       "  (85, 247, 71, 77, 0)),\n",
-       " ('B0002Z8MLC', 'ISO Bouncy Creme 8.5 oz.', (127, 141, 226, 206, 0)),\n",
-       " ('B0002Z8NJS',\n",
-       "  'Essie Nail Lacquer, Blanc, 0.5 Fluid Ounce',\n",
-       "  (152, 3, 162, 76, 0)),\n",
-       " ('B0002Z8OUG', 'Ruby Stone Nail File', (89, 211, 140, 206, 0)),\n",
-       " ('B0002Z8P1E', 'Quantum Classic Extra Body Acid Perm', (117, 173, 24, 91, 0)),\n",
-       " ('B0002Z8P8M', \"L'Oreal ColorZap Haircolor Remover\", (250, 21, 40, 53, 0)),\n",
-       " ('B0002Z8QG8',\n",
-       "  'Denman Styling Brush, Heavy Weight, 9-Row',\n",
-       "  (201, 122, 70, 160, 0)),\n",
-       " ('B0002Z8QHW', 'Creative Nail Design SolarOil .5oz', (141, 8, 163, 56, 0)),\n",
-       " ('B0002Z8RGC', 'Blax 4 mm Ponytail Holders - Black', (55, 79, 138, 169, 0)),\n",
-       " ('B0002Z8SE8',\n",
-       "  'Spilo: MISC Big Bondini Plus Brush-On Glue, 0.5 oz',\n",
-       "  (79, 161, 219, 133, 0)),\n",
-       " ('B0002Z8SZW',\n",
-       "  'Bain De Terre Serum Anti-Frizz Recovery Complex 1.7 oz.',\n",
-       "  (154, 202, 33, 195, 0)),\n",
-       " ('B0002Z90RM',\n",
-       "  'Rusk Thick Body and Texture Amplifier, 6 Ounce',\n",
-       "  (117, 100, 70, 114, 0)),\n",
-       " ('B0003009NK',\n",
-       "  'Hai Classic Convertible Ceramic Flat Iron, 1-1/4 Inch',\n",
-       "  (241, 196, 17, 34, 0)),\n",
-       " ('B000629ZHG', 'Tinted Moisturizer - Almond', (132, 103, 153, 169, 0)),\n",
-       " ('B00062A0JI',\n",
-       "  'Laura Mercier Secret Concealer # 1 0.08oz',\n",
-       "  (132, 62, 138, 169, 0)),\n",
-       " ('B00062ADJA',\n",
-       "  'Laura Mercier Loose Setting Powder - Ivory - 29g/1oz',\n",
-       "  (132, 159, 162, 34, 0)),\n",
-       " ('B0006433MM',\n",
-       "  \"18'' Detachable Long Handle Natural Boar Bristle Brush\",\n",
-       "  (28, 3, 201, 77, 0)),\n",
-       " ('B00064A5L4',\n",
-       "  'Be Delicious by Donna Karan Eau De Parfum Spray 1 oz for Women',\n",
-       "  (59, 202, 122, 15, 0)),\n",
-       " ('B00064H44K',\n",
-       "  'Vanderbilt by Gloria Vanderbilt for Women - 3.3 Ounce EDT Spray',\n",
-       "  (8, 120, 216, 27, 0)),\n",
-       " ('B00065AFFE',\n",
-       "  'Conair 1875 Watt Ergonomic Handle Hair Dryer',\n",
-       "  (226, 103, 254, 103, 0)),\n",
-       " ('B00066D2JE',\n",
-       "  'Giovanni Conditioner, Smooth as Silk for Damaged Hair, 8.5 fl oz Bottle',\n",
-       "  (13, 18, 27, 170, 0)),\n",
-       " ('B00066D4M4',\n",
-       "  'Giovanni Direct Leave-In Conditioner, Weightless moisture conditioner 8.5 fl oz Bottle',\n",
-       "  (173, 2, 40, 100, 0)),\n",
-       " ('B00066LD7C',\n",
-       "  'Xenna Curlaway Gradual Curl Relaxer Green Apple Fragrance -- 6 oz',\n",
-       "  (92, 173, 65, 53, 0)),\n",
-       " ('B00066YC34',\n",
-       "  'Conair Supreme Triple Curling Iron Pack  - 1/2 inch, 3/4 inch and 1 inch',\n",
-       "  (220, 122, 43, 206, 0)),\n",
-       " ('B000674XN2',\n",
-       "  'Laura Mercier Secret Brightener - Secret Brightener #1, .05 oz.',\n",
-       "  (132, 3, 24, 77, 0)),\n",
-       " ('B000674XNM',\n",
-       "  'Laura Mercier Secret Brightening Powder - Secret Brightening Powder #1, 0.14 oz.',\n",
-       "  (132, 46, 67, 169, 0)),\n",
-       " ('B000674Z8A',\n",
-       "  'Laura Mercier Accessories - Sponge, 4-pack',\n",
-       "  (155, 60, 82, 202, 0)),\n",
-       " ('B00067EJ4U', 'Laura Mercier Foundation Primer', (164, 248, 131, 236, 0)),\n",
-       " ('B00067YSLO',\n",
-       "  'Remington Wet 2 Straight 2&quot; Wide Plate Wet/Dry Ceramic Hair Straightening Iron with Tourmaline',\n",
-       "  (241, 40, 59, 169, 0)),\n",
-       " ('B00068A0AQ',\n",
-       "  'Straight Sexy Hair Smooth &amp; Seal Spray by Sexy Hair for Unisex - 8.1 Ounce Hairspray',\n",
-       "  (195, 3, 227, 114, 0)),\n",
-       " ('B00068AZ88',\n",
-       "  'CombaColor Quick Hair Color Applicator (Comb-a-Color)',\n",
-       "  (67, 4, 40, 12, 0)),\n",
-       " ('B00068CG3U', 'Philosophy The Microdelivery Peel', (235, 196, 254, 202, 0)),\n",
-       " ('B00069FJUG',\n",
-       "  'Philosophy Supernatural Airbrushed Canvas Powder, SPF 15, Bronze',\n",
-       "  (13, 122, 40, 38, 0)),\n",
-       " ('B0006B12KK', 'NARS Velvet Matte Lip Pencil, Bahama', (5, 87, 153, 167, 0)),\n",
-       " ('B0006B66D8',\n",
-       "  'Super Solano Professional Hair Dryer - Black',\n",
-       "  (80, 47, 24, 159, 0)),\n",
-       " ('B0006BDMIU', 'Caudalie Beauty Elixir 1 fl oz.', (44, 3, 27, 103, 0)),\n",
-       " ('B0006BDO5G',\n",
-       "  'Peter Thomas Roth Sulfur Cooling Masque 5 Ounce.',\n",
-       "  (102, 47, 33, 169, 0)),\n",
-       " ('B0006BDO5Q',\n",
-       "  'Peter Thomas Roth Glycolic Acid 10% Hydrating Gel, 2 Ounce',\n",
-       "  (235, 135, 153, 77, 0)),\n",
-       " ('B0006BDQDG',\n",
-       "  'Peter Thomas Roth AHA/BHA Acne Clearing Gel 2 fl oz',\n",
-       "  (56, 3, 71, 160, 0)),\n",
-       " ('B0006D3IAU', 'Cococare Coconut Oil, 9 Ounce', (161, 161, 71, 103, 0)),\n",
-       " ('B0006DS3KU',\n",
-       "  'Ojon Restorative Hair Treatment 4.6 oz (140 g)',\n",
-       "  (242, 47, 122, 103, 0)),\n",
-       " ('B0006FMK98',\n",
-       "  'Pharmaceutical Specialties Free and Clear Shampoo 12 oz.',\n",
-       "  (93, 103, 216, 103, 0)),\n",
-       " ('B0006FMKB6', 'Robathol Bath Oil - 16 oz', (102, 62, 192, 53, 0)),\n",
-       " ('B0006G2EX4', 'Obagi Foaming Gel Cleanser-6.7 oz', (144, 202, 24, 206, 0)),\n",
-       " ('B0006GBEYE',\n",
-       "  'Coretex SunX SPF30 Sunscreen with Towelettes - 25 Individual Foil Pouch with Towelettes/Box, PABA Free, Oil-free, Water and Sweat Resistant, UVA/UVB Protection',\n",
-       "  (226, 103, 33, 202, 0)),\n",
-       " ('B0006GZAKI',\n",
-       "  'Honeysuckle Rose Moisturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
-       "  (71, 47, 162, 171, 0)),\n",
-       " ('B0006GZBMA',\n",
-       "  'White Camellia Ultra-Smoothing Conditioner Aubrey Organics 11 oz Liquid',\n",
-       "  (71, 47, 162, 169, 0)),\n",
-       " ('B0006GZC4W',\n",
-       "  'Aubrey Organics - Island Natural Conditioner, 11 fl oz liquid',\n",
-       "  (71, 180, 162, 15, 0)),\n",
-       " ('B0006GZCVK',\n",
-       "  'Honeysuckle Rose Conditioner Aubrey Organics 11 oz Liquid',\n",
-       "  (201, 134, 59, 77, 0)),\n",
-       " ('B0006GZDE6',\n",
-       "  'Aubrey Organics green tea Clarifying Shampoo',\n",
-       "  (71, 134, 234, 159, 0)),\n",
-       " ('B0006GZDEQ',\n",
-       "  'White Camellia Ultra-Smoothing Shampoo 11oz',\n",
-       "  (71, 134, 246, 104, 0)),\n",
-       " ('B0006GZE0O',\n",
-       "  'Calaguala Texturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
-       "  (71, 134, 246, 152, 0)),\n",
-       " ('B0006HCE60',\n",
-       "  'Cococare 100% Vitamin E Oil, 1 Ounce',\n",
-       "  (133, 180, 201, 53, 0)),\n",
-       " ('B0006HRCL2', '4 Dozen (48) Long Pink Perm Rods', (241, 149, 67, 91, 0)),\n",
-       " ('B0006I9YK8', 'Baggallini Complete Cosmetic Bagg', (34, 62, 59, 77, 0)),\n",
-       " ('B0006IHDQ0',\n",
-       "  'Liz Claiborne Curve Crush EDC Spray 4.2 oz',\n",
-       "  (92, 173, 201, 76, 0)),\n",
-       " ('B0006II74C',\n",
-       "  \"Nature's Secret 5-Day Fast and Cleanse Kit\",\n",
-       "  (252, 247, 139, 34, 0)),\n",
-       " ('B0006IJA5C',\n",
-       "  'Jhirmack Silver Brightening Shampoo 20 Oz.',\n",
-       "  (63, 2, 140, 13, 0)),\n",
-       " ('B0006IXSG4',\n",
-       "  'John Varvatos by John Varvatos for Men - 7 ml EDT Splash',\n",
-       "  (121, 223, 122, 219, 0)),\n",
-       " ('B0006LNC2G',\n",
-       "  'MAC Eye Kohl Smolder Eye Liner for Women, 0.048 Ounce',\n",
-       "  (81, 115, 24, 195, 0)),\n",
-       " ('B0006LNJ56', 'MAC Satin Lipstick - Rebel', (81, 20, 24, 170, 0)),\n",
-       " ('B0006LNKXW',\n",
-       "  'MAC Tinted Lipglass -- Viva Glam V (Boxed) 4.8g/.17oz',\n",
-       "  (189, 60, 163, 202, 0)),\n",
-       " ('B0006LNKYG', 'MAC Matte Lipstick - Russian Red', (92, 129, 82, 170, 0)),\n",
-       " ('B0006LNMIU', 'MAC Powder Blush Fever', (152, 159, 163, 13, 0)),\n",
-       " ('B0006M5566', 'eb5 Facial Cream (4 Ounces)', (71, 129, 139, 114, 0)),\n",
-       " ('B0006M56BK',\n",
-       "  'Wilkinson Sword Double Edge single Razor Cartridge, 5 blades',\n",
-       "  (195, 211, 24, 77, 0)),\n",
-       " ('B0006MSVCG',\n",
-       "  'Burberry Brit ~ Women 3.3 oz / 100 ml Eau de Parfum Spray',\n",
-       "  (63, 236, 27, 219, 0)),\n",
-       " ('B0006N29AU',\n",
-       "  \"Dr. King's Natural Medicine Back, Muscle and Joint Relief, 2 Fluid Ounce\",\n",
-       "  (117, 59, 234, 53, 0)),\n",
-       " ('B0006NXBJ8', 'Mason Pearson Detangler', (89, 32, 71, 77, 0)),\n",
-       " ('B0006NY35E',\n",
-       "  'Pre de Provence Soap, Cucumber, 7 Ounce',\n",
-       "  (120, 149, 43, 76, 0)),\n",
-       " ('B0006NYCT6',\n",
-       "  'Algemarin Bubble Bath 750ml bubble bath by Algemarin',\n",
-       "  (47, 211, 254, 27, 0)),\n",
-       " ('B0006NYDOU',\n",
-       "  'Herbacin Kamille Hand Cream with Glycerine - 2.5 oz',\n",
-       "  (71, 173, 246, 131, 0)),\n",
-       " ('B0006O2IQ4',\n",
-       "  'Fragrances Of Ireland Inis The Energy Of The Sea Cologne Spray, 1.7 Fluid Ounce',\n",
-       "  (63, 115, 227, 13, 0)),\n",
-       " ('B0006O4MCM',\n",
-       "  'Suki - Concentrated Balancing Toner  4.0 fl oz',\n",
-       "  (102, 131, 216, 15, 0)),\n",
-       " ('B0006O9ZAG',\n",
-       "  'Palladio Herbal Dual Wet and Dry Foundation, Ivory Myrrh, 0.28 Ounce',\n",
-       "  (44, 103, 27, 236, 0)),\n",
-       " ('B0006OU06E', 'Semi Oval Wild Boar Hair Brush', (79, 161, 250, 99, 0)),\n",
-       " ('B0006OU078', 'Bass Brushes Facial Cleansing Brush', (195, 21, 33, 160, 0)),\n",
-       " ('B0006PJRP8', 'Zoya Nail Polish .5 oz. Lola', (194, 236, 65, 103, 0)),\n",
-       " ('B0006PJSES',\n",
-       "  'CND: Treatments/Prep Stickey Base Coat, 2.3 oz',\n",
-       "  (79, 122, 163, 123, 0)),\n",
-       " ('B0006PJSSO',\n",
-       "  'Essie Nail Lacquer, First Base Base Coat, 0.46 Fluid Ounce',\n",
-       "  (201, 21, 43, 195, 0)),\n",
-       " ('B0006PJTB0',\n",
-       "  'Seche Base Ridge Filling Base Coat .5 oz. [Health and Beauty]',\n",
-       "  (28, 96, 201, 219, 0)),\n",
-       " ('B0006PKGJY',\n",
-       "  'Alba Botanica Natural Even Advanced Daily Cream -- 2 oz',\n",
-       "  (94, 115, 70, 77, 0)),\n",
-       " ('B0006PLNSC', 'Zoya Nail Polish .50 fl oz Charisma', (230, 145, 201, 76, 0)),\n",
-       " ('B0006PLPE4', 'ZOYA Armor Topcoat/UV Block 0.5oz', (18, 21, 163, 76, 0)),\n",
-       " ('B0006PS3XU', 'Marianna Bobby Pins 1 lb. - Black', (200, 12, 124, 160, 0)),\n",
-       " ('B0006PS3Y4', 'Marianna Bobby Pins 1 lb. - Brown', (200, 12, 153, 195, 0)),\n",
-       " ('B0006PT6TU',\n",
-       "  'City Lips Advanced in NUDE York Lip Plumper',\n",
-       "  (99, 79, 24, 219, 0)),\n",
-       " ('B0006Q00IK',\n",
-       "  'For Pro Cozie Liners Hand or Foot 100-ct.',\n",
-       "  (155, 115, 22, 76, 0)),\n",
-       " ('B0006Q00ZI', 'Gena Healthy Hoof Cream, 4 Ounce', (201, 3, 65, 206, 0)),\n",
-       " ('B0006Q0102', 'Gena Nail Brite with Brush, 4 Ounce', (180, 180, 71, 170, 0)),\n",
-       " ('B0006Q01EI', 'Hot Sock Diffuser', (80, 80, 192, 103, 0)),\n",
-       " ('B0006Q01WK', 'King Dy-zoff Pads 80-ct.', (67, 89, 27, 77, 0)),\n",
-       " ('B0006Q0HEC',\n",
-       "  'Wen by Chaz Dean Tea Tree Cleansing Conditioner 16 oz.',\n",
-       "  (24, 245, 11, 72, 0)),\n",
-       " ('B0006Q23H6',\n",
-       "  'John Masters Organics Evening Primrose Shampoo for Dry Hair 8 fl oz / 236 ml',\n",
-       "  (44, 66, 139, 100, 0)),\n",
-       " ('B0006Q3NTS',\n",
-       "  'WEN by Chaz Dean Fig Cleansing Conditioner 16 oz',\n",
-       "  (24, 245, 65, 100, 0)),\n",
-       " ('B0006Q3O3S',\n",
-       "  'WEN&reg; Sweet Almond Mint Styling Creme 6oz',\n",
-       "  (120, 96, 124, 100, 0)),\n",
-       " ('B0006Q3P50',\n",
-       "  'WEN&reg; Sweet Almond Mint Texture Balm 3oz',\n",
-       "  (79, 161, 192, 21, 0)),\n",
-       " ('B0006SFXXK',\n",
-       "  'John Masters Organics - Detangler Citrus &amp; Neroli - 8 oz.',\n",
-       "  (12, 2, 246, 114, 0)),\n",
-       " ('B0006SFXYY',\n",
-       "  'John Masters Organics John Masters Organics Deep Scalp Follicle Treatment &amp; Volumizer for Thinning Hair 4.2 fl oz - 4.2 fl oz',\n",
-       "  (128, 47, 139, 76, 0)),\n",
-       " ('B0006U81GY', 'Cricket Ultra Clean UC130', (92, 80, 24, 236, 0)),\n",
-       " ('B0006V7T9I',\n",
-       "  'Bee Bar Lotion by Honey House Naturals - Vanilla Scent - Long Lasting Lotion Bar Moisturizes and Leaves Skin Smelling Fresh - 2 ounces',\n",
-       "  (232, 66, 226, 220, 0)),\n",
-       " ('B0006ZEVU4',\n",
-       "  'Paul Mitchell Tea Tree Special Shampoo Unisex, 16.9 Ounce',\n",
-       "  (109, 202, 71, 77, 0)),\n",
-       " ('B0006ZHCK0', \"Murphy's Oil Soap, 32-Ounce\", (228, 129, 139, 34, 0)),\n",
-       " ('B0006ZHK7A',\n",
-       "  'Pureology Hydrate Shampoo, 8.5 Ounce',\n",
-       "  (224, 202, 201, 15, 0)),\n",
-       " ('B00070E8IS',\n",
-       "  'Elchim Professional 2001 2000 Watt Classic Hair Dryer (Colors May Vary)',\n",
-       "  (201, 66, 153, 77, 0)),\n",
-       " ('B00070QF20',\n",
-       "  'AcneFree Clear Skin System, 3-Step Kit (Purifying Cleanser, Renewing Toner, Repair Lotion)',\n",
-       "  (21, 105, 24, 99, 0)),\n",
-       " ('B00074XC68',\n",
-       "  'Helen of Troy Mini 1/2 Inch Professional Curling Iron with Ergonomic Handle for Short Hair, Bangs &amp; Wisps (Model: 1503n)',\n",
-       "  (16, 96, 246, 169, 0)),\n",
-       " ('B0007506U2',\n",
-       "  'TIGI Catwalk Curl Collection Curlesque Curls Rock Amplifier, 5.07 Ounce, Packaging May Vary',\n",
-       "  (111, 96, 246, 182, 0)),\n",
-       " ('B00076X0BS',\n",
-       "  'Alba Botanica Revitalizing Green Tea Hawaiian Eye Gel, 1 Ounce Bottles',\n",
-       "  (45, 96, 201, 103, 0)),\n",
-       " ('B0007CIEBI',\n",
-       "  'Colorescience Pro Sunforgettable SPF 30 Brush- Fair 0.21 Oz',\n",
-       "  (109, 135, 254, 77, 0)),\n",
-       " ('B0007CWVLM',\n",
-       "  'Tweezerman  Spirit 2000 Styling Shears',\n",
-       "  (189, 185, 140, 116, 0)),\n",
-       " ('B0007CXWQU',\n",
-       "  'Aveda Blue Malva Conditioner, 8.5-Ounce Tube',\n",
-       "  (194, 96, 139, 170, 0)),\n",
-       " ('B0007CXWRE',\n",
-       "  'Aveda Brilliant Anti-Humectant Pomade, 2.6 Ounces',\n",
-       "  (47, 47, 254, 162, 0)),\n",
-       " ('B0007CXWTW', 'Aveda Clove Conditioner 8.5 Ounces', (126, 29, 138, 38, 0)),\n",
-       " ('B0007CXWUG', 'Aveda Confixor Liquid Gel, 8.5 Ounces', (24, 96, 216, 15, 0)),\n",
-       " ('B0007CXWWY',\n",
-       "  'Aveda Madder Root Conditioner 8.5 Ounces',\n",
-       "  (98, 46, 71, 42, 0)),\n",
-       " ('B0007CXWXI', 'Aveda Phomollient 6.7 Ounces', (98, 46, 246, 38, 0)),\n",
-       " ('B0007CXX82',\n",
-       "  'Biosilk Silk Therapy Serum, Packaging May Vary, 5.64 Ounces',\n",
-       "  (170, 47, 122, 15, 0)),\n",
-       " ('B0007CXXDC',\n",
-       "  'Biolage by Matrix Color Care Conditioner 33.8 Ounces',\n",
-       "  (132, 103, 227, 77, 0)),\n",
-       " ('B0007CXXDM',\n",
-       "  'Biolage by Matrix Color Care Shampoo 33.8 Ounces',\n",
-       "  (132, 247, 138, 53, 0)),\n",
-       " ('B0007CXXE6',\n",
-       "  'Biolage by Matrix Conditioning Balm 16.9 Ounces',\n",
-       "  (24, 248, 65, 104, 0)),\n",
-       " ('B0007CXXGO',\n",
-       "  'Biolage by Matrix Hydratherapie Hydrating Shampoo 33.8 Ounces',\n",
-       "  (191, 115, 162, 103, 0)),\n",
-       " ('B0007CXXIC',\n",
-       "  'Biolage by Matrix Normalizing Shampoo 33.8 Ounces',\n",
-       "  (191, 247, 67, 202, 0)),\n",
-       " ('B0007CXXJQ',\n",
-       "  'Biolage by Matrix Ultra-Hydrating Conditioning Balm 16.9 Ounces',\n",
-       "  (47, 121, 71, 101, 0)),\n",
-       " ('B0007CXXVE',\n",
-       "  'Nexxus Humectress Ultimate Moisturizing Conditioner 33.8 Ounces',\n",
-       "  (171, 47, 163, 169, 0)),\n",
-       " ('B0007CXXYG',\n",
-       "  'Paul Mitchell Awapuhi Shampoo, 33.8-Ounce Bottle',\n",
-       "  (47, 3, 65, 231, 0)),\n",
-       " ('B0007CXY5E',\n",
-       "  'Paul Mitchell Shampoo  1   33.8 Ounces',\n",
-       "  (99, 105, 24, 27, 0)),\n",
-       " ('B0007CXY5O',\n",
-       "  'Paul Mitchell Shampoo  3   16.9 Ounces',\n",
-       "  (99, 145, 153, 38, 0)),\n",
-       " ('B0007CXYAE',\n",
-       "  'Paul Mitchell The Detangler 33.8 Ounces',\n",
-       "  (99, 29, 70, 170, 0)),\n",
-       " ('B0007CXYP4',\n",
-       "  'Sebastian Collection Potion 9   5.1 Ounces',\n",
-       "  (170, 66, 67, 91, 0)),\n",
-       " ('B0007D2F3K', 'Pedi,Quick Salon Pedicure Kit', (200, 180, 234, 116, 0)),\n",
-       " ('B0007DHMBK',\n",
-       "  'Henna Black Cream Surya Nature, Inc 2.31 oz Cream',\n",
-       "  (128, 180, 43, 219, 0)),\n",
-       " ('B0007DHMCO',\n",
-       "  'Henna Burgundy Cream Surya Nature, Inc 2.31 oz Cream',\n",
-       "  (128, 47, 226, 160, 0)),\n",
-       " ('B0007IFB2W',\n",
-       "  'Spornette 25 Wood Handle &quot;Porcupine&quot; Brush With Genuine Boar Bristle * Made In Germany',\n",
-       "  (60, 161, 65, 27, 0)),\n",
-       " ('B0007IQMVG',\n",
-       "  'Digestive Advantage Intensive Bowel Support, 96 Counts Capsules',\n",
-       "  (70, 202, 231, 163, 0)),\n",
-       " ('B0007LE3EG', 'Total Hair Makeover Kit 90001', (174, 122, 254, 236, 0)),\n",
-       " ('B0007NIRUU',\n",
-       "  'Nexxus Humectress Ultimate Moisturizing Conditioner, 13.5Ounce Bottles',\n",
-       "  (198, 3, 192, 170, 0)),\n",
-       " ('B0007R7ABS',\n",
-       "  'Kenra Volume Spray 25 Super Hold Finishing Spray (Aerosol) (10 oz.)',\n",
-       "  (201, 100, 163, 76, 0)),\n",
-       " ('B0007RBYA6',\n",
-       "  'Philosophy Microdelivery Peel Pads, 60 Count',\n",
-       "  (151, 47, 22, 34, 0)),\n",
-       " ('B0007V6PFQ',\n",
-       "  'Brush - Large Oval Cushion Wood Bristles Wood Handle Bass Brushes 1 Brush',\n",
-       "  (253, 159, 219, 37, 0)),\n",
-       " ('B0007V9API', 'Bella B Tummy Honey Cream - 4 oz', (133, 80, 27, 170, 0)),\n",
-       " ('B0007W1R58',\n",
-       "  'Olay Regenerist Night Recovery Cream 1.7 Oz',\n",
-       "  (41, 196, 162, 34, 0)),\n",
-       " ('B0007WL21M',\n",
-       "  'PETER THOMAS ROTH - Anti-Aging Cleansing Gel 8.5oz',\n",
-       "  (185, 4, 124, 38, 0)),\n",
-       " ('B0007WZ7YU',\n",
-       "  'Elchim 2001hp High Pressure 2000 Watt Hair Dryer, White',\n",
-       "  (15, 115, 139, 114, 0)),\n",
-       " ('B0007X74CW',\n",
-       "  'Redken Hair Cleansing Cream Shampoo for All Hair Types, 10.1-Ounces',\n",
-       "  (24, 120, 153, 114, 0)),\n",
-       " ('B0007X74E0',\n",
-       "  'Smooth Down Heat Glide Protective Smoother Unisex Smoother by Redken, 5 Ounce',\n",
-       "  (128, 149, 227, 60, 0)),\n",
-       " ('B0007X74IQ',\n",
-       "  'Extreme Cat Protein Treatment Unisex Treatment by Redken, 5 Ounce',\n",
-       "  (252, 122, 37, 34, 0)),\n",
-       " ('B0007XFB2W', 'Original Sprout Miracle Detangler', (252, 173, 138, 99, 0)),\n",
-       " ('B0007YJ5QY',\n",
-       "  'AMERICAN CREW Daily Moisturizing Shampoo, 8.4 Ounce',\n",
-       "  (110, 3, 162, 27, 0)),\n",
-       " ('B00080DK86',\n",
-       "  'Glycolix Elite Treatment Pads 20 Percent 60 count',\n",
-       "  (133, 21, 40, 103, 0)),\n",
-       " ('B00081J4P8',\n",
-       "  'NO-AD Sunblock Lotion, SPF 15, 16 Ounces',\n",
-       "  (104, 96, 10, 38, 0)),\n",
-       " ('B00082379Q', 'Orly Polish Thinner', (70, 149, 55, 219, 0)),\n",
-       " ('B0008394GA', 'NARS Lip Liner Pencil, Jungle Red', (79, 196, 65, 21, 0)),\n",
-       " ('B000851N9E',\n",
-       "  'BurnOut - Ocean Tested Physical Sunscreen 30 SPF - 3.4 oz.',\n",
-       "  (12, 47, 17, 53, 0)),\n",
-       " ('B00085DX0Q',\n",
-       "  \"Grandpa's Soap Company Wonder Pine Tar Shampoo, 8 Ounce\",\n",
-       "  (164, 46, 246, 103, 0)),\n",
-       " ('B0008ENT8I',\n",
-       "  'ProVersa JWM6CF Wall Caddy Hair Dryer with 2-Speed and 3-Heat Settings, 1600-Watts, White Finish',\n",
-       "  (174, 21, 140, 77, 0)),\n",
-       " ('B0008F6ZDI',\n",
-       "  'Philosophy The Present Clear Makeup, 2 Ounce',\n",
-       "  (136, 122, 240, 182, 0)),\n",
-       " ('B0008FUY0I',\n",
-       "  'Flowery Swedish Clover Foot File * #530',\n",
-       "  (241, 134, 139, 15, 0)),\n",
-       " ('B0008GIZKS',\n",
-       "  'Malibu C Hard Water Wellness Shampoo, 1 Bottle, 9 oz',\n",
-       "  (133, 59, 70, 100, 0)),\n",
-       " ('B0008IV7BU',\n",
-       "  'American Crew American Crew Fiber Fiber - 3 oz',\n",
-       "  (80, 62, 24, 77, 0)),\n",
-       " ('B00092M2VO',\n",
-       "  'Andis Professional 1875 Watt Ceramic Ionic Hair Dryer - Black Chrome (82005)',\n",
-       "  (243, 115, 219, 34, 0)),\n",
-       " ('B00092M2XC',\n",
-       "  'Hot Tools Professional 1069S 1600 Watt Professional Turbo Hair Dryer, Silver/Black',\n",
-       "  (3, 103, 153, 114, 0)),\n",
-       " ('B00092M2ZU',\n",
-       "  'Ceramic Tools CT2555 1&quot; Professional Ceramic Flat Iron',\n",
-       "  (187, 59, 219, 34, 0)),\n",
-       " ('B00092M36I',\n",
-       "  'Ceramic Tools CT155S 1-1/2&quot; Dual Voltage Professional Ceramic Spring Curling Iron',\n",
-       "  (30, 161, 163, 170, 0)),\n",
-       " ('B00092M386',\n",
-       "  'Belson Profiles Spa Single Hand Nail Dryer P1013',\n",
-       "  (181, 8, 70, 195, 0)),\n",
-       " ('B00094O6D4',\n",
-       "  'LaLicious Sugar Souffle Body Scrub 16 fl oz.',\n",
-       "  (2, 2, 147, 100, 0)),\n",
-       " ('B000960QJA',\n",
-       "  'Vaseline Aloe Fresh Hydrating Body Lotion 20.3Oz',\n",
-       "  (102, 8, 65, 34, 0)),\n",
-       " ('B000977Q8I',\n",
-       "  'Bobbi Brown Bobbi Brown Shimmer Brick',\n",
-       "  (149, 211, 3, 114, 0)),\n",
-       " ('B000980PGM',\n",
-       "  'Dermalogica Daily Microfoliant, 2.6-Ounce',\n",
-       "  (133, 3, 59, 104, 0)),\n",
-       " ('B000980PH6',\n",
-       "  'Dermalogica Oil Control Lotion (2 oz.)',\n",
-       "  (245, 134, 226, 103, 0)),\n",
-       " ('B0009953JA',\n",
-       "  'Jerdon JP7507NB 8-Inch Two-Sided Swivel Wall Mount Mirror with 7x Magnification, 13.5-Inch Extension, Nickel Beaded Finish',\n",
-       "  (215, 66, 140, 231, 0)),\n",
-       " ('B00099E8ZA',\n",
-       "  'Neutrogena Shampoo, Anti-Residue Formula, 6 Ounce',\n",
-       "  (41, 115, 122, 53, 0)),\n",
-       " ('B00099QZOW',\n",
-       "  'Colorful Neutral Protein Filler 1.2 oz.',\n",
-       "  (47, 196, 201, 160, 0)),\n",
-       " ('B00099Z2OQ',\n",
-       "  'Giovanni Hair Care - Direct Leave-In Conditioner, 8.5 fl oz liquid',\n",
-       "  (13, 161, 139, 159, 0)),\n",
-       " ('B0009BVK0E',\n",
-       "  'Organix South Maximum Strength Neem Soap Bar 4 oz',\n",
-       "  (71, 134, 246, 202, 0)),\n",
-       " ('B0009DT39W',\n",
-       "  'Panasonic EH2351AC Heated Eyelash Curling Wand',\n",
-       "  (200, 131, 24, 116, 0)),\n",
-       " ('B0009DVDTU',\n",
-       "  'Revision Teamine Eye Complex, 0.5 Ounce',\n",
-       "  (141, 247, 67, 157, 0)),\n",
-       " ('B0009DVMAU', 'Lafes Deodorant Spray with MSM, 8oz', (44, 62, 65, 76, 0)),\n",
-       " ('B0009EILHG',\n",
-       "  'Biore Pore Perfect Blemish Fighting Ice Cleanser, Cools &amp; Clears , 6.7 fl oz (198 ml)',\n",
-       "  (154, 173, 106, 103, 0)),\n",
-       " ('B0009EILI0',\n",
-       "  'Biore Deep Cleansing Pore Strips Combo 14 Ea',\n",
-       "  (238, 180, 226, 103, 0)),\n",
-       " ('B0009EILKS',\n",
-       "  'Biore  Deep Cleansing Pore Strips , 14 Nose Strips',\n",
-       "  (155, 180, 122, 103, 0)),\n",
-       " ('B0009EILVM',\n",
-       "  'Zia Natural Skincare Ultimate Body Firming Treatment, 7 fl oz',\n",
-       "  (233, 202, 70, 27, 0)),\n",
-       " ('B0009ET3SC',\n",
-       "  'Aquis Microfiber Body Towel, Lisse Crepe, White (29 x 55-Inches)',\n",
-       "  (28, 120, 65, 91, 0)),\n",
-       " ('B0009ET4VI',\n",
-       "  'Beauty without Cruelty Vitamin C, Vitality Serum, 1-Ounce',\n",
-       "  (167, 213, 132, 37, 0)),\n",
-       " ('B0009ET5H6',\n",
-       "  \"Burt's Bees Poison Ivy Soap, 2-Ounce\",\n",
-       "  (119, 161, 246, 159, 0)),\n",
-       " ('B0009ET5JE',\n",
-       "  \"Burt's Bees  Lip Shimmer, Champagne, 0.09 oz.\",\n",
-       "  (65, 122, 254, 77, 0)),\n",
-       " ('B0009ET5O4',\n",
-       "  \"Burt's Bees Healthy Treatment Repair Serum, 1 Fluid Ounce\",\n",
-       "  (252, 247, 139, 53, 0)),\n",
-       " ('B0009ET6BQ',\n",
-       "  'derma e Hyaluronic Acid Day Cr&egrave;me, 2-Ounces',\n",
-       "  (56, 202, 65, 60, 0)),\n",
-       " ('B0009EXM52',\n",
-       "  'OPI: Treatment &amp; Finish Natural Nail Base Coat, 0.5 oz',\n",
-       "  (192, 145, 227, 135, 0)),\n",
-       " ('B0009EXMB6',\n",
-       "  \"Palmer's Sunless Tanner &amp; Instant Bronzer SPF 15 5.25oz\",\n",
-       "  (71, 145, 98, 206, 0)),\n",
-       " ('B0009EXONC',\n",
-       "  'Big Sexy Hair Volumizing Hairspray, Spray &amp; Play, 10.0 oz (284 g)',\n",
-       "  (195, 59, 234, 53, 0)),\n",
-       " ('B0009EXOO6',\n",
-       "  'HEALTHY SEXY SOY TRI-WHEAT LEAVE-IN CONDITIONER 8.5 OZ -PACKAGING MAY VARY',\n",
-       "  (58, 185, 234, 159, 0)),\n",
-       " ('B0009EXOP0',\n",
-       "  'SHORT SEXY HAIR CONTOL MANIAC WAX 1.8 OZ-PACKAGING MAY VARY',\n",
-       "  (192, 145, 41, 13, 0)),\n",
-       " ('B0009EXOXW',\n",
-       "  'Source Naturals Skin Eternal Serum, 1.7  Ounce',\n",
-       "  (102, 211, 65, 152, 0)),\n",
-       " ('B0009F3M6A',\n",
-       "  'Eucerin Dry Skin Therapy Foot Creme, Plus Intensive Repair, 3-Ounce Tubes (Pack of 3)',\n",
-       "  (239, 161, 27, 99, 0)),\n",
-       " ('B0009F3NGE',\n",
-       "  \"L'Oreal Dermo-Expertise Sublime Bronze Self-Tanning Gelee, Medium-Natural , 5 fl oz (150 ml)\",\n",
-       "  (44, 159, 254, 37, 0)),\n",
-       " ('B0009F3NYQ',\n",
-       "  'Neutrogena MicroMist Tanning Sunless Spray, Deep, 5.3 Ounce',\n",
-       "  (13, 21, 70, 100, 0)),\n",
-       " ('B0009F3O18',\n",
-       "  'Neutrogena Makeup Remover Cleansing Towelettes, 25 Count',\n",
-       "  (201, 8, 227, 77, 0)),\n",
-       " ('B0009F3O8Q',\n",
-       "  \"Palmer's Cocoa Butter Formula with Vitamin E, 13.5 fl oz (400 ml)\",\n",
-       "  (231, 180, 163, 152, 0)),\n",
-       " ('B0009F3OOA',\n",
-       "  'Queen Helene Refreshing Natural Facial Scrub Mint Julep -- 6 oz',\n",
-       "  (102, 211, 254, 15, 0)),\n",
-       " ('B0009F3OR2',\n",
-       "  'Redken All Soft Heavy Cream, Avocado Oil , 8.5 fl oz (250 ml)',\n",
-       "  (127, 161, 122, 100, 0)),\n",
-       " ('B0009F3OS6',\n",
-       "  'Redken Extreme Anti-Snap, Lipids/Proteins , 8.5 fl oz (250 ml)',\n",
-       "  (117, 173, 59, 60, 0)),\n",
-       " ('B0009F3OX6', 'Revlon Cuticle Nippers, Half-Jaw', (155, 89, 10, 195, 0)),\n",
-       " ('B0009F3R7E', \"Burt's Bees Hand Repair Kit\", (149, 191, 162, 27, 0)),\n",
-       " ('B0009FHJOG',\n",
-       "  'Talika Lash Conditioning Cleanser - 4.06 oz',\n",
-       "  (245, 173, 27, 76, 0)),\n",
-       " ('B0009FHJRS',\n",
-       "  'Toppik Regular Dark Brown Hair Building Fibers, 0.42 Ounce',\n",
-       "  (70, 122, 163, 114, 0)),\n",
-       " ('B0009G05W8', 'Cricket Cool Down Iron Travel Case', (17, 199, 219, 171, 0)),\n",
-       " ('B0009GGY96',\n",
-       "  'AcneFree Acne and Blackhead Terminator (.75 Ounce Tube)',\n",
-       "  (47, 115, 11, 116, 0)),\n",
-       " ('B0009H04LO', 'NARS Bronzer Blush Duo, Orgasm/Laguna', (219, 3, 153, 60, 0)),\n",
-       " ('B0009HIFRY',\n",
-       "  'Pravana Intense Therapy Leave-In Treatment 10.1 oz',\n",
-       "  (132, 47, 227, 170, 0)),\n",
-       " ('B0009HJBPE',\n",
-       "  'Yves Saint Laurent TOUCHE ECLATRadiant Touch 2 Luminous Ivory',\n",
-       "  (144, 159, 216, 182, 0)),\n",
-       " ('B0009I4J4G',\n",
-       "  'Lubriderm Daily Moisture Lotion with Shea and Cocoa Butter, 16  Ounce',\n",
-       "  (132, 138, 162, 34, 0)),\n",
-       " ('B0009I4M02', 'TIGI Bed Head Small Talk, 8 Ounce', (169, 159, 24, 34, 0)),\n",
-       " ('B0009I4MCU',\n",
-       "  'Pureology Anti-Fade Complex Hydrate Condition, 8.5 Ounce',\n",
-       "  (231, 122, 124, 15, 0)),\n",
-       " ('B0009I4MFW', 'Pureology Pure Volume Shampoo 10.1 oz', (24, 21, 122, 30, 0)),\n",
-       " ('B0009I4MG6',\n",
-       "  'Pureology Safeguard Your Color Purify Shampoo, 10.1 Ounces',\n",
-       "  (170, 145, 70, 103, 0)),\n",
-       " ('B0009I4MGQ',\n",
-       "  'Pureology Anti-Fade Complex Pure Volume Condition, 8.5 Ounce Bottle (Packaging May Vary)',\n",
-       "  (117, 47, 201, 99, 0)),\n",
-       " ('B0009I4MKW', 'Nexxus shampoo therappe, 33.8oz', (183, 173, 227, 53, 0)),\n",
-       " ('B0009IMRLS',\n",
-       "  'Christian Dior Diorshow Waterproof Mascara, No. 090 Black, 0.38 Ounce',\n",
-       "  (132, 161, 140, 76, 0)),\n",
-       " ('B0009ION0G',\n",
-       "  'TIGI Catwalk Your Highness Root Boost Spray, 8.1 Ounce',\n",
-       "  (1, 66, 55, 76, 0)),\n",
-       " ('B0009J6ER0',\n",
-       "  'Grow Hair Faster with Grow Shampoo and Conditioner for Faster Growing Hair',\n",
-       "  (12, 134, 151, 103, 0)),\n",
-       " ('B0009JQFF6',\n",
-       "  '100% Organic West African Shea Butter 16 oz',\n",
-       "  (239, 196, 70, 202, 0)),\n",
-       " ('B0009M0C5W',\n",
-       "  'FusionBeauty LipFusion Xl 2x Micro-Injected Collagen Advanced Lip Plumping Therapy',\n",
-       "  (144, 211, 227, 13, 0)),\n",
-       " ('B0009MHJE4',\n",
-       "  'African Shea Butter Cream (100% Pure &amp; Raw, Gold) 8 Oz.',\n",
-       "  (26, 4, 40, 12, 0)),\n",
-       " ('B0009MHJS0',\n",
-       "  'Raw Unrefined Yellow Shea Butter FILTERED &amp; CREAMY 5 0z',\n",
-       "  (252, 89, 246, 53, 0)),\n",
-       " ('B0009MJE74',\n",
-       "  'Nubian Heritage - Body Wash African Black Soap - 13 oz.',\n",
-       "  (21, 145, 71, 15, 0)),\n",
-       " ('B0009ML5M6', 'African Shea Oil - 8 ozs.', (100, 115, 246, 169, 0)),\n",
-       " ('B0009MMK5M',\n",
-       "  'Egyptian Magic - All Purpose Skin Cream - 4 oz.',\n",
-       "  (214, 54, 138, 60, 0)),\n",
-       " ('B0009MSHR2',\n",
-       "  'Madina African Black Soap Cocoa Butter W/Vitamin E 3.5 oz',\n",
-       "  (127, 96, 246, 75, 0)),\n",
-       " ('B0009MYMJY',\n",
-       "  'Shalimar By Guerlain For Women. Eau De Parfum Spray 2.5 Oz.',\n",
-       "  (175, 100, 3, 152, 0)),\n",
-       " ('B0009N35D2',\n",
-       "  'Glow By Jennifer Lopez For Women. Eau De Toilette Spray 1 Ounces',\n",
-       "  (175, 89, 11, 75, 0)),\n",
-       " ('B0009N5EZY',\n",
-       "  'Kenneth Cole Reaction By Kenneth Cole Eau De Toilette Spray 3.4 Oz',\n",
-       "  (121, 134, 59, 77, 0)),\n",
-       " ('B0009OAFUM',\n",
-       "  'Remington HKVAC-2000 Precision Vacuum Haircut Kit',\n",
-       "  (220, 122, 219, 160, 0)),\n",
-       " ('B0009OAGRE',\n",
-       "  'Eternity by Calvin Klein for Women, Eau De Parfum, 3.4 Ounce',\n",
-       "  (175, 120, 98, 206, 0)),\n",
-       " ('B0009OAGSI',\n",
-       "  'Escape by Calvin Klein for Men, Eau De Toilette Spray, 3.4-Fluid Ounce (100 ml)',\n",
-       "  (47, 145, 65, 103, 0)),\n",
-       " ('B0009OAGT2',\n",
-       "  'Escape by Calvin Klein for Women, Eau De Parfum Spray, 1.7 Ounce',\n",
-       "  (175, 4, 22, 12, 0)),\n",
-       " ('B0009OAGWE',\n",
-       "  'Ralph Lauren Polo Sport Eau de Toilette Spray for Men, 4.2 Ounce',\n",
-       "  (8, 4, 10, 12, 0)),\n",
-       " ('B0009OAGXI',\n",
-       "  'Acqua Di Gio By Giorgio Armani For Men. Eau De Toilette Spray 1.7 Oz.',\n",
-       "  (93, 77, 192, 195, 0)),\n",
-       " ('B0009OAGZ6',\n",
-       "  'Burberry By Burberry For Men. Eau De Toilette Spray 3.3 Ounces',\n",
-       "  (8, 24, 43, 206, 0)),\n",
-       " ('B0009OAH0A',\n",
-       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 1.7 Oz.',\n",
-       "  (8, 4, 3, 34, 0)),\n",
-       " ('B0009OAHBE',\n",
-       "  'COOL WATER by Davidoff Cologne for Men (EDT SPRAY 1.35 OZ)',\n",
-       "  (132, 232, 41, 38, 0)),\n",
-       " ('B0009OAHC8',\n",
-       "  'Cool Water By Davidoff For Men. Eau De Toilette Spray 4.2 Ounces',\n",
-       "  (175, 4, 10, 12, 0)),\n",
-       " ('B0009OAHEQ',\n",
-       "  'Joop! By Joop! For Men. Eau De Toilette Spray 4.2 Ounces',\n",
-       "  (175, 89, 10, 75, 0)),\n",
-       " ('B0009OAHIM',\n",
-       "  'Dolce &amp; Gabbana By Dolce &amp; Gabbana For Men. Eau De Toilette Spray 4.2 Ounce',\n",
-       "  (8, 135, 118, 77, 0)),\n",
-       " ('B0009OAHIW',\n",
-       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 1.6 Ounces',\n",
-       "  (31, 21, 153, 53, 0)),\n",
-       " ('B0009OAHNM', 'Curve Men Cologne Spray, 4.2-Ounce', (245, 66, 70, 103, 0)),\n",
-       " ('B0009OAHOG',\n",
-       "  'Mambo By Liz Claiborne For Men. Cologne Spray 1.7 Ounces',\n",
-       "  (92, 100, 201, 27, 0)),\n",
-       " ('B0009OAHQ4',\n",
-       "  'Bora Bora by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
-       "  (92, 77, 201, 53, 0)),\n",
-       " ('B0009OAHQE',\n",
-       "  'Bora Bora by Liz Claiborne for Women, Eau De Parfum Spray, 3.4-Ounce',\n",
-       "  (8, 120, 216, 77, 0)),\n",
-       " ('B0009OAHQY',\n",
-       "  'Claiborne by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
-       "  (212, 173, 33, 77, 0)),\n",
-       " ('B0009OAHRI',\n",
-       "  'Lucky You By Lucky Brand For Women. Eau De Toilette Spray 3.4 Oz.',\n",
-       "  (237, 135, 22, 15, 0)),\n",
-       " ('B0009OAHRS',\n",
-       "  'Lucky You By Lucky Brand For Men. Cologne Spray 3.4 Oz.',\n",
-       "  (92, 105, 201, 103, 0)),\n",
-       " ('B0009OAHVO',\n",
-       "  \"L'eau D'issey (issey Miyake) by Issey Miyake for Men - EDT Spray\",\n",
-       "  (8, 80, 27, 104, 0)),\n",
-       " ('B0009OAHWI',\n",
-       "  'Lolita Lempicka By Lolita Lempicka For Women. Eau De Parfum Spray 3.4 Oz.',\n",
-       "  (175, 55, 27, 75, 0)),\n",
-       " ('B0009OAI18',\n",
-       "  'Sunflowers By Elizabeth Arden For Women. Eau De Toilette Spray 3.4 Oz.',\n",
-       "  (32, 47, 22, 77, 0)),\n",
-       " ('B0009OAI1S',\n",
-       "  'Red Door By Elizabeth Arden For Women. Eau De Toilette Spray 3.3 Ounces',\n",
-       "  (175, 89, 3, 75, 0)),\n",
-       " ('B0009OAI2C',\n",
-       "  'Arden Beauty By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
-       "  (8, 100, 10, 77, 0)),\n",
-       " ('B0009OAI3G',\n",
-       "  'Blue Grass By Elizabeth Arden For Women. Eau De Parfum Spray 1.7 Ounces',\n",
-       "  (8, 100, 37, 27, 0)),\n",
-       " ('B0009OAI40',\n",
-       "  'Green Tea By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
-       "  (175, 55, 3, 75, 0)),\n",
-       " ('B0009OAI7C',\n",
-       "  'Passion by Elizabeth Taylor for Men, Cologne Spray, 4-Ounce',\n",
-       "  (92, 236, 201, 91, 0)),\n",
-       " ('B0009OAI8G',\n",
-       "  'White Diamonds By Elizabeth Taylor For Women, Eau De Toilette Spray, 1.7 Ounces',\n",
-       "  (8, 55, 10, 75, 0)),\n",
-       " ('B0009OAIB8',\n",
-       "  'Black Pearls by Elizabeth Taylor for Women, Eau De Parfum Spray, 3.3-Ounce',\n",
-       "  (175, 120, 192, 104, 0)),\n",
-       " ('B0009OAIE0',\n",
-       "  'Pleasures by Estee Lauder for Women, Eau De Toilette Spray, 1.7 Ounce',\n",
-       "  (8, 120, 27, 75, 0)),\n",
-       " ('B0009OAIIG',\n",
-       "  'Gucci Rush By Gucci For Women. Eau De Toilette Spray 2.5 Ounces',\n",
-       "  (31, 24, 3, 75, 0)),\n",
-       " ('B0009OAIKY',\n",
-       "  'Halston 1-12 by Halston for Men, Cologne Spray, 4.2-Ounce',\n",
-       "  (92, 149, 201, 103, 0)),\n",
-       " ('B0009OAIQI',\n",
-       "  'Design by Paul Sebastian for Women, Fine Parfum Spray, 1.7-Ounce',\n",
-       "  (31, 120, 40, 75, 0)),\n",
-       " ('B0009OAISG',\n",
-       "  'True Love by Elizabeth Arden for Women - 1.7 Ounce EDT Spray',\n",
-       "  (32, 120, 82, 27, 0)),\n",
-       " ('B0009OMNGQ',\n",
-       "  'Awapuhi Moisture Mist Unisex Mist by Paul Mitchell, 16.9 Ounce',\n",
-       "  (237, 122, 162, 76, 0)),\n",
-       " ('B0009OXHC0',\n",
-       "  \"Nature's Blessings Hair Pomade 4 oz.\",\n",
-       "  (154, 210, 139, 15, 0)),\n",
-       " ('B0009P4PZC', 'Cococare Coconut Oil 100% Pure 4 Oz', (170, 47, 122, 38, 0)),\n",
-       " ('B0009PVV36',\n",
-       "  'Conair CD86SCS Instant Heat Iron and Straightener, 1.5 Inch',\n",
-       "  (241, 223, 246, 103, 0)),\n",
-       " ('B0009PVV40',\n",
-       "  'Conair BC171NCS Ceramic Ionic Hot Air Brush, Black, 1.25 Inch',\n",
-       "  (24, 66, 140, 170, 0)),\n",
-       " ('B0009PVV4A',\n",
-       "  'Infiniti Pro by Conair Professional 1 Inch  Tourmaline Ceramic Straightener',\n",
-       "  (70, 122, 163, 99, 0)),\n",
-       " ('B0009QJ3TE',\n",
-       "  'Model in a Bottle Sensitive Makeup Setting Spray - 1.7 oz',\n",
-       "  (13, 4, 10, 76, 0)),\n",
-       " ('B0009QZXP2',\n",
-       "  \"Africa's Best Organics Hair Mayonnaise, 15 oz\",\n",
-       "  (245, 4, 234, 37, 0)),\n",
-       " ('B0009QZXQ6',\n",
-       "  \"Africa's Best Organics Olive Oil Dry Hair and Scalp Therapy, 7.5 oz.\",\n",
-       "  (133, 96, 24, 77, 0)),\n",
-       " ('B0009QZYBU',\n",
-       "  'Soft Sheen Carson Care Free Curl Gold Instant Activator 16Oz/473Ml',\n",
-       "  (47, 138, 250, 169, 0)),\n",
-       " ('B0009QZYLU',\n",
-       "  'Clairol BW2 - 8 oz. Tub Powder Lightener',\n",
-       "  (91, 100, 201, 114, 0)),\n",
-       " ('B0009QZYN8',\n",
-       "  'CLAIROL Jazzing Gentle Temporary Semi Permanent Hair Color #58 RUBY RED',\n",
-       "  (63, 21, 71, 99, 0)),\n",
-       " ('B0009QZYO2', 'Clairol Pure White 30 Volume 16 oz.', (115, 236, 71, 27, 0)),\n",
-       " ('B0009QZZ4Q',\n",
-       "  'Demert Wig Luster Conditioner, 9.76 Ounce',\n",
-       "  (123, 149, 70, 91, 0)),\n",
-       " ('B0009R14NG',\n",
-       "  'Africas Best Herbal Gro Super 5.25oz Jar',\n",
-       "  (92, 202, 153, 15, 0)),\n",
-       " ('B0009R14Q8',\n",
-       "  \"Africa's Best Organincs Mayo Leave In Conditioner 6 oz.\",\n",
-       "  (92, 228, 59, 77, 0)),\n",
-       " ('B0009R14XQ',\n",
-       "  'Australian Gold SPF 15 Spray Gel with Bronzer, 8 Ounce',\n",
-       "  (136, 24, 10, 27, 0)),\n",
-       " ('B0009R16O8',\n",
-       "  \"Dr. Bronner's Magic Soaps: Liquid Castile Soap, Tea Tree 32 oz\",\n",
-       "  (194, 180, 153, 15, 0)),\n",
-       " ('B0009R33U8',\n",
-       "  'Aphogee Two-step Treatment Protein for Damaged Hair 16 oz.',\n",
-       "  (215, 134, 231, 100, 0)),\n",
-       " ('B0009R35GA',\n",
-       "  'Clairol Pure White 40 Volume 16 oz.',\n",
-       "  (115, 236, 122, 126, 0)),\n",
-       " ('B0009R5AYA',\n",
-       "  'Aussie Sydney Smooth 3 Minute Miracle Smoothing Treatment-8oz',\n",
-       "  (164, 187, 219, 160, 0)),\n",
-       " ('B0009R5B8A', 'Avon SSS Original OIL 5oz', (24, 234, 221, 37, 0)),\n",
-       " ('B0009R5CMK',\n",
-       "  'Doo Gro Medicated Hair Vitalizer Mega Thick Anti-Thinning Formula, 4 Ounce',\n",
-       "  (44, 66, 227, 60, 0)),\n",
-       " ('B0009RF9OG',\n",
-       "  'Rogaine for Women Hair Regrowth Treatment, 2 Ounce',\n",
-       "  (255, 51, 59, 15, 1)),\n",
-       " ('B0009RFAOK',\n",
-       "  'Clean &amp; Clear Deep Cleaning Astringent, Oil Fighting, 8 Ounce',\n",
-       "  (132, 62, 162, 77, 0)),\n",
-       " ('B0009RFAPY',\n",
-       "  'Purpose Gentle Cleansing Wash, 6-Ounce Pump Bottle',\n",
-       "  (194, 7, 40, 91, 0)),\n",
-       " ('B0009RFB76',\n",
-       "  'RoC Retinol Correxion Eye Cream, 0.5 Ounce',\n",
-       "  (12, 115, 208, 194, 0)),\n",
-       " ('B0009RMN0E',\n",
-       "  'Conair CS25WNCS Ultra Slim Ceramic Straightener, Red, 1 5/8 Inch',\n",
-       "  (182, 66, 201, 38, 0)),\n",
-       " ('B0009STDD8',\n",
-       "  \"Calvin Klein Women's Obsession Eau de Parfum Spray, 3.4 fl. oz.\",\n",
-       "  (175, 129, 153, 170, 0)),\n",
-       " ('B0009TNBVC',\n",
-       "  'Estee Lauder Double Wear Stay-In-Place Makeup SPF 10 18 Linen',\n",
-       "  (132, 159, 216, 13, 0)),\n",
-       " ('B0009V1YR8',\n",
-       "  'Farouk CHI 1 Inch Ceramic Flat Hairstyling Iron',\n",
-       "  (105, 62, 153, 100, 0)),\n",
-       " ('B0009V1YRS',\n",
-       "  'CHI Turbo Microchip Ceramic Hairstyling Iron 1&quot;',\n",
-       "  (241, 134, 122, 202, 0)),\n",
-       " ('B0009V1YS2',\n",
-       "  'Farouk CHI GF1539 Turbo Big 2 Inch Ceramic Flat Iron Hair Straightener',\n",
-       "  (67, 202, 226, 188, 0)),\n",
-       " ('B0009V2S8M',\n",
-       "  'Glo Minerals GloPressed Base Beige Dark 0.35oz',\n",
-       "  (132, 47, 162, 139, 0)),\n",
-       " ('B0009V8N4U',\n",
-       "  'Sun Laboratories Dark Sunsation Self Tanning Lotion - Very Dark 8 fl oz.',\n",
-       "  (118, 115, 70, 220, 0)),\n",
-       " ('B0009V8N5E',\n",
-       "  'Sun Self Tanning Lotion Ultra Dark Instant Tint - Dark 8oz/236ml',\n",
-       "  (62, 211, 71, 77, 0)),\n",
-       " ('B0009VD8YU',\n",
-       "  'AUBREY Collagen Restorative Moisturizer 1.7 fl.oz',\n",
-       "  (71, 202, 246, 219, 0)),\n",
-       " ('B0009VDBWY',\n",
-       "  'Sun Laboratories Ultra Dark Self Tanning Spray Can (6 oz)',\n",
-       "  (70, 115, 219, 60, 0)),\n",
-       " ('B0009VIJV2',\n",
-       "  'Floxite Fl-10h 10x Hand Held 2-sided Mirror with Stand, Clear',\n",
-       "  (200, 77, 33, 34, 0)),\n",
-       " ('B0009VNI2C',\n",
-       "  'Fruit Of The Earth Bogo Cream Aloe Vera 4oz. Jar',\n",
-       "  (127, 62, 234, 13, 0)),\n",
-       " ('B0009VNI40',\n",
-       "  'Fruit Of The Earth 100 % Aloe Vera Gel, 12 oz, 1-Pack',\n",
-       "  (117, 134, 163, 60, 0)),\n",
-       " ('B0009VQ8X8',\n",
-       "  'Got2b Glued Blasting Freeze Spray, 12 Ounce',\n",
-       "  (67, 120, 216, 100, 0)),\n",
-       " ('B0009WB49K',\n",
-       "  'Alterna Caviar Anti-Aging Working Hair Spray, 15.5 Ounce',\n",
-       "  (14, 21, 254, 103, 0)),\n",
-       " ('B0009WY4U6',\n",
-       "  'St. Ives Swiss Formula Makeup Remover &amp; Facial Cleanser, All Skin Types, 6 oz (170 g)',\n",
-       "  (245, 21, 219, 219, 0)),\n",
-       " ('B0009WY4UQ',\n",
-       "  'TRESemme Curl Enhancing Mousse, 10.5 oz',\n",
-       "  (71, 213, 43, 219, 0)),\n",
-       " ('B0009WY54G',\n",
-       "  'TRESemme Shampoo, 24 Hour Body, Healthy Volume, 32 Ounce',\n",
-       "  (183, 134, 67, 76, 0)),\n",
-       " ('B0009XH6SC',\n",
-       "  'Andis 1-Inch Ceramic Clamp Flat Iron (67095)',\n",
-       "  (174, 66, 208, 99, 0)),\n",
-       " ('B0009XH6TG',\n",
-       "  'Andis RC-2 Ionic1875W Ceramic Hair Dryer with Folding Handle and Retractable Cord (80020)',\n",
-       "  (241, 117, 208, 34, 0)),\n",
-       " ('B0009XH6UU',\n",
-       "  'Andis 40055 Pro Style 1600 Watt Hair Dryer - White',\n",
-       "  (80, 103, 219, 76, 0)),\n",
-       " ('B0009XH6V4',\n",
-       "  'Andis Micro Turbo 1600 Watt Dual Voltage Hair Dryer - White (33805)',\n",
-       "  (155, 145, 71, 53, 0)),\n",
-       " ('B0009Y6RHM',\n",
-       "  'Banana Boat Summer Color Self-Tanning Lotion - Deep Dark: 6 OZ',\n",
-       "  (199, 47, 27, 37, 0)),\n",
-       " ('B0009YDO32',\n",
-       "  'Zum Mist Aromatherapy Room and Body Spray Frankincense And Myrrh -- 4 fl oz',\n",
-       "  (114, 62, 59, 103, 0)),\n",
-       " ('B000A0ADT8',\n",
-       "  'Ole Henriksen Truth Serum Collagen Booster, 1.0 Fluid Ounce',\n",
-       "  (245, 134, 162, 170, 0)),\n",
-       " ('B000A0ADUW',\n",
-       "  'Ole Henriksen Blue Black Berry Enzyme Facial Mask, 3.5 Fluid Ounce',\n",
-       "  (123, 21, 122, 15, 0)),\n",
-       " ('B000A35L56',\n",
-       "  'Max Green Alchemy Scalp Rescue Shampoo 8.8 oz',\n",
-       "  (245, 24, 208, 99, 0)),\n",
-       " ('B000A3I2X4',\n",
-       "  'Revlon RV408 1875 Watt Full-Size Turbo Dryer, Black',\n",
-       "  (140, 159, 24, 34, 0)),\n",
-       " ('B000A3V1SW',\n",
-       "  \"Hask Placenta Henna 'n' Placenta for Extremely Damaged Hair 237ml/8oz\",\n",
-       "  (178, 60, 163, 171, 0)),\n",
-       " ('B000A3V1XC', 'Hollywood Beauty Tea Tree Oil 2 oz.', (56, 21, 65, 114, 0)),\n",
-       " ('B000A3V2PE',\n",
-       "  \"L'Oreal Excellence Hicolor Hilights Red 1.2 oz.\",\n",
-       "  (128, 141, 43, 76, 0)),\n",
-       " ('B000A3XI4M',\n",
-       "  'S-Curl Activator and Moisturizer 32 oz',\n",
-       "  (170, 141, 140, 60, 0)),\n",
-       " ('B000A3ZMLO', 'Hollywood Beauty Carrot Oil 8 oz.', (234, 255, 227, 60, 0)),\n",
-       " ('B000A3ZN7M',\n",
-       "  \"L'Oreal Oreor 30 Volume Creme Developer 16 oz.\",\n",
-       "  (47, 47, 201, 15, 0)),\n",
-       " ('B000A3ZN7W',\n",
-       "  'Loreal Oreor Creme 40 Volume Developer 16 Oz',\n",
-       "  (47, 149, 201, 103, 0)),\n",
-       " ('B000A408VC',\n",
-       "  'Sally Hansen Half Jaw Cuticle Nippers',\n",
-       "  (16, 96, 155, 103, 0)),\n",
-       " ('B000A4094I',\n",
-       "  'Liquid Glass Nail Laminate With Sunscreen 0.5oz',\n",
-       "  (138, 21, 33, 195, 0)),\n",
-       " ('B000A409J8',\n",
-       "  \"L'Oreal Quick Blue Powder Bleach 1 Lb\",\n",
-       "  (28, 135, 59, 170, 0)),\n",
-       " ('B000A7VRYG',\n",
-       "  'Ocusoft Lid Scrub Foaming Eyelid Cleanser (7.25 fl. oz.)',\n",
-       "  (24, 89, 201, 160, 0)),\n",
-       " ('B000AA2XQ4',\n",
-       "  'Rothco Compartment Travel Toiletry Bag',\n",
-       "  (89, 202, 153, 91, 0)),\n",
-       " ('B000AA5VD6',\n",
-       "  'Sally Hansen Insta, Dri Insta, Dri Anti, Chip Top Coat',\n",
-       "  (200, 196, 122, 195, 0)),\n",
-       " ('B000AA5VZ4', 'Super Nail Cuticle Oil 4 oz.', (194, 173, 153, 160, 0)),\n",
-       " ('B000AA5VZY', 'Super Nail 16 oz. Pure Acetone', (222, 135, 216, 100, 0)),\n",
-       " ('B000AA9H3Q',\n",
-       "  'Sally Hansen Natural Nail Growth Activator # 2741 0.45 oz.',\n",
-       "  (11, 167, 201, 34, 0)),\n",
-       " ('B000AA9HQ8',\n",
-       "  'Super Nail Cuticle Softener, 8 Ounce',\n",
-       "  (210, 80, 202, 100, 0)),\n",
-       " ('B000AAAVTU',\n",
-       "  'Sally Hansen No Chip Acrylic Top Coat, 0.45 Fluid Ounce',\n",
-       "  (13, 122, 70, 76, 0)),\n",
-       " ('B000AAAW9Y',\n",
-       "  \"Mane 'n Tail Moisture Enriched Hair Strengthener, Bonus, 6 oz.\",\n",
-       "  (102, 80, 11, 220, 0)),\n",
-       " ('B000AAAWAI',\n",
-       "  \"Straight Arrow Mane 'N Tail Herbal Gro Maximum 5.5oz\",\n",
-       "  (39, 129, 59, 114, 0)),\n",
-       " ('B000AAAWEO',\n",
-       "  'Sun In Hair Lightner Lemon 4.7 oz. Pump',\n",
-       "  (94, 202, 11, 34, 0)),\n",
-       " ('B000AAC8VO',\n",
-       "  'DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ',\n",
-       "  (164, 173, 43, 103, 0)),\n",
-       " ('B000AADENA',\n",
-       "  'Sally Hansen Cuticle Massage Cream -- 0.4 fl oz',\n",
-       "  (71, 8, 122, 236, 0)),\n",
-       " ('B000AADEP8',\n",
-       "  'Sally Hansen Double Duty Base and Top Coat, 0.45 Fluid Ounce',\n",
-       "  (28, 87, 138, 195, 0)),\n",
-       " ('B000AADF0M',\n",
-       "  'Sally Hansen Airbrush Face Tanner 1.8oz Spray [Health and Beauty]',\n",
-       "  (196, 164, 43, 171, 0)),\n",
-       " ('B000AADF20',\n",
-       "  'Sally Hansen Vitamin E Nail and Cuticle Oil, 0.45 Fluid Ounce',\n",
-       "  (128, 135, 27, 77, 0)),\n",
-       " ('B000AADG0G',\n",
-       "  'Village Naturals Bath Shoppe Lavender &amp; Chamomile Foaming Milk Bath 28 fl oz.(Pack of 1)',\n",
-       "  (178, 62, 43, 169, 0)),\n",
-       " ('B000AADG8I',\n",
-       "  'Wella Cc Liquid #0811/8N Light Blonde Haircolor',\n",
-       "  (201, 134, 59, 77, 1)),\n",
-       " ('B000ABOLZ4',\n",
-       "  'Kirks Original Coco Castile Soap, 24 Bars 1/2 Case',\n",
-       "  (144, 159, 33, 116, 0)),\n",
-       " ('B000ACAV8O', 'Henna Bright Red Pwd 4oz 4 Ounces', (45, 21, 216, 77, 1)),\n",
-       " ('B000ACB09I',\n",
-       "  \"Nature's Gate Organics C for Yourself, (1.7 fl oz) (50 ml)\",\n",
-       "  (100, 196, 162, 34, 0)),\n",
-       " ('B000ACB0C0',\n",
-       "  \"Nature's Gate Organics Oh What a Night Cream, (1 oz) (28 g)\",\n",
-       "  (145, 66, 24, 219, 0)),\n",
-       " ('B000ALBJ40',\n",
-       "  'Fekkai Glossing Hair Conditioner 2 Fl Oz',\n",
-       "  (41, 3, 153, 100, 0)),\n",
-       " ('B000ALBKGW',\n",
-       "  'Bliss Diamancel Diamond Nail File No.2, Medium',\n",
-       "  (9, 122, 163, 162, 0)),\n",
-       " ('B000ALBNGE',\n",
-       "  'Clinique Superfine Liner for Brows 01 Soft Blonde',\n",
-       "  (189, 46, 163, 99, 0)),\n",
-       " ('B000ALCJR6',\n",
-       "  'T3 Tourmaline 83808 Professional Featherweight Ceramic Ionic Hair Dryer',\n",
-       "  (105, 228, 139, 116, 0)),\n",
-       " ('B000ALDJ7A',\n",
-       "  'Diamancel Diamond Tough Foot Buffer No.11, Medium',\n",
-       "  (200, 120, 67, 83, 0)),\n",
-       " ('B000ALDK1A',\n",
-       "  'Fruit Of The Earth 100% Aloe Vera 24oz Gel Pump',\n",
-       "  (185, 62, 254, 160, 0)),\n",
-       " ('B000ALDLJG',\n",
-       "  'Clinique Quickliner for Eyes 07 Really Black',\n",
-       "  (18, 96, 201, 169, 0)),\n",
-       " ('B000ALFROS',\n",
-       "  'Fekkai Advanced Brilliant Glossing Cream 4 fl oz (113 g)',\n",
-       "  (128, 105, 201, 99, 0)),\n",
-       " ('B000ALFTUU',\n",
-       "  'Clinique 7 Day Scrub Cream Rinse-Off Formula 3.4 oz',\n",
-       "  (132, 173, 138, 169, 0)),\n",
-       " ('B000ALFTVO',\n",
-       "  'Clinique Take the Day off makeup remover 4.2 oz /125ml',\n",
-       "  (132, 149, 43, 76, 0)),\n",
-       " ('B000AM82US',\n",
-       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Towel, Pink (19 x 39-Inches)\",\n",
-       "  (80, 134, 139, 12, 0)),\n",
-       " ('B000AMA43Q',\n",
-       "  'Aquis Microfiber Hair Towel, Waffle, Linen (19 x 39-Inches)',\n",
-       "  (28, 149, 139, 91, 0)),\n",
-       " ('B000AMA4DQ',\n",
-       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Turban, Patented Design, White\",\n",
-       "  (210, 21, 162, 210, 0)),\n",
-       " ('B000AMBF5W',\n",
-       "  'Nivea Soft Refreshingly Soft Moisturizing Creme with Jojoba Oil &amp; Vitamin E, 6.8 Ounces',\n",
-       "  (92, 202, 59, 53, 0)),\n",
-       " ('B000AMHWCW',\n",
-       "  'Too Faced Cosmetics Bronzer, Snow Bunny, 0.28-Ounce',\n",
-       "  (188, 103, 254, 77, 0)),\n",
-       " ('B000AO2NXS',\n",
-       "  'Dove Body Wash with NutriumMoisture, Sensitive Skin Nourishing, 24 Ounce',\n",
-       "  (99, 255, 153, 53, 0)),\n",
-       " ('B000AQF50Y',\n",
-       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 3.3 Ounces',\n",
-       "  (31, 24, 27, 75, 0)),\n",
-       " ('B000AQI2EK',\n",
-       "  'Paul Mitchell Tea Tree Special Conditioner, 33.8 Ounce',\n",
-       "  (33, 196, 98, 76, 0)),\n",
-       " ('B000ARDBH2',\n",
-       "  'Guess Eau de Parfum Spray for Women, 2.5 Fluid Ounce',\n",
-       "  (141, 3, 201, 152, 0)),\n",
-       " ('B000ASDGK8',\n",
-       "  'BaByliss Pro BAB2000 Ceramix Xtreme  Dryer',\n",
-       "  (181, 202, 122, 195, 0)),\n",
-       " ('B000ASDX3S',\n",
-       "  'BaByliss Pro BAB2590 Porcelain Ceramic Straightening Iron with Removable Comb, 1.5 Inch',\n",
-       "  (132, 66, 246, 169, 0)),\n",
-       " ('B000AU15E0',\n",
-       "  'Blue Lizard Australian SUNSCREEN SPF 30+, Baby, SPF 30+, 8.75-Ounces',\n",
-       "  (128, 77, 43, 77, 0)),\n",
-       " ('B000AUTH0E',\n",
-       "  'Rusk Thermal Shine Spray Unisex, 4.4 Ounce',\n",
-       "  (71, 96, 138, 100, 0)),\n",
-       " ('B000AUTHEA',\n",
-       "  'Straight Sexy Hair Smooth &amp; Seal Aerated Anti-Frizz Spray (8.1 oz)',\n",
-       "  (154, 131, 139, 195, 0)),\n",
-       " ('B000AYKVOG', 'Dove Pink Beauty Bar, 8 Count', (115, 24, 208, 170, 0)),\n",
-       " ('B000B45BUE',\n",
-       "  'Silver Metallic Perfume Atomizer Spray 10 ML for purse or travel Refillable',\n",
-       "  (3, 236, 122, 202, 0)),\n",
-       " ('B000B5S2LS',\n",
-       "  'Focus 21 Sea Plasma Hair and Skin Moisturizer 32oz',\n",
-       "  (40, 100, 226, 76, 0)),\n",
-       " ('B000B5UPF4', 'OC Eight Professional Mattifying Gel', (232, 77, 226, 77, 0)),\n",
-       " ('B000B63Y1K',\n",
-       "  'Dermorganic Conditioning Shampoo, 12 Ounce',\n",
-       "  (63, 115, 11, 184, 0)),\n",
-       " ('B000B6VIPY',\n",
-       "  'Princereigns Shaving Gel Used to Remove Ingrown Hair and Razor Bumps',\n",
-       "  (28, 3, 59, 19, 0)),\n",
-       " ('B000B7VO66',\n",
-       "  'PCA Skin Eyexcellence (Phaze 12), 0.5 Ounce',\n",
-       "  (233, 21, 33, 15, 0)),\n",
-       " ('B000B82TD2',\n",
-       "  'NeoStrata Ultra Smoothing Cream AHA 10, 1.4 Ounce',\n",
-       "  (44, 115, 27, 195, 0)),\n",
-       " ('B000B8FW0Y', 'Lansky Dual Grit Sharpener', (220, 173, 71, 34, 0)),\n",
-       " ('B000B8VBJK',\n",
-       "  'HOT TOOLS 2108 Nano Ceramic Marcel Curling Iron, Black/Purple, 1 Inch',\n",
-       "  (67, 202, 226, 77, 0)),\n",
-       " ('B000B9J3GM', 'Mary Kay Indulge Soothing Eye Gel', (132, 211, 17, 13, 0)),\n",
-       " ('B000B9N0N4',\n",
-       "  'Mary Kay Blemish Control Toner: Formula 3',\n",
-       "  (78, 180, 139, 182, 0)),\n",
-       " ('B000B9W3CI',\n",
-       "  'White Plastic Jar with Dome Lid 4 Oz - 12 Per Bag',\n",
-       "  (241, 46, 3, 77, 0)),\n",
-       " ('B000B9W4MC',\n",
-       "  'White Plastic Jar with Flat Lid 8 Oz - 6 Per Bag',\n",
-       "  (241, 46, 234, 99, 0)),\n",
-       " ('B000BB9L0S',\n",
-       "  'Nailtiques Formula 2 Protein, .5 Ounce',\n",
-       "  (6, 159, 226, 91, 0)),\n",
-       " ('B000BBGP4I', 'Mason Pearson Detangling Comb', (210, 8, 33, 153, 0)),\n",
-       " ('B000BD0SEE',\n",
-       "  'MyChelle Fruit Enzyme Cleanser, 4.4 Ounce Bottle',\n",
-       "  (204, 8, 139, 91, 0)),\n",
-       " ('B000BFTAMS',\n",
-       "  'Ponds Caring Classic Extra, Rich Dry Skin Cream, 10.1 oz',\n",
-       "  (170, 4, 40, 12, 0)),\n",
-       " ('B000BGIYWY',\n",
-       "  \"Africa's Best Kids Organics Hair Lotion, Shea Butter Detangling Moisturizing 12 oz\",\n",
-       "  (177, 180, 162, 159, 0)),\n",
-       " ('B000BH92J2',\n",
-       "  'Shea Moisture Shea Butter Leave in Conditioner 8oz',\n",
-       "  (132, 234, 219, 34, 0)),\n",
-       " ('B000BIPJ20',\n",
-       "  'Perfekt Skin Perfection Gel Luminous 1 oz',\n",
-       "  (133, 196, 71, 236, 0)),\n",
-       " ('B000BIUGRI', 'Bumble and Bumble Prep (8 Ounces)', (117, 3, 140, 241, 0)),\n",
-       " ('B000BIUGSM',\n",
-       "  'Bumble and Bumble Styling Lotion (8 Ounces)',\n",
-       "  (3, 134, 153, 206, 0)),\n",
-       " ('B000BIUGUA',\n",
-       "  'Bumble and Bumble Sunday Shampoo (8 Ounces)',\n",
-       "  (10, 122, 153, 160, 0)),\n",
-       " ('B000BIUGUK',\n",
-       "  'Bumble and Bumble Thickening Conditioner (8 Ounces)',\n",
-       "  (123, 122, 153, 169, 0)),\n",
-       " ('B000BIUGV4',\n",
-       "  'Bumble and Bumble Thickening Hair Spray (8 Ounces)',\n",
-       "  (79, 145, 246, 241, 0)),\n",
-       " ('B000BIUGXM',\n",
-       "  'Bumble and Bumble Curl Conscious Defining Creme 8.5 oz',\n",
-       "  (117, 122, 208, 114, 0)),\n",
-       " ('B000BIVXZW',\n",
-       "  'Bumble and Bumble Classic Hairspray (10 Ounces)',\n",
-       "  (19, 62, 24, 160, 0)),\n",
-       " ('B000BIVY0G',\n",
-       "  'Bumble and Bumble  DeFRIZZ (4 Ounces)',\n",
-       "  (92, 250, 201, 220, 0)),\n",
-       " ('B000BIVY10',\n",
-       "  'Kerastase Nutritive Bain Satin 2 Complete Nutrition Shampoo For Dry and Sensitised Hair, 8.5 Oz.',\n",
-       "  (132, 7, 226, 15, 0)),\n",
-       " ('B000BIVY1U',\n",
-       "  'Bumble and Bumble Seaweed Conditioner (8 Ounces)',\n",
-       "  (177, 21, 10, 116, 0)),\n",
-       " ('B000BIVY24',\n",
-       "  'Bumble and Bumble Tonic Lotion, 8-Ounce Spray Bottle',\n",
-       "  (151, 21, 216, 152, 0)),\n",
-       " ('B000BIXP30',\n",
-       "  'Kerastase Resistance Bain Volumactive Volumizing Shampoo For Fine, Vulnerable Hair, 8.5 Ounce',\n",
-       "  (1, 180, 55, 91, 0)),\n",
-       " ('B000BIXP3K',\n",
-       "  'Bumble and Bumble Grooming Cream (5 Ounces)',\n",
-       "  (201, 247, 71, 91, 0)),\n",
-       " ('B000BIXP3U',\n",
-       "  'Bumble and Bumble Leave in Conditioner (8 Ounces)',\n",
-       "  (79, 206, 41, 37, 0)),\n",
-       " ('B000BIXP4O',\n",
-       "  'Bumble and Bumble Styling Creme, 8-Ounce Bottle',\n",
-       "  (3, 180, 216, 114, 0)),\n",
-       " ('B000BIXP5I',\n",
-       "  'Bumble and Bumble Surf Spray, 4-Ounce Bottle',\n",
-       "  (3, 3, 208, 75, 0)),\n",
-       " ('B000BIXP5S',\n",
-       "  'Kerastase Nutritive Oleo-Relax Serum, 4.2 Ounces',\n",
-       "  (99, 196, 67, 53, 0)),\n",
-       " ('B000BIXP62',\n",
-       "  'Kerastase Nutritive Bain Oleo-Relax Smoothing Shampoo For Dry and Rebellious Hair, 8.5 Ounce',\n",
-       "  (132, 115, 227, 34, 0)),\n",
-       " ('B000BIZSUS',\n",
-       "  'Bumble and Bumble Brilliantine (2 Ounces)',\n",
-       "  (3, 47, 192, 37, 0)),\n",
-       " ('B000BIZSV2',\n",
-       "  'Bumble and Bumble Deeep, 5-Ounce Tube',\n",
-       "  (177, 173, 208, 100, 0)),\n",
-       " ('B000BIZSYO',\n",
-       "  'Kerastase Resistance Bain De Force Fortifying Shampoo For Weakened to Fragile Hair, 8.5 Ounce',\n",
-       "  (132, 249, 71, 180, 0)),\n",
-       " ('B000BJ1CGQ', 'GiGi Mini Pro Waxing Kit', (113, 196, 201, 15, 0)),\n",
-       " ('B000BK1P9E',\n",
-       "  'Kerasilk Rich Care Instant Silk Fluid By Goldwell for Unisex, 4.2 Ounce',\n",
-       "  (47, 115, 22, 27, 0)),\n",
-       " ('B000BKLBES',\n",
-       "  'Marc Anthony Instantly Thick Hair Thickening Cream, 6 oz',\n",
-       "  (13, 159, 162, 103, 0)),\n",
-       " ('B000BKLCDS',\n",
-       "  'Super Nail Acetone Polish Remover, 16 Ounce',\n",
-       "  (3, 21, 153, 38, 0)),\n",
-       " ('B000BKXGXW',\n",
-       "  'NOW Foods -  Red Clay Powder Moroccan, 6 OZ.',\n",
-       "  (245, 103, 71, 159, 0)),\n",
-       " ('B000BNG4VU',\n",
-       "  'Coty Airspun Loose Powder, Translucent, 2.3 Ounce',\n",
-       "  (123, 238, 10, 12, 0)),\n",
-       " ('B000BP81IM', 'NARS Loose Powder, Eden', (173, 96, 71, 170, 0)),\n",
-       " ('B000BPBV2U',\n",
-       "  'Liquid Gold Brush-on Bonding Adhesive for Cold Fusion Hair Extensions and Braids - .5oz',\n",
-       "  (12, 149, 139, 202, 0)),\n",
-       " ('B000BPMB2Y',\n",
-       "  'Roux Lash &amp; Brow Tint, Black,40 Count',\n",
-       "  (92, 29, 70, 27, 0)),\n",
-       " ('B000BPQG6Q', 'Roux Tween Time Hair Crayon, Auburn', (3, 47, 219, 53, 0)),\n",
-       " ('B000BPT0TG',\n",
-       "  'Roux Fanci-full Rinse #49 Ultra White Minx',\n",
-       "  (238, 236, 162, 170, 0)),\n",
-       " ('B000BR50M0',\n",
-       "  'Symbiotics Colostrum Plus, 240 Capsules',\n",
-       "  (252, 47, 219, 34, 0)),\n",
-       " ('B000BR5B2Y',\n",
-       "  'Komenuka Bijin Japanese All-Natural Essence Whitening Cream with Rice Bran',\n",
-       "  (99, 149, 3, 169, 0)),\n",
-       " ('B000BR766S',\n",
-       "  'Komenuka Bijin Premium Hair Care Set: Moisturizing Hair Shampoo &amp; Hair Treatment / Conditioner',\n",
-       "  (132, 161, 226, 99, 0)),\n",
-       " ('B000BRGAOM',\n",
-       "  'Biosilk Therapy Shine On Spray, 5.30 Ounce',\n",
-       "  (132, 228, 70, 34, 0)),\n",
-       " ('B000BRO6O8',\n",
-       "  'Coty Airspun Loose Powder, Naturelle, 2.3 Ounce',\n",
-       "  (123, 4, 40, 12, 0)),\n",
-       " ('B000BRPMJG',\n",
-       "  'Sebamed moisturizing lotion, for sensitive skin, 6.8-Fluid Ounce',\n",
-       "  (63, 46, 139, 48, 0)),\n",
-       " ('B000BRSHXE',\n",
-       "  'Sebamed Moisturing Cream, Sensitive Skin, 2.6-Ounce',\n",
-       "  (78, 159, 122, 202, 0)),\n",
-       " ('B000BRY5UI',\n",
-       "  'Komenuka Bijin Facial Cleansing Powder from Natural Rice Bran - 30 Packs',\n",
-       "  (78, 180, 219, 77, 0)),\n",
-       " ('B000BRYMKG',\n",
-       "  'Hylexin Serious Dark Circles by Bremenn Research Labs .39 oz. 11.53 ml',\n",
-       "  (252, 247, 227, 103, 0)),\n",
-       " ('B000BTM2K6',\n",
-       "  'White Shoulders By Evyan For Women, Eau De Cologne Spray (4.5 Ounces)',\n",
-       "  (92, 180, 192, 91, 0)),\n",
-       " ('B000BTO6CI',\n",
-       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 3.4 Oz.',\n",
-       "  (8, 4, 3, 34, 1)),\n",
-       " ('B000BTO6EG',\n",
-       "  'PALOMA PICASSO For Women By PALOMA PICASSO Eau De Parfum Spray,3.4 Oz',\n",
-       "  (17, 24, 118, 77, 0)),\n",
-       " ('B000BTQRFM',\n",
-       "  'Paul Sebastian by Paul Sebastian for Men - 8 Ounce EDC De Luxe Splash',\n",
-       "  (177, 131, 226, 202, 0)),\n",
-       " ('B000BU7G1A',\n",
-       "  'ApHogee Intensive Two Minute Keratin Reconstructor',\n",
-       "  (47, 196, 75, 202, 0)),\n",
-       " ('B000BU7SMM', 'Aphogee Balancing Moisturizer 16 Oz', (41, 4, 40, 12, 0)),\n",
-       " ('B000BUFFPO', \"Burt's Bees Herbal Deodorant\", (70, 236, 122, 15, 0)),\n",
-       " ('B000BVCSP8',\n",
-       "  'Scruples High Definition Shape Spray, 10.6 Ounce',\n",
-       "  (117, 149, 254, 15, 0)),\n",
-       " ('B000BW4U58', 'Vitamin E Skin Oil 10000 IU. 4.6 Oz', (99, 202, 122, 37, 0)),\n",
-       " ('B000BX1Z00',\n",
-       "  'CHI Silk Infusion Leave-In Treatment, 12 Ounce',\n",
-       "  (39, 46, 43, 114, 0)),\n",
-       " ('B000BX5FS8',\n",
-       "  'CHI Straight Guard Smoothing Styling Cream 8.5 oz',\n",
-       "  (155, 145, 201, 152, 0)),\n",
-       " ('B000BY2N7S', 'NOW Foods Biotin 5000mcg, 120 Vcaps', (167, 54, 41, 246, 0)),\n",
-       " ('B000BYQBT4',\n",
-       "  'Obagi Nu Derm Exfoderm Skin Smoothing Lotion-2 oz',\n",
-       "  (196, 1, 163, 38, 0)),\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "items_with_tuples"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,

From 438560d6e572f6967e652964a1ac5d2c5e77ed4a Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 2 Jan 2025 22:35:17 +0300
Subject: [PATCH 025/175] tiger next_item_predictions

---
 configs/train/tiger_train_config.json   |  4 ++--
 modeling/dataloader/batch_processors.py | 12 ++---------
 modeling/models/tiger.py                | 28 ++++++++++++++++++-------
 review.md                               |  1 +
 4 files changed, 25 insertions(+), 20 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 1688a56b..463a984a 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -9,7 +9,6 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "num_negatives_val": 100,
       "type": "next_item_prediction",
       "negative_sampler_type": "random"
     }
@@ -45,6 +44,7 @@
     "trie": "../data/Beauty/rqvae/trie.pkl",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
+    "positive_prefix": "positive",
     "labels_prefix": "labels",
     "embedding_dim": 64,
     "num_heads": 2,
@@ -70,7 +70,7 @@
       {
         "type": "ce",
         "predictions_prefix": "logits",
-        "labels_prefix": "semantic.labels",
+        "labels_prefix": "semantic.positive",
         "output_prefix": "downstream_loss",
         "weight": 1.0
       }
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index f1d17e0c..8d51bca9 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,3 +1,4 @@
+from collections import defaultdict
 import json
 import torch
 from models.base import BaseModel
@@ -56,22 +57,13 @@ def get_semantic_ids(self, item_ids):
         return semantic_ids
 
     def __call__(self, batch):
-        processed_batch = {}
+        processed_batch = defaultdict(list)
 
         for key in batch[0].keys():
             if key.endswith('.ids'):
                 prefix = key.split('.')[0]
                 assert '{}.length'.format(prefix) in batch[0]
 
-                processed_batch[f'{prefix}.ids'] = []
-                processed_batch[f'{prefix}.length'] = []
-                
-                processed_batch[f'semantic.{prefix}.ids'] = []
-                processed_batch[f'semantic.{prefix}.length'] = []
-                
-                # item_ids = list(itertools.chain(*semantic_ids))
-                # length = len(item_ids) # sample[f'{prefix}.length']
-
                 for sample in batch:
                     item_ids = sample[f'{prefix}.ids']
                     length = sample[f'{prefix}.length']
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 7078e1b0..794f0761 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -13,6 +13,7 @@ def __init__(
             trie,
             sequence_prefix,
             pred_prefix,
+            positive_prefix,
             labels_prefix,
             num_items,
             max_sequence_length,
@@ -56,6 +57,7 @@ def __init__(
         
         self._sequence_prefix = sequence_prefix
         self._pred_prefix = pred_prefix
+        self._positive_prefix = positive_prefix
         self._labels_prefix = labels_prefix
         
         self._semantic_id_arr = semantic_id_arr
@@ -76,6 +78,7 @@ def create_from_config(cls, config, **kwargs):
             trie=trie,
             sequence_prefix=config['sequence_prefix'],
             pred_prefix=config['predictions_prefix'],
+            positive_prefix=config['positive_prefix'],
             labels_prefix=config['labels_prefix'],
             num_items=kwargs['num_items'],
             max_sequence_length=kwargs['max_sequence_length'],
@@ -87,31 +90,40 @@ def create_from_config(cls, config, **kwargs):
             dropout=config.get('dropout', 0.0),
             initializer_range=config.get('initializer_range', 0.02)
         )
-
-    def forward(self, inputs):
-        all_sample_events = inputs['semantic.{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['semantic.{}.length'.format(self._sequence_prefix)]  # (batch_size)
         
+    def get_logits(self, inputs, prefix, all_sample_events, all_sample_lengths):
         # TODOPK pass parameter as args (self._semantic_prefix)
         
-        label_events = inputs['semantic.{}.ids'.format(self._labels_prefix)]
-        label_lengths = inputs['semantic.{}.length'.format(self._labels_prefix)]
+        label_events = inputs['semantic.{}.ids'.format(prefix)]
+        label_lengths = inputs['semantic.{}.length'.format(prefix)]
     
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths, add_codebook_embeddings=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
         
-        decoder_outputs = self._apply_decoder(label_events, label_lengths, embeddings, mask) # (batch_size, label_len, embedding_dim)
+        decoder_outputs = self._apply_decoder(
+            label_events, label_lengths, embeddings, mask
+        ) # (batch_size, label_len, embedding_dim)
         # todo pk correct place for projection? or view -> projection
         logits = self._projection(decoder_outputs)  # (batch_size, seq_len, _semantic_id_arr[0])
         
-        logits = logits.view(-1, self._semantic_id_arr[0]) # (batch_size * seq_len, _semantic_id_arr[0])
+        return logits, mask
+
+    def forward(self, inputs):
+        all_sample_events = inputs['semantic.{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['semantic.{}.length'.format(self._sequence_prefix)]  # (batch_size)
         
         if self.training:
+            logits, mask = self.get_logits(inputs, self._positive_prefix, all_sample_events, all_sample_lengths)
+            
+            logits = logits[mask] # TODOPK correct?
+            
             return {
                 self._pred_prefix: logits
             }
         else:
+            logits, mask = self.get_logits(inputs, self._labels_prefix, all_sample_events, all_sample_lengths)
+            
             preds = logits.argmax(dim=-1).view(len(all_sample_lengths), len(self._semantic_id_arr)) # Shape: (batch_size, seq_len)
             ids = torch.tensor(self._apply_trie(preds))
             return ids
diff --git a/review.md b/review.md
index f9534ce0..12af8109 100644
--- a/review.md
+++ b/review.md
@@ -2,6 +2,7 @@
 
 ## Todos
 
+- restore my amazon beauty changes (`data_full.pt`)
 - max_sequence_length (TODOPK), why +1? не смог найти где дописывается в батч сама длина
 - positions = positions // self._semantic_id_length или reverse?
 как именно учитываем codebook_post & item_pos (тот же порядок или inverted)

From 3a59be8b427396982bbe5e56b8e9b28cfc5f4340 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 2 Jan 2025 22:53:05 +0300
Subject: [PATCH 026/175] add coverage & sequence dataset

---
 configs/train/tiger_train_config.json | 26 ++++++++++++-
 modeling/dataset/base.py              | 56 +++++++++++++++++++++++++++
 2 files changed, 81 insertions(+), 1 deletion(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 463a984a..1b7e5115 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -3,7 +3,7 @@
   "train_epochs_num": 10,
   "best_metric": "validation/ndcg@20",
   "dataset": {
-    "type": "rqvae_scientific",
+    "type": "rqvae_sequence",
     "path_to_data_dir": "../data",
     "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "name": "Beauty",
@@ -114,6 +114,18 @@
           "recall@20": {
             "type": "recall",
             "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
           }
         }
       },
@@ -146,6 +158,18 @@
           "recall@20": {
             "type": "recall",
             "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
           }
         }
       }
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 1b883d5f..9a4e3278 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -618,6 +618,62 @@ def meta(self):
             'max_sequence_length': self.max_sequence_length
         }
         
+class RqvaeSequenceDataset(SequenceDataset, config_name='rqvae_sequence'):
+
+    def __init__(
+        self,
+        train_sampler,
+        validation_sampler,
+        test_sampler,
+        num_users,
+        num_items,
+        max_sequence_length,
+        semantic_id_arr
+    ):
+        super().__init__(
+            train_sampler=train_sampler,
+            validation_sampler=validation_sampler,
+            test_sampler=test_sampler,
+            num_users=num_users,
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+        )
+        self._semantic_id_arr = semantic_id_arr
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_config = json.load(open(config['rqvae_train_config_path']))
+        semantic_id_arr = rqvae_config['model']['codebook_sizes']
+        
+        scientific_instance = SequenceDataset.create_from_config(config, **kwargs)
+        
+        return cls(
+            train_sampler=scientific_instance._train_sampler,
+            validation_sampler=scientific_instance._validation_sampler,
+            test_sampler=scientific_instance._test_sampler,
+            num_users=scientific_instance.num_users,
+            num_items=scientific_instance.num_items,
+            max_sequence_length=scientific_instance.max_sequence_length,
+            semantic_id_arr=semantic_id_arr,
+        )
+
+    @property
+    def num_items(self):
+        return self._semantic_id_arr[0] # TODOPK?
+
+    @property
+    def max_sequence_length(self):
+        return self._max_sequence_length * len(self._semantic_id_arr)
+
+    @property
+    def meta(self):
+        return {
+            'num_users': self.num_users,
+            'num_items': self.num_items,
+            'max_sequence_length': self.max_sequence_length,
+            'semantic_id_arr': self._semantic_id_arr
+        }
+        
 class RqvaeScientificDataset(ScientificDataset, config_name='rqvae_scientific'):
 
     def __init__(

From e78a5aedd0457ff6e406d63cfcc5aa6ab2f41065 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 3 Jan 2025 13:10:27 +0300
Subject: [PATCH 027/175] unify datasets

---
 configs/train/tiger_train_config.json |   4 +-
 modeling/dataset/base.py              | 111 --------------------------
 modeling/models/tiger.py              |  13 +--
 3 files changed, 10 insertions(+), 118 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 1b7e5115..0f28fa01 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -3,9 +3,8 @@
   "train_epochs_num": 10,
   "best_metric": "validation/ndcg@20",
   "dataset": {
-    "type": "rqvae_sequence",
+    "type": "sequence",
     "path_to_data_dir": "../data",
-    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
@@ -42,6 +41,7 @@
   "model": {
     "type": "tiger",
     "trie": "../data/Beauty/rqvae/trie.pkl",
+    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
     "positive_prefix": "positive",
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 9a4e3278..dce14d8f 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -617,119 +617,8 @@ def meta(self):
             'num_items': self.num_items,
             'max_sequence_length': self.max_sequence_length
         }
-        
-class RqvaeSequenceDataset(SequenceDataset, config_name='rqvae_sequence'):
 
-    def __init__(
-        self,
-        train_sampler,
-        validation_sampler,
-        test_sampler,
-        num_users,
-        num_items,
-        max_sequence_length,
-        semantic_id_arr
-    ):
-        super().__init__(
-            train_sampler=train_sampler,
-            validation_sampler=validation_sampler,
-            test_sampler=test_sampler,
-            num_users=num_users,
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-        )
-        self._semantic_id_arr = semantic_id_arr
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        rqvae_config = json.load(open(config['rqvae_train_config_path']))
-        semantic_id_arr = rqvae_config['model']['codebook_sizes']
-        
-        scientific_instance = SequenceDataset.create_from_config(config, **kwargs)
-        
-        return cls(
-            train_sampler=scientific_instance._train_sampler,
-            validation_sampler=scientific_instance._validation_sampler,
-            test_sampler=scientific_instance._test_sampler,
-            num_users=scientific_instance.num_users,
-            num_items=scientific_instance.num_items,
-            max_sequence_length=scientific_instance.max_sequence_length,
-            semantic_id_arr=semantic_id_arr,
-        )
-
-    @property
-    def num_items(self):
-        return self._semantic_id_arr[0] # TODOPK?
-
-    @property
-    def max_sequence_length(self):
-        return self._max_sequence_length * len(self._semantic_id_arr)
-
-    @property
-    def meta(self):
-        return {
-            'num_users': self.num_users,
-            'num_items': self.num_items,
-            'max_sequence_length': self.max_sequence_length,
-            'semantic_id_arr': self._semantic_id_arr
-        }
-        
-class RqvaeScientificDataset(ScientificDataset, config_name='rqvae_scientific'):
 
-    def __init__(
-        self,
-        train_sampler,
-        validation_sampler,
-        test_sampler,
-        num_users,
-        num_items,
-        max_sequence_length,
-        semantic_id_arr
-    ):
-        super().__init__(
-            train_sampler=train_sampler,
-            validation_sampler=validation_sampler,
-            test_sampler=test_sampler,
-            num_users=num_users,
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-        )
-        self._semantic_id_arr = semantic_id_arr
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        rqvae_config = json.load(open(config['rqvae_train_config_path']))
-        semantic_id_arr = rqvae_config['model']['codebook_sizes']
-        
-        scientific_instance = ScientificDataset.create_from_config(config, **kwargs)
-        
-        return cls(
-            train_sampler=scientific_instance._train_sampler,
-            validation_sampler=scientific_instance._validation_sampler,
-            test_sampler=scientific_instance._test_sampler,
-            num_users=scientific_instance.num_users,
-            num_items=scientific_instance.num_items,
-            max_sequence_length=scientific_instance.max_sequence_length,
-            semantic_id_arr=semantic_id_arr,
-        )
-
-    @property
-    def num_items(self):
-        return self._semantic_id_arr[0] # TODOPK?
-
-    @property
-    def max_sequence_length(self):
-        return self._max_sequence_length * len(self._semantic_id_arr)
-
-    @property
-    def meta(self):
-        return {
-            'num_users': self.num_users,
-            'num_items': self.num_items,
-            'max_sequence_length': self.max_sequence_length,
-            'semantic_id_arr': self._semantic_id_arr
-        }
-        
 class RqVaeDataset(BaseDataset, config_name='rqvae'):
 
     def __init__(
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 794f0761..0c567217 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,9 +1,9 @@
+import json
 from utils import DEVICE, create_masked_tensor, get_activation_function
 from models.base import SequentialTorchModel
 import pickle
 import torch
 from torch import nn
-import random
 
 
 class TigerModel(SequentialTorchModel, config_name='tiger'):
@@ -52,7 +52,6 @@ def __init__(
         self._decoder = nn.TransformerDecoder(transformer_decoder_layer, num_layers)
         self._trie = trie
         
-        assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
         self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
         
         self._sequence_prefix = sequence_prefix
@@ -73,6 +72,10 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         with open(config['trie'], 'rb') as f:
             trie = pickle.load(f)
+            
+        rqvae_config = json.load(open(config['rqvae_train_config_path']))
+        semantic_id_arr = rqvae_config['model']['codebook_sizes']
+        assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
     
         return cls(
             trie=trie,
@@ -80,13 +83,13 @@ def create_from_config(cls, config, **kwargs):
             pred_prefix=config['predictions_prefix'],
             positive_prefix=config['positive_prefix'],
             labels_prefix=config['labels_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
+            num_items=semantic_id_arr[0],
+            max_sequence_length=kwargs['max_sequence_length'] * len(semantic_id_arr),
             embedding_dim=config['embedding_dim'],
             num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
             num_layers=config['num_layers'],
             dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            semantic_id_arr=kwargs['semantic_id_arr'],
+            semantic_id_arr=semantic_id_arr,
             dropout=config.get('dropout', 0.0),
             initializer_range=config.get('initializer_range', 0.02)
         )

From b0809f5140133d21d7c15d9717cc86b111d41fe3 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 3 Jan 2025 13:51:37 +0300
Subject: [PATCH 028/175] small fxs

---
 configs/train/tiger_train_config.json | 1 -
 modeling/models/rqvae.py              | 3 ---
 modeling/models/tiger.py              | 2 +-
 3 files changed, 1 insertion(+), 5 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 0f28fa01..ae27de61 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -52,7 +52,6 @@
     "dim_feedforward": 256,
     "dropout": 0.3,
     "activation": "gelu",
-    "use_ce": true,
     "layer_norm_eps": 1e-9,
     "initializer_range": 0.02
   },
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index de4bbf58..56185a4d 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,9 +1,6 @@
 from models.base import TorchModel
 
 import torch
-
-import torch
-
 import faiss
 
 class RqVaeModel(TorchModel, config_name='rqvae'):
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 0c567217..9ab87e14 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -107,7 +107,7 @@ def get_logits(self, inputs, prefix, all_sample_events, all_sample_lengths):
         decoder_outputs = self._apply_decoder(
             label_events, label_lengths, embeddings, mask
         ) # (batch_size, label_len, embedding_dim)
-        # todo pk correct place for projection? or view -> projection
+        # todopk correct place for projection? or view -> projection
         logits = self._projection(decoder_outputs)  # (batch_size, seq_len, _semantic_id_arr[0])
         
         return logits, mask

From 2f70ea77b26d3d038edc756f3edce3e374730e31 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 3 Jan 2025 13:57:35 +0300
Subject: [PATCH 029/175] remove multi domain scientific dataset

---
 modeling/dataset/base.py | 120 ---------------------------------------
 1 file changed, 120 deletions(-)

diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index dce14d8f..1cb01562 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -687,123 +687,3 @@ def meta(self):
             'num_items': self.num_items,
             'train_sampler': self._train_sampler
         }
-
-
-class MultiDomainScientificDataset(ScientificDataset, config_name='multi_domain_scientific'):
-
-    def __init__(
-            self,
-            train_sampler,
-            validation_sampler,
-            test_sampler,
-            num_users,
-            num_items,
-            max_sequence_length,
-            target_domain,
-            other_domains
-    ):
-        super().__init__(train_sampler, validation_sampler, test_sampler, num_users, num_items, max_sequence_length)
-        self._target_domain = target_domain
-        self._other_domains = other_domains
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
-        target_domain, other_domains = config['target_domain'], config['other_domains']
-        domains = [target_domain] + other_domains
-        max_sequence_length = config['max_sequence_length']
-        max_user_idx, max_item_idx = 0, 0
-
-        train_dataset, validation_dataset, test_dataset = {}, {}, {}
-        max_user_idx_by_domain, max_item_idx_by_domain = {}, {}
-
-        for domain in domains:
-            dataset_path = os.path.join(data_dir_path, domain, '{}.txt'.format('all_data'))
-            with open(dataset_path, 'r') as f:
-                data = f.readlines()
-            train_dataset[domain], validation_dataset[domain], test_dataset[domain] = [], [], []
-
-            for sample in data:
-                sample = sample.strip('\n').split(' ')
-                user_idx = int(sample[0])
-                item_ids = [int(item_id) for item_id in sample[1:]]
-
-                max_user_idx = max(max_user_idx, user_idx)
-                max_item_idx = max(max_item_idx, max(item_ids))
-
-                assert len(item_ids) >= 5
-
-                train_dataset[domain].append({
-                    'user.ids': [user_idx],
-                    'user.length': 1,
-                    'item.ids': item_ids[:-2][-max_sequence_length:],
-                    'item.length': len(item_ids[:-2][-max_sequence_length:])
-                })
-                assert len(item_ids[:-2][-max_sequence_length:]) == len(set(item_ids[:-2][-max_sequence_length:]))
-                validation_dataset[domain].append({
-                    'user.ids': [user_idx],
-                    'user.length': 1,
-                    'item.ids': item_ids[:-1][-max_sequence_length:],
-                    'item.length': len(item_ids[:-1][-max_sequence_length:])
-                })
-                assert len(item_ids[:-1][-max_sequence_length:]) == len(set(item_ids[:-1][-max_sequence_length:]))
-                test_dataset[domain].append({
-                    'user.ids': [user_idx],
-                    'user.length': 1,
-                    'item.ids': item_ids[-max_sequence_length:],
-                    'item.length': len(item_ids[-max_sequence_length:])
-                })
-                assert len(item_ids[-max_sequence_length:]) == len(set(item_ids[-max_sequence_length:]))
-
-            max_user_idx_by_domain[domain] = max_user_idx
-            max_item_idx_by_domain[domain] = max_item_idx
-
-        logger.info('Max user idx: {}'.format(max_user_idx))
-        logger.info('Max item idx: {}'.format(max_item_idx))
-        logger.info('Max sequence length: {}'.format(max_sequence_length))
-        for domain in domains:
-            logger.info('{} domain Train dataset size: {}'.format(domain, len(train_dataset[domain])))
-            logger.info('{} domain Test dataset size: {}'.format(domain, len(test_dataset[domain])))
-            logger.info('{} domain dataset sparsity: {}'.format(
-                domain, (len(train_dataset[domain]) + len(test_dataset[domain])) / max_user_idx_by_domain[domain] / max_item_idx_by_domain[domain]
-            ))
-
-        # TODO replace unodomain samplers with multidomain ones
-        train_sampler = TrainSampler.create_from_config(
-            dict(config['samplers'],
-                 **{'target_domain': target_domain,
-                    'other_domains': other_domains
-            }),
-            dataset=train_dataset,
-            num_users=max_user_idx,
-            num_items=max_item_idx
-        )
-        validation_sampler = EvalSampler.create_from_config(
-            dict(config['samplers'],
-                 **{'target_domain': target_domain,
-                    'other_domains': other_domains
-            }),
-            dataset=validation_dataset,
-            num_users=max_user_idx,
-            num_items=max_item_idx
-        )
-        test_sampler = EvalSampler.create_from_config(
-            dict(config['samplers'],
-                 **{'target_domain': target_domain,
-                    'other_domains': other_domains
-            }),
-            dataset=test_dataset,
-            num_users=max_user_idx,
-            num_items=max_item_idx
-        )
-
-        return cls(
-            train_sampler=train_sampler,
-            validation_sampler=validation_sampler,
-            test_sampler=test_sampler,
-            num_users=max_user_idx,
-            num_items=max_item_idx,
-            max_sequence_length=max_sequence_length,
-            target_domain=target_domain,
-            other_domains=other_domains
-        )

From d35f291f38d5fc965a846f9d0de02cd6cfed2fc3 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 3 Jan 2025 14:36:56 +0300
Subject: [PATCH 030/175] add item<->embedding mapping

---
 notebooks/AmazonBeautyDatasetStatistics.ipynb | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/notebooks/AmazonBeautyDatasetStatistics.ipynb b/notebooks/AmazonBeautyDatasetStatistics.ipynb
index dab1ab5b..e010eecc 100644
--- a/notebooks/AmazonBeautyDatasetStatistics.ipynb
+++ b/notebooks/AmazonBeautyDatasetStatistics.ipynb
@@ -376,6 +376,29 @@
     "        ] + [str(test_sample['next_interaction']['item_id'])]))\n",
     "        f.write('\\n')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "868d5db5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "\n",
+    "deduped_mapping = df.drop_duplicates(subset=['item_id', 'raw_item_id'])\n",
+    "\n",
+    "embs = torch.load('../data/df_with_embs.pt')\n",
+    "\n",
+    "merged = pd.merge(deduped_mapping, embs, 'inner', left_on='raw_item_id', right_on='asin')\n",
+    "merged['item_id'] = merged['item_id'].map(lambda x: item_mapping[x])\n",
+    "    \n",
+    "assert len(merged) == len(merged.item_id.unique())\n",
+    "merged = merged.set_index('item_id')\n",
+    "\n",
+    "torch.save(merged, '../data/Beauty/data_full.pt')"
+   ]
   }
  ],
  "metadata": {

From a097a0eaf5826c97316575f635f6044bdafa32a8 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 4 Jan 2025 14:31:30 +0300
Subject: [PATCH 031/175] broken grid config (no eval / validation callbacks)

---
 configs/train/rqvae_train_grid_config.json | 86 ++++++++++++++++++++++
 1 file changed, 86 insertions(+)
 create mode 100644 configs/train/rqvae_train_grid_config.json

diff --git a/configs/train/rqvae_train_grid_config.json b/configs/train/rqvae_train_grid_config.json
new file mode 100644
index 00000000..e90a6cfa
--- /dev/null
+++ b/configs/train/rqvae_train_grid_config.json
@@ -0,0 +1,86 @@
+{
+  "experiment_name": "rqvae_beauty_grid",
+  "train_epochs_num": 50,
+  "dataset": {
+    "type": "rqvae",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "samplers": {
+      "type": "identity"
+    }
+  },
+  "dataset_params": {
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "embed"
+      },
+      "drop_last": false,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "embed"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "rqvae",
+    "input_dim": 512,
+    "codebook_sizes": [256, 256, 256, 256],
+    "should_init_codebooks": true,
+    "should_reinit_unused_clusters": true,
+    "initializer_range": 0.02
+  },
+  "model_params": {
+    "n_iter": [
+      100,
+      500,
+      2000
+    ],
+    "hidden_dim": [
+      128,
+      512,
+      2048
+    ]
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 1e-4
+    },
+    "clip_grad_threshold": 5.0,
+    "scheduler": {
+      "type": "step",
+      "step_size": 100, 
+      "gamma": 0.96
+    }
+  },
+  "optimizer_params": {
+  },
+  "loss": {
+    "type": "rqvae_loss",
+    "beta": 0.25,
+    "output_prefix": "loss"
+  },
+  "loss_params": {
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      }
+    ]
+  }
+}

From 93064c66b43d62e30f96a1a1306c98aeb0c4e531 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 4 Jan 2025 18:45:27 +0300
Subject: [PATCH 032/175] tiger_new model

---
 configs/train/tiger_train_config.json |  11 +-
 modeling/models/__init__.py           |   3 +-
 modeling/models/tiger_new.py          | 221 ++++++++++++++++++++++++++
 review.md                             |   3 +-
 4 files changed, 231 insertions(+), 7 deletions(-)
 create mode 100644 modeling/models/tiger_new.py

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index ae27de61..e6a9e1d0 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -39,18 +39,19 @@
     }
   },
   "model": {
-    "type": "tiger",
+    "type": "tiger_new",
     "trie": "../data/Beauty/rqvae/trie.pkl",
     "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
     "positive_prefix": "positive",
     "labels_prefix": "labels",
-    "embedding_dim": 64,
-    "num_heads": 2,
-    "num_layers": 2,
+    "embedding_dim": 128,
+    "num_heads": 4,
+    "num_encoder_layers": 6,
+    "num_decoder_layers": 6,
     "dim_feedforward": 256,
-    "dropout": 0.3,
+    "dropout": 0.2,
     "activation": "gelu",
     "layer_norm_eps": 1e-9,
     "initializer_range": 0.02
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index a169d41e..60ba64b6 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -16,4 +16,5 @@
 from .sasrec_ce import SasRecCeModel
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
-from .tiger import TigerModel
+# from .tiger import TigerModel
+from .tiger_new import TigerModel
diff --git a/modeling/models/tiger_new.py b/modeling/models/tiger_new.py
new file mode 100644
index 00000000..1f7d19b8
--- /dev/null
+++ b/modeling/models/tiger_new.py
@@ -0,0 +1,221 @@
+import json
+from turtle import pos
+from utils import DEVICE, create_masked_tensor, get_activation_function
+from models.base import SequentialTorchModel
+import pickle
+import torch
+from torch import nn
+
+
+class TigerModel(SequentialTorchModel, config_name="tiger_new"):
+
+    def __init__(
+        self,
+        trie,
+        sequence_prefix,
+        pred_prefix,
+        positive_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_encoder_layers,
+        num_decoder_layers,
+        dim_feedforward,
+        semantic_id_arr,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_encoder_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True,
+        )
+
+        self._trie = trie
+
+        self._output_projection = nn.Linear(embedding_dim, semantic_id_arr[0])
+
+        self._sequence_prefix = sequence_prefix
+        self._pred_prefix = pred_prefix
+        self._positive_prefix = positive_prefix
+        self._labels_prefix = labels_prefix
+
+        self._semantic_id_arr = semantic_id_arr
+
+        self._level_embeddings = nn.ModuleList(
+            [
+                nn.Embedding(num_items + 2, embedding_dim)
+                for _ in range(len(semantic_id_arr))
+            ]
+        )
+
+        self._positional_embeddings = nn.Embedding(max_sequence_length, embedding_dim)
+
+        self._codebook_embeddings = nn.Embedding(
+            num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
+        )
+
+        self.transformer = nn.Transformer(
+            d_model=embedding_dim,
+            nhead=num_heads,
+            num_encoder_layers=num_encoder_layers,
+            num_decoder_layers=num_decoder_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=get_activation_function(activation),
+            layer_norm_eps=layer_norm_eps,
+            batch_first=True,
+        )
+
+        self._init_weights(initializer_range)
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        with open(config["trie"], "rb") as f:
+            trie = pickle.load(f)
+
+        rqvae_config = json.load(open(config["rqvae_train_config_path"]))
+        semantic_id_arr = rqvae_config["model"]["codebook_sizes"]
+        assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
+
+        return cls(
+            trie=trie,
+            sequence_prefix=config["sequence_prefix"],
+            pred_prefix=config["predictions_prefix"],
+            positive_prefix=config["positive_prefix"],
+            labels_prefix=config["labels_prefix"],
+            num_items=semantic_id_arr[0],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_encoder_layers=config["num_encoder_layers"],
+            num_decoder_layers=config["num_decoder_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            semantic_id_arr=semantic_id_arr,
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
+        )
+
+    def get_logits(self, inputs, prefix, flattened_events, lengths):
+        src_embeddings, src_mask = self.get_embeddings(
+            flattened_events, lengths
+        )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
+
+        tgt_flattened_events = inputs[
+            "semantic.{}.ids".format(prefix)
+        ]  # (all_batch_events)
+        tgt_lengths = inputs["semantic.{}.length".format(prefix)]  # (batch_size)
+
+        tgt_embeddings, tgt_mask = self.get_embeddings(
+            tgt_flattened_events, tgt_lengths
+        )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
+
+        transformer_output = self.transformer(
+            src_embeddings,
+            tgt_embeddings,
+            src_key_padding_mask=~src_mask,
+            tgt_key_padding_mask=~tgt_mask,
+        ) # (batch_size, seq_len, embedding_dim)
+
+        logits = self._output_projection(transformer_output)
+
+        return logits, tgt_mask
+
+    def forward(self, inputs):
+        all_sample_events = inputs[
+            "semantic.{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "semantic.{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+
+        if self.training:
+            logits, tgt_mask = self.get_logits(
+                inputs, self._positive_prefix, all_sample_events, all_sample_lengths
+            ) # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
+
+            logits = logits[tgt_mask]
+
+            return {self._pred_prefix: logits}
+        else:
+            logits, _ = self.get_logits(
+                inputs, self._labels_prefix, all_sample_events, all_sample_lengths
+            ) # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
+            preds = logits.argmax(dim=-1)
+            ids = torch.tensor(self._apply_trie(preds))
+            return ids
+
+    def _apply_trie(self, preds):  # TODOPK make this faster (how?)
+        native_repr = [tuple(row.tolist()) for row in preds]
+        ids = []
+        for semantic_id in native_repr:
+            cur_result = set()
+            prefixes = [semantic_id[:i] for i in range(len(semantic_id), 0, -1)]
+            for prefix in prefixes:
+                prefix_ids = self._trie.search_prefix(
+                    prefix
+                )  # todo handle collisions (not overwrite)
+                for id in prefix_ids:
+                    cur_result.add(id)
+                    if len(cur_result) >= 20:
+                        break
+                if len(cur_result) >= 20:
+                    break
+
+            cur_result = list(cur_result)
+            while len(cur_result) < 20:
+                cur_result.append(0)  # solve empty event if shortest prefix
+            ids.append(cur_result)
+        return ids
+
+    def get_embeddings(self, flattened_events, lengths):
+        num_levels = len(self._semantic_id_arr)
+
+        # Heirarchical embeddings
+        level_indices = torch.arange(len(flattened_events), device=DEVICE) % num_levels
+        item_embeddings = torch.zeros(
+            (len(flattened_events), self._embedding_dim), device=DEVICE
+        )
+        for level in range(num_levels):
+            level_mask = level_indices == level
+            item_embeddings[level_mask] = self._level_embeddings[level](
+                flattened_events[level_mask]
+            )
+
+        item_embeddings, mask = create_masked_tensor(
+            data=item_embeddings, lengths=lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        batch_size = mask.shape[0]
+        seq_len = mask.shape[1]
+
+        # Positional embeddings
+        positions = torch.arange(seq_len, device=DEVICE).repeat(batch_size, 1)
+        positions = positions.masked_fill(~mask, 0)  # (batch_size, max_len)
+        pos_embeds = self._positional_embeddings(
+            positions // num_levels
+        )  # (batch_size, seq_len, embedding_dim)
+
+        # Codebook embeddings
+        codebook_indices = (
+            torch.arange(seq_len, device=DEVICE).repeat(batch_size, 1) % num_levels
+        )
+        codebook_indices = codebook_indices.masked_fill(
+            ~mask, 0
+        )  # (batch_size, seq_len)
+        hierarchy_embeds = self._codebook_embeddings(
+            codebook_indices
+        )  # (batch_size, seq_len, embedding_dim)
+
+        return item_embeddings + pos_embeds + hierarchy_embeds, mask
diff --git a/review.md b/review.md
index 12af8109..09079c57 100644
--- a/review.md
+++ b/review.md
@@ -2,7 +2,8 @@
 
 ## Todos
 
-- restore my amazon beauty changes (`data_full.pt`)
+- check enc / dec model
+- correct logits indexing with tgt_mask?
 - max_sequence_length (TODOPK), why +1? не смог найти где дописывается в батч сама длина
 - positions = positions // self._semantic_id_length или reverse?
 как именно учитываем codebook_post & item_pos (тот же порядок или inverted)

From eb88dfa36f5a376e6ad959a20afb33a8b70454e6 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 4 Jan 2025 21:58:05 +0300
Subject: [PATCH 033/175] fix tiger (separate layers for decoder)

---
 modeling/models/base.py      |  35 ++--
 modeling/models/tiger.py     | 333 +++++++++++++++++++----------------
 modeling/models/tiger_new.py | 221 -----------------------
 review.md                    |   7 +-
 4 files changed, 201 insertions(+), 395 deletions(-)
 delete mode 100644 modeling/models/tiger_new.py

diff --git a/modeling/models/base.py b/modeling/models/base.py
index ccd02ce7..6405d960 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -4,6 +4,7 @@
 
 import torch
 import torch.nn as nn
+from utils import DEVICE, MetaParent, create_masked_tensor, get_activation_function
 
 
 class BaseModel(metaclass=MetaParent):
@@ -80,7 +81,7 @@ def __init__(
             embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value # TODOPK
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
             embedding_dim=embedding_dim
         )
 
@@ -98,7 +99,7 @@ def __init__(
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
 
-    def _apply_sequential_encoder(self, events, lengths, add_cls_token=False, add_codebook_embeddings=False):
+    def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
         embeddings = self._item_embeddings(events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
@@ -108,17 +109,10 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False, add_co
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
-        
-        position_embeddings = self._get_position_embeddings(
-            embeddings, lengths, mask, batch_size, seq_len
-        ) # (batch_size, seq_len, embedding_dim)
-        
-        if add_codebook_embeddings:
-            codebook_embeddings = self._get_codebook_embeddings(
-                embeddings, lengths, mask, batch_size, seq_len
-            ) # (batch_size, seq_len, embedding_dim)
-            embeddings = embeddings + codebook_embeddings
-        
+
+        position_embeddings = self._encoder_pos_embeddings(lengths, mask)
+        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
+
         embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -146,15 +140,14 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False, add_co
             )  # (batch_size, seq_len, embedding_dim)
 
         return embeddings, mask
-    
-    def _get_codebook_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
-        raise NotImplementedError
 
-    def _get_position_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
-        positions = torch.arange( # TODOPK invert decoder (position.reverse)
+    def _encoder_pos_embeddings(self, lengths, mask):
+        batch_size = mask.shape[0]
+        seq_len = mask.shape[1]
+
+        positions = torch.arange(
             start=seq_len - 1, end=-1, step=-1, device=mask.device
         )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
-        
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
@@ -163,10 +156,8 @@ def _get_position_embeddings(self, embeddings, lengths, mask, batch_size, seq_le
             data=position_embeddings,
             lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
-        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
-        
         return position_embeddings
-    
+
     @staticmethod
     def _add_cls_token(items, lengths, cls_token_id=0):
         num_items = items.shape[0]
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 9ab87e14..3fb4ecb2 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,31 +1,32 @@
 import json
-from utils import DEVICE, create_masked_tensor, get_activation_function
-from models.base import SequentialTorchModel
 import pickle
+
 import torch
+from models.base import SequentialTorchModel
 from torch import nn
+from utils import DEVICE, create_masked_tensor, get_activation_function
 
 
-class TigerModel(SequentialTorchModel, config_name='tiger'):
+class TigerModel(SequentialTorchModel, config_name="tiger"):
 
     def __init__(
-            self,
-            trie,
-            sequence_prefix,
-            pred_prefix,
-            positive_prefix,
-            labels_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            semantic_id_arr,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        trie,
+        sequence_prefix,
+        pred_prefix,
+        positive_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        semantic_id_arr,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -37,9 +38,17 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
-        
+        self._decoder_position_embeddings = nn.Embedding(
+            num_embeddings=max_sequence_length
+            + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim,
+        )
+        self._decoder_codebook_embeddings = nn.Embedding(
+            num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
+        )
+
         transformer_decoder_layer = nn.TransformerDecoderLayer(
             d_model=embedding_dim,
             nhead=num_heads,
@@ -47,193 +56,219 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True
+            batch_first=True,
         )
         self._decoder = nn.TransformerDecoder(transformer_decoder_layer, num_layers)
         self._trie = trie
-        
+
         self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
-        
+
         self._sequence_prefix = sequence_prefix
         self._pred_prefix = pred_prefix
         self._positive_prefix = positive_prefix
         self._labels_prefix = labels_prefix
-        
+
         self._semantic_id_arr = semantic_id_arr
-        
+
         self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(semantic_id_arr),  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim
+            num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
         )
 
+        self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
+        self._decoder_dropout = nn.Dropout(dropout)
+
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        with open(config['trie'], 'rb') as f:
+        with open(config["trie"], "rb") as f:
             trie = pickle.load(f)
-            
-        rqvae_config = json.load(open(config['rqvae_train_config_path']))
-        semantic_id_arr = rqvae_config['model']['codebook_sizes']
+
+        rqvae_config = json.load(open(config["rqvae_train_config_path"]))
+        semantic_id_arr = rqvae_config["model"]["codebook_sizes"]
         assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
-    
+
         return cls(
             trie=trie,
-            sequence_prefix=config['sequence_prefix'],
-            pred_prefix=config['predictions_prefix'],
-            positive_prefix=config['positive_prefix'],
-            labels_prefix=config['labels_prefix'],
+            sequence_prefix=config["sequence_prefix"],
+            pred_prefix=config["predictions_prefix"],
+            positive_prefix=config["positive_prefix"],
+            labels_prefix=config["labels_prefix"],
             num_items=semantic_id_arr[0],
-            max_sequence_length=kwargs['max_sequence_length'] * len(semantic_id_arr),
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
             semantic_id_arr=semantic_id_arr,
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
-        
+
     def get_logits(self, inputs, prefix, all_sample_events, all_sample_lengths):
-        # TODOPK pass parameter as args (self._semantic_prefix)
-        
-        label_events = inputs['semantic.{}.ids'.format(prefix)]
-        label_lengths = inputs['semantic.{}.length'.format(prefix)]
-    
-        embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths, add_codebook_embeddings=True
-        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        
+        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
+
+        label_events = inputs["semantic.{}.ids".format(prefix)]
+        label_lengths = inputs["semantic.{}.length".format(prefix)]
+
         decoder_outputs = self._apply_decoder(
-            label_events, label_lengths, embeddings, mask
-        ) # (batch_size, label_len, embedding_dim)
-        # todopk correct place for projection? or view -> projection
-        logits = self._projection(decoder_outputs)  # (batch_size, seq_len, _semantic_id_arr[0])
-        
-        return logits, mask
+            label_events, label_lengths, encoder_embeddings, encoder_mask
+        )  # (batch_size, label_len, embedding_dim)
+
+        # TODOPK correct place for projection? or view -> projection
+        logits = self._projection(
+            decoder_outputs
+        )  # (batch_size, dec_seq_len, _semantic_id_arr[0])
+
+        return logits
 
     def forward(self, inputs):
-        all_sample_events = inputs['semantic.{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['semantic.{}.length'.format(self._sequence_prefix)]  # (batch_size)
-        
+        all_sample_events = inputs[
+            "semantic.{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "semantic.{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+
         if self.training:
-            logits, mask = self.get_logits(inputs, self._positive_prefix, all_sample_events, all_sample_lengths)
-            
-            logits = logits[mask] # TODOPK correct?
-            
-            return {
-                self._pred_prefix: logits
-            }
+            logits = self.get_logits(
+                inputs, self._positive_prefix, all_sample_events, all_sample_lengths
+            )
+
+            return {self._pred_prefix: logits}
         else:
-            logits, mask = self.get_logits(inputs, self._labels_prefix, all_sample_events, all_sample_lengths)
-            
-            preds = logits.argmax(dim=-1).view(len(all_sample_lengths), len(self._semantic_id_arr)) # Shape: (batch_size, seq_len)
+            logits = self.get_logits(
+                inputs, self._labels_prefix, all_sample_events, all_sample_lengths
+            )
+
+            preds = logits.argmax(dim=-1)  # (batch_size, dec_seq_len)
             ids = torch.tensor(self._apply_trie(preds))
             return ids
-    
-    def _apply_trie(self, preds): # TODOPK make this faster (how?)
+
+    def _apply_trie(self, preds):  # TODOPK make this faster (how?)
         native_repr = [tuple(row.tolist()) for row in preds]
         ids = []
         for semantic_id in native_repr:
             cur_result = set()
-            prefixes = [
-                semantic_id[:i] for i in range(len(semantic_id), 0, -1)
-            ]
+            prefixes = [semantic_id[:i] for i in range(len(semantic_id), 0, -1)]
             for prefix in prefixes:
-                prefix_ids = self._trie.search_prefix(prefix) # todo handle collisions (not overwrite)
+                prefix_ids = self._trie.search_prefix(
+                    prefix
+                )  # todo handle collisions (not overwrite)
                 for id in prefix_ids:
                     cur_result.add(id)
                     if len(cur_result) >= 20:
                         break
                 if len(cur_result) >= 20:
                     break
-            
+
             cur_result = list(cur_result)
             while len(cur_result) < 20:
-                cur_result.append(0) # solve empty event if shortest prefix
+                cur_result.append(0)  # solve empty event if shortest prefix
             ids.append(cur_result)
         return ids
-        
-    def _apply_decoder(self, label_events, label_lengths, encoder_embeddings, encoder_mask):
-        # делаем по аналогии с encoder'ом?
-        embeddings = self._item_embeddings(label_events)  # (batch_size * label_len, embedding_dim)
-
-        embeddings, mask = create_masked_tensor(
-            data=embeddings,
-            lengths=label_lengths
+
+    def _apply_decoder(
+        self, label_events, label_lengths, encoder_embeddings, encoder_mask
+    ):
+        tgt_embeddings = self._item_embeddings(
+            label_events
+        )  # (all_batch_events, embedding_dim)
+        # TODOPK share same embeddings with encoder
+
+        tgt_embeddings, tgt_mask = create_masked_tensor(
+            data=tgt_embeddings, lengths=label_lengths
         )  # (batch_size, label_len, embedding_dim), (batch_size, label_len)
-        
-        batch_size = mask.shape[0]
-        label_len = mask.shape[1]
-        
-        position_embeddings = self._get_position_embeddings(
-            embeddings, label_lengths, mask, batch_size, label_len
-        ) # (batch_size, label_len, embedding_dim)
-        codebook_embeddings = self._get_codebook_embeddings(
-            embeddings, label_lengths, mask, batch_size, label_len
-        ) # (batch_size, label_len, embedding_dim)
-       
-        embeddings = embeddings + codebook_embeddings
-        embeddings = embeddings + position_embeddings
-        
-        embeddings = self._layernorm(embeddings)
-        embeddings = self._dropout(embeddings)
 
-        embeddings[~mask] = 0
+        label_len = tgt_mask.shape[1]
+
+        assert label_len == len(self._semantic_id_arr)
+
+        position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
+        assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
 
-        causal_mask = torch.tril(
-            torch.ones(label_len, label_len)
-        ).bool().to(DEVICE)  # (label_len, label_len)
+        tgt_embeddings = tgt_embeddings + position_embeddings
+
+        tgt_embeddings = self._decoder_layernorm(
+            tgt_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
+        tgt_embeddings = self._decoder_dropout(
+            tgt_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
+
+        tgt_embeddings[~tgt_mask] = 0
+
+        causal_mask = (
+            torch.tril(torch.ones(label_len, label_len)).bool().to(DEVICE)
+        )  # (seq_len, seq_len)
+        # TODOPK -inf?
 
         decoder_outputs = self._decoder(
-            tgt=embeddings,
+            tgt=tgt_embeddings,
             memory=encoder_embeddings,
             tgt_mask=~causal_mask,
             memory_key_padding_mask=~encoder_mask,
-            tgt_key_padding_mask=~mask
+            tgt_key_padding_mask=~tgt_mask,
         )  # (batch_size, label_len, embedding_dim)
-        
+
         return decoder_outputs
-        
-    def _get_position_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
-        positions = torch.arange( # TODOPK invert decoder (position.reverse)
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
-        
-        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
-        positions = positions[positions_mask]  # (all_batch_events)
-        
-        positions = positions // len(self._semantic_id_arr)
-        # 5 5 5 5 4 4 4 4 3 3 3 3 ...
-        
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
-        position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=lengths
-        )  # (batch_size, seq_len, embedding_dim)
-        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
-        
-        return position_embeddings
-        
-    def _get_codebook_embeddings(self, embeddings, lengths, mask, batch_size, seq_len):
-        positions = torch.arange( # TODOPK invert decoder (position.reverse)
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
-        
+    def _decoder_pos_embeddings(self, lengths, mask):
+        def position_lambda(x):  # TODOPK share layers with encoder & fix
+            return x // len(self._semantic_id_arr)  # 5 5 5 4 4 4 3 3 3 ...
+
+        position_embeddings = self._get_position_embeddings(
+            lengths, mask, position_lambda, self._decoder_position_embeddings
+        )
+
+        def codebook_lambda(x):
+            return x % len(self._semantic_id_arr)  # 2 1 0 2 1 0 ...
+
+        codebook_embeddings = self._get_codebook_embeddings(
+            lengths, mask, codebook_lambda, self._decoder_codebook_embeddings
+        )
+
+        return position_embeddings + codebook_embeddings
+
+    def _encoder_pos_embeddings(self, lengths, mask):
+        def position_lambda(x):
+            return x // len(self._semantic_id_arr)  # 5 5 5 4 4 4 3 3 3 ...
+
+        position_embeddings = self._get_position_embeddings(
+            lengths, mask, position_lambda, self._position_embeddings
+        )
+
+        def codebook_lambda(x):
+            return x % len(self._semantic_id_arr)  # 2 1 0 2 1 0 ...
+
+        codebook_embeddings = self._get_codebook_embeddings(
+            lengths, mask, codebook_lambda, self._codebook_embeddings
+        )
+
+        return position_embeddings + codebook_embeddings
+
+    def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
+        batch_size = mask.shape[0]
+        seq_len = mask.shape[1]
+
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        
-        positions = positions.flip(-1) % len(self._semantic_id_arr) # (all_batch_events)
-        # flip so first item has (0, 1, 2, 3) not (3, 2, 1, 0) semantic_id embeddings
-        # 0 1 2 3 0 1 2 3 0 1 2 3
-        
-        position_embeddings = self._codebook_embeddings(positions)  # (all_batch_events, embedding_dim)
+
+        positions = position_lambda(positions)  # (all_batch_events)
+
+        position_embeddings = embedding_layer(
+            positions
+        )  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=lengths
+            data=position_embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
-        assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
-        
+
         return position_embeddings
diff --git a/modeling/models/tiger_new.py b/modeling/models/tiger_new.py
deleted file mode 100644
index 1f7d19b8..00000000
--- a/modeling/models/tiger_new.py
+++ /dev/null
@@ -1,221 +0,0 @@
-import json
-from turtle import pos
-from utils import DEVICE, create_masked_tensor, get_activation_function
-from models.base import SequentialTorchModel
-import pickle
-import torch
-from torch import nn
-
-
-class TigerModel(SequentialTorchModel, config_name="tiger_new"):
-
-    def __init__(
-        self,
-        trie,
-        sequence_prefix,
-        pred_prefix,
-        positive_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_encoder_layers,
-        num_decoder_layers,
-        dim_feedforward,
-        semantic_id_arr,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-    ):
-        super().__init__(
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-            embedding_dim=embedding_dim,
-            num_heads=num_heads,
-            num_layers=num_encoder_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=activation,
-            layer_norm_eps=layer_norm_eps,
-            is_causal=True,
-        )
-
-        self._trie = trie
-
-        self._output_projection = nn.Linear(embedding_dim, semantic_id_arr[0])
-
-        self._sequence_prefix = sequence_prefix
-        self._pred_prefix = pred_prefix
-        self._positive_prefix = positive_prefix
-        self._labels_prefix = labels_prefix
-
-        self._semantic_id_arr = semantic_id_arr
-
-        self._level_embeddings = nn.ModuleList(
-            [
-                nn.Embedding(num_items + 2, embedding_dim)
-                for _ in range(len(semantic_id_arr))
-            ]
-        )
-
-        self._positional_embeddings = nn.Embedding(max_sequence_length, embedding_dim)
-
-        self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
-        )
-
-        self.transformer = nn.Transformer(
-            d_model=embedding_dim,
-            nhead=num_heads,
-            num_encoder_layers=num_encoder_layers,
-            num_decoder_layers=num_decoder_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=get_activation_function(activation),
-            layer_norm_eps=layer_norm_eps,
-            batch_first=True,
-        )
-
-        self._init_weights(initializer_range)
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        with open(config["trie"], "rb") as f:
-            trie = pickle.load(f)
-
-        rqvae_config = json.load(open(config["rqvae_train_config_path"]))
-        semantic_id_arr = rqvae_config["model"]["codebook_sizes"]
-        assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
-
-        return cls(
-            trie=trie,
-            sequence_prefix=config["sequence_prefix"],
-            pred_prefix=config["predictions_prefix"],
-            positive_prefix=config["positive_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=semantic_id_arr[0],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_encoder_layers=config["num_encoder_layers"],
-            num_decoder_layers=config["num_decoder_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            semantic_id_arr=semantic_id_arr,
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-        )
-
-    def get_logits(self, inputs, prefix, flattened_events, lengths):
-        src_embeddings, src_mask = self.get_embeddings(
-            flattened_events, lengths
-        )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
-
-        tgt_flattened_events = inputs[
-            "semantic.{}.ids".format(prefix)
-        ]  # (all_batch_events)
-        tgt_lengths = inputs["semantic.{}.length".format(prefix)]  # (batch_size)
-
-        tgt_embeddings, tgt_mask = self.get_embeddings(
-            tgt_flattened_events, tgt_lengths
-        )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
-
-        transformer_output = self.transformer(
-            src_embeddings,
-            tgt_embeddings,
-            src_key_padding_mask=~src_mask,
-            tgt_key_padding_mask=~tgt_mask,
-        ) # (batch_size, seq_len, embedding_dim)
-
-        logits = self._output_projection(transformer_output)
-
-        return logits, tgt_mask
-
-    def forward(self, inputs):
-        all_sample_events = inputs[
-            "semantic.{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "semantic.{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-
-        if self.training:
-            logits, tgt_mask = self.get_logits(
-                inputs, self._positive_prefix, all_sample_events, all_sample_lengths
-            ) # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
-
-            logits = logits[tgt_mask]
-
-            return {self._pred_prefix: logits}
-        else:
-            logits, _ = self.get_logits(
-                inputs, self._labels_prefix, all_sample_events, all_sample_lengths
-            ) # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
-            preds = logits.argmax(dim=-1)
-            ids = torch.tensor(self._apply_trie(preds))
-            return ids
-
-    def _apply_trie(self, preds):  # TODOPK make this faster (how?)
-        native_repr = [tuple(row.tolist()) for row in preds]
-        ids = []
-        for semantic_id in native_repr:
-            cur_result = set()
-            prefixes = [semantic_id[:i] for i in range(len(semantic_id), 0, -1)]
-            for prefix in prefixes:
-                prefix_ids = self._trie.search_prefix(
-                    prefix
-                )  # todo handle collisions (not overwrite)
-                for id in prefix_ids:
-                    cur_result.add(id)
-                    if len(cur_result) >= 20:
-                        break
-                if len(cur_result) >= 20:
-                    break
-
-            cur_result = list(cur_result)
-            while len(cur_result) < 20:
-                cur_result.append(0)  # solve empty event if shortest prefix
-            ids.append(cur_result)
-        return ids
-
-    def get_embeddings(self, flattened_events, lengths):
-        num_levels = len(self._semantic_id_arr)
-
-        # Heirarchical embeddings
-        level_indices = torch.arange(len(flattened_events), device=DEVICE) % num_levels
-        item_embeddings = torch.zeros(
-            (len(flattened_events), self._embedding_dim), device=DEVICE
-        )
-        for level in range(num_levels):
-            level_mask = level_indices == level
-            item_embeddings[level_mask] = self._level_embeddings[level](
-                flattened_events[level_mask]
-            )
-
-        item_embeddings, mask = create_masked_tensor(
-            data=item_embeddings, lengths=lengths
-        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-
-        batch_size = mask.shape[0]
-        seq_len = mask.shape[1]
-
-        # Positional embeddings
-        positions = torch.arange(seq_len, device=DEVICE).repeat(batch_size, 1)
-        positions = positions.masked_fill(~mask, 0)  # (batch_size, max_len)
-        pos_embeds = self._positional_embeddings(
-            positions // num_levels
-        )  # (batch_size, seq_len, embedding_dim)
-
-        # Codebook embeddings
-        codebook_indices = (
-            torch.arange(seq_len, device=DEVICE).repeat(batch_size, 1) % num_levels
-        )
-        codebook_indices = codebook_indices.masked_fill(
-            ~mask, 0
-        )  # (batch_size, seq_len)
-        hierarchy_embeds = self._codebook_embeddings(
-            codebook_indices
-        )  # (batch_size, seq_len, embedding_dim)
-
-        return item_embeddings + pos_embeds + hierarchy_embeds, mask
diff --git a/review.md b/review.md
index 09079c57..c1c6c755 100644
--- a/review.md
+++ b/review.md
@@ -2,9 +2,10 @@
 
 ## Todos
 
-- check enc / dec model
-- correct logits indexing with tgt_mask?
-- max_sequence_length (TODOPK), why +1? не смог найти где дописывается в батч сама длина
+- level embeddings
+- fix dataset (take last max_seq items)
+- single sample from single user (honest comparison)
+- correct logits indexing with tgt_mask? (upper remark fixes)
 - positions = positions // self._semantic_id_length или reverse?
 как именно учитываем codebook_post & item_pos (тот же порядок или inverted)
 - как именно находим ближайшего при пересечении по embedding? (не понял о каком embedding речь)

From b45bf39ee8d298b29293cc249e83c1b7ebe5f774 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 4 Jan 2025 23:27:01 +0300
Subject: [PATCH 034/175] add bos to tiger

---
 configs/train/tiger_train_config.json | 12 ++++-----
 modeling/models/__init__.py           |  3 +--
 modeling/models/tiger.py              | 39 +++++++++++++++++++++------
 review.md                             | 37 ++++++++++++-------------
 4 files changed, 55 insertions(+), 36 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index e6a9e1d0..4a172ff7 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -39,18 +39,18 @@
     }
   },
   "model": {
-    "type": "tiger_new",
+    "type": "tiger",
     "trie": "../data/Beauty/rqvae/trie.pkl",
     "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
-    "positive_prefix": "positive",
+    "positive_prefix": "labels",
     "labels_prefix": "labels",
-    "embedding_dim": 128,
+    "embedding_dim": 64,
     "num_heads": 4,
     "num_encoder_layers": 6,
     "num_decoder_layers": 6,
-    "dim_feedforward": 256,
+    "dim_feedforward": 128,
     "dropout": 0.2,
     "activation": "gelu",
     "layer_norm_eps": 1e-9,
@@ -70,7 +70,7 @@
       {
         "type": "ce",
         "predictions_prefix": "logits",
-        "labels_prefix": "semantic.positive",
+        "labels_prefix": "semantic.labels",
         "output_prefix": "downstream_loss",
         "weight": 1.0
       }
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index 60ba64b6..a169d41e 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -16,5 +16,4 @@
 from .sasrec_ce import SasRecCeModel
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
-# from .tiger import TigerModel
-from .tiger_new import TigerModel
+from .tiger import TigerModel
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 3fb4ecb2..bf5b1a32 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -20,7 +20,8 @@ def __init__(
         max_sequence_length,
         embedding_dim,
         num_heads,
-        num_layers,
+        num_encoder_layers,
+        num_decoder_layers,
         dim_feedforward,
         semantic_id_arr,
         dropout=0.0,
@@ -33,7 +34,7 @@ def __init__(
             max_sequence_length=max_sequence_length,
             embedding_dim=embedding_dim,
             num_heads=num_heads,
-            num_layers=num_layers,
+            num_layers=num_encoder_layers,
             dim_feedforward=dim_feedforward,
             dropout=dropout,
             activation=activation,
@@ -58,7 +59,9 @@ def __init__(
             layer_norm_eps=layer_norm_eps,
             batch_first=True,
         )
-        self._decoder = nn.TransformerDecoder(transformer_decoder_layer, num_layers)
+        self._decoder = nn.TransformerDecoder(
+            transformer_decoder_layer, num_decoder_layers
+        )
         self._trie = trie
 
         self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
@@ -70,6 +73,8 @@ def __init__(
 
         self._semantic_id_arr = semantic_id_arr
 
+        self._bos_embedding = nn.Embedding(1, embedding_dim)
+
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
         )
@@ -98,7 +103,8 @@ def create_from_config(cls, config, **kwargs):
             max_sequence_length=kwargs["max_sequence_length"],
             embedding_dim=config["embedding_dim"],
             num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
+            num_encoder_layers=config["num_encoder_layers"],
+            num_decoder_layers=config["num_decoder_layers"],
             dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
             semantic_id_arr=semantic_id_arr,
             dropout=config.get("dropout", 0.0),
@@ -135,7 +141,10 @@ def forward(self, inputs):
         if self.training:
             logits = self.get_logits(
                 inputs, self._positive_prefix, all_sample_events, all_sample_lengths
-            )
+            ) # (batch_size, dec_seq_len, _semantic_id_arr[0])
+
+            logits = logits.view(-1, self._semantic_id_arr[0])
+            # TODOPK check if correct flattening
 
             return {self._pred_prefix: logits}
         else:
@@ -173,6 +182,16 @@ def _apply_trie(self, preds):  # TODOPK make this faster (how?)
     def _apply_decoder(
         self, label_events, label_lengths, encoder_embeddings, encoder_mask
     ):
+        matrix_label_events = label_events.view(-1, label_lengths[0])
+        matrix_label_events = torch.cat(
+            [torch.full((len(label_lengths), 1), 256), matrix_label_events], dim=1
+        )
+        # TODOPK 256 hardcoded
+
+        label_events = matrix_label_events.view(-1)
+        
+        label_lengths = label_lengths + 1
+
         tgt_embeddings = self._item_embeddings(
             label_events
         )  # (all_batch_events, embedding_dim)
@@ -184,7 +203,7 @@ def _apply_decoder(
 
         label_len = tgt_mask.shape[1]
 
-        assert label_len == len(self._semantic_id_arr)
+        assert label_len == len(self._semantic_id_arr) + 1 # TODOPK
 
         position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
         assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
@@ -212,6 +231,8 @@ def _apply_decoder(
             memory_key_padding_mask=~encoder_mask,
             tgt_key_padding_mask=~tgt_mask,
         )  # (batch_size, label_len, embedding_dim)
+        
+        decoder_outputs = decoder_outputs[:, 1:, :]  # TODOPK remove bos token
 
         return decoder_outputs
 
@@ -226,10 +247,12 @@ def position_lambda(x):  # TODOPK share layers with encoder & fix
         def codebook_lambda(x):
             return x % len(self._semantic_id_arr)  # 2 1 0 2 1 0 ...
 
-        codebook_embeddings = self._get_codebook_embeddings(
+        codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._decoder_codebook_embeddings
         )
 
+        # TODOPK fix codebook indexing
+
         return position_embeddings + codebook_embeddings
 
     def _encoder_pos_embeddings(self, lengths, mask):
@@ -243,7 +266,7 @@ def position_lambda(x):
         def codebook_lambda(x):
             return x % len(self._semantic_id_arr)  # 2 1 0 2 1 0 ...
 
-        codebook_embeddings = self._get_codebook_embeddings(
+        codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings
         )
 
diff --git a/review.md b/review.md
index c1c6c755..0bda9419 100644
--- a/review.md
+++ b/review.md
@@ -2,29 +2,11 @@
 
 ## Todos
 
+- sos / bos embedding correct train fix
 - level embeddings
-- fix dataset (take last max_seq items)
-- single sample from single user (honest comparison)
-- correct logits indexing with tgt_mask? (upper remark fixes)
 - positions = positions // self._semantic_id_length или reverse?
 как именно учитываем codebook_post & item_pos (тот же порядок или inverted)
 - как именно находим ближайшего при пересечении по embedding? (не понял о каком embedding речь)
-- почему
-
-```python
-candidate_scores = torch.einsum(
-    'bd,nd->bn',
-    predictions,
-    self._item_embeddings.weight
-)
-```
-
-- next_item_pred / last_item_pred (какие задачи учим и как именно) # can be both tasks
-- предсказываем item = предсказываем 4 semantic id? # yes
-- как составить датасет для обучения # (map item seq -> semantic id seq)
-- берем правдивые semantic id # yes
-
-- у нас авторегрессионный next item prediction? # no (teacher learning)
 
 то есть:
 
@@ -40,12 +22,27 @@ target  -> (b_size x 4) [(1, 2, 3, 4); (29, 6, 7, 4); ...]
 decoder: (bos, 1, 2, 3) -> (1, 2, 3, 4) # causal mask so (bos -> 1), (bos, 1 -> 2), ...
            \___ learnable embed
 
+## Fixed
+
+- next_item_pred / last_item_pred (какие задачи учим и как именно) # can be both tasks
+- предсказываем item = предсказываем 4 semantic id? # yes
+- как составить датасет для обучения # (map item seq -> semantic id seq)
+- берем правдивые semantic id # yes
+- у нас авторегрессионный next item prediction? # no (teacher learning)
+
+- fix dataset (take last max_seq items) (last_item fixed)
+- single sample from single user (honest comparison)
+- correct logits indexing with tgt_mask? (upper remark fixes)
+
 - posterior collapse (как будто все сваливается в один индекс в кодбуке) (fixed eval code)
-- обязательно использование reinit unused clusters! (mark)
 - в Amazon датасете пофиг на rating? получается учитываются только implicit действия? # байтовый датасет (любое взаимодействие)
 - TODO какой базовый класс использовать для seq2seq модели? (LastPred?) # use encoder from SequentialTorchModel
 - TODO имя для модели (tiger) # tmp
 
+## Remarks
+
+- обязательно использование reinit unused clusters! (mark)
+
 ## Links
 
 - [dataset](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html)

From 6e3d32e49ea507a37a612b1dd15abc437e1ad231 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 5 Jan 2025 12:10:57 +0300
Subject: [PATCH 035/175] use mps & fix encoder pos embeddings

---
 modeling/dataloader/batch_processors.py |  4 +++-
 modeling/models/tiger.py                | 19 ++++++++++++-------
 modeling/utils/__init__.py              |  8 ++++++--
 3 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 8d51bca9..e71200cd 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -41,7 +41,9 @@ def create_from_config(cls, config, **kwargs):
         embs_extractor = torch.load(config['embs_extractor_path'])
         
         item_ids = embs_extractor.index.tolist()
-        embeddings = torch.stack([emb for emb in embs_extractor['embeddings'].tolist()])
+        embeddings = torch.stack([
+            emb for emb in embs_extractor['embeddings'].tolist()
+        ]).to(DEVICE)
         semantic_ids = list(rqvae_model({"embeddings": embeddings}))
         
         item_id_to_semantic_id = {
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index bf5b1a32..7c6e7ad5 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -141,7 +141,7 @@ def forward(self, inputs):
         if self.training:
             logits = self.get_logits(
                 inputs, self._positive_prefix, all_sample_events, all_sample_lengths
-            ) # (batch_size, dec_seq_len, _semantic_id_arr[0])
+            )  # (batch_size, dec_seq_len, _semantic_id_arr[0])
 
             logits = logits.view(-1, self._semantic_id_arr[0])
             # TODOPK check if correct flattening
@@ -184,12 +184,12 @@ def _apply_decoder(
     ):
         matrix_label_events = label_events.view(-1, label_lengths[0])
         matrix_label_events = torch.cat(
-            [torch.full((len(label_lengths), 1), 256), matrix_label_events], dim=1
+            [torch.full((len(label_lengths), 1), 256).to(DEVICE), matrix_label_events], dim=1
         )
         # TODOPK 256 hardcoded
 
         label_events = matrix_label_events.view(-1)
-        
+
         label_lengths = label_lengths + 1
 
         tgt_embeddings = self._item_embeddings(
@@ -203,7 +203,7 @@ def _apply_decoder(
 
         label_len = tgt_mask.shape[1]
 
-        assert label_len == len(self._semantic_id_arr) + 1 # TODOPK
+        assert label_len == len(self._semantic_id_arr) + 1  # TODOPK
 
         position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
         assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
@@ -231,12 +231,13 @@ def _apply_decoder(
             memory_key_padding_mask=~encoder_mask,
             tgt_key_padding_mask=~tgt_mask,
         )  # (batch_size, label_len, embedding_dim)
-        
+
         decoder_outputs = decoder_outputs[:, 1:, :]  # TODOPK remove bos token
 
         return decoder_outputs
 
     def _decoder_pos_embeddings(self, lengths, mask):
+        # TODOPK fix decoder positional
         def position_lambda(x):  # TODOPK share layers with encoder & fix
             return x // len(self._semantic_id_arr)  # 5 5 5 4 4 4 3 3 3 ...
 
@@ -245,7 +246,9 @@ def position_lambda(x):  # TODOPK share layers with encoder & fix
         )
 
         def codebook_lambda(x):
-            return x % len(self._semantic_id_arr)  # 2 1 0 2 1 0 ...
+            return (len(self._semantic_id_arr) - 1) - x % len(
+                self._semantic_id_arr
+            )  # 0 1 2 0 1 2 ...
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._decoder_codebook_embeddings
@@ -264,7 +267,9 @@ def position_lambda(x):
         )
 
         def codebook_lambda(x):
-            return x % len(self._semantic_id_arr)  # 2 1 0 2 1 0 ...
+            return (len(self._semantic_id_arr) - 1) - x % len(
+                self._semantic_id_arr
+            )  # 0 1 2 0 1 2 ...
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index c366aeb6..5cdc705a 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -10,8 +10,12 @@
 
 import torch
 
-DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-# DEVICE = torch.device('cpu')
+if torch.cuda.is_available():
+    DEVICE = torch.device('cuda')
+elif torch.backends.mps.is_available():
+    DEVICE = torch.device("mps")
+else:
+    DEVICE = torch.device('cpu')
 
 
 def parse_args():

From e40c3212f1580535f1fd0cb5f1453d8d95f3a47b Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 5 Jan 2025 13:08:59 +0300
Subject: [PATCH 036/175] fixed position embeddings

---
 modeling/models/tiger.py | 54 +++++++++++++++++++---------------------
 1 file changed, 25 insertions(+), 29 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 7c6e7ad5..910e4a47 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -42,13 +42,9 @@ def __init__(
             is_causal=True,
         )
         self._decoder_position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
+            num_embeddings=2,  # bos token + label item id (always 0)
             embedding_dim=embedding_dim,
         )
-        self._decoder_codebook_embeddings = nn.Embedding(
-            num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
-        )
 
         transformer_decoder_layer = nn.TransformerDecoderLayer(
             d_model=embedding_dim,
@@ -73,11 +69,11 @@ def __init__(
 
         self._semantic_id_arr = semantic_id_arr
 
-        self._bos_embedding = nn.Embedding(1, embedding_dim)
+        self._bos_token_id = semantic_id_arr[0]
 
         self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(semantic_id_arr), embedding_dim=embedding_dim
-        )
+            num_embeddings=len(semantic_id_arr) + 1, embedding_dim=embedding_dim
+        )  # + 1 for bos token
 
         self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
         self._decoder_dropout = nn.Dropout(dropout)
@@ -179,27 +175,30 @@ def _apply_trie(self, preds):  # TODOPK make this faster (how?)
             ids.append(cur_result)
         return ids
 
+    def _prepend_bos(self, label_events, label_lengths):
+        batch_size = len(label_lengths)
+        label_events = label_events.view(batch_size, -1)
+        bos_tokens = torch.full(
+            (batch_size, 1), self._bos_token_id, device=label_events.device
+        )
+        label_events = torch.cat(
+            [bos_tokens, label_events], dim=1
+        )  # (batch_size, dec_seq_len + 1)
+        label_lengths = label_lengths + 1
+        return label_events.view(-1), label_lengths
+
     def _apply_decoder(
         self, label_events, label_lengths, encoder_embeddings, encoder_mask
     ):
-        matrix_label_events = label_events.view(-1, label_lengths[0])
-        matrix_label_events = torch.cat(
-            [torch.full((len(label_lengths), 1), 256).to(DEVICE), matrix_label_events], dim=1
-        )
-        # TODOPK 256 hardcoded
-
-        label_events = matrix_label_events.view(-1)
-
-        label_lengths = label_lengths + 1
+        label_events, label_lengths = self._prepend_bos(label_events, label_lengths)
 
         tgt_embeddings = self._item_embeddings(
             label_events
         )  # (all_batch_events, embedding_dim)
-        # TODOPK share same embeddings with encoder
 
         tgt_embeddings, tgt_mask = create_masked_tensor(
             data=tgt_embeddings, lengths=label_lengths
-        )  # (batch_size, label_len, embedding_dim), (batch_size, label_len)
+        )  # (batch_size, dec_seq_len + 1, embedding_dim), (batch_size, dec_seq_len + 1)
 
         label_len = tgt_mask.shape[1]
 
@@ -237,25 +236,22 @@ def _apply_decoder(
         return decoder_outputs
 
     def _decoder_pos_embeddings(self, lengths, mask):
-        # TODOPK fix decoder positional
-        def position_lambda(x):  # TODOPK share layers with encoder & fix
-            return x // len(self._semantic_id_arr)  # 5 5 5 4 4 4 3 3 3 ...
+        def position_lambda(x):
+            return x // len(self._semantic_id_arr)  #  1 0 0 0 1 0 0 0 ...
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._decoder_position_embeddings
         )
 
         def codebook_lambda(x):
-            return (len(self._semantic_id_arr) - 1) - x % len(
-                self._semantic_id_arr
-            )  # 0 1 2 0 1 2 ...
+            non_bos = x < len(self._semantic_id_arr)
+            x[non_bos] = (len(self._semantic_id_arr) - 1) - x[non_bos]
+            return x # 3, 0, 1, 2, 3, 0, 1, 2 ...
 
         codebook_embeddings = self._get_position_embeddings(
-            lengths, mask, codebook_lambda, self._decoder_codebook_embeddings
+            lengths, mask, codebook_lambda, self._codebook_embeddings
         )
 
-        # TODOPK fix codebook indexing
-
         return position_embeddings + codebook_embeddings
 
     def _encoder_pos_embeddings(self, lengths, mask):
@@ -269,7 +265,7 @@ def position_lambda(x):
         def codebook_lambda(x):
             return (len(self._semantic_id_arr) - 1) - x % len(
                 self._semantic_id_arr
-            )  # 0 1 2 0 1 2 ...
+            )  # 0 1 2 3 0 1 2 3 ...
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings

From fee9aaf8b0bce70a6ea31b217d03150f91808549 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 5 Jan 2025 13:21:05 +0300
Subject: [PATCH 037/175] remove todos

---
 modeling/models/tiger.py | 12 +++++-------
 review.md                |  3 ++-
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 910e4a47..14f075ee 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -140,7 +140,6 @@ def forward(self, inputs):
             )  # (batch_size, dec_seq_len, _semantic_id_arr[0])
 
             logits = logits.view(-1, self._semantic_id_arr[0])
-            # TODOPK check if correct flattening
 
             return {self._pred_prefix: logits}
         else:
@@ -211,17 +210,16 @@ def _apply_decoder(
 
         tgt_embeddings = self._decoder_layernorm(
             tgt_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        )  # (batch_size, dec_seq_len + 1, embedding_dim)
         tgt_embeddings = self._decoder_dropout(
             tgt_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        )  # (batch_size, dec_seq_len + 1, embedding_dim)
 
         tgt_embeddings[~tgt_mask] = 0
 
         causal_mask = (
             torch.tril(torch.ones(label_len, label_len)).bool().to(DEVICE)
-        )  # (seq_len, seq_len)
-        # TODOPK -inf?
+        )  # (dec_seq_len + 1, dec_seq_len + 1)
 
         decoder_outputs = self._decoder(
             tgt=tgt_embeddings,
@@ -229,9 +227,9 @@ def _apply_decoder(
             tgt_mask=~causal_mask,
             memory_key_padding_mask=~encoder_mask,
             tgt_key_padding_mask=~tgt_mask,
-        )  # (batch_size, label_len, embedding_dim)
+        )  # (batch_size, dec_seq_len + 1, embedding_dim)
 
-        decoder_outputs = decoder_outputs[:, 1:, :]  # TODOPK remove bos token
+        decoder_outputs = decoder_outputs[:, 1:, :]  # remove bos token
 
         return decoder_outputs
 
diff --git a/review.md b/review.md
index 0bda9419..2c612b4b 100644
--- a/review.md
+++ b/review.md
@@ -2,8 +2,9 @@
 
 ## Todos
 
-- sos / bos embedding correct train fix
 - level embeddings
+- fix trie eval
+- sos / bos embedding correct train fix
 - positions = positions // self._semantic_id_length или reverse?
 как именно учитываем codebook_post & item_pos (тот же порядок или inverted)
 - как именно находим ближайшего при пересечении по embedding? (не понял о каком embedding речь)

From 92f584bbfb9fbca919f8003ada7c8c515c577f07 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 5 Jan 2025 13:48:01 +0300
Subject: [PATCH 038/175] add remark

---
 review.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/review.md b/review.md
index 2c612b4b..8904976e 100644
--- a/review.md
+++ b/review.md
@@ -2,6 +2,7 @@
 
 ## Todos
 
+- Train dataset size: 16972 (in `sasrec`)
 - level embeddings
 - fix trie eval
 - sos / bos embedding correct train fix

From c3397317a69011b7bbe3063ce1d9fc275c92677e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 15 Jan 2025 00:36:30 +0700
Subject: [PATCH 039/175] fix item_embeddings (now take from rqvae)

---
 configs/train/tiger_train_config.json |  2 +-
 modeling/models/base.py               |  5 ++-
 modeling/models/tiger.py              | 54 ++++++++++++++++++++++++---
 3 files changed, 53 insertions(+), 8 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 4a172ff7..fa711076 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -42,11 +42,11 @@
     "type": "tiger",
     "trie": "../data/Beauty/rqvae/trie.pkl",
     "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+    "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
     "positive_prefix": "labels",
     "labels_prefix": "labels",
-    "embedding_dim": 64,
     "num_heads": 4,
     "num_encoder_layers": 6,
     "num_decoder_layers": 6,
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 6405d960..43fee7b9 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -98,9 +98,12 @@ def __init__(
             batch_first=True
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
+    
+    def get_item_embeddings(self, events):
+        return self._item_embeddings(events)
 
     def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
-        embeddings = self._item_embeddings(events)  # (all_batch_events, embedding_dim)
+        embeddings = self.get_item_embeddings(events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
             data=embeddings,
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 14f075ee..eb7da626 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -6,12 +6,15 @@
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
+from .rqvae import RqVaeModel
+
 
 class TigerModel(SequentialTorchModel, config_name="tiger"):
 
     def __init__(
         self,
         trie,
+        rqvae_model,
         sequence_prefix,
         pred_prefix,
         positive_prefix,
@@ -58,7 +61,11 @@ def __init__(
         self._decoder = nn.TransformerDecoder(
             transformer_decoder_layer, num_decoder_layers
         )
+        
+        self._codebook_item_embeddings = torch.cat([codebook for codebook in rqvae_model.codebooks], dim=0)
+
         self._trie = trie
+        self._rqvae_model = rqvae_model
 
         self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
 
@@ -67,9 +74,18 @@ def __init__(
         self._positive_prefix = positive_prefix
         self._labels_prefix = labels_prefix
 
+        # TODOPK
+        #
+        # 128 128
+        # emb       0[0, 255]   1[0, 255]   2[0, 255]
+        # 12101 ->     123         123         34      ->  lookups (+embedding)
+        #
+        # item_emb in [0, 255]
+
         self._semantic_id_arr = semantic_id_arr
 
         self._bos_token_id = semantic_id_arr[0]
+        self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
 
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(semantic_id_arr) + 1, embedding_dim=embedding_dim
@@ -89,24 +105,48 @@ def create_from_config(cls, config, **kwargs):
         semantic_id_arr = rqvae_config["model"]["codebook_sizes"]
         assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
 
+        rqvae_config["model"]["should_init_codebooks"] = False
+        rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
+        rqvae_model.load_state_dict(
+            torch.load(config["rqvae_checkpoint_path"], weights_only=True)
+        )
+        rqvae_model.eval()
+
+        embedding_dim = rqvae_model.encoder.weight.shape[0] # inner rqvae dim
+
         return cls(
             trie=trie,
+            rqvae_model=rqvae_model,
             sequence_prefix=config["sequence_prefix"],
             pred_prefix=config["predictions_prefix"],
             positive_prefix=config["positive_prefix"],
             labels_prefix=config["labels_prefix"],
             num_items=semantic_id_arr[0],
             max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            embedding_dim=embedding_dim,
+            num_heads=config.get("num_heads", int(embedding_dim // 64)),
             num_encoder_layers=config["num_encoder_layers"],
             num_decoder_layers=config["num_decoder_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dim_feedforward=config.get("dim_feedforward", 4 * embedding_dim),
             semantic_id_arr=semantic_id_arr,
             dropout=config.get("dropout", 0.0),
             initializer_range=config.get("initializer_range", 0.02),
         )
 
+    def get_item_embeddings(self, events):
+        bos_mask = (events == self._bos_token_id)
+        
+        codebook_events = events[~bos_mask]
+        positions = torch.arange(len(codebook_events), device=events.device)
+        codebook_positions = positions % len(self._semantic_id_arr)
+        emb_indices = codebook_positions * self._semantic_id_arr[0] + codebook_events
+        
+        embeddings = torch.zeros((len(events), self._bos_weight.shape[0]), device=events.device)
+        embeddings[bos_mask] = self._bos_weight
+        embeddings[~bos_mask] = self._codebook_item_embeddings[emb_indices]
+                
+        return embeddings
+
     def get_logits(self, inputs, prefix, all_sample_events, all_sample_lengths):
         encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
@@ -145,7 +185,7 @@ def forward(self, inputs):
         else:
             logits = self.get_logits(
                 inputs, self._labels_prefix, all_sample_events, all_sample_lengths
-            )
+            )  # batch_size, dec_seq_len, emb_dim (_semantic_id_arr[0])
 
             preds = logits.argmax(dim=-1)  # (batch_size, dec_seq_len)
             ids = torch.tensor(self._apply_trie(preds))
@@ -153,6 +193,8 @@ def forward(self, inputs):
 
     def _apply_trie(self, preds):  # TODOPK make this faster (how?)
         native_repr = [tuple(row.tolist()) for row in preds]
+        # TODOPK add residual
+        # add flag if item taken (take other items in up level)
         ids = []
         for semantic_id in native_repr:
             cur_result = set()
@@ -191,7 +233,7 @@ def _apply_decoder(
     ):
         label_events, label_lengths = self._prepend_bos(label_events, label_lengths)
 
-        tgt_embeddings = self._item_embeddings(
+        tgt_embeddings = self.get_item_embeddings(
             label_events
         )  # (all_batch_events, embedding_dim)
 
@@ -244,7 +286,7 @@ def position_lambda(x):
         def codebook_lambda(x):
             non_bos = x < len(self._semantic_id_arr)
             x[non_bos] = (len(self._semantic_id_arr) - 1) - x[non_bos]
-            return x # 3, 0, 1, 2, 3, 0, 1, 2 ...
+            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ...
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings

From b6ddb0feb68885d973b37dd53e86e0a739f1dc91 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 15 Jan 2025 12:54:49 +0700
Subject: [PATCH 040/175] remove todo

---
 modeling/models/tiger.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index eb7da626..43efd644 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -74,14 +74,6 @@ def __init__(
         self._positive_prefix = positive_prefix
         self._labels_prefix = labels_prefix
 
-        # TODOPK
-        #
-        # 128 128
-        # emb       0[0, 255]   1[0, 255]   2[0, 255]
-        # 12101 ->     123         123         34      ->  lookups (+embedding)
-        #
-        # item_emb in [0, 255]
-
         self._semantic_id_arr = semantic_id_arr
 
         self._bos_token_id = semantic_id_arr[0]

From e13947fb82722602640e7b771298cb79df036de5 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 15 Jan 2025 22:48:18 +0700
Subject: [PATCH 041/175] add collision solver

---
 modeling/models/collision_solver.py | 111 +++++++++
 modeling/models/tiger.py            |   7 +
 notebooks/CollisionSolver.ipynb     | 369 ++++++++++++++++++++++++++++
 3 files changed, 487 insertions(+)
 create mode 100644 modeling/models/collision_solver.py
 create mode 100644 notebooks/CollisionSolver.ipynb

diff --git a/modeling/models/collision_solver.py b/modeling/models/collision_solver.py
new file mode 100644
index 00000000..9af18bed
--- /dev/null
+++ b/modeling/models/collision_solver.py
@@ -0,0 +1,111 @@
+from collections import defaultdict
+from typing import List, Tuple, Dict
+import torch
+
+class CollisionSolver:
+    def __init__(self, residual_length, semantic_id_length, device: torch.device = torch.device('cpu')):
+        """
+        :param residual_length: Длина остатка для каждого semantic_id
+        :param semantic_id_length: Длина semantic_id (без токена решающего коллизии)
+        :param device: Устройство
+        """
+        self._semantic_id_dict = defaultdict(list)
+        self.residual_length = residual_length
+        self.semantic_id_length = semantic_id_length
+        self.device = device
+
+    def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:
+        """
+        Перенос тензора на устройство
+        """
+        if tensor.device != self.device:
+            tensor = tensor.to(self.device)
+        return tensor
+
+    def add_item(self, semantic_id: List[int] | torch.Tensor, residual: torch.Tensor) -> None:
+        """
+        Добавляет новый элемент в словарь хранящий semantic_ids с остатками
+
+        :param semantic_id: Semantic id (без токена решающего коллизии)
+        :param residual: Тензор с остатком для данного semantic_id
+        """
+        if isinstance(semantic_id, torch.Tensor):
+            semantic_id = semantic_id.tolist()
+
+        assert isinstance(residual, torch.Tensor)
+        assert residual.shape == (self.residual_length,)
+        assert len(semantic_id) == self.semantic_id_length
+
+        residual = self._to_device(residual)
+        key = tuple(semantic_id)
+        self._semantic_id_dict[key].append((len(self._semantic_id_dict[key]), residual))
+
+
+    def create_query_candidates_dict(self, semantic_ids: torch.Tensor | List[List[int]], residuals: torch.Tensor | List[List[int]]) -> None:
+        """
+        Создает словарь, который содержит сгруппирированные по semantic id элементы, к ним добавлены токены решающие коллизии (добавляются по порядку начиная с нуля)
+
+        :param semantic_ids: Тензор или список всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)
+        :param residuals: Тензор или список остатков для каждого semantic_id
+        """
+        residuals_count = residuals.shape[0] if isinstance(residuals, torch.Tensor) else len(residuals)
+        semantic_ids_count = semantic_ids.shape[0] if isinstance(semantic_ids, torch.Tensor) else len(semantic_ids)
+        assert(residuals_count == semantic_ids_count)
+
+        if isinstance(residuals, list):
+            residuals = torch.tensor(residuals, device=self.device)
+        residuals = self._to_device(residuals)
+
+        for semantic_id, residual in zip(semantic_ids, residuals):
+            self.add_item(semantic_id, residual)
+
+    def get_candidates_tensor(self, query_prefixes: List[List[int]]) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)
+
+        :return: Кортеж из двух тензоров:
+        - candidates_tensor (размерность: [num_prefixes, max_collisions, residual_dim]): тензор, содержащий остатки кандидатов для каждого префикса
+          `max_collisions` — максимальное количество кандидатов для каждого префикса
+        - mask (размерность: [num_prefixes, max_collisions]): Маска для candidates_tensor
+
+        Примечание:
+            Предполагаем что все префиксы из `query_prefixes` уже есть в словаре semantic ids
+            Если префикс не найден, будет выброшено исключение
+        """
+        assert isinstance(query_prefixes, list)
+        assert(self.residual_length == len(self._semantic_id_dict[tuple(query_prefixes[0])][0][1]))
+        assert(len(query_prefixes[0]) == self.semantic_id_length)
+
+        max_collision_len = max(len(x) for x in self._semantic_id_dict.values())
+        candidates_tensor = torch.zeros(len(query_prefixes), max_collision_len, self.residual_length, dtype=torch.float32, device=self.device)
+        mask = torch.zeros(len(query_prefixes), max_collision_len, dtype=torch.bool, device=self.device)
+
+        for i, semantic_id in enumerate(query_prefixes):
+            key = tuple(semantic_id)
+            assert key in self._semantic_id_dict.keys(), f"Не найдено обьектов с semantic id {key}" # нужно что-то с этим делать
+            for j, residual in self._semantic_id_dict[key]: #сохранение порядка
+                candidates_tensor[i, j] = residual
+                mask[i, j] = True
+        return candidates_tensor, mask
+
+    def get_semantic_ids(self, query_prefixes: torch.Tensor, query_residuals: torch.Tensor) -> torch.Tensor:
+        """
+        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)
+
+        :return: semantic_ids: [num_prefixes, prefix_len + 1] список из semantic id с токенами решающие коллизии
+        """
+        assert isinstance(query_prefixes, torch.Tensor)
+        assert isinstance(query_residuals, torch.Tensor)
+        assert(query_prefixes.shape[0] == query_residuals.shape[0])
+        assert(query_prefixes.shape[1] == self.semantic_id_length)
+        assert(query_residuals.shape[1] == self.residual_length)
+
+        query_prefixes = self._to_device(query_prefixes)
+        query_residuals = self._to_device(query_residuals)
+
+        candidates_tensor, mask = self.get_candidates_tensor(query_prefixes.tolist())
+
+        masked_dot_products = torch.einsum('ijk,ik->ij', candidates_tensor, query_residuals).masked_fill(~mask, float('-inf'))
+        max_indices = torch.argmax(masked_dot_products, dim=1)
+        best_semantic_ids = torch.concat((query_prefixes, max_indices.unsqueeze(1)), dim=1)
+        return best_semantic_ids
\ No newline at end of file
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 43efd644..99084e64 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -2,6 +2,7 @@
 import pickle
 
 import torch
+from models.collision_solver import CollisionSolver
 from models.base import SequentialTorchModel
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
@@ -66,6 +67,11 @@ def __init__(
 
         self._trie = trie
         self._rqvae_model = rqvae_model
+        self._collision_solver = CollisionSolver(embedding_dim, len(semantic_id_arr), DEVICE)
+
+        # TODO
+        # self._collision_solver.create_query_candidates_dict(semantic_ids[:, :-1], residuals)
+        # self._collision_solver.get_semantic_ids(query_prefixes, query_residuals)
 
         self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
 
@@ -182,6 +188,7 @@ def forward(self, inputs):
             preds = logits.argmax(dim=-1)  # (batch_size, dec_seq_len)
             ids = torch.tensor(self._apply_trie(preds))
             return ids
+        
 
     def _apply_trie(self, preds):  # TODOPK make this faster (how?)
         native_repr = [tuple(row.tolist()) for row in preds]
diff --git a/notebooks/CollisionSolver.ipynb b/notebooks/CollisionSolver.ipynb
new file mode 100644
index 00000000..b6fe5605
--- /dev/null
+++ b/notebooks/CollisionSolver.ipynb
@@ -0,0 +1,369 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "from collections import defaultdict\n",
+        "from typing import List, Tuple, Dict\n",
+        "import torch"
+      ],
+      "metadata": {
+        "id": "CH-xb-IDScxO"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "WH6rvC8BSTD1"
+      },
+      "outputs": [],
+      "source": [
+        "class CollisionSolver:\n",
+        "    def __init__(self, residual_length, semantic_id_length, device: torch.device = torch.device('cpu')):\n",
+        "        \"\"\"\n",
+        "        :param residual_length: Длина остатка для каждого semantic_id\n",
+        "        :param semantic_id_length: Длина semantic_id (без токена решающего коллизии)\n",
+        "        :param device: Устройство\n",
+        "        \"\"\"\n",
+        "        self._semantic_id_dict = defaultdict(list)\n",
+        "        self.residual_length = residual_length\n",
+        "        self.semantic_id_length = semantic_id_length\n",
+        "        self.device = device\n",
+        "\n",
+        "    def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:\n",
+        "        \"\"\"\n",
+        "        Перенос тензора на устройство\n",
+        "        \"\"\"\n",
+        "        if tensor.device != self.device:\n",
+        "            tensor = tensor.to(self.device)\n",
+        "        return tensor\n",
+        "\n",
+        "    def add_item(self, semantic_id: List[int] | torch.Tensor, residual: torch.Tensor) -> None:\n",
+        "        \"\"\"\n",
+        "        Добавляет новый элемент в словарь хранящий semantic_ids с остатками\n",
+        "\n",
+        "        :param semantic_id: Semantic id (без токена решающего коллизии)\n",
+        "        :param residual: Тензор с остатком для данного semantic_id\n",
+        "        \"\"\"\n",
+        "        if isinstance(semantic_id, torch.Tensor):\n",
+        "            semantic_id = semantic_id.tolist()\n",
+        "\n",
+        "        assert isinstance(residual, torch.Tensor)\n",
+        "        assert residual.shape == (self.residual_length,)\n",
+        "        assert len(semantic_id) == self.semantic_id_length\n",
+        "\n",
+        "        residual = self._to_device(residual)\n",
+        "        key = tuple(semantic_id)\n",
+        "        self._semantic_id_dict[key].append((len(self._semantic_id_dict[key]), residual))\n",
+        "\n",
+        "\n",
+        "    def create_query_candidates_dict(self, semantic_ids: torch.Tensor | List[List[int]], residuals: torch.Tensor | List[List[int]]) -> None:\n",
+        "        \"\"\"\n",
+        "        Создает словарь, который содержит сгруппирированные по semantic id элементы, к ним добавлены токены решающие коллизии (добавляются по порядку начиная с нуля)\n",
+        "\n",
+        "        :param semantic_ids: Тензор или список всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)\n",
+        "        :param residuals: Тензор или список остатков для каждого semantic_id\n",
+        "        \"\"\"\n",
+        "        residuals_count = residuals.shape[0] if isinstance(residuals, torch.Tensor) else len(residuals)\n",
+        "        semantic_ids_count = semantic_ids.shape[0] if isinstance(semantic_ids, torch.Tensor) else len(semantic_ids)\n",
+        "        assert(residuals_count == semantic_ids_count)\n",
+        "\n",
+        "        if isinstance(residuals, list):\n",
+        "            residuals = torch.tensor(residuals, device=self.device)\n",
+        "        residuals = self._to_device(residuals)\n",
+        "\n",
+        "        for semantic_id, residual in zip(semantic_ids, residuals):\n",
+        "            self.add_item(semantic_id, residual)\n",
+        "\n",
+        "    def get_candidates_tensor(self, query_prefixes: List[List[int]]) -> Tuple[torch.Tensor, torch.Tensor]:\n",
+        "        \"\"\"\n",
+        "        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)\n",
+        "\n",
+        "        :return: Кортеж из двух тензоров:\n",
+        "        - candidates_tensor (размерность: [num_prefixes, max_collisions, residual_dim]): тензор, содержащий остатки кандидатов для каждого префикса\n",
+        "          `max_collisions` — максимальное количество кандидатов для каждого префикса\n",
+        "        - mask (размерность: [num_prefixes, max_collisions]): Маска для candidates_tensor\n",
+        "\n",
+        "        Примечание:\n",
+        "            Предполагаем что все префиксы из `query_prefixes` уже есть в словаре semantic ids\n",
+        "            Если префикс не найден, будет выброшено исключение\n",
+        "        \"\"\"\n",
+        "        assert isinstance(query_prefixes, list)\n",
+        "        assert(self.residual_length == len(self._semantic_id_dict[tuple(query_prefixes[0])][0][1]))\n",
+        "        assert(len(query_prefixes[0]) == self.semantic_id_length)\n",
+        "\n",
+        "        max_collision_len = max(len(x) for x in self._semantic_id_dict.values())\n",
+        "        candidates_tensor = torch.zeros(len(query_prefixes), max_collision_len, self.residual_length, dtype=torch.float32, device=self.device)\n",
+        "        mask = torch.zeros(len(query_prefixes), max_collision_len, dtype=torch.bool, device=self.device)\n",
+        "\n",
+        "        for i, semantic_id in enumerate(query_prefixes):\n",
+        "            key = tuple(semantic_id)\n",
+        "            assert key in self._semantic_id_dict.keys(), f\"Не найдено обьектов с semantic id {key}\" # нужно что-то с этим делать\n",
+        "            for j, residual in self._semantic_id_dict[key]: #сохранение порядка\n",
+        "                candidates_tensor[i, j] = residual\n",
+        "                mask[i, j] = True\n",
+        "        return candidates_tensor, mask\n",
+        "\n",
+        "    def get_semantic_ids(self, query_prefixes: torch.Tensor, query_residuals: torch.Tensor) -> torch.Tensor:\n",
+        "        \"\"\"\n",
+        "        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)\n",
+        "\n",
+        "        :return: semantic_ids: [num_prefixes, prefix_len + 1] список из semantic id с токенами решающие коллизии\n",
+        "        \"\"\"\n",
+        "        assert isinstance(query_prefixes, torch.Tensor)\n",
+        "        assert isinstance(query_residuals, torch.Tensor)\n",
+        "        assert(query_prefixes.shape[0] == query_residuals.shape[0])\n",
+        "        assert(query_prefixes.shape[1] == self.semantic_id_length)\n",
+        "        assert(query_residuals.shape[1] == self.residual_length)\n",
+        "\n",
+        "        query_prefixes = self._to_device(query_prefixes)\n",
+        "        query_residuals = self._to_device(query_residuals)\n",
+        "\n",
+        "        candidates_tensor, mask = self.get_candidates_tensor(query_prefixes.tolist())\n",
+        "\n",
+        "        masked_dot_products = torch.einsum('ijk,ik->ij', candidates_tensor, query_residuals).masked_fill(~mask, float('-inf'))\n",
+        "        max_indices = torch.argmax(masked_dot_products, dim=1)\n",
+        "        best_semantic_ids = torch.concat((query_prefixes, max_indices.unsqueeze(1)), dim=1)\n",
+        "        return best_semantic_ids"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Пример использования"
+      ],
+      "metadata": {
+        "id": "AhJgbuGESnWd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "residual_length = 12\n",
+        "semantic_ids_length = 3\n",
+        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+        "\n",
+        "semantic_ids = torch.tensor([\n",
+        "    [1, 2, 3, 0],\n",
+        "    [1, 2, 3, 1],\n",
+        "    [1, 2, 3, 2],\n",
+        "    [1, 2, 3, 3],\n",
+        "    [1, 2, 4, 0],\n",
+        "    [1, 2, 4, 1],\n",
+        "    [1, 2, 4, 2],\n",
+        "    [5, 2, 3, 0],\n",
+        "    [5, 2, 3, 1],\n",
+        "    [5, 2, 3, 2],\n",
+        "    [5, 2, 3, 3],\n",
+        "    [5, 2, 3, 4],\n",
+        "    [5, 2, 3, 5],\n",
+        "    [5, 2, 3, 6],\n",
+        "    [2, 8, 7, 6],\n",
+        "], device=torch.device('cpu'))\n",
+        "\n",
+        "residuals = torch.rand(semantic_ids.shape[0], residual_length)\n",
+        "\n",
+        "query_prefixes = torch.tensor([\n",
+        "    [1, 2, 3],\n",
+        "    [1, 2, 4],\n",
+        "    [5, 2, 3]\n",
+        "], device=device)  # [num_prefixes, prefix_len]\n",
+        "\n",
+        "query_residuals = torch.rand(query_prefixes.shape[0], residual_length, device=torch.device('cpu'))  # [num_prefixes, emb_dim]"
+      ],
+      "metadata": {
+        "id": "pBjXwVLVSWMO"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "solver = CollisionSolver(residual_length, semantic_ids_length)\n",
+        "\n",
+        "solver.create_query_candidates_dict(semantic_ids[:, :-1], residuals)\n",
+        "\n",
+        "solver.get_semantic_ids(query_prefixes, query_residuals)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wn7Uk8V_TgrE",
+        "outputId": "540b25c6-63bc-4613-90b1-5510ea4d6224"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([[1, 2, 3, 2],\n",
+              "        [1, 2, 4, 0],\n",
+              "        [5, 2, 3, 6]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "solver._semantic_id_dict"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3HTju2grZm-F",
+        "outputId": "442795a8-a952-466c-8c48-fb3d6f14bc81"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "defaultdict(list,\n",
+              "            {(1,\n",
+              "              2,\n",
+              "              3): [(0,\n",
+              "               tensor([0.2521, 0.4989, 0.8696, 0.9459, 0.4614, 0.3054, 0.1708, 0.8725, 0.5294,\n",
+              "                       0.7386, 0.8668, 0.9158])), (1,\n",
+              "               tensor([0.3066, 0.7874, 0.8942, 0.5829, 0.5947, 0.1307, 0.1789, 0.7914, 0.6617,\n",
+              "                       0.7250, 0.4264, 0.6968])), (2,\n",
+              "               tensor([0.8502, 0.4814, 0.2935, 0.2933, 0.5334, 0.5938, 0.4865, 0.7920, 0.9304,\n",
+              "                       0.4737, 0.4883, 0.9769])), (3,\n",
+              "               tensor([0.4382, 0.4937, 0.5311, 0.1205, 0.0210, 0.2097, 0.7704, 0.9061, 0.6019,\n",
+              "                       0.9187, 0.3894, 0.1716]))],\n",
+              "             (1,\n",
+              "              2,\n",
+              "              4): [(0,\n",
+              "               tensor([0.9046, 0.2291, 0.9002, 0.7236, 0.7392, 0.1195, 0.0039, 0.1333, 0.2854,\n",
+              "                       0.3425, 0.9413, 0.6365])), (1,\n",
+              "               tensor([0.3013, 0.1465, 0.4424, 0.9448, 0.0412, 0.5664, 0.3587, 0.1531, 0.5751,\n",
+              "                       0.8052, 0.0830, 0.5028])), (2,\n",
+              "               tensor([0.9886, 0.6681, 0.4602, 0.3818, 0.8741, 0.3990, 0.1009, 0.8240, 0.9018,\n",
+              "                       0.1647, 0.0799, 0.0188]))],\n",
+              "             (5,\n",
+              "              2,\n",
+              "              3): [(0,\n",
+              "               tensor([0.6242, 0.6769, 0.8397, 0.6340, 0.9251, 0.2997, 0.9545, 0.6810, 0.4468,\n",
+              "                       0.3179, 0.5830, 0.2547])), (1,\n",
+              "               tensor([0.9159, 0.3269, 0.6216, 0.8065, 0.9175, 0.3175, 0.0664, 0.1575, 0.1457,\n",
+              "                       0.6718, 0.7908, 0.2829])), (2,\n",
+              "               tensor([0.1270, 0.7954, 0.7779, 0.9226, 0.0595, 0.6361, 0.4578, 0.7727, 0.4038,\n",
+              "                       0.6136, 0.8738, 0.6714])), (3,\n",
+              "               tensor([0.0647, 0.4849, 0.2900, 0.4458, 0.3928, 0.1550, 0.6921, 0.8732, 0.7545,\n",
+              "                       0.0995, 0.7739, 0.3181])), (4,\n",
+              "               tensor([0.2761, 0.3357, 0.2239, 0.4669, 0.9118, 0.2321, 0.7169, 0.5600, 0.5067,\n",
+              "                       0.4533, 0.6332, 0.6862])), (5,\n",
+              "               tensor([0.9039, 0.0986, 0.9541, 0.7187, 0.6052, 0.8883, 0.1887, 0.1329, 0.3411,\n",
+              "                       0.9943, 0.0430, 0.0611])), (6,\n",
+              "               tensor([0.7541, 0.7320, 0.2690, 0.1116, 0.0751, 0.7047, 0.5125, 0.9484, 0.2797,\n",
+              "                       0.5430, 0.9255, 0.6738]))],\n",
+              "             (2,\n",
+              "              8,\n",
+              "              7): [(0,\n",
+              "               tensor([0.6241, 0.6990, 0.7154, 0.6228, 0.1982, 0.0796, 0.1684, 0.9888, 0.0580,\n",
+              "                       0.7725, 0.8443, 0.4938]))]})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Альтернативное решение только через torch"
+      ],
+      "metadata": {
+        "id": "ji7PzyQeTxuP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "semantic_ids = semantic_ids.to(device)\n",
+        "residuals = residuals.to(device)\n",
+        "query_prefixes = query_prefixes.to(device)\n",
+        "query_residuals = query_residuals.to(device)\n",
+        "\n",
+        "batch_size, max_length = semantic_ids.shape\n",
+        "num_prefixes, prefix_len = query_prefixes.shape\n",
+        "\n",
+        "#привожу к одной размерности чтобы найти совпадения по префиксам\n",
+        "semantic_ids_exp = semantic_ids[:, :prefix_len].unsqueeze(0).expand(num_prefixes, batch_size, prefix_len) # [num_prefixes, batch_size, prefix_len]\n",
+        "prefixes_exp = query_prefixes.unsqueeze(1).expand(num_prefixes, batch_size, prefix_len) #torch.tile\n",
+        "is_prefix_match = (semantic_ids_exp == prefixes_exp).all(dim=2)  # [num_prefixes, batch_size]\n",
+        "\n",
+        "# Шаг 2: Маскирование residuals для каждого префикса\n",
+        "residuals_exp = residuals.unsqueeze(0).expand(num_prefixes, batch_size, -1)  # [num_prefixes, batch_size, emb_dim]\n",
+        "masked_residuals = residuals_exp * is_prefix_match.unsqueeze(2).float()  # Зануляем строки, не соответствующие префиксам\n",
+        "dot_products = torch.einsum('ijk,ik->ij', masked_residuals, query_residuals)\n",
+        "max_indices = torch.argmax(dot_products, dim=1)  # [num_prefixes] #\n",
+        "\n",
+        "best_semantic_ids = semantic_ids[max_indices]  # [num_prefixes, max_length]\n",
+        "best_residuals = residuals[max_indices]  # [num_prefixes, emb_dim]\n",
+        "\n",
+        "\n",
+        "for i, prefix in enumerate(query_prefixes):\n",
+        "    print(f\"Префикс: {prefix.tolist()}\")\n",
+        "    print(f\"Лучший semantic_id: {best_semantic_ids[i].tolist()}\")\n",
+        "    print(f\"Соответствующий residual: {best_residuals[i]}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eh7jwIkHTr4_",
+        "outputId": "d9bccfba-c12f-4036-8bd3-0ef5d1d5b5e1"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Префикс: [1, 2, 3]\n",
+            "Лучший semantic_id: [1, 2, 3, 2]\n",
+            "Соответствующий residual: tensor([0.8502, 0.4814, 0.2935, 0.2933, 0.5334, 0.5938, 0.4865, 0.7920, 0.9304,\n",
+            "        0.4737, 0.4883, 0.9769], device='cuda:0')\n",
+            "Префикс: [1, 2, 4]\n",
+            "Лучший semantic_id: [1, 2, 4, 0]\n",
+            "Соответствующий residual: tensor([0.9046, 0.2291, 0.9002, 0.7236, 0.7392, 0.1195, 0.0039, 0.1333, 0.2854,\n",
+            "        0.3425, 0.9413, 0.6365], device='cuda:0')\n",
+            "Префикс: [5, 2, 3]\n",
+            "Лучший semantic_id: [5, 2, 3, 6]\n",
+            "Соответствующий residual: tensor([0.7541, 0.7320, 0.2690, 0.1116, 0.0751, 0.7047, 0.5125, 0.9484, 0.2797,\n",
+            "        0.5430, 0.9255, 0.6738], device='cuda:0')\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 37a22d86946ee011c75ab8b85052f90214d59086 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 19 Jan 2025 13:23:44 +0700
Subject: [PATCH 042/175] add draft code (nan breaking)

---
 configs/train/rqvae_train_config.json   |    5 +-
 configs/train/tiger_train_config.json   |   11 +-
 modeling/dataloader/__init__.py         |    2 +-
 modeling/dataloader/batch_processors.py |   65 +-
 modeling/main.ipynb                     | 2749 ++++++++++++++++++++++-
 modeling/models/base.py                 |    6 +-
 modeling/models/collision_solver.py     |   27 +-
 modeling/models/rqvae.py                |    9 +-
 modeling/models/tiger.py                |  363 +--
 modeling/rqvae/collisions.py            |   13 +
 modeling/rqvae/trie.py                  |  188 ++
 modeling/trie.py                        |   41 -
 notebooks/CollisionSolver.ipynb         |  258 ++-
 13 files changed, 3352 insertions(+), 385 deletions(-)
 create mode 100644 modeling/rqvae/trie.py
 delete mode 100644 modeling/trie.py

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index ba2cfb05..0d54dba5 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "train_steps_num": 1024,
+  "train_steps_num": 512,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
@@ -31,8 +31,7 @@
   },
   "model": {
     "type": "rqvae",
-    "input_dim": 512,
-    "hidden_dim": 128,
+    "embedding_dim": 512,
     "n_iter": 100,
     "codebook_sizes": [256, 256, 256, 256],
     "should_init_codebooks": true,
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index fa711076..b1163b3a 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -17,10 +17,7 @@
       "type": "torch",
       "batch_size": 256,
       "batch_processor": {
-        "type": "rqvae",
-        "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
-        "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-        "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt"
+        "type": "basic"
       },
       "drop_last": true,
       "shuffle": true
@@ -29,10 +26,7 @@
       "type": "torch",
       "batch_size": 256,
       "batch_processor": {
-        "type": "rqvae",
-        "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
-        "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-        "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt"
+        "type": "basic"
       },
       "drop_last": false,
       "shuffle": false
@@ -43,6 +37,7 @@
     "trie": "../data/Beauty/rqvae/trie.pkl",
     "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+    "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
     "sequence_prefix": "item",
     "predictions_prefix": "logits",
     "positive_prefix": "labels",
diff --git a/modeling/dataloader/__init__.py b/modeling/dataloader/__init__.py
index e134259b..a141bb21 100644
--- a/modeling/dataloader/__init__.py
+++ b/modeling/dataloader/__init__.py
@@ -1,2 +1,2 @@
 from .base import BaseDataloader
-from .batch_processors import BaseBatchProcessor, IdentityBatchProcessor, RqVaeProcessor
+from .batch_processors import BaseBatchProcessor, IdentityBatchProcessor
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index e71200cd..9991a073 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,9 +1,5 @@
-from collections import defaultdict
-import json
 import torch
-from models.base import BaseModel
-from utils import DEVICE, MetaParent
-import itertools
+from utils import MetaParent
 
 
 class BaseBatchProcessor(metaclass=MetaParent):
@@ -24,65 +20,6 @@ def __call__(self, batch):
         embeds = torch.stack([entry['item.embed'] for entry in batch])
         
         return {'ids': ids, 'embeddings': embeds}
-    
-class RqVaeProcessor(BaseBatchProcessor, config_name='rqvae'):
-    def __init__(self, item_id_to_semantic_id):
-        self._item_id_to_semantic_id = item_id_to_semantic_id
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        rqvae_train_config = json.load(open(config['rqvae_train_config_path']))
-        rqvae_train_config['model']['should_init_codebooks'] = False
-        
-        rqvae_model = BaseModel.create_from_config(rqvae_train_config['model']).to(DEVICE)
-        rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
-        rqvae_model.eval()
-        
-        embs_extractor = torch.load(config['embs_extractor_path'])
-        
-        item_ids = embs_extractor.index.tolist()
-        embeddings = torch.stack([
-            emb for emb in embs_extractor['embeddings'].tolist()
-        ]).to(DEVICE)
-        semantic_ids = list(rqvae_model({"embeddings": embeddings}))
-        
-        item_id_to_semantic_id = {
-            item_id: semantic_id for (item_id, semantic_id) in zip(item_ids, semantic_ids)
-        }
-
-        return cls(item_id_to_semantic_id)
-    
-    def get_semantic_ids(self, item_ids):
-        semantic_ids = []
-        for item_id in item_ids:
-            semantic_ids.append(self._item_id_to_semantic_id[item_id])
-        return semantic_ids
-
-    def __call__(self, batch):
-        processed_batch = defaultdict(list)
-
-        for key in batch[0].keys():
-            if key.endswith('.ids'):
-                prefix = key.split('.')[0]
-                assert '{}.length'.format(prefix) in batch[0]
-
-                for sample in batch:
-                    item_ids = sample[f'{prefix}.ids']
-                    length = sample[f'{prefix}.length']
-                    
-                    processed_batch[f'{prefix}.ids'].extend(item_ids)
-                    processed_batch[f'{prefix}.length'].append(length)
-                    
-                    if prefix != 'user':
-                        semantic_ids = self.get_semantic_ids(item_ids)
-                        semantic_ids = list(itertools.chain(*semantic_ids))
-                        processed_batch[f'semantic.{prefix}.ids'].extend(semantic_ids)
-                        processed_batch[f'semantic.{prefix}.length'].append(len(semantic_ids))
-                    
-        for part, values in processed_batch.items():
-            processed_batch[part] = torch.tensor(values, dtype=torch.long)
-
-        return processed_batch
 
 
 class BasicBatchProcessor(BaseBatchProcessor, config_name='basic'):
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index 0924a494..63993428 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -4,7 +4,92 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4a57704c044240659047a8fd9d54123b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b06a168ba57445c3b10c24c0d5f08352",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc28303c594d4b2b9a710ab312e403eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25987885c1624b1a9be880896a607272",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ba30015b4ed421eb2e07333b3ba727c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc28335fa6294753be34419468a5e815",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import torch\n",
     "\n",
@@ -66,6 +151,2668 @@
     "assert len(df) == len(set(item[-1] for item in items_with_tuples))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('7806397051',\n",
+       "  'WAWO 15 Color Professionl Makeup Eyeshadow Camouflage Facial Concealer Neutral Palette',\n",
+       "  (23, 49, 13, 166, 0)),\n",
+       " ('9759091062', 'Xtreme Brite Brightening Gel 1oz.', (171, 230, 188, 70, 0)),\n",
+       " ('9788072216',\n",
+       "  'Prada Candy By Prada Eau De Parfum Spray 1.7 Oz For Women',\n",
+       "  (189, 0, 26, 166, 0)),\n",
+       " ('9790790961',\n",
+       "  'Versace Bright Crystal Eau de Toilette Spray for Women, 3 Ounce',\n",
+       "  (84, 225, 207, 237, 0)),\n",
+       " ('9790794231', 'Stella McCartney Stella', (233, 61, 1, 68, 0)),\n",
+       " ('B00004TMFE',\n",
+       "  'Avalon Biotin B-Complex Thickening Conditioner, 14 Ounce',\n",
+       "  (194, 83, 125, 220, 0)),\n",
+       " ('B00004TUBL',\n",
+       "  'Better Living Classic Two Chamber Dispenser, White',\n",
+       "  (57, 199, 129, 249, 0)),\n",
+       " ('B00004TUBV',\n",
+       "  'Better Living The Ulti-Mate Dispenser',\n",
+       "  (120, 180, 151, 115, 0)),\n",
+       " ('B00004U9UY',\n",
+       "  'Crabtree &amp; Evelyn 2792 Gardeners Hand Therapy (100ml, 3.4 oz)',\n",
+       "  (115, 7, 22, 70, 0)),\n",
+       " ('B00004U9V2',\n",
+       "  \"Crabtree &amp; Evelyn - Gardener's Ultra-Moisturising Hand Therapy Pump - 250g/8.8 OZ\",\n",
+       "  (18, 0, 150, 28, 0)),\n",
+       " ('B000050B6U', 'Instant Heat Curling Iron, 1-Inch', (87, 1, 150, 18, 0)),\n",
+       " ('B000052WY7',\n",
+       "  'Maybelline New York Expert Wear Brow &amp; Eyeliner, 151 Velvet Black , .011 oz (310 mg)',\n",
+       "  (135, 203, 84, 254, 0)),\n",
+       " ('B000052WYD',\n",
+       "  'Maybelline New York Cover Stick Concealer, Ivory, Light 2, 0.16 Ounce',\n",
+       "  (52, 90, 157, 136, 0)),\n",
+       " ('B000052WYL',\n",
+       "  'Maybelline New York Expert Eyes Moisturizing Eye Makeup Remover, 2.3 Fl. Oz.',\n",
+       "  (70, 222, 128, 136, 0)),\n",
+       " ('B000052WYN',\n",
+       "  'Maybelline New York Ultra-Liner Liquid Liner, Waterproof, Black 135L-01 , .25 fl oz (7.3 ml)',\n",
+       "  (24, 145, 1, 54, 0)),\n",
+       " ('B000052X9G',\n",
+       "  'Nullo Internal Deodorant, Coated Caplets, 135 Caplets',\n",
+       "  (157, 0, 10, 88, 0)),\n",
+       " ('B000052XIA',\n",
+       "  'Vagisil Anti-Itch Creme, Original Formula - 1 oz',\n",
+       "  (232, 72, 177, 42, 0)),\n",
+       " ('B000052XW5',\n",
+       "  'Pretty Feet &amp; Hands Rough Skin Remover-Exfoliant, 3-Ounce Bottles (Pack of 3)',\n",
+       "  (183, 50, 157, 222, 0)),\n",
+       " ('B000052XYQ', 'DHS Clear Shampoo 8 Fl Oz', (157, 39, 12, 233, 0)),\n",
+       " ('B000052XZP',\n",
+       "  'Neutrogena T-Gel Shampoo, Stubborn Itch Control, 4.4 Ounce',\n",
+       "  (75, 1, 176, 32, 0)),\n",
+       " ('B000052XZX',\n",
+       "  'Neutrogena T/Sal Shampoo, Scalp Build-up Control, 4.5 fl oz',\n",
+       "  (241, 0, 177, 42, 0)),\n",
+       " ('B000052Y25',\n",
+       "  'DHS Conditioning Rinse With Panthenol 8 fl oz',\n",
+       "  (48, 145, 248, 254, 0)),\n",
+       " ('B000052Y33',\n",
+       "  'Alberto VO5 Moisturizing Hot Oil Treatment, 0.5 Ounce, 2-Count Tubes (Pack of 6)',\n",
+       "  (175, 215, 150, 248, 0)),\n",
+       " ('B000052Y5F',\n",
+       "  'Konsyl Pharmaceuticals Psyllium Fiber 15.9 oz',\n",
+       "  (222, 72, 176, 32, 0)),\n",
+       " ('B000052Y6Z',\n",
+       "  'Williams Lectric Shave Electric Razor Pre-Shave, Original, 3 fl oz',\n",
+       "  (235, 212, 230, 24, 0)),\n",
+       " ('B000052YJC',\n",
+       "  'Buf-Puf Double-Sided Body Sponge - 1 ea',\n",
+       "  (189, 52, 248, 28, 0)),\n",
+       " ('B000052YJD',\n",
+       "  'Buf-Puf Reusable Facial Sponge, Extra Gentle, - 1 ea',\n",
+       "  (157, 72, 10, 215, 0)),\n",
+       " ('B000052YJH',\n",
+       "  'Buf-Puf Reusable Facial Sponge (Pack of 6)',\n",
+       "  (182, 39, 197, 28, 0)),\n",
+       " ('B000052YJM',\n",
+       "  'Clean &amp; Clear ESSENTIALS Dual Action Moisturizer, 4 Ounce',\n",
+       "  (1, 13, 49, 205, 0)),\n",
+       " ('B000052YJX',\n",
+       "  'Neutrogena OntheSpot Acne Treatment, Vanishing Formula, 0.75 Ounce',\n",
+       "  (241, 1, 165, 175, 0)),\n",
+       " ('B000052YKI',\n",
+       "  'Purpose Dual Treatment Moisture Lotion with SPF 15 4 fl oz (120 ml)',\n",
+       "  (42, 62, 157, 249, 0)),\n",
+       " ('B000052YKM',\n",
+       "  'Stri-Dex Medicated Pads, Maximum Strength, 90-Count Containers, (Pack of 3)',\n",
+       "  (29, 0, 137, 222, 0)),\n",
+       " ('B000052YKS',\n",
+       "  'Basis Cleaner Clean Face Wash, 6 Fluid Ounces',\n",
+       "  (2, 151, 48, 237, 0)),\n",
+       " ('B000052YKY',\n",
+       "  'Cetaphil Gentle Cleansing Bar, Antibacterial - 4.5 oz',\n",
+       "  (224, 49, 18, 115, 0)),\n",
+       " ('B000052YL1',\n",
+       "  'Cuticura Medicated Anti-Bacterial Bar Soap, Original Formula - 5.25 oz bar',\n",
+       "  (231, 35, 248, 222, 0)),\n",
+       " ('B000052YLX',\n",
+       "  'Purpose Gentle Cleansing Bar 6 oz (170 g)',\n",
+       "  (148, 93, 207, 141, 0)),\n",
+       " ('B000052YM0',\n",
+       "  'Fruit Of The Earth 100% Aloe Vera 6oz. Gel Tube',\n",
+       "  (32, 199, 150, 237, 0)),\n",
+       " ('B000052YM3',\n",
+       "  'Alpha Hydrox AHA Enhanced Cream 2 oz.',\n",
+       "  (157, 35, 150, 28, 0)),\n",
+       " ('B000052YM4',\n",
+       "  'Alpha Hydrox Foaming Face Wash -- 6 fl oz',\n",
+       "  (145, 61, 136, 136, 0)),\n",
+       " ('B000052YM7',\n",
+       "  'Alpha Hydrox Enhanced Lotion 10 percent Glycolic AHA -- 6 fl oz Anti-Wrinkle',\n",
+       "  (145, 230, 128, 233, 0)),\n",
+       " ('B000052YM8',\n",
+       "  'Alpha Hydrox Oil Free Treatment 10 Percent AHA 1.7 fl oz.',\n",
+       "  (145, 35, 128, 68, 0)),\n",
+       " ('B000052YMG', 'Aquanil Cleanser 16 fl oz.', (231, 35, 203, 214, 0)),\n",
+       " ('B000052YMQ',\n",
+       "  'Cetaphil Moisturizing Cream, Fragrance Free - 16 oz',\n",
+       "  (157, 250, 32, 237, 0)),\n",
+       " ('B000052YMR',\n",
+       "  'Cetaphil Moisturizing Cream, 3.0 - Ounces Tube (Pack of 3)',\n",
+       "  (10, 84, 136, 215, 0)),\n",
+       " ('B000052YMS',\n",
+       "  'Cetaphil Moisturizing Lotion, Fragrance Free - 16 fl oz',\n",
+       "  (231, 151, 176, 233, 0)),\n",
+       " ('B000052YMT',\n",
+       "  'Cetaphil Gentle Skin Cleanser, 4.0 -Ounce Bottles (Pack of 6)',\n",
+       "  (58, 119, 183, 223, 0)),\n",
+       " ('B000052YMV',\n",
+       "  'Cetaphil Gentle Skin Cleanser - 16 fl oz',\n",
+       "  (9, 119, 188, 233, 0)),\n",
+       " ('B000052YMX',\n",
+       "  'Complex 15 Therapeutic Moisturizing Face Cream - 2.5 Ounce',\n",
+       "  (10, 93, 32, 141, 0)),\n",
+       " ('B000052YN5',\n",
+       "  'DML Daily Facial Moisturizer, SPF 25 - 1.5 Oz',\n",
+       "  (157, 241, 10, 28, 0)),\n",
+       " ('B000052YN7', 'DML Moisturizing Lotion 16 oz.', (32, 199, 136, 237, 0)),\n",
+       " ('B000052YOL',\n",
+       "  'Neutrogena Healthy Skin Anti-Wrinkle Cream, SPF 15, 1.4 Ounce',\n",
+       "  (75, 84, 188, 237, 0)),\n",
+       " ('B000052YOR',\n",
+       "  'Neutrogena Body Oil, Light Sesame Formula, 8.5 Ounce',\n",
+       "  (25, 151, 184, 18, 0)),\n",
+       " ('B000052YOX',\n",
+       "  'Neutrogena Oil-Free Moisture, Sensitive Skin, 4 Ounce',\n",
+       "  (235, 84, 150, 255, 0)),\n",
+       " ('B000052YP4',\n",
+       "  'Neutrogena Light Night Cream, 2.25 Ounce',\n",
+       "  (75, 230, 18, 166, 0)),\n",
+       " ('B000052YP6',\n",
+       "  'Neutrogena Norwegian Formula Hand Cream, Fragrance-Free (2 Ounce)',\n",
+       "  (32, 151, 203, 236, 0)),\n",
+       " ('B000052YQ0',\n",
+       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 Normal 4 Fl Oz',\n",
+       "  (189, 1, 157, 214, 0)),\n",
+       " ('B000052YQ2',\n",
+       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 - Sensitive 4 Fl Oz',\n",
+       "  (189, 93, 157, 105, 0)),\n",
+       " ('B000052YQN', \"POND'S Cold Cream Cleanser, 3.5 oz.\", (109, 93, 207, 214, 0)),\n",
+       " ('B000052YQU',\n",
+       "  \"POND'S Dry Skin Cream Facial Moisturizer, 3.9-Ounce (Packaging May Vary)\",\n",
+       "  (233, 14, 137, 254, 0)),\n",
+       " ('B000052Z5B',\n",
+       "  'Coty Airspun Face Powder, Naturally Neutral, 2.3 oz',\n",
+       "  (46, 35, 25, 28, 0)),\n",
+       " ('B000052Z8D',\n",
+       "  'Max Factor Pan-Stik Ultra Creamy Makeup, Sun Tone 137 .5 oz (14 g)',\n",
+       "  (15, 160, 130, 136, 0)),\n",
+       " ('B000052ZB1',\n",
+       "  'Neutrogena Intensified Day Moisture, SPF 15, 2.25 Ounce',\n",
+       "  (42, 222, 183, 251, 0)),\n",
+       " ('B000052ZB2',\n",
+       "  'Neutrogena Extra Gentle Cleanser, 6.7 Ounce',\n",
+       "  (42, 93, 48, 177, 0)),\n",
+       " ('B000052ZB4',\n",
+       "  'Neutrogena Deep Clean Facial Cleanser, Normal to Oily Skin, 6.7 Ounce',\n",
+       "  (242, 230, 205, 105, 0)),\n",
+       " ('B000052ZBD',\n",
+       "  'Neutrogena Rainbath Gel, Original, 16  Ounce',\n",
+       "  (224, 0, 48, 166, 0)),\n",
+       " ('B000052ZBH',\n",
+       "  'Almay One Coat Nourishing Mascara, Thickening, Black 402, 0.4-Ounce Package',\n",
+       "  (167, 39, 197, 254, 0)),\n",
+       " ('B000052ZBP',\n",
+       "  'Almay Moisturizing Eye Makeup Remover Pads, 80-Pads',\n",
+       "  (171, 39, 230, 61, 0)),\n",
+       " ('B000052ZTY',\n",
+       "  'Hugo By Hugo Boss For Men. Eau De Toilette Spray 1.3 Ounces',\n",
+       "  (26, 83, 150, 68, 0)),\n",
+       " ('B00005302B',\n",
+       "  'Jergen Extra Moisturizing Liquid Hand Wash 7.5 Oz',\n",
+       "  (15, 160, 12, 220, 0)),\n",
+       " ('B00005304H', 'Q-tips Cotton Swabs, 500 Count', (26, 88, 12, 68, 0)),\n",
+       " ('B0000530ED',\n",
+       "  \"L'Oreal Paris Feria Hair Color, 36 Dark Burgundy Brown/Chocolate Cherry\",\n",
+       "  (115, 199, 68, 215, 0)),\n",
+       " ('B0000530EZ',\n",
+       "  'Aussie Hair Insurance Leave-In Conditioner 8 Fl Oz (Pack of 3)',\n",
+       "  (88, 0, 215, 214, 0)),\n",
+       " ('B0000530G6',\n",
+       "  'Suave Professionals Conditioner, Humectant - 12.6 Ounce',\n",
+       "  (88, 215, 197, 70, 0)),\n",
+       " ('B0000530LO',\n",
+       "  'Suave Naturals Shampoo, Daily Clarifying - 22.5oz.',\n",
+       "  (109, 39, 53, 136, 0)),\n",
+       " ('B0000530LP',\n",
+       "  'Suave Shampoo, Daily Clarifying - 12oz.',\n",
+       "  (21, 7, 203, 222, 0)),\n",
+       " ('B0000530LV',\n",
+       "  'Suave Naturals Shampoo, Sun Ripened Strawberry 22.5oz',\n",
+       "  (189, 0, 177, 42, 0)),\n",
+       " ('B0000530M0',\n",
+       "  'Suave Professionals Humectant Moisture Shampoo , 12.6 fl Ounce (373 ml)',\n",
+       "  (75, 93, 183, 22, 0)),\n",
+       " ('B0000530NA',\n",
+       "  'Olay Daily Care Refreshing Toner, 7.2-Fluid Ounce',\n",
+       "  (80, 199, 248, 136, 0)),\n",
+       " ('B0000530O9',\n",
+       "  'Olay Age Defying Classic Eye Gel 0.5 Oz',\n",
+       "  (2, 151, 188, 4, 0)),\n",
+       " ('B0000531GD',\n",
+       "  'Maybelline New York Shine Free Oil Control Pressed Powder, Ivory - 1 ea',\n",
+       "  (39, 199, 137, 249, 0)),\n",
+       " ('B0000531GV',\n",
+       "  'Maybelline New York Expert Eyes 100% Oil-Free Eye Makeup Remover, 2.3 Fl. Oz.',\n",
+       "  (201, 145, 248, 254, 0)),\n",
+       " ('B0000531II',\n",
+       "  'Maybelline New York Shine Free Oil Control Loose Powder, Light, 0.7 Ounce',\n",
+       "  (93, 39, 151, 187, 0)),\n",
+       " ('B0000531NU',\n",
+       "  \"Maybelline New York Volum' Express - Waterproof Mascara, Very Black - .34 fl oz\",\n",
+       "  (183, 215, 137, 251, 0)),\n",
+       " ('B0000531SW',\n",
+       "  \"L'Oreal Paris Visible Lift Line-Minimizing &amp; Tone-Enhancing Makeup, Soft Ivory, 1.25 Ounces\",\n",
+       "  (147, 119, 215, 215, 0)),\n",
+       " ('B0000531WZ',\n",
+       "  'Revlon Super Lustrous Creme Lipstick, Fire and Ice 720, 0.15 Ounce',\n",
+       "  (8, 222, 248, 22, 0)),\n",
+       " ('B00005321Z',\n",
+       "  \"Revlon Eterna '27' Moisture Cream with Progenitin, 2 Ounce\",\n",
+       "  (37, 1, 188, 105, 0)),\n",
+       " ('B0000532AN', 'Revlon Lash Curlers, 1 Count', (87, 199, 177, 68, 0)),\n",
+       " ('B0000532VT',\n",
+       "  'Neutrogena Clean Conditioner, Replenishing , 10.1 Fluid Ounce',\n",
+       "  (26, 83, 31, 237, 0)),\n",
+       " ('B0000532YN',\n",
+       "  'Kiss My Face Bar Soap, 8.0 oz, Pure Olive Oil. 1-Bar',\n",
+       "  (151, 230, 215, 88, 0)),\n",
+       " ('B00005333G',\n",
+       "  \"L'Oreal Paris Hydra-Renewal Continuous Moisture Cream, 1.7 Ounce\",\n",
+       "  (130, 145, 53, 220, 0)),\n",
+       " ('B00005333I',\n",
+       "  \"L'Oreal Paris Eye Defense, 0.5 Fluid Ounce\",\n",
+       "  (2, 49, 150, 166, 0)),\n",
+       " ('B0000533CC',\n",
+       "  'Instead 12 Hour Feminine Protection Cup 24 ea',\n",
+       "  (120, 64, 157, 68, 0)),\n",
+       " ('B0000534VO',\n",
+       "  'Clean &amp; Clear Continuous Control Acne Cleanser,  5 oz  (141g)',\n",
+       "  (148, 145, 248, 254, 0)),\n",
+       " ('B0000535CH',\n",
+       "  \"Nature's Cure Two-part Acne Treatment System for Women 60 Tablets 1 Oz Cream\",\n",
+       "  (232, 72, 12, 68, 0)),\n",
+       " ('B0000535OF',\n",
+       "  'Alpha Hydrox Night Replenishing Cream 2 oz.',\n",
+       "  (157, 167, 215, 22, 0)),\n",
+       " ('B0000535RA',\n",
+       "  'Neutrogena Pore Refining Toner, Alpha and Beta Hydroxy Formula, 8.5 Ounce',\n",
+       "  (42, 225, 157, 251, 0)),\n",
+       " ('B0000535RC',\n",
+       "  'NeutrogenaClear Pore Oil Eliminating Astringent, 8 Ounce',\n",
+       "  (224, 93, 129, 214, 0)),\n",
+       " ('B0000535RD',\n",
+       "  'Neutrogena Body Clear Body Wash for Clean, Clear Skin, 8.5 Ounce',\n",
+       "  (58, 39, 26, 228, 0)),\n",
+       " ('B0000535U2', 'Jason C-Effects Creme, 2 Ounces', (120, 64, 177, 68, 0)),\n",
+       " ('B0000535UA', 'Jason Shampoo, Tea Tree, 17.5 Ounce', (120, 88, 40, 68, 0)),\n",
+       " ('B0000535UM',\n",
+       "  'Jason Thin-To-Thick Extra Volume Shampoo, 8 Ounce',\n",
+       "  (120, 64, 64, 68, 0)),\n",
+       " ('B0000535UN',\n",
+       "  'Thin to Thick Extra Volume Conditioner 8 oz',\n",
+       "  (69, 49, 53, 61, 0)),\n",
+       " ('B0000535UT',\n",
+       "  'Jason Purifying Tea Tree Body Wash 30 fl oz',\n",
+       "  (157, 0, 12, 28, 0)),\n",
+       " ('B0000535UX',\n",
+       "  'Jason Pure Natural Hand Soap, Purifying Tea Tree, 16 Ounce',\n",
+       "  (120, 64, 177, 68, 1)),\n",
+       " ('B0000535VH',\n",
+       "  'Alpha Hydrox Optimum Series, Retinol Night ResQ, Anti-Wrinkle Firming Complex - 1.05 oz',\n",
+       "  (157, 52, 12, 220, 0)),\n",
+       " ('B000053676',\n",
+       "  'Avalon Organics Vitamin C Revitalizing Eye Creme, 1 Ounce Bottle',\n",
+       "  (231, 0, 176, 175, 0)),\n",
+       " ('B0000536A3',\n",
+       "  'Tommy Hilfiger Tommy Cologne Spray for Men - 3.4 Fluid Ounces',\n",
+       "  (181, 62, 203, 177, 0)),\n",
+       " ('B0000536EK',\n",
+       "  \"Mother's Special Blend All Natural Skin Toning Oil, 8-Ounce Bottle\",\n",
+       "  (58, 63, 157, 115, 0)),\n",
+       " ('B0000536EW',\n",
+       "  'Aveeno Anti-Itch Concentrated Lotion, 4 Ounce',\n",
+       "  (220, 72, 129, 88, 0)),\n",
+       " ('B0000536F0',\n",
+       "  'Aveeno Moisturizing Bar with Natural Colloidal Oatmeal for Dry Skin, Fragrance Free, 3.5 oz',\n",
+       "  (2, 231, 136, 129, 0)),\n",
+       " ('B0000536M2',\n",
+       "  \"Maybelline New York Full 'N Soft Mascara, Very Black, 0.28 Oz.\",\n",
+       "  (151, 0, 22, 136, 0)),\n",
+       " ('B0000536M3',\n",
+       "  \"Maybelline Full' N Soft Mascara: Brownish Black #302\",\n",
+       "  (204, 14, 183, 248, 0)),\n",
+       " ('B0000536P3',\n",
+       "  'Olay Total Effects 7-In-1 Anti-Aging Daily Moisturizer 1.7 Fl. Oz.',\n",
+       "  (242, 199, 48, 22, 0)),\n",
+       " ('B0000536P4',\n",
+       "  'Olay Total Effects Anti-Aging Fragrance Free Moisturizer 1.7 Fl Oz',\n",
+       "  (242, 199, 170, 22, 0)),\n",
+       " ('B00005375C',\n",
+       "  'Aveeno Daily Moisturizing Lotion, 8 Ounce',\n",
+       "  (203, 84, 188, 175, 0)),\n",
+       " ('B0000537NH',\n",
+       "  'Neutrogena Sensitive Skin Sunscreen Lotion, SPF 30, 4 Ounce',\n",
+       "  (94, 230, 25, 88, 0)),\n",
+       " ('B0000537SD', 'ZapZyt Acne Treatment Gel, 1 oz', (189, 49, 129, 61, 0)),\n",
+       " ('B000053L6W',\n",
+       "  'Clubman Pinaud Virgin Island Bay Rum, 12 Ounce',\n",
+       "  (143, 49, 48, 220, 0)),\n",
+       " ('B000056I89',\n",
+       "  'Cetaphil Daily Facial Cleanser, Normal to Oily Skin - 8 fl oz',\n",
+       "  (42, 151, 1, 85, 0)),\n",
+       " ('B000056W5Y',\n",
+       "  \"Palmer's Cocoa Butter Formula Massage Cream for Stretch Marks, 4.4 Ounce\",\n",
+       "  (33, 39, 215, 166, 0)),\n",
+       " ('B000056W74',\n",
+       "  \"Palmer's Cocoa Butter Formula Moisturizing Body Oil with Vitamin E -- 8.5 fl oz\",\n",
+       "  (80, 230, 53, 214, 0)),\n",
+       " ('B00005A43X',\n",
+       "  'Conair Supreme 2-In-1 Hot Air Styling Brush',\n",
+       "  (57, 18, 205, 177, 0)),\n",
+       " ('B00005A443',\n",
+       "  'Conair Double-Sided Battery-Operated Lighted Makeup Mirror, Polished Chrome Finish',\n",
+       "  (57, 49, 150, 54, 0)),\n",
+       " ('B00005A9WP',\n",
+       "  'Coanir Facial Sauna System with Timer',\n",
+       "  (120, 90, 207, 41, 0)),\n",
+       " ('B00005AVC7',\n",
+       "  'Clean &amp; Clear Instant Oil-Absorbing Sheets 50 sheets',\n",
+       "  (116, 7, 22, 214, 0)),\n",
+       " ('B00005B9FV',\n",
+       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Lotion SPF, 4 Ounce',\n",
+       "  (118, 160, 136, 32, 0)),\n",
+       " ('B00005CDSP',\n",
+       "  'Revlon Perfect Heat 1-1/2inch Curling Iron',\n",
+       "  (87, 93, 205, 222, 0)),\n",
+       " ('B00005IBW0',\n",
+       "  'St. Ives, Sensitive Skin Apricot Scrub, 6-Ounce (Pack of 6)',\n",
+       "  (75, 14, 13, 166, 0)),\n",
+       " ('B00005LBRT',\n",
+       "  'Avalon Organics Lavender Luminosity Daily Moisturizer, 2 Ounce',\n",
+       "  (194, 83, 90, 220, 0)),\n",
+       " ('B00005NAOD',\n",
+       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Creme, 1.7 Ounce Jar',\n",
+       "  (118, 230, 170, 68, 0)),\n",
+       " ('B00005NAOJ',\n",
+       "  'Cetaphil Gentle Cleansing Bar - 4.5 oz',\n",
+       "  (157, 61, 248, 28, 0)),\n",
+       " ('B00005NASR',\n",
+       "  'Ecco Bella Original Organic Water-Free Herbal Body Lotion, Vanilla, 8-Ounce Bottle',\n",
+       "  (2, 225, 187, 24, 0)),\n",
+       " ('B00005NFBD',\n",
+       "  'Freeman Facial Masque, Purifying - 6 fl oz',\n",
+       "  (157, 52, 157, 70, 0)),\n",
+       " ('B00005NFBJ',\n",
+       "  'Freeman Cucumber Facial Peel-Off Mask - 6 oz',\n",
+       "  (1, 84, 192, 105, 0)),\n",
+       " ('B00005O0MZ',\n",
+       "  'Conair 1875 Watt Ionic Conditioning Hair Dryer',\n",
+       "  (180, 102, 129, 249, 0)),\n",
+       " ('B00005R1H2',\n",
+       "  'Skin Success Eventone Fade Milk with Vitamin E and Alpha Hydroxy - 8.5 Fluid Ounces',\n",
+       "  (145, 41, 128, 228, 0)),\n",
+       " ('B00005R1H3',\n",
+       "  'Skin Success Eventone Fade Cream, For Oily Skin - 2.7 oz',\n",
+       "  (157, 52, 129, 28, 0)),\n",
+       " ('B00005REAQ',\n",
+       "  'Alba BotanicaTM Even Advanced Natural Moisturizer Sea Moss SPF 15 -- 2 fl oz',\n",
+       "  (41, 52, 187, 42, 0)),\n",
+       " ('B00005REAT',\n",
+       "  'Body Bath Honey Mango Alba Botanica 12 oz Liquid',\n",
+       "  (29, 7, 156, 22, 0)),\n",
+       " ('B00005S81T',\n",
+       "  'Skin Success Eventone Fade Cream, Regular - 2.7 oz',\n",
+       "  (157, 52, 129, 28, 1)),\n",
+       " ('B00005T83D',\n",
+       "  \"Palmer's Cocoa Butter Formula, Cream Soap Bar with Vitamin E, 3.5 oz - 1 ea\",\n",
+       "  (231, 41, 187, 115, 0)),\n",
+       " ('B00005TZU8',\n",
+       "  'DreamTime Inner Peace Eye Pillow, Lavender Velvet',\n",
+       "  (203, 215, 183, 115, 0)),\n",
+       " ('B00005UN90',\n",
+       "  'Suave Naturals Body Wash, Ocean Breeze - 12oz.',\n",
+       "  (238, 49, 188, 141, 0)),\n",
+       " ('B00005UQSP',\n",
+       "  'EO Shampoo for Fine/Oily Hair, Rosemary &amp; Mint, 8 fl oz (240 ml)',\n",
+       "  (175, 39, 205, 199, 0)),\n",
+       " ('B00005V5OU',\n",
+       "  'Neutrogena Healthy Defense Daily Moisturizer, SPF 30, Light Tint 1.7 Ounce',\n",
+       "  (10, 160, 130, 61, 0)),\n",
+       " ('B00005V5OW',\n",
+       "  'Maybelline Lash Discovery Washable Mascara, Very Black - .16 fl oz',\n",
+       "  (15, 90, 187, 158, 0)),\n",
+       " ('B00005V5P6',\n",
+       "  'Maybelline New York Purestay Powder &amp; Foundation SPF 15, Ivory - .34 oz',\n",
+       "  (27, 22, 248, 141, 0)),\n",
+       " ('B0000632EE',\n",
+       "  'Fallene Total Block UVA/UVB Complete Broad Spectrum Sun Protection, SPF 65 Clear, 2 fl Ounces (59 ml)',\n",
+       "  (115, 61, 10, 28, 0)),\n",
+       " ('B0000632EN',\n",
+       "  'Aveeno Positively Radiant Skin Brightening Daily Scrub, 5 Ounce',\n",
+       "  (2, 151, 170, 22, 0)),\n",
+       " ('B00006598X',\n",
+       "  'Aquis Microfiber Hair Towel, Lisse Crepe, Pink (19 x 39-Inches)',\n",
+       "  (85, 215, 183, 88, 0)),\n",
+       " ('B000065DJY',\n",
+       "  'Revlon Perfect Heat 1875W Shine Boosting Hair Dryer, RV484',\n",
+       "  (180, 49, 48, 237, 0)),\n",
+       " ('B000065DK4',\n",
+       "  'Vidal Sassoon VS184C Three-Barrel Waver',\n",
+       "  (57, 199, 26, 175, 0)),\n",
+       " ('B000066CKS',\n",
+       "  'Badger - Healing Balm For Hardworking Hands',\n",
+       "  (189, 52, 203, 237, 0)),\n",
+       " ('B0000682T7', 'Badger Balm, Sleep Balm - 2 oz', (203, 0, 183, 115, 0)),\n",
+       " ('B0000683O5',\n",
+       "  'CoverGirl Lipslicks Lip Gloss, Clear - .14 oz',\n",
+       "  (1, 52, 1, 251, 0)),\n",
+       " ('B000068U49',\n",
+       "  'Kingsley Shave Soap Bowl with Lid Dark Wood',\n",
+       "  (5, 93, 205, 61, 0)),\n",
+       " ('B00006FDU6',\n",
+       "  'Conair Straight Waves 3-in-1 Specialty Styler',\n",
+       "  (226, 222, 188, 28, 0)),\n",
+       " ('B00006FE30',\n",
+       "  'Neutrogena Clear Pore Cleanser/Mask, 4.2 Fluid Ounce (125 ml)',\n",
+       "  (100, 84, 136, 236, 0)),\n",
+       " ('B00006FRW7',\n",
+       "  'Aveeno Clear Complexion Daily Moisturizer, 4 Ounce',\n",
+       "  (203, 3, 188, 88, 0)),\n",
+       " ('B00006IGL3',\n",
+       "  'Kiss My Face Organics Break Out, Botanical Acne Gel, 1 fl oz',\n",
+       "  (242, 151, 128, 171, 0)),\n",
+       " ('B00006IGL8',\n",
+       "  'Kiss My Face Deep Cleansing Mask Pore Shrink , 2 Fluid Ounce',\n",
+       "  (29, 145, 183, 251, 0)),\n",
+       " ('B00006IV22',\n",
+       "  'Conair 1875 Watt Dual Voltage Folding Handle Hair Dryer',\n",
+       "  (87, 35, 53, 115, 0)),\n",
+       " ('B00006IV2E',\n",
+       "  'Conair BC40JBC Hot Brush, White, 0.75 Inch',\n",
+       "  (141, 22, 207, 196, 0)),\n",
+       " ('B00006IV2F', 'Instant Heat Hot Brush, 3/4-Inch', (110, 72, 129, 249, 0)),\n",
+       " ('B00006IV30', 'Conair Instant Heat Travel Hairsetter', (43, 39, 184, 18, 0)),\n",
+       " ('B00006JN4F',\n",
+       "  \"L'Oreal Paris Superior Preference Hair Color, LB-01 Extra Light Ash Blonde\",\n",
+       "  (115, 248, 177, 214, 0)),\n",
+       " ('B00006JT6O',\n",
+       "  'Olay Total Effects Anti-Aging Night Firming Treatment - 1.7 Fl Oz',\n",
+       "  (242, 199, 205, 22, 0)),\n",
+       " ('B00006K14U',\n",
+       "  'Nivea for Men Energy Face Scrub, 4.4 Ounce Tube',\n",
+       "  (75, 119, 53, 199, 0)),\n",
+       " ('B00006L9KV',\n",
+       "  'Thicker Fuller Hair Revitalizing Shampoo-12 oz',\n",
+       "  (203, 84, 192, 136, 0)),\n",
+       " ('B00006L9KW',\n",
+       "  'Thicker Fuller Hair Weightless Conditioner-12 oz',\n",
+       "  (157, 0, 157, 28, 0)),\n",
+       " ('B00007FCQ1',\n",
+       "  'Blue Lizard Australian Sunscreen, Regular, SPF 30+, 8.75-Ounce Bottle',\n",
+       "  (148, 39, 26, 199, 0)),\n",
+       " ('B00007KQF3',\n",
+       "  'Barielle Nail Strengthener Cream, 1 Ounce',\n",
+       "  (204, 41, 188, 40, 0)),\n",
+       " ('B00007M0CP',\n",
+       "  'Conair Xtreme Instant Heat Multisized Hot Rollers, Pink',\n",
+       "  (57, 49, 129, 237, 0)),\n",
+       " ('B000087L6T',\n",
+       "  'Rose Milk Skin Care Lotion - 8 fl oz',\n",
+       "  (235, 84, 205, 171, 0)),\n",
+       " ('B000089SA4', 'Soap Glycerine Vitamin-E 4 Ounces', (26, 88, 49, 214, 0)),\n",
+       " ('B00008BNZ4',\n",
+       "  'Wahl 79900B Clip-N-Trim 23-Piece Complete Haircut Kit',\n",
+       "  (87, 35, 197, 215, 0)),\n",
+       " ('B00008CMOQ',\n",
+       "  'Queen Helene Mint Julep Masque, 8 Ounce',\n",
+       "  (174, 13, 157, 228, 0)),\n",
+       " ('B00008CMOR',\n",
+       "  'Queen Helene Original Formula Antioxidant Grape Seed Extract Peel Off Masque -- 6 oz',\n",
+       "  (241, 199, 12, 207, 0)),\n",
+       " ('B00008CMOS', 'Queen Helene Masque, Mud Pack - 8 oz', (157, 52, 12, 214, 0)),\n",
+       " ('B00008DPP8',\n",
+       "  'Softsoap Naturals Moisturizing Liquid Hand Soap with Milk &amp; Honey, Pump - 7.5 fl oz',\n",
+       "  (32, 14, 48, 91, 0)),\n",
+       " ('B00008J2XQ',\n",
+       "  'Aveeno Active Naturals Skin Relief Hand Cream, 3.5 Ounce',\n",
+       "  (74, 215, 48, 236, 0)),\n",
+       " ('B00008KA7P',\n",
+       "  'No Rinse Shampoo - 16 floz (473.1 mL)',\n",
+       "  (100, 175, 215, 22, 0)),\n",
+       " ('B00008KA8D',\n",
+       "  'Revlon ColorStay Overtime Liquid Lipcolor, Keep Blushing',\n",
+       "  (15, 119, 183, 210, 0)),\n",
+       " ('B00008MNZH',\n",
+       "  'Eucerin Daily Protection Moisturizing Face Lotion, Broad Spectrum SPF 30',\n",
+       "  (235, 47, 197, 237, 0)),\n",
+       " ('B00008MOQE',\n",
+       "  'Maybelline Great Lash Mascara, Very Black -.43 Fluid Ounce',\n",
+       "  (61, 50, 129, 42, 0)),\n",
+       " ('B00008MOQJ',\n",
+       "  'Maybelline Great Lash Curved - Very Black',\n",
+       "  (61, 72, 205, 70, 0)),\n",
+       " ('B00008MOQN',\n",
+       "  'Maybelline Great Lash Waterproof Mascara, Very Black- .43 Fluid Ounce',\n",
+       "  (61, 50, 129, 42, 1)),\n",
+       " ('B00008O2X5',\n",
+       "  'Almay Anti-Perspirant &amp; Deodorant, Clear Gel, Fragrance Free - 2.25 oz',\n",
+       "  (69, 49, 136, 205, 0)),\n",
+       " ('B00008PC1O',\n",
+       "  'Sun-In Spray-In Hair Lightener, Original - 4.7 fl oz',\n",
+       "  (23, 93, 157, 61, 0)),\n",
+       " ('B00008ZPG9',\n",
+       "  'Olay Regenerist Deep Hydration Regenerating Cream Facial Moisturirzer 1.7 Fl Oz',\n",
+       "  (235, 1, 156, 237, 0)),\n",
+       " ('B00008ZPGA',\n",
+       "  'Olay Regenerist Regenerating Serum Moisturizer 1.7 Fl Oz',\n",
+       "  (109, 57, 230, 105, 0)),\n",
+       " ('B000092OX4',\n",
+       "  'Olay Regenerist Regenerating Lotion With Sunscreen Broad Spectrum SPF 15 2.5 Fl Oz',\n",
+       "  (241, 39, 26, 18, 0)),\n",
+       " ('B000093A0U',\n",
+       "  'Hugo By Hugo Boss For Women. Eau De Toilette Spray 1.3 Ounces',\n",
+       "  (26, 83, 150, 218, 0)),\n",
+       " ('B000093I60', 'No-Rinse Body Bath, 16 oz', (6, 225, 130, 237, 0)),\n",
+       " ('B000094Q6C',\n",
+       "  'Tournmaline Ceramic Curling Iron, 3/4-Inch',\n",
+       "  (87, 47, 129, 237, 0)),\n",
+       " ('B000094ZDX',\n",
+       "  'Conair 1600 Watt Folding Handle Hair Dryer',\n",
+       "  (110, 0, 156, 228, 0)),\n",
+       " ('B0000950LF',\n",
+       "  'Promensil Menopausal Relief Supplement Tablets, 60-Count Box',\n",
+       "  (120, 199, 157, 68, 0)),\n",
+       " ('B000095SDS', 'Scalpicin Max Dermatology', (3, 123, 128, 220, 0)),\n",
+       " ('B00009WO0W', \"Weleda Children's Tooth Gel, 1.7 oz\", (203, 0, 25, 220, 0)),\n",
+       " ('B0000AFUTL',\n",
+       "  'Jerdon JP7506CF 8-Inch Two-Sided Swivel Wall Mount Mirror with 5x Magnification, 13.5-Inch Extension, Chrome Finish',\n",
+       "  (57, 49, 13, 54, 0)),\n",
+       " ('B0000AJ3PT',\n",
+       "  'Aveeno Active Naturals Creamy Moisturizing Oil, 12 Ounce',\n",
+       "  (203, 84, 156, 220, 0)),\n",
+       " ('B0000AJ3PU',\n",
+       "  'Aveeno Active Naturals Stress Relief Moisturizing Lotion, 12 Ounce',\n",
+       "  (9, 93, 188, 214, 0)),\n",
+       " ('B0000AJ3Q3',\n",
+       "  \"L'Oreal Paris Double Extend Mascara, Blackest Black, 0.33 Ounces\",\n",
+       "  (118, 95, 49, 215, 0)),\n",
+       " ('B0000AN9L9',\n",
+       "  'Neutrogena Transparent Facial Bar Bonus Pack, Original Formula - 6 ea, 3.5 oz each, total 21 oz',\n",
+       "  (224, 49, 248, 42, 0)),\n",
+       " ('B0000AS54S',\n",
+       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 4 Ounce Tube',\n",
+       "  (189, 61, 136, 115, 0)),\n",
+       " ('B0000BV14G',\n",
+       "  'Cool Water By Davidoff Eau-de-toilette Spray, 1.7-Ounce',\n",
+       "  (143, 50, 157, 68, 0)),\n",
+       " ('B0000C0XL8',\n",
+       "  'Desert Essence Organic Jojoba Oil -- 4 fl oz',\n",
+       "  (145, 187, 176, 248, 0)),\n",
+       " ('B0000C4COV',\n",
+       "  'derma e Soothing Skin Treatment, Tea Tree &amp; E Antiseptic Cr&egrave;me, 4 oz (113 g)',\n",
+       "  (10, 222, 207, 61, 0)),\n",
+       " ('B0000CC64W',\n",
+       "  'Olay Regenerist Daily Regenerating Serum - Fragrance Free Moisturizer 1.7 Fl Oz',\n",
+       "  (235, 194, 188, 105, 0)),\n",
+       " ('B0000CDVN6', 'Conair Spiral Styler, 0.75 Inch', (26, 88, 59, 68, 0)),\n",
+       " ('B0000CNR0E',\n",
+       "  'Household Essentials Travel Tie Case, Black',\n",
+       "  (43, 49, 203, 251, 0)),\n",
+       " ('B0000CNR0J',\n",
+       "  'Household Essentials Grooming Travel Bag Organizer, Black',\n",
+       "  (20, 0, 207, 28, 0)),\n",
+       " ('B0000UJJH0',\n",
+       "  'Desert Essence Tea Tree Oil 100% Pure, 2-Ounce',\n",
+       "  (187, 49, 188, 22, 0)),\n",
+       " ('B0000UTUS8',\n",
+       "  'Aquis Microfiber Hair Turban, Lisse Crepe, Patented Design, White',\n",
+       "  (204, 215, 183, 88, 0)),\n",
+       " ('B0000UTUVU',\n",
+       "  \"Mrs. Meyer's Clean Day Dish Soap, Lavender, 16 Ounce Bottle\",\n",
+       "  (15, 7, 187, 199, 0)),\n",
+       " ('B0000UTUW4',\n",
+       "  \"Mrs. Meyer's Clean Day Liquid Hand Soap, Lavender, 12.5 Ounce Bottle\",\n",
+       "  (32, 90, 53, 249, 0)),\n",
+       " ('B0000VC1YC',\n",
+       "  'Neutrogena Body Oil, Light Sesame Formula, 32 Ounce',\n",
+       "  (162, 7, 197, 222, 0)),\n",
+       " ('B0000XMQTA',\n",
+       "  'Irene Gari Cover Your Grey for Women Touch Up Stick Medium Brown',\n",
+       "  (91, 84, 205, 105, 0)),\n",
+       " ('B0000Y3C3I', 'Afirm 3X', (145, 61, 48, 24, 0)),\n",
+       " ('B0000Y3COC', 'PanOxyl Bar 10% - 4 oz - 1 Bar', (189, 222, 18, 251, 0)),\n",
+       " ('B0000Y3CRY', 'Nizoral Anti-Dandruff Shampoo, 4 oz.', (210, 7, 203, 81, 0)),\n",
+       " ('B0000Y3CSI',\n",
+       "  'Neutrogena T/Gel Original Shampoo - 16 oz',\n",
+       "  (189, 50, 177, 136, 0)),\n",
+       " ('B0000Y3D4G',\n",
+       "  'Neutrogena Norwegian Formula Lip Moisturizer, SPF 15, 0.15 Ounce',\n",
+       "  (210, 119, 130, 222, 0)),\n",
+       " ('B0000Y3D4Q',\n",
+       "  'Rezamid Acne Treatment Lotion, 2 oz.',\n",
+       "  (157, 199, 45, 215, 0)),\n",
+       " ('B0000Y3DAK', 'DHS Clear Shampoo 8 oz', (218, 50, 188, 166, 0)),\n",
+       " ('B0000Y3FQC', 'Diamancel Tough Buffer #11', (120, 248, 177, 28, 0)),\n",
+       " ('B0000Y3FRG',\n",
+       "  'Obagi Nu-Derm Foaming Gel -- 6.7 fl oz',\n",
+       "  (162, 49, 188, 167, 0)),\n",
+       " ('B0000Y3GF2',\n",
+       "  'Free &amp; Clear Hairspray Firm Hold, 8 Ounce',\n",
+       "  (29, 0, 48, 215, 0)),\n",
+       " ('B0000Y3GIE',\n",
+       "  'Nailtiques Formula 2 Nail Growth Formula Treatments, 0.25 Ounce',\n",
+       "  (18, 39, 48, 175, 0)),\n",
+       " ('B0000Y3LAC',\n",
+       "  'Jobst It Stays Roll-on Body Adhesive 2 oz. (3 pack)',\n",
+       "  (133, 41, 22, 141, 0)),\n",
+       " ('B0000Y8H3S',\n",
+       "  'Rogaine for Men Hair Regrowth Treatment, Extra Strength Original Unscented, Set of 3, 2-Ounce Bottles',\n",
+       "  (120, 87, 157, 214, 0)),\n",
+       " ('B0000YUWY0',\n",
+       "  'jerome russell Hair Color Thickener for Thinning Hair, Black, 3.5 Ounce',\n",
+       "  (217, 0, 150, 215, 0)),\n",
+       " ('B0000YUX4O', 'Mebco Tortoise Shower Detangler', (184, 39, 184, 222, 0)),\n",
+       " ('B0000YUXGW', 'Mavala Scientifique (Nail Hardener)', (194, 110, 32, 249, 0)),\n",
+       " ('B0000YUXI0',\n",
+       "  'Mavala Stop - Helps Cure Nail Biting and Thumb Sucking, 0.3-Fluid Ounce',\n",
+       "  (53, 151, 10, 205, 0)),\n",
+       " ('B0000YV6BI', 'ApHogee Shampoo for Damaged Hair', (179, 35, 25, 248, 0)),\n",
+       " ('B0000YV6YK', \"One 'n Only Colorfix\", (183, 0, 129, 136, 0)),\n",
+       " ('B0000YV83O', 'Facial-Flex Facial Exercise System', (168, 52, 177, 215, 0)),\n",
+       " ('B0000ZHH5G',\n",
+       "  'Vanicream Moisturizing Skin Cream, 16 Ounces',\n",
+       "  (10, 13, 177, 248, 0)),\n",
+       " ('B0000ZHOEU',\n",
+       "  'Free &amp; Clear Liquid Cleanser, 8 Ounce',\n",
+       "  (1, 230, 84, 24, 0)),\n",
+       " ('B0000ZLEFU',\n",
+       "  'Auric Blends Perfume Oil, 0.33 oz - Coco Mango',\n",
+       "  (222, 230, 128, 24, 0)),\n",
+       " ('B0000ZLEX2',\n",
+       "  'Black Opium - Auric Blends Perfume Oils',\n",
+       "  (17, 225, 128, 199, 0)),\n",
+       " ('B00011JI88',\n",
+       "  'Ardell Fashion Lashes - 120 Black Demi',\n",
+       "  (43, 93, 130, 233, 0)),\n",
+       " ('B00011JOQY', 'Cricket Static Free Fast Flo', (141, 22, 10, 222, 0)),\n",
+       " ('B00011JOR8',\n",
+       "  'Cricket Hair Brush Static Free, Mini Fast Flo, 1.28 Ounce',\n",
+       "  (46, 39, 26, 222, 0)),\n",
+       " ('B00011JTFA',\n",
+       "  'Star Nail Pen-Style Polish Corrector',\n",
+       "  (11, 145, 248, 254, 0)),\n",
+       " ('B00011QTZI',\n",
+       "  'Cricket Technique Barrel Hair Brush, XX-large Round, 2 Inch',\n",
+       "  (141, 35, 26, 222, 0)),\n",
+       " ('B00011QUDE',\n",
+       "  'Helen of Troy 1514 Brush Iron, White, 1 1/2 Inches Barrel',\n",
+       "  (110, 50, 53, 42, 0)),\n",
+       " ('B00011QUKW',\n",
+       "  'Hot Tools HT1108 Professional Marcel Iron, 1 Inch',\n",
+       "  (252, 222, 184, 251, 0)),\n",
+       " ('B00011QVHO',\n",
+       "  'Spornette Porcupine Rounder Brush, 2-Inch Diameter',\n",
+       "  (190, 93, 188, 241, 0)),\n",
+       " ('B00011QVIS',\n",
+       "  'Spornette Prego Brush, 2-Inch Diameter',\n",
+       "  (165, 222, 12, 220, 0)),\n",
+       " ('B00011QVUG',\n",
+       "  'For Pro Stretch Terry Headband with Velcro 3in.',\n",
+       "  (176, 119, 22, 42, 0)),\n",
+       " ('B000127UUA',\n",
+       "  'Camille Beckman Glycerin Hand Therapy, White Lilac, 8 Ounce',\n",
+       "  (120, 22, 205, 251, 0)),\n",
+       " ('B000127UVY',\n",
+       "  'Camille Beckman Glycerin Hand Therapy, Glycerine Rosewater, 6 Ounce',\n",
+       "  (120, 22, 205, 251, 1)),\n",
+       " ('B000127VFE',\n",
+       "  'Camille Beckman Silky Body Moisturizing Cream, Glycerine Rosewater, 16 Ounce',\n",
+       "  (6, 1, 191, 105, 0)),\n",
+       " ('B00012NEYG', \"Burt's Bees Head to Toe Starter Kit\", (165, 93, 12, 199, 0)),\n",
+       " ('B00012NI7E',\n",
+       "  \"Burt's Bees Avocado Butter Pre-Shampoo Hair Treatment with Nettles and Rosemary - 4.34 fl oz\",\n",
+       "  (107, 62, 156, 22, 0)),\n",
+       " ('B00012NJZA',\n",
+       "  \"Burt's Bees Orange Essence Facial Cleanser, 4.3 Ounces\",\n",
+       "  (189, 199, 1, 115, 0)),\n",
+       " ('B00012NKGS',\n",
+       "  \"Burt's Bees: Citrus Facial Scrub, 2 oz\",\n",
+       "  (144, 242, 226, 205, 0)),\n",
+       " ('B000136P7S',\n",
+       "  'Miracle of Aloe Miracle Hand Repair Cream 8 Oz',\n",
+       "  (157, 151, 157, 215, 0)),\n",
+       " ('B00013TQRE',\n",
+       "  'Rogaine for Women Hair Regrowth Treatment 3- 2 ounce bottles',\n",
+       "  (120, 7, 157, 105, 0)),\n",
+       " ('B00013YYS0',\n",
+       "  \"Burt's Bees - Cuticle Creme Lemon Butter - 0.6 oz.\",\n",
+       "  (191, 61, 188, 237, 0)),\n",
+       " ('B00013YZ7U', \"Dr. Bronner's Almond Soap 32oz\", (144, 242, 194, 68, 0)),\n",
+       " ('B00013Z15A',\n",
+       "  \"Burt's Bees Day Creme Carrot Nutritive -- 2 fl oz\",\n",
+       "  (203, 199, 187, 214, 0)),\n",
+       " ('B000141O6E',\n",
+       "  'Redken Extreme Anti-Snap Leave-in Treatment, 8.5 Ounce',\n",
+       "  (178, 215, 1, 105, 0)),\n",
+       " ('B000142AZ8',\n",
+       "  'Bain de Soleil Mega Tan Sunscreen Lotion With Self Tanner, SPF 4 - 4 fl oz',\n",
+       "  (94, 160, 12, 220, 0)),\n",
+       " ('B000142C1A',\n",
+       "  'Essie Nail Polish Marshmallow, .5 Ounce',\n",
+       "  (194, 88, 12, 88, 0)),\n",
+       " ('B000142FVW',\n",
+       "  'Opi Nail Lacquer, Not So Bora Pink, 0.5 Fluid Ounce',\n",
+       "  (194, 225, 177, 61, 0)),\n",
+       " ('B000142JDG',\n",
+       "  'Roux Fanci-Full Mousse, 18 Spun Sand, 6 Fluid Ounce',\n",
+       "  (187, 119, 150, 241, 0)),\n",
+       " ('B000142KZ8',\n",
+       "  'Pretty Nails Instant Polish Remover Regular',\n",
+       "  (70, 215, 48, 22, 0)),\n",
+       " ('B000142MGA',\n",
+       "  'Fran Wilson Eye Tees Precision Makeup Applicators 80 Cotton Swabs',\n",
+       "  (91, 160, 7, 68, 0)),\n",
+       " ('B000142NDC',\n",
+       "  'Bigen Permanent Powder Hair Color, Dark Chestnut, .21 Ounce',\n",
+       "  (85, 84, 48, 199, 0)),\n",
+       " ('B000142OZY',\n",
+       "  'Aphogee Conditioner Pro-Vitamin Leave-In 8 oz.',\n",
+       "  (23, 151, 183, 24, 0)),\n",
+       " ('B000142P12',\n",
+       "  'ApHogee Keratin and Green Tea Restructurizer 8 fl. oz',\n",
+       "  (132, 14, 176, 236, 0)),\n",
+       " ('B000142PUS',\n",
+       "  'Clairol Professional Beautiful Collection Semi-permanent Hair Color, Light Reddish Brown',\n",
+       "  (74, 84, 207, 54, 0)),\n",
+       " ('B000142Q6G', 'Colora Henna Veg-Hair Chestnut 2oz', (210, 119, 10, 222, 0)),\n",
+       " ('B000142TU4',\n",
+       "  'Sally Hansen Polish Remover 8 oz. Strengthening',\n",
+       "  (171, 22, 130, 215, 0)),\n",
+       " ('B000142U9E',\n",
+       "  'Fantasia High Potency IC Hair Polisher Styling Gel, with Sparkle Lites, 16 oz.',\n",
+       "  (15, 151, 184, 88, 0)),\n",
+       " ('B000142ZFS', 'Super Nail Polish Thinner 4oz', (135, 119, 151, 236, 0)),\n",
+       " ('B0001431AG',\n",
+       "  'Fran Wilson Cake Eyeliner, Black, 0.09 Ounce',\n",
+       "  (184, 22, 203, 88, 0)),\n",
+       " ('B0001432S2',\n",
+       "  'OPI Natural Nail Strengthener Treatment, 0.5-Fluid Ounce',\n",
+       "  (123, 52, 22, 40, 0)),\n",
+       " ('B0001433OU',\n",
+       "  'OPI Nail Lacquer, Pirates of The Caribbean Collection, Stranger Tides, 0.5 Fluid Ounce',\n",
+       "  (251, 39, 136, 141, 0)),\n",
+       " ('B0001433VI',\n",
+       "  'OPI Start-to-finish Base Coat, Top Coat and Nail Strengthener, 0.5-Fluid Ounce',\n",
+       "  (200, 119, 157, 237, 0)),\n",
+       " ('B000143AY8', 'Fran Wilson Yellow Mood Matcher', (143, 0, 32, 28, 0)),\n",
+       " ('B000143CFA', 'Andrea Eyelash Adhesive: Clear', (125, 35, 1, 68, 0)),\n",
+       " ('B000143K2A',\n",
+       "  \"Mane 'n Tail Deep Moisturizing Conditioner\",\n",
+       "  (178, 175, 156, 248, 0)),\n",
+       " ('B000143O7G',\n",
+       "  'Instant Peel Natural Dead Skin Remover',\n",
+       "  (204, 199, 150, 177, 0)),\n",
+       " ('B000146L62',\n",
+       "  'Cassia Clove 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
+       "  (60, 84, 129, 177, 0)),\n",
+       " ('B000146LKS',\n",
+       "  'Patchouli 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
+       "  (6, 84, 129, 251, 0)),\n",
+       " ('B00014D138',\n",
+       "  'Jason Pure Natural Moisturizing Creme, Anti-Aging Tea Time, 4 Ounce',\n",
+       "  (120, 64, 177, 68, 2)),\n",
+       " ('B00014D1OW', 'Lemon Clarifying Shampoo 11 Ounces', (189, 1, 86, 22, 0)),\n",
+       " ('B00014D322',\n",
+       "  'MillCreek - Biotin Shampoo, 16 fl oz gel',\n",
+       "  (242, 35, 157, 236, 0)),\n",
+       " ('B00014D5O8', \"Burt's Bees Hand Salve, 3 oz Tin\", (165, 93, 177, 249, 0)),\n",
+       " ('B00014DGCO',\n",
+       "  'Plantlife Lavender 100% Pure Essential Oil - Bulgarian - 10 ml',\n",
+       "  (242, 50, 28, 85, 0)),\n",
+       " ('B00014DIKE',\n",
+       "  \"Burt's Bees Therapeutic Bath Crystals, 1 Pound\",\n",
+       "  (189, 199, 12, 115, 0)),\n",
+       " ('B00014DKKC',\n",
+       "  \"Burt's Bees Lemon &amp; Vitamin E Bath &amp; Body Oil, 4 Fluid Ounce\",\n",
+       "  (80, 225, 129, 61, 0)),\n",
+       " ('B00014DM5K',\n",
+       "  'Facial Toner &amp; Freshener-Aloe Vera Earth Science 8 oz Liquid',\n",
+       "  (157, 193, 215, 214, 0)),\n",
+       " ('B00014DMQE',\n",
+       "  'Reviva Labs 10% Glycolic Acid Cream -- 1.5 oz',\n",
+       "  (163, 199, 157, 222, 0)),\n",
+       " ('B00014DMRI',\n",
+       "  'Aubrey Organics - Natural Mist Herbal Hairspray (Regular Hold), 8 fl oz spray',\n",
+       "  (10, 199, 177, 215, 0)),\n",
+       " ('B00014DMUA',\n",
+       "  'Moisturizer-Daily Essential Jojoba/Aloe Desert Essence 4 oz Cream',\n",
+       "  (42, 199, 176, 203, 0)),\n",
+       " ('B00014DTY4',\n",
+       "  'Nutribiotic Skin Ointment, 0.5 Ounce',\n",
+       "  (163, 129, 150, 220, 0)),\n",
+       " ('B00014DU6G',\n",
+       "  'Earth Science Fragrance Free Clarifying Facial Wash 8 oz',\n",
+       "  (10, 52, 129, 105, 0)),\n",
+       " ('B00014DZ2K', 'AmeriLeather Leather Toiletry Bag', (176, 52, 128, 171, 0)),\n",
+       " ('B00014EH2C',\n",
+       "  'Apricot Intensive Night Cream, 1.65oz',\n",
+       "  (2, 151, 156, 141, 0)),\n",
+       " ('B00014EKJC',\n",
+       "  \"Nature's Plus Natural Beauty Cleansing Bar -- 3 oz\",\n",
+       "  (202, 222, 248, 249, 0)),\n",
+       " ('B00014FTTM', 'Reviva Labs DMAE Firming Fluid - 1 oz', (41, 50, 22, 61, 0)),\n",
+       " ('B00014GT78',\n",
+       "  'Dermablend Cover Creme - Chroma 1 - Rose Beige, 1 oz',\n",
+       "  (100, 222, 165, 32, 0)),\n",
+       " ('B00014GTWS',\n",
+       "  'Dermablend Leg and Body Cover Make-Up SPF 15, Medium, 3.4 Ounce',\n",
+       "  (29, 50, 10, 203, 0)),\n",
+       " ('B00014HF94',\n",
+       "  \"Vitamin E 14,000 IU Nature's Plus 0.5 oz Liquid\",\n",
+       "  (108, 199, 207, 214, 0)),\n",
+       " ('B00014JKG0',\n",
+       "  \"Nature's Gate Tea Tree Moisturizing Lotion for Irritated, Distressed Skin, 18-Ounce Pump Bottle\",\n",
+       "  (210, 215, 151, 88, 0)),\n",
+       " ('B00014JOHA',\n",
+       "  'Kiss My Face - Whenever Shampoo, 11 fl oz liquid',\n",
+       "  (10, 52, 150, 22, 0)),\n",
+       " ('B00014WW38',\n",
+       "  'Naturtint Permanent Hair Colorant Sandy Blonde',\n",
+       "  (203, 39, 157, 105, 0)),\n",
+       " ('B00015GWQU',\n",
+       "  'Fran Wilson Nail Tees Precision Makeup Applicators 120 Cotton Swabs',\n",
+       "  (91, 160, 248, 88, 0)),\n",
+       " ('B00015HBD8',\n",
+       "  'Phillips Light Touch 1 Cushion Hair Brush',\n",
+       "  (34, 119, 129, 18, 0)),\n",
+       " ('B00015HBEC',\n",
+       "  'Spornette Large Oval Nylon Bristle Cushion Brush',\n",
+       "  (54, 90, 12, 88, 0)),\n",
+       " ('B00015HBEM',\n",
+       "  'Spornette Double Stranded XL Nylon Rounder Brush, 2-Inch Diameter',\n",
+       "  (47, 41, 215, 215, 0)),\n",
+       " ('B00015XAQ0',\n",
+       "  'Avalon Organics Vitamin C Renewal Refreshing Cleansing Gel, 8.5 Ounce Bottle',\n",
+       "  (6, 87, 163, 70, 0)),\n",
+       " ('B00016QYOO',\n",
+       "  'Desert Essence Thoroughly Clean Face Wash - Original -- 8.5 fl oz',\n",
+       "  (203, 93, 150, 228, 0)),\n",
+       " ('B00016RIIA', 'Yerba Prima Tampico Skin Brush 1 Ea', (48, 230, 187, 199, 0)),\n",
+       " ('B00016WSIK',\n",
+       "  'Earth Therapeutics Body Sponge, Anti-Bacterial, 1 Count',\n",
+       "  (6, 93, 137, 171, 0)),\n",
+       " ('B00016WUEW',\n",
+       "  'Heritage Products Rosewater &amp; Glycerin, 4 Ounce',\n",
+       "  (15, 199, 150, 251, 0)),\n",
+       " ('B00016WULK',\n",
+       "  'Avalon Organics Vitamin C Renewal Moisture Plus Lotion SPF 15 -- 4 fl oz',\n",
+       "  (194, 83, 90, 220, 1)),\n",
+       " ('B00016WV6E', 'Henna Persia Light Brown 4 Ounces', (217, 230, 48, 28, 0)),\n",
+       " ('B00016WW8Q', \"Dickinson's Witch Hazel 16 oz\", (169, 145, 1, 136, 0)),\n",
+       " ('B00016WXEY',\n",
+       "  'LILY OF THE DESERT, Aloe Vera Gelly - 12 oz',\n",
+       "  (25, 187, 215, 215, 0)),\n",
+       " ('B00016X06O',\n",
+       "  'Herbatint Permanent Herbal Haircolor Gel, Golden Chestnut 4D, 4.56-Ounce',\n",
+       "  (18, 72, 136, 141, 0)),\n",
+       " ('B00016X3G6',\n",
+       "  'Herbal Salt Scrub Lavender 20.50 Ounces',\n",
+       "  (159, 72, 177, 215, 0)),\n",
+       " ('B00016X3L6',\n",
+       "  'GIOVANNI COSMETICS, Nutrafix Hair Reconstructor - 8.5 oz',\n",
+       "  (250, 52, 10, 141, 0)),\n",
+       " ('B00016X604',\n",
+       "  \"Smiles' Prid Homeopathic Drawing Salve, 18 GM, 1 each\",\n",
+       "  (70, 39, 183, 136, 0)),\n",
+       " ('B00016X68Q',\n",
+       "  'J.R. Liggett Bar Shampoo, Original Formula, 3.5 Ounce',\n",
+       "  (85, 230, 165, 199, 0)),\n",
+       " ('B00016XJ4M',\n",
+       "  'Thayers - Rose Petal Witch Hazel with Aloe Vera Alcohol-Free Toner - 12 oz.',\n",
+       "  (232, 0, 157, 215, 0)),\n",
+       " ('B00016XJ7O',\n",
+       "  'Thayers Witch Hazel w/Aloe Vera Lemon 12 oz',\n",
+       "  (48, 170, 192, 82, 0)),\n",
+       " ('B00016XJ8S',\n",
+       "  'Arbordoun Abundantly Herbal Calendula Cream 4 Oz',\n",
+       "  (241, 22, 156, 24, 0)),\n",
+       " ('B000172R76', 'Ace Teasing Tail Comb 8&quot; * Black', (110, 0, 188, 68, 0)),\n",
+       " ('B000177FXW',\n",
+       "  \"Bar Soap-Tea Tree Dr. Bronner's 5 oz Bar\",\n",
+       "  (84, 160, 10, 141, 0)),\n",
+       " ('B00017XWJ8',\n",
+       "  'Ralph Lauren Eau de Toilette Spray For Women, 1.7 Oz',\n",
+       "  (181, 84, 146, 248, 0)),\n",
+       " ('B000186X4I',\n",
+       "  'Shiseido Shiseido Cleansing Massage Brush',\n",
+       "  (191, 240, 230, 248, 0)),\n",
+       " ('B000195GAY',\n",
+       "  'Blue Grass By Elizabeth Arden Deodorant Cream, 1.5-Ounce',\n",
+       "  (76, 181, 150, 42, 0)),\n",
+       " ('B000196U04', 'Diamon Deb Nail File 8&quot;', (212, 7, 12, 22, 0)),\n",
+       " ('B0001AGMWE',\n",
+       "  'Miracell ProEar-for Itchy, Irritated Ears .5 OZ',\n",
+       "  (115, 240, 151, 203, 0)),\n",
+       " ('B0001AQCII',\n",
+       "  'China Glaze Nail Polish, Jetstream, 0.5 Fluid Ounce',\n",
+       "  (61, 222, 18, 54, 0)),\n",
+       " ('B0001B86HM',\n",
+       "  'Conair Pro Style 1875 Watt Hard Hat Hair Dryer',\n",
+       "  (57, 61, 191, 237, 0)),\n",
+       " ('B0001EKTZQ',\n",
+       "  'Dermalogica Multi-active Toner, 8.4 Fluid Ounce',\n",
+       "  (2, 225, 197, 227, 0)),\n",
+       " ('B0001EKWPI',\n",
+       "  'Vanicream Cleansing Bar, Fragrance Free, 3.9 Ounce Bars (Pack of 3)',\n",
+       "  (157, 193, 10, 233, 0)),\n",
+       " ('B0001EKYL0',\n",
+       "  'Exuviance Evening Restorative Complex, 1.75 Fluid Ounce',\n",
+       "  (242, 230, 183, 230, 0)),\n",
+       " ('B0001EL0CW',\n",
+       "  'Basis Cleans and Smoothes Sensitive Skin Bar',\n",
+       "  (221, 35, 150, 88, 0)),\n",
+       " ('B0001EL5R2', 'PCA Skin Acne Cream, 0.5 Ounce', (255, 41, 215, 22, 0)),\n",
+       " ('B0001EL658',\n",
+       "  'Bare Escentuals Maximum Coverage Concealer Brush',\n",
+       "  (146, 230, 197, 141, 0)),\n",
+       " ('B0001FF80G', 'Dopp Jumbo Kit w/ Zip Bottom', (176, 180, 145, 222, 0)),\n",
+       " ('B0001I8ZT4',\n",
+       "  'Mar-V-Cide Shampoo Brush &amp; Invigorator',\n",
+       "  (106, 187, 215, 166, 0)),\n",
+       " ('B0001LH6YG', 'My Spots Are Consealed Light Color', (18, 175, 22, 61, 0)),\n",
+       " ('B0001M3K4U',\n",
+       "  'Soft N Style Pro-Cap Professional Processing Caps, Extra Large, 30-count',\n",
+       "  (190, 90, 205, 237, 0)),\n",
+       " ('B0001M7D7A',\n",
+       "  'Marianna Single Prong Curl Clip 80 pcs',\n",
+       "  (125, 225, 187, 199, 0)),\n",
+       " ('B0001MC06Y',\n",
+       "  'Peter Thomas Roth Beta Hydroxy Acid 2% Acne Wash 8.5 fl oz',\n",
+       "  (224, 222, 129, 136, 0)),\n",
+       " ('B0001MROQA',\n",
+       "  'Hot Tools Big Bumper Spring Curling Iron, 1-1/2&quot;',\n",
+       "  (43, 13, 150, 215, 0)),\n",
+       " ('B0001TJX2Q',\n",
+       "  'Home Health Castor Oil, Cold Pressed and Cold Processed, 32-Ounce',\n",
+       "  (2, 14, 200, 40, 0)),\n",
+       " ('B0001TMDF0',\n",
+       "  'Heritage Store Rose Petals Rosewater 8 oz',\n",
+       "  (169, 222, 12, 233, 0)),\n",
+       " ('B0001TO3NA', \"Grandpa's Pine Tar Soap Medium Size\", (171, 0, 136, 230, 0)),\n",
+       " ('B0001TODNK',\n",
+       "  'Earth Therapeutics Hydro Exfoliating Towel, 1 each',\n",
+       "  (116, 39, 188, 228, 0)),\n",
+       " ('B0001TOH8G',\n",
+       "  'Exfoliating Hydro Gloves-Natural Earth Therapeutics 1 Set',\n",
+       "  (168, 62, 207, 141, 0)),\n",
+       " ('B0001TQ5MW', 'Margarite Zinc Cream -- 1 oz', (224, 151, 32, 230, 0)),\n",
+       " ('B0001TQ9WI',\n",
+       "  'Mountain Ocean Skin Trip Moisturizer, Coconut, 8-Ounce',\n",
+       "  (217, 230, 129, 251, 0)),\n",
+       " ('B0001TSJPI',\n",
+       "  'Bar Soap - Sandalwood, 6 Units / 2.6 oz',\n",
+       "  (251, 199, 137, 222, 0)),\n",
+       " ('B0001TSWWS',\n",
+       "  'Home Health Roll-On Deodorant Herbal Scent -- 3 fl oz',\n",
+       "  (84, 151, 207, 22, 0)),\n",
+       " ('B0001UWRCI',\n",
+       "  'Yu-Be Moisturizing Skin Cream 1.25 oz Tube',\n",
+       "  (203, 57, 177, 136, 0)),\n",
+       " ('B0001VKL7U', \"Burt's Bees Lip Shimmer, Rhubarb\", (189, 90, 26, 54, 0)),\n",
+       " ('B0001VOJWI',\n",
+       "  'Pheromone By Marilyn Miglin For Women. Eau De Parfum Spray 1.7 Oz / 50 Ml.',\n",
+       "  (191, 67, 53, 245, 0)),\n",
+       " ('B0001XKA38',\n",
+       "  'Roux Lash and Brow Tint, Brown, 40 Count',\n",
+       "  (15, 22, 10, 61, 0)),\n",
+       " ('B0001XLSQ6', 'Steam Facial By Kaz', (88, 0, 26, 187, 0)),\n",
+       " ('B0001XLSR0',\n",
+       "  'ROUX Tween Time Instant Haircolor Touch-Up Stick BLACK 1/3 oz/10g',\n",
+       "  (62, 22, 157, 136, 0)),\n",
+       " ('B0001XQNMK',\n",
+       "  'Roux Fanci Full Mousse #52 White Mink 6 oz',\n",
+       "  (183, 41, 203, 236, 0)),\n",
+       " ('B0001Y74VS',\n",
+       "  'Ouidad by Ouidad Ouidad Climate Control Heat and Humidity Gel for Unisex, 8.5 Ounce',\n",
+       "  (233, 7, 157, 241, 0)),\n",
+       " ('B0001Y74XG',\n",
+       "  'Ouidad Water Works Clarifying Shampoo, 8.5 Ounce',\n",
+       "  (27, 230, 184, 241, 0)),\n",
+       " ('B0001YOMD6',\n",
+       "  'Touch with Love Eau De Parfum Spray for Women by Fred Hayman, 1.7 Ounce',\n",
+       "  (115, 88, 40, 68, 0)),\n",
+       " ('B0001YXXUY',\n",
+       "  'Hot Tools HT1074 Anti-Static Ionic Hot Air Brush with Ion Technology, 1000 Watts, 1 1/2 Inches',\n",
+       "  (62, 215, 137, 70, 0)),\n",
+       " ('B0001ZA3Y2', 'U-Lactin Dry Skin Lotion - 16 oz.', (145, 1, 53, 199, 0)),\n",
+       " ('B0001ZA4CS',\n",
+       "  'NeoStrata Exuviance CoverBlend Multi Function Concealer SPF15 - Sand, .5 fl oz',\n",
+       "  (247, 50, 230, 251, 0)),\n",
+       " ('B0001ZYL3Q',\n",
+       "  'TheraNeem Cream - Original Organix South 2 oz Cream \\nVanilla',\n",
+       "  (250, 222, 129, 249, 0)),\n",
+       " ('B0001ZYLAO',\n",
+       "  'Emerita Pro-gest Natural Balancing Cream, 2 Ounce',\n",
+       "  (220, 145, 136, 199, 0)),\n",
+       " ('B0001ZZH0M',\n",
+       "  'Naturally Fresh, Deodorant Crystal Spray Mist, 4 Oz&nbsp;',\n",
+       "  (18, 199, 146, 166, 0)),\n",
+       " ('B00020DY2O',\n",
+       "  'TheraNeem Gentle Therape Shampoo Organix South 12 oz Liquid',\n",
+       "  (48, 72, 25, 215, 0)),\n",
+       " ('B00020E1P8', 'Shiseido Shiseido Facial Cotton', (171, 39, 10, 199, 0)),\n",
+       " ('B00021AK4I',\n",
+       "  'Calvin Klein Ck One EDT Spray 1.7 oz for Unisex',\n",
+       "  (106, 39, 207, 54, 0)),\n",
+       " ('B00021AM9G',\n",
+       "  'Hanae Mori by Hanae Mori for Women - 3.4 Ounce EDT Spray',\n",
+       "  (26, 83, 150, 218, 1)),\n",
+       " ('B00021AQCY',\n",
+       "  'Fekkai Shea Butter Moisturizing Shampoo Hair Products 8 Fl Oz',\n",
+       "  (21, 39, 176, 241, 0)),\n",
+       " ('B00021AYF8',\n",
+       "  'Mustela Foam Shampoo for Newborns - 5.07 oz.',\n",
+       "  (189, 230, 128, 136, 0)),\n",
+       " ('B00021B8L2',\n",
+       "  'Philosophy Hope in a Jar Daily Moisturizer, Dry Skin, 2 Ounce',\n",
+       "  (25, 22, 32, 248, 0)),\n",
+       " ('B00021B95W',\n",
+       "  'Philosophy Coconut Frosting Shampoo/Shower Gel/Bubble Bath, 16 Ounces',\n",
+       "  (46, 145, 136, 220, 0)),\n",
+       " ('B00021BEXY', 'Blinc Kiss Me Mascara, Dark Brown', (194, 230, 128, 42, 0)),\n",
+       " ('B00021C1LI',\n",
+       "  'Philosophy Purity Made Simple One-Step Facial Cleanser, 16 Ounces',\n",
+       "  (201, 39, 230, 177, 0)),\n",
+       " ('B00021C49W',\n",
+       "  'Philosophy Amazing Grace Shower Gel, 16 Ounces',\n",
+       "  (123, 145, 165, 166, 0)),\n",
+       " ('B00021C5Y6',\n",
+       "  'STILL JENNIFER LOPEZ by Jennifer Lopez EAU DE PARFUM SPRAY 3.4 OZ',\n",
+       "  (191, 199, 53, 68, 0)),\n",
+       " ('B00021C9BA', 'NARS Cream Blush, Lokoum', (39, 230, 205, 220, 0)),\n",
+       " ('B00021CNCK',\n",
+       "  'CASHMERE MIST by Donna Karan EDT SPRAY 3.4 OZ',\n",
+       "  (210, 61, 184, 215, 0)),\n",
+       " ('B00021CSBG', 'NARS Duo Eyeshadow, All About Eve', (47, 222, 136, 251, 0)),\n",
+       " ('B00021CT10', 'NARS Matte Eyeshadow, Thunderball', (151, 215, 26, 42, 0)),\n",
+       " ('B00021CT56', 'NARS Shimmer Eyeshadow, Nepal', (151, 215, 26, 61, 0)),\n",
+       " ('B00021D5FY',\n",
+       "  'Desire by Alfred Dunhill for Men - 1.7 Ounce EDT Spray',\n",
+       "  (169, 191, 12, 199, 0)),\n",
+       " ('B00021D6JE',\n",
+       "  'FCUK by French Connection Eau De Toilette Spray 1.7 oz for Men',\n",
+       "  (76, 50, 170, 54, 0)),\n",
+       " ('B00021DDR4', 'NARS The Multiple, Maui', (151, 119, 26, 171, 0)),\n",
+       " ('B00021DDYW', 'NARS Sheer Lipstick, Barbarella', (193, 160, 26, 199, 0)),\n",
+       " ('B00021DE14', 'NARS Semi-Matte Lipstick, Morocco', (193, 160, 26, 199, 1)),\n",
+       " ('B00021DG80', 'NARS Lip Gloss, Turkish Delight', (39, 41, 130, 42, 0)),\n",
+       " ('B00021DJ32', 'NARS Blush, Taj Mahal', (39, 39, 177, 22, 0)),\n",
+       " ('B00021DPHW', 'NARS Bronzing Powder, Casino', (197, 145, 248, 254, 0)),\n",
+       " ('B00021DTEG',\n",
+       "  'Bare Escentuals bareMinerals Glimmer Celestine',\n",
+       "  (1, 93, 177, 166, 0)),\n",
+       " ('B00021DUM2',\n",
+       "  \"bareMinerals Blush - I'm Amused Rouge\",\n",
+       "  (237, 160, 248, 61, 0)),\n",
+       " ('B00021DVCQ',\n",
+       "  'BareMinerals Original Foundation Broad Spectrum SPF 15 8 g/0.28 Oz (Fair C10 8g/0.28 oz)',\n",
+       "  (189, 22, 157, 136, 0)),\n",
+       " ('B00021DZCC',\n",
+       "  'Philosophy Falling in Love Spray Fragrance, 2 Ounce',\n",
+       "  (133, 61, 1, 249, 0)),\n",
+       " ('B00021EA9Y',\n",
+       "  'Mustela Hydra-Bebe Body Lotion w/ Pump 10.14 US fl. oz',\n",
+       "  (84, 199, 187, 22, 0)),\n",
+       " ('B00021KD2C',\n",
+       "  'derma e Anti-Wrinkle Vitamin A Retinyl Palmitate Cr&egrave;me , 4-Ounce Jar',\n",
+       "  (10, 49, 248, 230, 0)),\n",
+       " ('B00021PDN6',\n",
+       "  'Cacharel Anais Anais By Cacharel - Eau De Toilette Spray 3.4 Oz, 3.4 oz',\n",
+       "  (148, 0, 1, 141, 0)),\n",
+       " ('B00021UR3C',\n",
+       "  'Green Tea by Elizabeth Arden, 1.5 Ounce',\n",
+       "  (169, 199, 1, 42, 0)),\n",
+       " ('B00021W26M',\n",
+       "  'Prestige Liquid Eyeliner, Legend, 0.1 Ounce',\n",
+       "  (201, 215, 165, 18, 0)),\n",
+       " ('B00021WZVE',\n",
+       "  'Christian Super Long Lash Mascara Waterproof Seven Oils Black',\n",
+       "  (168, 160, 207, 177, 0)),\n",
+       " ('B000225ZL0', 'Fresh Farmacy Cleanser by LUSH', (220, 93, 188, 214, 0)),\n",
+       " ('B000225ZOC', 'Karma Handmade Soap by LUSH', (204, 35, 183, 199, 0)),\n",
+       " ('B0002260DW', 'Sex Bomb Bath Bomb by LUSH', (66, 197, 64, 5, 0)),\n",
+       " ('B0002260L4', 'Big Blue Bath Bomb by LUSH', (29, 1, 177, 251, 0)),\n",
+       " ('B0002260N2', 'Karma Komba Solid Shampoo by LUSH', (71, 1, 18, 237, 0)),\n",
+       " ('B00022F1DW',\n",
+       "  'Organic Excellence  Mint Shampoo, 16 Ounce',\n",
+       "  (189, 199, 150, 136, 0)),\n",
+       " ('B00022WA9K', 'Revlon Shine Enchancing Hot Air Kit', (43, 145, 53, 136, 0)),\n",
+       " ('B00022WA9U',\n",
+       "  'Revlon RV261 20-Roller Ionic Professional Hairsetter, Purple',\n",
+       "  (194, 52, 177, 215, 0)),\n",
+       " ('B00023DIBI',\n",
+       "  'Cucina Coriander and Olive Tree 33.8 oz Purifying Hand Wash Refill',\n",
+       "  (15, 39, 188, 228, 0)),\n",
+       " ('B00023KDAW',\n",
+       "  'amazing grace | perfumed firming body emulsion | philosophy 8 oz.',\n",
+       "  (255, 41, 187, 222, 0)),\n",
+       " ('B000248HLI',\n",
+       "  'StriVectin-SD Intensive Repair for Existing Stretch Marks, 6 oz.',\n",
+       "  (214, 7, 165, 115, 0)),\n",
+       " ('B000260KIO',\n",
+       "  'Ouidad Botanical Boost Moisture Infusing and Refreshing Spray, 8.5 Ounce',\n",
+       "  (17, 119, 151, 18, 0)),\n",
+       " ('B0002716FO',\n",
+       "  'Ardell Duralash Flare Short Black (56 Lashes)',\n",
+       "  (43, 225, 151, 187, 0)),\n",
+       " ('B000271KR8',\n",
+       "  'Clairol Natural Instincts  02 Sahara Light Blonde 1 Kit  (Pack of 3) (packaging may vary)',\n",
+       "  (100, 1, 248, 215, 0)),\n",
+       " ('B000273PEO',\n",
+       "  'Happy By Clinique For Women. Eau De Parfum Spray 1.7 Ounces',\n",
+       "  (26, 83, 150, 133, 0)),\n",
+       " ('B000273RI8', 'M.D. Forte Facial Cream III', (58, 57, 26, 18, 0)),\n",
+       " ('B000277N7Y',\n",
+       "  'Happy By Clinique For Men. Cologne Spray 1.7 Oz.',\n",
+       "  (147, 151, 26, 166, 0)),\n",
+       " ('B00027C9CS',\n",
+       "  'Suave For Men, Deep Clean Peppermint, Shampoo12.6 fl Ounce 373 ml)',\n",
+       "  (52, 90, 183, 136, 0)),\n",
+       " ('B00027CDY2',\n",
+       "  'Neutrogena Triple Moisture Silk Touch Leave-In Cream, 6 Fluid Ounce',\n",
+       "  (203, 222, 26, 18, 0)),\n",
+       " ('B00027CDYM',\n",
+       "  'Neutrogena Clean Replenishing Deep Recovery Hair Mask, 6 oz',\n",
+       "  (203, 22, 25, 18, 0)),\n",
+       " ('B00027CGWQ',\n",
+       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 12 Ounce Jar',\n",
+       "  (189, 160, 156, 199, 0)),\n",
+       " ('B00027D0DU',\n",
+       "  'Neutrogena Cosmetics  Makeup Remover, 3.8 oz',\n",
+       "  (117, 84, 165, 228, 0)),\n",
+       " ('B00027D6SE',\n",
+       "  'Bye Bye Blemish Drying Lotion - 1 fl oz',\n",
+       "  (175, 14, 170, 54, 0)),\n",
+       " ('B00027D8IC', 'Duo Lash Adhesive, Clear, 0.25 Ounce', (87, 199, 136, 18, 0)),\n",
+       " ('B00027D98Q',\n",
+       "  'Ardell Brow &amp; Lash Growth Accelerator Treatment Gel 7ml/0.25oz',\n",
+       "  (18, 199, 32, 22, 0)),\n",
+       " ('B00027DDEQ',\n",
+       "  'Clean &amp; Clear Morning Glow Moisturizer, SPF 15, 4 Ounce',\n",
+       "  (80, 50, 165, 175, 0)),\n",
+       " ('B00027DDOQ',\n",
+       "  'Clean &amp; Clear Clear Advantage Acne Spot Treatment, 0.75 Ounce',\n",
+       "  (80, 14, 1, 233, 0)),\n",
+       " ('B00027DHYM',\n",
+       "  'Natures Cure Body Acne Treatment Spray 3.5 Oz',\n",
+       "  (203, 0, 157, 136, 0)),\n",
+       " ('B00027DMI8',\n",
+       "  'RoC Retinol Correxion Deep Wrinkle Night Cream, 1-Ounce',\n",
+       "  (236, 205, 183, 199, 0)),\n",
+       " ('B00027DMJW',\n",
+       "  'Roc Retinol Correxion Deep Wrinkle Daily Moisturizer, SPF 30, 1-ounce Tube',\n",
+       "  (202, 29, 183, 24, 0)),\n",
+       " ('B00027DMLK',\n",
+       "  'Frownies Corners Of Eyes And Mouth, 144 Patches',\n",
+       "  (53, 7, 32, 222, 0)),\n",
+       " ('B00027DMSI',\n",
+       "  'Frownies Forehead &amp; Between Eyes, 144 Patches',\n",
+       "  (53, 199, 215, 88, 0)),\n",
+       " ('B00027EG9C',\n",
+       "  'Neutrogena Fresh Foaming Cleanser, 6.7 Ounce',\n",
+       "  (203, 1, 128, 222, 0)),\n",
+       " ('B00027YZKM', 'Mirror* Hand* Large* Black', (67, 50, 32, 141, 0)),\n",
+       " ('B00028F0NM',\n",
+       "  'Alba Botanica Maximum Very Emollient Body Lotion, 12 Ounce Bottle',\n",
+       "  (120, 93, 192, 41, 0)),\n",
+       " ('B00028LN1K',\n",
+       "  \"GRANDPA'S BRANDS, Pine Tar Soap Bath Size - 4.25 oz\",\n",
+       "  (191, 0, 150, 22, 0)),\n",
+       " ('B00028LURW', 'Henna Red 4 Ounces', (120, 7, 191, 22, 0)),\n",
+       " ('B00028LV4Y',\n",
+       "  'Natural Missst Herbal Hairspray Mist-Super Hold Aubrey Organics 8 oz Spray',\n",
+       "  (48, 199, 49, 237, 0)),\n",
+       " ('B00028M3N2',\n",
+       "  'Now Foods Essential Oil, Lemon, 4 Fluid Ounce',\n",
+       "  (29, 0, 183, 68, 0)),\n",
+       " ('B00028MLG6',\n",
+       "  'NOW Foods Apricot Kernel Oil (Liquid), 16 oz',\n",
+       "  (203, 199, 26, 88, 0)),\n",
+       " ('B00028MLHA',\n",
+       "  'Cocoa Butter with jojoba oil - 100% Pure 6.5 fl.oz',\n",
+       "  (29, 50, 188, 248, 0)),\n",
+       " ('B00028O7ZO',\n",
+       "  \"Castile Liquid Soap-Lavender Dr. Bronner's 1 Gallon Liquid\",\n",
+       "  (37, 1, 188, 88, 0)),\n",
+       " ('B00028OC4K',\n",
+       "  'Throughly Clean Face Wash - Original - 8.5 oz. (250 ml) - Liquid',\n",
+       "  (157, 62, 45, 215, 0)),\n",
+       " ('B00028OPZ6',\n",
+       "  'GPB (Glycogen Protein Balance) Conditioner Jumbo Size - 16 oz - Liquid',\n",
+       "  (10, 0, 177, 215, 0)),\n",
+       " ('B00028OQZA',\n",
+       "  'Sparkling Glacier Therapy Complexion Mist Aubrey Organics 3.4 oz (100ml) Liquid',\n",
+       "  (118, 240, 25, 68, 0)),\n",
+       " ('B00028OSI0', 'Aloe Life Skin Gel and Herbs, 8 oz', (203, 49, 130, 40, 0)),\n",
+       " ('B00028OTAM',\n",
+       "  'Gabriel Cosmetics Inc. - Moisturizing Liquid Foundation Soft Beige 18 SPF - 1 oz.',\n",
+       "  (26, 110, 64, 251, 0)),\n",
+       " ('B00028OTNY',\n",
+       "  'Mandarin Magic Hair Gel Aubrey Organics 8 oz Liquid',\n",
+       "  (84, 151, 188, 237, 0)),\n",
+       " ('B00028OU5Q', 'Blue Green Algae Hair Mask 4 oz', (157, 145, 10, 237, 0)),\n",
+       " ('B00028PDWK', 'Apricot Gentle Facial Scrub, 4 Ounce', (2, 39, 177, 136, 0)),\n",
+       " ('B00028PFF0', 'WITCH HAZEL PADS EA 1/60 PADS', (18, 0, 12, 187, 0)),\n",
+       " ('B00028PXAC', 'Viobin Wheat Germ Oil 16 oz', (31, 93, 28, 237, 0)),\n",
+       " ('B0002A65LQ',\n",
+       "  'Philosophy Help Me Retinol Night Treatment, 1.05 Ounce',\n",
+       "  (148, 90, 205, 220, 0)),\n",
+       " ('B0002B0R14',\n",
+       "  'H2Ocean 1.5 FL OZ Purified Ocean Salt Water Piercing Aftercare',\n",
+       "  (26, 110, 64, 214, 0)),\n",
+       " ('B0002BB3YO',\n",
+       "  'NaturalCare Homeopathic Ultra Vein-Gard Leg Therapy Cream, 2.25-Ounce Package',\n",
+       "  (32, 199, 10, 251, 0)),\n",
+       " ('B0002BPIZO',\n",
+       "  'Philosophy Pure Grace Shampoo, Bath &amp; Shower gel, 16 Ounces',\n",
+       "  (123, 52, 177, 249, 0)),\n",
+       " ('B0002CTSRM', 'Amber Butter', (143, 0, 136, 251, 0)),\n",
+       " ('B0002DLEJG',\n",
+       "  'Nailtek Foundation No.2 Ridge-Filling Nail Strengthener Base Coat, 0.5 Fluid Ounce',\n",
+       "  (15, 160, 130, 222, 0)),\n",
+       " ('B0002DNZAC', 'MAC Eye Shadow Frost Satin Taupe', (34, 160, 136, 237, 0)),\n",
+       " ('B0002DO1RI',\n",
+       "  'MAC Studio Fix Powder Plus Foundation NC35 for Women, 0.52 Ounce',\n",
+       "  (121, 39, 151, 61, 0)),\n",
+       " ('B0002DO1RS',\n",
+       "  'MAC Studio Fix Powder Plus Foundation NC45',\n",
+       "  (121, 233, 28, 222, 0)),\n",
+       " ('B0002DPG1I', 'Fat Girl Slim by Bliss', (84, 47, 205, 88, 0)),\n",
+       " ('B0002DTV0K',\n",
+       "  'Emu Oil Soap Bar (110g) Brand: SoapWorks',\n",
+       "  (235, 49, 48, 61, 0)),\n",
+       " ('B0002DUSR0', 'PediFix Visco GEL Toe Spacers, Medium', (15, 7, 22, 214, 0)),\n",
+       " ('B0002DUSUW', 'PediFix Double-Toe Straightener #P57', (5, 49, 205, 237, 0)),\n",
+       " ('B0002E4WOO',\n",
+       "  \"Miracle of Aloe Miracle Hand Repair Cream 8 Oz Relieve Dry, Cracked, Flacking Hands Immediately! Therapeutic Formula Contains 60% Ultra Aloe - The Purest Most Potent Form of Whole Leaf Aloe Vera Gel. Fast Acting Relief, Say Good Bye to Dry, Cracked Hands Now! Keep Your Hands Healthy &amp; Warm this Winter! Reduces Flaking and Redness, Use on Hands, Elbows and Knees, Exclusive Fast Acting Formula Penetrates Deep Into Damaged Skin Layers to Moisturize Where It's Needed Most. Leaves Hands Feeling Silky Smooth and Comfortable. Olay, Ponds, Vaseline, Nivea, Eucerin, Aveeno.\",\n",
+       "  (157, 52, 230, 237, 0)),\n",
+       " ('B0002EBI82', 'Toppik Hair Fattener, 4-Ounce', (120, 84, 156, 105, 0)),\n",
+       " ('B0002EZXUG',\n",
+       "  'Vaseline Intensive Care Moisturizing Bath Beads, Gentle Breeze, 24 Ounces',\n",
+       "  (173, 93, 32, 205, 0)),\n",
+       " ('B0002F1H6E',\n",
+       "  'Jergens All-Purpose Face Cream 425g/15oz',\n",
+       "  (52, 225, 137, 222, 0)),\n",
+       " ('B0002FBOLW',\n",
+       "  'Atopalm Intensive Moisturizing Cream, 3.4 fl. Ounce',\n",
+       "  (203, 230, 205, 175, 0)),\n",
+       " ('B0002FCCH2',\n",
+       "  'Suave Naturals Shampoo, Tropical Coconut  - 22.5oz.',\n",
+       "  (21, 39, 165, 228, 0)),\n",
+       " ('B0002FCD5I',\n",
+       "  'Suave Naturals Conditioner, Tropical Coconut - 22.5oz.',\n",
+       "  (109, 39, 156, 24, 0)),\n",
+       " ('B0002FCDHQ',\n",
+       "  'Suave For Kids Double Dutch Apple Detangler Spray Conditioner 10.5Ounces',\n",
+       "  (88, 90, 215, 28, 0)),\n",
+       " ('B0002G214U', 'Conair Soft Bonnet Hair Dryer', (3, 84, 191, 88, 0)),\n",
+       " ('B0002G21GI',\n",
+       "  'Ion Shine&reg; by Conair Instant Heat Compact Styling Setter',\n",
+       "  (43, 39, 151, 136, 0)),\n",
+       " ('B0002JG2NI',\n",
+       "  'Home Health Castor Oil Cold Pressed &amp; Cold Processed 8 fl. oz.',\n",
+       "  (189, 90, 215, 203, 0)),\n",
+       " ('B0002JGIZA',\n",
+       "  'Avalon Organics Night Cream Lavender Luminosity -- 2 oz',\n",
+       "  (202, 29, 183, 91, 0)),\n",
+       " ('B0002JGU30',\n",
+       "  'Avalon Organics Hand &amp; Body Lotion, Peppermint, 12 Ounce',\n",
+       "  (194, 83, 150, 220, 0)),\n",
+       " ('B0002JHI1I',\n",
+       "  'Avalon Organics CoQ10 Facial Cleansing Milk -- 8.5 fl oz',\n",
+       "  (118, 145, 248, 254, 0)),\n",
+       " ('B0002JIHT0',\n",
+       "  'Home Health Antidandruff Shampoo, Everclean, Unscented, 8-Ounce',\n",
+       "  (143, 199, 192, 199, 0)),\n",
+       " ('B0002JISX0',\n",
+       "  'Earth Science A-D-E Creamy Fruit Oil Cleanser 8 oz.',\n",
+       "  (177, 230, 26, 91, 0)),\n",
+       " ('B0002JIUDS',\n",
+       "  'Nutricology Ox Bile, 500 mg,  100 Vegetarian Capsules',\n",
+       "  (189, 218, 10, 228, 0)),\n",
+       " ('B0002JKPA4', 'Caruso C97958 ION Steam Hairsetter', (87, 230, 188, 70, 0)),\n",
+       " ('B0002JKQ8K',\n",
+       "  'Caruso Professional Molecular Steam Rollers with Shields, Small (6-Pack)',\n",
+       "  (61, 52, 188, 88, 0)),\n",
+       " ('B0002JMZ7A',\n",
+       "  'Yves Saint Laurent Volume Effet Faux Cils Luxurious Mascara for a False Lash Effect 1 High Density Black',\n",
+       "  (17, 233, 203, 249, 0)),\n",
+       " ('B0002JNRCC',\n",
+       "  'Redken All Soft Conditioner Unisex Conditioner, 8.5 Ounce',\n",
+       "  (100, 215, 230, 24, 0)),\n",
+       " ('B0002KS5VE',\n",
+       "  'Body Fantasies Body Spray for Women, Cotton Candy Fantasy Fragrance, 8 Ounce',\n",
+       "  (210, 62, 184, 115, 0)),\n",
+       " ('B0002L2JLU',\n",
+       "  'Topix Pharm SPF 40 Citrix Antioxidant Sunscreen, 3.5 Fluid Ounce',\n",
+       "  (10, 145, 151, 24, 0)),\n",
+       " ('B0002M9OYY',\n",
+       "  'Fake Bake Passion Fruit Body Polish 6 oz.',\n",
+       "  (163, 61, 157, 214, 0)),\n",
+       " ('B0002NIAC0',\n",
+       "  'Obagi C-Exfoliating Day Lotion 2 fl oz.',\n",
+       "  (232, 62, 165, 199, 0)),\n",
+       " ('B0002OCNMC',\n",
+       "  'Goat Milk Soap - Original (3 Pack) Canus Vermont 3 Bar Soap',\n",
+       "  (169, 215, 84, 177, 0)),\n",
+       " ('B0002OMOIA',\n",
+       "  'Brahmi Oil - Ayurvedic Hair Growth massage oil',\n",
+       "  (203, 50, 192, 215, 0)),\n",
+       " ('B0002PCH2M',\n",
+       "  'Focus 21 Sea Plasma All Purpose Skin and Hair Moisturizing Spray 12 oz',\n",
+       "  (10, 14, 13, 136, 0)),\n",
+       " ('B0002Q8W9I',\n",
+       "  'NEW cnd Nail Design Stickey Base Coat 0.33Oz',\n",
+       "  (5, 93, 203, 249, 0)),\n",
+       " ('B0002QB1SC',\n",
+       "  'Creative Nail Design Creative Nail Solar Oil, 0.5 Ounce',\n",
+       "  (80, 49, 137, 199, 0)),\n",
+       " ('B0002RI2PG',\n",
+       "  'Dermablend Loose Setting Powder Original, 1 Ounce',\n",
+       "  (183, 41, 1, 141, 0)),\n",
+       " ('B0002RPTX4', 'Shampoo Scalp Massage Brush- 1 Brush', (24, 222, 203, 28, 0)),\n",
+       " ('B0002RYHTQ',\n",
+       "  'Spa Sister Womens Cotton Moisture Enhancing Overnight Gloves',\n",
+       "  (100, 119, 184, 175, 0)),\n",
+       " ('B0002SA9BU',\n",
+       "  'Best Hair Growth Vitamins NEW ADVANCED Hair Formula 37 Hair Vitamins for Faster Growing Healthy Hair 1 month Supply 60 Capsules',\n",
+       "  (120, 145, 248, 254, 0)),\n",
+       " ('B0002SGNOM',\n",
+       "  'California Tan Sunsets Tanning Eyewear 1-PR Random Picked Colors',\n",
+       "  (230, 39, 12, 199, 0)),\n",
+       " ('B0002SGSNI', 'China Glaze Strong Adhesion Base Coat', (77, 52, 248, 88, 0)),\n",
+       " ('B0002SI7DW',\n",
+       "  'NAIL TEK Regular Size Crystal File with Free Blue Case',\n",
+       "  (191, 205, 22, 22, 0)),\n",
+       " ('B0002SISY0',\n",
+       "  'Spornette My Favorite Mini Nylon Tipped Paddle Brush',\n",
+       "  (5, 199, 1, 139, 0)),\n",
+       " ('B0002SQ6YO', 'Sponge Company Cosmetic Silk Sponge 1', (46, 52, 25, 68, 0)),\n",
+       " ('B0002TSCZY',\n",
+       "  'Solia Tourmaline Ceramic Ion Flat Iron (1&quot;)',\n",
+       "  (207, 145, 1, 22, 0)),\n",
+       " ('B0002TSD08',\n",
+       "  'Solia Professional Ceramic Ion Flat Iron (1-3/4&quot;)Solia',\n",
+       "  (243, 199, 53, 199, 0)),\n",
+       " ('B0002UEDWE', 'Evian Facial Water Spray 1.7 Oz', (203, 1, 157, 251, 0)),\n",
+       " ('B0002VHBTU', 'Thermal Spa Hair Wrap - White', (146, 35, 151, 105, 0)),\n",
+       " ('B0002VJIH8',\n",
+       "  'Best Hair Vitamin to Grow Hair Faster with Fast Grow Ethnic Hair Growth Enhancer 90 capsules 30 Day Supply',\n",
+       "  (120, 47, 26, 248, 0)),\n",
+       " ('B0002VQ0WO', 'Hair Dryer Stand', (45, 225, 32, 251, 0)),\n",
+       " ('B0002WUHTU',\n",
+       "  'Nailtiques Nail Protein Formula 2 Plus',\n",
+       "  (18, 50, 170, 136, 0)),\n",
+       " ('B0002X4F0Q',\n",
+       "  'PCA Skin Blemish Control Bar (Phaze 32), 3.3 Ounce',\n",
+       "  (143, 199, 150, 177, 0)),\n",
+       " ('B0002X9038',\n",
+       "  'PCA Skin Pigment Gel (Phaze 13), 1 Fluid Ounce',\n",
+       "  (32, 35, 129, 68, 0)),\n",
+       " ('B0002XBTVO', 'PCA Skin Clearskin, 1.7 Ounce', (222, 35, 177, 214, 0)),\n",
+       " ('B0002XG9X2',\n",
+       "  'Panasonic EH2511A Pore Cleanser with Micro-fine Mist',\n",
+       "  (120, 93, 184, 105, 0)),\n",
+       " ('B0002XY3QC', 'duri Miracote .61oz', (230, 13, 192, 241, 0)),\n",
+       " ('B0002YE8F2',\n",
+       "  'Revlon RV2513 Color Swirl Detangling Comb, Colors may vary',\n",
+       "  (125, 50, 183, 220, 0)),\n",
+       " ('B0002YE8IO',\n",
+       "  'Revlon RV2641 Soft Touch Porcupine Cushion Brush, Colors May Vary',\n",
+       "  (125, 230, 150, 220, 0)),\n",
+       " ('B0002YE8V6',\n",
+       "  'Vidal Sassoon VS7963 Ionic Thermal All Purpose Brush',\n",
+       "  (17, 205, 26, 105, 0)),\n",
+       " ('B0002YE8WA',\n",
+       "  'Vidal Sassoon VS14434 No Headache Headbands (2 Headbands)',\n",
+       "  (91, 175, 184, 18, 0)),\n",
+       " ('B0002YFQ6W',\n",
+       "  'Cloud Star Corporation Buddy Wash Lavender &amp; Mint',\n",
+       "  (61, 199, 205, 249, 0)),\n",
+       " ('B0002YLY9K', 'Obagi Nu-Derm Toner 6.7 oz.', (178, 225, 1, 203, 0)),\n",
+       " ('B0002Z0R9C',\n",
+       "  'Spornette No.215 Super Looper Hair Brush',\n",
+       "  (146, 240, 156, 171, 0)),\n",
+       " ('B0002Z0U5I',\n",
+       "  'Helen of Troy Professional Tangle-Free Hot Air Brush Iron',\n",
+       "  (212, 86, 10, 28, 0)),\n",
+       " ('B0002Z0UA8',\n",
+       "  'FusionBeauty LipFusion Micro-Injected Collagen Lip Plump Color Shine, Summer',\n",
+       "  (148, 1, 12, 141, 0)),\n",
+       " ('B0002Z1JJ4', 'Acquarella Remover', (120, 84, 26, 41, 0)),\n",
+       " ('B0002Z1JK8', 'Acquarella Conditioner', (120, 84, 26, 18, 0)),\n",
+       " ('B0002Z81O0',\n",
+       "  'Peter Thomas Roth Max Complexion Correction Pads? (60 Pads)',\n",
+       "  (242, 35, 165, 141, 0)),\n",
+       " ('B0002Z8GEK', 'Gena Hoof Lacquer, 5 Ounce', (1, 175, 187, 105, 0)),\n",
+       " ('B0002Z8HAI',\n",
+       "  'Essie Nail Lacquer, Sugar Daddy, 0.5 Fluid Ounce',\n",
+       "  (251, 35, 136, 54, 0)),\n",
+       " ('B0002Z8IV6',\n",
+       "  'Ardell Hair Color Bottle, Unred, 0.25 Ounce',\n",
+       "  (70, 0, 136, 236, 0)),\n",
+       " ('B0002Z8LLI',\n",
+       "  'Sensories Smoother Passionflower and Aloe Leave In Texturizing Condition By Rusk, 33.8 Ounce',\n",
+       "  (169, 151, 205, 42, 0)),\n",
+       " ('B0002Z8MLC', 'ISO Bouncy Creme 8.5 oz.', (32, 225, 170, 61, 0)),\n",
+       " ('B0002Z8NJS',\n",
+       "  'Essie Nail Lacquer, Blanc, 0.5 Fluid Ounce',\n",
+       "  (40, 151, 197, 54, 0)),\n",
+       " ('B0002Z8OUG', 'Ruby Stone Nail File', (176, 215, 12, 215, 0)),\n",
+       " ('B0002Z8P1E', 'Quantum Classic Extra Body Acid Perm', (1, 49, 192, 230, 0)),\n",
+       " ('B0002Z8P8M', \"L'Oreal ColorZap Haircolor Remover\", (165, 1, 18, 199, 0)),\n",
+       " ('B0002Z8QG8',\n",
+       "  'Denman Styling Brush, Heavy Weight, 9-Row',\n",
+       "  (190, 84, 205, 237, 0)),\n",
+       " ('B0002Z8QHW', 'Creative Nail Design SolarOil .5oz', (29, 7, 207, 177, 0)),\n",
+       " ('B0002Z8RGC', 'Blax 4 mm Ponytail Holders - Black', (99, 160, 195, 105, 0)),\n",
+       " ('B0002Z8SE8',\n",
+       "  'Spilo: MISC Big Bondini Plus Brush-On Glue, 0.5 oz',\n",
+       "  (61, 0, 32, 28, 0)),\n",
+       " ('B0002Z8SZW',\n",
+       "  'Bain De Terre Serum Anti-Frizz Recovery Complex 1.7 oz.',\n",
+       "  (222, 72, 170, 141, 0)),\n",
+       " ('B0002Z90RM',\n",
+       "  'Rusk Thick Body and Texture Amplifier, 6 Ounce',\n",
+       "  (1, 222, 18, 141, 0)),\n",
+       " ('B0003009NK',\n",
+       "  'Hai Classic Convertible Ceramic Flat Iron, 1-1/4 Inch',\n",
+       "  (223, 225, 53, 228, 0)),\n",
+       " ('B000629ZHG', 'Tinted Moisturizer - Almond', (168, 22, 48, 141, 0)),\n",
+       " ('B00062A0JI',\n",
+       "  'Laura Mercier Secret Concealer # 1 0.08oz',\n",
+       "  (178, 215, 1, 22, 0)),\n",
+       " ('B00062ADJA',\n",
+       "  'Laura Mercier Loose Setting Powder - Ivory - 29g/1oz',\n",
+       "  (168, 145, 137, 22, 0)),\n",
+       " ('B0006433MM',\n",
+       "  \"18'' Detachable Long Handle Natural Boar Bristle Brush\",\n",
+       "  (183, 39, 26, 251, 0)),\n",
+       " ('B00064A5L4',\n",
+       "  'Be Delicious by Donna Karan Eau De Parfum Spray 1 oz for Women',\n",
+       "  (215, 7, 25, 32, 0)),\n",
+       " ('B00064H44K',\n",
+       "  'Vanderbilt by Gloria Vanderbilt for Women - 3.3 Ounce EDT Spray',\n",
+       "  (26, 83, 150, 68, 1)),\n",
+       " ('B00065AFFE',\n",
+       "  'Conair 1875 Watt Ergonomic Handle Hair Dryer',\n",
+       "  (206, 35, 203, 220, 0)),\n",
+       " ('B00066D2JE',\n",
+       "  'Giovanni Conditioner, Smooth as Silk for Damaged Hair, 8.5 fl oz Bottle',\n",
+       "  (217, 57, 205, 254, 0)),\n",
+       " ('B00066D4M4',\n",
+       "  'Giovanni Direct Leave-In Conditioner, Weightless moisture conditioner 8.5 fl oz Bottle',\n",
+       "  (217, 189, 18, 22, 0)),\n",
+       " ('B00066LD7C',\n",
+       "  'Xenna Curlaway Gradual Curl Relaxer Green Apple Fragrance -- 6 oz',\n",
+       "  (106, 50, 137, 28, 0)),\n",
+       " ('B00066YC34',\n",
+       "  'Conair Supreme Triple Curling Iron Pack  - 1/2 inch, 3/4 inch and 1 inch',\n",
+       "  (119, 49, 146, 136, 0)),\n",
+       " ('B000674XN2',\n",
+       "  'Laura Mercier Secret Brightener - Secret Brightener #1, .05 oz.',\n",
+       "  (168, 22, 53, 22, 0)),\n",
+       " ('B000674XNM',\n",
+       "  'Laura Mercier Secret Brightening Powder - Secret Brightening Powder #1, 0.14 oz.',\n",
+       "  (13, 84, 207, 222, 0)),\n",
+       " ('B000674Z8A',\n",
+       "  'Laura Mercier Accessories - Sponge, 4-pack',\n",
+       "  (5, 49, 129, 237, 0)),\n",
+       " ('B00067EJ4U', 'Laura Mercier Foundation Primer', (137, 54, 25, 228, 0)),\n",
+       " ('B00067YSLO',\n",
+       "  'Remington Wet 2 Straight 2&quot; Wide Plate Wet/Dry Ceramic Hair Straightening Iron with Tourmaline',\n",
+       "  (176, 52, 129, 18, 0)),\n",
+       " ('B00068A0AQ',\n",
+       "  'Straight Sexy Hair Smooth &amp; Seal Spray by Sexy Hair for Unisex - 8.1 Ounce Hairspray',\n",
+       "  (227, 39, 150, 42, 0)),\n",
+       " ('B00068AZ88',\n",
+       "  'CombaColor Quick Hair Color Applicator (Comb-a-Color)',\n",
+       "  (71, 7, 128, 105, 0)),\n",
+       " ('B00068CG3U', 'Philosophy The Microdelivery Peel', (242, 1, 177, 237, 0)),\n",
+       " ('B00069FJUG',\n",
+       "  'Philosophy Supernatural Airbrushed Canvas Powder, SPF 15, Bronze',\n",
+       "  (217, 49, 188, 248, 0)),\n",
+       " ('B0006B12KK',\n",
+       "  'NARS Velvet Matte Lip Pencil, Bahama',\n",
+       "  (217, 93, 248, 171, 0)),\n",
+       " ('B0006B66D8',\n",
+       "  'Super Solano Professional Hair Dryer - Black',\n",
+       "  (90, 1, 26, 228, 0)),\n",
+       " ('B0006BDMIU', 'Caudalie Beauty Elixir 1 fl oz.', (173, 129, 150, 115, 0)),\n",
+       " ('B0006BDO5G',\n",
+       "  'Peter Thomas Roth Sulfur Cooling Masque 5 Ounce.',\n",
+       "  (10, 160, 129, 42, 0)),\n",
+       " ('B0006BDO5Q',\n",
+       "  'Peter Thomas Roth Glycolic Acid 10% Hydrating Gel, 2 Ounce',\n",
+       "  (224, 93, 32, 28, 0)),\n",
+       " ('B0006BDQDG',\n",
+       "  'Peter Thomas Roth AHA/BHA Acne Clearing Gel 2 fl oz',\n",
+       "  (10, 93, 215, 22, 0)),\n",
+       " ('B0006D3IAU', 'Cococare Coconut Oil, 9 Ounce', (80, 225, 205, 249, 0)),\n",
+       " ('B0006DS3KU',\n",
+       "  'Ojon Restorative Hair Treatment 4.6 oz (140 g)',\n",
+       "  (120, 50, 183, 32, 0)),\n",
+       " ('B0006FMK98',\n",
+       "  'Pharmaceutical Specialties Free and Clear Shampoo 12 oz.',\n",
+       "  (189, 199, 1, 220, 0)),\n",
+       " ('B0006FMKB6', 'Robathol Bath Oil - 16 oz', (224, 35, 203, 205, 0)),\n",
+       " ('B0006G2EX4', 'Obagi Foaming Gel Cleanser-6.7 oz', (203, 84, 207, 70, 0)),\n",
+       " ('B0006GBEYE',\n",
+       "  'Coretex SunX SPF30 Sunscreen with Towelettes - 25 Individual Foil Pouch with Towelettes/Box, PABA Free, Oil-free, Water and Sweat Resistant, UVA/UVB Protection',\n",
+       "  (43, 50, 129, 237, 0)),\n",
+       " ('B0006GZAKI',\n",
+       "  'Honeysuckle Rose Moisturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
+       "  (118, 240, 177, 68, 0)),\n",
+       " ('B0006GZBMA',\n",
+       "  'White Camellia Ultra-Smoothing Conditioner Aubrey Organics 11 oz Liquid',\n",
+       "  (10, 61, 32, 141, 0)),\n",
+       " ('B0006GZC4W',\n",
+       "  'Aubrey Organics - Island Natural Conditioner, 11 fl oz liquid',\n",
+       "  (10, 199, 128, 222, 0)),\n",
+       " ('B0006GZCVK',\n",
+       "  'Honeysuckle Rose Conditioner Aubrey Organics 11 oz Liquid',\n",
+       "  (147, 151, 22, 42, 0)),\n",
+       " ('B0006GZDE6',\n",
+       "  'Aubrey Organics green tea Clarifying Shampoo',\n",
+       "  (157, 92, 10, 222, 0)),\n",
+       " ('B0006GZDEQ',\n",
+       "  'White Camellia Ultra-Smoothing Shampoo 11oz',\n",
+       "  (10, 199, 187, 215, 0)),\n",
+       " ('B0006GZE0O',\n",
+       "  'Calaguala Texturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
+       "  (157, 199, 10, 215, 0)),\n",
+       " ('B0006HCE60',\n",
+       "  'Cococare 100% Vitamin E Oil, 1 Ounce',\n",
+       "  (242, 151, 150, 68, 0)),\n",
+       " ('B0006HRCL2', '4 Dozen (48) Long Pink Perm Rods', (223, 225, 156, 199, 0)),\n",
+       " ('B0006I9YK8', 'Baggallini Complete Cosmetic Bagg', (176, 119, 128, 215, 0)),\n",
+       " ('B0006IHDQ0',\n",
+       "  'Liz Claiborne Curve Crush EDC Spray 4.2 oz',\n",
+       "  (106, 52, 165, 54, 0)),\n",
+       " ('B0006II74C',\n",
+       "  \"Nature's Secret 5-Day Fast and Cleanse Kit\",\n",
+       "  (6, 1, 188, 228, 0)),\n",
+       " ('B0006IJA5C',\n",
+       "  'Jhirmack Silver Brightening Shampoo 20 Oz.',\n",
+       "  (18, 0, 188, 220, 0)),\n",
+       " ('B0006IXSG4',\n",
+       "  'John Varvatos by John Varvatos for Men - 7 ml EDT Splash',\n",
+       "  (63, 160, 157, 205, 0)),\n",
+       " ('B0006LNC2G',\n",
+       "  'MAC Eye Kohl Smolder Eye Liner for Women, 0.048 Ounce',\n",
+       "  (5, 199, 25, 141, 0)),\n",
+       " ('B0006LNJ56', 'MAC Satin Lipstick - Rebel', (252, 199, 25, 141, 0)),\n",
+       " ('B0006LNKXW',\n",
+       "  'MAC Tinted Lipglass -- Viva Glam V (Boxed) 4.8g/.17oz',\n",
+       "  (63, 119, 205, 54, 0)),\n",
+       " ('B0006LNKYG', 'MAC Matte Lipstick - Russian Red', (123, 215, 156, 136, 0)),\n",
+       " ('B0006LNMIU', 'MAC Powder Blush Fever', (200, 205, 129, 214, 0)),\n",
+       " ('B0006M5566', 'eb5 Facial Cream (4 Ounces)', (148, 90, 129, 230, 0)),\n",
+       " ('B0006M56BK',\n",
+       "  'Wilkinson Sword Double Edge single Razor Cartridge, 5 blades',\n",
+       "  (184, 77, 129, 88, 0)),\n",
+       " ('B0006MSVCG',\n",
+       "  'Burberry Brit ~ Women 3.3 oz / 100 ml Eau de Parfum Spray',\n",
+       "  (32, 199, 183, 249, 0)),\n",
+       " ('B0006N29AU',\n",
+       "  \"Dr. King's Natural Medicine Back, Muscle and Joint Relief, 2 Fluid Ounce\",\n",
+       "  (177, 0, 197, 228, 0)),\n",
+       " ('B0006NXBJ8', 'Mason Pearson Detangler', (3, 29, 26, 28, 0)),\n",
+       " ('B0006NY35E',\n",
+       "  'Pre de Provence Soap, Cucumber, 7 Ounce',\n",
+       "  (23, 225, 183, 251, 0)),\n",
+       " ('B0006NYCT6',\n",
+       "  'Algemarin Bubble Bath 750ml bubble bath by Algemarin',\n",
+       "  (163, 61, 12, 141, 0)),\n",
+       " ('B0006NYDOU',\n",
+       "  'Herbacin Kamille Hand Cream with Glycerine - 2.5 oz',\n",
+       "  (157, 199, 49, 215, 0)),\n",
+       " ('B0006O2IQ4',\n",
+       "  'Fragrances Of Ireland Inis The Energy Of The Sea Cologne Spray, 1.7 Fluid Ounce',\n",
+       "  (148, 169, 25, 237, 0)),\n",
+       " ('B0006O4MCM',\n",
+       "  'Suki - Concentrated Balancing Toner  4.0 fl oz',\n",
+       "  (69, 230, 32, 241, 0)),\n",
+       " ('B0006O9ZAG',\n",
+       "  'Palladio Herbal Dual Wet and Dry Foundation, Ivory Myrrh, 0.28 Ounce',\n",
+       "  (48, 225, 84, 166, 0)),\n",
+       " ('B0006OU06E', 'Semi Oval Wild Boar Hair Brush', (15, 90, 187, 222, 0)),\n",
+       " ('B0006OU078', 'Bass Brushes Facial Cleansing Brush', (67, 151, 201, 228, 0)),\n",
+       " ('B0006PJRP8', 'Zoya Nail Polish .5 oz. Lola', (183, 0, 203, 54, 0)),\n",
+       " ('B0006PJSES',\n",
+       "  'CND: Treatments/Prep Stickey Base Coat, 2.3 oz',\n",
+       "  (61, 0, 32, 22, 0)),\n",
+       " ('B0006PJSSO',\n",
+       "  'Essie Nail Lacquer, First Base Base Coat, 0.46 Fluid Ounce',\n",
+       "  (98, 86, 177, 166, 0)),\n",
+       " ('B0006PJTB0',\n",
+       "  'Seche Base Ridge Filling Base Coat .5 oz. [Health and Beauty]',\n",
+       "  (85, 160, 128, 171, 0)),\n",
+       " ('B0006PKGJY',\n",
+       "  'Alba Botanica Natural Even Advanced Daily Cream -- 2 oz',\n",
+       "  (29, 7, 146, 44, 0)),\n",
+       " ('B0006PLNSC', 'Zoya Nail Polish .50 fl oz Charisma', (206, 230, 183, 68, 0)),\n",
+       " ('B0006PLPE4', 'ZOYA Armor Topcoat/UV Block 0.5oz', (89, 215, 129, 214, 0)),\n",
+       " ('B0006PS3XU', 'Marianna Bobby Pins 1 lb. - Black', (43, 225, 22, 249, 0)),\n",
+       " ('B0006PS3Y4', 'Marianna Bobby Pins 1 lb. - Brown', (43, 225, 22, 237, 0)),\n",
+       " ('B0006PT6TU',\n",
+       "  'City Lips Advanced in NUDE York Lip Plumper',\n",
+       "  (8, 37, 192, 241, 0)),\n",
+       " ('B0006Q00IK',\n",
+       "  'For Pro Cozie Liners Hand or Foot 100-ct.',\n",
+       "  (5, 49, 150, 141, 0)),\n",
+       " ('B0006Q00ZI', 'Gena Healthy Hoof Cream, 4 Ounce', (245, 41, 22, 215, 0)),\n",
+       " ('B0006Q0102', 'Gena Nail Brite with Brush, 4 Ounce', (1, 160, 12, 171, 0)),\n",
+       " ('B0006Q01EI', 'Hot Sock Diffuser', (47, 41, 28, 32, 0)),\n",
+       " ('B0006Q01WK', 'King Dy-zoff Pads 80-ct.', (141, 145, 10, 24, 0)),\n",
+       " ('B0006Q0HEC',\n",
+       "  'Wen by Chaz Dean Tea Tree Cleansing Conditioner 16 oz.',\n",
+       "  (191, 199, 194, 70, 0)),\n",
+       " ('B0006Q23H6',\n",
+       "  'John Masters Organics Evening Primrose Shampoo for Dry Hair 8 fl oz / 236 ml',\n",
+       "  (48, 222, 28, 136, 0)),\n",
+       " ('B0006Q3NTS',\n",
+       "  'WEN by Chaz Dean Fig Cleansing Conditioner 16 oz',\n",
+       "  (253, 123, 64, 42, 0)),\n",
+       " ('B0006Q3O3S',\n",
+       "  'WEN&reg; Sweet Almond Mint Styling Creme 6oz',\n",
+       "  (116, 222, 183, 88, 0)),\n",
+       " ('B0006Q3P50',\n",
+       "  'WEN&reg; Sweet Almond Mint Texture Balm 3oz',\n",
+       "  (61, 90, 73, 205, 0)),\n",
+       " ('B0006SFXXK',\n",
+       "  'John Masters Organics - Detangler Citrus &amp; Neroli - 8 oz.',\n",
+       "  (120, 62, 170, 237, 0)),\n",
+       " ('B0006SFXYY',\n",
+       "  'John Masters Organics John Masters Organics Deep Scalp Follicle Treatment &amp; Volumizer for Thinning Hair 4.2 fl oz - 4.2 fl oz',\n",
+       "  (189, 41, 176, 220, 0)),\n",
+       " ('B0006U81GY', 'Cricket Ultra Clean UC130', (233, 229, 205, 136, 0)),\n",
+       " ('B0006V7T9I',\n",
+       "  'Bee Bar Lotion by Honey House Naturals - Vanilla Scent - Long Lasting Lotion Bar Moisturizes and Leaves Skin Smelling Fresh - 2 ounces',\n",
+       "  (20, 93, 157, 141, 0)),\n",
+       " ('B0006ZEVU4',\n",
+       "  'Paul Mitchell Tea Tree Special Shampoo Unisex, 16.9 Ounce',\n",
+       "  (80, 222, 53, 199, 0)),\n",
+       " ('B0006ZHCK0', \"Murphy's Oil Soap, 32-Ounce\", (53, 194, 187, 214, 0)),\n",
+       " ('B0006ZHK7A', 'Pureology Hydrate Shampoo, 8.5 Ounce', (86, 49, 156, 166, 0)),\n",
+       " ('B00070E8IS',\n",
+       "  'Elchim Professional 2001 2000 Watt Classic Hair Dryer (Colors May Vary)',\n",
+       "  (192, 225, 130, 22, 0)),\n",
+       " ('B00070QF20',\n",
+       "  'AcneFree Clear Skin System, 3-Step Kit (Purifying Cleanser, Renewing Toner, Repair Lotion)',\n",
+       "  (203, 57, 215, 222, 0)),\n",
+       " ('B00074XC68',\n",
+       "  'Helen of Troy Mini 1/2 Inch Professional Curling Iron with Ergonomic Handle for Short Hair, Bangs &amp; Wisps (Model: 1503n)',\n",
+       "  (110, 72, 48, 237, 0)),\n",
+       " ('B0007506U2',\n",
+       "  'TIGI Catwalk Curl Collection Curlesque Curls Rock Amplifier, 5.07 Ounce, Packaging May Vary',\n",
+       "  (1, 90, 84, 141, 0)),\n",
+       " ('B00076X0BS',\n",
+       "  'Alba Botanica Revitalizing Green Tea Hawaiian Eye Gel, 1 Ounce Bottles',\n",
+       "  (120, 93, 192, 41, 1)),\n",
+       " ('B0007CIEBI',\n",
+       "  'Colorescience Pro Sunforgettable SPF 30 Brush- Fair 0.21 Oz',\n",
+       "  (32, 1, 207, 215, 0)),\n",
+       " ('B0007CWVLM', 'Tweezerman  Spirit 2000 Styling Shears', (3, 57, 26, 246, 0)),\n",
+       " ('B0007CXWQU',\n",
+       "  'Aveda Blue Malva Conditioner, 8.5-Ounce Tube',\n",
+       "  (171, 84, 128, 166, 0)),\n",
+       " ('B0007CXWRE',\n",
+       "  'Aveda Brilliant Anti-Humectant Pomade, 2.6 Ounces',\n",
+       "  (144, 123, 1, 42, 0)),\n",
+       " ('B0007CXWTW', 'Aveda Clove Conditioner 8.5 Ounces', (3, 119, 136, 171, 0)),\n",
+       " ('B0007CXWUG',\n",
+       "  'Aveda Confixor Liquid Gel, 8.5 Ounces',\n",
+       "  (227, 50, 176, 228, 0)),\n",
+       " ('B0007CXWWY',\n",
+       "  'Aveda Madder Root Conditioner 8.5 Ounces',\n",
+       "  (227, 61, 1, 228, 0)),\n",
+       " ('B0007CXWXI', 'Aveda Phomollient 6.7 Ounces', (3, 62, 136, 237, 0)),\n",
+       " ('B0007CXX82',\n",
+       "  'Biosilk Silk Therapy Serum, Packaging May Vary, 5.64 Ounces',\n",
+       "  (163, 199, 170, 228, 0)),\n",
+       " ('B0007CXXDC',\n",
+       "  'Biolage by Matrix Color Care Conditioner 33.8 Ounces',\n",
+       "  (178, 22, 26, 199, 0)),\n",
+       " ('B0007CXXDM',\n",
+       "  'Biolage by Matrix Color Care Shampoo 33.8 Ounces',\n",
+       "  (168, 1, 176, 203, 0)),\n",
+       " ('B0007CXXE6',\n",
+       "  'Biolage by Matrix Conditioning Balm 16.9 Ounces',\n",
+       "  (3, 93, 230, 214, 0)),\n",
+       " ('B0007CXXGO',\n",
+       "  'Biolage by Matrix Hydratherapie Hydrating Shampoo 33.8 Ounces',\n",
+       "  (76, 225, 150, 207, 0)),\n",
+       " ('B0007CXXIC',\n",
+       "  'Biolage by Matrix Normalizing Shampoo 33.8 Ounces',\n",
+       "  (76, 225, 130, 207, 0)),\n",
+       " ('B0007CXXJQ',\n",
+       "  'Biolage by Matrix Ultra-Hydrating Conditioning Balm 16.9 Ounces',\n",
+       "  (103, 151, 205, 249, 0)),\n",
+       " ('B0007CXXVE',\n",
+       "  'Nexxus Humectress Ultimate Moisturizing Conditioner 33.8 Ounces',\n",
+       "  (103, 225, 156, 220, 0)),\n",
+       " ('B0007CXXYG',\n",
+       "  'Paul Mitchell Awapuhi Shampoo, 33.8-Ounce Bottle',\n",
+       "  (211, 61, 156, 220, 0)),\n",
+       " ('B0007CXY5E',\n",
+       "  'Paul Mitchell Shampoo  1   33.8 Ounces',\n",
+       "  (83, 93, 177, 88, 0)),\n",
+       " ('B0007CXY5O',\n",
+       "  'Paul Mitchell Shampoo  3   16.9 Ounces',\n",
+       "  (233, 7, 151, 177, 0)),\n",
+       " ('B0007CXYAE',\n",
+       "  'Paul Mitchell The Detangler 33.8 Ounces',\n",
+       "  (235, 181, 203, 105, 0)),\n",
+       " ('B0007CXYP4',\n",
+       "  'Sebastian Collection Potion 9   5.1 Ounces',\n",
+       "  (227, 1, 32, 251, 0)),\n",
+       " ('B0007D2F3K', 'Pedi,Quick Salon Pedicure Kit', (43, 13, 13, 91, 0)),\n",
+       " ('B0007DHMBK',\n",
+       "  'Henna Black Cream Surya Nature, Inc 2.31 oz Cream',\n",
+       "  (189, 49, 136, 141, 0)),\n",
+       " ('B0007DHMCO',\n",
+       "  'Henna Burgundy Cream Surya Nature, Inc 2.31 oz Cream',\n",
+       "  (189, 49, 177, 254, 0)),\n",
+       " ('B0007IFB2W',\n",
+       "  'Spornette 25 Wood Handle &quot;Porcupine&quot; Brush With Genuine Boar Bristle * Made In Germany',\n",
+       "  (191, 77, 12, 28, 0)),\n",
+       " ('B0007IQMVG',\n",
+       "  'Digestive Advantage Intensive Bowel Support, 96 Counts Capsules',\n",
+       "  (75, 151, 205, 22, 0)),\n",
+       " ('B0007LE3EG', 'Total Hair Makeover Kit 90001', (87, 84, 205, 248, 0)),\n",
+       " ('B0007NIRUU',\n",
+       "  'Nexxus Humectress Ultimate Moisturizing Conditioner, 13.5Ounce Bottles',\n",
+       "  (238, 49, 207, 136, 0)),\n",
+       " ('B0007R7ABS',\n",
+       "  'Kenra Volume Spray 25 Super Hold Finishing Spray (Aerosol) (10 oz.)',\n",
+       "  (24, 41, 1, 68, 0)),\n",
+       " ('B0007RBYA6',\n",
+       "  'Philosophy Microdelivery Peel Pads, 60 Count',\n",
+       "  (25, 222, 129, 222, 0)),\n",
+       " ('B0007V6PFQ',\n",
+       "  'Brush - Large Oval Cushion Wood Bristles Wood Handle Bass Brushes 1 Brush',\n",
+       "  (90, 215, 10, 214, 0)),\n",
+       " ('B0007V9API', 'Bella B Tummy Honey Cream - 4 oz', (2, 39, 157, 249, 0)),\n",
+       " ('B0007W1R58',\n",
+       "  'Olay Regenerist Night Recovery Cream 1.7 Oz',\n",
+       "  (109, 93, 188, 237, 0)),\n",
+       " ('B0007WL21M',\n",
+       "  'PETER THOMAS ROTH - Anti-Aging Cleansing Gel 8.5oz',\n",
+       "  (25, 151, 192, 230, 0)),\n",
+       " ('B0007WZ7YU',\n",
+       "  'Elchim 2001hp High Pressure 2000 Watt Hair Dryer, White',\n",
+       "  (153, 199, 207, 88, 0)),\n",
+       " ('B0007X74CW',\n",
+       "  'Redken Hair Cleansing Cream Shampoo for All Hair Types, 10.1-Ounces',\n",
+       "  (235, 194, 157, 88, 0)),\n",
+       " ('B0007X74E0',\n",
+       "  'Smooth Down Heat Glide Protective Smoother Unisex Smoother by Redken, 5 Ounce',\n",
+       "  (204, 57, 187, 237, 0)),\n",
+       " ('B0007X74IQ',\n",
+       "  'Extreme Cat Protein Treatment Unisex Treatment by Redken, 5 Ounce',\n",
+       "  (203, 7, 12, 22, 0)),\n",
+       " ('B0007XFB2W', 'Original Sprout Miracle Detangler', (189, 52, 150, 177, 0)),\n",
+       " ('B0007YJ5QY',\n",
+       "  'AMERICAN CREW Daily Moisturizing Shampoo, 8.4 Ounce',\n",
+       "  (52, 1, 191, 222, 0)),\n",
+       " ('B00080DK86',\n",
+       "  'Glycolix Elite Treatment Pads 20 Percent 60 count',\n",
+       "  (10, 222, 12, 24, 0)),\n",
+       " ('B00081J4P8',\n",
+       "  'NO-AD Sunblock Lotion, SPF 15, 16 Ounces',\n",
+       "  (23, 50, 10, 199, 0)),\n",
+       " ('B00082379Q', 'Orly Polish Thinner', (8, 222, 129, 28, 0)),\n",
+       " ('B0008394GA', 'NARS Lip Liner Pencil, Jungle Red', (230, 49, 129, 175, 0)),\n",
+       " ('B000851N9E',\n",
+       "  'BurnOut - Ocean Tested Physical Sunscreen 30 SPF - 3.4 oz.',\n",
+       "  (214, 205, 165, 199, 0)),\n",
+       " ('B00085DX0Q',\n",
+       "  \"Grandpa's Soap Company Wonder Pine Tar Shampoo, 8 Ounce\",\n",
+       "  (247, 7, 18, 220, 0)),\n",
+       " ('B0008ENT8I',\n",
+       "  'ProVersa JWM6CF Wall Caddy Hair Dryer with 2-Speed and 3-Heat Settings, 1600-Watts, White Finish',\n",
+       "  (87, 84, 129, 237, 0)),\n",
+       " ('B0008F6ZDI',\n",
+       "  'Philosophy The Present Clear Makeup, 2 Ounce',\n",
+       "  (28, 39, 188, 175, 0)),\n",
+       " ('B0008FUY0I',\n",
+       "  'Flowery Swedish Clover Foot File * #530',\n",
+       "  (176, 119, 32, 215, 0)),\n",
+       " ('B0008GIZKS',\n",
+       "  'Malibu C Hard Water Wellness Shampoo, 1 Bottle, 9 oz',\n",
+       "  (242, 0, 205, 187, 0)),\n",
+       " ('B0008IV7BU',\n",
+       "  'American Crew American Crew Fiber Fiber - 3 oz',\n",
+       "  (5, 93, 188, 251, 0)),\n",
+       " ('B00092M2VO',\n",
+       "  'Andis Professional 1875 Watt Ceramic Ionic Hair Dryer - Black Chrome (82005)',\n",
+       "  (176, 1, 248, 222, 0)),\n",
+       " ('B00092M2XC',\n",
+       "  'Hot Tools Professional 1069S 1600 Watt Professional Turbo Hair Dryer, Silver/Black',\n",
+       "  (153, 215, 10, 88, 0)),\n",
+       " ('B00092M2ZU',\n",
+       "  'Ceramic Tools CT2555 1&quot; Professional Ceramic Flat Iron',\n",
+       "  (24, 39, 25, 251, 0)),\n",
+       " ('B00092M36I',\n",
+       "  'Ceramic Tools CT155S 1-1/2&quot; Dual Voltage Professional Ceramic Spring Curling Iron',\n",
+       "  (110, 72, 184, 249, 0)),\n",
+       " ('B00092M386',\n",
+       "  'Belson Profiles Spa Single Hand Nail Dryer P1013',\n",
+       "  (110, 160, 248, 136, 0)),\n",
+       " ('B00094O6D4',\n",
+       "  'LaLicious Sugar Souffle Body Scrub 16 fl oz.',\n",
+       "  (10, 84, 203, 215, 0)),\n",
+       " ('B000960QJA',\n",
+       "  'Vaseline Aloe Fresh Hydrating Body Lotion 20.3Oz',\n",
+       "  (162, 49, 188, 167, 1)),\n",
+       " ('B000977Q8I', 'Bobbi Brown Bobbi Brown Shimmer Brick', (15, 7, 165, 70, 0)),\n",
+       " ('B000980PGM',\n",
+       "  'Dermalogica Daily Microfoliant, 2.6-Ounce',\n",
+       "  (224, 222, 129, 251, 0)),\n",
+       " ('B000980PH6',\n",
+       "  'Dermalogica Oil Control Lotion (2 oz.)',\n",
+       "  (224, 222, 177, 175, 0)),\n",
+       " ('B0009953JA',\n",
+       "  'Jerdon JP7507NB 8-Inch Two-Sided Swivel Wall Mount Mirror with 7x Magnification, 13.5-Inch Extension, Nickel Beaded Finish',\n",
+       "  (87, 57, 230, 220, 0)),\n",
+       " ('B00099E8ZA',\n",
+       "  'Neutrogena Shampoo, Anti-Residue Formula, 6 Ounce',\n",
+       "  (109, 57, 12, 220, 0)),\n",
+       " ('B00099QZOW',\n",
+       "  'Colorful Neutral Protein Filler 1.2 oz.',\n",
+       "  (211, 230, 188, 115, 0)),\n",
+       " ('B00099Z2OQ',\n",
+       "  'Giovanni Hair Care - Direct Leave-In Conditioner, 8.5 fl oz liquid',\n",
+       "  (180, 35, 188, 54, 0)),\n",
+       " ('B0009BVK0E',\n",
+       "  'Organix South Maximum Strength Neem Soap Bar 4 oz',\n",
+       "  (157, 41, 10, 215, 0)),\n",
+       " ('B0009DT39W',\n",
+       "  'Panasonic EH2351AC Heated Eyelash Curling Wand',\n",
+       "  (43, 225, 10, 91, 0)),\n",
+       " ('B0009DVDTU',\n",
+       "  'Revision Teamine Eye Complex, 0.5 Ounce',\n",
+       "  (6, 71, 183, 141, 0)),\n",
+       " ('B0009DVMAU', 'Lafes Deodorant Spray with MSM, 8oz', (250, 230, 156, 68, 0)),\n",
+       " ('B0009EILHG',\n",
+       "  'Biore Pore Perfect Blemish Fighting Ice Cleanser, Cools &amp; Clears , 6.7 fl oz (198 ml)',\n",
+       "  (185, 52, 203, 222, 0)),\n",
+       " ('B0009EILI0',\n",
+       "  'Biore Deep Cleansing Pore Strips Combo 14 Ea',\n",
+       "  (185, 52, 12, 42, 0)),\n",
+       " ('B0009EILKS',\n",
+       "  'Biore  Deep Cleansing Pore Strips , 14 Nose Strips',\n",
+       "  (46, 72, 187, 215, 0)),\n",
+       " ('B0009EILVM',\n",
+       "  'Zia Natural Skincare Ultimate Body Firming Treatment, 7 fl oz',\n",
+       "  (224, 225, 10, 251, 0)),\n",
+       " ('B0009ET3SC',\n",
+       "  'Aquis Microfiber Body Towel, Lisse Crepe, White (29 x 55-Inches)',\n",
+       "  (85, 215, 192, 105, 0)),\n",
+       " ('B0009ET4VI',\n",
+       "  'Beauty without Cruelty Vitamin C, Vitality Serum, 1-Ounce',\n",
+       "  (157, 72, 177, 215, 0)),\n",
+       " ('B0009ET5H6',\n",
+       "  \"Burt's Bees Poison Ivy Soap, 2-Ounce\",\n",
+       "  (194, 199, 177, 141, 0)),\n",
+       " ('B0009ET5JE',\n",
+       "  \"Burt's Bees  Lip Shimmer, Champagne, 0.09 oz.\",\n",
+       "  (151, 215, 156, 233, 0)),\n",
+       " ('B0009ET5O4',\n",
+       "  \"Burt's Bees Healthy Treatment Repair Serum, 1 Fluid Ounce\",\n",
+       "  (6, 230, 188, 220, 0)),\n",
+       " ('B0009ET6BQ',\n",
+       "  'derma e Hyaluronic Acid Day Cr&egrave;me, 2-Ounces',\n",
+       "  (10, 93, 32, 251, 0)),\n",
+       " ('B0009EXM52',\n",
+       "  'OPI: Treatment &amp; Finish Natural Nail Base Coat, 0.5 oz',\n",
+       "  (87, 199, 53, 68, 0)),\n",
+       " ('B0009EXMB6',\n",
+       "  \"Palmer's Sunless Tanner &amp; Instant Bronzer SPF 15 5.25oz\",\n",
+       "  (189, 0, 25, 42, 0)),\n",
+       " ('B0009EXONC',\n",
+       "  'Big Sexy Hair Volumizing Hairspray, Spray &amp; Play, 10.0 oz (284 g)',\n",
+       "  (147, 52, 207, 28, 0)),\n",
+       " ('B0009EXOO6',\n",
+       "  'HEALTHY SEXY SOY TRI-WHEAT LEAVE-IN CONDITIONER 8.5 OZ -PACKAGING MAY VARY',\n",
+       "  (233, 187, 10, 251, 0)),\n",
+       " ('B0009EXOP0',\n",
+       "  'SHORT SEXY HAIR CONTOL MANIAC WAX 1.8 OZ-PACKAGING MAY VARY',\n",
+       "  (37, 88, 12, 214, 0)),\n",
+       " ('B0009EXOXW',\n",
+       "  'Source Naturals Skin Eternal Serum, 1.7  Ounce',\n",
+       "  (2, 145, 1, 105, 0)),\n",
+       " ('B0009F3M6A',\n",
+       "  'Eucerin Dry Skin Therapy Foot Creme, Plus Intensive Repair, 3-Ounce Tubes (Pack of 3)',\n",
+       "  (203, 145, 188, 251, 0)),\n",
+       " ('B0009F3NGE',\n",
+       "  \"L'Oreal Dermo-Expertise Sublime Bronze Self-Tanning Gelee, Medium-Natural , 5 fl oz (150 ml)\",\n",
+       "  (48, 145, 136, 136, 0)),\n",
+       " ('B0009F3NYQ',\n",
+       "  'Neutrogena MicroMist Tanning Sunless Spray, Deep, 5.3 Ounce',\n",
+       "  (117, 145, 230, 136, 0)),\n",
+       " ('B0009F3O18',\n",
+       "  'Neutrogena Makeup Remover Cleansing Towelettes, 25 Count',\n",
+       "  (98, 90, 205, 136, 0)),\n",
+       " ('B0009F3O8Q',\n",
+       "  \"Palmer's Cocoa Butter Formula with Vitamin E, 13.5 fl oz (400 ml)\",\n",
+       "  (10, 225, 176, 215, 0)),\n",
+       " ('B0009F3OOA',\n",
+       "  'Queen Helene Refreshing Natural Facial Scrub Mint Julep -- 6 oz',\n",
+       "  (177, 230, 215, 175, 0)),\n",
+       " ('B0009F3OR2',\n",
+       "  'Redken All Soft Heavy Cream, Avocado Oil , 8.5 fl oz (250 ml)',\n",
+       "  (25, 52, 183, 248, 0)),\n",
+       " ('B0009F3OS6',\n",
+       "  'Redken Extreme Anti-Snap, Lipids/Proteins , 8.5 fl oz (250 ml)',\n",
+       "  (32, 170, 177, 205, 0)),\n",
+       " ('B0009F3OX6', 'Revlon Cuticle Nippers, Half-Jaw', (192, 57, 207, 28, 0)),\n",
+       " ('B0009F3R7E', \"Burt's Bees Hand Repair Kit\", (18, 90, 12, 214, 0)),\n",
+       " ('B0009FHJOG',\n",
+       "  'Talika Lash Conditioning Cleanser - 4.06 oz',\n",
+       "  (69, 49, 165, 249, 0)),\n",
+       " ('B0009FHJRS',\n",
+       "  'Toppik Regular Dark Brown Hair Building Fibers, 0.42 Ounce',\n",
+       "  (75, 215, 184, 32, 0)),\n",
+       " ('B0009G05W8', 'Cricket Cool Down Iron Travel Case', (26, 88, 10, 28, 0)),\n",
+       " ('B0009GGY96',\n",
+       "  'AcneFree Acne and Blackhead Terminator (.75 Ounce Tube)',\n",
+       "  (121, 7, 129, 220, 0)),\n",
+       " ('B0009H04LO', 'NARS Bronzer Blush Duo, Orgasm/Laguna', (23, 7, 203, 237, 0)),\n",
+       " ('B0009HIFRY',\n",
+       "  'Pravana Intense Therapy Leave-In Treatment 10.1 oz',\n",
+       "  (13, 84, 151, 136, 0)),\n",
+       " ('B0009HJBPE',\n",
+       "  'Yves Saint Laurent TOUCHE ECLATRadiant Touch 2 Luminous Ivory',\n",
+       "  (189, 39, 203, 105, 0)),\n",
+       " ('B0009I4J4G',\n",
+       "  'Lubriderm Daily Moisture Lotion with Shea and Cocoa Butter, 16  Ounce',\n",
+       "  (132, 84, 28, 249, 0)),\n",
+       " ('B0009I4M02', 'TIGI Bed Head Small Talk, 8 Ounce', (3, 145, 248, 254, 0)),\n",
+       " ('B0009I4MCU',\n",
+       "  'Pureology Anti-Fade Complex Hydrate Condition, 8.5 Ounce',\n",
+       "  (25, 225, 136, 215, 0)),\n",
+       " ('B0009I4MFW',\n",
+       "  'Pureology Pure Volume Shampoo 10.1 oz',\n",
+       "  (235, 61, 170, 24, 0)),\n",
+       " ('B0009I4MG6',\n",
+       "  'Pureology Safeguard Your Color Purify Shampoo, 10.1 Ounces',\n",
+       "  (163, 61, 177, 88, 0)),\n",
+       " ('B0009I4MGQ',\n",
+       "  'Pureology Anti-Fade Complex Pure Volume Condition, 8.5 Ounce Bottle (Packaging May Vary)',\n",
+       "  (80, 119, 177, 24, 0)),\n",
+       " ('B0009I4MKW', 'Nexxus shampoo therappe, 33.8oz', (88, 7, 187, 68, 0)),\n",
+       " ('B0009IMRLS',\n",
+       "  'Christian Dior Diorshow Waterproof Mascara, No. 090 Black, 0.38 Ounce',\n",
+       "  (168, 180, 192, 24, 0)),\n",
+       " ('B0009ION0G',\n",
+       "  'TIGI Catwalk Your Highness Root Boost Spray, 8.1 Ounce',\n",
+       "  (187, 90, 18, 141, 0)),\n",
+       " ('B0009J6ER0',\n",
+       "  'Grow Hair Faster with Grow Shampoo and Conditioner for Faster Growing Hair',\n",
+       "  (202, 52, 177, 28, 0)),\n",
+       " ('B0009JQFF6',\n",
+       "  '100% Organic West African Shea Butter 16 oz',\n",
+       "  (203, 129, 130, 18, 0)),\n",
+       " ('B0009M0C5W',\n",
+       "  'FusionBeauty LipFusion Xl 2x Micro-Injected Collagen Advanced Lip Plumping Therapy',\n",
+       "  (203, 7, 156, 115, 0)),\n",
+       " ('B0009MHJE4',\n",
+       "  'African Shea Butter Cream (100% Pure &amp; Raw, Gold) 8 Oz.',\n",
+       "  (242, 35, 177, 70, 0)),\n",
+       " ('B0009MHJS0',\n",
+       "  'Raw Unrefined Yellow Shea Butter FILTERED &amp; CREAMY 5 0z',\n",
+       "  (145, 22, 203, 91, 0)),\n",
+       " ('B0009MJE74',\n",
+       "  'Nubian Heritage - Body Wash African Black Soap - 13 oz.',\n",
+       "  (202, 222, 183, 22, 0)),\n",
+       " ('B0009ML5M6', 'African Shea Oil - 8 ozs.', (145, 49, 207, 105, 0)),\n",
+       " ('B0009MMK5M',\n",
+       "  'Egyptian Magic - All Purpose Skin Cream - 4 oz.',\n",
+       "  (37, 87, 157, 214, 0)),\n",
+       " ('B0009MSHR2',\n",
+       "  'Madina African Black Soap Cocoa Butter W/Vitamin E 3.5 oz',\n",
+       "  (25, 119, 170, 241, 0)),\n",
+       " ('B0009MYMJY',\n",
+       "  'Shalimar By Guerlain For Women. Eau De Parfum Spray 2.5 Oz.',\n",
+       "  (115, 88, 177, 68, 0)),\n",
+       " ('B0009N35D2',\n",
+       "  'Glow By Jennifer Lopez For Women. Eau De Toilette Spray 1 Ounces',\n",
+       "  (26, 83, 150, 133, 1)),\n",
+       " ('B0009N5EZY',\n",
+       "  'Kenneth Cole Reaction By Kenneth Cole Eau De Toilette Spray 3.4 Oz',\n",
+       "  (76, 61, 157, 68, 0)),\n",
+       " ('B0009OAFUM',\n",
+       "  'Remington HKVAC-2000 Precision Vacuum Haircut Kit',\n",
+       "  (0, 90, 10, 203, 0)),\n",
+       " ('B0009OAGRE',\n",
+       "  'Eternity by Calvin Klein for Women, Eau De Parfum, 3.4 Ounce',\n",
+       "  (228, 88, 177, 68, 0)),\n",
+       " ('B0009OAGSI',\n",
+       "  'Escape by Calvin Klein for Men, Eau De Toilette Spray, 3.4-Fluid Ounce (100 ml)',\n",
+       "  (211, 61, 25, 68, 0)),\n",
+       " ('B0009OAGT2',\n",
+       "  'Escape by Calvin Klein for Women, Eau De Parfum Spray, 1.7 Ounce',\n",
+       "  (26, 83, 150, 68, 2)),\n",
+       " ('B0009OAGWE',\n",
+       "  'Ralph Lauren Polo Sport Eau de Toilette Spray for Men, 4.2 Ounce',\n",
+       "  (26, 83, 150, 68, 3)),\n",
+       " ('B0009OAGXI',\n",
+       "  'Acqua Di Gio By Giorgio Armani For Men. Eau De Toilette Spray 1.7 Oz.',\n",
+       "  (203, 0, 25, 220, 1)),\n",
+       " ('B0009OAGZ6',\n",
+       "  'Burberry By Burberry For Men. Eau De Toilette Spray 3.3 Ounces',\n",
+       "  (26, 83, 226, 218, 0)),\n",
+       " ('B0009OAH0A',\n",
+       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 1.7 Oz.',\n",
+       "  (26, 83, 150, 68, 4)),\n",
+       " ('B0009OAHBE',\n",
+       "  'COOL WATER by Davidoff Cologne for Men (EDT SPRAY 1.35 OZ)',\n",
+       "  (216, 49, 12, 24, 0)),\n",
+       " ('B0009OAHC8',\n",
+       "  'Cool Water By Davidoff For Men. Eau De Toilette Spray 4.2 Ounces',\n",
+       "  (26, 83, 150, 68, 5)),\n",
+       " ('B0009OAHEQ',\n",
+       "  'Joop! By Joop! For Men. Eau De Toilette Spray 4.2 Ounces',\n",
+       "  (26, 83, 150, 218, 2)),\n",
+       " ('B0009OAHIM',\n",
+       "  'Dolce &amp; Gabbana By Dolce &amp; Gabbana For Men. Eau De Toilette Spray 4.2 Ounce',\n",
+       "  (26, 83, 150, 68, 6)),\n",
+       " ('B0009OAHIW',\n",
+       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 1.6 Ounces',\n",
+       "  (26, 83, 150, 218, 3)),\n",
+       " ('B0009OAHNM', 'Curve Men Cologne Spray, 4.2-Ounce', (15, 230, 248, 166, 0)),\n",
+       " ('B0009OAHOG',\n",
+       "  'Mambo By Liz Claiborne For Men. Cologne Spray 1.7 Ounces',\n",
+       "  (219, 39, 22, 205, 0)),\n",
+       " ('B0009OAHQ4',\n",
+       "  'Bora Bora by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
+       "  (219, 7, 22, 249, 0)),\n",
+       " ('B0009OAHQE',\n",
+       "  'Bora Bora by Liz Claiborne for Women, Eau De Parfum Spray, 3.4-Ounce',\n",
+       "  (26, 83, 150, 218, 4)),\n",
+       " ('B0009OAHQY',\n",
+       "  'Claiborne by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
+       "  (181, 222, 203, 70, 0)),\n",
+       " ('B0009OAHRI',\n",
+       "  'Lucky You By Lucky Brand For Women. Eau De Toilette Spray 3.4 Oz.',\n",
+       "  (83, 50, 1, 68, 0)),\n",
+       " ('B0009OAHRS',\n",
+       "  'Lucky You By Lucky Brand For Men. Cologne Spray 3.4 Oz.',\n",
+       "  (210, 22, 165, 175, 0)),\n",
+       " ('B0009OAHVO',\n",
+       "  \"L'eau D'issey (issey Miyake) by Issey Miyake for Men - EDT Spray\",\n",
+       "  (26, 83, 150, 68, 7)),\n",
+       " ('B0009OAHWI',\n",
+       "  'Lolita Lempicka By Lolita Lempicka For Women. Eau De Parfum Spray 3.4 Oz.',\n",
+       "  (26, 83, 150, 218, 5)),\n",
+       " ('B0009OAI18',\n",
+       "  'Sunflowers By Elizabeth Arden For Women. Eau De Toilette Spray 3.4 Oz.',\n",
+       "  (235, 61, 48, 24, 0)),\n",
+       " ('B0009OAI1S',\n",
+       "  'Red Door By Elizabeth Arden For Women. Eau De Toilette Spray 3.3 Ounces',\n",
+       "  (228, 88, 177, 68, 1)),\n",
+       " ('B0009OAI2C',\n",
+       "  'Arden Beauty By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
+       "  (26, 83, 150, 133, 2)),\n",
+       " ('B0009OAI3G',\n",
+       "  'Blue Grass By Elizabeth Arden For Women. Eau De Parfum Spray 1.7 Ounces',\n",
+       "  (26, 83, 150, 133, 3)),\n",
+       " ('B0009OAI40',\n",
+       "  'Green Tea By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
+       "  (26, 83, 150, 218, 6)),\n",
+       " ('B0009OAI7C',\n",
+       "  'Passion by Elizabeth Taylor for Men, Cologne Spray, 4-Ounce',\n",
+       "  (147, 145, 248, 254, 0)),\n",
+       " ('B0009OAI8G',\n",
+       "  'White Diamonds By Elizabeth Taylor For Women, Eau De Toilette Spray, 1.7 Ounces',\n",
+       "  (26, 83, 150, 68, 8)),\n",
+       " ('B0009OAIB8',\n",
+       "  'Black Pearls by Elizabeth Taylor for Women, Eau De Parfum Spray, 3.3-Ounce',\n",
+       "  (228, 88, 177, 68, 2)),\n",
+       " ('B0009OAIE0',\n",
+       "  'Pleasures by Estee Lauder for Women, Eau De Toilette Spray, 1.7 Ounce',\n",
+       "  (26, 83, 150, 218, 7)),\n",
+       " ('B0009OAIIG',\n",
+       "  'Gucci Rush By Gucci For Women. Eau De Toilette Spray 2.5 Ounces',\n",
+       "  (26, 83, 150, 133, 4)),\n",
+       " ('B0009OAIKY',\n",
+       "  'Halston 1-12 by Halston for Men, Cologne Spray, 4.2-Ounce',\n",
+       "  (219, 215, 137, 251, 0)),\n",
+       " ('B0009OAIQI',\n",
+       "  'Design by Paul Sebastian for Women, Fine Parfum Spray, 1.7-Ounce',\n",
+       "  (228, 88, 177, 68, 3)),\n",
+       " ('B0009OAISG',\n",
+       "  'True Love by Elizabeth Arden for Women - 1.7 Ounce EDT Spray',\n",
+       "  (106, 41, 1, 54, 0)),\n",
+       " ('B0009OMNGQ',\n",
+       "  'Awapuhi Moisture Mist Unisex Mist by Paul Mitchell, 16.9 Ounce',\n",
+       "  (83, 50, 25, 171, 0)),\n",
+       " ('B0009OXHC0',\n",
+       "  \"Nature's Blessings Hair Pomade 4 oz.\",\n",
+       "  (185, 170, 184, 205, 0)),\n",
+       " ('B0009P4PZC', 'Cococare Coconut Oil 100% Pure 4 Oz', (163, 67, 157, 88, 0)),\n",
+       " ('B0009PVV36',\n",
+       "  'Conair CD86SCS Instant Heat Iron and Straightener, 1.5 Inch',\n",
+       "  (83, 180, 130, 88, 0)),\n",
+       " ('B0009PVV40',\n",
+       "  'Conair BC171NCS Ceramic Ionic Hot Air Brush, Black, 1.25 Inch',\n",
+       "  (83, 52, 165, 222, 0)),\n",
+       " ('B0009PVV4A',\n",
+       "  'Infiniti Pro by Conair Professional 1 Inch  Tourmaline Ceramic Straightener',\n",
+       "  (100, 119, 191, 88, 0)),\n",
+       " ('B0009QJ3TE',\n",
+       "  'Model in a Bottle Sensitive Makeup Setting Spray - 1.7 oz',\n",
+       "  (23, 57, 28, 177, 0)),\n",
+       " ('B0009QZXP2',\n",
+       "  \"Africa's Best Organics Hair Mayonnaise, 15 oz\",\n",
+       "  (224, 93, 10, 222, 0)),\n",
+       " ('B0009QZXQ6',\n",
+       "  \"Africa's Best Organics Olive Oil Dry Hair and Scalp Therapy, 7.5 oz.\",\n",
+       "  (242, 225, 48, 105, 0)),\n",
+       " ('B0009QZYBU',\n",
+       "  'Soft Sheen Carson Care Free Curl Gold Instant Activator 16Oz/473Ml',\n",
+       "  (17, 30, 156, 22, 0)),\n",
+       " ('B0009QZYLU',\n",
+       "  'Clairol BW2 - 8 oz. Tub Powder Lightener',\n",
+       "  (211, 41, 136, 61, 0)),\n",
+       " ('B0009QZYN8',\n",
+       "  'CLAIROL Jazzing Gentle Temporary Semi Permanent Hair Color #58 RUBY RED',\n",
+       "  (32, 145, 200, 28, 0)),\n",
+       " ('B0009QZYO2', 'Clairol Pure White 30 Volume 16 oz.', (88, 62, 130, 248, 0)),\n",
+       " ('B0009QZZ4Q',\n",
+       "  'Demert Wig Luster Conditioner, 9.76 Ounce',\n",
+       "  (166, 84, 205, 88, 0)),\n",
+       " ('B0009R14NG',\n",
+       "  'Africas Best Herbal Gro Super 5.25oz Jar',\n",
+       "  (147, 151, 22, 222, 0)),\n",
+       " ('B0009R14Q8',\n",
+       "  \"Africa's Best Organincs Mayo Leave In Conditioner 6 oz.\",\n",
+       "  (106, 1, 150, 222, 0)),\n",
+       " ('B0009R14XQ',\n",
+       "  'Australian Gold SPF 15 Spray Gel with Bronzer, 8 Ounce',\n",
+       "  (28, 145, 150, 251, 0)),\n",
+       " ('B0009R16O8',\n",
+       "  \"Dr. Bronner's Magic Soaps: Liquid Castile Soap, Tea Tree 32 oz\",\n",
+       "  (18, 222, 248, 248, 0)),\n",
+       " ('B0009R33U8',\n",
+       "  'Aphogee Two-step Treatment Protein for Damaged Hair 16 oz.',\n",
+       "  (120, 61, 230, 214, 0)),\n",
+       " ('B0009R35GA', 'Clairol Pure White 40 Volume 16 oz.', (88, 62, 187, 136, 0)),\n",
+       " ('B0009R5AYA',\n",
+       "  'Aussie Sydney Smooth 3 Minute Miracle Smoothing Treatment-8oz',\n",
+       "  (181, 160, 207, 220, 0)),\n",
+       " ('B0009R5B8A', 'Avon SSS Original OIL 5oz', (3, 123, 136, 237, 0)),\n",
+       " ('B0009R5CMK',\n",
+       "  'Doo Gro Medicated Hair Vitalizer Mega Thick Anti-Thinning Formula, 4 Ounce',\n",
+       "  (42, 222, 215, 88, 0)),\n",
+       " ('B0009RF9OG',\n",
+       "  'Rogaine for Women Hair Regrowth Treatment, 2 Ounce',\n",
+       "  (120, 7, 157, 105, 1)),\n",
+       " ('B0009RFAOK',\n",
+       "  'Clean &amp; Clear Deep Cleaning Astringent, Oil Fighting, 8 Ounce',\n",
+       "  (178, 90, 1, 42, 0)),\n",
+       " ('B0009RFAPY',\n",
+       "  'Purpose Gentle Cleansing Wash, 6-Ounce Pump Bottle',\n",
+       "  (148, 22, 207, 136, 0)),\n",
+       " ('B0009RFB76',\n",
+       "  'RoC Retinol Correxion Eye Cream, 0.5 Ounce',\n",
+       "  (120, 57, 226, 251, 0)),\n",
+       " ('B0009RMN0E',\n",
+       "  'Conair CS25WNCS Ultra Slim Ceramic Straightener, Red, 1 5/8 Inch',\n",
+       "  (62, 90, 192, 220, 0)),\n",
+       " ('B0009STDD8',\n",
+       "  \"Calvin Klein Women's Obsession Eau de Parfum Spray, 3.4 fl. oz.\",\n",
+       "  (228, 88, 177, 68, 4)),\n",
+       " ('B0009TNBVC',\n",
+       "  'Estee Lauder Double Wear Stay-In-Place Makeup SPF 10 18 Linen',\n",
+       "  (125, 61, 53, 160, 0)),\n",
+       " ('B0009V1YR8',\n",
+       "  'Farouk CHI 1 Inch Ceramic Flat Hairstyling Iron',\n",
+       "  (54, 0, 26, 222, 0)),\n",
+       " ('B0009V1YRS',\n",
+       "  'CHI Turbo Microchip Ceramic Hairstyling Iron 1&quot;',\n",
+       "  (83, 52, 130, 222, 0)),\n",
+       " ('B0009V1YS2',\n",
+       "  'Farouk CHI GF1539 Turbo Big 2 Inch Ceramic Flat Iron Hair Straightener',\n",
+       "  (20, 145, 248, 254, 0)),\n",
+       " ('B0009V2S8M',\n",
+       "  'Glo Minerals GloPressed Base Beige Dark 0.35oz',\n",
+       "  (132, 35, 48, 141, 0)),\n",
+       " ('B0009V8N4U',\n",
+       "  'Sun Laboratories Dark Sunsation Self Tanning Lotion - Very Dark 8 fl oz.',\n",
+       "  (42, 93, 129, 249, 0)),\n",
+       " ('B0009V8N5E',\n",
+       "  'Sun Self Tanning Lotion Ultra Dark Instant Tint - Dark 8oz/236ml',\n",
+       "  (203, 1, 156, 70, 0)),\n",
+       " ('B0009VD8YU',\n",
+       "  'AUBREY Collagen Restorative Moisturizer 1.7 fl.oz',\n",
+       "  (10, 145, 10, 54, 0)),\n",
+       " ('B0009VDBWY',\n",
+       "  'Sun Laboratories Ultra Dark Self Tanning Spray Can (6 oz)',\n",
+       "  (75, 93, 129, 214, 0)),\n",
+       " ('B0009VIJV2',\n",
+       "  'Floxite Fl-10h 10x Hand Held 2-sided Mirror with Stand, Clear',\n",
+       "  (12, 0, 156, 141, 0)),\n",
+       " ('B0009VNI2C',\n",
+       "  'Fruit Of The Earth Bogo Cream Aloe Vera 4oz. Jar',\n",
+       "  (220, 250, 10, 241, 0)),\n",
+       " ('B0009VNI40',\n",
+       "  'Fruit Of The Earth 100 % Aloe Vera Gel, 12 oz, 1-Pack',\n",
+       "  (32, 199, 136, 167, 0)),\n",
+       " ('B0009VQ8X8',\n",
+       "  'Got2b Glued Blasting Freeze Spray, 12 Ounce',\n",
+       "  (141, 7, 31, 215, 0)),\n",
+       " ('B0009WB49K',\n",
+       "  'Alterna Caviar Anti-Aging Working Hair Spray, 15.5 Ounce',\n",
+       "  (52, 90, 129, 61, 0)),\n",
+       " ('B0009WY4U6',\n",
+       "  'St. Ives Swiss Formula Makeup Remover &amp; Facial Cleanser, All Skin Types, 6 oz (170 g)',\n",
+       "  (52, 93, 22, 233, 0)),\n",
+       " ('B0009WY4UQ',\n",
+       "  'TRESemme Curl Enhancing Mousse, 10.5 oz',\n",
+       "  (84, 52, 197, 230, 0)),\n",
+       " ('B0009WY54G',\n",
+       "  'TRESemme Shampoo, 24 Hour Body, Healthy Volume, 32 Ounce',\n",
+       "  (21, 0, 205, 136, 0)),\n",
+       " ('B0009XH6SC',\n",
+       "  'Andis 1-Inch Ceramic Clamp Flat Iron (67095)',\n",
+       "  (87, 93, 129, 237, 0)),\n",
+       " ('B0009XH6TG',\n",
+       "  'Andis RC-2 Ionic1875W Ceramic Hair Dryer with Folding Handle and Retractable Cord (80020)',\n",
+       "  (176, 39, 129, 222, 0)),\n",
+       " ('B0009XH6UU',\n",
+       "  'Andis 40055 Pro Style 1600 Watt Hair Dryer - White',\n",
+       "  (5, 84, 157, 249, 0)),\n",
+       " ('B0009XH6V4',\n",
+       "  'Andis Micro Turbo 1600 Watt Dual Voltage Hair Dryer - White (33805)',\n",
+       "  (5, 129, 183, 205, 0)),\n",
+       " ('B0009Y6RHM',\n",
+       "  'Banana Boat Summer Color Self-Tanning Lotion - Deep Dark: 6 OZ',\n",
+       "  (204, 57, 157, 199, 0)),\n",
+       " ('B0009YDO32',\n",
+       "  'Zum Mist Aromatherapy Room and Body Spray Frankincense And Myrrh -- 4 fl oz',\n",
+       "  (84, 39, 12, 141, 0)),\n",
+       " ('B000A0ADT8',\n",
+       "  'Ole Henriksen Truth Serum Collagen Booster, 1.0 Fluid Ounce',\n",
+       "  (2, 151, 177, 88, 0)),\n",
+       " ('B000A0ADUW',\n",
+       "  'Ole Henriksen Blue Black Berry Enzyme Facial Mask, 3.5 Fluid Ounce',\n",
+       "  (222, 225, 170, 40, 0)),\n",
+       " ('B000A35L56',\n",
+       "  'Max Green Alchemy Scalp Rescue Shampoo 8.8 oz',\n",
+       "  (2, 39, 183, 199, 0)),\n",
+       " ('B000A3I2X4',\n",
+       "  'Revlon RV408 1875 Watt Full-Size Turbo Dryer, Black',\n",
+       "  (12, 7, 197, 228, 0)),\n",
+       " ('B000A3V1SW',\n",
+       "  \"Hask Placenta Henna 'n' Placenta for Extremely Damaged Hair 237ml/8oz\",\n",
+       "  (59, 61, 53, 228, 0)),\n",
+       " ('B000A3V1XC', 'Hollywood Beauty Tea Tree Oil 2 oz.', (58, 145, 248, 254, 0)),\n",
+       " ('B000A3V2PE',\n",
+       "  \"L'Oreal Excellence Hicolor Hilights Red 1.2 oz.\",\n",
+       "  (189, 160, 128, 222, 0)),\n",
+       " ('B000A3XI4M',\n",
+       "  'S-Curl Activator and Moisturizer 32 oz',\n",
+       "  (227, 160, 192, 251, 0)),\n",
+       " ('B000A3ZMLO', 'Hollywood Beauty Carrot Oil 8 oz.', (210, 180, 154, 203, 0)),\n",
+       " ('B000A3ZN7M',\n",
+       "  \"L'Oreal Oreor 30 Volume Creme Developer 16 oz.\",\n",
+       "  (17, 213, 1, 68, 0)),\n",
+       " ('B000A3ZN7W',\n",
+       "  'Loreal Oreor Creme 40 Volume Developer 16 Oz',\n",
+       "  (211, 14, 1, 233, 0)),\n",
+       " ('B000A408VC',\n",
+       "  'Sally Hansen Half Jaw Cuticle Nippers',\n",
+       "  (110, 72, 177, 68, 0)),\n",
+       " ('B000A4094I',\n",
+       "  'Liquid Glass Nail Laminate With Sunscreen 0.5oz',\n",
+       "  (70, 52, 165, 215, 0)),\n",
+       " ('B000A409J8',\n",
+       "  \"L'Oreal Quick Blue Powder Bleach 1 Lb\",\n",
+       "  (14, 39, 151, 248, 0)),\n",
+       " ('B000A7VRYG',\n",
+       "  'Ocusoft Lid Scrub Foaming Eyelid Cleanser (7.25 fl. oz.)',\n",
+       "  (227, 1, 25, 54, 0)),\n",
+       " ('B000AA2XQ4',\n",
+       "  'Rothco Compartment Travel Toiletry Bag',\n",
+       "  (176, 52, 12, 175, 0)),\n",
+       " ('B000AA5VD6',\n",
+       "  'Sally Hansen Insta, Dri Insta, Dri Anti, Chip Top Coat',\n",
+       "  (15, 215, 170, 98, 0)),\n",
+       " ('B000AA5VZ4', 'Super Nail Cuticle Oil 4 oz.', (18, 215, 10, 254, 0)),\n",
+       " ('B000AA5VZY', 'Super Nail 16 oz. Pure Acetone', (200, 14, 184, 54, 0)),\n",
+       " ('B000AA9H3Q',\n",
+       "  'Sally Hansen Natural Nail Growth Activator # 2741 0.45 oz.',\n",
+       "  (63, 160, 248, 233, 0)),\n",
+       " ('B000AA9HQ8', 'Super Nail Cuticle Softener, 8 Ounce', (194, 41, 136, 88, 0)),\n",
+       " ('B000AAAVTU',\n",
+       "  'Sally Hansen No Chip Acrylic Top Coat, 0.45 Fluid Ounce',\n",
+       "  (217, 62, 151, 136, 0)),\n",
+       " ('B000AAAW9Y',\n",
+       "  \"Mane 'n Tail Moisture Enriched Hair Strengthener, Bonus, 6 oz.\",\n",
+       "  (74, 93, 157, 171, 0)),\n",
+       " ('B000AAAWAI',\n",
+       "  \"Straight Arrow Mane 'N Tail Herbal Gro Maximum 5.5oz\",\n",
+       "  (171, 1, 28, 241, 0)),\n",
+       " ('B000AAAWEO',\n",
+       "  'Sun In Hair Lightner Lemon 4.7 oz. Pump',\n",
+       "  (80, 93, 12, 207, 0)),\n",
+       " ('B000AAC8VO',\n",
+       "  'DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ',\n",
+       "  (181, 57, 165, 24, 0)),\n",
+       " ('B000AADENA',\n",
+       "  'Sally Hansen Cuticle Massage Cream -- 0.4 fl oz',\n",
+       "  (18, 41, 197, 205, 0)),\n",
+       " ('B000AADEP8',\n",
+       "  'Sally Hansen Double Duty Base and Top Coat, 0.45 Fluid Ounce',\n",
+       "  (204, 130, 170, 177, 0)),\n",
+       " ('B000AADF0M',\n",
+       "  'Sally Hansen Airbrush Face Tanner 1.8oz Spray [Health and Beauty]',\n",
+       "  (48, 160, 157, 214, 0)),\n",
+       " ('B000AADF20',\n",
+       "  'Sally Hansen Vitamin E Nail and Cuticle Oil, 0.45 Fluid Ounce',\n",
+       "  (42, 52, 130, 177, 0)),\n",
+       " ('B000AADG0G',\n",
+       "  'Village Naturals Bath Shoppe Lavender &amp; Chamomile Foaming Milk Bath 28 fl oz.(Pack of 1)',\n",
+       "  (191, 199, 25, 214, 0)),\n",
+       " ('B000AADG8I',\n",
+       "  'Wella Cc Liquid #0811/8N Light Blonde Haircolor',\n",
+       "  (222, 41, 187, 215, 0)),\n",
+       " ('B000ABOLZ4',\n",
+       "  'Kirks Original Coco Castile Soap, 24 Bars 1/2 Case',\n",
+       "  (60, 84, 22, 251, 0)),\n",
+       " ('B000ACAV8O', 'Henna Bright Red Pwd 4oz 4 Ounces', (120, 7, 22, 141, 0)),\n",
+       " ('B000ACB09I',\n",
+       "  \"Nature's Gate Organics C for Yourself, (1.7 fl oz) (50 ml)\",\n",
+       "  (145, 1, 205, 171, 0)),\n",
+       " ('B000ACB0C0',\n",
+       "  \"Nature's Gate Organics Oh What a Night Cream, (1 oz) (28 g)\",\n",
+       "  (203, 1, 177, 141, 0)),\n",
+       " ('B000ALBJ40',\n",
+       "  'Fekkai Glossing Hair Conditioner 2 Fl Oz',\n",
+       "  (21, 215, 130, 230, 0)),\n",
+       " ('B000ALBKGW',\n",
+       "  'Bliss Diamancel Diamond Nail File No.2, Medium',\n",
+       "  (34, 41, 183, 248, 0)),\n",
+       " ('B000ALBNGE',\n",
+       "  'Clinique Superfine Liner for Brows 01 Soft Blonde',\n",
+       "  (104, 225, 150, 249, 0)),\n",
+       " ('B000ALCJR6',\n",
+       "  'T3 Tourmaline 83808 Professional Featherweight Ceramic Ionic Hair Dryer',\n",
+       "  (140, 92, 177, 228, 0)),\n",
+       " ('B000ALDJ7A',\n",
+       "  'Diamancel Diamond Tough Foot Buffer No.11, Medium',\n",
+       "  (15, 90, 137, 158, 0)),\n",
+       " ('B000ALDK1A',\n",
+       "  'Fruit Of The Earth 100% Aloe Vera 24oz Gel Pump',\n",
+       "  (220, 151, 177, 237, 0)),\n",
+       " ('B000ALDLJG',\n",
+       "  'Clinique Quickliner for Eyes 07 Really Black',\n",
+       "  (89, 93, 22, 28, 0)),\n",
+       " ('B000ALFROS',\n",
+       "  'Fekkai Advanced Brilliant Glossing Cream 4 fl oz (113 g)',\n",
+       "  (189, 119, 205, 136, 0)),\n",
+       " ('B000ALFTUU',\n",
+       "  'Clinique 7 Day Scrub Cream Rinse-Off Formula 3.4 oz',\n",
+       "  (17, 119, 48, 222, 0)),\n",
+       " ('B000ALFTVO',\n",
+       "  'Clinique Take the Day off makeup remover 4.2 oz /125ml',\n",
+       "  (168, 22, 203, 105, 0)),\n",
+       " ('B000AM82US',\n",
+       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Towel, Pink (19 x 39-Inches)\",\n",
+       "  (62, 39, 26, 42, 0)),\n",
+       " ('B000AMA43Q',\n",
+       "  'Aquis Microfiber Hair Towel, Waffle, Linen (19 x 39-Inches)',\n",
+       "  (85, 215, 192, 199, 0)),\n",
+       " ('B000AMA4DQ',\n",
+       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Turban, Patented Design, White\",\n",
+       "  (62, 39, 26, 199, 0)),\n",
+       " ('B000AMBF5W',\n",
+       "  'Nivea Soft Refreshingly Soft Moisturizing Creme with Jojoba Oil &amp; Vitamin E, 6.8 Ounces',\n",
+       "  (172, 151, 215, 215, 0)),\n",
+       " ('B000AMHWCW',\n",
+       "  'Too Faced Cosmetics Bronzer, Snow Bunny, 0.28-Ounce',\n",
+       "  (143, 49, 248, 166, 0)),\n",
+       " ('B000AO2NXS',\n",
+       "  'Dove Body Wash with NutriumMoisture, Sensitive Skin Nourishing, 24 Ounce',\n",
+       "  (100, 151, 205, 220, 0)),\n",
+       " ('B000AQF50Y',\n",
+       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 3.3 Ounces',\n",
+       "  (26, 83, 150, 68, 9)),\n",
+       " ('B000AQI2EK',\n",
+       "  'Paul Mitchell Tea Tree Special Conditioner, 33.8 Ounce',\n",
+       "  (1, 93, 150, 171, 0)),\n",
+       " ('B000ARDBH2',\n",
+       "  'Guess Eau de Parfum Spray for Women, 2.5 Fluid Ounce',\n",
+       "  (29, 50, 205, 54, 0)),\n",
+       " ('B000ASDGK8',\n",
+       "  'BaByliss Pro BAB2000 Ceramix Xtreme  Dryer',\n",
+       "  (24, 49, 136, 205, 0)),\n",
+       " ('B000ASDX3S',\n",
+       "  'BaByliss Pro BAB2590 Porcelain Ceramic Straightening Iron with Removable Comb, 1.5 Inch',\n",
+       "  (247, 62, 184, 249, 0)),\n",
+       " ('B000AU15E0',\n",
+       "  'Blue Lizard Australian SUNSCREEN SPF 30+, Baby, SPF 30+, 8.75-Ounces',\n",
+       "  (148, 39, 192, 199, 0)),\n",
+       " ('B000AUTH0E',\n",
+       "  'Rusk Thermal Shine Spray Unisex, 4.4 Ounce',\n",
+       "  (18, 151, 48, 61, 0)),\n",
+       " ('B000AUTHEA',\n",
+       "  'Straight Sexy Hair Smooth &amp; Seal Aerated Anti-Frizz Spray (8.1 oz)',\n",
+       "  (210, 119, 184, 28, 0)),\n",
+       " ('B000AYKVOG', 'Dove Pink Beauty Bar, 8 Count', (88, 41, 10, 61, 0)),\n",
+       " ('B000B45BUE',\n",
+       "  'Silver Metallic Perfume Atomizer Spray 10 ML for purse or travel Refillable',\n",
+       "  (46, 215, 192, 24, 0)),\n",
+       " ('B000B5S2LS',\n",
+       "  'Focus 21 Sea Plasma Hair and Skin Moisturizer 32oz',\n",
+       "  (2, 62, 129, 237, 0)),\n",
+       " ('B000B5UPF4', 'OC Eight Professional Mattifying Gel', (6, 1, 184, 171, 0)),\n",
+       " ('B000B63Y1K',\n",
+       "  'Dermorganic Conditioning Shampoo, 12 Ounce',\n",
+       "  (84, 192, 207, 249, 0)),\n",
+       " ('B000B6VIPY',\n",
+       "  'Princereigns Shaving Gel Used to Remove Ingrown Hair and Razor Bumps',\n",
+       "  (14, 7, 248, 251, 0)),\n",
+       " ('B000B7VO66',\n",
+       "  'PCA Skin Eyexcellence (Phaze 12), 0.5 Ounce',\n",
+       "  (224, 0, 130, 228, 0)),\n",
+       " ('B000B82TD2',\n",
+       "  'NeoStrata Ultra Smoothing Cream AHA 10, 1.4 Ounce',\n",
+       "  (42, 222, 18, 251, 0)),\n",
+       " ('B000B8FW0Y', 'Lansky Dual Grit Sharpener', (43, 50, 207, 26, 0)),\n",
+       " ('B000B8VBJK',\n",
+       "  'HOT TOOLS 2108 Nano Ceramic Marcel Curling Iron, Black/Purple, 1 Inch',\n",
+       "  (71, 35, 18, 91, 0)),\n",
+       " ('B000B9J3GM', 'Mary Kay Indulge Soothing Eye Gel', (187, 230, 25, 214, 0)),\n",
+       " ('B000B9N0N4',\n",
+       "  'Mary Kay Blemish Control Toner: Formula 3',\n",
+       "  (32, 199, 150, 249, 0)),\n",
+       " ('B000B9W3CI',\n",
+       "  'White Plastic Jar with Dome Lid 4 Oz - 12 Per Bag',\n",
+       "  (67, 35, 183, 105, 0)),\n",
+       " ('B000B9W4MC',\n",
+       "  'White Plastic Jar with Flat Lid 8 Oz - 6 Per Bag',\n",
+       "  (190, 7, 223, 237, 0)),\n",
+       " ('B000BB9L0S', 'Nailtiques Formula 2 Protein, .5 Ounce', (18, 1, 177, 88, 0)),\n",
+       " ('B000BBGP4I', 'Mason Pearson Detangling Comb', (5, 82, 170, 220, 0)),\n",
+       " ('B000BD0SEE',\n",
+       "  'MyChelle Fruit Enzyme Cleanser, 4.4 Ounce Bottle',\n",
+       "  (80, 127, 48, 175, 0)),\n",
+       " ('B000BFTAMS',\n",
+       "  'Ponds Caring Classic Extra, Rich Dry Skin Cream, 10.1 oz',\n",
+       "  (221, 35, 207, 88, 0)),\n",
+       " ('B000BGIYWY',\n",
+       "  \"Africa's Best Kids Organics Hair Lotion, Shea Butter Detangling Moisturizing 12 oz\",\n",
+       "  (222, 199, 137, 249, 0)),\n",
+       " ('B000BH92J2',\n",
+       "  'Shea Moisture Shea Butter Leave in Conditioner 8oz',\n",
+       "  (178, 22, 1, 68, 0)),\n",
+       " ('B000BIPJ20',\n",
+       "  'Perfekt Skin Perfection Gel Luminous 1 oz',\n",
+       "  (242, 84, 53, 22, 0)),\n",
+       " ('B000BIUGRI', 'Bumble and Bumble Prep (8 Ounces)', (1, 225, 129, 166, 0)),\n",
+       " ('B000BIUGSM',\n",
+       "  'Bumble and Bumble Styling Lotion (8 Ounces)',\n",
+       "  (24, 230, 177, 24, 0)),\n",
+       " ('B000BIUGUA',\n",
+       "  'Bumble and Bumble Sunday Shampoo (8 Ounces)',\n",
+       "  (184, 229, 150, 136, 0)),\n",
+       " ('B000BIUGUK',\n",
+       "  'Bumble and Bumble Thickening Conditioner (8 Ounces)',\n",
+       "  (28, 39, 137, 88, 0)),\n",
+       " ('B000BIUGV4',\n",
+       "  'Bumble and Bumble Thickening Hair Spray (8 Ounces)',\n",
+       "  (61, 199, 183, 136, 0)),\n",
+       " ('B000BIUGXM',\n",
+       "  'Bumble and Bumble Curl Conscious Defining Creme 8.5 oz',\n",
+       "  (1, 222, 187, 251, 0)),\n",
+       " ('B000BIVXZW',\n",
+       "  'Bumble and Bumble Classic Hairspray (10 Ounces)',\n",
+       "  (153, 0, 177, 214, 0)),\n",
+       " ('B000BIVY0G',\n",
+       "  'Bumble and Bumble  DeFRIZZ (4 Ounces)',\n",
+       "  (123, 22, 187, 222, 0)),\n",
+       " ('B000BIVY10',\n",
+       "  'Kerastase Nutritive Bain Satin 2 Complete Nutrition Shampoo For Dry and Sensitised Hair, 8.5 Oz.',\n",
+       "  (244, 50, 205, 220, 0)),\n",
+       " ('B000BIVY1U',\n",
+       "  'Bumble and Bumble Seaweed Conditioner (8 Ounces)',\n",
+       "  (245, 35, 207, 233, 0)),\n",
+       " ('B000BIVY24',\n",
+       "  'Bumble and Bumble Tonic Lotion, 8-Ounce Spray Bottle',\n",
+       "  (46, 151, 151, 91, 0)),\n",
+       " ('B000BIXP30',\n",
+       "  'Kerastase Resistance Bain Volumactive Volumizing Shampoo For Fine, Vulnerable Hair, 8.5 Ounce',\n",
+       "  (169, 145, 18, 61, 0)),\n",
+       " ('B000BIXP3K',\n",
+       "  'Bumble and Bumble Grooming Cream (5 Ounces)',\n",
+       "  (135, 225, 184, 205, 0)),\n",
+       " ('B000BIXP3U',\n",
+       "  'Bumble and Bumble Leave in Conditioner (8 Ounces)',\n",
+       "  (193, 72, 188, 177, 0)),\n",
+       " ('B000BIXP4O',\n",
+       "  'Bumble and Bumble Styling Creme, 8-Ounce Bottle',\n",
+       "  (24, 50, 18, 24, 0)),\n",
+       " ('B000BIXP5I',\n",
+       "  'Bumble and Bumble Surf Spray, 4-Ounce Bottle',\n",
+       "  (97, 41, 183, 88, 0)),\n",
+       " ('B000BIXP5S',\n",
+       "  'Kerastase Nutritive Oleo-Relax Serum, 4.2 Ounces',\n",
+       "  (172, 41, 176, 215, 0)),\n",
+       " ('B000BIXP62',\n",
+       "  'Kerastase Nutritive Bain Oleo-Relax Smoothing Shampoo For Dry and Rebellious Hair, 8.5 Ounce',\n",
+       "  (183, 145, 53, 236, 0)),\n",
+       " ('B000BIZSUS',\n",
+       "  'Bumble and Bumble Brilliantine (2 Ounces)',\n",
+       "  (46, 215, 157, 220, 0)),\n",
+       " ('B000BIZSV2',\n",
+       "  'Bumble and Bumble Deeep, 5-Ounce Tube',\n",
+       "  (27, 222, 191, 222, 0)),\n",
+       " ('B000BIZSYO',\n",
+       "  'Kerastase Resistance Bain De Force Fortifying Shampoo For Weakened to Fragile Hair, 8.5 Ounce',\n",
+       "  (132, 61, 188, 214, 0)),\n",
+       " ('B000BJ1CGQ', 'GiGi Mini Pro Waxing Kit', (191, 175, 26, 214, 0)),\n",
+       " ('B000BK1P9E',\n",
+       "  'Kerasilk Rich Care Instant Silk Fluid By Goldwell for Unisex, 4.2 Ounce',\n",
+       "  (121, 50, 188, 88, 0)),\n",
+       " ('B000BKLBES',\n",
+       "  'Marc Anthony Instantly Thick Hair Thickening Cream, 6 oz',\n",
+       "  (204, 199, 230, 88, 0)),\n",
+       " ('B000BKLCDS',\n",
+       "  'Super Nail Acetone Polish Remover, 16 Ounce',\n",
+       "  (24, 22, 1, 68, 0)),\n",
+       " ('B000BKXGXW',\n",
+       "  'NOW Foods -  Red Clay Powder Moroccan, 6 OZ.',\n",
+       "  (161, 1, 215, 28, 0)),\n",
+       " ('B000BNG4VU',\n",
+       "  'Coty Airspun Loose Powder, Translucent, 2.3 Ounce',\n",
+       "  (93, 151, 188, 136, 0)),\n",
+       " ('B000BP81IM', 'NARS Loose Powder, Eden', (218, 19, 184, 166, 0)),\n",
+       " ('B000BPBV2U',\n",
+       "  'Liquid Gold Brush-on Bonding Adhesive for Cold Fusion Hair Extensions and Braids - .5oz',\n",
+       "  (120, 215, 184, 237, 0)),\n",
+       " ('B000BPMB2Y',\n",
+       "  'Roux Lash &amp; Brow Tint, Black,40 Count',\n",
+       "  (233, 119, 203, 177, 0)),\n",
+       " ('B000BPQG6Q', 'Roux Tween Time Hair Crayon, Auburn', (196, 39, 48, 88, 0)),\n",
+       " ('B000BPT0TG',\n",
+       "  'Roux Fanci-full Rinse #49 Ultra White Minx',\n",
+       "  (210, 52, 12, 215, 0)),\n",
+       " ('B000BR50M0',\n",
+       "  'Symbiotics Colostrum Plus, 240 Capsules',\n",
+       "  (145, 7, 151, 105, 0)),\n",
+       " ('B000BR5B2Y',\n",
+       "  'Komenuka Bijin Japanese All-Natural Essence Whitening Cream with Rice Bran',\n",
+       "  (100, 84, 146, 61, 0)),\n",
+       " ('B000BR766S',\n",
+       "  'Komenuka Bijin Premium Hair Care Set: Moisturizing Hair Shampoo &amp; Hair Treatment / Conditioner',\n",
+       "  (247, 49, 32, 166, 0)),\n",
+       " ('B000BRGAOM',\n",
+       "  'Biosilk Therapy Shine On Spray, 5.30 Ounce',\n",
+       "  (62, 41, 156, 166, 0)),\n",
+       " ('B000BRO6O8',\n",
+       "  'Coty Airspun Loose Powder, Naturelle, 2.3 Ounce',\n",
+       "  (93, 151, 188, 136, 1)),\n",
+       " ('B000BRPMJG',\n",
+       "  'Sebamed moisturizing lotion, for sensitive skin, 6.8-Fluid Ounce',\n",
+       "  (32, 7, 32, 91, 0)),\n",
+       " ('B000BRSHXE',\n",
+       "  'Sebamed Moisturing Cream, Sensitive Skin, 2.6-Ounce',\n",
+       "  (32, 7, 32, 91, 1)),\n",
+       " ('B000BRY5UI',\n",
+       "  'Komenuka Bijin Facial Cleansing Powder from Natural Rice Bran - 30 Packs',\n",
+       "  (177, 7, 197, 215, 0)),\n",
+       " ('B000BRYMKG',\n",
+       "  'Hylexin Serious Dark Circles by Bremenn Research Labs .39 oz. 11.53 ml',\n",
+       "  (120, 230, 53, 141, 0)),\n",
+       " ('B000BTM2K6',\n",
+       "  'White Shoulders By Evyan For Women, Eau De Cologne Spray (4.5 Ounces)',\n",
+       "  (147, 222, 32, 166, 0)),\n",
+       " ('B000BTO6CI',\n",
+       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 3.4 Oz.',\n",
+       "  (26, 83, 150, 68, 10)),\n",
+       " ('B000BTO6EG',\n",
+       "  'PALOMA PICASSO For Women By PALOMA PICASSO Eau De Parfum Spray,3.4 Oz',\n",
+       "  (26, 83, 226, 68, 0)),\n",
+       " ('B000BTQRFM',\n",
+       "  'Paul Sebastian by Paul Sebastian for Men - 8 Ounce EDC De Luxe Splash',\n",
+       "  (70, 0, 18, 42, 0)),\n",
+       " ('B000BU7G1A',\n",
+       "  'ApHogee Intensive Two Minute Keratin Reconstructor',\n",
+       "  (178, 215, 156, 24, 0)),\n",
+       " ('B000BU7SMM', 'Aphogee Balancing Moisturizer 16 Oz', (21, 215, 157, 254, 0)),\n",
+       " ('B000BUFFPO', \"Burt's Bees Herbal Deodorant\", (238, 72, 136, 199, 0)),\n",
+       " ('B000BVCSP8',\n",
+       "  'Scruples High Definition Shape Spray, 10.6 Ounce',\n",
+       "  (1, 90, 203, 249, 0)),\n",
+       " ('B000BW4U58', 'Vitamin E Skin Oil 10000 IU. 4.6 Oz', (235, 230, 156, 5, 0)),\n",
+       " ('B000BX1Z00',\n",
+       "  'CHI Silk Infusion Leave-In Treatment, 12 Ounce',\n",
+       "  (62, 93, 32, 40, 0)),\n",
+       " ('B000BX5FS8',\n",
+       "  'CHI Straight Guard Smoothing Styling Cream 8.5 oz',\n",
+       "  (5, 199, 18, 105, 0)),\n",
+       " ('B000BY2N7S', 'NOW Foods Biotin 5000mcg, 120 Vcaps', (241, 83, 249, 218, 0)),\n",
+       " ('B000BYQBT4',\n",
+       "  'Obagi Nu Derm Exfoderm Skin Smoothing Lotion-2 oz',\n",
+       "  (118, 62, 156, 171, 0)),\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "items_with_tuples"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 43fee7b9..6e1bd68b 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -99,11 +99,13 @@ def __init__(
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
     
-    def get_item_embeddings(self, events):
+    def get_encoder_embeddings(self, events):
         return self._item_embeddings(events)
 
     def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
-        embeddings = self.get_item_embeddings(events)  # (all_batch_events, embedding_dim)
+        embeddings = self.get_encoder_embeddings(events)  # (all_batch_events, embedding_dim)
+        
+        assert embeddings.shape[0] == sum(lengths)
 
         embeddings, mask = create_masked_tensor(
             data=embeddings,
diff --git a/modeling/models/collision_solver.py b/modeling/models/collision_solver.py
index 9af18bed..6172c825 100644
--- a/modeling/models/collision_solver.py
+++ b/modeling/models/collision_solver.py
@@ -108,4 +108,29 @@ def get_semantic_ids(self, query_prefixes: torch.Tensor, query_residuals: torch.
         masked_dot_products = torch.einsum('ijk,ik->ij', candidates_tensor, query_residuals).masked_fill(~mask, float('-inf'))
         max_indices = torch.argmax(masked_dot_products, dim=1)
         best_semantic_ids = torch.concat((query_prefixes, max_indices.unsqueeze(1)), dim=1)
-        return best_semantic_ids
\ No newline at end of file
+        return best_semantic_ids
+    
+    def get_closest_torch(self, query_prefixes: torch.Tensor, query_residuals: torch.Tensor):
+        raise NotImplementedError("get_closest_torch is not implemented")
+    
+        query_prefixes = query_prefixes.to(self.device)
+        query_residuals = query_residuals.to(self.device)
+
+        batch_size, max_length = self._semantic_ids.shape
+        num_prefixes, prefix_len = query_prefixes.shape
+
+        # привожу к одной размерности чтобы найти совпадения по префиксам
+        semantic_ids_exp = self._semantic_ids[:, :prefix_len].unsqueeze(0).expand(num_prefixes, batch_size, prefix_len) # [num_prefixes, batch_size, prefix_len]
+        prefixes_exp = query_prefixes.unsqueeze(1).expand(num_prefixes, batch_size, prefix_len) #torch.tile
+        is_prefix_match = (semantic_ids_exp == prefixes_exp).all(dim=2)  # [num_prefixes, batch_size]
+
+        # Шаг 2: Маскирование residuals для каждого префикса
+        residuals_exp = self._residuals.unsqueeze(0).expand(num_prefixes, batch_size, -1)  # [num_prefixes, batch_size, emb_dim]
+        masked_residuals = residuals_exp * is_prefix_match.unsqueeze(2).float()  # Зануляем строки, не соответствующие префиксам
+        dot_products = torch.einsum('ijk,ik->ij', masked_residuals, query_residuals)
+        max_indices = torch.argmax(dot_products, dim=1)  # [num_prefixes] #
+
+        best_semantic_ids = self._semantic_ids[max_indices]  # [num_prefixes, max_length]
+        best_residuals = self._residuals[max_indices]  # [num_prefixes, emb_dim]
+        
+        return best_semantic_ids, best_residuals
\ No newline at end of file
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 56185a4d..bb6abb16 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -32,6 +32,9 @@ def __init__(
 
         # Default initialization of codebook
         self.codebooks = torch.nn.ParameterList()
+        
+        self.codebook_sizes = codebook_sizes
+        
         for codebook_size in codebook_sizes:
             cb = torch.FloatTensor(codebook_size, hidden_dim)
             self.codebooks.append(cb)
@@ -51,8 +54,8 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         return cls(
             train_sampler=kwargs.get('train_sampler'),
-            input_dim=config['input_dim'],
-            hidden_dim=config['hidden_dim'],
+            input_dim=config['embedding_dim'],
+            hidden_dim=config['embedding_dim'],
             n_iter=config['n_iter'],
             codebook_sizes=config['codebook_sizes'],
             should_init_codebooks=config.get('should_init_codebooks', False),
@@ -144,7 +147,7 @@ def eval_pass(self, embeddings):
             codebook_vectors = codebook[codebook_indices]
             ind_lists.append(codebook_indices.cpu().numpy())
             remainder = remainder - codebook_vectors
-        return zip(*ind_lists)
+        return list(zip(*ind_lists)), remainder
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 99084e64..62c660ff 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,9 +1,9 @@
 import json
-import pickle
 
 import torch
-from models.collision_solver import CollisionSolver
+from rqvae.trie import Item, Trie
 from models.base import SequentialTorchModel
+from models.collision_solver import CollisionSolver
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
@@ -14,8 +14,11 @@ class TigerModel(SequentialTorchModel, config_name="tiger"):
 
     def __init__(
         self,
-        trie,
         rqvae_model,
+        item_id_to_semantic_id,
+        item_id_to_residual,
+        item_id_to_embedding,
+        solver,
         sequence_prefix,
         pred_prefix,
         positive_prefix,
@@ -27,7 +30,7 @@ def __init__(
         num_encoder_layers,
         num_decoder_layers,
         dim_feedforward,
-        semantic_id_arr,
+        codebook_sizes,
         dropout=0.0,
         activation="relu",
         layer_norm_eps=1e-9,
@@ -45,10 +48,11 @@ def __init__(
             layer_norm_eps=layer_norm_eps,
             is_causal=True,
         )
-        self._decoder_position_embeddings = nn.Embedding(
-            num_embeddings=2,  # bos token + label item id (always 0)
-            embedding_dim=embedding_dim,
-        )
+
+        self._sequence_prefix = sequence_prefix
+        self._pred_prefix = pred_prefix
+        self._positive_prefix = positive_prefix
+        self._labels_prefix = labels_prefix
 
         transformer_decoder_layer = nn.TransformerDecoderLayer(
             d_model=embedding_dim,
@@ -59,190 +63,250 @@ def __init__(
             layer_norm_eps=layer_norm_eps,
             batch_first=True,
         )
+        
         self._decoder = nn.TransformerDecoder(
             transformer_decoder_layer, num_decoder_layers
         )
+    
+        self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
+        self._decoder_dropout = nn.Dropout(dropout)
         
-        self._codebook_item_embeddings = torch.cat([codebook for codebook in rqvae_model.codebooks], dim=0)
-
-        self._trie = trie
-        self._rqvae_model = rqvae_model
-        self._collision_solver = CollisionSolver(embedding_dim, len(semantic_id_arr), DEVICE)
-
-        # TODO
-        # self._collision_solver.create_query_candidates_dict(semantic_ids[:, :-1], residuals)
-        # self._collision_solver.get_semantic_ids(query_prefixes, query_residuals)
-
-        self._projection = nn.Linear(embedding_dim, semantic_id_arr[0])
-
-        self._sequence_prefix = sequence_prefix
-        self._pred_prefix = pred_prefix
-        self._positive_prefix = positive_prefix
-        self._labels_prefix = labels_prefix
-
-        self._semantic_id_arr = semantic_id_arr
+        self._solver = solver
+        
+        self._codebook_sizes = codebook_sizes
+        self._codebook_item_embeddings_flattened = torch.cat([codebook for codebook in rqvae_model.codebooks], dim=0)
+        self._codebook_item_embeddings_stacked = torch.stack([codebook for codebook in rqvae_model.codebooks])
+        
+        self._item_id_to_semantic_id = item_id_to_semantic_id
+        self._item_id_to_residual = item_id_to_residual
+        self._item_id_to_embedding = item_id_to_embedding
+        
+        self._trie = Trie()
+        for item_id, semantic_id in item_id_to_semantic_id.items():
+            self._trie.insert(Item(semantic_id, item_id_to_residual[item_id])) # TODO no dedup tokens here
 
-        self._bos_token_id = semantic_id_arr[0]
+        self._bos_token_id = codebook_sizes[0]
         self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
-
+        
+        self._decoder_position_embeddings = nn.Embedding(
+            num_embeddings=2,  # bos token + label item id (always 0)
+            embedding_dim=embedding_dim,
+        )
         self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(semantic_id_arr) + 1, embedding_dim=embedding_dim
-        )  # + 1 for bos token
-
-        self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
-        self._decoder_dropout = nn.Dropout(dropout)
+            num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim # TODO
+        )  # + 2 for bos token & residual
 
         self._init_weights(initializer_range)
-
+        
     @classmethod
-    def create_from_config(cls, config, **kwargs):
-        with open(config["trie"], "rb") as f:
-            trie = pickle.load(f)
-
+    def init_rqvae(cls, config):
         rqvae_config = json.load(open(config["rqvae_train_config_path"]))
-        semantic_id_arr = rqvae_config["model"]["codebook_sizes"]
-        assert all([book_size == semantic_id_arr[0] for book_size in semantic_id_arr])
-
         rqvae_config["model"]["should_init_codebooks"] = False
-        rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
-        rqvae_model.load_state_dict(
-            torch.load(config["rqvae_checkpoint_path"], weights_only=True)
-        )
+        
+        rqvae_model = RqVaeModel.create_from_config(rqvae_config['model']).to(DEVICE)
+        rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
         rqvae_model.eval()
+        for param in rqvae_model.parameters(): # TODO
+            param.requires_grad = False
+        
+        codebook_sizes = rqvae_model.codebook_sizes
+        assert all([book_size == codebook_sizes[0] for book_size in codebook_sizes])
+        
+        return rqvae_model
 
-        embedding_dim = rqvae_model.encoder.weight.shape[0] # inner rqvae dim
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_model = cls.init_rqvae(config)
+        embedding_dim = rqvae_model.encoder.weight.shape[0] # TODO
+        embs_extractor = torch.load(config['embs_extractor_path'])
+        
+        item_ids = embs_extractor.index.tolist()
+        assert sorted(item_ids) == list(range(1, len(item_ids) + 1))
+        
+        embeddings = torch.stack(embs_extractor['embeddings'].tolist()).to(DEVICE)
+
+        semantic_ids, residuals = rqvae_model({"embeddings": embeddings})
+        
+        solver = CollisionSolver(residuals.shape[1], len(semantic_ids[0]), device=DEVICE)
+        solver.create_query_candidates_dict(semantic_ids, residuals) # TODO
+        
+        item_id_to_embedding = {
+            item_id: embedding for (item_id, embedding) in zip(item_ids, embeddings)
+        }
+        item_id_to_semantic_id = {
+            item_id: torch.tensor(semantic_id, device=DEVICE) for (item_id, semantic_id) in zip(item_ids, semantic_ids)
+        }
+        item_id_to_residual = {
+            item_id: remainder for (item_id, remainder) in zip(item_ids, residuals)
+        }
 
         return cls(
-            trie=trie,
             rqvae_model=rqvae_model,
+            item_id_to_semantic_id=item_id_to_semantic_id,
+            item_id_to_residual=item_id_to_residual,
+            item_id_to_embedding=item_id_to_embedding,
+            solver=solver,
             sequence_prefix=config["sequence_prefix"],
             pred_prefix=config["predictions_prefix"],
             positive_prefix=config["positive_prefix"],
             labels_prefix=config["labels_prefix"],
-            num_items=semantic_id_arr[0],
+            num_items=rqvae_model.codebook_sizes[0], # unused
             max_sequence_length=kwargs["max_sequence_length"],
             embedding_dim=embedding_dim,
             num_heads=config.get("num_heads", int(embedding_dim // 64)),
             num_encoder_layers=config["num_encoder_layers"],
             num_decoder_layers=config["num_decoder_layers"],
             dim_feedforward=config.get("dim_feedforward", 4 * embedding_dim),
-            semantic_id_arr=semantic_id_arr,
+            codebook_sizes=rqvae_model.codebook_sizes,
             dropout=config.get("dropout", 0.0),
             initializer_range=config.get("initializer_range", 0.02),
         )
 
-    def get_item_embeddings(self, events):
-        bos_mask = (events == self._bos_token_id)
+    def get_encoder_embeddings(self, events):
+        events = events.tolist()
+        
+        # convert to semantic ids
+        semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
+        semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
+        
+        # convert to rqvae embeddings
+        positions = torch.arange(len(semantic_events), device=DEVICE)
+        codebook_positions = positions % len(self._codebook_sizes)
+        emb_indices = codebook_positions * self._codebook_sizes[0] + semantic_events
+        semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
+        semantic_embeddings = semantic_embeddings.view(
+            len(events), len(self._codebook_sizes), self._embedding_dim
+        )
+        
+        # get residuals
+        text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
+        residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = residual.unsqueeze(1)
         
-        codebook_events = events[~bos_mask]
-        positions = torch.arange(len(codebook_events), device=events.device)
-        codebook_positions = positions % len(self._semantic_id_arr)
-        emb_indices = codebook_positions * self._semantic_id_arr[0] + codebook_events
+        # get true item embeddings
+        item_embeddings = torch.cat([semantic_embeddings, residual], dim=1)
+        item_embeddings = item_embeddings.view(-1, self._embedding_dim)
         
-        embeddings = torch.zeros((len(events), self._bos_weight.shape[0]), device=events.device)
-        embeddings[bos_mask] = self._bos_weight
-        embeddings[~bos_mask] = self._codebook_item_embeddings[emb_indices]
-                
-        return embeddings
+        return item_embeddings
 
-    def get_logits(self, inputs, prefix, all_sample_events, all_sample_lengths):
+    def get_logits(self, label_events, label_lengths, all_sample_events, all_sample_lengths):
+        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1) # TODO residual gets add as event
+        # TODO encoder_mask sees residual as event (is it correct?)
+        
         encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
-
-        label_events = inputs["semantic.{}.ids".format(prefix)]
-        label_lengths = inputs["semantic.{}.length".format(prefix)]
-
+        
+        print(f"{encoder_embeddings.isnan().any()=}")
+        
         decoder_outputs = self._apply_decoder(
             label_events, label_lengths, encoder_embeddings, encoder_mask
         )  # (batch_size, label_len, embedding_dim)
+        
+        decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
+        
+        return decoder_prefix_scores
+    
+    def get_decoder_embeddings(self, events):
+        events = events.tolist()
+        
+        # convert to semantic ids
+        semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
+        semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
+        
+        # convert to rqvae embeddings
+        positions = torch.arange(len(semantic_events), device=DEVICE)
+        codebook_positions = positions % len(self._codebook_sizes)
+        emb_indices = codebook_positions * self._codebook_sizes[0] + semantic_events
+        semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
+        semantic_embeddings = semantic_embeddings.view(
+            len(events), len(self._codebook_sizes), self._embedding_dim
+        )
+        
+        # get residuals
+        text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
+        residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = residual.unsqueeze(1)
+        
+        # apply collision solver
+        # deduped_semantic_ids = self._solver.get_semantic_ids(semantic_ids, residual) # TODO
+        
+        # get true item embeddings
+        item_embeddings = torch.cat([semantic_embeddings, residual], dim=1)
+        item_embeddings = item_embeddings.view(-1, self._embedding_dim)
+        
+        return item_embeddings
 
-        # TODOPK correct place for projection? or view -> projection
-        logits = self._projection(
-            decoder_outputs
-        )  # (batch_size, dec_seq_len, _semantic_id_arr[0])
-
-        return logits
-
+    # semantic ids come with dedup token
     def forward(self, inputs):
         all_sample_events = inputs[
-            "semantic.{}.ids".format(self._sequence_prefix)
+            "{}.ids".format(self._sequence_prefix)
         ]  # (all_batch_events)
         all_sample_lengths = inputs[
-            "semantic.{}.length".format(self._sequence_prefix)
+            "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
         if self.training:
-            logits = self.get_logits(
-                inputs, self._positive_prefix, all_sample_events, all_sample_lengths
-            )  # (batch_size, dec_seq_len, _semantic_id_arr[0])
-
-            logits = logits.view(-1, self._semantic_id_arr[0])
-
-            return {self._pred_prefix: logits}
+            label_events = inputs["{}.ids".format(self._positive_prefix)]
+            label_lengths = inputs["{}.length".format(self._positive_prefix)]
+        
+            decoder_prefix_scores = self.get_logits(
+                label_events, label_lengths, all_sample_events, all_sample_lengths
+            )  # (batch_size, dec_seq_len, _codebook_sizes[0])
+            
+            logits = decoder_prefix_scores.reshape(-1, self._codebook_sizes[0])
+            
+            semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in label_events.tolist()]) # len(events), len(codebook_sizes)
+            semantic_events = semantic_ids.view(-1)
+            
+            return {self._pred_prefix: logits, f"semantic.{self._labels_prefix}.ids": semantic_events}
         else:
-            logits = self.get_logits(
-                inputs, self._labels_prefix, all_sample_events, all_sample_lengths
-            )  # batch_size, dec_seq_len, emb_dim (_semantic_id_arr[0])
-
-            preds = logits.argmax(dim=-1)  # (batch_size, dec_seq_len)
-            ids = torch.tensor(self._apply_trie(preds))
+            label_events = inputs["{}.ids".format(self._labels_prefix)]
+            label_lengths = inputs["{}.length".format(self._labels_prefix)]
+            
+            decoder_prefix_scores = self.get_logits(
+                label_events, label_lengths, all_sample_events, all_sample_lengths
+            )  # batch_size, dec_seq_len, emb_dim (_codebook_sizes[0])
+
+            preds = decoder_prefix_scores.argmax(dim=-1)  # (batch_size, dec_seq_len)
+            ids = self._apply_trie(preds)
             return ids
-        
 
-    def _apply_trie(self, preds):  # TODOPK make this faster (how?)
-        native_repr = [tuple(row.tolist()) for row in preds]
-        # TODOPK add residual
-        # add flag if item taken (take other items in up level)
+    def _apply_trie(self, preds):
+        semantic_ids = [tuple(row.tolist()) for row in preds]
+        
         ids = []
-        for semantic_id in native_repr:
-            cur_result = set()
-            prefixes = [semantic_id[:i] for i in range(len(semantic_id), 0, -1)]
-            for prefix in prefixes:
-                prefix_ids = self._trie.search_prefix(
-                    prefix
-                )  # todo handle collisions (not overwrite)
-                for id in prefix_ids:
-                    cur_result.add(id)
-                    if len(cur_result) >= 20:
-                        break
-                if len(cur_result) >= 20:
-                    break
-
-            cur_result = list(cur_result)
-            while len(cur_result) < 20:
-                cur_result.append(0)  # solve empty event if shortest prefix
-            ids.append(cur_result)
-        return ids
-
-    def _prepend_bos(self, label_events, label_lengths):
-        batch_size = len(label_lengths)
-        label_events = label_events.view(batch_size, -1)
-        bos_tokens = torch.full(
-            (batch_size, 1), self._bos_token_id, device=label_events.device
-        )
-        label_events = torch.cat(
-            [bos_tokens, label_events], dim=1
-        )  # (batch_size, dec_seq_len + 1)
-        label_lengths = label_lengths + 1
-        return label_events.view(-1), label_lengths
+        for semantic_id in semantic_ids:
+            item = Item(semantic_id, torch.rand(self._embedding_dim)) # TODO use true residuals
+            closest_items = self._trie.find_n_closest(item, n=20)
+            ids.append(closest_items)
+            
+        print(f"{ids=}")
+        
+        return torch.tensor(ids)
 
     def _apply_decoder(
         self, label_events, label_lengths, encoder_embeddings, encoder_mask
     ):
-        label_events, label_lengths = self._prepend_bos(label_events, label_lengths)
-
-        tgt_embeddings = self.get_item_embeddings(
+        label_lengths = label_lengths * (len(self._codebook_sizes) + 1) # TODO bos prepending, residual removing
+        tgt_embeddings = self.get_decoder_embeddings( # TODO residual embs
             label_events
         )  # (all_batch_events, embedding_dim)
-
+        
         tgt_embeddings, tgt_mask = create_masked_tensor(
             data=tgt_embeddings, lengths=label_lengths
-        )  # (batch_size, dec_seq_len + 1, embedding_dim), (batch_size, dec_seq_len + 1)
-
+        )  # (batch_size, dec_seq_len, embedding_dim), (batch_size, dec_seq_len)
+        
+        batch_size = tgt_embeddings.shape[0]
+        bos_embeddings = self._bos_weight.unsqueeze(0).expand(batch_size, 1, -1)  # (batch_size, 1, embedding_dim)
+        
+        tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1) # TODO remove residuals (batch_size, dec_seq_len, embedding_dim)
+        # tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1) # (batch_size, dec_seq_len, embedding_dim)
+        # TODO here :-1 removes dedup token, however sizes doesn't match (decoder_output = b x len(codebook_sizes) + 1 x emb_dim)
+        
         label_len = tgt_mask.shape[1]
 
-        assert label_len == len(self._semantic_id_arr) + 1  # TODOPK
+        assert label_len == len(self._codebook_sizes) + 1 # TODO +1 for bos
 
         position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
         assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
@@ -251,40 +315,38 @@ def _apply_decoder(
 
         tgt_embeddings = self._decoder_layernorm(
             tgt_embeddings
-        )  # (batch_size, dec_seq_len + 1, embedding_dim)
+        )  # (batch_size, dec_seq_len, embedding_dim)
         tgt_embeddings = self._decoder_dropout(
             tgt_embeddings
-        )  # (batch_size, dec_seq_len + 1, embedding_dim)
+        )  # (batch_size, dec_seq_len, embedding_dim)
 
         tgt_embeddings[~tgt_mask] = 0
 
         causal_mask = (
             torch.tril(torch.ones(label_len, label_len)).bool().to(DEVICE)
-        )  # (dec_seq_len + 1, dec_seq_len + 1)
-
+        )  # (dec_seq_len, dec_seq_len)
+        
         decoder_outputs = self._decoder(
             tgt=tgt_embeddings,
             memory=encoder_embeddings,
             tgt_mask=~causal_mask,
             memory_key_padding_mask=~encoder_mask,
             tgt_key_padding_mask=~tgt_mask,
-        )  # (batch_size, dec_seq_len + 1, embedding_dim)
-
-        decoder_outputs = decoder_outputs[:, 1:, :]  # remove bos token
+        )  # (batch_size, dec_seq_len, embedding_dim)
 
         return decoder_outputs
 
     def _decoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
-            return x // len(self._semantic_id_arr)  #  1 0 0 0 1 0 0 0 ...
+            return x // len(self._codebook_sizes)  #  1 0 0 0 1 0 0 0 ...
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._decoder_position_embeddings
         )
 
         def codebook_lambda(x):
-            non_bos = x < len(self._semantic_id_arr)
-            x[non_bos] = (len(self._semantic_id_arr) - 1) - x[non_bos]
+            non_bos = x < len(self._codebook_sizes)
+            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
             return x  # 3, 0, 1, 2, 3, 0, 1, 2 ...
 
         codebook_embeddings = self._get_position_embeddings(
@@ -295,21 +357,27 @@ def codebook_lambda(x):
 
     def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
-            return x // len(self._semantic_id_arr)  # 5 5 5 4 4 4 3 3 3 ...
+            return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
+        # TODO +1 for residual embedding
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._position_embeddings
         )
+        
+        # print(f"{position_embeddings.isnan().any()=}") # TODO fix NaN in pos_embs
 
         def codebook_lambda(x):
-            return (len(self._semantic_id_arr) - 1) - x % len(
-                self._semantic_id_arr
-            )  # 0 1 2 3 0 1 2 3 ...
+            x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
+            x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
+            # 0 1 2 3 5 0 1 2 3 5 ... # len(self._codebook_sizes) for bos, len(self._codebook_sizes) + 1 for residual
+            return x
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings
         )
 
+        # print(f"{codebook_embeddings.isnan().any()=}") # TODO fix NaN in pos_embs
+
         return position_embeddings + codebook_embeddings
 
     def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
@@ -317,7 +385,7 @@ def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_lay
         seq_len = mask.shape[1]
 
         positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=DEVICE)[None]
             .tile([batch_size, 1])
             .long()
         )  # (batch_size, seq_len)
@@ -326,10 +394,15 @@ def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_lay
         positions = positions[positions_mask]  # (all_batch_events)
 
         positions = position_lambda(positions)  # (all_batch_events)
+        
+        # print(f"{positions.tolist()[:20]=}")
+        
+        assert (positions >= 0).all() and (positions < embedding_layer.num_embeddings).all()
 
         position_embeddings = embedding_layer(
             positions
         )  # (all_batch_events, embedding_dim)
+        # print(f"{position_embeddings.isnan().any()=}") # TODO without embeddings also NaN
         position_embeddings, _ = create_masked_tensor(
             data=position_embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
diff --git a/modeling/rqvae/collisions.py b/modeling/rqvae/collisions.py
index 263f76d7..10ad039e 100644
--- a/modeling/rqvae/collisions.py
+++ b/modeling/rqvae/collisions.py
@@ -1,3 +1,6 @@
+from collections import defaultdict
+
+
 def dedup(data):
     count_dict = {}
 
@@ -12,3 +15,13 @@ def dedup(data):
         result.append((item[0], item[1], new_last_element))
 
     return result
+
+def dedup_semantic_ids(semantic_ids): # TODOPK
+    result = []
+    count_dict = defaultdict(int)
+    for semantic_id in semantic_ids:
+        unique_index = count_dict[semantic_id]
+        count_dict[semantic_id] += 1
+        new_last_element = (*semantic_id, unique_index)
+        result.append(new_last_element)
+    return result
diff --git a/modeling/rqvae/trie.py b/modeling/rqvae/trie.py
new file mode 100644
index 00000000..eb2d26c7
--- /dev/null
+++ b/modeling/rqvae/trie.py
@@ -0,0 +1,188 @@
+from collections import defaultdict
+import random
+
+import torch
+
+
+class Item:
+    """
+    Represents one data entry with:
+      - hierarchical_id: a tuple of int, e.g. (1, 2, 5)
+      - residual: a torch.Tensor of shape (emb_dim,)
+    """
+    def __init__(self, hierarchical_id: tuple[int, ...], residual: torch.Tensor):
+        self.hierarchical_id = hierarchical_id
+        self.residual = residual
+
+    def __repr__(self):
+        return f"Item(id={self.hierarchical_id}, residual={self.residual})"
+
+
+class TrieNode:
+    """
+    A node in the Trie.
+      - children: dict<int, TrieNode>
+      - items: list of Item, non-empty only if this node is
+               a terminal for those items' hierarchical_id.
+    """
+    def __init__(self):
+        self.children: dict[int, "TrieNode"] = {}
+        self.items: list[Item] = []
+
+    def is_leaf(self) -> bool:
+        """
+        Return True if this node has no children.
+        """
+        return len(self.children) == 0
+
+
+class Trie:
+    """
+    Trie for storing items keyed by their hierarchical_id.
+    """
+
+    def __init__(self):
+        self.root = TrieNode()
+
+    def insert(self, item: Item) -> None:
+        """
+        Insert an Item into the Trie by walking its hierarchical_id.
+        """
+        node = self.root
+        for idx in item.hierarchical_id:
+            if idx not in node.children:
+                node.children[idx] = TrieNode()
+            node = node.children[idx]
+        # This node is now the leaf for item.hierarchical_id
+        node.items.append(item)
+
+    def _find_longest_prefix_node(
+        self, hierarchical_id: tuple[int, ...]
+    ) -> tuple[TrieNode, tuple[int, ...]]:
+        """
+        Walk the Trie to find the node that matches the longest possible prefix
+        of `hierarchical_id`.
+        Returns the node and the prefix (as a tuple of int) actually matched.
+        """
+        node = self.root
+        matched_prefix = []
+
+        for idx in hierarchical_id:
+            if idx in node.children:
+                node = node.children[idx]
+                matched_prefix.append(idx)
+            else:
+                break
+
+        return node, tuple(matched_prefix)
+
+    def _gather_subtree_items(self, node: TrieNode) -> list[Item]:
+        """
+        Collect all items in the entire subtree rooted at `node`.
+        We do a DFS (or BFS) to gather items from every leaf below.
+        """
+        stack = [node]
+        all_items = []
+
+        while stack:
+            current = stack.pop()
+            # If current is a leaf, it might have items
+            if current.items:
+                all_items.extend(current.items)
+            # Traverse children
+            for child in current.children.values():
+                stack.append(child)
+
+        return all_items
+
+    def _euclidean_distance(self, a: torch.Tensor, b: torch.Tensor) -> float:
+        """
+        Euclidean (L2) distance between two torch.Tensors.
+        """
+        return torch.norm(a - b).item()
+
+    def find_n_closest(self, query_item: Item, n: int) -> list[Item]:
+        """
+        Find up to `n` closest items to `query_item` by:
+          1. Finding the longest existing matching prefix.
+          2. First include ALL items from longest matching prefix node
+          3. If more slots remain:
+             - Go up prefix levels and gather more items
+             - Within each prefix level, sort by distance
+          4. Never return more than n items total
+        """
+        # Step 1: Longest prefix node
+        node, matched_prefix = self._find_longest_prefix_node(query_item.hierarchical_id)
+
+        # Track items by their prefix length and distance
+        collected_by_prefix: dict[int, list[tuple[Item, float]]] = defaultdict(list)
+        already_seen = set()
+
+        def gather_and_append(target_node: TrieNode, prefix_length: int):
+            """
+            Gather items from subtree of `target_node`, compute distances,
+            and group them by prefix length.
+            """
+            subtree_items = self._gather_subtree_items(target_node)
+            for it in subtree_items:
+                if it not in already_seen:
+                    dist = self._euclidean_distance(it.residual, query_item.residual)
+                    collected_by_prefix[prefix_length].append((it, dist))
+                    already_seen.add(it)
+
+        # First gather items from longest prefix node
+        prefix_len = len(matched_prefix)
+        gather_and_append(node, prefix_len)
+
+        # If we need more items, move up prefix levels
+        while prefix_len > 0 and sum(len(items) for items in collected_by_prefix.values()) < n:
+            prefix_len -= 1
+            parent = self.root
+            for idx in matched_prefix[:prefix_len]:
+                parent = parent.children[idx]
+            gather_and_append(parent, prefix_len)
+
+        # If still not enough and we're not at root, gather from root
+        if prefix_len != 0 and sum(len(items) for items in collected_by_prefix.values()) < n:
+            gather_and_append(self.root, 0)
+
+        # Build final result prioritizing longer prefixes
+        result = []
+        # Start from longest prefix and work down
+        for prefix_length in sorted(collected_by_prefix.keys(), reverse=True):
+            items = collected_by_prefix[prefix_length]
+            # Sort items within this prefix length by distance
+            items.sort(key=lambda x: x[1])
+            # Add items from this level until we hit n
+            remaining_slots = n - len(result)
+            result.extend(item for item, _ in items[:remaining_slots])
+            if len(result) >= n:
+                break
+
+        return result
+
+
+# ------------------------
+# Example Usage:
+# ------------------------
+if __name__ == "__main__":
+    trie = Trie()
+    
+    # generate random items and stress test
+    emb_dim = 4
+    for i in range(12000):
+        semantic_id = tuple(random.randint(0, 255) for _ in range(4))
+        item = Item(semantic_id, torch.rand(emb_dim))
+        trie.insert(item)
+
+    # generate query items
+    query_items = []
+    for i in range(256):
+        semantic_id = tuple(random.randint(0, 255) for _ in range(4))
+        query_item = Item(semantic_id, torch.rand(emb_dim))
+        query_items.append(query_item)
+        
+    for query_item in query_items:
+        closest_items = trie.find_n_closest(query_item, n=20)
+        print(f"{len(closest_items)=}")
+        # print("Closest items to", query_item, ":\n", closest_items)
diff --git a/modeling/trie.py b/modeling/trie.py
deleted file mode 100644
index 1c98c362..00000000
--- a/modeling/trie.py
+++ /dev/null
@@ -1,41 +0,0 @@
-class TrieNode:
-    def __init__(self):
-        self.children = {}
-        self.id = None
-
-
-class Trie:
-    def __init__(self):
-        self.root = TrieNode()
-
-    def insert(self, tuple_key, id):
-        node = self.root
-        for number in tuple_key:
-            if number not in node.children:
-                node.children[number] = TrieNode()
-            node = node.children[number]
-        node.id = id
-
-    def search(self, tuple_key):
-        node = self.root
-        for number in tuple_key:
-            if number not in node.children:
-                return None
-            node = node.children[number]
-        return node.id
-
-    def _get_all_leaf_ids(self, node):
-        result = []
-        if node.id is not None:
-            result.append(node.id)
-        for child in node.children.values():
-            result.extend(self._get_all_leaf_ids(child))
-        return result
-
-    def search_prefix(self, prefix):
-        node = self.root
-        for number in prefix:
-            if number not in node.children:
-                return []
-            node = node.children[number]
-        return self._get_all_leaf_ids(node)
\ No newline at end of file
diff --git a/notebooks/CollisionSolver.ipynb b/notebooks/CollisionSolver.ipynb
index b6fe5605..4fabd090 100644
--- a/notebooks/CollisionSolver.ipynb
+++ b/notebooks/CollisionSolver.ipynb
@@ -1,37 +1,21 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "CH-xb-IDScxO"
+      },
+      "outputs": [],
       "source": [
         "from collections import defaultdict\n",
         "from typing import List, Tuple, Dict\n",
         "import torch"
-      ],
-      "metadata": {
-        "id": "CH-xb-IDScxO"
-      },
-      "execution_count": 1,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 2,
       "metadata": {
         "id": "WH6rvC8BSTD1"
       },
@@ -148,15 +132,20 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Пример использования"
-      ],
       "metadata": {
         "id": "AhJgbuGESnWd"
-      }
+      },
+      "source": [
+        "# Пример использования"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "pBjXwVLVSWMO"
+      },
+      "outputs": [],
       "source": [
         "residual_length = 12\n",
         "semantic_ids_length = 3\n",
@@ -185,26 +174,16 @@
         "query_prefixes = torch.tensor([\n",
         "    [1, 2, 3],\n",
         "    [1, 2, 4],\n",
+        "    [5, 2, 3],\n",
         "    [5, 2, 3]\n",
         "], device=device)  # [num_prefixes, prefix_len]\n",
         "\n",
         "query_residuals = torch.rand(query_prefixes.shape[0], residual_length, device=torch.device('cpu'))  # [num_prefixes, emb_dim]"
-      ],
-      "metadata": {
-        "id": "pBjXwVLVSWMO"
-      },
-      "execution_count": 32,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "solver = CollisionSolver(residual_length, semantic_ids_length)\n",
-        "\n",
-        "solver.create_query_candidates_dict(semantic_ids[:, :-1], residuals)\n",
-        "\n",
-        "solver.get_semantic_ids(query_prefixes, query_residuals)"
-      ],
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -212,27 +191,32 @@
         "id": "wn7Uk8V_TgrE",
         "outputId": "540b25c6-63bc-4613-90b1-5510ea4d6224"
       },
-      "execution_count": 33,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "tensor([[1, 2, 3, 2],\n",
+              "tensor([[1, 2, 3, 0],\n",
               "        [1, 2, 4, 0],\n",
-              "        [5, 2, 3, 6]])"
+              "        [5, 2, 3, 0],\n",
+              "        [5, 2, 3, 3]])"
             ]
           },
+          "execution_count": 8,
           "metadata": {},
-          "execution_count": 33
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "solver = CollisionSolver(residual_length, semantic_ids_length)\n",
+        "\n",
+        "solver.create_query_candidates_dict(semantic_ids[:, :-1], residuals)\n",
+        "\n",
+        "solver.get_semantic_ids(query_prefixes, query_residuals)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "solver._semantic_id_dict"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -240,73 +224,107 @@
         "id": "3HTju2grZm-F",
         "outputId": "442795a8-a952-466c-8c48-fb3d6f14bc81"
       },
-      "execution_count": 34,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "defaultdict(list,\n",
               "            {(1,\n",
               "              2,\n",
               "              3): [(0,\n",
-              "               tensor([0.2521, 0.4989, 0.8696, 0.9459, 0.4614, 0.3054, 0.1708, 0.8725, 0.5294,\n",
-              "                       0.7386, 0.8668, 0.9158])), (1,\n",
-              "               tensor([0.3066, 0.7874, 0.8942, 0.5829, 0.5947, 0.1307, 0.1789, 0.7914, 0.6617,\n",
-              "                       0.7250, 0.4264, 0.6968])), (2,\n",
-              "               tensor([0.8502, 0.4814, 0.2935, 0.2933, 0.5334, 0.5938, 0.4865, 0.7920, 0.9304,\n",
-              "                       0.4737, 0.4883, 0.9769])), (3,\n",
-              "               tensor([0.4382, 0.4937, 0.5311, 0.1205, 0.0210, 0.2097, 0.7704, 0.9061, 0.6019,\n",
-              "                       0.9187, 0.3894, 0.1716]))],\n",
+              "               tensor([0.7220, 0.9496, 0.4006, 0.8832, 0.6087, 0.4947, 0.1341, 0.9645, 0.7408,\n",
+              "                       0.5972, 0.3433, 0.8700])), (1,\n",
+              "               tensor([0.4457, 0.4410, 0.1333, 0.4391, 0.4153, 0.1703, 0.3044, 0.0940, 0.2773,\n",
+              "                       0.5258, 0.5838, 0.0273])), (2,\n",
+              "               tensor([0.6268, 0.1060, 0.0841, 0.0750, 0.4090, 0.2886, 0.4343, 0.1945, 0.0429,\n",
+              "                       0.8477, 0.1418, 0.6465])), (3,\n",
+              "               tensor([0.0077, 0.8171, 0.1344, 0.2223, 0.9616, 0.2790, 0.3448, 0.1485, 0.7148,\n",
+              "                       0.5900, 0.0154, 0.4752]))],\n",
               "             (1,\n",
               "              2,\n",
               "              4): [(0,\n",
-              "               tensor([0.9046, 0.2291, 0.9002, 0.7236, 0.7392, 0.1195, 0.0039, 0.1333, 0.2854,\n",
-              "                       0.3425, 0.9413, 0.6365])), (1,\n",
-              "               tensor([0.3013, 0.1465, 0.4424, 0.9448, 0.0412, 0.5664, 0.3587, 0.1531, 0.5751,\n",
-              "                       0.8052, 0.0830, 0.5028])), (2,\n",
-              "               tensor([0.9886, 0.6681, 0.4602, 0.3818, 0.8741, 0.3990, 0.1009, 0.8240, 0.9018,\n",
-              "                       0.1647, 0.0799, 0.0188]))],\n",
+              "               tensor([0.3480, 0.3537, 0.3771, 0.1443, 0.6877, 0.4845, 0.8278, 0.9831, 0.4941,\n",
+              "                       0.0682, 0.0900, 0.2485])), (1,\n",
+              "               tensor([0.4048, 0.3308, 0.2278, 0.4890, 0.4899, 0.9994, 0.1511, 0.9374, 0.8730,\n",
+              "                       0.7538, 0.0409, 0.1444])), (2,\n",
+              "               tensor([0.3601, 0.7909, 0.0766, 0.7096, 0.5745, 0.2606, 0.4412, 0.8748, 0.1248,\n",
+              "                       0.5816, 0.2185, 0.2352]))],\n",
               "             (5,\n",
               "              2,\n",
               "              3): [(0,\n",
-              "               tensor([0.6242, 0.6769, 0.8397, 0.6340, 0.9251, 0.2997, 0.9545, 0.6810, 0.4468,\n",
-              "                       0.3179, 0.5830, 0.2547])), (1,\n",
-              "               tensor([0.9159, 0.3269, 0.6216, 0.8065, 0.9175, 0.3175, 0.0664, 0.1575, 0.1457,\n",
-              "                       0.6718, 0.7908, 0.2829])), (2,\n",
-              "               tensor([0.1270, 0.7954, 0.7779, 0.9226, 0.0595, 0.6361, 0.4578, 0.7727, 0.4038,\n",
-              "                       0.6136, 0.8738, 0.6714])), (3,\n",
-              "               tensor([0.0647, 0.4849, 0.2900, 0.4458, 0.3928, 0.1550, 0.6921, 0.8732, 0.7545,\n",
-              "                       0.0995, 0.7739, 0.3181])), (4,\n",
-              "               tensor([0.2761, 0.3357, 0.2239, 0.4669, 0.9118, 0.2321, 0.7169, 0.5600, 0.5067,\n",
-              "                       0.4533, 0.6332, 0.6862])), (5,\n",
-              "               tensor([0.9039, 0.0986, 0.9541, 0.7187, 0.6052, 0.8883, 0.1887, 0.1329, 0.3411,\n",
-              "                       0.9943, 0.0430, 0.0611])), (6,\n",
-              "               tensor([0.7541, 0.7320, 0.2690, 0.1116, 0.0751, 0.7047, 0.5125, 0.9484, 0.2797,\n",
-              "                       0.5430, 0.9255, 0.6738]))],\n",
+              "               tensor([0.9524, 0.6976, 0.7598, 0.9994, 0.2881, 0.9854, 0.2537, 0.6400, 0.5632,\n",
+              "                       0.5768, 0.2833, 0.0570])), (1,\n",
+              "               tensor([0.6778, 0.6523, 0.7592, 0.1953, 0.0077, 0.0960, 0.0714, 0.8100, 0.9999,\n",
+              "                       0.0407, 0.1231, 0.7192])), (2,\n",
+              "               tensor([0.3652, 0.1131, 0.6800, 0.7445, 0.3586, 0.6498, 0.6479, 0.3792, 0.3110,\n",
+              "                       0.7605, 0.9031, 0.4177])), (3,\n",
+              "               tensor([0.5732, 0.6359, 0.1402, 0.0661, 0.6557, 0.5067, 0.7383, 0.7173, 0.3075,\n",
+              "                       0.3920, 0.7497, 0.9602])), (4,\n",
+              "               tensor([0.3407, 0.3068, 0.0815, 0.3887, 0.5861, 0.8103, 0.1236, 0.2175, 0.7513,\n",
+              "                       0.5872, 0.7065, 0.0919])), (5,\n",
+              "               tensor([0.0248, 0.9603, 0.5902, 0.8559, 0.9800, 0.9306, 0.9737, 0.9035, 0.2527,\n",
+              "                       0.0049, 0.3355, 0.2858])), (6,\n",
+              "               tensor([0.2779, 0.6830, 0.5133, 0.5767, 0.2029, 0.9013, 0.9562, 0.4474, 0.0377,\n",
+              "                       0.0205, 0.6822, 0.1314]))],\n",
               "             (2,\n",
               "              8,\n",
               "              7): [(0,\n",
-              "               tensor([0.6241, 0.6990, 0.7154, 0.6228, 0.1982, 0.0796, 0.1684, 0.9888, 0.0580,\n",
-              "                       0.7725, 0.8443, 0.4938]))]})"
+              "               tensor([0.5204, 0.4737, 0.7302, 0.0674, 0.0289, 0.5045, 0.1267, 0.8282, 0.7559,\n",
+              "                       0.3856, 0.4455, 0.1655]))]})"
             ]
           },
+          "execution_count": 9,
           "metadata": {},
-          "execution_count": 34
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "solver._semantic_id_dict"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Альтернативное решение только через torch"
-      ],
       "metadata": {
         "id": "ji7PzyQeTxuP"
-      }
+      },
+      "source": [
+        "# Альтернативное решение только через torch"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eh7jwIkHTr4_",
+        "outputId": "d9bccfba-c12f-4036-8bd3-0ef5d1d5b5e1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Префикс: [1, 2, 3]\n",
+            "Лучший semantic_id: [1, 2, 3, 0]\n",
+            "Соответствующий residual: tensor([0.7220, 0.9496, 0.4006, 0.8832, 0.6087, 0.4947, 0.1341, 0.9645, 0.7408,\n",
+            "        0.5972, 0.3433, 0.8700])\n",
+            "Префикс: [1, 2, 4]\n",
+            "Лучший semantic_id: [1, 2, 4, 0]\n",
+            "Соответствующий residual: tensor([0.3480, 0.3537, 0.3771, 0.1443, 0.6877, 0.4845, 0.8278, 0.9831, 0.4941,\n",
+            "        0.0682, 0.0900, 0.2485])\n",
+            "Префикс: [5, 2, 3]\n",
+            "Лучший semantic_id: [5, 2, 3, 0]\n",
+            "Соответствующий residual: tensor([0.9524, 0.6976, 0.7598, 0.9994, 0.2881, 0.9854, 0.2537, 0.6400, 0.5632,\n",
+            "        0.5768, 0.2833, 0.0570])\n",
+            "Префикс: [5, 2, 3]\n",
+            "Лучший semantic_id: [5, 2, 3, 3]\n",
+            "Соответствующий residual: tensor([0.5732, 0.6359, 0.1402, 0.0661, 0.6557, 0.5067, 0.7383, 0.7173, 0.3075,\n",
+            "        0.3920, 0.7497, 0.9602])\n"
+          ]
+        }
+      ],
       "source": [
         "semantic_ids = semantic_ids.to(device)\n",
         "residuals = residuals.to(device)\n",
@@ -330,40 +348,48 @@
         "best_semantic_ids = semantic_ids[max_indices]  # [num_prefixes, max_length]\n",
         "best_residuals = residuals[max_indices]  # [num_prefixes, emb_dim]\n",
         "\n",
-        "\n",
         "for i, prefix in enumerate(query_prefixes):\n",
         "    print(f\"Префикс: {prefix.tolist()}\")\n",
         "    print(f\"Лучший semantic_id: {best_semantic_ids[i].tolist()}\")\n",
         "    print(f\"Соответствующий residual: {best_residuals[i]}\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eh7jwIkHTr4_",
-        "outputId": "d9bccfba-c12f-4036-8bd3-0ef5d1d5b5e1"
-      },
-      "execution_count": 35,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Префикс: [1, 2, 3]\n",
-            "Лучший semantic_id: [1, 2, 3, 2]\n",
-            "Соответствующий residual: tensor([0.8502, 0.4814, 0.2935, 0.2933, 0.5334, 0.5938, 0.4865, 0.7920, 0.9304,\n",
-            "        0.4737, 0.4883, 0.9769], device='cuda:0')\n",
-            "Префикс: [1, 2, 4]\n",
-            "Лучший semantic_id: [1, 2, 4, 0]\n",
-            "Соответствующий residual: tensor([0.9046, 0.2291, 0.9002, 0.7236, 0.7392, 0.1195, 0.0039, 0.1333, 0.2854,\n",
-            "        0.3425, 0.9413, 0.6365], device='cuda:0')\n",
-            "Префикс: [5, 2, 3]\n",
-            "Лучший semantic_id: [5, 2, 3, 6]\n",
-            "Соответствующий residual: tensor([0.7541, 0.7320, 0.2690, 0.1116, 0.0751, 0.7047, 0.5125, 0.9484, 0.2797,\n",
-            "        0.5430, 0.9255, 0.6738], device='cuda:0')\n"
-          ]
-        }
       ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": []
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From c2e7ee21bedd7e22dd5cd5f9e39bd12f3ff1fccd Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 19 Jan 2025 18:05:39 +0700
Subject: [PATCH 043/175] fix NaNs

---
 configs/train/tiger_train_config.json |   6 +-
 modeling/models/base.py               |   8 +-
 modeling/models/tiger.py              | 173 +++++++----------
 modeling/optimizer/base.py            |   3 +-
 modeling/rqvae/trie.py                | 259 +++++++++++---------------
 5 files changed, 188 insertions(+), 261 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index b1163b3a..be57d320 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -42,9 +42,9 @@
     "predictions_prefix": "logits",
     "positive_prefix": "labels",
     "labels_prefix": "labels",
-    "num_heads": 4,
-    "num_encoder_layers": 6,
-    "num_decoder_layers": 6,
+    "num_heads": 2,
+    "num_encoder_layers": 2,
+    "num_decoder_layers": 2,
     "dim_feedforward": 128,
     "dropout": 0.2,
     "activation": "gelu",
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 6e1bd68b..b3abef1e 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -1,7 +1,3 @@
-from utils import MetaParent
-
-from utils import DEVICE, create_masked_tensor, get_activation_function
-
 import torch
 import torch.nn as nn
 from utils import DEVICE, MetaParent, create_masked_tensor, get_activation_function
@@ -99,11 +95,11 @@ def __init__(
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
     
-    def get_encoder_embeddings(self, events):
+    def get_item_embeddings(self, events):
         return self._item_embeddings(events)
 
     def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
-        embeddings = self.get_encoder_embeddings(events)  # (all_batch_events, embedding_dim)
+        embeddings = self.get_item_embeddings(events)  # (all_batch_events, embedding_dim)
         
         assert embeddings.shape[0] == sum(lengths)
 
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 62c660ff..c71917fe 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,9 +1,10 @@
 import json
 
 import torch
-from rqvae.trie import Item, Trie
+from tqdm import tqdm
 from models.base import SequentialTorchModel
 from models.collision_solver import CollisionSolver
+from rqvae.trie import Item, Trie
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
@@ -162,81 +163,6 @@ def create_from_config(cls, config, **kwargs):
             initializer_range=config.get("initializer_range", 0.02),
         )
 
-    def get_encoder_embeddings(self, events):
-        events = events.tolist()
-        
-        # convert to semantic ids
-        semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
-        semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
-        
-        # convert to rqvae embeddings
-        positions = torch.arange(len(semantic_events), device=DEVICE)
-        codebook_positions = positions % len(self._codebook_sizes)
-        emb_indices = codebook_positions * self._codebook_sizes[0] + semantic_events
-        semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
-        semantic_embeddings = semantic_embeddings.view(
-            len(events), len(self._codebook_sizes), self._embedding_dim
-        )
-        
-        # get residuals
-        text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
-        residual = text_embeddings - semantic_embeddings.sum(dim=1)
-        residual = residual.unsqueeze(1)
-        
-        # get true item embeddings
-        item_embeddings = torch.cat([semantic_embeddings, residual], dim=1)
-        item_embeddings = item_embeddings.view(-1, self._embedding_dim)
-        
-        return item_embeddings
-
-    def get_logits(self, label_events, label_lengths, all_sample_events, all_sample_lengths):
-        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1) # TODO residual gets add as event
-        # TODO encoder_mask sees residual as event (is it correct?)
-        
-        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
-        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
-        
-        print(f"{encoder_embeddings.isnan().any()=}")
-        
-        decoder_outputs = self._apply_decoder(
-            label_events, label_lengths, encoder_embeddings, encoder_mask
-        )  # (batch_size, label_len, embedding_dim)
-        
-        decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
-        
-        return decoder_prefix_scores
-    
-    def get_decoder_embeddings(self, events):
-        events = events.tolist()
-        
-        # convert to semantic ids
-        semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
-        semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
-        
-        # convert to rqvae embeddings
-        positions = torch.arange(len(semantic_events), device=DEVICE)
-        codebook_positions = positions % len(self._codebook_sizes)
-        emb_indices = codebook_positions * self._codebook_sizes[0] + semantic_events
-        semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
-        semantic_embeddings = semantic_embeddings.view(
-            len(events), len(self._codebook_sizes), self._embedding_dim
-        )
-        
-        # get residuals
-        text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
-        residual = text_embeddings - semantic_embeddings.sum(dim=1)
-        residual = residual.unsqueeze(1)
-        
-        # apply collision solver
-        # deduped_semantic_ids = self._solver.get_semantic_ids(semantic_ids, residual) # TODO
-        
-        # get true item embeddings
-        item_embeddings = torch.cat([semantic_embeddings, residual], dim=1)
-        item_embeddings = item_embeddings.view(-1, self._embedding_dim)
-        
-        return item_embeddings
-
     # semantic ids come with dedup token
     def forward(self, inputs):
         all_sample_events = inputs[
@@ -254,7 +180,7 @@ def forward(self, inputs):
                 label_events, label_lengths, all_sample_events, all_sample_lengths
             )  # (batch_size, dec_seq_len, _codebook_sizes[0])
             
-            logits = decoder_prefix_scores.reshape(-1, self._codebook_sizes[0])
+            logits = decoder_prefix_scores.reshape(-1, self._codebook_sizes[0]) # (batch_size * dec_seq_len, _codebook_sizes[0])
             
             semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in label_events.tolist()]) # len(events), len(codebook_sizes)
             semantic_events = semantic_ids.view(-1)
@@ -276,20 +202,34 @@ def _apply_trie(self, preds):
         semantic_ids = [tuple(row.tolist()) for row in preds]
         
         ids = []
-        for semantic_id in semantic_ids:
-            item = Item(semantic_id, torch.rand(self._embedding_dim)) # TODO use true residuals
-            closest_items = self._trie.find_n_closest(item, n=20)
+        for semantic_id in tqdm(semantic_ids):
+            item = Item(semantic_id, torch.rand(self._embedding_dim).to(DEVICE)) # TODO use true residuals
+            closest_items = self._trie.find_closest(item, n=20)
             ids.append(closest_items)
-            
-        print(f"{ids=}")
         
         return torch.tensor(ids)
+    
+    def get_logits(self, label_events, label_lengths, all_sample_events, all_sample_lengths):
+        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1) # TODO residual gets add as event
+        # TODO encoder_mask sees residual as event (is it correct?)
+        
+        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
+        
+        decoder_outputs = self._apply_decoder(
+            label_events, label_lengths, encoder_embeddings, encoder_mask
+        )  # (batch_size, label_len, embedding_dim)
+        
+        decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
+        
+        return decoder_prefix_scores
 
     def _apply_decoder(
         self, label_events, label_lengths, encoder_embeddings, encoder_mask
     ):
-        label_lengths = label_lengths * (len(self._codebook_sizes) + 1) # TODO bos prepending, residual removing
-        tgt_embeddings = self.get_decoder_embeddings( # TODO residual embs
+        label_lengths = label_lengths * (len(self._codebook_sizes) + 1) # TODO bos prepending
+        tgt_embeddings = self.get_item_embeddings( # TODO residual embs
             label_events
         )  # (all_batch_events, embedding_dim)
         
@@ -331,29 +271,37 @@ def _apply_decoder(
             memory=encoder_embeddings,
             tgt_mask=~causal_mask,
             memory_key_padding_mask=~encoder_mask,
-            tgt_key_padding_mask=~tgt_mask,
+            # TODO tgt_key_padding_mask=~tgt_mask,
         )  # (batch_size, dec_seq_len, embedding_dim)
 
         return decoder_outputs
-
-    def _decoder_pos_embeddings(self, lengths, mask):
-        def position_lambda(x):
-            return x // len(self._codebook_sizes)  #  1 0 0 0 1 0 0 0 ...
-
-        position_embeddings = self._get_position_embeddings(
-            lengths, mask, position_lambda, self._decoder_position_embeddings
-        )
-
-        def codebook_lambda(x):
-            non_bos = x < len(self._codebook_sizes)
-            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
-            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ...
-
-        codebook_embeddings = self._get_position_embeddings(
-            lengths, mask, codebook_lambda, self._codebook_embeddings
+    
+    def get_item_embeddings(self, events):
+        events = events.tolist()
+        
+        # convert to semantic ids
+        semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
+        semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
+        
+        # convert to rqvae embeddings
+        positions = torch.arange(len(semantic_events), device=DEVICE)
+        codebook_positions = positions % len(self._codebook_sizes)
+        emb_indices = codebook_positions * self._codebook_sizes[0] + semantic_events
+        semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
+        semantic_embeddings = semantic_embeddings.view(
+            len(events), len(self._codebook_sizes), self._embedding_dim
         )
-
-        return position_embeddings + codebook_embeddings
+        
+        # get residuals
+        text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
+        residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = residual.unsqueeze(1)
+        
+        # get true item embeddings
+        item_embeddings = torch.cat([semantic_embeddings, residual], dim=1) 
+        item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
+        
+        return item_embeddings
 
     def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
@@ -380,6 +328,25 @@ def codebook_lambda(x):
 
         return position_embeddings + codebook_embeddings
 
+    def _decoder_pos_embeddings(self, lengths, mask):
+        def position_lambda(x):
+            return x // len(self._codebook_sizes)  #  1 0 0 0 1 0 0 0 ...
+
+        position_embeddings = self._get_position_embeddings(
+            lengths, mask, position_lambda, self._decoder_position_embeddings
+        )
+
+        def codebook_lambda(x):
+            non_bos = x < len(self._codebook_sizes)
+            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
+            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ...
+
+        codebook_embeddings = self._get_position_embeddings(
+            lengths, mask, codebook_lambda, self._codebook_embeddings
+        )
+
+        return position_embeddings + codebook_embeddings
+    
     def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
diff --git a/modeling/optimizer/base.py b/modeling/optimizer/base.py
index ede62be2..86c63537 100644
--- a/modeling/optimizer/base.py
+++ b/modeling/optimizer/base.py
@@ -1,8 +1,7 @@
 import copy
 
-from utils import MetaParent
-
 import torch
+from utils import MetaParent
 
 OPTIMIZERS = {
     'sgd': torch.optim.SGD,
diff --git a/modeling/rqvae/trie.py b/modeling/rqvae/trie.py
index eb2d26c7..e5a94067 100644
--- a/modeling/rqvae/trie.py
+++ b/modeling/rqvae/trie.py
@@ -1,188 +1,153 @@
-from collections import defaultdict
-import random
-
 import torch
-
+from typing import List, Tuple, Dict, Optional
 
 class Item:
     """
-    Represents one data entry with:
-      - hierarchical_id: a tuple of int, e.g. (1, 2, 5)
+    Represents an item with:
+      - hierarchical_id: a tuple of ints
       - residual: a torch.Tensor of shape (emb_dim,)
     """
-    def __init__(self, hierarchical_id: tuple[int, ...], residual: torch.Tensor):
+    def __init__(self, hierarchical_id: Tuple[int, ...], residual: torch.Tensor):
         self.hierarchical_id = hierarchical_id
         self.residual = residual
 
     def __repr__(self):
-        return f"Item(id={self.hierarchical_id}, residual={self.residual})"
+        return f"Item(hier_id={self.hierarchical_id}, resid_shape={tuple(self.residual.shape)})"
 
 
 class TrieNode:
     """
-    A node in the Trie.
-      - children: dict<int, TrieNode>
-      - items: list of Item, non-empty only if this node is
-               a terminal for those items' hierarchical_id.
+    A node in the trie.
+      - children: dict(int -> TrieNode)
+      - items: list of Items (only non-empty if this node is the end of some hierarchical_id(s))
     """
     def __init__(self):
-        self.children: dict[int, "TrieNode"] = {}
-        self.items: list[Item] = []
-
-    def is_leaf(self) -> bool:
-        """
-        Return True if this node has no children.
-        """
-        return len(self.children) == 0
+        self.children: Dict[int, TrieNode] = {}
+        self.items: List[Item] = []  # collisions end up here if they share the same path
 
 
 class Trie:
     """
-    Trie for storing items keyed by their hierarchical_id.
+    The trie structure that can:
+      1. Insert an Item by its hierarchical_id.
+      2. Find up to n closest items to a query Item by the described prefix-truncation + dot-product logic.
     """
-
     def __init__(self):
         self.root = TrieNode()
 
     def insert(self, item: Item) -> None:
         """
-        Insert an Item into the Trie by walking its hierarchical_id.
+        Insert an Item into the trie following item.hierarchical_id.
         """
-        node = self.root
+        current_node = self.root
         for idx in item.hierarchical_id:
-            if idx not in node.children:
-                node.children[idx] = TrieNode()
-            node = node.children[idx]
-        # This node is now the leaf for item.hierarchical_id
-        node.items.append(item)
-
-    def _find_longest_prefix_node(
-        self, hierarchical_id: tuple[int, ...]
-    ) -> tuple[TrieNode, tuple[int, ...]]:
-        """
-        Walk the Trie to find the node that matches the longest possible prefix
-        of `hierarchical_id`.
-        Returns the node and the prefix (as a tuple of int) actually matched.
-        """
-        node = self.root
-        matched_prefix = []
-
-        for idx in hierarchical_id:
-            if idx in node.children:
-                node = node.children[idx]
-                matched_prefix.append(idx)
-            else:
-                break
+            if idx not in current_node.children:
+                current_node.children[idx] = TrieNode()
+            current_node = current_node.children[idx]
+        # At the leaf, store the item
+        current_node.items.append(item)
 
-        return node, tuple(matched_prefix)
-
-    def _gather_subtree_items(self, node: TrieNode) -> list[Item]:
+    def _gather_subtree_items(self, node: TrieNode) -> List[Item]:
         """
-        Collect all items in the entire subtree rooted at `node`.
-        We do a DFS (or BFS) to gather items from every leaf below.
+        Collect all items in the sub-tree rooted at 'node'.
         """
+        collected = []
         stack = [node]
-        all_items = []
-
         while stack:
-            current = stack.pop()
-            # If current is a leaf, it might have items
-            if current.items:
-                all_items.extend(current.items)
-            # Traverse children
-            for child in current.children.values():
-                stack.append(child)
-
-        return all_items
-
-    def _euclidean_distance(self, a: torch.Tensor, b: torch.Tensor) -> float:
+            cur = stack.pop()
+            collected.extend(cur.items)
+            for child_node in cur.children.values():
+                stack.append(child_node)
+        return collected
+
+    def _dot_scores(
+        self, 
+        query_residual: torch.Tensor, 
+        candidates: List[Item]
+    ) -> List[Tuple[Item, float]]:
         """
-        Euclidean (L2) distance between two torch.Tensors.
+        Compute dot-product scores between query_residual and each candidate's residual.
+        Return list of (candidate_item, score).
         """
-        return torch.norm(a - b).item()
-
-    def find_n_closest(self, query_item: Item, n: int) -> list[Item]:
+        # Note: if your embeddings are large, you may prefer a more efficient approach
+        # (e.g., batched matrix multiplication). For clarity, we do a simple loop here.
+        scored = []
+        for c in candidates:
+            score = torch.dot(query_residual, c.residual).item()
+            scored.append((c, score))
+        return scored
+
+    def find_closest(self, query_item: Item, n: int) -> List[Item]:
         """
-        Find up to `n` closest items to `query_item` by:
-          1. Finding the longest existing matching prefix.
-          2. First include ALL items from longest matching prefix node
-          3. If more slots remain:
-             - Go up prefix levels and gather more items
-             - Within each prefix level, sort by distance
-          4. Never return more than n items total
+        Find up to n closest items to 'query_item' by the described rules:
+          1) Find longest existing matching prefix.
+          2) Gather sub-tree items:
+             - If >= n items, pick top n by dot product.
+             - Else, move to parent, gather sub-tree, etc.
+          3) If root is reached, return all if < n, else top n by dot product.
         """
-        # Step 1: Longest prefix node
-        node, matched_prefix = self._find_longest_prefix_node(query_item.hierarchical_id)
-
-        # Track items by their prefix length and distance
-        collected_by_prefix: dict[int, list[tuple[Item, float]]] = defaultdict(list)
-        already_seen = set()
-
-        def gather_and_append(target_node: TrieNode, prefix_length: int):
-            """
-            Gather items from subtree of `target_node`, compute distances,
-            and group them by prefix length.
-            """
-            subtree_items = self._gather_subtree_items(target_node)
-            for it in subtree_items:
-                if it not in already_seen:
-                    dist = self._euclidean_distance(it.residual, query_item.residual)
-                    collected_by_prefix[prefix_length].append((it, dist))
-                    already_seen.add(it)
-
-        # First gather items from longest prefix node
-        prefix_len = len(matched_prefix)
-        gather_and_append(node, prefix_len)
-
-        # If we need more items, move up prefix levels
-        while prefix_len > 0 and sum(len(items) for items in collected_by_prefix.values()) < n:
-            prefix_len -= 1
-            parent = self.root
-            for idx in matched_prefix[:prefix_len]:
-                parent = parent.children[idx]
-            gather_and_append(parent, prefix_len)
-
-        # If still not enough and we're not at root, gather from root
-        if prefix_len != 0 and sum(len(items) for items in collected_by_prefix.values()) < n:
-            gather_and_append(self.root, 0)
-
-        # Build final result prioritizing longer prefixes
-        result = []
-        # Start from longest prefix and work down
-        for prefix_length in sorted(collected_by_prefix.keys(), reverse=True):
-            items = collected_by_prefix[prefix_length]
-            # Sort items within this prefix length by distance
-            items.sort(key=lambda x: x[1])
-            # Add items from this level until we hit n
-            remaining_slots = n - len(result)
-            result.extend(item for item, _ in items[:remaining_slots])
-            if len(result) >= n:
+        # 1) Descend as far as possible
+        path_stack = [(self.root, None)]  # (node, parent_node) pairs for easy upward climb
+        current_node = self.root
+
+        # Traverse hierarchy while possible
+        for idx in query_item.hierarchical_id:
+            if idx in current_node.children:
+                parent_node = current_node
+                current_node = current_node.children[idx]
+                path_stack.append((current_node, parent_node))
+            else:
+                # Can't go deeper, break
                 break
 
-        return result
-
-
-# ------------------------
-# Example Usage:
-# ------------------------
+        # Now path_stack[-1][0] is the deepest node we matched.
+        # We'll climb up if needed.
+        while path_stack:
+            node, parent = path_stack[-1]
+            subtree_items = self._gather_subtree_items(node)
+            if len(subtree_items) >= n:
+                # We can pick the top n by dot product
+                scored = self._dot_scores(query_item.residual, subtree_items)
+                # Sort descending by score
+                scored.sort(key=lambda x: x[1], reverse=True)
+                return [itm for itm, _ in scored[:n]]
+            else:
+                # Not enough items: move up one level
+                # (pop the current node off the stack and keep going)
+                path_stack.pop()
+                if not path_stack:
+                    # We are at the root (the last pop).
+                    # If still not enough items, we simply return everything we have from the root
+                    # or if root has more than n, we pick top n.
+                    scored = self._dot_scores(query_item.residual, subtree_items)
+                    scored.sort(key=lambda x: x[1], reverse=True)
+                    return [itm for itm, _ in scored[:n]]
+                # Otherwise, continue in the loop (which means gather from the parent's node)
+
+        # Safety net (should never get here logically, but just in case):
+        return []
+
+
+# ---------------------------
+# Usage example (toy):
 if __name__ == "__main__":
+    # Suppose emb_dim = 3 for example
     trie = Trie()
-    
-    # generate random items and stress test
-    emb_dim = 4
-    for i in range(12000):
-        semantic_id = tuple(random.randint(0, 255) for _ in range(4))
-        item = Item(semantic_id, torch.rand(emb_dim))
-        trie.insert(item)
-
-    # generate query items
-    query_items = []
-    for i in range(256):
-        semantic_id = tuple(random.randint(0, 255) for _ in range(4))
-        query_item = Item(semantic_id, torch.rand(emb_dim))
-        query_items.append(query_item)
-        
-    for query_item in query_items:
-        closest_items = trie.find_n_closest(query_item, n=20)
-        print(f"{len(closest_items)=}")
-        # print("Closest items to", query_item, ":\n", closest_items)
+
+    # Insert a few items
+    items_to_insert = [
+        Item((1, 2, 3), torch.tensor([1.0, 0.5, 0.2])),
+        Item((1, 2, 3), torch.tensor([1.1, 0.4, 0.0])),  # collision on same path
+        Item((1, 2, 4), torch.tensor([0.9, 0.9, 0.9])),
+        Item((1, 5),     torch.tensor([-0.1, 0.2, 1.0])),
+        Item((2,),       torch.tensor([0.3, 0.3, 0.3])),
+    ]
+    for it in items_to_insert:
+        trie.insert(it)
+
+    # Query item
+    query = Item((1, 2, 3, 99), torch.tensor([1.0, 1.0, 1.0]))  # (1,2,3,99) partially matches deeper
+    closest = trie.find_closest(query, n=3)
+    print("Closest items:")
+    for c in closest:
+        print(c)

From 465702f7740fa0d20532c12c5bedda8bc733e544 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 22 Jan 2025 10:03:34 +0700
Subject: [PATCH 044/175] add new collsion solver

---
 modeling/models/collision_solver.py           | 136 ------
 modeling/models/tiger.py                      |  14 +-
 modeling/rqvae/__init__.py                    |   0
 modeling/rqvae/collisions.py                  |  27 --
 modeling/rqvae_utils/__init__.py              |   2 +
 modeling/rqvae_utils/collision_solver.py      | 103 +++++
 modeling/{rqvae => rqvae_utils}/rqvae_data.py |   0
 modeling/{rqvae => rqvae_utils}/trie.py       |   0
 notebooks/CollisionSolver.ipynb               | 395 ------------------
 9 files changed, 114 insertions(+), 563 deletions(-)
 delete mode 100644 modeling/models/collision_solver.py
 delete mode 100644 modeling/rqvae/__init__.py
 delete mode 100644 modeling/rqvae/collisions.py
 create mode 100644 modeling/rqvae_utils/__init__.py
 create mode 100644 modeling/rqvae_utils/collision_solver.py
 rename modeling/{rqvae => rqvae_utils}/rqvae_data.py (100%)
 rename modeling/{rqvae => rqvae_utils}/trie.py (100%)
 delete mode 100644 notebooks/CollisionSolver.ipynb

diff --git a/modeling/models/collision_solver.py b/modeling/models/collision_solver.py
deleted file mode 100644
index 6172c825..00000000
--- a/modeling/models/collision_solver.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from collections import defaultdict
-from typing import List, Tuple, Dict
-import torch
-
-class CollisionSolver:
-    def __init__(self, residual_length, semantic_id_length, device: torch.device = torch.device('cpu')):
-        """
-        :param residual_length: Длина остатка для каждого semantic_id
-        :param semantic_id_length: Длина semantic_id (без токена решающего коллизии)
-        :param device: Устройство
-        """
-        self._semantic_id_dict = defaultdict(list)
-        self.residual_length = residual_length
-        self.semantic_id_length = semantic_id_length
-        self.device = device
-
-    def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:
-        """
-        Перенос тензора на устройство
-        """
-        if tensor.device != self.device:
-            tensor = tensor.to(self.device)
-        return tensor
-
-    def add_item(self, semantic_id: List[int] | torch.Tensor, residual: torch.Tensor) -> None:
-        """
-        Добавляет новый элемент в словарь хранящий semantic_ids с остатками
-
-        :param semantic_id: Semantic id (без токена решающего коллизии)
-        :param residual: Тензор с остатком для данного semantic_id
-        """
-        if isinstance(semantic_id, torch.Tensor):
-            semantic_id = semantic_id.tolist()
-
-        assert isinstance(residual, torch.Tensor)
-        assert residual.shape == (self.residual_length,)
-        assert len(semantic_id) == self.semantic_id_length
-
-        residual = self._to_device(residual)
-        key = tuple(semantic_id)
-        self._semantic_id_dict[key].append((len(self._semantic_id_dict[key]), residual))
-
-
-    def create_query_candidates_dict(self, semantic_ids: torch.Tensor | List[List[int]], residuals: torch.Tensor | List[List[int]]) -> None:
-        """
-        Создает словарь, который содержит сгруппирированные по semantic id элементы, к ним добавлены токены решающие коллизии (добавляются по порядку начиная с нуля)
-
-        :param semantic_ids: Тензор или список всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)
-        :param residuals: Тензор или список остатков для каждого semantic_id
-        """
-        residuals_count = residuals.shape[0] if isinstance(residuals, torch.Tensor) else len(residuals)
-        semantic_ids_count = semantic_ids.shape[0] if isinstance(semantic_ids, torch.Tensor) else len(semantic_ids)
-        assert(residuals_count == semantic_ids_count)
-
-        if isinstance(residuals, list):
-            residuals = torch.tensor(residuals, device=self.device)
-        residuals = self._to_device(residuals)
-
-        for semantic_id, residual in zip(semantic_ids, residuals):
-            self.add_item(semantic_id, residual)
-
-    def get_candidates_tensor(self, query_prefixes: List[List[int]]) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)
-
-        :return: Кортеж из двух тензоров:
-        - candidates_tensor (размерность: [num_prefixes, max_collisions, residual_dim]): тензор, содержащий остатки кандидатов для каждого префикса
-          `max_collisions` — максимальное количество кандидатов для каждого префикса
-        - mask (размерность: [num_prefixes, max_collisions]): Маска для candidates_tensor
-
-        Примечание:
-            Предполагаем что все префиксы из `query_prefixes` уже есть в словаре semantic ids
-            Если префикс не найден, будет выброшено исключение
-        """
-        assert isinstance(query_prefixes, list)
-        assert(self.residual_length == len(self._semantic_id_dict[tuple(query_prefixes[0])][0][1]))
-        assert(len(query_prefixes[0]) == self.semantic_id_length)
-
-        max_collision_len = max(len(x) for x in self._semantic_id_dict.values())
-        candidates_tensor = torch.zeros(len(query_prefixes), max_collision_len, self.residual_length, dtype=torch.float32, device=self.device)
-        mask = torch.zeros(len(query_prefixes), max_collision_len, dtype=torch.bool, device=self.device)
-
-        for i, semantic_id in enumerate(query_prefixes):
-            key = tuple(semantic_id)
-            assert key in self._semantic_id_dict.keys(), f"Не найдено обьектов с semantic id {key}" # нужно что-то с этим делать
-            for j, residual in self._semantic_id_dict[key]: #сохранение порядка
-                candidates_tensor[i, j] = residual
-                mask[i, j] = True
-        return candidates_tensor, mask
-
-    def get_semantic_ids(self, query_prefixes: torch.Tensor, query_residuals: torch.Tensor) -> torch.Tensor:
-        """
-        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)
-
-        :return: semantic_ids: [num_prefixes, prefix_len + 1] список из semantic id с токенами решающие коллизии
-        """
-        assert isinstance(query_prefixes, torch.Tensor)
-        assert isinstance(query_residuals, torch.Tensor)
-        assert(query_prefixes.shape[0] == query_residuals.shape[0])
-        assert(query_prefixes.shape[1] == self.semantic_id_length)
-        assert(query_residuals.shape[1] == self.residual_length)
-
-        query_prefixes = self._to_device(query_prefixes)
-        query_residuals = self._to_device(query_residuals)
-
-        candidates_tensor, mask = self.get_candidates_tensor(query_prefixes.tolist())
-
-        masked_dot_products = torch.einsum('ijk,ik->ij', candidates_tensor, query_residuals).masked_fill(~mask, float('-inf'))
-        max_indices = torch.argmax(masked_dot_products, dim=1)
-        best_semantic_ids = torch.concat((query_prefixes, max_indices.unsqueeze(1)), dim=1)
-        return best_semantic_ids
-    
-    def get_closest_torch(self, query_prefixes: torch.Tensor, query_residuals: torch.Tensor):
-        raise NotImplementedError("get_closest_torch is not implemented")
-    
-        query_prefixes = query_prefixes.to(self.device)
-        query_residuals = query_residuals.to(self.device)
-
-        batch_size, max_length = self._semantic_ids.shape
-        num_prefixes, prefix_len = query_prefixes.shape
-
-        # привожу к одной размерности чтобы найти совпадения по префиксам
-        semantic_ids_exp = self._semantic_ids[:, :prefix_len].unsqueeze(0).expand(num_prefixes, batch_size, prefix_len) # [num_prefixes, batch_size, prefix_len]
-        prefixes_exp = query_prefixes.unsqueeze(1).expand(num_prefixes, batch_size, prefix_len) #torch.tile
-        is_prefix_match = (semantic_ids_exp == prefixes_exp).all(dim=2)  # [num_prefixes, batch_size]
-
-        # Шаг 2: Маскирование residuals для каждого префикса
-        residuals_exp = self._residuals.unsqueeze(0).expand(num_prefixes, batch_size, -1)  # [num_prefixes, batch_size, emb_dim]
-        masked_residuals = residuals_exp * is_prefix_match.unsqueeze(2).float()  # Зануляем строки, не соответствующие префиксам
-        dot_products = torch.einsum('ijk,ik->ij', masked_residuals, query_residuals)
-        max_indices = torch.argmax(dot_products, dim=1)  # [num_prefixes] #
-
-        best_semantic_ids = self._semantic_ids[max_indices]  # [num_prefixes, max_length]
-        best_residuals = self._residuals[max_indices]  # [num_prefixes, emb_dim]
-        
-        return best_semantic_ids, best_residuals
\ No newline at end of file
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index c71917fe..60bc6319 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -3,8 +3,7 @@
 import torch
 from tqdm import tqdm
 from models.base import SequentialTorchModel
-from models.collision_solver import CollisionSolver
-from rqvae.trie import Item, Trie
+from rqvae_utils import CollisionSolver, Trie, Item
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
@@ -100,7 +99,7 @@ def __init__(
         self._init_weights(initializer_range)
         
     @classmethod
-    def init_rqvae(cls, config):
+    def init_rqvae(cls, config) -> RqVaeModel:
         rqvae_config = json.load(open(config["rqvae_train_config_path"]))
         rqvae_config["model"]["should_init_codebooks"] = False
         
@@ -128,8 +127,13 @@ def create_from_config(cls, config, **kwargs):
 
         semantic_ids, residuals = rqvae_model({"embeddings": embeddings})
         
-        solver = CollisionSolver(residuals.shape[1], len(semantic_ids[0]), device=DEVICE)
-        solver.create_query_candidates_dict(semantic_ids, residuals) # TODO
+        solver = CollisionSolver(
+            residual_dim=residuals.shape[1], 
+            emb_dim=len(rqvae_model.codebook_sizes), 
+            codebook_size=len(rqvae_model.codebook_sizes[0]), 
+            device=DEVICE
+        )
+        solver.create_query_candidates_dict(semantic_ids, residuals)
         
         item_id_to_embedding = {
             item_id: embedding for (item_id, embedding) in zip(item_ids, embeddings)
diff --git a/modeling/rqvae/__init__.py b/modeling/rqvae/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/modeling/rqvae/collisions.py b/modeling/rqvae/collisions.py
deleted file mode 100644
index 10ad039e..00000000
--- a/modeling/rqvae/collisions.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from collections import defaultdict
-
-
-def dedup(data):
-    count_dict = {}
-
-    result = []
-    for item in data:
-        code = item[2]
-        if code not in count_dict:
-            count_dict[code] = 0
-        unique_index = count_dict[code]
-        count_dict[code] += 1
-        new_last_element = (*code, unique_index)
-        result.append((item[0], item[1], new_last_element))
-
-    return result
-
-def dedup_semantic_ids(semantic_ids): # TODOPK
-    result = []
-    count_dict = defaultdict(int)
-    for semantic_id in semantic_ids:
-        unique_index = count_dict[semantic_id]
-        count_dict[semantic_id] += 1
-        new_last_element = (*semantic_id, unique_index)
-        result.append(new_last_element)
-    return result
diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
new file mode 100644
index 00000000..a25b6189
--- /dev/null
+++ b/modeling/rqvae_utils/__init__.py
@@ -0,0 +1,2 @@
+from .collision_solver import CollisionSolver
+from .trie import Item, Trie
\ No newline at end of file
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
new file mode 100644
index 00000000..f81b9454
--- /dev/null
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -0,0 +1,103 @@
+import torch
+
+class CollisionSolver:
+    def __init__(self, residual_dim, emb_dim, codebook_size, device: torch.device = torch.device('cpu')):
+        """
+        :param residual_dim: Длина остатка
+        :param codebook_size: Количество элементов в одном кодбуке
+        :param emb_dim: Длина semantic_id (без токена решающего коллизии)
+        :param device: Устройство
+        """
+        self._semantic_id_dict = None
+        self._unique_ids = None
+        self.counts = None
+        self.residual_dim = residual_dim
+        self.emb_dim = emb_dim
+        self.codebook_size = codebook_size
+        self.device = device
+
+        self.key = torch.tensor([self.codebook_size ** i for i in range(self.emb_dim)], dtype=torch.long, device=self.device)
+
+    def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:
+        """
+        Перенос тензора на устройство
+        """
+        if tensor.device != self.device:
+            tensor = tensor.to(self.device)
+        return tensor
+
+    def create_query_candidates_dict(self, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
+        """
+        Создает разреженный тензор, который содержит сгруппированные по semantic id элементы
+
+        :param semantic_ids: Тензор всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)
+        :param residuals: Тензор остатков для каждого semantic_id
+        """
+        residuals_count, residual_length = residuals.shape
+        semantic_ids_count, semantic_id_length = semantic_ids.shape
+
+        assert residuals_count == semantic_ids_count
+        assert semantic_id_length == self.emb_dim
+        assert residual_length == self.residual_dim
+
+        residuals = self._to_device(residuals)
+        semantic_ids = self._to_device(semantic_ids)
+        unique_id = torch.einsum('nc,c->n', semantic_ids, self.key)
+        unique_ids, inverse_indices = torch.unique(unique_id, return_inverse=True)
+        sorted_indices = torch.argsort(inverse_indices)
+        counts = torch.bincount(inverse_indices)
+        max_residuals_count = counts.max().item()
+        offsets = torch.cumsum(torch.cat((torch.tensor([0], dtype=torch.long, device=self.device), counts[:-1])), dim=0)
+        row_indices = inverse_indices[sorted_indices]
+        col_indices = torch.arange(semantic_ids_count) - offsets[row_indices]
+        indices = torch.stack([
+            row_indices,
+            col_indices
+        ], dim=0)
+
+        self._semantic_id_dict = torch.sparse_coo_tensor(indices, residuals[sorted_indices], size=(len(unique_ids), max_residuals_count, self.residual_dim), device=self.device)
+        self._unique_ids = unique_ids
+        self.counts = counts
+
+
+    def get_residuals_by_semantic_id(self, semantic_id: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param semantic_id: [emb_dim] semantic id (без токена решающего коллизии)
+
+        :return: candidates: [max_residuals_count, residual_dim] список остатков с таким же semantic id
+        :return: mask: [max_residuals_count] маска для списка остатков
+        """
+        assert semantic_id.shape == (self.emb_dim,)
+
+        semantic_id = self._to_device(semantic_id)
+
+        unique_id = torch.einsum('c,c->', semantic_id, self.key)
+        target_index = (self._unique_ids == unique_id).nonzero(as_tuple=True)[0]
+
+        if len(target_index) == 0:
+            empty_candidates = torch.empty((0, self.residual_dim), device=self.device)
+            empty_mask = torch.empty((0,), dtype=torch.bool, device=self.device)
+            return empty_candidates, empty_mask # Если unique_id отсутствует
+
+        candidates = self._semantic_id_dict[target_index.item()].to_dense()
+        mask = torch.arange(candidates.size(0)) < self.counts[target_index.item()]
+
+        return candidates, mask
+
+    def get_scores(self, semantic_id, residual) -> tuple[torch.Tensor, int]:
+        """
+        :param semantic_id: [emb_dim] semantic id (без токена решающего коллизии
+        :param residual: [residual_dim] Остаток
+
+        :return: scores: [residuals_count] Вероятности для остатков
+        :return: index: Индекс наибольшего значения в scores
+        """
+        assert semantic_id.shape == (self.emb_dim,)
+        assert residual.shape == (self.residual_dim,)
+
+        residual = self._to_device(residual)
+        candidates, mask = self.get_residuals_by_semantic_id(semantic_id)
+        scores = torch.softmax(torch.einsum('jk,k->j', candidates[mask], residual), dim=0)
+        if scores.shape[0] == 0:
+            return torch.empty((0,), device=self.device), torch.empty((0,), device=self.device)
+        return scores, torch.argmax(scores)
\ No newline at end of file
diff --git a/modeling/rqvae/rqvae_data.py b/modeling/rqvae_utils/rqvae_data.py
similarity index 100%
rename from modeling/rqvae/rqvae_data.py
rename to modeling/rqvae_utils/rqvae_data.py
diff --git a/modeling/rqvae/trie.py b/modeling/rqvae_utils/trie.py
similarity index 100%
rename from modeling/rqvae/trie.py
rename to modeling/rqvae_utils/trie.py
diff --git a/notebooks/CollisionSolver.ipynb b/notebooks/CollisionSolver.ipynb
deleted file mode 100644
index 4fabd090..00000000
--- a/notebooks/CollisionSolver.ipynb
+++ /dev/null
@@ -1,395 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "CH-xb-IDScxO"
-      },
-      "outputs": [],
-      "source": [
-        "from collections import defaultdict\n",
-        "from typing import List, Tuple, Dict\n",
-        "import torch"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "WH6rvC8BSTD1"
-      },
-      "outputs": [],
-      "source": [
-        "class CollisionSolver:\n",
-        "    def __init__(self, residual_length, semantic_id_length, device: torch.device = torch.device('cpu')):\n",
-        "        \"\"\"\n",
-        "        :param residual_length: Длина остатка для каждого semantic_id\n",
-        "        :param semantic_id_length: Длина semantic_id (без токена решающего коллизии)\n",
-        "        :param device: Устройство\n",
-        "        \"\"\"\n",
-        "        self._semantic_id_dict = defaultdict(list)\n",
-        "        self.residual_length = residual_length\n",
-        "        self.semantic_id_length = semantic_id_length\n",
-        "        self.device = device\n",
-        "\n",
-        "    def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:\n",
-        "        \"\"\"\n",
-        "        Перенос тензора на устройство\n",
-        "        \"\"\"\n",
-        "        if tensor.device != self.device:\n",
-        "            tensor = tensor.to(self.device)\n",
-        "        return tensor\n",
-        "\n",
-        "    def add_item(self, semantic_id: List[int] | torch.Tensor, residual: torch.Tensor) -> None:\n",
-        "        \"\"\"\n",
-        "        Добавляет новый элемент в словарь хранящий semantic_ids с остатками\n",
-        "\n",
-        "        :param semantic_id: Semantic id (без токена решающего коллизии)\n",
-        "        :param residual: Тензор с остатком для данного semantic_id\n",
-        "        \"\"\"\n",
-        "        if isinstance(semantic_id, torch.Tensor):\n",
-        "            semantic_id = semantic_id.tolist()\n",
-        "\n",
-        "        assert isinstance(residual, torch.Tensor)\n",
-        "        assert residual.shape == (self.residual_length,)\n",
-        "        assert len(semantic_id) == self.semantic_id_length\n",
-        "\n",
-        "        residual = self._to_device(residual)\n",
-        "        key = tuple(semantic_id)\n",
-        "        self._semantic_id_dict[key].append((len(self._semantic_id_dict[key]), residual))\n",
-        "\n",
-        "\n",
-        "    def create_query_candidates_dict(self, semantic_ids: torch.Tensor | List[List[int]], residuals: torch.Tensor | List[List[int]]) -> None:\n",
-        "        \"\"\"\n",
-        "        Создает словарь, который содержит сгруппирированные по semantic id элементы, к ним добавлены токены решающие коллизии (добавляются по порядку начиная с нуля)\n",
-        "\n",
-        "        :param semantic_ids: Тензор или список всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)\n",
-        "        :param residuals: Тензор или список остатков для каждого semantic_id\n",
-        "        \"\"\"\n",
-        "        residuals_count = residuals.shape[0] if isinstance(residuals, torch.Tensor) else len(residuals)\n",
-        "        semantic_ids_count = semantic_ids.shape[0] if isinstance(semantic_ids, torch.Tensor) else len(semantic_ids)\n",
-        "        assert(residuals_count == semantic_ids_count)\n",
-        "\n",
-        "        if isinstance(residuals, list):\n",
-        "            residuals = torch.tensor(residuals, device=self.device)\n",
-        "        residuals = self._to_device(residuals)\n",
-        "\n",
-        "        for semantic_id, residual in zip(semantic_ids, residuals):\n",
-        "            self.add_item(semantic_id, residual)\n",
-        "\n",
-        "    def get_candidates_tensor(self, query_prefixes: List[List[int]]) -> Tuple[torch.Tensor, torch.Tensor]:\n",
-        "        \"\"\"\n",
-        "        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)\n",
-        "\n",
-        "        :return: Кортеж из двух тензоров:\n",
-        "        - candidates_tensor (размерность: [num_prefixes, max_collisions, residual_dim]): тензор, содержащий остатки кандидатов для каждого префикса\n",
-        "          `max_collisions` — максимальное количество кандидатов для каждого префикса\n",
-        "        - mask (размерность: [num_prefixes, max_collisions]): Маска для candidates_tensor\n",
-        "\n",
-        "        Примечание:\n",
-        "            Предполагаем что все префиксы из `query_prefixes` уже есть в словаре semantic ids\n",
-        "            Если префикс не найден, будет выброшено исключение\n",
-        "        \"\"\"\n",
-        "        assert isinstance(query_prefixes, list)\n",
-        "        assert(self.residual_length == len(self._semantic_id_dict[tuple(query_prefixes[0])][0][1]))\n",
-        "        assert(len(query_prefixes[0]) == self.semantic_id_length)\n",
-        "\n",
-        "        max_collision_len = max(len(x) for x in self._semantic_id_dict.values())\n",
-        "        candidates_tensor = torch.zeros(len(query_prefixes), max_collision_len, self.residual_length, dtype=torch.float32, device=self.device)\n",
-        "        mask = torch.zeros(len(query_prefixes), max_collision_len, dtype=torch.bool, device=self.device)\n",
-        "\n",
-        "        for i, semantic_id in enumerate(query_prefixes):\n",
-        "            key = tuple(semantic_id)\n",
-        "            assert key in self._semantic_id_dict.keys(), f\"Не найдено обьектов с semantic id {key}\" # нужно что-то с этим делать\n",
-        "            for j, residual in self._semantic_id_dict[key]: #сохранение порядка\n",
-        "                candidates_tensor[i, j] = residual\n",
-        "                mask[i, j] = True\n",
-        "        return candidates_tensor, mask\n",
-        "\n",
-        "    def get_semantic_ids(self, query_prefixes: torch.Tensor, query_residuals: torch.Tensor) -> torch.Tensor:\n",
-        "        \"\"\"\n",
-        "        :param query_prefixes: [num_prefixes, prefix_len] список из semantic id (без токенов решающих коллизии)\n",
-        "\n",
-        "        :return: semantic_ids: [num_prefixes, prefix_len + 1] список из semantic id с токенами решающие коллизии\n",
-        "        \"\"\"\n",
-        "        assert isinstance(query_prefixes, torch.Tensor)\n",
-        "        assert isinstance(query_residuals, torch.Tensor)\n",
-        "        assert(query_prefixes.shape[0] == query_residuals.shape[0])\n",
-        "        assert(query_prefixes.shape[1] == self.semantic_id_length)\n",
-        "        assert(query_residuals.shape[1] == self.residual_length)\n",
-        "\n",
-        "        query_prefixes = self._to_device(query_prefixes)\n",
-        "        query_residuals = self._to_device(query_residuals)\n",
-        "\n",
-        "        candidates_tensor, mask = self.get_candidates_tensor(query_prefixes.tolist())\n",
-        "\n",
-        "        masked_dot_products = torch.einsum('ijk,ik->ij', candidates_tensor, query_residuals).masked_fill(~mask, float('-inf'))\n",
-        "        max_indices = torch.argmax(masked_dot_products, dim=1)\n",
-        "        best_semantic_ids = torch.concat((query_prefixes, max_indices.unsqueeze(1)), dim=1)\n",
-        "        return best_semantic_ids"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "AhJgbuGESnWd"
-      },
-      "source": [
-        "# Пример использования"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "id": "pBjXwVLVSWMO"
-      },
-      "outputs": [],
-      "source": [
-        "residual_length = 12\n",
-        "semantic_ids_length = 3\n",
-        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-        "\n",
-        "semantic_ids = torch.tensor([\n",
-        "    [1, 2, 3, 0],\n",
-        "    [1, 2, 3, 1],\n",
-        "    [1, 2, 3, 2],\n",
-        "    [1, 2, 3, 3],\n",
-        "    [1, 2, 4, 0],\n",
-        "    [1, 2, 4, 1],\n",
-        "    [1, 2, 4, 2],\n",
-        "    [5, 2, 3, 0],\n",
-        "    [5, 2, 3, 1],\n",
-        "    [5, 2, 3, 2],\n",
-        "    [5, 2, 3, 3],\n",
-        "    [5, 2, 3, 4],\n",
-        "    [5, 2, 3, 5],\n",
-        "    [5, 2, 3, 6],\n",
-        "    [2, 8, 7, 6],\n",
-        "], device=torch.device('cpu'))\n",
-        "\n",
-        "residuals = torch.rand(semantic_ids.shape[0], residual_length)\n",
-        "\n",
-        "query_prefixes = torch.tensor([\n",
-        "    [1, 2, 3],\n",
-        "    [1, 2, 4],\n",
-        "    [5, 2, 3],\n",
-        "    [5, 2, 3]\n",
-        "], device=device)  # [num_prefixes, prefix_len]\n",
-        "\n",
-        "query_residuals = torch.rand(query_prefixes.shape[0], residual_length, device=torch.device('cpu'))  # [num_prefixes, emb_dim]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "wn7Uk8V_TgrE",
-        "outputId": "540b25c6-63bc-4613-90b1-5510ea4d6224"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "tensor([[1, 2, 3, 0],\n",
-              "        [1, 2, 4, 0],\n",
-              "        [5, 2, 3, 0],\n",
-              "        [5, 2, 3, 3]])"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solver = CollisionSolver(residual_length, semantic_ids_length)\n",
-        "\n",
-        "solver.create_query_candidates_dict(semantic_ids[:, :-1], residuals)\n",
-        "\n",
-        "solver.get_semantic_ids(query_prefixes, query_residuals)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "3HTju2grZm-F",
-        "outputId": "442795a8-a952-466c-8c48-fb3d6f14bc81"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "defaultdict(list,\n",
-              "            {(1,\n",
-              "              2,\n",
-              "              3): [(0,\n",
-              "               tensor([0.7220, 0.9496, 0.4006, 0.8832, 0.6087, 0.4947, 0.1341, 0.9645, 0.7408,\n",
-              "                       0.5972, 0.3433, 0.8700])), (1,\n",
-              "               tensor([0.4457, 0.4410, 0.1333, 0.4391, 0.4153, 0.1703, 0.3044, 0.0940, 0.2773,\n",
-              "                       0.5258, 0.5838, 0.0273])), (2,\n",
-              "               tensor([0.6268, 0.1060, 0.0841, 0.0750, 0.4090, 0.2886, 0.4343, 0.1945, 0.0429,\n",
-              "                       0.8477, 0.1418, 0.6465])), (3,\n",
-              "               tensor([0.0077, 0.8171, 0.1344, 0.2223, 0.9616, 0.2790, 0.3448, 0.1485, 0.7148,\n",
-              "                       0.5900, 0.0154, 0.4752]))],\n",
-              "             (1,\n",
-              "              2,\n",
-              "              4): [(0,\n",
-              "               tensor([0.3480, 0.3537, 0.3771, 0.1443, 0.6877, 0.4845, 0.8278, 0.9831, 0.4941,\n",
-              "                       0.0682, 0.0900, 0.2485])), (1,\n",
-              "               tensor([0.4048, 0.3308, 0.2278, 0.4890, 0.4899, 0.9994, 0.1511, 0.9374, 0.8730,\n",
-              "                       0.7538, 0.0409, 0.1444])), (2,\n",
-              "               tensor([0.3601, 0.7909, 0.0766, 0.7096, 0.5745, 0.2606, 0.4412, 0.8748, 0.1248,\n",
-              "                       0.5816, 0.2185, 0.2352]))],\n",
-              "             (5,\n",
-              "              2,\n",
-              "              3): [(0,\n",
-              "               tensor([0.9524, 0.6976, 0.7598, 0.9994, 0.2881, 0.9854, 0.2537, 0.6400, 0.5632,\n",
-              "                       0.5768, 0.2833, 0.0570])), (1,\n",
-              "               tensor([0.6778, 0.6523, 0.7592, 0.1953, 0.0077, 0.0960, 0.0714, 0.8100, 0.9999,\n",
-              "                       0.0407, 0.1231, 0.7192])), (2,\n",
-              "               tensor([0.3652, 0.1131, 0.6800, 0.7445, 0.3586, 0.6498, 0.6479, 0.3792, 0.3110,\n",
-              "                       0.7605, 0.9031, 0.4177])), (3,\n",
-              "               tensor([0.5732, 0.6359, 0.1402, 0.0661, 0.6557, 0.5067, 0.7383, 0.7173, 0.3075,\n",
-              "                       0.3920, 0.7497, 0.9602])), (4,\n",
-              "               tensor([0.3407, 0.3068, 0.0815, 0.3887, 0.5861, 0.8103, 0.1236, 0.2175, 0.7513,\n",
-              "                       0.5872, 0.7065, 0.0919])), (5,\n",
-              "               tensor([0.0248, 0.9603, 0.5902, 0.8559, 0.9800, 0.9306, 0.9737, 0.9035, 0.2527,\n",
-              "                       0.0049, 0.3355, 0.2858])), (6,\n",
-              "               tensor([0.2779, 0.6830, 0.5133, 0.5767, 0.2029, 0.9013, 0.9562, 0.4474, 0.0377,\n",
-              "                       0.0205, 0.6822, 0.1314]))],\n",
-              "             (2,\n",
-              "              8,\n",
-              "              7): [(0,\n",
-              "               tensor([0.5204, 0.4737, 0.7302, 0.0674, 0.0289, 0.5045, 0.1267, 0.8282, 0.7559,\n",
-              "                       0.3856, 0.4455, 0.1655]))]})"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solver._semantic_id_dict"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ji7PzyQeTxuP"
-      },
-      "source": [
-        "# Альтернативное решение только через torch"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "eh7jwIkHTr4_",
-        "outputId": "d9bccfba-c12f-4036-8bd3-0ef5d1d5b5e1"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Префикс: [1, 2, 3]\n",
-            "Лучший semantic_id: [1, 2, 3, 0]\n",
-            "Соответствующий residual: tensor([0.7220, 0.9496, 0.4006, 0.8832, 0.6087, 0.4947, 0.1341, 0.9645, 0.7408,\n",
-            "        0.5972, 0.3433, 0.8700])\n",
-            "Префикс: [1, 2, 4]\n",
-            "Лучший semantic_id: [1, 2, 4, 0]\n",
-            "Соответствующий residual: tensor([0.3480, 0.3537, 0.3771, 0.1443, 0.6877, 0.4845, 0.8278, 0.9831, 0.4941,\n",
-            "        0.0682, 0.0900, 0.2485])\n",
-            "Префикс: [5, 2, 3]\n",
-            "Лучший semantic_id: [5, 2, 3, 0]\n",
-            "Соответствующий residual: tensor([0.9524, 0.6976, 0.7598, 0.9994, 0.2881, 0.9854, 0.2537, 0.6400, 0.5632,\n",
-            "        0.5768, 0.2833, 0.0570])\n",
-            "Префикс: [5, 2, 3]\n",
-            "Лучший semantic_id: [5, 2, 3, 3]\n",
-            "Соответствующий residual: tensor([0.5732, 0.6359, 0.1402, 0.0661, 0.6557, 0.5067, 0.7383, 0.7173, 0.3075,\n",
-            "        0.3920, 0.7497, 0.9602])\n"
-          ]
-        }
-      ],
-      "source": [
-        "semantic_ids = semantic_ids.to(device)\n",
-        "residuals = residuals.to(device)\n",
-        "query_prefixes = query_prefixes.to(device)\n",
-        "query_residuals = query_residuals.to(device)\n",
-        "\n",
-        "batch_size, max_length = semantic_ids.shape\n",
-        "num_prefixes, prefix_len = query_prefixes.shape\n",
-        "\n",
-        "#привожу к одной размерности чтобы найти совпадения по префиксам\n",
-        "semantic_ids_exp = semantic_ids[:, :prefix_len].unsqueeze(0).expand(num_prefixes, batch_size, prefix_len) # [num_prefixes, batch_size, prefix_len]\n",
-        "prefixes_exp = query_prefixes.unsqueeze(1).expand(num_prefixes, batch_size, prefix_len) #torch.tile\n",
-        "is_prefix_match = (semantic_ids_exp == prefixes_exp).all(dim=2)  # [num_prefixes, batch_size]\n",
-        "\n",
-        "# Шаг 2: Маскирование residuals для каждого префикса\n",
-        "residuals_exp = residuals.unsqueeze(0).expand(num_prefixes, batch_size, -1)  # [num_prefixes, batch_size, emb_dim]\n",
-        "masked_residuals = residuals_exp * is_prefix_match.unsqueeze(2).float()  # Зануляем строки, не соответствующие префиксам\n",
-        "dot_products = torch.einsum('ijk,ik->ij', masked_residuals, query_residuals)\n",
-        "max_indices = torch.argmax(dot_products, dim=1)  # [num_prefixes] #\n",
-        "\n",
-        "best_semantic_ids = semantic_ids[max_indices]  # [num_prefixes, max_length]\n",
-        "best_residuals = residuals[max_indices]  # [num_prefixes, emb_dim]\n",
-        "\n",
-        "for i, prefix in enumerate(query_prefixes):\n",
-        "    print(f\"Префикс: {prefix.tolist()}\")\n",
-        "    print(f\"Лучший semantic_id: {best_semantic_ids[i].tolist()}\")\n",
-        "    print(f\"Соответствующий residual: {best_residuals[i]}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.11"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

From 9cd9b8c304b79f86e98b33aef8bb38c109ef00e4 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 22 Jan 2025 10:09:44 +0700
Subject: [PATCH 045/175] upudate tiger

---
 modeling/main.ipynb      | 2746 +-------------------------------------
 modeling/models/tiger.py |    3 +-
 2 files changed, 5 insertions(+), 2744 deletions(-)

diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index 63993428..e7152949 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -2,94 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a57704c044240659047a8fd9d54123b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b06a168ba57445c3b10c24c0d5f08352",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc28303c594d4b2b9a710ab312e403eb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "25987885c1624b1a9be880896a607272",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6ba30015b4ed421eb2e07333b3ba727c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc28335fa6294753be34419468a5e815",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "\n",
@@ -153,2662 +68,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('7806397051',\n",
-       "  'WAWO 15 Color Professionl Makeup Eyeshadow Camouflage Facial Concealer Neutral Palette',\n",
-       "  (23, 49, 13, 166, 0)),\n",
-       " ('9759091062', 'Xtreme Brite Brightening Gel 1oz.', (171, 230, 188, 70, 0)),\n",
-       " ('9788072216',\n",
-       "  'Prada Candy By Prada Eau De Parfum Spray 1.7 Oz For Women',\n",
-       "  (189, 0, 26, 166, 0)),\n",
-       " ('9790790961',\n",
-       "  'Versace Bright Crystal Eau de Toilette Spray for Women, 3 Ounce',\n",
-       "  (84, 225, 207, 237, 0)),\n",
-       " ('9790794231', 'Stella McCartney Stella', (233, 61, 1, 68, 0)),\n",
-       " ('B00004TMFE',\n",
-       "  'Avalon Biotin B-Complex Thickening Conditioner, 14 Ounce',\n",
-       "  (194, 83, 125, 220, 0)),\n",
-       " ('B00004TUBL',\n",
-       "  'Better Living Classic Two Chamber Dispenser, White',\n",
-       "  (57, 199, 129, 249, 0)),\n",
-       " ('B00004TUBV',\n",
-       "  'Better Living The Ulti-Mate Dispenser',\n",
-       "  (120, 180, 151, 115, 0)),\n",
-       " ('B00004U9UY',\n",
-       "  'Crabtree &amp; Evelyn 2792 Gardeners Hand Therapy (100ml, 3.4 oz)',\n",
-       "  (115, 7, 22, 70, 0)),\n",
-       " ('B00004U9V2',\n",
-       "  \"Crabtree &amp; Evelyn - Gardener's Ultra-Moisturising Hand Therapy Pump - 250g/8.8 OZ\",\n",
-       "  (18, 0, 150, 28, 0)),\n",
-       " ('B000050B6U', 'Instant Heat Curling Iron, 1-Inch', (87, 1, 150, 18, 0)),\n",
-       " ('B000052WY7',\n",
-       "  'Maybelline New York Expert Wear Brow &amp; Eyeliner, 151 Velvet Black , .011 oz (310 mg)',\n",
-       "  (135, 203, 84, 254, 0)),\n",
-       " ('B000052WYD',\n",
-       "  'Maybelline New York Cover Stick Concealer, Ivory, Light 2, 0.16 Ounce',\n",
-       "  (52, 90, 157, 136, 0)),\n",
-       " ('B000052WYL',\n",
-       "  'Maybelline New York Expert Eyes Moisturizing Eye Makeup Remover, 2.3 Fl. Oz.',\n",
-       "  (70, 222, 128, 136, 0)),\n",
-       " ('B000052WYN',\n",
-       "  'Maybelline New York Ultra-Liner Liquid Liner, Waterproof, Black 135L-01 , .25 fl oz (7.3 ml)',\n",
-       "  (24, 145, 1, 54, 0)),\n",
-       " ('B000052X9G',\n",
-       "  'Nullo Internal Deodorant, Coated Caplets, 135 Caplets',\n",
-       "  (157, 0, 10, 88, 0)),\n",
-       " ('B000052XIA',\n",
-       "  'Vagisil Anti-Itch Creme, Original Formula - 1 oz',\n",
-       "  (232, 72, 177, 42, 0)),\n",
-       " ('B000052XW5',\n",
-       "  'Pretty Feet &amp; Hands Rough Skin Remover-Exfoliant, 3-Ounce Bottles (Pack of 3)',\n",
-       "  (183, 50, 157, 222, 0)),\n",
-       " ('B000052XYQ', 'DHS Clear Shampoo 8 Fl Oz', (157, 39, 12, 233, 0)),\n",
-       " ('B000052XZP',\n",
-       "  'Neutrogena T-Gel Shampoo, Stubborn Itch Control, 4.4 Ounce',\n",
-       "  (75, 1, 176, 32, 0)),\n",
-       " ('B000052XZX',\n",
-       "  'Neutrogena T/Sal Shampoo, Scalp Build-up Control, 4.5 fl oz',\n",
-       "  (241, 0, 177, 42, 0)),\n",
-       " ('B000052Y25',\n",
-       "  'DHS Conditioning Rinse With Panthenol 8 fl oz',\n",
-       "  (48, 145, 248, 254, 0)),\n",
-       " ('B000052Y33',\n",
-       "  'Alberto VO5 Moisturizing Hot Oil Treatment, 0.5 Ounce, 2-Count Tubes (Pack of 6)',\n",
-       "  (175, 215, 150, 248, 0)),\n",
-       " ('B000052Y5F',\n",
-       "  'Konsyl Pharmaceuticals Psyllium Fiber 15.9 oz',\n",
-       "  (222, 72, 176, 32, 0)),\n",
-       " ('B000052Y6Z',\n",
-       "  'Williams Lectric Shave Electric Razor Pre-Shave, Original, 3 fl oz',\n",
-       "  (235, 212, 230, 24, 0)),\n",
-       " ('B000052YJC',\n",
-       "  'Buf-Puf Double-Sided Body Sponge - 1 ea',\n",
-       "  (189, 52, 248, 28, 0)),\n",
-       " ('B000052YJD',\n",
-       "  'Buf-Puf Reusable Facial Sponge, Extra Gentle, - 1 ea',\n",
-       "  (157, 72, 10, 215, 0)),\n",
-       " ('B000052YJH',\n",
-       "  'Buf-Puf Reusable Facial Sponge (Pack of 6)',\n",
-       "  (182, 39, 197, 28, 0)),\n",
-       " ('B000052YJM',\n",
-       "  'Clean &amp; Clear ESSENTIALS Dual Action Moisturizer, 4 Ounce',\n",
-       "  (1, 13, 49, 205, 0)),\n",
-       " ('B000052YJX',\n",
-       "  'Neutrogena OntheSpot Acne Treatment, Vanishing Formula, 0.75 Ounce',\n",
-       "  (241, 1, 165, 175, 0)),\n",
-       " ('B000052YKI',\n",
-       "  'Purpose Dual Treatment Moisture Lotion with SPF 15 4 fl oz (120 ml)',\n",
-       "  (42, 62, 157, 249, 0)),\n",
-       " ('B000052YKM',\n",
-       "  'Stri-Dex Medicated Pads, Maximum Strength, 90-Count Containers, (Pack of 3)',\n",
-       "  (29, 0, 137, 222, 0)),\n",
-       " ('B000052YKS',\n",
-       "  'Basis Cleaner Clean Face Wash, 6 Fluid Ounces',\n",
-       "  (2, 151, 48, 237, 0)),\n",
-       " ('B000052YKY',\n",
-       "  'Cetaphil Gentle Cleansing Bar, Antibacterial - 4.5 oz',\n",
-       "  (224, 49, 18, 115, 0)),\n",
-       " ('B000052YL1',\n",
-       "  'Cuticura Medicated Anti-Bacterial Bar Soap, Original Formula - 5.25 oz bar',\n",
-       "  (231, 35, 248, 222, 0)),\n",
-       " ('B000052YLX',\n",
-       "  'Purpose Gentle Cleansing Bar 6 oz (170 g)',\n",
-       "  (148, 93, 207, 141, 0)),\n",
-       " ('B000052YM0',\n",
-       "  'Fruit Of The Earth 100% Aloe Vera 6oz. Gel Tube',\n",
-       "  (32, 199, 150, 237, 0)),\n",
-       " ('B000052YM3',\n",
-       "  'Alpha Hydrox AHA Enhanced Cream 2 oz.',\n",
-       "  (157, 35, 150, 28, 0)),\n",
-       " ('B000052YM4',\n",
-       "  'Alpha Hydrox Foaming Face Wash -- 6 fl oz',\n",
-       "  (145, 61, 136, 136, 0)),\n",
-       " ('B000052YM7',\n",
-       "  'Alpha Hydrox Enhanced Lotion 10 percent Glycolic AHA -- 6 fl oz Anti-Wrinkle',\n",
-       "  (145, 230, 128, 233, 0)),\n",
-       " ('B000052YM8',\n",
-       "  'Alpha Hydrox Oil Free Treatment 10 Percent AHA 1.7 fl oz.',\n",
-       "  (145, 35, 128, 68, 0)),\n",
-       " ('B000052YMG', 'Aquanil Cleanser 16 fl oz.', (231, 35, 203, 214, 0)),\n",
-       " ('B000052YMQ',\n",
-       "  'Cetaphil Moisturizing Cream, Fragrance Free - 16 oz',\n",
-       "  (157, 250, 32, 237, 0)),\n",
-       " ('B000052YMR',\n",
-       "  'Cetaphil Moisturizing Cream, 3.0 - Ounces Tube (Pack of 3)',\n",
-       "  (10, 84, 136, 215, 0)),\n",
-       " ('B000052YMS',\n",
-       "  'Cetaphil Moisturizing Lotion, Fragrance Free - 16 fl oz',\n",
-       "  (231, 151, 176, 233, 0)),\n",
-       " ('B000052YMT',\n",
-       "  'Cetaphil Gentle Skin Cleanser, 4.0 -Ounce Bottles (Pack of 6)',\n",
-       "  (58, 119, 183, 223, 0)),\n",
-       " ('B000052YMV',\n",
-       "  'Cetaphil Gentle Skin Cleanser - 16 fl oz',\n",
-       "  (9, 119, 188, 233, 0)),\n",
-       " ('B000052YMX',\n",
-       "  'Complex 15 Therapeutic Moisturizing Face Cream - 2.5 Ounce',\n",
-       "  (10, 93, 32, 141, 0)),\n",
-       " ('B000052YN5',\n",
-       "  'DML Daily Facial Moisturizer, SPF 25 - 1.5 Oz',\n",
-       "  (157, 241, 10, 28, 0)),\n",
-       " ('B000052YN7', 'DML Moisturizing Lotion 16 oz.', (32, 199, 136, 237, 0)),\n",
-       " ('B000052YOL',\n",
-       "  'Neutrogena Healthy Skin Anti-Wrinkle Cream, SPF 15, 1.4 Ounce',\n",
-       "  (75, 84, 188, 237, 0)),\n",
-       " ('B000052YOR',\n",
-       "  'Neutrogena Body Oil, Light Sesame Formula, 8.5 Ounce',\n",
-       "  (25, 151, 184, 18, 0)),\n",
-       " ('B000052YOX',\n",
-       "  'Neutrogena Oil-Free Moisture, Sensitive Skin, 4 Ounce',\n",
-       "  (235, 84, 150, 255, 0)),\n",
-       " ('B000052YP4',\n",
-       "  'Neutrogena Light Night Cream, 2.25 Ounce',\n",
-       "  (75, 230, 18, 166, 0)),\n",
-       " ('B000052YP6',\n",
-       "  'Neutrogena Norwegian Formula Hand Cream, Fragrance-Free (2 Ounce)',\n",
-       "  (32, 151, 203, 236, 0)),\n",
-       " ('B000052YQ0',\n",
-       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 Normal 4 Fl Oz',\n",
-       "  (189, 1, 157, 214, 0)),\n",
-       " ('B000052YQ2',\n",
-       "  'Olay Complete All Day Moisturizer With Sunscreen Broad Spectrum SPF 15 - Sensitive 4 Fl Oz',\n",
-       "  (189, 93, 157, 105, 0)),\n",
-       " ('B000052YQN', \"POND'S Cold Cream Cleanser, 3.5 oz.\", (109, 93, 207, 214, 0)),\n",
-       " ('B000052YQU',\n",
-       "  \"POND'S Dry Skin Cream Facial Moisturizer, 3.9-Ounce (Packaging May Vary)\",\n",
-       "  (233, 14, 137, 254, 0)),\n",
-       " ('B000052Z5B',\n",
-       "  'Coty Airspun Face Powder, Naturally Neutral, 2.3 oz',\n",
-       "  (46, 35, 25, 28, 0)),\n",
-       " ('B000052Z8D',\n",
-       "  'Max Factor Pan-Stik Ultra Creamy Makeup, Sun Tone 137 .5 oz (14 g)',\n",
-       "  (15, 160, 130, 136, 0)),\n",
-       " ('B000052ZB1',\n",
-       "  'Neutrogena Intensified Day Moisture, SPF 15, 2.25 Ounce',\n",
-       "  (42, 222, 183, 251, 0)),\n",
-       " ('B000052ZB2',\n",
-       "  'Neutrogena Extra Gentle Cleanser, 6.7 Ounce',\n",
-       "  (42, 93, 48, 177, 0)),\n",
-       " ('B000052ZB4',\n",
-       "  'Neutrogena Deep Clean Facial Cleanser, Normal to Oily Skin, 6.7 Ounce',\n",
-       "  (242, 230, 205, 105, 0)),\n",
-       " ('B000052ZBD',\n",
-       "  'Neutrogena Rainbath Gel, Original, 16  Ounce',\n",
-       "  (224, 0, 48, 166, 0)),\n",
-       " ('B000052ZBH',\n",
-       "  'Almay One Coat Nourishing Mascara, Thickening, Black 402, 0.4-Ounce Package',\n",
-       "  (167, 39, 197, 254, 0)),\n",
-       " ('B000052ZBP',\n",
-       "  'Almay Moisturizing Eye Makeup Remover Pads, 80-Pads',\n",
-       "  (171, 39, 230, 61, 0)),\n",
-       " ('B000052ZTY',\n",
-       "  'Hugo By Hugo Boss For Men. Eau De Toilette Spray 1.3 Ounces',\n",
-       "  (26, 83, 150, 68, 0)),\n",
-       " ('B00005302B',\n",
-       "  'Jergen Extra Moisturizing Liquid Hand Wash 7.5 Oz',\n",
-       "  (15, 160, 12, 220, 0)),\n",
-       " ('B00005304H', 'Q-tips Cotton Swabs, 500 Count', (26, 88, 12, 68, 0)),\n",
-       " ('B0000530ED',\n",
-       "  \"L'Oreal Paris Feria Hair Color, 36 Dark Burgundy Brown/Chocolate Cherry\",\n",
-       "  (115, 199, 68, 215, 0)),\n",
-       " ('B0000530EZ',\n",
-       "  'Aussie Hair Insurance Leave-In Conditioner 8 Fl Oz (Pack of 3)',\n",
-       "  (88, 0, 215, 214, 0)),\n",
-       " ('B0000530G6',\n",
-       "  'Suave Professionals Conditioner, Humectant - 12.6 Ounce',\n",
-       "  (88, 215, 197, 70, 0)),\n",
-       " ('B0000530LO',\n",
-       "  'Suave Naturals Shampoo, Daily Clarifying - 22.5oz.',\n",
-       "  (109, 39, 53, 136, 0)),\n",
-       " ('B0000530LP',\n",
-       "  'Suave Shampoo, Daily Clarifying - 12oz.',\n",
-       "  (21, 7, 203, 222, 0)),\n",
-       " ('B0000530LV',\n",
-       "  'Suave Naturals Shampoo, Sun Ripened Strawberry 22.5oz',\n",
-       "  (189, 0, 177, 42, 0)),\n",
-       " ('B0000530M0',\n",
-       "  'Suave Professionals Humectant Moisture Shampoo , 12.6 fl Ounce (373 ml)',\n",
-       "  (75, 93, 183, 22, 0)),\n",
-       " ('B0000530NA',\n",
-       "  'Olay Daily Care Refreshing Toner, 7.2-Fluid Ounce',\n",
-       "  (80, 199, 248, 136, 0)),\n",
-       " ('B0000530O9',\n",
-       "  'Olay Age Defying Classic Eye Gel 0.5 Oz',\n",
-       "  (2, 151, 188, 4, 0)),\n",
-       " ('B0000531GD',\n",
-       "  'Maybelline New York Shine Free Oil Control Pressed Powder, Ivory - 1 ea',\n",
-       "  (39, 199, 137, 249, 0)),\n",
-       " ('B0000531GV',\n",
-       "  'Maybelline New York Expert Eyes 100% Oil-Free Eye Makeup Remover, 2.3 Fl. Oz.',\n",
-       "  (201, 145, 248, 254, 0)),\n",
-       " ('B0000531II',\n",
-       "  'Maybelline New York Shine Free Oil Control Loose Powder, Light, 0.7 Ounce',\n",
-       "  (93, 39, 151, 187, 0)),\n",
-       " ('B0000531NU',\n",
-       "  \"Maybelline New York Volum' Express - Waterproof Mascara, Very Black - .34 fl oz\",\n",
-       "  (183, 215, 137, 251, 0)),\n",
-       " ('B0000531SW',\n",
-       "  \"L'Oreal Paris Visible Lift Line-Minimizing &amp; Tone-Enhancing Makeup, Soft Ivory, 1.25 Ounces\",\n",
-       "  (147, 119, 215, 215, 0)),\n",
-       " ('B0000531WZ',\n",
-       "  'Revlon Super Lustrous Creme Lipstick, Fire and Ice 720, 0.15 Ounce',\n",
-       "  (8, 222, 248, 22, 0)),\n",
-       " ('B00005321Z',\n",
-       "  \"Revlon Eterna '27' Moisture Cream with Progenitin, 2 Ounce\",\n",
-       "  (37, 1, 188, 105, 0)),\n",
-       " ('B0000532AN', 'Revlon Lash Curlers, 1 Count', (87, 199, 177, 68, 0)),\n",
-       " ('B0000532VT',\n",
-       "  'Neutrogena Clean Conditioner, Replenishing , 10.1 Fluid Ounce',\n",
-       "  (26, 83, 31, 237, 0)),\n",
-       " ('B0000532YN',\n",
-       "  'Kiss My Face Bar Soap, 8.0 oz, Pure Olive Oil. 1-Bar',\n",
-       "  (151, 230, 215, 88, 0)),\n",
-       " ('B00005333G',\n",
-       "  \"L'Oreal Paris Hydra-Renewal Continuous Moisture Cream, 1.7 Ounce\",\n",
-       "  (130, 145, 53, 220, 0)),\n",
-       " ('B00005333I',\n",
-       "  \"L'Oreal Paris Eye Defense, 0.5 Fluid Ounce\",\n",
-       "  (2, 49, 150, 166, 0)),\n",
-       " ('B0000533CC',\n",
-       "  'Instead 12 Hour Feminine Protection Cup 24 ea',\n",
-       "  (120, 64, 157, 68, 0)),\n",
-       " ('B0000534VO',\n",
-       "  'Clean &amp; Clear Continuous Control Acne Cleanser,  5 oz  (141g)',\n",
-       "  (148, 145, 248, 254, 0)),\n",
-       " ('B0000535CH',\n",
-       "  \"Nature's Cure Two-part Acne Treatment System for Women 60 Tablets 1 Oz Cream\",\n",
-       "  (232, 72, 12, 68, 0)),\n",
-       " ('B0000535OF',\n",
-       "  'Alpha Hydrox Night Replenishing Cream 2 oz.',\n",
-       "  (157, 167, 215, 22, 0)),\n",
-       " ('B0000535RA',\n",
-       "  'Neutrogena Pore Refining Toner, Alpha and Beta Hydroxy Formula, 8.5 Ounce',\n",
-       "  (42, 225, 157, 251, 0)),\n",
-       " ('B0000535RC',\n",
-       "  'NeutrogenaClear Pore Oil Eliminating Astringent, 8 Ounce',\n",
-       "  (224, 93, 129, 214, 0)),\n",
-       " ('B0000535RD',\n",
-       "  'Neutrogena Body Clear Body Wash for Clean, Clear Skin, 8.5 Ounce',\n",
-       "  (58, 39, 26, 228, 0)),\n",
-       " ('B0000535U2', 'Jason C-Effects Creme, 2 Ounces', (120, 64, 177, 68, 0)),\n",
-       " ('B0000535UA', 'Jason Shampoo, Tea Tree, 17.5 Ounce', (120, 88, 40, 68, 0)),\n",
-       " ('B0000535UM',\n",
-       "  'Jason Thin-To-Thick Extra Volume Shampoo, 8 Ounce',\n",
-       "  (120, 64, 64, 68, 0)),\n",
-       " ('B0000535UN',\n",
-       "  'Thin to Thick Extra Volume Conditioner 8 oz',\n",
-       "  (69, 49, 53, 61, 0)),\n",
-       " ('B0000535UT',\n",
-       "  'Jason Purifying Tea Tree Body Wash 30 fl oz',\n",
-       "  (157, 0, 12, 28, 0)),\n",
-       " ('B0000535UX',\n",
-       "  'Jason Pure Natural Hand Soap, Purifying Tea Tree, 16 Ounce',\n",
-       "  (120, 64, 177, 68, 1)),\n",
-       " ('B0000535VH',\n",
-       "  'Alpha Hydrox Optimum Series, Retinol Night ResQ, Anti-Wrinkle Firming Complex - 1.05 oz',\n",
-       "  (157, 52, 12, 220, 0)),\n",
-       " ('B000053676',\n",
-       "  'Avalon Organics Vitamin C Revitalizing Eye Creme, 1 Ounce Bottle',\n",
-       "  (231, 0, 176, 175, 0)),\n",
-       " ('B0000536A3',\n",
-       "  'Tommy Hilfiger Tommy Cologne Spray for Men - 3.4 Fluid Ounces',\n",
-       "  (181, 62, 203, 177, 0)),\n",
-       " ('B0000536EK',\n",
-       "  \"Mother's Special Blend All Natural Skin Toning Oil, 8-Ounce Bottle\",\n",
-       "  (58, 63, 157, 115, 0)),\n",
-       " ('B0000536EW',\n",
-       "  'Aveeno Anti-Itch Concentrated Lotion, 4 Ounce',\n",
-       "  (220, 72, 129, 88, 0)),\n",
-       " ('B0000536F0',\n",
-       "  'Aveeno Moisturizing Bar with Natural Colloidal Oatmeal for Dry Skin, Fragrance Free, 3.5 oz',\n",
-       "  (2, 231, 136, 129, 0)),\n",
-       " ('B0000536M2',\n",
-       "  \"Maybelline New York Full 'N Soft Mascara, Very Black, 0.28 Oz.\",\n",
-       "  (151, 0, 22, 136, 0)),\n",
-       " ('B0000536M3',\n",
-       "  \"Maybelline Full' N Soft Mascara: Brownish Black #302\",\n",
-       "  (204, 14, 183, 248, 0)),\n",
-       " ('B0000536P3',\n",
-       "  'Olay Total Effects 7-In-1 Anti-Aging Daily Moisturizer 1.7 Fl. Oz.',\n",
-       "  (242, 199, 48, 22, 0)),\n",
-       " ('B0000536P4',\n",
-       "  'Olay Total Effects Anti-Aging Fragrance Free Moisturizer 1.7 Fl Oz',\n",
-       "  (242, 199, 170, 22, 0)),\n",
-       " ('B00005375C',\n",
-       "  'Aveeno Daily Moisturizing Lotion, 8 Ounce',\n",
-       "  (203, 84, 188, 175, 0)),\n",
-       " ('B0000537NH',\n",
-       "  'Neutrogena Sensitive Skin Sunscreen Lotion, SPF 30, 4 Ounce',\n",
-       "  (94, 230, 25, 88, 0)),\n",
-       " ('B0000537SD', 'ZapZyt Acne Treatment Gel, 1 oz', (189, 49, 129, 61, 0)),\n",
-       " ('B000053L6W',\n",
-       "  'Clubman Pinaud Virgin Island Bay Rum, 12 Ounce',\n",
-       "  (143, 49, 48, 220, 0)),\n",
-       " ('B000056I89',\n",
-       "  'Cetaphil Daily Facial Cleanser, Normal to Oily Skin - 8 fl oz',\n",
-       "  (42, 151, 1, 85, 0)),\n",
-       " ('B000056W5Y',\n",
-       "  \"Palmer's Cocoa Butter Formula Massage Cream for Stretch Marks, 4.4 Ounce\",\n",
-       "  (33, 39, 215, 166, 0)),\n",
-       " ('B000056W74',\n",
-       "  \"Palmer's Cocoa Butter Formula Moisturizing Body Oil with Vitamin E -- 8.5 fl oz\",\n",
-       "  (80, 230, 53, 214, 0)),\n",
-       " ('B00005A43X',\n",
-       "  'Conair Supreme 2-In-1 Hot Air Styling Brush',\n",
-       "  (57, 18, 205, 177, 0)),\n",
-       " ('B00005A443',\n",
-       "  'Conair Double-Sided Battery-Operated Lighted Makeup Mirror, Polished Chrome Finish',\n",
-       "  (57, 49, 150, 54, 0)),\n",
-       " ('B00005A9WP',\n",
-       "  'Coanir Facial Sauna System with Timer',\n",
-       "  (120, 90, 207, 41, 0)),\n",
-       " ('B00005AVC7',\n",
-       "  'Clean &amp; Clear Instant Oil-Absorbing Sheets 50 sheets',\n",
-       "  (116, 7, 22, 214, 0)),\n",
-       " ('B00005B9FV',\n",
-       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Lotion SPF, 4 Ounce',\n",
-       "  (118, 160, 136, 32, 0)),\n",
-       " ('B00005CDSP',\n",
-       "  'Revlon Perfect Heat 1-1/2inch Curling Iron',\n",
-       "  (87, 93, 205, 222, 0)),\n",
-       " ('B00005IBW0',\n",
-       "  'St. Ives, Sensitive Skin Apricot Scrub, 6-Ounce (Pack of 6)',\n",
-       "  (75, 14, 13, 166, 0)),\n",
-       " ('B00005LBRT',\n",
-       "  'Avalon Organics Lavender Luminosity Daily Moisturizer, 2 Ounce',\n",
-       "  (194, 83, 90, 220, 0)),\n",
-       " ('B00005NAOD',\n",
-       "  'Eucerin Q10 Anti-Wrinkle Sensitive Skin Creme, 1.7 Ounce Jar',\n",
-       "  (118, 230, 170, 68, 0)),\n",
-       " ('B00005NAOJ',\n",
-       "  'Cetaphil Gentle Cleansing Bar - 4.5 oz',\n",
-       "  (157, 61, 248, 28, 0)),\n",
-       " ('B00005NASR',\n",
-       "  'Ecco Bella Original Organic Water-Free Herbal Body Lotion, Vanilla, 8-Ounce Bottle',\n",
-       "  (2, 225, 187, 24, 0)),\n",
-       " ('B00005NFBD',\n",
-       "  'Freeman Facial Masque, Purifying - 6 fl oz',\n",
-       "  (157, 52, 157, 70, 0)),\n",
-       " ('B00005NFBJ',\n",
-       "  'Freeman Cucumber Facial Peel-Off Mask - 6 oz',\n",
-       "  (1, 84, 192, 105, 0)),\n",
-       " ('B00005O0MZ',\n",
-       "  'Conair 1875 Watt Ionic Conditioning Hair Dryer',\n",
-       "  (180, 102, 129, 249, 0)),\n",
-       " ('B00005R1H2',\n",
-       "  'Skin Success Eventone Fade Milk with Vitamin E and Alpha Hydroxy - 8.5 Fluid Ounces',\n",
-       "  (145, 41, 128, 228, 0)),\n",
-       " ('B00005R1H3',\n",
-       "  'Skin Success Eventone Fade Cream, For Oily Skin - 2.7 oz',\n",
-       "  (157, 52, 129, 28, 0)),\n",
-       " ('B00005REAQ',\n",
-       "  'Alba BotanicaTM Even Advanced Natural Moisturizer Sea Moss SPF 15 -- 2 fl oz',\n",
-       "  (41, 52, 187, 42, 0)),\n",
-       " ('B00005REAT',\n",
-       "  'Body Bath Honey Mango Alba Botanica 12 oz Liquid',\n",
-       "  (29, 7, 156, 22, 0)),\n",
-       " ('B00005S81T',\n",
-       "  'Skin Success Eventone Fade Cream, Regular - 2.7 oz',\n",
-       "  (157, 52, 129, 28, 1)),\n",
-       " ('B00005T83D',\n",
-       "  \"Palmer's Cocoa Butter Formula, Cream Soap Bar with Vitamin E, 3.5 oz - 1 ea\",\n",
-       "  (231, 41, 187, 115, 0)),\n",
-       " ('B00005TZU8',\n",
-       "  'DreamTime Inner Peace Eye Pillow, Lavender Velvet',\n",
-       "  (203, 215, 183, 115, 0)),\n",
-       " ('B00005UN90',\n",
-       "  'Suave Naturals Body Wash, Ocean Breeze - 12oz.',\n",
-       "  (238, 49, 188, 141, 0)),\n",
-       " ('B00005UQSP',\n",
-       "  'EO Shampoo for Fine/Oily Hair, Rosemary &amp; Mint, 8 fl oz (240 ml)',\n",
-       "  (175, 39, 205, 199, 0)),\n",
-       " ('B00005V5OU',\n",
-       "  'Neutrogena Healthy Defense Daily Moisturizer, SPF 30, Light Tint 1.7 Ounce',\n",
-       "  (10, 160, 130, 61, 0)),\n",
-       " ('B00005V5OW',\n",
-       "  'Maybelline Lash Discovery Washable Mascara, Very Black - .16 fl oz',\n",
-       "  (15, 90, 187, 158, 0)),\n",
-       " ('B00005V5P6',\n",
-       "  'Maybelline New York Purestay Powder &amp; Foundation SPF 15, Ivory - .34 oz',\n",
-       "  (27, 22, 248, 141, 0)),\n",
-       " ('B0000632EE',\n",
-       "  'Fallene Total Block UVA/UVB Complete Broad Spectrum Sun Protection, SPF 65 Clear, 2 fl Ounces (59 ml)',\n",
-       "  (115, 61, 10, 28, 0)),\n",
-       " ('B0000632EN',\n",
-       "  'Aveeno Positively Radiant Skin Brightening Daily Scrub, 5 Ounce',\n",
-       "  (2, 151, 170, 22, 0)),\n",
-       " ('B00006598X',\n",
-       "  'Aquis Microfiber Hair Towel, Lisse Crepe, Pink (19 x 39-Inches)',\n",
-       "  (85, 215, 183, 88, 0)),\n",
-       " ('B000065DJY',\n",
-       "  'Revlon Perfect Heat 1875W Shine Boosting Hair Dryer, RV484',\n",
-       "  (180, 49, 48, 237, 0)),\n",
-       " ('B000065DK4',\n",
-       "  'Vidal Sassoon VS184C Three-Barrel Waver',\n",
-       "  (57, 199, 26, 175, 0)),\n",
-       " ('B000066CKS',\n",
-       "  'Badger - Healing Balm For Hardworking Hands',\n",
-       "  (189, 52, 203, 237, 0)),\n",
-       " ('B0000682T7', 'Badger Balm, Sleep Balm - 2 oz', (203, 0, 183, 115, 0)),\n",
-       " ('B0000683O5',\n",
-       "  'CoverGirl Lipslicks Lip Gloss, Clear - .14 oz',\n",
-       "  (1, 52, 1, 251, 0)),\n",
-       " ('B000068U49',\n",
-       "  'Kingsley Shave Soap Bowl with Lid Dark Wood',\n",
-       "  (5, 93, 205, 61, 0)),\n",
-       " ('B00006FDU6',\n",
-       "  'Conair Straight Waves 3-in-1 Specialty Styler',\n",
-       "  (226, 222, 188, 28, 0)),\n",
-       " ('B00006FE30',\n",
-       "  'Neutrogena Clear Pore Cleanser/Mask, 4.2 Fluid Ounce (125 ml)',\n",
-       "  (100, 84, 136, 236, 0)),\n",
-       " ('B00006FRW7',\n",
-       "  'Aveeno Clear Complexion Daily Moisturizer, 4 Ounce',\n",
-       "  (203, 3, 188, 88, 0)),\n",
-       " ('B00006IGL3',\n",
-       "  'Kiss My Face Organics Break Out, Botanical Acne Gel, 1 fl oz',\n",
-       "  (242, 151, 128, 171, 0)),\n",
-       " ('B00006IGL8',\n",
-       "  'Kiss My Face Deep Cleansing Mask Pore Shrink , 2 Fluid Ounce',\n",
-       "  (29, 145, 183, 251, 0)),\n",
-       " ('B00006IV22',\n",
-       "  'Conair 1875 Watt Dual Voltage Folding Handle Hair Dryer',\n",
-       "  (87, 35, 53, 115, 0)),\n",
-       " ('B00006IV2E',\n",
-       "  'Conair BC40JBC Hot Brush, White, 0.75 Inch',\n",
-       "  (141, 22, 207, 196, 0)),\n",
-       " ('B00006IV2F', 'Instant Heat Hot Brush, 3/4-Inch', (110, 72, 129, 249, 0)),\n",
-       " ('B00006IV30', 'Conair Instant Heat Travel Hairsetter', (43, 39, 184, 18, 0)),\n",
-       " ('B00006JN4F',\n",
-       "  \"L'Oreal Paris Superior Preference Hair Color, LB-01 Extra Light Ash Blonde\",\n",
-       "  (115, 248, 177, 214, 0)),\n",
-       " ('B00006JT6O',\n",
-       "  'Olay Total Effects Anti-Aging Night Firming Treatment - 1.7 Fl Oz',\n",
-       "  (242, 199, 205, 22, 0)),\n",
-       " ('B00006K14U',\n",
-       "  'Nivea for Men Energy Face Scrub, 4.4 Ounce Tube',\n",
-       "  (75, 119, 53, 199, 0)),\n",
-       " ('B00006L9KV',\n",
-       "  'Thicker Fuller Hair Revitalizing Shampoo-12 oz',\n",
-       "  (203, 84, 192, 136, 0)),\n",
-       " ('B00006L9KW',\n",
-       "  'Thicker Fuller Hair Weightless Conditioner-12 oz',\n",
-       "  (157, 0, 157, 28, 0)),\n",
-       " ('B00007FCQ1',\n",
-       "  'Blue Lizard Australian Sunscreen, Regular, SPF 30+, 8.75-Ounce Bottle',\n",
-       "  (148, 39, 26, 199, 0)),\n",
-       " ('B00007KQF3',\n",
-       "  'Barielle Nail Strengthener Cream, 1 Ounce',\n",
-       "  (204, 41, 188, 40, 0)),\n",
-       " ('B00007M0CP',\n",
-       "  'Conair Xtreme Instant Heat Multisized Hot Rollers, Pink',\n",
-       "  (57, 49, 129, 237, 0)),\n",
-       " ('B000087L6T',\n",
-       "  'Rose Milk Skin Care Lotion - 8 fl oz',\n",
-       "  (235, 84, 205, 171, 0)),\n",
-       " ('B000089SA4', 'Soap Glycerine Vitamin-E 4 Ounces', (26, 88, 49, 214, 0)),\n",
-       " ('B00008BNZ4',\n",
-       "  'Wahl 79900B Clip-N-Trim 23-Piece Complete Haircut Kit',\n",
-       "  (87, 35, 197, 215, 0)),\n",
-       " ('B00008CMOQ',\n",
-       "  'Queen Helene Mint Julep Masque, 8 Ounce',\n",
-       "  (174, 13, 157, 228, 0)),\n",
-       " ('B00008CMOR',\n",
-       "  'Queen Helene Original Formula Antioxidant Grape Seed Extract Peel Off Masque -- 6 oz',\n",
-       "  (241, 199, 12, 207, 0)),\n",
-       " ('B00008CMOS', 'Queen Helene Masque, Mud Pack - 8 oz', (157, 52, 12, 214, 0)),\n",
-       " ('B00008DPP8',\n",
-       "  'Softsoap Naturals Moisturizing Liquid Hand Soap with Milk &amp; Honey, Pump - 7.5 fl oz',\n",
-       "  (32, 14, 48, 91, 0)),\n",
-       " ('B00008J2XQ',\n",
-       "  'Aveeno Active Naturals Skin Relief Hand Cream, 3.5 Ounce',\n",
-       "  (74, 215, 48, 236, 0)),\n",
-       " ('B00008KA7P',\n",
-       "  'No Rinse Shampoo - 16 floz (473.1 mL)',\n",
-       "  (100, 175, 215, 22, 0)),\n",
-       " ('B00008KA8D',\n",
-       "  'Revlon ColorStay Overtime Liquid Lipcolor, Keep Blushing',\n",
-       "  (15, 119, 183, 210, 0)),\n",
-       " ('B00008MNZH',\n",
-       "  'Eucerin Daily Protection Moisturizing Face Lotion, Broad Spectrum SPF 30',\n",
-       "  (235, 47, 197, 237, 0)),\n",
-       " ('B00008MOQE',\n",
-       "  'Maybelline Great Lash Mascara, Very Black -.43 Fluid Ounce',\n",
-       "  (61, 50, 129, 42, 0)),\n",
-       " ('B00008MOQJ',\n",
-       "  'Maybelline Great Lash Curved - Very Black',\n",
-       "  (61, 72, 205, 70, 0)),\n",
-       " ('B00008MOQN',\n",
-       "  'Maybelline Great Lash Waterproof Mascara, Very Black- .43 Fluid Ounce',\n",
-       "  (61, 50, 129, 42, 1)),\n",
-       " ('B00008O2X5',\n",
-       "  'Almay Anti-Perspirant &amp; Deodorant, Clear Gel, Fragrance Free - 2.25 oz',\n",
-       "  (69, 49, 136, 205, 0)),\n",
-       " ('B00008PC1O',\n",
-       "  'Sun-In Spray-In Hair Lightener, Original - 4.7 fl oz',\n",
-       "  (23, 93, 157, 61, 0)),\n",
-       " ('B00008ZPG9',\n",
-       "  'Olay Regenerist Deep Hydration Regenerating Cream Facial Moisturirzer 1.7 Fl Oz',\n",
-       "  (235, 1, 156, 237, 0)),\n",
-       " ('B00008ZPGA',\n",
-       "  'Olay Regenerist Regenerating Serum Moisturizer 1.7 Fl Oz',\n",
-       "  (109, 57, 230, 105, 0)),\n",
-       " ('B000092OX4',\n",
-       "  'Olay Regenerist Regenerating Lotion With Sunscreen Broad Spectrum SPF 15 2.5 Fl Oz',\n",
-       "  (241, 39, 26, 18, 0)),\n",
-       " ('B000093A0U',\n",
-       "  'Hugo By Hugo Boss For Women. Eau De Toilette Spray 1.3 Ounces',\n",
-       "  (26, 83, 150, 218, 0)),\n",
-       " ('B000093I60', 'No-Rinse Body Bath, 16 oz', (6, 225, 130, 237, 0)),\n",
-       " ('B000094Q6C',\n",
-       "  'Tournmaline Ceramic Curling Iron, 3/4-Inch',\n",
-       "  (87, 47, 129, 237, 0)),\n",
-       " ('B000094ZDX',\n",
-       "  'Conair 1600 Watt Folding Handle Hair Dryer',\n",
-       "  (110, 0, 156, 228, 0)),\n",
-       " ('B0000950LF',\n",
-       "  'Promensil Menopausal Relief Supplement Tablets, 60-Count Box',\n",
-       "  (120, 199, 157, 68, 0)),\n",
-       " ('B000095SDS', 'Scalpicin Max Dermatology', (3, 123, 128, 220, 0)),\n",
-       " ('B00009WO0W', \"Weleda Children's Tooth Gel, 1.7 oz\", (203, 0, 25, 220, 0)),\n",
-       " ('B0000AFUTL',\n",
-       "  'Jerdon JP7506CF 8-Inch Two-Sided Swivel Wall Mount Mirror with 5x Magnification, 13.5-Inch Extension, Chrome Finish',\n",
-       "  (57, 49, 13, 54, 0)),\n",
-       " ('B0000AJ3PT',\n",
-       "  'Aveeno Active Naturals Creamy Moisturizing Oil, 12 Ounce',\n",
-       "  (203, 84, 156, 220, 0)),\n",
-       " ('B0000AJ3PU',\n",
-       "  'Aveeno Active Naturals Stress Relief Moisturizing Lotion, 12 Ounce',\n",
-       "  (9, 93, 188, 214, 0)),\n",
-       " ('B0000AJ3Q3',\n",
-       "  \"L'Oreal Paris Double Extend Mascara, Blackest Black, 0.33 Ounces\",\n",
-       "  (118, 95, 49, 215, 0)),\n",
-       " ('B0000AN9L9',\n",
-       "  'Neutrogena Transparent Facial Bar Bonus Pack, Original Formula - 6 ea, 3.5 oz each, total 21 oz',\n",
-       "  (224, 49, 248, 42, 0)),\n",
-       " ('B0000AS54S',\n",
-       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 4 Ounce Tube',\n",
-       "  (189, 61, 136, 115, 0)),\n",
-       " ('B0000BV14G',\n",
-       "  'Cool Water By Davidoff Eau-de-toilette Spray, 1.7-Ounce',\n",
-       "  (143, 50, 157, 68, 0)),\n",
-       " ('B0000C0XL8',\n",
-       "  'Desert Essence Organic Jojoba Oil -- 4 fl oz',\n",
-       "  (145, 187, 176, 248, 0)),\n",
-       " ('B0000C4COV',\n",
-       "  'derma e Soothing Skin Treatment, Tea Tree &amp; E Antiseptic Cr&egrave;me, 4 oz (113 g)',\n",
-       "  (10, 222, 207, 61, 0)),\n",
-       " ('B0000CC64W',\n",
-       "  'Olay Regenerist Daily Regenerating Serum - Fragrance Free Moisturizer 1.7 Fl Oz',\n",
-       "  (235, 194, 188, 105, 0)),\n",
-       " ('B0000CDVN6', 'Conair Spiral Styler, 0.75 Inch', (26, 88, 59, 68, 0)),\n",
-       " ('B0000CNR0E',\n",
-       "  'Household Essentials Travel Tie Case, Black',\n",
-       "  (43, 49, 203, 251, 0)),\n",
-       " ('B0000CNR0J',\n",
-       "  'Household Essentials Grooming Travel Bag Organizer, Black',\n",
-       "  (20, 0, 207, 28, 0)),\n",
-       " ('B0000UJJH0',\n",
-       "  'Desert Essence Tea Tree Oil 100% Pure, 2-Ounce',\n",
-       "  (187, 49, 188, 22, 0)),\n",
-       " ('B0000UTUS8',\n",
-       "  'Aquis Microfiber Hair Turban, Lisse Crepe, Patented Design, White',\n",
-       "  (204, 215, 183, 88, 0)),\n",
-       " ('B0000UTUVU',\n",
-       "  \"Mrs. Meyer's Clean Day Dish Soap, Lavender, 16 Ounce Bottle\",\n",
-       "  (15, 7, 187, 199, 0)),\n",
-       " ('B0000UTUW4',\n",
-       "  \"Mrs. Meyer's Clean Day Liquid Hand Soap, Lavender, 12.5 Ounce Bottle\",\n",
-       "  (32, 90, 53, 249, 0)),\n",
-       " ('B0000VC1YC',\n",
-       "  'Neutrogena Body Oil, Light Sesame Formula, 32 Ounce',\n",
-       "  (162, 7, 197, 222, 0)),\n",
-       " ('B0000XMQTA',\n",
-       "  'Irene Gari Cover Your Grey for Women Touch Up Stick Medium Brown',\n",
-       "  (91, 84, 205, 105, 0)),\n",
-       " ('B0000Y3C3I', 'Afirm 3X', (145, 61, 48, 24, 0)),\n",
-       " ('B0000Y3COC', 'PanOxyl Bar 10% - 4 oz - 1 Bar', (189, 222, 18, 251, 0)),\n",
-       " ('B0000Y3CRY', 'Nizoral Anti-Dandruff Shampoo, 4 oz.', (210, 7, 203, 81, 0)),\n",
-       " ('B0000Y3CSI',\n",
-       "  'Neutrogena T/Gel Original Shampoo - 16 oz',\n",
-       "  (189, 50, 177, 136, 0)),\n",
-       " ('B0000Y3D4G',\n",
-       "  'Neutrogena Norwegian Formula Lip Moisturizer, SPF 15, 0.15 Ounce',\n",
-       "  (210, 119, 130, 222, 0)),\n",
-       " ('B0000Y3D4Q',\n",
-       "  'Rezamid Acne Treatment Lotion, 2 oz.',\n",
-       "  (157, 199, 45, 215, 0)),\n",
-       " ('B0000Y3DAK', 'DHS Clear Shampoo 8 oz', (218, 50, 188, 166, 0)),\n",
-       " ('B0000Y3FQC', 'Diamancel Tough Buffer #11', (120, 248, 177, 28, 0)),\n",
-       " ('B0000Y3FRG',\n",
-       "  'Obagi Nu-Derm Foaming Gel -- 6.7 fl oz',\n",
-       "  (162, 49, 188, 167, 0)),\n",
-       " ('B0000Y3GF2',\n",
-       "  'Free &amp; Clear Hairspray Firm Hold, 8 Ounce',\n",
-       "  (29, 0, 48, 215, 0)),\n",
-       " ('B0000Y3GIE',\n",
-       "  'Nailtiques Formula 2 Nail Growth Formula Treatments, 0.25 Ounce',\n",
-       "  (18, 39, 48, 175, 0)),\n",
-       " ('B0000Y3LAC',\n",
-       "  'Jobst It Stays Roll-on Body Adhesive 2 oz. (3 pack)',\n",
-       "  (133, 41, 22, 141, 0)),\n",
-       " ('B0000Y8H3S',\n",
-       "  'Rogaine for Men Hair Regrowth Treatment, Extra Strength Original Unscented, Set of 3, 2-Ounce Bottles',\n",
-       "  (120, 87, 157, 214, 0)),\n",
-       " ('B0000YUWY0',\n",
-       "  'jerome russell Hair Color Thickener for Thinning Hair, Black, 3.5 Ounce',\n",
-       "  (217, 0, 150, 215, 0)),\n",
-       " ('B0000YUX4O', 'Mebco Tortoise Shower Detangler', (184, 39, 184, 222, 0)),\n",
-       " ('B0000YUXGW', 'Mavala Scientifique (Nail Hardener)', (194, 110, 32, 249, 0)),\n",
-       " ('B0000YUXI0',\n",
-       "  'Mavala Stop - Helps Cure Nail Biting and Thumb Sucking, 0.3-Fluid Ounce',\n",
-       "  (53, 151, 10, 205, 0)),\n",
-       " ('B0000YV6BI', 'ApHogee Shampoo for Damaged Hair', (179, 35, 25, 248, 0)),\n",
-       " ('B0000YV6YK', \"One 'n Only Colorfix\", (183, 0, 129, 136, 0)),\n",
-       " ('B0000YV83O', 'Facial-Flex Facial Exercise System', (168, 52, 177, 215, 0)),\n",
-       " ('B0000ZHH5G',\n",
-       "  'Vanicream Moisturizing Skin Cream, 16 Ounces',\n",
-       "  (10, 13, 177, 248, 0)),\n",
-       " ('B0000ZHOEU',\n",
-       "  'Free &amp; Clear Liquid Cleanser, 8 Ounce',\n",
-       "  (1, 230, 84, 24, 0)),\n",
-       " ('B0000ZLEFU',\n",
-       "  'Auric Blends Perfume Oil, 0.33 oz - Coco Mango',\n",
-       "  (222, 230, 128, 24, 0)),\n",
-       " ('B0000ZLEX2',\n",
-       "  'Black Opium - Auric Blends Perfume Oils',\n",
-       "  (17, 225, 128, 199, 0)),\n",
-       " ('B00011JI88',\n",
-       "  'Ardell Fashion Lashes - 120 Black Demi',\n",
-       "  (43, 93, 130, 233, 0)),\n",
-       " ('B00011JOQY', 'Cricket Static Free Fast Flo', (141, 22, 10, 222, 0)),\n",
-       " ('B00011JOR8',\n",
-       "  'Cricket Hair Brush Static Free, Mini Fast Flo, 1.28 Ounce',\n",
-       "  (46, 39, 26, 222, 0)),\n",
-       " ('B00011JTFA',\n",
-       "  'Star Nail Pen-Style Polish Corrector',\n",
-       "  (11, 145, 248, 254, 0)),\n",
-       " ('B00011QTZI',\n",
-       "  'Cricket Technique Barrel Hair Brush, XX-large Round, 2 Inch',\n",
-       "  (141, 35, 26, 222, 0)),\n",
-       " ('B00011QUDE',\n",
-       "  'Helen of Troy 1514 Brush Iron, White, 1 1/2 Inches Barrel',\n",
-       "  (110, 50, 53, 42, 0)),\n",
-       " ('B00011QUKW',\n",
-       "  'Hot Tools HT1108 Professional Marcel Iron, 1 Inch',\n",
-       "  (252, 222, 184, 251, 0)),\n",
-       " ('B00011QVHO',\n",
-       "  'Spornette Porcupine Rounder Brush, 2-Inch Diameter',\n",
-       "  (190, 93, 188, 241, 0)),\n",
-       " ('B00011QVIS',\n",
-       "  'Spornette Prego Brush, 2-Inch Diameter',\n",
-       "  (165, 222, 12, 220, 0)),\n",
-       " ('B00011QVUG',\n",
-       "  'For Pro Stretch Terry Headband with Velcro 3in.',\n",
-       "  (176, 119, 22, 42, 0)),\n",
-       " ('B000127UUA',\n",
-       "  'Camille Beckman Glycerin Hand Therapy, White Lilac, 8 Ounce',\n",
-       "  (120, 22, 205, 251, 0)),\n",
-       " ('B000127UVY',\n",
-       "  'Camille Beckman Glycerin Hand Therapy, Glycerine Rosewater, 6 Ounce',\n",
-       "  (120, 22, 205, 251, 1)),\n",
-       " ('B000127VFE',\n",
-       "  'Camille Beckman Silky Body Moisturizing Cream, Glycerine Rosewater, 16 Ounce',\n",
-       "  (6, 1, 191, 105, 0)),\n",
-       " ('B00012NEYG', \"Burt's Bees Head to Toe Starter Kit\", (165, 93, 12, 199, 0)),\n",
-       " ('B00012NI7E',\n",
-       "  \"Burt's Bees Avocado Butter Pre-Shampoo Hair Treatment with Nettles and Rosemary - 4.34 fl oz\",\n",
-       "  (107, 62, 156, 22, 0)),\n",
-       " ('B00012NJZA',\n",
-       "  \"Burt's Bees Orange Essence Facial Cleanser, 4.3 Ounces\",\n",
-       "  (189, 199, 1, 115, 0)),\n",
-       " ('B00012NKGS',\n",
-       "  \"Burt's Bees: Citrus Facial Scrub, 2 oz\",\n",
-       "  (144, 242, 226, 205, 0)),\n",
-       " ('B000136P7S',\n",
-       "  'Miracle of Aloe Miracle Hand Repair Cream 8 Oz',\n",
-       "  (157, 151, 157, 215, 0)),\n",
-       " ('B00013TQRE',\n",
-       "  'Rogaine for Women Hair Regrowth Treatment 3- 2 ounce bottles',\n",
-       "  (120, 7, 157, 105, 0)),\n",
-       " ('B00013YYS0',\n",
-       "  \"Burt's Bees - Cuticle Creme Lemon Butter - 0.6 oz.\",\n",
-       "  (191, 61, 188, 237, 0)),\n",
-       " ('B00013YZ7U', \"Dr. Bronner's Almond Soap 32oz\", (144, 242, 194, 68, 0)),\n",
-       " ('B00013Z15A',\n",
-       "  \"Burt's Bees Day Creme Carrot Nutritive -- 2 fl oz\",\n",
-       "  (203, 199, 187, 214, 0)),\n",
-       " ('B000141O6E',\n",
-       "  'Redken Extreme Anti-Snap Leave-in Treatment, 8.5 Ounce',\n",
-       "  (178, 215, 1, 105, 0)),\n",
-       " ('B000142AZ8',\n",
-       "  'Bain de Soleil Mega Tan Sunscreen Lotion With Self Tanner, SPF 4 - 4 fl oz',\n",
-       "  (94, 160, 12, 220, 0)),\n",
-       " ('B000142C1A',\n",
-       "  'Essie Nail Polish Marshmallow, .5 Ounce',\n",
-       "  (194, 88, 12, 88, 0)),\n",
-       " ('B000142FVW',\n",
-       "  'Opi Nail Lacquer, Not So Bora Pink, 0.5 Fluid Ounce',\n",
-       "  (194, 225, 177, 61, 0)),\n",
-       " ('B000142JDG',\n",
-       "  'Roux Fanci-Full Mousse, 18 Spun Sand, 6 Fluid Ounce',\n",
-       "  (187, 119, 150, 241, 0)),\n",
-       " ('B000142KZ8',\n",
-       "  'Pretty Nails Instant Polish Remover Regular',\n",
-       "  (70, 215, 48, 22, 0)),\n",
-       " ('B000142MGA',\n",
-       "  'Fran Wilson Eye Tees Precision Makeup Applicators 80 Cotton Swabs',\n",
-       "  (91, 160, 7, 68, 0)),\n",
-       " ('B000142NDC',\n",
-       "  'Bigen Permanent Powder Hair Color, Dark Chestnut, .21 Ounce',\n",
-       "  (85, 84, 48, 199, 0)),\n",
-       " ('B000142OZY',\n",
-       "  'Aphogee Conditioner Pro-Vitamin Leave-In 8 oz.',\n",
-       "  (23, 151, 183, 24, 0)),\n",
-       " ('B000142P12',\n",
-       "  'ApHogee Keratin and Green Tea Restructurizer 8 fl. oz',\n",
-       "  (132, 14, 176, 236, 0)),\n",
-       " ('B000142PUS',\n",
-       "  'Clairol Professional Beautiful Collection Semi-permanent Hair Color, Light Reddish Brown',\n",
-       "  (74, 84, 207, 54, 0)),\n",
-       " ('B000142Q6G', 'Colora Henna Veg-Hair Chestnut 2oz', (210, 119, 10, 222, 0)),\n",
-       " ('B000142TU4',\n",
-       "  'Sally Hansen Polish Remover 8 oz. Strengthening',\n",
-       "  (171, 22, 130, 215, 0)),\n",
-       " ('B000142U9E',\n",
-       "  'Fantasia High Potency IC Hair Polisher Styling Gel, with Sparkle Lites, 16 oz.',\n",
-       "  (15, 151, 184, 88, 0)),\n",
-       " ('B000142ZFS', 'Super Nail Polish Thinner 4oz', (135, 119, 151, 236, 0)),\n",
-       " ('B0001431AG',\n",
-       "  'Fran Wilson Cake Eyeliner, Black, 0.09 Ounce',\n",
-       "  (184, 22, 203, 88, 0)),\n",
-       " ('B0001432S2',\n",
-       "  'OPI Natural Nail Strengthener Treatment, 0.5-Fluid Ounce',\n",
-       "  (123, 52, 22, 40, 0)),\n",
-       " ('B0001433OU',\n",
-       "  'OPI Nail Lacquer, Pirates of The Caribbean Collection, Stranger Tides, 0.5 Fluid Ounce',\n",
-       "  (251, 39, 136, 141, 0)),\n",
-       " ('B0001433VI',\n",
-       "  'OPI Start-to-finish Base Coat, Top Coat and Nail Strengthener, 0.5-Fluid Ounce',\n",
-       "  (200, 119, 157, 237, 0)),\n",
-       " ('B000143AY8', 'Fran Wilson Yellow Mood Matcher', (143, 0, 32, 28, 0)),\n",
-       " ('B000143CFA', 'Andrea Eyelash Adhesive: Clear', (125, 35, 1, 68, 0)),\n",
-       " ('B000143K2A',\n",
-       "  \"Mane 'n Tail Deep Moisturizing Conditioner\",\n",
-       "  (178, 175, 156, 248, 0)),\n",
-       " ('B000143O7G',\n",
-       "  'Instant Peel Natural Dead Skin Remover',\n",
-       "  (204, 199, 150, 177, 0)),\n",
-       " ('B000146L62',\n",
-       "  'Cassia Clove 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
-       "  (60, 84, 129, 177, 0)),\n",
-       " ('B000146LKS',\n",
-       "  'Patchouli 100% Pure &amp; Natural Aromatherapy Herbal Soap- 4 oz (113g)',\n",
-       "  (6, 84, 129, 251, 0)),\n",
-       " ('B00014D138',\n",
-       "  'Jason Pure Natural Moisturizing Creme, Anti-Aging Tea Time, 4 Ounce',\n",
-       "  (120, 64, 177, 68, 2)),\n",
-       " ('B00014D1OW', 'Lemon Clarifying Shampoo 11 Ounces', (189, 1, 86, 22, 0)),\n",
-       " ('B00014D322',\n",
-       "  'MillCreek - Biotin Shampoo, 16 fl oz gel',\n",
-       "  (242, 35, 157, 236, 0)),\n",
-       " ('B00014D5O8', \"Burt's Bees Hand Salve, 3 oz Tin\", (165, 93, 177, 249, 0)),\n",
-       " ('B00014DGCO',\n",
-       "  'Plantlife Lavender 100% Pure Essential Oil - Bulgarian - 10 ml',\n",
-       "  (242, 50, 28, 85, 0)),\n",
-       " ('B00014DIKE',\n",
-       "  \"Burt's Bees Therapeutic Bath Crystals, 1 Pound\",\n",
-       "  (189, 199, 12, 115, 0)),\n",
-       " ('B00014DKKC',\n",
-       "  \"Burt's Bees Lemon &amp; Vitamin E Bath &amp; Body Oil, 4 Fluid Ounce\",\n",
-       "  (80, 225, 129, 61, 0)),\n",
-       " ('B00014DM5K',\n",
-       "  'Facial Toner &amp; Freshener-Aloe Vera Earth Science 8 oz Liquid',\n",
-       "  (157, 193, 215, 214, 0)),\n",
-       " ('B00014DMQE',\n",
-       "  'Reviva Labs 10% Glycolic Acid Cream -- 1.5 oz',\n",
-       "  (163, 199, 157, 222, 0)),\n",
-       " ('B00014DMRI',\n",
-       "  'Aubrey Organics - Natural Mist Herbal Hairspray (Regular Hold), 8 fl oz spray',\n",
-       "  (10, 199, 177, 215, 0)),\n",
-       " ('B00014DMUA',\n",
-       "  'Moisturizer-Daily Essential Jojoba/Aloe Desert Essence 4 oz Cream',\n",
-       "  (42, 199, 176, 203, 0)),\n",
-       " ('B00014DTY4',\n",
-       "  'Nutribiotic Skin Ointment, 0.5 Ounce',\n",
-       "  (163, 129, 150, 220, 0)),\n",
-       " ('B00014DU6G',\n",
-       "  'Earth Science Fragrance Free Clarifying Facial Wash 8 oz',\n",
-       "  (10, 52, 129, 105, 0)),\n",
-       " ('B00014DZ2K', 'AmeriLeather Leather Toiletry Bag', (176, 52, 128, 171, 0)),\n",
-       " ('B00014EH2C',\n",
-       "  'Apricot Intensive Night Cream, 1.65oz',\n",
-       "  (2, 151, 156, 141, 0)),\n",
-       " ('B00014EKJC',\n",
-       "  \"Nature's Plus Natural Beauty Cleansing Bar -- 3 oz\",\n",
-       "  (202, 222, 248, 249, 0)),\n",
-       " ('B00014FTTM', 'Reviva Labs DMAE Firming Fluid - 1 oz', (41, 50, 22, 61, 0)),\n",
-       " ('B00014GT78',\n",
-       "  'Dermablend Cover Creme - Chroma 1 - Rose Beige, 1 oz',\n",
-       "  (100, 222, 165, 32, 0)),\n",
-       " ('B00014GTWS',\n",
-       "  'Dermablend Leg and Body Cover Make-Up SPF 15, Medium, 3.4 Ounce',\n",
-       "  (29, 50, 10, 203, 0)),\n",
-       " ('B00014HF94',\n",
-       "  \"Vitamin E 14,000 IU Nature's Plus 0.5 oz Liquid\",\n",
-       "  (108, 199, 207, 214, 0)),\n",
-       " ('B00014JKG0',\n",
-       "  \"Nature's Gate Tea Tree Moisturizing Lotion for Irritated, Distressed Skin, 18-Ounce Pump Bottle\",\n",
-       "  (210, 215, 151, 88, 0)),\n",
-       " ('B00014JOHA',\n",
-       "  'Kiss My Face - Whenever Shampoo, 11 fl oz liquid',\n",
-       "  (10, 52, 150, 22, 0)),\n",
-       " ('B00014WW38',\n",
-       "  'Naturtint Permanent Hair Colorant Sandy Blonde',\n",
-       "  (203, 39, 157, 105, 0)),\n",
-       " ('B00015GWQU',\n",
-       "  'Fran Wilson Nail Tees Precision Makeup Applicators 120 Cotton Swabs',\n",
-       "  (91, 160, 248, 88, 0)),\n",
-       " ('B00015HBD8',\n",
-       "  'Phillips Light Touch 1 Cushion Hair Brush',\n",
-       "  (34, 119, 129, 18, 0)),\n",
-       " ('B00015HBEC',\n",
-       "  'Spornette Large Oval Nylon Bristle Cushion Brush',\n",
-       "  (54, 90, 12, 88, 0)),\n",
-       " ('B00015HBEM',\n",
-       "  'Spornette Double Stranded XL Nylon Rounder Brush, 2-Inch Diameter',\n",
-       "  (47, 41, 215, 215, 0)),\n",
-       " ('B00015XAQ0',\n",
-       "  'Avalon Organics Vitamin C Renewal Refreshing Cleansing Gel, 8.5 Ounce Bottle',\n",
-       "  (6, 87, 163, 70, 0)),\n",
-       " ('B00016QYOO',\n",
-       "  'Desert Essence Thoroughly Clean Face Wash - Original -- 8.5 fl oz',\n",
-       "  (203, 93, 150, 228, 0)),\n",
-       " ('B00016RIIA', 'Yerba Prima Tampico Skin Brush 1 Ea', (48, 230, 187, 199, 0)),\n",
-       " ('B00016WSIK',\n",
-       "  'Earth Therapeutics Body Sponge, Anti-Bacterial, 1 Count',\n",
-       "  (6, 93, 137, 171, 0)),\n",
-       " ('B00016WUEW',\n",
-       "  'Heritage Products Rosewater &amp; Glycerin, 4 Ounce',\n",
-       "  (15, 199, 150, 251, 0)),\n",
-       " ('B00016WULK',\n",
-       "  'Avalon Organics Vitamin C Renewal Moisture Plus Lotion SPF 15 -- 4 fl oz',\n",
-       "  (194, 83, 90, 220, 1)),\n",
-       " ('B00016WV6E', 'Henna Persia Light Brown 4 Ounces', (217, 230, 48, 28, 0)),\n",
-       " ('B00016WW8Q', \"Dickinson's Witch Hazel 16 oz\", (169, 145, 1, 136, 0)),\n",
-       " ('B00016WXEY',\n",
-       "  'LILY OF THE DESERT, Aloe Vera Gelly - 12 oz',\n",
-       "  (25, 187, 215, 215, 0)),\n",
-       " ('B00016X06O',\n",
-       "  'Herbatint Permanent Herbal Haircolor Gel, Golden Chestnut 4D, 4.56-Ounce',\n",
-       "  (18, 72, 136, 141, 0)),\n",
-       " ('B00016X3G6',\n",
-       "  'Herbal Salt Scrub Lavender 20.50 Ounces',\n",
-       "  (159, 72, 177, 215, 0)),\n",
-       " ('B00016X3L6',\n",
-       "  'GIOVANNI COSMETICS, Nutrafix Hair Reconstructor - 8.5 oz',\n",
-       "  (250, 52, 10, 141, 0)),\n",
-       " ('B00016X604',\n",
-       "  \"Smiles' Prid Homeopathic Drawing Salve, 18 GM, 1 each\",\n",
-       "  (70, 39, 183, 136, 0)),\n",
-       " ('B00016X68Q',\n",
-       "  'J.R. Liggett Bar Shampoo, Original Formula, 3.5 Ounce',\n",
-       "  (85, 230, 165, 199, 0)),\n",
-       " ('B00016XJ4M',\n",
-       "  'Thayers - Rose Petal Witch Hazel with Aloe Vera Alcohol-Free Toner - 12 oz.',\n",
-       "  (232, 0, 157, 215, 0)),\n",
-       " ('B00016XJ7O',\n",
-       "  'Thayers Witch Hazel w/Aloe Vera Lemon 12 oz',\n",
-       "  (48, 170, 192, 82, 0)),\n",
-       " ('B00016XJ8S',\n",
-       "  'Arbordoun Abundantly Herbal Calendula Cream 4 Oz',\n",
-       "  (241, 22, 156, 24, 0)),\n",
-       " ('B000172R76', 'Ace Teasing Tail Comb 8&quot; * Black', (110, 0, 188, 68, 0)),\n",
-       " ('B000177FXW',\n",
-       "  \"Bar Soap-Tea Tree Dr. Bronner's 5 oz Bar\",\n",
-       "  (84, 160, 10, 141, 0)),\n",
-       " ('B00017XWJ8',\n",
-       "  'Ralph Lauren Eau de Toilette Spray For Women, 1.7 Oz',\n",
-       "  (181, 84, 146, 248, 0)),\n",
-       " ('B000186X4I',\n",
-       "  'Shiseido Shiseido Cleansing Massage Brush',\n",
-       "  (191, 240, 230, 248, 0)),\n",
-       " ('B000195GAY',\n",
-       "  'Blue Grass By Elizabeth Arden Deodorant Cream, 1.5-Ounce',\n",
-       "  (76, 181, 150, 42, 0)),\n",
-       " ('B000196U04', 'Diamon Deb Nail File 8&quot;', (212, 7, 12, 22, 0)),\n",
-       " ('B0001AGMWE',\n",
-       "  'Miracell ProEar-for Itchy, Irritated Ears .5 OZ',\n",
-       "  (115, 240, 151, 203, 0)),\n",
-       " ('B0001AQCII',\n",
-       "  'China Glaze Nail Polish, Jetstream, 0.5 Fluid Ounce',\n",
-       "  (61, 222, 18, 54, 0)),\n",
-       " ('B0001B86HM',\n",
-       "  'Conair Pro Style 1875 Watt Hard Hat Hair Dryer',\n",
-       "  (57, 61, 191, 237, 0)),\n",
-       " ('B0001EKTZQ',\n",
-       "  'Dermalogica Multi-active Toner, 8.4 Fluid Ounce',\n",
-       "  (2, 225, 197, 227, 0)),\n",
-       " ('B0001EKWPI',\n",
-       "  'Vanicream Cleansing Bar, Fragrance Free, 3.9 Ounce Bars (Pack of 3)',\n",
-       "  (157, 193, 10, 233, 0)),\n",
-       " ('B0001EKYL0',\n",
-       "  'Exuviance Evening Restorative Complex, 1.75 Fluid Ounce',\n",
-       "  (242, 230, 183, 230, 0)),\n",
-       " ('B0001EL0CW',\n",
-       "  'Basis Cleans and Smoothes Sensitive Skin Bar',\n",
-       "  (221, 35, 150, 88, 0)),\n",
-       " ('B0001EL5R2', 'PCA Skin Acne Cream, 0.5 Ounce', (255, 41, 215, 22, 0)),\n",
-       " ('B0001EL658',\n",
-       "  'Bare Escentuals Maximum Coverage Concealer Brush',\n",
-       "  (146, 230, 197, 141, 0)),\n",
-       " ('B0001FF80G', 'Dopp Jumbo Kit w/ Zip Bottom', (176, 180, 145, 222, 0)),\n",
-       " ('B0001I8ZT4',\n",
-       "  'Mar-V-Cide Shampoo Brush &amp; Invigorator',\n",
-       "  (106, 187, 215, 166, 0)),\n",
-       " ('B0001LH6YG', 'My Spots Are Consealed Light Color', (18, 175, 22, 61, 0)),\n",
-       " ('B0001M3K4U',\n",
-       "  'Soft N Style Pro-Cap Professional Processing Caps, Extra Large, 30-count',\n",
-       "  (190, 90, 205, 237, 0)),\n",
-       " ('B0001M7D7A',\n",
-       "  'Marianna Single Prong Curl Clip 80 pcs',\n",
-       "  (125, 225, 187, 199, 0)),\n",
-       " ('B0001MC06Y',\n",
-       "  'Peter Thomas Roth Beta Hydroxy Acid 2% Acne Wash 8.5 fl oz',\n",
-       "  (224, 222, 129, 136, 0)),\n",
-       " ('B0001MROQA',\n",
-       "  'Hot Tools Big Bumper Spring Curling Iron, 1-1/2&quot;',\n",
-       "  (43, 13, 150, 215, 0)),\n",
-       " ('B0001TJX2Q',\n",
-       "  'Home Health Castor Oil, Cold Pressed and Cold Processed, 32-Ounce',\n",
-       "  (2, 14, 200, 40, 0)),\n",
-       " ('B0001TMDF0',\n",
-       "  'Heritage Store Rose Petals Rosewater 8 oz',\n",
-       "  (169, 222, 12, 233, 0)),\n",
-       " ('B0001TO3NA', \"Grandpa's Pine Tar Soap Medium Size\", (171, 0, 136, 230, 0)),\n",
-       " ('B0001TODNK',\n",
-       "  'Earth Therapeutics Hydro Exfoliating Towel, 1 each',\n",
-       "  (116, 39, 188, 228, 0)),\n",
-       " ('B0001TOH8G',\n",
-       "  'Exfoliating Hydro Gloves-Natural Earth Therapeutics 1 Set',\n",
-       "  (168, 62, 207, 141, 0)),\n",
-       " ('B0001TQ5MW', 'Margarite Zinc Cream -- 1 oz', (224, 151, 32, 230, 0)),\n",
-       " ('B0001TQ9WI',\n",
-       "  'Mountain Ocean Skin Trip Moisturizer, Coconut, 8-Ounce',\n",
-       "  (217, 230, 129, 251, 0)),\n",
-       " ('B0001TSJPI',\n",
-       "  'Bar Soap - Sandalwood, 6 Units / 2.6 oz',\n",
-       "  (251, 199, 137, 222, 0)),\n",
-       " ('B0001TSWWS',\n",
-       "  'Home Health Roll-On Deodorant Herbal Scent -- 3 fl oz',\n",
-       "  (84, 151, 207, 22, 0)),\n",
-       " ('B0001UWRCI',\n",
-       "  'Yu-Be Moisturizing Skin Cream 1.25 oz Tube',\n",
-       "  (203, 57, 177, 136, 0)),\n",
-       " ('B0001VKL7U', \"Burt's Bees Lip Shimmer, Rhubarb\", (189, 90, 26, 54, 0)),\n",
-       " ('B0001VOJWI',\n",
-       "  'Pheromone By Marilyn Miglin For Women. Eau De Parfum Spray 1.7 Oz / 50 Ml.',\n",
-       "  (191, 67, 53, 245, 0)),\n",
-       " ('B0001XKA38',\n",
-       "  'Roux Lash and Brow Tint, Brown, 40 Count',\n",
-       "  (15, 22, 10, 61, 0)),\n",
-       " ('B0001XLSQ6', 'Steam Facial By Kaz', (88, 0, 26, 187, 0)),\n",
-       " ('B0001XLSR0',\n",
-       "  'ROUX Tween Time Instant Haircolor Touch-Up Stick BLACK 1/3 oz/10g',\n",
-       "  (62, 22, 157, 136, 0)),\n",
-       " ('B0001XQNMK',\n",
-       "  'Roux Fanci Full Mousse #52 White Mink 6 oz',\n",
-       "  (183, 41, 203, 236, 0)),\n",
-       " ('B0001Y74VS',\n",
-       "  'Ouidad by Ouidad Ouidad Climate Control Heat and Humidity Gel for Unisex, 8.5 Ounce',\n",
-       "  (233, 7, 157, 241, 0)),\n",
-       " ('B0001Y74XG',\n",
-       "  'Ouidad Water Works Clarifying Shampoo, 8.5 Ounce',\n",
-       "  (27, 230, 184, 241, 0)),\n",
-       " ('B0001YOMD6',\n",
-       "  'Touch with Love Eau De Parfum Spray for Women by Fred Hayman, 1.7 Ounce',\n",
-       "  (115, 88, 40, 68, 0)),\n",
-       " ('B0001YXXUY',\n",
-       "  'Hot Tools HT1074 Anti-Static Ionic Hot Air Brush with Ion Technology, 1000 Watts, 1 1/2 Inches',\n",
-       "  (62, 215, 137, 70, 0)),\n",
-       " ('B0001ZA3Y2', 'U-Lactin Dry Skin Lotion - 16 oz.', (145, 1, 53, 199, 0)),\n",
-       " ('B0001ZA4CS',\n",
-       "  'NeoStrata Exuviance CoverBlend Multi Function Concealer SPF15 - Sand, .5 fl oz',\n",
-       "  (247, 50, 230, 251, 0)),\n",
-       " ('B0001ZYL3Q',\n",
-       "  'TheraNeem Cream - Original Organix South 2 oz Cream \\nVanilla',\n",
-       "  (250, 222, 129, 249, 0)),\n",
-       " ('B0001ZYLAO',\n",
-       "  'Emerita Pro-gest Natural Balancing Cream, 2 Ounce',\n",
-       "  (220, 145, 136, 199, 0)),\n",
-       " ('B0001ZZH0M',\n",
-       "  'Naturally Fresh, Deodorant Crystal Spray Mist, 4 Oz&nbsp;',\n",
-       "  (18, 199, 146, 166, 0)),\n",
-       " ('B00020DY2O',\n",
-       "  'TheraNeem Gentle Therape Shampoo Organix South 12 oz Liquid',\n",
-       "  (48, 72, 25, 215, 0)),\n",
-       " ('B00020E1P8', 'Shiseido Shiseido Facial Cotton', (171, 39, 10, 199, 0)),\n",
-       " ('B00021AK4I',\n",
-       "  'Calvin Klein Ck One EDT Spray 1.7 oz for Unisex',\n",
-       "  (106, 39, 207, 54, 0)),\n",
-       " ('B00021AM9G',\n",
-       "  'Hanae Mori by Hanae Mori for Women - 3.4 Ounce EDT Spray',\n",
-       "  (26, 83, 150, 218, 1)),\n",
-       " ('B00021AQCY',\n",
-       "  'Fekkai Shea Butter Moisturizing Shampoo Hair Products 8 Fl Oz',\n",
-       "  (21, 39, 176, 241, 0)),\n",
-       " ('B00021AYF8',\n",
-       "  'Mustela Foam Shampoo for Newborns - 5.07 oz.',\n",
-       "  (189, 230, 128, 136, 0)),\n",
-       " ('B00021B8L2',\n",
-       "  'Philosophy Hope in a Jar Daily Moisturizer, Dry Skin, 2 Ounce',\n",
-       "  (25, 22, 32, 248, 0)),\n",
-       " ('B00021B95W',\n",
-       "  'Philosophy Coconut Frosting Shampoo/Shower Gel/Bubble Bath, 16 Ounces',\n",
-       "  (46, 145, 136, 220, 0)),\n",
-       " ('B00021BEXY', 'Blinc Kiss Me Mascara, Dark Brown', (194, 230, 128, 42, 0)),\n",
-       " ('B00021C1LI',\n",
-       "  'Philosophy Purity Made Simple One-Step Facial Cleanser, 16 Ounces',\n",
-       "  (201, 39, 230, 177, 0)),\n",
-       " ('B00021C49W',\n",
-       "  'Philosophy Amazing Grace Shower Gel, 16 Ounces',\n",
-       "  (123, 145, 165, 166, 0)),\n",
-       " ('B00021C5Y6',\n",
-       "  'STILL JENNIFER LOPEZ by Jennifer Lopez EAU DE PARFUM SPRAY 3.4 OZ',\n",
-       "  (191, 199, 53, 68, 0)),\n",
-       " ('B00021C9BA', 'NARS Cream Blush, Lokoum', (39, 230, 205, 220, 0)),\n",
-       " ('B00021CNCK',\n",
-       "  'CASHMERE MIST by Donna Karan EDT SPRAY 3.4 OZ',\n",
-       "  (210, 61, 184, 215, 0)),\n",
-       " ('B00021CSBG', 'NARS Duo Eyeshadow, All About Eve', (47, 222, 136, 251, 0)),\n",
-       " ('B00021CT10', 'NARS Matte Eyeshadow, Thunderball', (151, 215, 26, 42, 0)),\n",
-       " ('B00021CT56', 'NARS Shimmer Eyeshadow, Nepal', (151, 215, 26, 61, 0)),\n",
-       " ('B00021D5FY',\n",
-       "  'Desire by Alfred Dunhill for Men - 1.7 Ounce EDT Spray',\n",
-       "  (169, 191, 12, 199, 0)),\n",
-       " ('B00021D6JE',\n",
-       "  'FCUK by French Connection Eau De Toilette Spray 1.7 oz for Men',\n",
-       "  (76, 50, 170, 54, 0)),\n",
-       " ('B00021DDR4', 'NARS The Multiple, Maui', (151, 119, 26, 171, 0)),\n",
-       " ('B00021DDYW', 'NARS Sheer Lipstick, Barbarella', (193, 160, 26, 199, 0)),\n",
-       " ('B00021DE14', 'NARS Semi-Matte Lipstick, Morocco', (193, 160, 26, 199, 1)),\n",
-       " ('B00021DG80', 'NARS Lip Gloss, Turkish Delight', (39, 41, 130, 42, 0)),\n",
-       " ('B00021DJ32', 'NARS Blush, Taj Mahal', (39, 39, 177, 22, 0)),\n",
-       " ('B00021DPHW', 'NARS Bronzing Powder, Casino', (197, 145, 248, 254, 0)),\n",
-       " ('B00021DTEG',\n",
-       "  'Bare Escentuals bareMinerals Glimmer Celestine',\n",
-       "  (1, 93, 177, 166, 0)),\n",
-       " ('B00021DUM2',\n",
-       "  \"bareMinerals Blush - I'm Amused Rouge\",\n",
-       "  (237, 160, 248, 61, 0)),\n",
-       " ('B00021DVCQ',\n",
-       "  'BareMinerals Original Foundation Broad Spectrum SPF 15 8 g/0.28 Oz (Fair C10 8g/0.28 oz)',\n",
-       "  (189, 22, 157, 136, 0)),\n",
-       " ('B00021DZCC',\n",
-       "  'Philosophy Falling in Love Spray Fragrance, 2 Ounce',\n",
-       "  (133, 61, 1, 249, 0)),\n",
-       " ('B00021EA9Y',\n",
-       "  'Mustela Hydra-Bebe Body Lotion w/ Pump 10.14 US fl. oz',\n",
-       "  (84, 199, 187, 22, 0)),\n",
-       " ('B00021KD2C',\n",
-       "  'derma e Anti-Wrinkle Vitamin A Retinyl Palmitate Cr&egrave;me , 4-Ounce Jar',\n",
-       "  (10, 49, 248, 230, 0)),\n",
-       " ('B00021PDN6',\n",
-       "  'Cacharel Anais Anais By Cacharel - Eau De Toilette Spray 3.4 Oz, 3.4 oz',\n",
-       "  (148, 0, 1, 141, 0)),\n",
-       " ('B00021UR3C',\n",
-       "  'Green Tea by Elizabeth Arden, 1.5 Ounce',\n",
-       "  (169, 199, 1, 42, 0)),\n",
-       " ('B00021W26M',\n",
-       "  'Prestige Liquid Eyeliner, Legend, 0.1 Ounce',\n",
-       "  (201, 215, 165, 18, 0)),\n",
-       " ('B00021WZVE',\n",
-       "  'Christian Super Long Lash Mascara Waterproof Seven Oils Black',\n",
-       "  (168, 160, 207, 177, 0)),\n",
-       " ('B000225ZL0', 'Fresh Farmacy Cleanser by LUSH', (220, 93, 188, 214, 0)),\n",
-       " ('B000225ZOC', 'Karma Handmade Soap by LUSH', (204, 35, 183, 199, 0)),\n",
-       " ('B0002260DW', 'Sex Bomb Bath Bomb by LUSH', (66, 197, 64, 5, 0)),\n",
-       " ('B0002260L4', 'Big Blue Bath Bomb by LUSH', (29, 1, 177, 251, 0)),\n",
-       " ('B0002260N2', 'Karma Komba Solid Shampoo by LUSH', (71, 1, 18, 237, 0)),\n",
-       " ('B00022F1DW',\n",
-       "  'Organic Excellence  Mint Shampoo, 16 Ounce',\n",
-       "  (189, 199, 150, 136, 0)),\n",
-       " ('B00022WA9K', 'Revlon Shine Enchancing Hot Air Kit', (43, 145, 53, 136, 0)),\n",
-       " ('B00022WA9U',\n",
-       "  'Revlon RV261 20-Roller Ionic Professional Hairsetter, Purple',\n",
-       "  (194, 52, 177, 215, 0)),\n",
-       " ('B00023DIBI',\n",
-       "  'Cucina Coriander and Olive Tree 33.8 oz Purifying Hand Wash Refill',\n",
-       "  (15, 39, 188, 228, 0)),\n",
-       " ('B00023KDAW',\n",
-       "  'amazing grace | perfumed firming body emulsion | philosophy 8 oz.',\n",
-       "  (255, 41, 187, 222, 0)),\n",
-       " ('B000248HLI',\n",
-       "  'StriVectin-SD Intensive Repair for Existing Stretch Marks, 6 oz.',\n",
-       "  (214, 7, 165, 115, 0)),\n",
-       " ('B000260KIO',\n",
-       "  'Ouidad Botanical Boost Moisture Infusing and Refreshing Spray, 8.5 Ounce',\n",
-       "  (17, 119, 151, 18, 0)),\n",
-       " ('B0002716FO',\n",
-       "  'Ardell Duralash Flare Short Black (56 Lashes)',\n",
-       "  (43, 225, 151, 187, 0)),\n",
-       " ('B000271KR8',\n",
-       "  'Clairol Natural Instincts  02 Sahara Light Blonde 1 Kit  (Pack of 3) (packaging may vary)',\n",
-       "  (100, 1, 248, 215, 0)),\n",
-       " ('B000273PEO',\n",
-       "  'Happy By Clinique For Women. Eau De Parfum Spray 1.7 Ounces',\n",
-       "  (26, 83, 150, 133, 0)),\n",
-       " ('B000273RI8', 'M.D. Forte Facial Cream III', (58, 57, 26, 18, 0)),\n",
-       " ('B000277N7Y',\n",
-       "  'Happy By Clinique For Men. Cologne Spray 1.7 Oz.',\n",
-       "  (147, 151, 26, 166, 0)),\n",
-       " ('B00027C9CS',\n",
-       "  'Suave For Men, Deep Clean Peppermint, Shampoo12.6 fl Ounce 373 ml)',\n",
-       "  (52, 90, 183, 136, 0)),\n",
-       " ('B00027CDY2',\n",
-       "  'Neutrogena Triple Moisture Silk Touch Leave-In Cream, 6 Fluid Ounce',\n",
-       "  (203, 222, 26, 18, 0)),\n",
-       " ('B00027CDYM',\n",
-       "  'Neutrogena Clean Replenishing Deep Recovery Hair Mask, 6 oz',\n",
-       "  (203, 22, 25, 18, 0)),\n",
-       " ('B00027CGWQ',\n",
-       "  'Udderly Smooth Udder Cream, Skin Moisturizer, 12 Ounce Jar',\n",
-       "  (189, 160, 156, 199, 0)),\n",
-       " ('B00027D0DU',\n",
-       "  'Neutrogena Cosmetics  Makeup Remover, 3.8 oz',\n",
-       "  (117, 84, 165, 228, 0)),\n",
-       " ('B00027D6SE',\n",
-       "  'Bye Bye Blemish Drying Lotion - 1 fl oz',\n",
-       "  (175, 14, 170, 54, 0)),\n",
-       " ('B00027D8IC', 'Duo Lash Adhesive, Clear, 0.25 Ounce', (87, 199, 136, 18, 0)),\n",
-       " ('B00027D98Q',\n",
-       "  'Ardell Brow &amp; Lash Growth Accelerator Treatment Gel 7ml/0.25oz',\n",
-       "  (18, 199, 32, 22, 0)),\n",
-       " ('B00027DDEQ',\n",
-       "  'Clean &amp; Clear Morning Glow Moisturizer, SPF 15, 4 Ounce',\n",
-       "  (80, 50, 165, 175, 0)),\n",
-       " ('B00027DDOQ',\n",
-       "  'Clean &amp; Clear Clear Advantage Acne Spot Treatment, 0.75 Ounce',\n",
-       "  (80, 14, 1, 233, 0)),\n",
-       " ('B00027DHYM',\n",
-       "  'Natures Cure Body Acne Treatment Spray 3.5 Oz',\n",
-       "  (203, 0, 157, 136, 0)),\n",
-       " ('B00027DMI8',\n",
-       "  'RoC Retinol Correxion Deep Wrinkle Night Cream, 1-Ounce',\n",
-       "  (236, 205, 183, 199, 0)),\n",
-       " ('B00027DMJW',\n",
-       "  'Roc Retinol Correxion Deep Wrinkle Daily Moisturizer, SPF 30, 1-ounce Tube',\n",
-       "  (202, 29, 183, 24, 0)),\n",
-       " ('B00027DMLK',\n",
-       "  'Frownies Corners Of Eyes And Mouth, 144 Patches',\n",
-       "  (53, 7, 32, 222, 0)),\n",
-       " ('B00027DMSI',\n",
-       "  'Frownies Forehead &amp; Between Eyes, 144 Patches',\n",
-       "  (53, 199, 215, 88, 0)),\n",
-       " ('B00027EG9C',\n",
-       "  'Neutrogena Fresh Foaming Cleanser, 6.7 Ounce',\n",
-       "  (203, 1, 128, 222, 0)),\n",
-       " ('B00027YZKM', 'Mirror* Hand* Large* Black', (67, 50, 32, 141, 0)),\n",
-       " ('B00028F0NM',\n",
-       "  'Alba Botanica Maximum Very Emollient Body Lotion, 12 Ounce Bottle',\n",
-       "  (120, 93, 192, 41, 0)),\n",
-       " ('B00028LN1K',\n",
-       "  \"GRANDPA'S BRANDS, Pine Tar Soap Bath Size - 4.25 oz\",\n",
-       "  (191, 0, 150, 22, 0)),\n",
-       " ('B00028LURW', 'Henna Red 4 Ounces', (120, 7, 191, 22, 0)),\n",
-       " ('B00028LV4Y',\n",
-       "  'Natural Missst Herbal Hairspray Mist-Super Hold Aubrey Organics 8 oz Spray',\n",
-       "  (48, 199, 49, 237, 0)),\n",
-       " ('B00028M3N2',\n",
-       "  'Now Foods Essential Oil, Lemon, 4 Fluid Ounce',\n",
-       "  (29, 0, 183, 68, 0)),\n",
-       " ('B00028MLG6',\n",
-       "  'NOW Foods Apricot Kernel Oil (Liquid), 16 oz',\n",
-       "  (203, 199, 26, 88, 0)),\n",
-       " ('B00028MLHA',\n",
-       "  'Cocoa Butter with jojoba oil - 100% Pure 6.5 fl.oz',\n",
-       "  (29, 50, 188, 248, 0)),\n",
-       " ('B00028O7ZO',\n",
-       "  \"Castile Liquid Soap-Lavender Dr. Bronner's 1 Gallon Liquid\",\n",
-       "  (37, 1, 188, 88, 0)),\n",
-       " ('B00028OC4K',\n",
-       "  'Throughly Clean Face Wash - Original - 8.5 oz. (250 ml) - Liquid',\n",
-       "  (157, 62, 45, 215, 0)),\n",
-       " ('B00028OPZ6',\n",
-       "  'GPB (Glycogen Protein Balance) Conditioner Jumbo Size - 16 oz - Liquid',\n",
-       "  (10, 0, 177, 215, 0)),\n",
-       " ('B00028OQZA',\n",
-       "  'Sparkling Glacier Therapy Complexion Mist Aubrey Organics 3.4 oz (100ml) Liquid',\n",
-       "  (118, 240, 25, 68, 0)),\n",
-       " ('B00028OSI0', 'Aloe Life Skin Gel and Herbs, 8 oz', (203, 49, 130, 40, 0)),\n",
-       " ('B00028OTAM',\n",
-       "  'Gabriel Cosmetics Inc. - Moisturizing Liquid Foundation Soft Beige 18 SPF - 1 oz.',\n",
-       "  (26, 110, 64, 251, 0)),\n",
-       " ('B00028OTNY',\n",
-       "  'Mandarin Magic Hair Gel Aubrey Organics 8 oz Liquid',\n",
-       "  (84, 151, 188, 237, 0)),\n",
-       " ('B00028OU5Q', 'Blue Green Algae Hair Mask 4 oz', (157, 145, 10, 237, 0)),\n",
-       " ('B00028PDWK', 'Apricot Gentle Facial Scrub, 4 Ounce', (2, 39, 177, 136, 0)),\n",
-       " ('B00028PFF0', 'WITCH HAZEL PADS EA 1/60 PADS', (18, 0, 12, 187, 0)),\n",
-       " ('B00028PXAC', 'Viobin Wheat Germ Oil 16 oz', (31, 93, 28, 237, 0)),\n",
-       " ('B0002A65LQ',\n",
-       "  'Philosophy Help Me Retinol Night Treatment, 1.05 Ounce',\n",
-       "  (148, 90, 205, 220, 0)),\n",
-       " ('B0002B0R14',\n",
-       "  'H2Ocean 1.5 FL OZ Purified Ocean Salt Water Piercing Aftercare',\n",
-       "  (26, 110, 64, 214, 0)),\n",
-       " ('B0002BB3YO',\n",
-       "  'NaturalCare Homeopathic Ultra Vein-Gard Leg Therapy Cream, 2.25-Ounce Package',\n",
-       "  (32, 199, 10, 251, 0)),\n",
-       " ('B0002BPIZO',\n",
-       "  'Philosophy Pure Grace Shampoo, Bath &amp; Shower gel, 16 Ounces',\n",
-       "  (123, 52, 177, 249, 0)),\n",
-       " ('B0002CTSRM', 'Amber Butter', (143, 0, 136, 251, 0)),\n",
-       " ('B0002DLEJG',\n",
-       "  'Nailtek Foundation No.2 Ridge-Filling Nail Strengthener Base Coat, 0.5 Fluid Ounce',\n",
-       "  (15, 160, 130, 222, 0)),\n",
-       " ('B0002DNZAC', 'MAC Eye Shadow Frost Satin Taupe', (34, 160, 136, 237, 0)),\n",
-       " ('B0002DO1RI',\n",
-       "  'MAC Studio Fix Powder Plus Foundation NC35 for Women, 0.52 Ounce',\n",
-       "  (121, 39, 151, 61, 0)),\n",
-       " ('B0002DO1RS',\n",
-       "  'MAC Studio Fix Powder Plus Foundation NC45',\n",
-       "  (121, 233, 28, 222, 0)),\n",
-       " ('B0002DPG1I', 'Fat Girl Slim by Bliss', (84, 47, 205, 88, 0)),\n",
-       " ('B0002DTV0K',\n",
-       "  'Emu Oil Soap Bar (110g) Brand: SoapWorks',\n",
-       "  (235, 49, 48, 61, 0)),\n",
-       " ('B0002DUSR0', 'PediFix Visco GEL Toe Spacers, Medium', (15, 7, 22, 214, 0)),\n",
-       " ('B0002DUSUW', 'PediFix Double-Toe Straightener #P57', (5, 49, 205, 237, 0)),\n",
-       " ('B0002E4WOO',\n",
-       "  \"Miracle of Aloe Miracle Hand Repair Cream 8 Oz Relieve Dry, Cracked, Flacking Hands Immediately! Therapeutic Formula Contains 60% Ultra Aloe - The Purest Most Potent Form of Whole Leaf Aloe Vera Gel. Fast Acting Relief, Say Good Bye to Dry, Cracked Hands Now! Keep Your Hands Healthy &amp; Warm this Winter! Reduces Flaking and Redness, Use on Hands, Elbows and Knees, Exclusive Fast Acting Formula Penetrates Deep Into Damaged Skin Layers to Moisturize Where It's Needed Most. Leaves Hands Feeling Silky Smooth and Comfortable. Olay, Ponds, Vaseline, Nivea, Eucerin, Aveeno.\",\n",
-       "  (157, 52, 230, 237, 0)),\n",
-       " ('B0002EBI82', 'Toppik Hair Fattener, 4-Ounce', (120, 84, 156, 105, 0)),\n",
-       " ('B0002EZXUG',\n",
-       "  'Vaseline Intensive Care Moisturizing Bath Beads, Gentle Breeze, 24 Ounces',\n",
-       "  (173, 93, 32, 205, 0)),\n",
-       " ('B0002F1H6E',\n",
-       "  'Jergens All-Purpose Face Cream 425g/15oz',\n",
-       "  (52, 225, 137, 222, 0)),\n",
-       " ('B0002FBOLW',\n",
-       "  'Atopalm Intensive Moisturizing Cream, 3.4 fl. Ounce',\n",
-       "  (203, 230, 205, 175, 0)),\n",
-       " ('B0002FCCH2',\n",
-       "  'Suave Naturals Shampoo, Tropical Coconut  - 22.5oz.',\n",
-       "  (21, 39, 165, 228, 0)),\n",
-       " ('B0002FCD5I',\n",
-       "  'Suave Naturals Conditioner, Tropical Coconut - 22.5oz.',\n",
-       "  (109, 39, 156, 24, 0)),\n",
-       " ('B0002FCDHQ',\n",
-       "  'Suave For Kids Double Dutch Apple Detangler Spray Conditioner 10.5Ounces',\n",
-       "  (88, 90, 215, 28, 0)),\n",
-       " ('B0002G214U', 'Conair Soft Bonnet Hair Dryer', (3, 84, 191, 88, 0)),\n",
-       " ('B0002G21GI',\n",
-       "  'Ion Shine&reg; by Conair Instant Heat Compact Styling Setter',\n",
-       "  (43, 39, 151, 136, 0)),\n",
-       " ('B0002JG2NI',\n",
-       "  'Home Health Castor Oil Cold Pressed &amp; Cold Processed 8 fl. oz.',\n",
-       "  (189, 90, 215, 203, 0)),\n",
-       " ('B0002JGIZA',\n",
-       "  'Avalon Organics Night Cream Lavender Luminosity -- 2 oz',\n",
-       "  (202, 29, 183, 91, 0)),\n",
-       " ('B0002JGU30',\n",
-       "  'Avalon Organics Hand &amp; Body Lotion, Peppermint, 12 Ounce',\n",
-       "  (194, 83, 150, 220, 0)),\n",
-       " ('B0002JHI1I',\n",
-       "  'Avalon Organics CoQ10 Facial Cleansing Milk -- 8.5 fl oz',\n",
-       "  (118, 145, 248, 254, 0)),\n",
-       " ('B0002JIHT0',\n",
-       "  'Home Health Antidandruff Shampoo, Everclean, Unscented, 8-Ounce',\n",
-       "  (143, 199, 192, 199, 0)),\n",
-       " ('B0002JISX0',\n",
-       "  'Earth Science A-D-E Creamy Fruit Oil Cleanser 8 oz.',\n",
-       "  (177, 230, 26, 91, 0)),\n",
-       " ('B0002JIUDS',\n",
-       "  'Nutricology Ox Bile, 500 mg,  100 Vegetarian Capsules',\n",
-       "  (189, 218, 10, 228, 0)),\n",
-       " ('B0002JKPA4', 'Caruso C97958 ION Steam Hairsetter', (87, 230, 188, 70, 0)),\n",
-       " ('B0002JKQ8K',\n",
-       "  'Caruso Professional Molecular Steam Rollers with Shields, Small (6-Pack)',\n",
-       "  (61, 52, 188, 88, 0)),\n",
-       " ('B0002JMZ7A',\n",
-       "  'Yves Saint Laurent Volume Effet Faux Cils Luxurious Mascara for a False Lash Effect 1 High Density Black',\n",
-       "  (17, 233, 203, 249, 0)),\n",
-       " ('B0002JNRCC',\n",
-       "  'Redken All Soft Conditioner Unisex Conditioner, 8.5 Ounce',\n",
-       "  (100, 215, 230, 24, 0)),\n",
-       " ('B0002KS5VE',\n",
-       "  'Body Fantasies Body Spray for Women, Cotton Candy Fantasy Fragrance, 8 Ounce',\n",
-       "  (210, 62, 184, 115, 0)),\n",
-       " ('B0002L2JLU',\n",
-       "  'Topix Pharm SPF 40 Citrix Antioxidant Sunscreen, 3.5 Fluid Ounce',\n",
-       "  (10, 145, 151, 24, 0)),\n",
-       " ('B0002M9OYY',\n",
-       "  'Fake Bake Passion Fruit Body Polish 6 oz.',\n",
-       "  (163, 61, 157, 214, 0)),\n",
-       " ('B0002NIAC0',\n",
-       "  'Obagi C-Exfoliating Day Lotion 2 fl oz.',\n",
-       "  (232, 62, 165, 199, 0)),\n",
-       " ('B0002OCNMC',\n",
-       "  'Goat Milk Soap - Original (3 Pack) Canus Vermont 3 Bar Soap',\n",
-       "  (169, 215, 84, 177, 0)),\n",
-       " ('B0002OMOIA',\n",
-       "  'Brahmi Oil - Ayurvedic Hair Growth massage oil',\n",
-       "  (203, 50, 192, 215, 0)),\n",
-       " ('B0002PCH2M',\n",
-       "  'Focus 21 Sea Plasma All Purpose Skin and Hair Moisturizing Spray 12 oz',\n",
-       "  (10, 14, 13, 136, 0)),\n",
-       " ('B0002Q8W9I',\n",
-       "  'NEW cnd Nail Design Stickey Base Coat 0.33Oz',\n",
-       "  (5, 93, 203, 249, 0)),\n",
-       " ('B0002QB1SC',\n",
-       "  'Creative Nail Design Creative Nail Solar Oil, 0.5 Ounce',\n",
-       "  (80, 49, 137, 199, 0)),\n",
-       " ('B0002RI2PG',\n",
-       "  'Dermablend Loose Setting Powder Original, 1 Ounce',\n",
-       "  (183, 41, 1, 141, 0)),\n",
-       " ('B0002RPTX4', 'Shampoo Scalp Massage Brush- 1 Brush', (24, 222, 203, 28, 0)),\n",
-       " ('B0002RYHTQ',\n",
-       "  'Spa Sister Womens Cotton Moisture Enhancing Overnight Gloves',\n",
-       "  (100, 119, 184, 175, 0)),\n",
-       " ('B0002SA9BU',\n",
-       "  'Best Hair Growth Vitamins NEW ADVANCED Hair Formula 37 Hair Vitamins for Faster Growing Healthy Hair 1 month Supply 60 Capsules',\n",
-       "  (120, 145, 248, 254, 0)),\n",
-       " ('B0002SGNOM',\n",
-       "  'California Tan Sunsets Tanning Eyewear 1-PR Random Picked Colors',\n",
-       "  (230, 39, 12, 199, 0)),\n",
-       " ('B0002SGSNI', 'China Glaze Strong Adhesion Base Coat', (77, 52, 248, 88, 0)),\n",
-       " ('B0002SI7DW',\n",
-       "  'NAIL TEK Regular Size Crystal File with Free Blue Case',\n",
-       "  (191, 205, 22, 22, 0)),\n",
-       " ('B0002SISY0',\n",
-       "  'Spornette My Favorite Mini Nylon Tipped Paddle Brush',\n",
-       "  (5, 199, 1, 139, 0)),\n",
-       " ('B0002SQ6YO', 'Sponge Company Cosmetic Silk Sponge 1', (46, 52, 25, 68, 0)),\n",
-       " ('B0002TSCZY',\n",
-       "  'Solia Tourmaline Ceramic Ion Flat Iron (1&quot;)',\n",
-       "  (207, 145, 1, 22, 0)),\n",
-       " ('B0002TSD08',\n",
-       "  'Solia Professional Ceramic Ion Flat Iron (1-3/4&quot;)Solia',\n",
-       "  (243, 199, 53, 199, 0)),\n",
-       " ('B0002UEDWE', 'Evian Facial Water Spray 1.7 Oz', (203, 1, 157, 251, 0)),\n",
-       " ('B0002VHBTU', 'Thermal Spa Hair Wrap - White', (146, 35, 151, 105, 0)),\n",
-       " ('B0002VJIH8',\n",
-       "  'Best Hair Vitamin to Grow Hair Faster with Fast Grow Ethnic Hair Growth Enhancer 90 capsules 30 Day Supply',\n",
-       "  (120, 47, 26, 248, 0)),\n",
-       " ('B0002VQ0WO', 'Hair Dryer Stand', (45, 225, 32, 251, 0)),\n",
-       " ('B0002WUHTU',\n",
-       "  'Nailtiques Nail Protein Formula 2 Plus',\n",
-       "  (18, 50, 170, 136, 0)),\n",
-       " ('B0002X4F0Q',\n",
-       "  'PCA Skin Blemish Control Bar (Phaze 32), 3.3 Ounce',\n",
-       "  (143, 199, 150, 177, 0)),\n",
-       " ('B0002X9038',\n",
-       "  'PCA Skin Pigment Gel (Phaze 13), 1 Fluid Ounce',\n",
-       "  (32, 35, 129, 68, 0)),\n",
-       " ('B0002XBTVO', 'PCA Skin Clearskin, 1.7 Ounce', (222, 35, 177, 214, 0)),\n",
-       " ('B0002XG9X2',\n",
-       "  'Panasonic EH2511A Pore Cleanser with Micro-fine Mist',\n",
-       "  (120, 93, 184, 105, 0)),\n",
-       " ('B0002XY3QC', 'duri Miracote .61oz', (230, 13, 192, 241, 0)),\n",
-       " ('B0002YE8F2',\n",
-       "  'Revlon RV2513 Color Swirl Detangling Comb, Colors may vary',\n",
-       "  (125, 50, 183, 220, 0)),\n",
-       " ('B0002YE8IO',\n",
-       "  'Revlon RV2641 Soft Touch Porcupine Cushion Brush, Colors May Vary',\n",
-       "  (125, 230, 150, 220, 0)),\n",
-       " ('B0002YE8V6',\n",
-       "  'Vidal Sassoon VS7963 Ionic Thermal All Purpose Brush',\n",
-       "  (17, 205, 26, 105, 0)),\n",
-       " ('B0002YE8WA',\n",
-       "  'Vidal Sassoon VS14434 No Headache Headbands (2 Headbands)',\n",
-       "  (91, 175, 184, 18, 0)),\n",
-       " ('B0002YFQ6W',\n",
-       "  'Cloud Star Corporation Buddy Wash Lavender &amp; Mint',\n",
-       "  (61, 199, 205, 249, 0)),\n",
-       " ('B0002YLY9K', 'Obagi Nu-Derm Toner 6.7 oz.', (178, 225, 1, 203, 0)),\n",
-       " ('B0002Z0R9C',\n",
-       "  'Spornette No.215 Super Looper Hair Brush',\n",
-       "  (146, 240, 156, 171, 0)),\n",
-       " ('B0002Z0U5I',\n",
-       "  'Helen of Troy Professional Tangle-Free Hot Air Brush Iron',\n",
-       "  (212, 86, 10, 28, 0)),\n",
-       " ('B0002Z0UA8',\n",
-       "  'FusionBeauty LipFusion Micro-Injected Collagen Lip Plump Color Shine, Summer',\n",
-       "  (148, 1, 12, 141, 0)),\n",
-       " ('B0002Z1JJ4', 'Acquarella Remover', (120, 84, 26, 41, 0)),\n",
-       " ('B0002Z1JK8', 'Acquarella Conditioner', (120, 84, 26, 18, 0)),\n",
-       " ('B0002Z81O0',\n",
-       "  'Peter Thomas Roth Max Complexion Correction Pads? (60 Pads)',\n",
-       "  (242, 35, 165, 141, 0)),\n",
-       " ('B0002Z8GEK', 'Gena Hoof Lacquer, 5 Ounce', (1, 175, 187, 105, 0)),\n",
-       " ('B0002Z8HAI',\n",
-       "  'Essie Nail Lacquer, Sugar Daddy, 0.5 Fluid Ounce',\n",
-       "  (251, 35, 136, 54, 0)),\n",
-       " ('B0002Z8IV6',\n",
-       "  'Ardell Hair Color Bottle, Unred, 0.25 Ounce',\n",
-       "  (70, 0, 136, 236, 0)),\n",
-       " ('B0002Z8LLI',\n",
-       "  'Sensories Smoother Passionflower and Aloe Leave In Texturizing Condition By Rusk, 33.8 Ounce',\n",
-       "  (169, 151, 205, 42, 0)),\n",
-       " ('B0002Z8MLC', 'ISO Bouncy Creme 8.5 oz.', (32, 225, 170, 61, 0)),\n",
-       " ('B0002Z8NJS',\n",
-       "  'Essie Nail Lacquer, Blanc, 0.5 Fluid Ounce',\n",
-       "  (40, 151, 197, 54, 0)),\n",
-       " ('B0002Z8OUG', 'Ruby Stone Nail File', (176, 215, 12, 215, 0)),\n",
-       " ('B0002Z8P1E', 'Quantum Classic Extra Body Acid Perm', (1, 49, 192, 230, 0)),\n",
-       " ('B0002Z8P8M', \"L'Oreal ColorZap Haircolor Remover\", (165, 1, 18, 199, 0)),\n",
-       " ('B0002Z8QG8',\n",
-       "  'Denman Styling Brush, Heavy Weight, 9-Row',\n",
-       "  (190, 84, 205, 237, 0)),\n",
-       " ('B0002Z8QHW', 'Creative Nail Design SolarOil .5oz', (29, 7, 207, 177, 0)),\n",
-       " ('B0002Z8RGC', 'Blax 4 mm Ponytail Holders - Black', (99, 160, 195, 105, 0)),\n",
-       " ('B0002Z8SE8',\n",
-       "  'Spilo: MISC Big Bondini Plus Brush-On Glue, 0.5 oz',\n",
-       "  (61, 0, 32, 28, 0)),\n",
-       " ('B0002Z8SZW',\n",
-       "  'Bain De Terre Serum Anti-Frizz Recovery Complex 1.7 oz.',\n",
-       "  (222, 72, 170, 141, 0)),\n",
-       " ('B0002Z90RM',\n",
-       "  'Rusk Thick Body and Texture Amplifier, 6 Ounce',\n",
-       "  (1, 222, 18, 141, 0)),\n",
-       " ('B0003009NK',\n",
-       "  'Hai Classic Convertible Ceramic Flat Iron, 1-1/4 Inch',\n",
-       "  (223, 225, 53, 228, 0)),\n",
-       " ('B000629ZHG', 'Tinted Moisturizer - Almond', (168, 22, 48, 141, 0)),\n",
-       " ('B00062A0JI',\n",
-       "  'Laura Mercier Secret Concealer # 1 0.08oz',\n",
-       "  (178, 215, 1, 22, 0)),\n",
-       " ('B00062ADJA',\n",
-       "  'Laura Mercier Loose Setting Powder - Ivory - 29g/1oz',\n",
-       "  (168, 145, 137, 22, 0)),\n",
-       " ('B0006433MM',\n",
-       "  \"18'' Detachable Long Handle Natural Boar Bristle Brush\",\n",
-       "  (183, 39, 26, 251, 0)),\n",
-       " ('B00064A5L4',\n",
-       "  'Be Delicious by Donna Karan Eau De Parfum Spray 1 oz for Women',\n",
-       "  (215, 7, 25, 32, 0)),\n",
-       " ('B00064H44K',\n",
-       "  'Vanderbilt by Gloria Vanderbilt for Women - 3.3 Ounce EDT Spray',\n",
-       "  (26, 83, 150, 68, 1)),\n",
-       " ('B00065AFFE',\n",
-       "  'Conair 1875 Watt Ergonomic Handle Hair Dryer',\n",
-       "  (206, 35, 203, 220, 0)),\n",
-       " ('B00066D2JE',\n",
-       "  'Giovanni Conditioner, Smooth as Silk for Damaged Hair, 8.5 fl oz Bottle',\n",
-       "  (217, 57, 205, 254, 0)),\n",
-       " ('B00066D4M4',\n",
-       "  'Giovanni Direct Leave-In Conditioner, Weightless moisture conditioner 8.5 fl oz Bottle',\n",
-       "  (217, 189, 18, 22, 0)),\n",
-       " ('B00066LD7C',\n",
-       "  'Xenna Curlaway Gradual Curl Relaxer Green Apple Fragrance -- 6 oz',\n",
-       "  (106, 50, 137, 28, 0)),\n",
-       " ('B00066YC34',\n",
-       "  'Conair Supreme Triple Curling Iron Pack  - 1/2 inch, 3/4 inch and 1 inch',\n",
-       "  (119, 49, 146, 136, 0)),\n",
-       " ('B000674XN2',\n",
-       "  'Laura Mercier Secret Brightener - Secret Brightener #1, .05 oz.',\n",
-       "  (168, 22, 53, 22, 0)),\n",
-       " ('B000674XNM',\n",
-       "  'Laura Mercier Secret Brightening Powder - Secret Brightening Powder #1, 0.14 oz.',\n",
-       "  (13, 84, 207, 222, 0)),\n",
-       " ('B000674Z8A',\n",
-       "  'Laura Mercier Accessories - Sponge, 4-pack',\n",
-       "  (5, 49, 129, 237, 0)),\n",
-       " ('B00067EJ4U', 'Laura Mercier Foundation Primer', (137, 54, 25, 228, 0)),\n",
-       " ('B00067YSLO',\n",
-       "  'Remington Wet 2 Straight 2&quot; Wide Plate Wet/Dry Ceramic Hair Straightening Iron with Tourmaline',\n",
-       "  (176, 52, 129, 18, 0)),\n",
-       " ('B00068A0AQ',\n",
-       "  'Straight Sexy Hair Smooth &amp; Seal Spray by Sexy Hair for Unisex - 8.1 Ounce Hairspray',\n",
-       "  (227, 39, 150, 42, 0)),\n",
-       " ('B00068AZ88',\n",
-       "  'CombaColor Quick Hair Color Applicator (Comb-a-Color)',\n",
-       "  (71, 7, 128, 105, 0)),\n",
-       " ('B00068CG3U', 'Philosophy The Microdelivery Peel', (242, 1, 177, 237, 0)),\n",
-       " ('B00069FJUG',\n",
-       "  'Philosophy Supernatural Airbrushed Canvas Powder, SPF 15, Bronze',\n",
-       "  (217, 49, 188, 248, 0)),\n",
-       " ('B0006B12KK',\n",
-       "  'NARS Velvet Matte Lip Pencil, Bahama',\n",
-       "  (217, 93, 248, 171, 0)),\n",
-       " ('B0006B66D8',\n",
-       "  'Super Solano Professional Hair Dryer - Black',\n",
-       "  (90, 1, 26, 228, 0)),\n",
-       " ('B0006BDMIU', 'Caudalie Beauty Elixir 1 fl oz.', (173, 129, 150, 115, 0)),\n",
-       " ('B0006BDO5G',\n",
-       "  'Peter Thomas Roth Sulfur Cooling Masque 5 Ounce.',\n",
-       "  (10, 160, 129, 42, 0)),\n",
-       " ('B0006BDO5Q',\n",
-       "  'Peter Thomas Roth Glycolic Acid 10% Hydrating Gel, 2 Ounce',\n",
-       "  (224, 93, 32, 28, 0)),\n",
-       " ('B0006BDQDG',\n",
-       "  'Peter Thomas Roth AHA/BHA Acne Clearing Gel 2 fl oz',\n",
-       "  (10, 93, 215, 22, 0)),\n",
-       " ('B0006D3IAU', 'Cococare Coconut Oil, 9 Ounce', (80, 225, 205, 249, 0)),\n",
-       " ('B0006DS3KU',\n",
-       "  'Ojon Restorative Hair Treatment 4.6 oz (140 g)',\n",
-       "  (120, 50, 183, 32, 0)),\n",
-       " ('B0006FMK98',\n",
-       "  'Pharmaceutical Specialties Free and Clear Shampoo 12 oz.',\n",
-       "  (189, 199, 1, 220, 0)),\n",
-       " ('B0006FMKB6', 'Robathol Bath Oil - 16 oz', (224, 35, 203, 205, 0)),\n",
-       " ('B0006G2EX4', 'Obagi Foaming Gel Cleanser-6.7 oz', (203, 84, 207, 70, 0)),\n",
-       " ('B0006GBEYE',\n",
-       "  'Coretex SunX SPF30 Sunscreen with Towelettes - 25 Individual Foil Pouch with Towelettes/Box, PABA Free, Oil-free, Water and Sweat Resistant, UVA/UVB Protection',\n",
-       "  (43, 50, 129, 237, 0)),\n",
-       " ('B0006GZAKI',\n",
-       "  'Honeysuckle Rose Moisturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
-       "  (118, 240, 177, 68, 0)),\n",
-       " ('B0006GZBMA',\n",
-       "  'White Camellia Ultra-Smoothing Conditioner Aubrey Organics 11 oz Liquid',\n",
-       "  (10, 61, 32, 141, 0)),\n",
-       " ('B0006GZC4W',\n",
-       "  'Aubrey Organics - Island Natural Conditioner, 11 fl oz liquid',\n",
-       "  (10, 199, 128, 222, 0)),\n",
-       " ('B0006GZCVK',\n",
-       "  'Honeysuckle Rose Conditioner Aubrey Organics 11 oz Liquid',\n",
-       "  (147, 151, 22, 42, 0)),\n",
-       " ('B0006GZDE6',\n",
-       "  'Aubrey Organics green tea Clarifying Shampoo',\n",
-       "  (157, 92, 10, 222, 0)),\n",
-       " ('B0006GZDEQ',\n",
-       "  'White Camellia Ultra-Smoothing Shampoo 11oz',\n",
-       "  (10, 199, 187, 215, 0)),\n",
-       " ('B0006GZE0O',\n",
-       "  'Calaguala Texturizing Shampoo Aubrey Organics 11 oz Liquid',\n",
-       "  (157, 199, 10, 215, 0)),\n",
-       " ('B0006HCE60',\n",
-       "  'Cococare 100% Vitamin E Oil, 1 Ounce',\n",
-       "  (242, 151, 150, 68, 0)),\n",
-       " ('B0006HRCL2', '4 Dozen (48) Long Pink Perm Rods', (223, 225, 156, 199, 0)),\n",
-       " ('B0006I9YK8', 'Baggallini Complete Cosmetic Bagg', (176, 119, 128, 215, 0)),\n",
-       " ('B0006IHDQ0',\n",
-       "  'Liz Claiborne Curve Crush EDC Spray 4.2 oz',\n",
-       "  (106, 52, 165, 54, 0)),\n",
-       " ('B0006II74C',\n",
-       "  \"Nature's Secret 5-Day Fast and Cleanse Kit\",\n",
-       "  (6, 1, 188, 228, 0)),\n",
-       " ('B0006IJA5C',\n",
-       "  'Jhirmack Silver Brightening Shampoo 20 Oz.',\n",
-       "  (18, 0, 188, 220, 0)),\n",
-       " ('B0006IXSG4',\n",
-       "  'John Varvatos by John Varvatos for Men - 7 ml EDT Splash',\n",
-       "  (63, 160, 157, 205, 0)),\n",
-       " ('B0006LNC2G',\n",
-       "  'MAC Eye Kohl Smolder Eye Liner for Women, 0.048 Ounce',\n",
-       "  (5, 199, 25, 141, 0)),\n",
-       " ('B0006LNJ56', 'MAC Satin Lipstick - Rebel', (252, 199, 25, 141, 0)),\n",
-       " ('B0006LNKXW',\n",
-       "  'MAC Tinted Lipglass -- Viva Glam V (Boxed) 4.8g/.17oz',\n",
-       "  (63, 119, 205, 54, 0)),\n",
-       " ('B0006LNKYG', 'MAC Matte Lipstick - Russian Red', (123, 215, 156, 136, 0)),\n",
-       " ('B0006LNMIU', 'MAC Powder Blush Fever', (200, 205, 129, 214, 0)),\n",
-       " ('B0006M5566', 'eb5 Facial Cream (4 Ounces)', (148, 90, 129, 230, 0)),\n",
-       " ('B0006M56BK',\n",
-       "  'Wilkinson Sword Double Edge single Razor Cartridge, 5 blades',\n",
-       "  (184, 77, 129, 88, 0)),\n",
-       " ('B0006MSVCG',\n",
-       "  'Burberry Brit ~ Women 3.3 oz / 100 ml Eau de Parfum Spray',\n",
-       "  (32, 199, 183, 249, 0)),\n",
-       " ('B0006N29AU',\n",
-       "  \"Dr. King's Natural Medicine Back, Muscle and Joint Relief, 2 Fluid Ounce\",\n",
-       "  (177, 0, 197, 228, 0)),\n",
-       " ('B0006NXBJ8', 'Mason Pearson Detangler', (3, 29, 26, 28, 0)),\n",
-       " ('B0006NY35E',\n",
-       "  'Pre de Provence Soap, Cucumber, 7 Ounce',\n",
-       "  (23, 225, 183, 251, 0)),\n",
-       " ('B0006NYCT6',\n",
-       "  'Algemarin Bubble Bath 750ml bubble bath by Algemarin',\n",
-       "  (163, 61, 12, 141, 0)),\n",
-       " ('B0006NYDOU',\n",
-       "  'Herbacin Kamille Hand Cream with Glycerine - 2.5 oz',\n",
-       "  (157, 199, 49, 215, 0)),\n",
-       " ('B0006O2IQ4',\n",
-       "  'Fragrances Of Ireland Inis The Energy Of The Sea Cologne Spray, 1.7 Fluid Ounce',\n",
-       "  (148, 169, 25, 237, 0)),\n",
-       " ('B0006O4MCM',\n",
-       "  'Suki - Concentrated Balancing Toner  4.0 fl oz',\n",
-       "  (69, 230, 32, 241, 0)),\n",
-       " ('B0006O9ZAG',\n",
-       "  'Palladio Herbal Dual Wet and Dry Foundation, Ivory Myrrh, 0.28 Ounce',\n",
-       "  (48, 225, 84, 166, 0)),\n",
-       " ('B0006OU06E', 'Semi Oval Wild Boar Hair Brush', (15, 90, 187, 222, 0)),\n",
-       " ('B0006OU078', 'Bass Brushes Facial Cleansing Brush', (67, 151, 201, 228, 0)),\n",
-       " ('B0006PJRP8', 'Zoya Nail Polish .5 oz. Lola', (183, 0, 203, 54, 0)),\n",
-       " ('B0006PJSES',\n",
-       "  'CND: Treatments/Prep Stickey Base Coat, 2.3 oz',\n",
-       "  (61, 0, 32, 22, 0)),\n",
-       " ('B0006PJSSO',\n",
-       "  'Essie Nail Lacquer, First Base Base Coat, 0.46 Fluid Ounce',\n",
-       "  (98, 86, 177, 166, 0)),\n",
-       " ('B0006PJTB0',\n",
-       "  'Seche Base Ridge Filling Base Coat .5 oz. [Health and Beauty]',\n",
-       "  (85, 160, 128, 171, 0)),\n",
-       " ('B0006PKGJY',\n",
-       "  'Alba Botanica Natural Even Advanced Daily Cream -- 2 oz',\n",
-       "  (29, 7, 146, 44, 0)),\n",
-       " ('B0006PLNSC', 'Zoya Nail Polish .50 fl oz Charisma', (206, 230, 183, 68, 0)),\n",
-       " ('B0006PLPE4', 'ZOYA Armor Topcoat/UV Block 0.5oz', (89, 215, 129, 214, 0)),\n",
-       " ('B0006PS3XU', 'Marianna Bobby Pins 1 lb. - Black', (43, 225, 22, 249, 0)),\n",
-       " ('B0006PS3Y4', 'Marianna Bobby Pins 1 lb. - Brown', (43, 225, 22, 237, 0)),\n",
-       " ('B0006PT6TU',\n",
-       "  'City Lips Advanced in NUDE York Lip Plumper',\n",
-       "  (8, 37, 192, 241, 0)),\n",
-       " ('B0006Q00IK',\n",
-       "  'For Pro Cozie Liners Hand or Foot 100-ct.',\n",
-       "  (5, 49, 150, 141, 0)),\n",
-       " ('B0006Q00ZI', 'Gena Healthy Hoof Cream, 4 Ounce', (245, 41, 22, 215, 0)),\n",
-       " ('B0006Q0102', 'Gena Nail Brite with Brush, 4 Ounce', (1, 160, 12, 171, 0)),\n",
-       " ('B0006Q01EI', 'Hot Sock Diffuser', (47, 41, 28, 32, 0)),\n",
-       " ('B0006Q01WK', 'King Dy-zoff Pads 80-ct.', (141, 145, 10, 24, 0)),\n",
-       " ('B0006Q0HEC',\n",
-       "  'Wen by Chaz Dean Tea Tree Cleansing Conditioner 16 oz.',\n",
-       "  (191, 199, 194, 70, 0)),\n",
-       " ('B0006Q23H6',\n",
-       "  'John Masters Organics Evening Primrose Shampoo for Dry Hair 8 fl oz / 236 ml',\n",
-       "  (48, 222, 28, 136, 0)),\n",
-       " ('B0006Q3NTS',\n",
-       "  'WEN by Chaz Dean Fig Cleansing Conditioner 16 oz',\n",
-       "  (253, 123, 64, 42, 0)),\n",
-       " ('B0006Q3O3S',\n",
-       "  'WEN&reg; Sweet Almond Mint Styling Creme 6oz',\n",
-       "  (116, 222, 183, 88, 0)),\n",
-       " ('B0006Q3P50',\n",
-       "  'WEN&reg; Sweet Almond Mint Texture Balm 3oz',\n",
-       "  (61, 90, 73, 205, 0)),\n",
-       " ('B0006SFXXK',\n",
-       "  'John Masters Organics - Detangler Citrus &amp; Neroli - 8 oz.',\n",
-       "  (120, 62, 170, 237, 0)),\n",
-       " ('B0006SFXYY',\n",
-       "  'John Masters Organics John Masters Organics Deep Scalp Follicle Treatment &amp; Volumizer for Thinning Hair 4.2 fl oz - 4.2 fl oz',\n",
-       "  (189, 41, 176, 220, 0)),\n",
-       " ('B0006U81GY', 'Cricket Ultra Clean UC130', (233, 229, 205, 136, 0)),\n",
-       " ('B0006V7T9I',\n",
-       "  'Bee Bar Lotion by Honey House Naturals - Vanilla Scent - Long Lasting Lotion Bar Moisturizes and Leaves Skin Smelling Fresh - 2 ounces',\n",
-       "  (20, 93, 157, 141, 0)),\n",
-       " ('B0006ZEVU4',\n",
-       "  'Paul Mitchell Tea Tree Special Shampoo Unisex, 16.9 Ounce',\n",
-       "  (80, 222, 53, 199, 0)),\n",
-       " ('B0006ZHCK0', \"Murphy's Oil Soap, 32-Ounce\", (53, 194, 187, 214, 0)),\n",
-       " ('B0006ZHK7A', 'Pureology Hydrate Shampoo, 8.5 Ounce', (86, 49, 156, 166, 0)),\n",
-       " ('B00070E8IS',\n",
-       "  'Elchim Professional 2001 2000 Watt Classic Hair Dryer (Colors May Vary)',\n",
-       "  (192, 225, 130, 22, 0)),\n",
-       " ('B00070QF20',\n",
-       "  'AcneFree Clear Skin System, 3-Step Kit (Purifying Cleanser, Renewing Toner, Repair Lotion)',\n",
-       "  (203, 57, 215, 222, 0)),\n",
-       " ('B00074XC68',\n",
-       "  'Helen of Troy Mini 1/2 Inch Professional Curling Iron with Ergonomic Handle for Short Hair, Bangs &amp; Wisps (Model: 1503n)',\n",
-       "  (110, 72, 48, 237, 0)),\n",
-       " ('B0007506U2',\n",
-       "  'TIGI Catwalk Curl Collection Curlesque Curls Rock Amplifier, 5.07 Ounce, Packaging May Vary',\n",
-       "  (1, 90, 84, 141, 0)),\n",
-       " ('B00076X0BS',\n",
-       "  'Alba Botanica Revitalizing Green Tea Hawaiian Eye Gel, 1 Ounce Bottles',\n",
-       "  (120, 93, 192, 41, 1)),\n",
-       " ('B0007CIEBI',\n",
-       "  'Colorescience Pro Sunforgettable SPF 30 Brush- Fair 0.21 Oz',\n",
-       "  (32, 1, 207, 215, 0)),\n",
-       " ('B0007CWVLM', 'Tweezerman  Spirit 2000 Styling Shears', (3, 57, 26, 246, 0)),\n",
-       " ('B0007CXWQU',\n",
-       "  'Aveda Blue Malva Conditioner, 8.5-Ounce Tube',\n",
-       "  (171, 84, 128, 166, 0)),\n",
-       " ('B0007CXWRE',\n",
-       "  'Aveda Brilliant Anti-Humectant Pomade, 2.6 Ounces',\n",
-       "  (144, 123, 1, 42, 0)),\n",
-       " ('B0007CXWTW', 'Aveda Clove Conditioner 8.5 Ounces', (3, 119, 136, 171, 0)),\n",
-       " ('B0007CXWUG',\n",
-       "  'Aveda Confixor Liquid Gel, 8.5 Ounces',\n",
-       "  (227, 50, 176, 228, 0)),\n",
-       " ('B0007CXWWY',\n",
-       "  'Aveda Madder Root Conditioner 8.5 Ounces',\n",
-       "  (227, 61, 1, 228, 0)),\n",
-       " ('B0007CXWXI', 'Aveda Phomollient 6.7 Ounces', (3, 62, 136, 237, 0)),\n",
-       " ('B0007CXX82',\n",
-       "  'Biosilk Silk Therapy Serum, Packaging May Vary, 5.64 Ounces',\n",
-       "  (163, 199, 170, 228, 0)),\n",
-       " ('B0007CXXDC',\n",
-       "  'Biolage by Matrix Color Care Conditioner 33.8 Ounces',\n",
-       "  (178, 22, 26, 199, 0)),\n",
-       " ('B0007CXXDM',\n",
-       "  'Biolage by Matrix Color Care Shampoo 33.8 Ounces',\n",
-       "  (168, 1, 176, 203, 0)),\n",
-       " ('B0007CXXE6',\n",
-       "  'Biolage by Matrix Conditioning Balm 16.9 Ounces',\n",
-       "  (3, 93, 230, 214, 0)),\n",
-       " ('B0007CXXGO',\n",
-       "  'Biolage by Matrix Hydratherapie Hydrating Shampoo 33.8 Ounces',\n",
-       "  (76, 225, 150, 207, 0)),\n",
-       " ('B0007CXXIC',\n",
-       "  'Biolage by Matrix Normalizing Shampoo 33.8 Ounces',\n",
-       "  (76, 225, 130, 207, 0)),\n",
-       " ('B0007CXXJQ',\n",
-       "  'Biolage by Matrix Ultra-Hydrating Conditioning Balm 16.9 Ounces',\n",
-       "  (103, 151, 205, 249, 0)),\n",
-       " ('B0007CXXVE',\n",
-       "  'Nexxus Humectress Ultimate Moisturizing Conditioner 33.8 Ounces',\n",
-       "  (103, 225, 156, 220, 0)),\n",
-       " ('B0007CXXYG',\n",
-       "  'Paul Mitchell Awapuhi Shampoo, 33.8-Ounce Bottle',\n",
-       "  (211, 61, 156, 220, 0)),\n",
-       " ('B0007CXY5E',\n",
-       "  'Paul Mitchell Shampoo  1   33.8 Ounces',\n",
-       "  (83, 93, 177, 88, 0)),\n",
-       " ('B0007CXY5O',\n",
-       "  'Paul Mitchell Shampoo  3   16.9 Ounces',\n",
-       "  (233, 7, 151, 177, 0)),\n",
-       " ('B0007CXYAE',\n",
-       "  'Paul Mitchell The Detangler 33.8 Ounces',\n",
-       "  (235, 181, 203, 105, 0)),\n",
-       " ('B0007CXYP4',\n",
-       "  'Sebastian Collection Potion 9   5.1 Ounces',\n",
-       "  (227, 1, 32, 251, 0)),\n",
-       " ('B0007D2F3K', 'Pedi,Quick Salon Pedicure Kit', (43, 13, 13, 91, 0)),\n",
-       " ('B0007DHMBK',\n",
-       "  'Henna Black Cream Surya Nature, Inc 2.31 oz Cream',\n",
-       "  (189, 49, 136, 141, 0)),\n",
-       " ('B0007DHMCO',\n",
-       "  'Henna Burgundy Cream Surya Nature, Inc 2.31 oz Cream',\n",
-       "  (189, 49, 177, 254, 0)),\n",
-       " ('B0007IFB2W',\n",
-       "  'Spornette 25 Wood Handle &quot;Porcupine&quot; Brush With Genuine Boar Bristle * Made In Germany',\n",
-       "  (191, 77, 12, 28, 0)),\n",
-       " ('B0007IQMVG',\n",
-       "  'Digestive Advantage Intensive Bowel Support, 96 Counts Capsules',\n",
-       "  (75, 151, 205, 22, 0)),\n",
-       " ('B0007LE3EG', 'Total Hair Makeover Kit 90001', (87, 84, 205, 248, 0)),\n",
-       " ('B0007NIRUU',\n",
-       "  'Nexxus Humectress Ultimate Moisturizing Conditioner, 13.5Ounce Bottles',\n",
-       "  (238, 49, 207, 136, 0)),\n",
-       " ('B0007R7ABS',\n",
-       "  'Kenra Volume Spray 25 Super Hold Finishing Spray (Aerosol) (10 oz.)',\n",
-       "  (24, 41, 1, 68, 0)),\n",
-       " ('B0007RBYA6',\n",
-       "  'Philosophy Microdelivery Peel Pads, 60 Count',\n",
-       "  (25, 222, 129, 222, 0)),\n",
-       " ('B0007V6PFQ',\n",
-       "  'Brush - Large Oval Cushion Wood Bristles Wood Handle Bass Brushes 1 Brush',\n",
-       "  (90, 215, 10, 214, 0)),\n",
-       " ('B0007V9API', 'Bella B Tummy Honey Cream - 4 oz', (2, 39, 157, 249, 0)),\n",
-       " ('B0007W1R58',\n",
-       "  'Olay Regenerist Night Recovery Cream 1.7 Oz',\n",
-       "  (109, 93, 188, 237, 0)),\n",
-       " ('B0007WL21M',\n",
-       "  'PETER THOMAS ROTH - Anti-Aging Cleansing Gel 8.5oz',\n",
-       "  (25, 151, 192, 230, 0)),\n",
-       " ('B0007WZ7YU',\n",
-       "  'Elchim 2001hp High Pressure 2000 Watt Hair Dryer, White',\n",
-       "  (153, 199, 207, 88, 0)),\n",
-       " ('B0007X74CW',\n",
-       "  'Redken Hair Cleansing Cream Shampoo for All Hair Types, 10.1-Ounces',\n",
-       "  (235, 194, 157, 88, 0)),\n",
-       " ('B0007X74E0',\n",
-       "  'Smooth Down Heat Glide Protective Smoother Unisex Smoother by Redken, 5 Ounce',\n",
-       "  (204, 57, 187, 237, 0)),\n",
-       " ('B0007X74IQ',\n",
-       "  'Extreme Cat Protein Treatment Unisex Treatment by Redken, 5 Ounce',\n",
-       "  (203, 7, 12, 22, 0)),\n",
-       " ('B0007XFB2W', 'Original Sprout Miracle Detangler', (189, 52, 150, 177, 0)),\n",
-       " ('B0007YJ5QY',\n",
-       "  'AMERICAN CREW Daily Moisturizing Shampoo, 8.4 Ounce',\n",
-       "  (52, 1, 191, 222, 0)),\n",
-       " ('B00080DK86',\n",
-       "  'Glycolix Elite Treatment Pads 20 Percent 60 count',\n",
-       "  (10, 222, 12, 24, 0)),\n",
-       " ('B00081J4P8',\n",
-       "  'NO-AD Sunblock Lotion, SPF 15, 16 Ounces',\n",
-       "  (23, 50, 10, 199, 0)),\n",
-       " ('B00082379Q', 'Orly Polish Thinner', (8, 222, 129, 28, 0)),\n",
-       " ('B0008394GA', 'NARS Lip Liner Pencil, Jungle Red', (230, 49, 129, 175, 0)),\n",
-       " ('B000851N9E',\n",
-       "  'BurnOut - Ocean Tested Physical Sunscreen 30 SPF - 3.4 oz.',\n",
-       "  (214, 205, 165, 199, 0)),\n",
-       " ('B00085DX0Q',\n",
-       "  \"Grandpa's Soap Company Wonder Pine Tar Shampoo, 8 Ounce\",\n",
-       "  (247, 7, 18, 220, 0)),\n",
-       " ('B0008ENT8I',\n",
-       "  'ProVersa JWM6CF Wall Caddy Hair Dryer with 2-Speed and 3-Heat Settings, 1600-Watts, White Finish',\n",
-       "  (87, 84, 129, 237, 0)),\n",
-       " ('B0008F6ZDI',\n",
-       "  'Philosophy The Present Clear Makeup, 2 Ounce',\n",
-       "  (28, 39, 188, 175, 0)),\n",
-       " ('B0008FUY0I',\n",
-       "  'Flowery Swedish Clover Foot File * #530',\n",
-       "  (176, 119, 32, 215, 0)),\n",
-       " ('B0008GIZKS',\n",
-       "  'Malibu C Hard Water Wellness Shampoo, 1 Bottle, 9 oz',\n",
-       "  (242, 0, 205, 187, 0)),\n",
-       " ('B0008IV7BU',\n",
-       "  'American Crew American Crew Fiber Fiber - 3 oz',\n",
-       "  (5, 93, 188, 251, 0)),\n",
-       " ('B00092M2VO',\n",
-       "  'Andis Professional 1875 Watt Ceramic Ionic Hair Dryer - Black Chrome (82005)',\n",
-       "  (176, 1, 248, 222, 0)),\n",
-       " ('B00092M2XC',\n",
-       "  'Hot Tools Professional 1069S 1600 Watt Professional Turbo Hair Dryer, Silver/Black',\n",
-       "  (153, 215, 10, 88, 0)),\n",
-       " ('B00092M2ZU',\n",
-       "  'Ceramic Tools CT2555 1&quot; Professional Ceramic Flat Iron',\n",
-       "  (24, 39, 25, 251, 0)),\n",
-       " ('B00092M36I',\n",
-       "  'Ceramic Tools CT155S 1-1/2&quot; Dual Voltage Professional Ceramic Spring Curling Iron',\n",
-       "  (110, 72, 184, 249, 0)),\n",
-       " ('B00092M386',\n",
-       "  'Belson Profiles Spa Single Hand Nail Dryer P1013',\n",
-       "  (110, 160, 248, 136, 0)),\n",
-       " ('B00094O6D4',\n",
-       "  'LaLicious Sugar Souffle Body Scrub 16 fl oz.',\n",
-       "  (10, 84, 203, 215, 0)),\n",
-       " ('B000960QJA',\n",
-       "  'Vaseline Aloe Fresh Hydrating Body Lotion 20.3Oz',\n",
-       "  (162, 49, 188, 167, 1)),\n",
-       " ('B000977Q8I', 'Bobbi Brown Bobbi Brown Shimmer Brick', (15, 7, 165, 70, 0)),\n",
-       " ('B000980PGM',\n",
-       "  'Dermalogica Daily Microfoliant, 2.6-Ounce',\n",
-       "  (224, 222, 129, 251, 0)),\n",
-       " ('B000980PH6',\n",
-       "  'Dermalogica Oil Control Lotion (2 oz.)',\n",
-       "  (224, 222, 177, 175, 0)),\n",
-       " ('B0009953JA',\n",
-       "  'Jerdon JP7507NB 8-Inch Two-Sided Swivel Wall Mount Mirror with 7x Magnification, 13.5-Inch Extension, Nickel Beaded Finish',\n",
-       "  (87, 57, 230, 220, 0)),\n",
-       " ('B00099E8ZA',\n",
-       "  'Neutrogena Shampoo, Anti-Residue Formula, 6 Ounce',\n",
-       "  (109, 57, 12, 220, 0)),\n",
-       " ('B00099QZOW',\n",
-       "  'Colorful Neutral Protein Filler 1.2 oz.',\n",
-       "  (211, 230, 188, 115, 0)),\n",
-       " ('B00099Z2OQ',\n",
-       "  'Giovanni Hair Care - Direct Leave-In Conditioner, 8.5 fl oz liquid',\n",
-       "  (180, 35, 188, 54, 0)),\n",
-       " ('B0009BVK0E',\n",
-       "  'Organix South Maximum Strength Neem Soap Bar 4 oz',\n",
-       "  (157, 41, 10, 215, 0)),\n",
-       " ('B0009DT39W',\n",
-       "  'Panasonic EH2351AC Heated Eyelash Curling Wand',\n",
-       "  (43, 225, 10, 91, 0)),\n",
-       " ('B0009DVDTU',\n",
-       "  'Revision Teamine Eye Complex, 0.5 Ounce',\n",
-       "  (6, 71, 183, 141, 0)),\n",
-       " ('B0009DVMAU', 'Lafes Deodorant Spray with MSM, 8oz', (250, 230, 156, 68, 0)),\n",
-       " ('B0009EILHG',\n",
-       "  'Biore Pore Perfect Blemish Fighting Ice Cleanser, Cools &amp; Clears , 6.7 fl oz (198 ml)',\n",
-       "  (185, 52, 203, 222, 0)),\n",
-       " ('B0009EILI0',\n",
-       "  'Biore Deep Cleansing Pore Strips Combo 14 Ea',\n",
-       "  (185, 52, 12, 42, 0)),\n",
-       " ('B0009EILKS',\n",
-       "  'Biore  Deep Cleansing Pore Strips , 14 Nose Strips',\n",
-       "  (46, 72, 187, 215, 0)),\n",
-       " ('B0009EILVM',\n",
-       "  'Zia Natural Skincare Ultimate Body Firming Treatment, 7 fl oz',\n",
-       "  (224, 225, 10, 251, 0)),\n",
-       " ('B0009ET3SC',\n",
-       "  'Aquis Microfiber Body Towel, Lisse Crepe, White (29 x 55-Inches)',\n",
-       "  (85, 215, 192, 105, 0)),\n",
-       " ('B0009ET4VI',\n",
-       "  'Beauty without Cruelty Vitamin C, Vitality Serum, 1-Ounce',\n",
-       "  (157, 72, 177, 215, 0)),\n",
-       " ('B0009ET5H6',\n",
-       "  \"Burt's Bees Poison Ivy Soap, 2-Ounce\",\n",
-       "  (194, 199, 177, 141, 0)),\n",
-       " ('B0009ET5JE',\n",
-       "  \"Burt's Bees  Lip Shimmer, Champagne, 0.09 oz.\",\n",
-       "  (151, 215, 156, 233, 0)),\n",
-       " ('B0009ET5O4',\n",
-       "  \"Burt's Bees Healthy Treatment Repair Serum, 1 Fluid Ounce\",\n",
-       "  (6, 230, 188, 220, 0)),\n",
-       " ('B0009ET6BQ',\n",
-       "  'derma e Hyaluronic Acid Day Cr&egrave;me, 2-Ounces',\n",
-       "  (10, 93, 32, 251, 0)),\n",
-       " ('B0009EXM52',\n",
-       "  'OPI: Treatment &amp; Finish Natural Nail Base Coat, 0.5 oz',\n",
-       "  (87, 199, 53, 68, 0)),\n",
-       " ('B0009EXMB6',\n",
-       "  \"Palmer's Sunless Tanner &amp; Instant Bronzer SPF 15 5.25oz\",\n",
-       "  (189, 0, 25, 42, 0)),\n",
-       " ('B0009EXONC',\n",
-       "  'Big Sexy Hair Volumizing Hairspray, Spray &amp; Play, 10.0 oz (284 g)',\n",
-       "  (147, 52, 207, 28, 0)),\n",
-       " ('B0009EXOO6',\n",
-       "  'HEALTHY SEXY SOY TRI-WHEAT LEAVE-IN CONDITIONER 8.5 OZ -PACKAGING MAY VARY',\n",
-       "  (233, 187, 10, 251, 0)),\n",
-       " ('B0009EXOP0',\n",
-       "  'SHORT SEXY HAIR CONTOL MANIAC WAX 1.8 OZ-PACKAGING MAY VARY',\n",
-       "  (37, 88, 12, 214, 0)),\n",
-       " ('B0009EXOXW',\n",
-       "  'Source Naturals Skin Eternal Serum, 1.7  Ounce',\n",
-       "  (2, 145, 1, 105, 0)),\n",
-       " ('B0009F3M6A',\n",
-       "  'Eucerin Dry Skin Therapy Foot Creme, Plus Intensive Repair, 3-Ounce Tubes (Pack of 3)',\n",
-       "  (203, 145, 188, 251, 0)),\n",
-       " ('B0009F3NGE',\n",
-       "  \"L'Oreal Dermo-Expertise Sublime Bronze Self-Tanning Gelee, Medium-Natural , 5 fl oz (150 ml)\",\n",
-       "  (48, 145, 136, 136, 0)),\n",
-       " ('B0009F3NYQ',\n",
-       "  'Neutrogena MicroMist Tanning Sunless Spray, Deep, 5.3 Ounce',\n",
-       "  (117, 145, 230, 136, 0)),\n",
-       " ('B0009F3O18',\n",
-       "  'Neutrogena Makeup Remover Cleansing Towelettes, 25 Count',\n",
-       "  (98, 90, 205, 136, 0)),\n",
-       " ('B0009F3O8Q',\n",
-       "  \"Palmer's Cocoa Butter Formula with Vitamin E, 13.5 fl oz (400 ml)\",\n",
-       "  (10, 225, 176, 215, 0)),\n",
-       " ('B0009F3OOA',\n",
-       "  'Queen Helene Refreshing Natural Facial Scrub Mint Julep -- 6 oz',\n",
-       "  (177, 230, 215, 175, 0)),\n",
-       " ('B0009F3OR2',\n",
-       "  'Redken All Soft Heavy Cream, Avocado Oil , 8.5 fl oz (250 ml)',\n",
-       "  (25, 52, 183, 248, 0)),\n",
-       " ('B0009F3OS6',\n",
-       "  'Redken Extreme Anti-Snap, Lipids/Proteins , 8.5 fl oz (250 ml)',\n",
-       "  (32, 170, 177, 205, 0)),\n",
-       " ('B0009F3OX6', 'Revlon Cuticle Nippers, Half-Jaw', (192, 57, 207, 28, 0)),\n",
-       " ('B0009F3R7E', \"Burt's Bees Hand Repair Kit\", (18, 90, 12, 214, 0)),\n",
-       " ('B0009FHJOG',\n",
-       "  'Talika Lash Conditioning Cleanser - 4.06 oz',\n",
-       "  (69, 49, 165, 249, 0)),\n",
-       " ('B0009FHJRS',\n",
-       "  'Toppik Regular Dark Brown Hair Building Fibers, 0.42 Ounce',\n",
-       "  (75, 215, 184, 32, 0)),\n",
-       " ('B0009G05W8', 'Cricket Cool Down Iron Travel Case', (26, 88, 10, 28, 0)),\n",
-       " ('B0009GGY96',\n",
-       "  'AcneFree Acne and Blackhead Terminator (.75 Ounce Tube)',\n",
-       "  (121, 7, 129, 220, 0)),\n",
-       " ('B0009H04LO', 'NARS Bronzer Blush Duo, Orgasm/Laguna', (23, 7, 203, 237, 0)),\n",
-       " ('B0009HIFRY',\n",
-       "  'Pravana Intense Therapy Leave-In Treatment 10.1 oz',\n",
-       "  (13, 84, 151, 136, 0)),\n",
-       " ('B0009HJBPE',\n",
-       "  'Yves Saint Laurent TOUCHE ECLATRadiant Touch 2 Luminous Ivory',\n",
-       "  (189, 39, 203, 105, 0)),\n",
-       " ('B0009I4J4G',\n",
-       "  'Lubriderm Daily Moisture Lotion with Shea and Cocoa Butter, 16  Ounce',\n",
-       "  (132, 84, 28, 249, 0)),\n",
-       " ('B0009I4M02', 'TIGI Bed Head Small Talk, 8 Ounce', (3, 145, 248, 254, 0)),\n",
-       " ('B0009I4MCU',\n",
-       "  'Pureology Anti-Fade Complex Hydrate Condition, 8.5 Ounce',\n",
-       "  (25, 225, 136, 215, 0)),\n",
-       " ('B0009I4MFW',\n",
-       "  'Pureology Pure Volume Shampoo 10.1 oz',\n",
-       "  (235, 61, 170, 24, 0)),\n",
-       " ('B0009I4MG6',\n",
-       "  'Pureology Safeguard Your Color Purify Shampoo, 10.1 Ounces',\n",
-       "  (163, 61, 177, 88, 0)),\n",
-       " ('B0009I4MGQ',\n",
-       "  'Pureology Anti-Fade Complex Pure Volume Condition, 8.5 Ounce Bottle (Packaging May Vary)',\n",
-       "  (80, 119, 177, 24, 0)),\n",
-       " ('B0009I4MKW', 'Nexxus shampoo therappe, 33.8oz', (88, 7, 187, 68, 0)),\n",
-       " ('B0009IMRLS',\n",
-       "  'Christian Dior Diorshow Waterproof Mascara, No. 090 Black, 0.38 Ounce',\n",
-       "  (168, 180, 192, 24, 0)),\n",
-       " ('B0009ION0G',\n",
-       "  'TIGI Catwalk Your Highness Root Boost Spray, 8.1 Ounce',\n",
-       "  (187, 90, 18, 141, 0)),\n",
-       " ('B0009J6ER0',\n",
-       "  'Grow Hair Faster with Grow Shampoo and Conditioner for Faster Growing Hair',\n",
-       "  (202, 52, 177, 28, 0)),\n",
-       " ('B0009JQFF6',\n",
-       "  '100% Organic West African Shea Butter 16 oz',\n",
-       "  (203, 129, 130, 18, 0)),\n",
-       " ('B0009M0C5W',\n",
-       "  'FusionBeauty LipFusion Xl 2x Micro-Injected Collagen Advanced Lip Plumping Therapy',\n",
-       "  (203, 7, 156, 115, 0)),\n",
-       " ('B0009MHJE4',\n",
-       "  'African Shea Butter Cream (100% Pure &amp; Raw, Gold) 8 Oz.',\n",
-       "  (242, 35, 177, 70, 0)),\n",
-       " ('B0009MHJS0',\n",
-       "  'Raw Unrefined Yellow Shea Butter FILTERED &amp; CREAMY 5 0z',\n",
-       "  (145, 22, 203, 91, 0)),\n",
-       " ('B0009MJE74',\n",
-       "  'Nubian Heritage - Body Wash African Black Soap - 13 oz.',\n",
-       "  (202, 222, 183, 22, 0)),\n",
-       " ('B0009ML5M6', 'African Shea Oil - 8 ozs.', (145, 49, 207, 105, 0)),\n",
-       " ('B0009MMK5M',\n",
-       "  'Egyptian Magic - All Purpose Skin Cream - 4 oz.',\n",
-       "  (37, 87, 157, 214, 0)),\n",
-       " ('B0009MSHR2',\n",
-       "  'Madina African Black Soap Cocoa Butter W/Vitamin E 3.5 oz',\n",
-       "  (25, 119, 170, 241, 0)),\n",
-       " ('B0009MYMJY',\n",
-       "  'Shalimar By Guerlain For Women. Eau De Parfum Spray 2.5 Oz.',\n",
-       "  (115, 88, 177, 68, 0)),\n",
-       " ('B0009N35D2',\n",
-       "  'Glow By Jennifer Lopez For Women. Eau De Toilette Spray 1 Ounces',\n",
-       "  (26, 83, 150, 133, 1)),\n",
-       " ('B0009N5EZY',\n",
-       "  'Kenneth Cole Reaction By Kenneth Cole Eau De Toilette Spray 3.4 Oz',\n",
-       "  (76, 61, 157, 68, 0)),\n",
-       " ('B0009OAFUM',\n",
-       "  'Remington HKVAC-2000 Precision Vacuum Haircut Kit',\n",
-       "  (0, 90, 10, 203, 0)),\n",
-       " ('B0009OAGRE',\n",
-       "  'Eternity by Calvin Klein for Women, Eau De Parfum, 3.4 Ounce',\n",
-       "  (228, 88, 177, 68, 0)),\n",
-       " ('B0009OAGSI',\n",
-       "  'Escape by Calvin Klein for Men, Eau De Toilette Spray, 3.4-Fluid Ounce (100 ml)',\n",
-       "  (211, 61, 25, 68, 0)),\n",
-       " ('B0009OAGT2',\n",
-       "  'Escape by Calvin Klein for Women, Eau De Parfum Spray, 1.7 Ounce',\n",
-       "  (26, 83, 150, 68, 2)),\n",
-       " ('B0009OAGWE',\n",
-       "  'Ralph Lauren Polo Sport Eau de Toilette Spray for Men, 4.2 Ounce',\n",
-       "  (26, 83, 150, 68, 3)),\n",
-       " ('B0009OAGXI',\n",
-       "  'Acqua Di Gio By Giorgio Armani For Men. Eau De Toilette Spray 1.7 Oz.',\n",
-       "  (203, 0, 25, 220, 1)),\n",
-       " ('B0009OAGZ6',\n",
-       "  'Burberry By Burberry For Men. Eau De Toilette Spray 3.3 Ounces',\n",
-       "  (26, 83, 226, 218, 0)),\n",
-       " ('B0009OAH0A',\n",
-       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 1.7 Oz.',\n",
-       "  (26, 83, 150, 68, 4)),\n",
-       " ('B0009OAHBE',\n",
-       "  'COOL WATER by Davidoff Cologne for Men (EDT SPRAY 1.35 OZ)',\n",
-       "  (216, 49, 12, 24, 0)),\n",
-       " ('B0009OAHC8',\n",
-       "  'Cool Water By Davidoff For Men. Eau De Toilette Spray 4.2 Ounces',\n",
-       "  (26, 83, 150, 68, 5)),\n",
-       " ('B0009OAHEQ',\n",
-       "  'Joop! By Joop! For Men. Eau De Toilette Spray 4.2 Ounces',\n",
-       "  (26, 83, 150, 218, 2)),\n",
-       " ('B0009OAHIM',\n",
-       "  'Dolce &amp; Gabbana By Dolce &amp; Gabbana For Men. Eau De Toilette Spray 4.2 Ounce',\n",
-       "  (26, 83, 150, 68, 6)),\n",
-       " ('B0009OAHIW',\n",
-       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 1.6 Ounces',\n",
-       "  (26, 83, 150, 218, 3)),\n",
-       " ('B0009OAHNM', 'Curve Men Cologne Spray, 4.2-Ounce', (15, 230, 248, 166, 0)),\n",
-       " ('B0009OAHOG',\n",
-       "  'Mambo By Liz Claiborne For Men. Cologne Spray 1.7 Ounces',\n",
-       "  (219, 39, 22, 205, 0)),\n",
-       " ('B0009OAHQ4',\n",
-       "  'Bora Bora by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
-       "  (219, 7, 22, 249, 0)),\n",
-       " ('B0009OAHQE',\n",
-       "  'Bora Bora by Liz Claiborne for Women, Eau De Parfum Spray, 3.4-Ounce',\n",
-       "  (26, 83, 150, 218, 4)),\n",
-       " ('B0009OAHQY',\n",
-       "  'Claiborne by Liz Claiborne for Men, Cologne Spray, 3.4-Ounce',\n",
-       "  (181, 222, 203, 70, 0)),\n",
-       " ('B0009OAHRI',\n",
-       "  'Lucky You By Lucky Brand For Women. Eau De Toilette Spray 3.4 Oz.',\n",
-       "  (83, 50, 1, 68, 0)),\n",
-       " ('B0009OAHRS',\n",
-       "  'Lucky You By Lucky Brand For Men. Cologne Spray 3.4 Oz.',\n",
-       "  (210, 22, 165, 175, 0)),\n",
-       " ('B0009OAHVO',\n",
-       "  \"L'eau D'issey (issey Miyake) by Issey Miyake for Men - EDT Spray\",\n",
-       "  (26, 83, 150, 68, 7)),\n",
-       " ('B0009OAHWI',\n",
-       "  'Lolita Lempicka By Lolita Lempicka For Women. Eau De Parfum Spray 3.4 Oz.',\n",
-       "  (26, 83, 150, 218, 5)),\n",
-       " ('B0009OAI18',\n",
-       "  'Sunflowers By Elizabeth Arden For Women. Eau De Toilette Spray 3.4 Oz.',\n",
-       "  (235, 61, 48, 24, 0)),\n",
-       " ('B0009OAI1S',\n",
-       "  'Red Door By Elizabeth Arden For Women. Eau De Toilette Spray 3.3 Ounces',\n",
-       "  (228, 88, 177, 68, 1)),\n",
-       " ('B0009OAI2C',\n",
-       "  'Arden Beauty By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
-       "  (26, 83, 150, 133, 2)),\n",
-       " ('B0009OAI3G',\n",
-       "  'Blue Grass By Elizabeth Arden For Women. Eau De Parfum Spray 1.7 Ounces',\n",
-       "  (26, 83, 150, 133, 3)),\n",
-       " ('B0009OAI40',\n",
-       "  'Green Tea By Elizabeth Arden For Women. Eau De Parfum Spray 3.3 Ounces',\n",
-       "  (26, 83, 150, 218, 6)),\n",
-       " ('B0009OAI7C',\n",
-       "  'Passion by Elizabeth Taylor for Men, Cologne Spray, 4-Ounce',\n",
-       "  (147, 145, 248, 254, 0)),\n",
-       " ('B0009OAI8G',\n",
-       "  'White Diamonds By Elizabeth Taylor For Women, Eau De Toilette Spray, 1.7 Ounces',\n",
-       "  (26, 83, 150, 68, 8)),\n",
-       " ('B0009OAIB8',\n",
-       "  'Black Pearls by Elizabeth Taylor for Women, Eau De Parfum Spray, 3.3-Ounce',\n",
-       "  (228, 88, 177, 68, 2)),\n",
-       " ('B0009OAIE0',\n",
-       "  'Pleasures by Estee Lauder for Women, Eau De Toilette Spray, 1.7 Ounce',\n",
-       "  (26, 83, 150, 218, 7)),\n",
-       " ('B0009OAIIG',\n",
-       "  'Gucci Rush By Gucci For Women. Eau De Toilette Spray 2.5 Ounces',\n",
-       "  (26, 83, 150, 133, 4)),\n",
-       " ('B0009OAIKY',\n",
-       "  'Halston 1-12 by Halston for Men, Cologne Spray, 4.2-Ounce',\n",
-       "  (219, 215, 137, 251, 0)),\n",
-       " ('B0009OAIQI',\n",
-       "  'Design by Paul Sebastian for Women, Fine Parfum Spray, 1.7-Ounce',\n",
-       "  (228, 88, 177, 68, 3)),\n",
-       " ('B0009OAISG',\n",
-       "  'True Love by Elizabeth Arden for Women - 1.7 Ounce EDT Spray',\n",
-       "  (106, 41, 1, 54, 0)),\n",
-       " ('B0009OMNGQ',\n",
-       "  'Awapuhi Moisture Mist Unisex Mist by Paul Mitchell, 16.9 Ounce',\n",
-       "  (83, 50, 25, 171, 0)),\n",
-       " ('B0009OXHC0',\n",
-       "  \"Nature's Blessings Hair Pomade 4 oz.\",\n",
-       "  (185, 170, 184, 205, 0)),\n",
-       " ('B0009P4PZC', 'Cococare Coconut Oil 100% Pure 4 Oz', (163, 67, 157, 88, 0)),\n",
-       " ('B0009PVV36',\n",
-       "  'Conair CD86SCS Instant Heat Iron and Straightener, 1.5 Inch',\n",
-       "  (83, 180, 130, 88, 0)),\n",
-       " ('B0009PVV40',\n",
-       "  'Conair BC171NCS Ceramic Ionic Hot Air Brush, Black, 1.25 Inch',\n",
-       "  (83, 52, 165, 222, 0)),\n",
-       " ('B0009PVV4A',\n",
-       "  'Infiniti Pro by Conair Professional 1 Inch  Tourmaline Ceramic Straightener',\n",
-       "  (100, 119, 191, 88, 0)),\n",
-       " ('B0009QJ3TE',\n",
-       "  'Model in a Bottle Sensitive Makeup Setting Spray - 1.7 oz',\n",
-       "  (23, 57, 28, 177, 0)),\n",
-       " ('B0009QZXP2',\n",
-       "  \"Africa's Best Organics Hair Mayonnaise, 15 oz\",\n",
-       "  (224, 93, 10, 222, 0)),\n",
-       " ('B0009QZXQ6',\n",
-       "  \"Africa's Best Organics Olive Oil Dry Hair and Scalp Therapy, 7.5 oz.\",\n",
-       "  (242, 225, 48, 105, 0)),\n",
-       " ('B0009QZYBU',\n",
-       "  'Soft Sheen Carson Care Free Curl Gold Instant Activator 16Oz/473Ml',\n",
-       "  (17, 30, 156, 22, 0)),\n",
-       " ('B0009QZYLU',\n",
-       "  'Clairol BW2 - 8 oz. Tub Powder Lightener',\n",
-       "  (211, 41, 136, 61, 0)),\n",
-       " ('B0009QZYN8',\n",
-       "  'CLAIROL Jazzing Gentle Temporary Semi Permanent Hair Color #58 RUBY RED',\n",
-       "  (32, 145, 200, 28, 0)),\n",
-       " ('B0009QZYO2', 'Clairol Pure White 30 Volume 16 oz.', (88, 62, 130, 248, 0)),\n",
-       " ('B0009QZZ4Q',\n",
-       "  'Demert Wig Luster Conditioner, 9.76 Ounce',\n",
-       "  (166, 84, 205, 88, 0)),\n",
-       " ('B0009R14NG',\n",
-       "  'Africas Best Herbal Gro Super 5.25oz Jar',\n",
-       "  (147, 151, 22, 222, 0)),\n",
-       " ('B0009R14Q8',\n",
-       "  \"Africa's Best Organincs Mayo Leave In Conditioner 6 oz.\",\n",
-       "  (106, 1, 150, 222, 0)),\n",
-       " ('B0009R14XQ',\n",
-       "  'Australian Gold SPF 15 Spray Gel with Bronzer, 8 Ounce',\n",
-       "  (28, 145, 150, 251, 0)),\n",
-       " ('B0009R16O8',\n",
-       "  \"Dr. Bronner's Magic Soaps: Liquid Castile Soap, Tea Tree 32 oz\",\n",
-       "  (18, 222, 248, 248, 0)),\n",
-       " ('B0009R33U8',\n",
-       "  'Aphogee Two-step Treatment Protein for Damaged Hair 16 oz.',\n",
-       "  (120, 61, 230, 214, 0)),\n",
-       " ('B0009R35GA', 'Clairol Pure White 40 Volume 16 oz.', (88, 62, 187, 136, 0)),\n",
-       " ('B0009R5AYA',\n",
-       "  'Aussie Sydney Smooth 3 Minute Miracle Smoothing Treatment-8oz',\n",
-       "  (181, 160, 207, 220, 0)),\n",
-       " ('B0009R5B8A', 'Avon SSS Original OIL 5oz', (3, 123, 136, 237, 0)),\n",
-       " ('B0009R5CMK',\n",
-       "  'Doo Gro Medicated Hair Vitalizer Mega Thick Anti-Thinning Formula, 4 Ounce',\n",
-       "  (42, 222, 215, 88, 0)),\n",
-       " ('B0009RF9OG',\n",
-       "  'Rogaine for Women Hair Regrowth Treatment, 2 Ounce',\n",
-       "  (120, 7, 157, 105, 1)),\n",
-       " ('B0009RFAOK',\n",
-       "  'Clean &amp; Clear Deep Cleaning Astringent, Oil Fighting, 8 Ounce',\n",
-       "  (178, 90, 1, 42, 0)),\n",
-       " ('B0009RFAPY',\n",
-       "  'Purpose Gentle Cleansing Wash, 6-Ounce Pump Bottle',\n",
-       "  (148, 22, 207, 136, 0)),\n",
-       " ('B0009RFB76',\n",
-       "  'RoC Retinol Correxion Eye Cream, 0.5 Ounce',\n",
-       "  (120, 57, 226, 251, 0)),\n",
-       " ('B0009RMN0E',\n",
-       "  'Conair CS25WNCS Ultra Slim Ceramic Straightener, Red, 1 5/8 Inch',\n",
-       "  (62, 90, 192, 220, 0)),\n",
-       " ('B0009STDD8',\n",
-       "  \"Calvin Klein Women's Obsession Eau de Parfum Spray, 3.4 fl. oz.\",\n",
-       "  (228, 88, 177, 68, 4)),\n",
-       " ('B0009TNBVC',\n",
-       "  'Estee Lauder Double Wear Stay-In-Place Makeup SPF 10 18 Linen',\n",
-       "  (125, 61, 53, 160, 0)),\n",
-       " ('B0009V1YR8',\n",
-       "  'Farouk CHI 1 Inch Ceramic Flat Hairstyling Iron',\n",
-       "  (54, 0, 26, 222, 0)),\n",
-       " ('B0009V1YRS',\n",
-       "  'CHI Turbo Microchip Ceramic Hairstyling Iron 1&quot;',\n",
-       "  (83, 52, 130, 222, 0)),\n",
-       " ('B0009V1YS2',\n",
-       "  'Farouk CHI GF1539 Turbo Big 2 Inch Ceramic Flat Iron Hair Straightener',\n",
-       "  (20, 145, 248, 254, 0)),\n",
-       " ('B0009V2S8M',\n",
-       "  'Glo Minerals GloPressed Base Beige Dark 0.35oz',\n",
-       "  (132, 35, 48, 141, 0)),\n",
-       " ('B0009V8N4U',\n",
-       "  'Sun Laboratories Dark Sunsation Self Tanning Lotion - Very Dark 8 fl oz.',\n",
-       "  (42, 93, 129, 249, 0)),\n",
-       " ('B0009V8N5E',\n",
-       "  'Sun Self Tanning Lotion Ultra Dark Instant Tint - Dark 8oz/236ml',\n",
-       "  (203, 1, 156, 70, 0)),\n",
-       " ('B0009VD8YU',\n",
-       "  'AUBREY Collagen Restorative Moisturizer 1.7 fl.oz',\n",
-       "  (10, 145, 10, 54, 0)),\n",
-       " ('B0009VDBWY',\n",
-       "  'Sun Laboratories Ultra Dark Self Tanning Spray Can (6 oz)',\n",
-       "  (75, 93, 129, 214, 0)),\n",
-       " ('B0009VIJV2',\n",
-       "  'Floxite Fl-10h 10x Hand Held 2-sided Mirror with Stand, Clear',\n",
-       "  (12, 0, 156, 141, 0)),\n",
-       " ('B0009VNI2C',\n",
-       "  'Fruit Of The Earth Bogo Cream Aloe Vera 4oz. Jar',\n",
-       "  (220, 250, 10, 241, 0)),\n",
-       " ('B0009VNI40',\n",
-       "  'Fruit Of The Earth 100 % Aloe Vera Gel, 12 oz, 1-Pack',\n",
-       "  (32, 199, 136, 167, 0)),\n",
-       " ('B0009VQ8X8',\n",
-       "  'Got2b Glued Blasting Freeze Spray, 12 Ounce',\n",
-       "  (141, 7, 31, 215, 0)),\n",
-       " ('B0009WB49K',\n",
-       "  'Alterna Caviar Anti-Aging Working Hair Spray, 15.5 Ounce',\n",
-       "  (52, 90, 129, 61, 0)),\n",
-       " ('B0009WY4U6',\n",
-       "  'St. Ives Swiss Formula Makeup Remover &amp; Facial Cleanser, All Skin Types, 6 oz (170 g)',\n",
-       "  (52, 93, 22, 233, 0)),\n",
-       " ('B0009WY4UQ',\n",
-       "  'TRESemme Curl Enhancing Mousse, 10.5 oz',\n",
-       "  (84, 52, 197, 230, 0)),\n",
-       " ('B0009WY54G',\n",
-       "  'TRESemme Shampoo, 24 Hour Body, Healthy Volume, 32 Ounce',\n",
-       "  (21, 0, 205, 136, 0)),\n",
-       " ('B0009XH6SC',\n",
-       "  'Andis 1-Inch Ceramic Clamp Flat Iron (67095)',\n",
-       "  (87, 93, 129, 237, 0)),\n",
-       " ('B0009XH6TG',\n",
-       "  'Andis RC-2 Ionic1875W Ceramic Hair Dryer with Folding Handle and Retractable Cord (80020)',\n",
-       "  (176, 39, 129, 222, 0)),\n",
-       " ('B0009XH6UU',\n",
-       "  'Andis 40055 Pro Style 1600 Watt Hair Dryer - White',\n",
-       "  (5, 84, 157, 249, 0)),\n",
-       " ('B0009XH6V4',\n",
-       "  'Andis Micro Turbo 1600 Watt Dual Voltage Hair Dryer - White (33805)',\n",
-       "  (5, 129, 183, 205, 0)),\n",
-       " ('B0009Y6RHM',\n",
-       "  'Banana Boat Summer Color Self-Tanning Lotion - Deep Dark: 6 OZ',\n",
-       "  (204, 57, 157, 199, 0)),\n",
-       " ('B0009YDO32',\n",
-       "  'Zum Mist Aromatherapy Room and Body Spray Frankincense And Myrrh -- 4 fl oz',\n",
-       "  (84, 39, 12, 141, 0)),\n",
-       " ('B000A0ADT8',\n",
-       "  'Ole Henriksen Truth Serum Collagen Booster, 1.0 Fluid Ounce',\n",
-       "  (2, 151, 177, 88, 0)),\n",
-       " ('B000A0ADUW',\n",
-       "  'Ole Henriksen Blue Black Berry Enzyme Facial Mask, 3.5 Fluid Ounce',\n",
-       "  (222, 225, 170, 40, 0)),\n",
-       " ('B000A35L56',\n",
-       "  'Max Green Alchemy Scalp Rescue Shampoo 8.8 oz',\n",
-       "  (2, 39, 183, 199, 0)),\n",
-       " ('B000A3I2X4',\n",
-       "  'Revlon RV408 1875 Watt Full-Size Turbo Dryer, Black',\n",
-       "  (12, 7, 197, 228, 0)),\n",
-       " ('B000A3V1SW',\n",
-       "  \"Hask Placenta Henna 'n' Placenta for Extremely Damaged Hair 237ml/8oz\",\n",
-       "  (59, 61, 53, 228, 0)),\n",
-       " ('B000A3V1XC', 'Hollywood Beauty Tea Tree Oil 2 oz.', (58, 145, 248, 254, 0)),\n",
-       " ('B000A3V2PE',\n",
-       "  \"L'Oreal Excellence Hicolor Hilights Red 1.2 oz.\",\n",
-       "  (189, 160, 128, 222, 0)),\n",
-       " ('B000A3XI4M',\n",
-       "  'S-Curl Activator and Moisturizer 32 oz',\n",
-       "  (227, 160, 192, 251, 0)),\n",
-       " ('B000A3ZMLO', 'Hollywood Beauty Carrot Oil 8 oz.', (210, 180, 154, 203, 0)),\n",
-       " ('B000A3ZN7M',\n",
-       "  \"L'Oreal Oreor 30 Volume Creme Developer 16 oz.\",\n",
-       "  (17, 213, 1, 68, 0)),\n",
-       " ('B000A3ZN7W',\n",
-       "  'Loreal Oreor Creme 40 Volume Developer 16 Oz',\n",
-       "  (211, 14, 1, 233, 0)),\n",
-       " ('B000A408VC',\n",
-       "  'Sally Hansen Half Jaw Cuticle Nippers',\n",
-       "  (110, 72, 177, 68, 0)),\n",
-       " ('B000A4094I',\n",
-       "  'Liquid Glass Nail Laminate With Sunscreen 0.5oz',\n",
-       "  (70, 52, 165, 215, 0)),\n",
-       " ('B000A409J8',\n",
-       "  \"L'Oreal Quick Blue Powder Bleach 1 Lb\",\n",
-       "  (14, 39, 151, 248, 0)),\n",
-       " ('B000A7VRYG',\n",
-       "  'Ocusoft Lid Scrub Foaming Eyelid Cleanser (7.25 fl. oz.)',\n",
-       "  (227, 1, 25, 54, 0)),\n",
-       " ('B000AA2XQ4',\n",
-       "  'Rothco Compartment Travel Toiletry Bag',\n",
-       "  (176, 52, 12, 175, 0)),\n",
-       " ('B000AA5VD6',\n",
-       "  'Sally Hansen Insta, Dri Insta, Dri Anti, Chip Top Coat',\n",
-       "  (15, 215, 170, 98, 0)),\n",
-       " ('B000AA5VZ4', 'Super Nail Cuticle Oil 4 oz.', (18, 215, 10, 254, 0)),\n",
-       " ('B000AA5VZY', 'Super Nail 16 oz. Pure Acetone', (200, 14, 184, 54, 0)),\n",
-       " ('B000AA9H3Q',\n",
-       "  'Sally Hansen Natural Nail Growth Activator # 2741 0.45 oz.',\n",
-       "  (63, 160, 248, 233, 0)),\n",
-       " ('B000AA9HQ8', 'Super Nail Cuticle Softener, 8 Ounce', (194, 41, 136, 88, 0)),\n",
-       " ('B000AAAVTU',\n",
-       "  'Sally Hansen No Chip Acrylic Top Coat, 0.45 Fluid Ounce',\n",
-       "  (217, 62, 151, 136, 0)),\n",
-       " ('B000AAAW9Y',\n",
-       "  \"Mane 'n Tail Moisture Enriched Hair Strengthener, Bonus, 6 oz.\",\n",
-       "  (74, 93, 157, 171, 0)),\n",
-       " ('B000AAAWAI',\n",
-       "  \"Straight Arrow Mane 'N Tail Herbal Gro Maximum 5.5oz\",\n",
-       "  (171, 1, 28, 241, 0)),\n",
-       " ('B000AAAWEO',\n",
-       "  'Sun In Hair Lightner Lemon 4.7 oz. Pump',\n",
-       "  (80, 93, 12, 207, 0)),\n",
-       " ('B000AAC8VO',\n",
-       "  'DKNY BE DELICIOUS by Donna Karan Womens EAU DE PARFUM SPRAY 1 OZ',\n",
-       "  (181, 57, 165, 24, 0)),\n",
-       " ('B000AADENA',\n",
-       "  'Sally Hansen Cuticle Massage Cream -- 0.4 fl oz',\n",
-       "  (18, 41, 197, 205, 0)),\n",
-       " ('B000AADEP8',\n",
-       "  'Sally Hansen Double Duty Base and Top Coat, 0.45 Fluid Ounce',\n",
-       "  (204, 130, 170, 177, 0)),\n",
-       " ('B000AADF0M',\n",
-       "  'Sally Hansen Airbrush Face Tanner 1.8oz Spray [Health and Beauty]',\n",
-       "  (48, 160, 157, 214, 0)),\n",
-       " ('B000AADF20',\n",
-       "  'Sally Hansen Vitamin E Nail and Cuticle Oil, 0.45 Fluid Ounce',\n",
-       "  (42, 52, 130, 177, 0)),\n",
-       " ('B000AADG0G',\n",
-       "  'Village Naturals Bath Shoppe Lavender &amp; Chamomile Foaming Milk Bath 28 fl oz.(Pack of 1)',\n",
-       "  (191, 199, 25, 214, 0)),\n",
-       " ('B000AADG8I',\n",
-       "  'Wella Cc Liquid #0811/8N Light Blonde Haircolor',\n",
-       "  (222, 41, 187, 215, 0)),\n",
-       " ('B000ABOLZ4',\n",
-       "  'Kirks Original Coco Castile Soap, 24 Bars 1/2 Case',\n",
-       "  (60, 84, 22, 251, 0)),\n",
-       " ('B000ACAV8O', 'Henna Bright Red Pwd 4oz 4 Ounces', (120, 7, 22, 141, 0)),\n",
-       " ('B000ACB09I',\n",
-       "  \"Nature's Gate Organics C for Yourself, (1.7 fl oz) (50 ml)\",\n",
-       "  (145, 1, 205, 171, 0)),\n",
-       " ('B000ACB0C0',\n",
-       "  \"Nature's Gate Organics Oh What a Night Cream, (1 oz) (28 g)\",\n",
-       "  (203, 1, 177, 141, 0)),\n",
-       " ('B000ALBJ40',\n",
-       "  'Fekkai Glossing Hair Conditioner 2 Fl Oz',\n",
-       "  (21, 215, 130, 230, 0)),\n",
-       " ('B000ALBKGW',\n",
-       "  'Bliss Diamancel Diamond Nail File No.2, Medium',\n",
-       "  (34, 41, 183, 248, 0)),\n",
-       " ('B000ALBNGE',\n",
-       "  'Clinique Superfine Liner for Brows 01 Soft Blonde',\n",
-       "  (104, 225, 150, 249, 0)),\n",
-       " ('B000ALCJR6',\n",
-       "  'T3 Tourmaline 83808 Professional Featherweight Ceramic Ionic Hair Dryer',\n",
-       "  (140, 92, 177, 228, 0)),\n",
-       " ('B000ALDJ7A',\n",
-       "  'Diamancel Diamond Tough Foot Buffer No.11, Medium',\n",
-       "  (15, 90, 137, 158, 0)),\n",
-       " ('B000ALDK1A',\n",
-       "  'Fruit Of The Earth 100% Aloe Vera 24oz Gel Pump',\n",
-       "  (220, 151, 177, 237, 0)),\n",
-       " ('B000ALDLJG',\n",
-       "  'Clinique Quickliner for Eyes 07 Really Black',\n",
-       "  (89, 93, 22, 28, 0)),\n",
-       " ('B000ALFROS',\n",
-       "  'Fekkai Advanced Brilliant Glossing Cream 4 fl oz (113 g)',\n",
-       "  (189, 119, 205, 136, 0)),\n",
-       " ('B000ALFTUU',\n",
-       "  'Clinique 7 Day Scrub Cream Rinse-Off Formula 3.4 oz',\n",
-       "  (17, 119, 48, 222, 0)),\n",
-       " ('B000ALFTVO',\n",
-       "  'Clinique Take the Day off makeup remover 4.2 oz /125ml',\n",
-       "  (168, 22, 203, 105, 0)),\n",
-       " ('B000AM82US',\n",
-       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Towel, Pink (19 x 39-Inches)\",\n",
-       "  (62, 39, 26, 42, 0)),\n",
-       " ('B000AMA43Q',\n",
-       "  'Aquis Microfiber Hair Towel, Waffle, Linen (19 x 39-Inches)',\n",
-       "  (85, 215, 192, 199, 0)),\n",
-       " ('B000AMA4DQ',\n",
-       "  \"Mimi's Diva Dryer by Aquis Microfiber Hair Turban, Patented Design, White\",\n",
-       "  (62, 39, 26, 199, 0)),\n",
-       " ('B000AMBF5W',\n",
-       "  'Nivea Soft Refreshingly Soft Moisturizing Creme with Jojoba Oil &amp; Vitamin E, 6.8 Ounces',\n",
-       "  (172, 151, 215, 215, 0)),\n",
-       " ('B000AMHWCW',\n",
-       "  'Too Faced Cosmetics Bronzer, Snow Bunny, 0.28-Ounce',\n",
-       "  (143, 49, 248, 166, 0)),\n",
-       " ('B000AO2NXS',\n",
-       "  'Dove Body Wash with NutriumMoisture, Sensitive Skin Nourishing, 24 Ounce',\n",
-       "  (100, 151, 205, 220, 0)),\n",
-       " ('B000AQF50Y',\n",
-       "  'D &amp; G Light Blue By Dolce &amp; Gabbana For Women. Eau De Toilette Spray 3.3 Ounces',\n",
-       "  (26, 83, 150, 68, 9)),\n",
-       " ('B000AQI2EK',\n",
-       "  'Paul Mitchell Tea Tree Special Conditioner, 33.8 Ounce',\n",
-       "  (1, 93, 150, 171, 0)),\n",
-       " ('B000ARDBH2',\n",
-       "  'Guess Eau de Parfum Spray for Women, 2.5 Fluid Ounce',\n",
-       "  (29, 50, 205, 54, 0)),\n",
-       " ('B000ASDGK8',\n",
-       "  'BaByliss Pro BAB2000 Ceramix Xtreme  Dryer',\n",
-       "  (24, 49, 136, 205, 0)),\n",
-       " ('B000ASDX3S',\n",
-       "  'BaByliss Pro BAB2590 Porcelain Ceramic Straightening Iron with Removable Comb, 1.5 Inch',\n",
-       "  (247, 62, 184, 249, 0)),\n",
-       " ('B000AU15E0',\n",
-       "  'Blue Lizard Australian SUNSCREEN SPF 30+, Baby, SPF 30+, 8.75-Ounces',\n",
-       "  (148, 39, 192, 199, 0)),\n",
-       " ('B000AUTH0E',\n",
-       "  'Rusk Thermal Shine Spray Unisex, 4.4 Ounce',\n",
-       "  (18, 151, 48, 61, 0)),\n",
-       " ('B000AUTHEA',\n",
-       "  'Straight Sexy Hair Smooth &amp; Seal Aerated Anti-Frizz Spray (8.1 oz)',\n",
-       "  (210, 119, 184, 28, 0)),\n",
-       " ('B000AYKVOG', 'Dove Pink Beauty Bar, 8 Count', (88, 41, 10, 61, 0)),\n",
-       " ('B000B45BUE',\n",
-       "  'Silver Metallic Perfume Atomizer Spray 10 ML for purse or travel Refillable',\n",
-       "  (46, 215, 192, 24, 0)),\n",
-       " ('B000B5S2LS',\n",
-       "  'Focus 21 Sea Plasma Hair and Skin Moisturizer 32oz',\n",
-       "  (2, 62, 129, 237, 0)),\n",
-       " ('B000B5UPF4', 'OC Eight Professional Mattifying Gel', (6, 1, 184, 171, 0)),\n",
-       " ('B000B63Y1K',\n",
-       "  'Dermorganic Conditioning Shampoo, 12 Ounce',\n",
-       "  (84, 192, 207, 249, 0)),\n",
-       " ('B000B6VIPY',\n",
-       "  'Princereigns Shaving Gel Used to Remove Ingrown Hair and Razor Bumps',\n",
-       "  (14, 7, 248, 251, 0)),\n",
-       " ('B000B7VO66',\n",
-       "  'PCA Skin Eyexcellence (Phaze 12), 0.5 Ounce',\n",
-       "  (224, 0, 130, 228, 0)),\n",
-       " ('B000B82TD2',\n",
-       "  'NeoStrata Ultra Smoothing Cream AHA 10, 1.4 Ounce',\n",
-       "  (42, 222, 18, 251, 0)),\n",
-       " ('B000B8FW0Y', 'Lansky Dual Grit Sharpener', (43, 50, 207, 26, 0)),\n",
-       " ('B000B8VBJK',\n",
-       "  'HOT TOOLS 2108 Nano Ceramic Marcel Curling Iron, Black/Purple, 1 Inch',\n",
-       "  (71, 35, 18, 91, 0)),\n",
-       " ('B000B9J3GM', 'Mary Kay Indulge Soothing Eye Gel', (187, 230, 25, 214, 0)),\n",
-       " ('B000B9N0N4',\n",
-       "  'Mary Kay Blemish Control Toner: Formula 3',\n",
-       "  (32, 199, 150, 249, 0)),\n",
-       " ('B000B9W3CI',\n",
-       "  'White Plastic Jar with Dome Lid 4 Oz - 12 Per Bag',\n",
-       "  (67, 35, 183, 105, 0)),\n",
-       " ('B000B9W4MC',\n",
-       "  'White Plastic Jar with Flat Lid 8 Oz - 6 Per Bag',\n",
-       "  (190, 7, 223, 237, 0)),\n",
-       " ('B000BB9L0S', 'Nailtiques Formula 2 Protein, .5 Ounce', (18, 1, 177, 88, 0)),\n",
-       " ('B000BBGP4I', 'Mason Pearson Detangling Comb', (5, 82, 170, 220, 0)),\n",
-       " ('B000BD0SEE',\n",
-       "  'MyChelle Fruit Enzyme Cleanser, 4.4 Ounce Bottle',\n",
-       "  (80, 127, 48, 175, 0)),\n",
-       " ('B000BFTAMS',\n",
-       "  'Ponds Caring Classic Extra, Rich Dry Skin Cream, 10.1 oz',\n",
-       "  (221, 35, 207, 88, 0)),\n",
-       " ('B000BGIYWY',\n",
-       "  \"Africa's Best Kids Organics Hair Lotion, Shea Butter Detangling Moisturizing 12 oz\",\n",
-       "  (222, 199, 137, 249, 0)),\n",
-       " ('B000BH92J2',\n",
-       "  'Shea Moisture Shea Butter Leave in Conditioner 8oz',\n",
-       "  (178, 22, 1, 68, 0)),\n",
-       " ('B000BIPJ20',\n",
-       "  'Perfekt Skin Perfection Gel Luminous 1 oz',\n",
-       "  (242, 84, 53, 22, 0)),\n",
-       " ('B000BIUGRI', 'Bumble and Bumble Prep (8 Ounces)', (1, 225, 129, 166, 0)),\n",
-       " ('B000BIUGSM',\n",
-       "  'Bumble and Bumble Styling Lotion (8 Ounces)',\n",
-       "  (24, 230, 177, 24, 0)),\n",
-       " ('B000BIUGUA',\n",
-       "  'Bumble and Bumble Sunday Shampoo (8 Ounces)',\n",
-       "  (184, 229, 150, 136, 0)),\n",
-       " ('B000BIUGUK',\n",
-       "  'Bumble and Bumble Thickening Conditioner (8 Ounces)',\n",
-       "  (28, 39, 137, 88, 0)),\n",
-       " ('B000BIUGV4',\n",
-       "  'Bumble and Bumble Thickening Hair Spray (8 Ounces)',\n",
-       "  (61, 199, 183, 136, 0)),\n",
-       " ('B000BIUGXM',\n",
-       "  'Bumble and Bumble Curl Conscious Defining Creme 8.5 oz',\n",
-       "  (1, 222, 187, 251, 0)),\n",
-       " ('B000BIVXZW',\n",
-       "  'Bumble and Bumble Classic Hairspray (10 Ounces)',\n",
-       "  (153, 0, 177, 214, 0)),\n",
-       " ('B000BIVY0G',\n",
-       "  'Bumble and Bumble  DeFRIZZ (4 Ounces)',\n",
-       "  (123, 22, 187, 222, 0)),\n",
-       " ('B000BIVY10',\n",
-       "  'Kerastase Nutritive Bain Satin 2 Complete Nutrition Shampoo For Dry and Sensitised Hair, 8.5 Oz.',\n",
-       "  (244, 50, 205, 220, 0)),\n",
-       " ('B000BIVY1U',\n",
-       "  'Bumble and Bumble Seaweed Conditioner (8 Ounces)',\n",
-       "  (245, 35, 207, 233, 0)),\n",
-       " ('B000BIVY24',\n",
-       "  'Bumble and Bumble Tonic Lotion, 8-Ounce Spray Bottle',\n",
-       "  (46, 151, 151, 91, 0)),\n",
-       " ('B000BIXP30',\n",
-       "  'Kerastase Resistance Bain Volumactive Volumizing Shampoo For Fine, Vulnerable Hair, 8.5 Ounce',\n",
-       "  (169, 145, 18, 61, 0)),\n",
-       " ('B000BIXP3K',\n",
-       "  'Bumble and Bumble Grooming Cream (5 Ounces)',\n",
-       "  (135, 225, 184, 205, 0)),\n",
-       " ('B000BIXP3U',\n",
-       "  'Bumble and Bumble Leave in Conditioner (8 Ounces)',\n",
-       "  (193, 72, 188, 177, 0)),\n",
-       " ('B000BIXP4O',\n",
-       "  'Bumble and Bumble Styling Creme, 8-Ounce Bottle',\n",
-       "  (24, 50, 18, 24, 0)),\n",
-       " ('B000BIXP5I',\n",
-       "  'Bumble and Bumble Surf Spray, 4-Ounce Bottle',\n",
-       "  (97, 41, 183, 88, 0)),\n",
-       " ('B000BIXP5S',\n",
-       "  'Kerastase Nutritive Oleo-Relax Serum, 4.2 Ounces',\n",
-       "  (172, 41, 176, 215, 0)),\n",
-       " ('B000BIXP62',\n",
-       "  'Kerastase Nutritive Bain Oleo-Relax Smoothing Shampoo For Dry and Rebellious Hair, 8.5 Ounce',\n",
-       "  (183, 145, 53, 236, 0)),\n",
-       " ('B000BIZSUS',\n",
-       "  'Bumble and Bumble Brilliantine (2 Ounces)',\n",
-       "  (46, 215, 157, 220, 0)),\n",
-       " ('B000BIZSV2',\n",
-       "  'Bumble and Bumble Deeep, 5-Ounce Tube',\n",
-       "  (27, 222, 191, 222, 0)),\n",
-       " ('B000BIZSYO',\n",
-       "  'Kerastase Resistance Bain De Force Fortifying Shampoo For Weakened to Fragile Hair, 8.5 Ounce',\n",
-       "  (132, 61, 188, 214, 0)),\n",
-       " ('B000BJ1CGQ', 'GiGi Mini Pro Waxing Kit', (191, 175, 26, 214, 0)),\n",
-       " ('B000BK1P9E',\n",
-       "  'Kerasilk Rich Care Instant Silk Fluid By Goldwell for Unisex, 4.2 Ounce',\n",
-       "  (121, 50, 188, 88, 0)),\n",
-       " ('B000BKLBES',\n",
-       "  'Marc Anthony Instantly Thick Hair Thickening Cream, 6 oz',\n",
-       "  (204, 199, 230, 88, 0)),\n",
-       " ('B000BKLCDS',\n",
-       "  'Super Nail Acetone Polish Remover, 16 Ounce',\n",
-       "  (24, 22, 1, 68, 0)),\n",
-       " ('B000BKXGXW',\n",
-       "  'NOW Foods -  Red Clay Powder Moroccan, 6 OZ.',\n",
-       "  (161, 1, 215, 28, 0)),\n",
-       " ('B000BNG4VU',\n",
-       "  'Coty Airspun Loose Powder, Translucent, 2.3 Ounce',\n",
-       "  (93, 151, 188, 136, 0)),\n",
-       " ('B000BP81IM', 'NARS Loose Powder, Eden', (218, 19, 184, 166, 0)),\n",
-       " ('B000BPBV2U',\n",
-       "  'Liquid Gold Brush-on Bonding Adhesive for Cold Fusion Hair Extensions and Braids - .5oz',\n",
-       "  (120, 215, 184, 237, 0)),\n",
-       " ('B000BPMB2Y',\n",
-       "  'Roux Lash &amp; Brow Tint, Black,40 Count',\n",
-       "  (233, 119, 203, 177, 0)),\n",
-       " ('B000BPQG6Q', 'Roux Tween Time Hair Crayon, Auburn', (196, 39, 48, 88, 0)),\n",
-       " ('B000BPT0TG',\n",
-       "  'Roux Fanci-full Rinse #49 Ultra White Minx',\n",
-       "  (210, 52, 12, 215, 0)),\n",
-       " ('B000BR50M0',\n",
-       "  'Symbiotics Colostrum Plus, 240 Capsules',\n",
-       "  (145, 7, 151, 105, 0)),\n",
-       " ('B000BR5B2Y',\n",
-       "  'Komenuka Bijin Japanese All-Natural Essence Whitening Cream with Rice Bran',\n",
-       "  (100, 84, 146, 61, 0)),\n",
-       " ('B000BR766S',\n",
-       "  'Komenuka Bijin Premium Hair Care Set: Moisturizing Hair Shampoo &amp; Hair Treatment / Conditioner',\n",
-       "  (247, 49, 32, 166, 0)),\n",
-       " ('B000BRGAOM',\n",
-       "  'Biosilk Therapy Shine On Spray, 5.30 Ounce',\n",
-       "  (62, 41, 156, 166, 0)),\n",
-       " ('B000BRO6O8',\n",
-       "  'Coty Airspun Loose Powder, Naturelle, 2.3 Ounce',\n",
-       "  (93, 151, 188, 136, 1)),\n",
-       " ('B000BRPMJG',\n",
-       "  'Sebamed moisturizing lotion, for sensitive skin, 6.8-Fluid Ounce',\n",
-       "  (32, 7, 32, 91, 0)),\n",
-       " ('B000BRSHXE',\n",
-       "  'Sebamed Moisturing Cream, Sensitive Skin, 2.6-Ounce',\n",
-       "  (32, 7, 32, 91, 1)),\n",
-       " ('B000BRY5UI',\n",
-       "  'Komenuka Bijin Facial Cleansing Powder from Natural Rice Bran - 30 Packs',\n",
-       "  (177, 7, 197, 215, 0)),\n",
-       " ('B000BRYMKG',\n",
-       "  'Hylexin Serious Dark Circles by Bremenn Research Labs .39 oz. 11.53 ml',\n",
-       "  (120, 230, 53, 141, 0)),\n",
-       " ('B000BTM2K6',\n",
-       "  'White Shoulders By Evyan For Women, Eau De Cologne Spray (4.5 Ounces)',\n",
-       "  (147, 222, 32, 166, 0)),\n",
-       " ('B000BTO6CI',\n",
-       "  'Jessica Mcclintock By Jessica Mcclintock For Women. Eau De Parfum Spray 3.4 Oz.',\n",
-       "  (26, 83, 150, 68, 10)),\n",
-       " ('B000BTO6EG',\n",
-       "  'PALOMA PICASSO For Women By PALOMA PICASSO Eau De Parfum Spray,3.4 Oz',\n",
-       "  (26, 83, 226, 68, 0)),\n",
-       " ('B000BTQRFM',\n",
-       "  'Paul Sebastian by Paul Sebastian for Men - 8 Ounce EDC De Luxe Splash',\n",
-       "  (70, 0, 18, 42, 0)),\n",
-       " ('B000BU7G1A',\n",
-       "  'ApHogee Intensive Two Minute Keratin Reconstructor',\n",
-       "  (178, 215, 156, 24, 0)),\n",
-       " ('B000BU7SMM', 'Aphogee Balancing Moisturizer 16 Oz', (21, 215, 157, 254, 0)),\n",
-       " ('B000BUFFPO', \"Burt's Bees Herbal Deodorant\", (238, 72, 136, 199, 0)),\n",
-       " ('B000BVCSP8',\n",
-       "  'Scruples High Definition Shape Spray, 10.6 Ounce',\n",
-       "  (1, 90, 203, 249, 0)),\n",
-       " ('B000BW4U58', 'Vitamin E Skin Oil 10000 IU. 4.6 Oz', (235, 230, 156, 5, 0)),\n",
-       " ('B000BX1Z00',\n",
-       "  'CHI Silk Infusion Leave-In Treatment, 12 Ounce',\n",
-       "  (62, 93, 32, 40, 0)),\n",
-       " ('B000BX5FS8',\n",
-       "  'CHI Straight Guard Smoothing Styling Cream 8.5 oz',\n",
-       "  (5, 199, 18, 105, 0)),\n",
-       " ('B000BY2N7S', 'NOW Foods Biotin 5000mcg, 120 Vcaps', (241, 83, 249, 218, 0)),\n",
-       " ('B000BYQBT4',\n",
-       "  'Obagi Nu Derm Exfoderm Skin Smoothing Lotion-2 oz',\n",
-       "  (118, 62, 156, 171, 0)),\n",
-       " ...]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "items_with_tuples"
    ]
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 60bc6319..81d29168 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -126,6 +126,7 @@ def create_from_config(cls, config, **kwargs):
         embeddings = torch.stack(embs_extractor['embeddings'].tolist()).to(DEVICE)
 
         semantic_ids, residuals = rqvae_model({"embeddings": embeddings})
+        assert embedding_dim == residuals.shape[1]
         
         solver = CollisionSolver(
             residual_dim=residuals.shape[1], 
@@ -245,8 +246,6 @@ def _apply_decoder(
         bos_embeddings = self._bos_weight.unsqueeze(0).expand(batch_size, 1, -1)  # (batch_size, 1, embedding_dim)
         
         tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1) # TODO remove residuals (batch_size, dec_seq_len, embedding_dim)
-        # tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1) # (batch_size, dec_seq_len, embedding_dim)
-        # TODO here :-1 removes dedup token, however sizes doesn't match (decoder_output = b x len(codebook_sizes) + 1 x emb_dim)
         
         label_len = tgt_mask.shape[1]
 

From 5a913d9e0edc98ff07ced14e25f68d44aff2bb10 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 24 Jan 2025 11:39:36 +0700
Subject: [PATCH 046/175] fix solver init

---
 modeling/models/tiger.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 81d29168..44a8b624 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -131,10 +131,9 @@ def create_from_config(cls, config, **kwargs):
         solver = CollisionSolver(
             residual_dim=residuals.shape[1], 
             emb_dim=len(rqvae_model.codebook_sizes), 
-            codebook_size=len(rqvae_model.codebook_sizes[0]), 
-            device=DEVICE
+            codebook_size=rqvae_model.codebook_sizes[0]
         )
-        solver.create_query_candidates_dict(semantic_ids, residuals)
+        solver.create_query_candidates_dict(torch.tensor(semantic_ids), residuals)
         
         item_id_to_embedding = {
             item_id: embedding for (item_id, embedding) in zip(item_ids, embeddings)
@@ -190,6 +189,8 @@ def forward(self, inputs):
             semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in label_events.tolist()]) # len(events), len(codebook_sizes)
             semantic_events = semantic_ids.view(-1)
             
+            # TODO batch_logsoftmax don't match shapes (example bert4rec cls)
+            
             return {self._pred_prefix: logits, f"semantic.{self._labels_prefix}.ids": semantic_events}
         else:
             label_events = inputs["{}.ids".format(self._labels_prefix)]

From 5d15b3262607b56f30d1a004b48b38888efc9de4 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 24 Jan 2025 12:06:37 +0700
Subject: [PATCH 047/175] sasrec compare

---
 configs/train/sasrec_train_config.json |   2 +-
 modeling/main.ipynb                    | 468 +++++++++++++++++++++++++
 modeling/models/sasrec.py              |  11 +
 3 files changed, 480 insertions(+), 1 deletion(-)

diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index aa29a029..690541d1 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -37,7 +37,7 @@
     "positive_prefix": "positive",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
-    "embedding_dim": 64,
+    "embedding_dim": 512,
     "num_heads": 2,
     "num_layers": 2,
     "dim_feedforward": 256,
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index e7152949..e0e0b985 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -228,6 +228,474 @@
     "mask"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "df = torch.load('../data/Beauty/data_full.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[ 0.0115, -0.0265, -0.0014,  ..., -0.0403, -0.0064, -0.0646],\n",
+       "        [ 0.0522,  0.0051, -0.0317,  ..., -0.0215, -0.0284,  0.0104],\n",
+       "        [ 0.0657, -0.0131, -0.0234,  ..., -0.0370, -0.0462, -0.0503],\n",
+       "        ...,\n",
+       "        [ 0.0432,  0.0406, -0.0517,  ..., -0.0368,  0.0365, -0.0342],\n",
+       "        [ 0.0523,  0.0635, -0.0556,  ..., -0.0422,  0.0264, -0.0267],\n",
+       "        [ 0.0603,  0.0612, -0.0532,  ..., -0.0728,  0.0288, -0.0492]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.stack(df.sort_index().embeddings.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>raw_user_id</th>\n",
+       "      <th>raw_item_id</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>user_id</th>\n",
+       "      <th>asin</th>\n",
+       "      <th>description</th>\n",
+       "      <th>title</th>\n",
+       "      <th>imUrl</th>\n",
+       "      <th>salesRank</th>\n",
+       "      <th>categories</th>\n",
+       "      <th>price</th>\n",
+       "      <th>related</th>\n",
+       "      <th>brand</th>\n",
+       "      <th>combined_text</th>\n",
+       "      <th>embeddings</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>item_id</th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A20NUABVL6KKTV</td>\n",
+       "      <td>B002OVV7F0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1344556800</td>\n",
+       "      <td>651772</td>\n",
+       "      <td>B002OVV7F0</td>\n",
+       "      <td>Ten shades of eye shadow that features bold co...</td>\n",
+       "      <td>NYX Cosmetics Eye Shadow Palette 10 Color, Jaz...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/41PVzq7M...</td>\n",
+       "      <td>{'Beauty': 138235}</td>\n",
+       "      <td>[[Beauty, Makeup, Eyes, Eye Shadow]]</td>\n",
+       "      <td>7.97</td>\n",
+       "      <td>{'also_bought': ['B00B1ZPFT4', 'B00A5YDBWK', '...</td>\n",
+       "      <td>NYX</td>\n",
+       "      <td>Description: Ten shades of eye shadow that fea...</td>\n",
+       "      <td>[tensor(0.0115), tensor(-0.0265), tensor(-0.00...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A2CNWQEZHQ3K6R</td>\n",
+       "      <td>B006GQPZ8E</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1392940800</td>\n",
+       "      <td>952110</td>\n",
+       "      <td>B006GQPZ8E</td>\n",
+       "      <td>Color burst lip butter combines beautiful colo...</td>\n",
+       "      <td>REVLON Colorburst Lip Butter, Peach Parfait, 0...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/31r1scO3...</td>\n",
+       "      <td>{'Beauty': 12827}</td>\n",
+       "      <td>[[Beauty, Makeup, Lips, Lipstick]]</td>\n",
+       "      <td>6.35</td>\n",
+       "      <td>{'also_bought': ['B006GQTZ8A', 'B006GQEI0A', '...</td>\n",
+       "      <td>Revlon</td>\n",
+       "      <td>Description: Color burst lip butter combines b...</td>\n",
+       "      <td>[tensor(0.0522), tensor(0.0051), tensor(-0.031...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A1MVAPY2WT4D4M</td>\n",
+       "      <td>B0002DNZAC</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1404086400</td>\n",
+       "      <td>83086</td>\n",
+       "      <td>B0002DNZAC</td>\n",
+       "      <td>Buy MAC Eyeshadows - MAC Eye Shadow Frost Sati...</td>\n",
+       "      <td>MAC Eye Shadow Frost Satin Taupe</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/41PNABCE...</td>\n",
+       "      <td>{'Beauty': 107237}</td>\n",
+       "      <td>[[Beauty, Makeup, Eyes, Eye Shadow]]</td>\n",
+       "      <td>21.19</td>\n",
+       "      <td>{'also_bought': ['B0018HPFNG', 'B00BEH4UU4', '...</td>\n",
+       "      <td>M.A.C</td>\n",
+       "      <td>Description: Buy MAC Eyeshadows - MAC Eye Shad...</td>\n",
+       "      <td>[tensor(0.0657), tensor(-0.0131), tensor(-0.02...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AO2GZG0N16FCD</td>\n",
+       "      <td>B0000UTUVU</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1384905600</td>\n",
+       "      <td>36346</td>\n",
+       "      <td>B0000UTUVU</td>\n",
+       "      <td>Here's something you simply can't live without...</td>\n",
+       "      <td>Mrs. Meyer's Clean Day Dish Soap, Lavender, 16...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/31H9M36F...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>[[Beauty, Skin Care, Body, Moisturizers, Lotio...</td>\n",
+       "      <td>8.19</td>\n",
+       "      <td>{'also_bought': ['B0000UTUV0', 'B004ZY1J6G', '...</td>\n",
+       "      <td>Mrs. Meyer&amp;#39;s Clean Day</td>\n",
+       "      <td>Description: Here's something you simply can't...</td>\n",
+       "      <td>[tensor(0.0452), tensor(0.0012), tensor(0.0066...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>ASNGEUJ0LCACM</td>\n",
+       "      <td>B000F8HWXU</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1320796800</td>\n",
+       "      <td>27187</td>\n",
+       "      <td>B000F8HWXU</td>\n",
+       "      <td>Queen Helene has been a leader in quality beau...</td>\n",
+       "      <td>Queen Helene Mint Julep Masque, 2 Ounce Travel...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/41YKB8lA...</td>\n",
+       "      <td>{'Beauty': 10453}</td>\n",
+       "      <td>[[Beauty, Skin Care, Face, Treatments &amp; Masks,...</td>\n",
+       "      <td>3.03</td>\n",
+       "      <td>{'also_bought': ['B0072CTONS', 'B00CYI3RAG', '...</td>\n",
+       "      <td>Queen Helene</td>\n",
+       "      <td>Description: Queen Helene has been a leader in...</td>\n",
+       "      <td>[tensor(0.0141), tensor(-0.0156), tensor(-0.03...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12097</th>\n",
+       "      <td>A26ZA5ZV0BPRXK</td>\n",
+       "      <td>B00LCEROA2</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1404259200</td>\n",
+       "      <td>1186930</td>\n",
+       "      <td>B00LCEROA2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Dr Song Rosehip Oil 4oz (4 oz)</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/412qdoPc...</td>\n",
+       "      <td>{'Beauty': 7597}</td>\n",
+       "      <td>[[Beauty, Skin Care, Face, Oils &amp; Serums]]</td>\n",
+       "      <td>19.99</td>\n",
+       "      <td>{'also_bought': ['B00LNVW1IE', 'B00JYKGFWY', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Description: empty. Title: Dr Song Rosehip Oil...</td>\n",
+       "      <td>[tensor(0.0204), tensor(0.0244), tensor(0.0625...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12098</th>\n",
+       "      <td>A3DXSM2289U79E</td>\n",
+       "      <td>B00IBMV2ME</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1392768000</td>\n",
+       "      <td>1188037</td>\n",
+       "      <td>B00IBMV2ME</td>\n",
+       "      <td>The Best BOTANICAL HYALURONIC ACID (5.0%) Gel ...</td>\n",
+       "      <td>Best Botanical Hyaluronic Acid Anti Aging Faci...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/4171BmUV...</td>\n",
+       "      <td>{'Beauty': 116649}</td>\n",
+       "      <td>[[Beauty, Skin Care, Face, Oils &amp; Serums]]</td>\n",
+       "      <td>24.50</td>\n",
+       "      <td>{'also_bought': ['B00IC8JBIE', 'B00IC9AG5A', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Description: The Best BOTANICAL HYALURONIC ACI...</td>\n",
+       "      <td>[tensor(0.0454), tensor(0.0540), tensor(-0.023...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12099</th>\n",
+       "      <td>A3DXSM2289U79E</td>\n",
+       "      <td>B00IC9AG5A</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1392768000</td>\n",
+       "      <td>1188037</td>\n",
+       "      <td>B00IC9AG5A</td>\n",
+       "      <td>Announcing a Dermatologist Grade Skin Treatmen...</td>\n",
+       "      <td>Anti Aging All In One Facial Treatment (Replac...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/314b-jZn...</td>\n",
+       "      <td>{'Beauty': 84262}</td>\n",
+       "      <td>[[Beauty, Skin Care, Eyes, Combinations]]</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>{'also_bought': ['B00IC8JBIE', 'B00IC7L3JK', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Description: Announcing a Dermatologist Grade ...</td>\n",
+       "      <td>[tensor(0.0432), tensor(0.0406), tensor(-0.051...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12100</th>\n",
+       "      <td>A2BWXFJAQNH8LC</td>\n",
+       "      <td>B00IKKORVU</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1393632000</td>\n",
+       "      <td>1188048</td>\n",
+       "      <td>B00IKKORVU</td>\n",
+       "      <td>Announcing The Ultimate Vitamin C Anti Aging S...</td>\n",
+       "      <td>Best Vitamin C Anti Aging 6 Item System &amp;amp; ...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/51yIcFHj...</td>\n",
+       "      <td>{'Beauty': 87595}</td>\n",
+       "      <td>[[Beauty, Skin Care, Sets &amp; Kits]]</td>\n",
+       "      <td>125.00</td>\n",
+       "      <td>{'also_viewed': ['B00IC8JBIE', 'B00GYJWL7G', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Description: Announcing The Ultimate Vitamin C...</td>\n",
+       "      <td>[tensor(0.0523), tensor(0.0635), tensor(-0.055...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12101</th>\n",
+       "      <td>A3DXSM2289U79E</td>\n",
+       "      <td>B00IC8JBIE</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1392768000</td>\n",
+       "      <td>1188037</td>\n",
+       "      <td>B00IC8JBIE</td>\n",
+       "      <td>For The Best Natural &amp; Organic Ingredient Base...</td>\n",
+       "      <td>Best Anti Aging Facial Creme &amp;amp; Face Cream ...</td>\n",
+       "      <td>http://ecx.images-amazon.com/images/I/41EwlMp3...</td>\n",
+       "      <td>{'Beauty': 28766}</td>\n",
+       "      <td>[[Beauty, Skin Care, Face, Creams &amp; Moisturize...</td>\n",
+       "      <td>33.00</td>\n",
+       "      <td>{'also_bought': ['B00IC7L3JK', 'B00IC9AG5A', '...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Description: For The Best Natural &amp; Organic In...</td>\n",
+       "      <td>[tensor(0.0603), tensor(0.0612), tensor(-0.053...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12101 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            raw_user_id raw_item_id  rating   timestamp  user_id        asin  \\\n",
+       "item_id                                                                        \n",
+       "1        A20NUABVL6KKTV  B002OVV7F0     4.0  1344556800   651772  B002OVV7F0   \n",
+       "2        A2CNWQEZHQ3K6R  B006GQPZ8E     2.0  1392940800   952110  B006GQPZ8E   \n",
+       "3        A1MVAPY2WT4D4M  B0002DNZAC     4.0  1404086400    83086  B0002DNZAC   \n",
+       "4         AO2GZG0N16FCD  B0000UTUVU     5.0  1384905600    36346  B0000UTUVU   \n",
+       "5         ASNGEUJ0LCACM  B000F8HWXU     5.0  1320796800    27187  B000F8HWXU   \n",
+       "...                 ...         ...     ...         ...      ...         ...   \n",
+       "12097    A26ZA5ZV0BPRXK  B00LCEROA2     5.0  1404259200  1186930  B00LCEROA2   \n",
+       "12098    A3DXSM2289U79E  B00IBMV2ME     5.0  1392768000  1188037  B00IBMV2ME   \n",
+       "12099    A3DXSM2289U79E  B00IC9AG5A     5.0  1392768000  1188037  B00IC9AG5A   \n",
+       "12100    A2BWXFJAQNH8LC  B00IKKORVU     5.0  1393632000  1188048  B00IKKORVU   \n",
+       "12101    A3DXSM2289U79E  B00IC8JBIE     5.0  1392768000  1188037  B00IC8JBIE   \n",
+       "\n",
+       "                                               description  \\\n",
+       "item_id                                                      \n",
+       "1        Ten shades of eye shadow that features bold co...   \n",
+       "2        Color burst lip butter combines beautiful colo...   \n",
+       "3        Buy MAC Eyeshadows - MAC Eye Shadow Frost Sati...   \n",
+       "4        Here's something you simply can't live without...   \n",
+       "5        Queen Helene has been a leader in quality beau...   \n",
+       "...                                                    ...   \n",
+       "12097                                                  NaN   \n",
+       "12098    The Best BOTANICAL HYALURONIC ACID (5.0%) Gel ...   \n",
+       "12099    Announcing a Dermatologist Grade Skin Treatmen...   \n",
+       "12100    Announcing The Ultimate Vitamin C Anti Aging S...   \n",
+       "12101    For The Best Natural & Organic Ingredient Base...   \n",
+       "\n",
+       "                                                     title  \\\n",
+       "item_id                                                      \n",
+       "1        NYX Cosmetics Eye Shadow Palette 10 Color, Jaz...   \n",
+       "2        REVLON Colorburst Lip Butter, Peach Parfait, 0...   \n",
+       "3                         MAC Eye Shadow Frost Satin Taupe   \n",
+       "4        Mrs. Meyer's Clean Day Dish Soap, Lavender, 16...   \n",
+       "5        Queen Helene Mint Julep Masque, 2 Ounce Travel...   \n",
+       "...                                                    ...   \n",
+       "12097                       Dr Song Rosehip Oil 4oz (4 oz)   \n",
+       "12098    Best Botanical Hyaluronic Acid Anti Aging Faci...   \n",
+       "12099    Anti Aging All In One Facial Treatment (Replac...   \n",
+       "12100    Best Vitamin C Anti Aging 6 Item System &amp; ...   \n",
+       "12101    Best Anti Aging Facial Creme &amp; Face Cream ...   \n",
+       "\n",
+       "                                                     imUrl  \\\n",
+       "item_id                                                      \n",
+       "1        http://ecx.images-amazon.com/images/I/41PVzq7M...   \n",
+       "2        http://ecx.images-amazon.com/images/I/31r1scO3...   \n",
+       "3        http://ecx.images-amazon.com/images/I/41PNABCE...   \n",
+       "4        http://ecx.images-amazon.com/images/I/31H9M36F...   \n",
+       "5        http://ecx.images-amazon.com/images/I/41YKB8lA...   \n",
+       "...                                                    ...   \n",
+       "12097    http://ecx.images-amazon.com/images/I/412qdoPc...   \n",
+       "12098    http://ecx.images-amazon.com/images/I/4171BmUV...   \n",
+       "12099    http://ecx.images-amazon.com/images/I/314b-jZn...   \n",
+       "12100    http://ecx.images-amazon.com/images/I/51yIcFHj...   \n",
+       "12101    http://ecx.images-amazon.com/images/I/41EwlMp3...   \n",
+       "\n",
+       "                  salesRank  \\\n",
+       "item_id                       \n",
+       "1        {'Beauty': 138235}   \n",
+       "2         {'Beauty': 12827}   \n",
+       "3        {'Beauty': 107237}   \n",
+       "4                       NaN   \n",
+       "5         {'Beauty': 10453}   \n",
+       "...                     ...   \n",
+       "12097      {'Beauty': 7597}   \n",
+       "12098    {'Beauty': 116649}   \n",
+       "12099     {'Beauty': 84262}   \n",
+       "12100     {'Beauty': 87595}   \n",
+       "12101     {'Beauty': 28766}   \n",
+       "\n",
+       "                                                categories   price  \\\n",
+       "item_id                                                              \n",
+       "1                     [[Beauty, Makeup, Eyes, Eye Shadow]]    7.97   \n",
+       "2                       [[Beauty, Makeup, Lips, Lipstick]]    6.35   \n",
+       "3                     [[Beauty, Makeup, Eyes, Eye Shadow]]   21.19   \n",
+       "4        [[Beauty, Skin Care, Body, Moisturizers, Lotio...    8.19   \n",
+       "5        [[Beauty, Skin Care, Face, Treatments & Masks,...    3.03   \n",
+       "...                                                    ...     ...   \n",
+       "12097           [[Beauty, Skin Care, Face, Oils & Serums]]   19.99   \n",
+       "12098           [[Beauty, Skin Care, Face, Oils & Serums]]   24.50   \n",
+       "12099            [[Beauty, Skin Care, Eyes, Combinations]]   26.50   \n",
+       "12100                   [[Beauty, Skin Care, Sets & Kits]]  125.00   \n",
+       "12101    [[Beauty, Skin Care, Face, Creams & Moisturize...   33.00   \n",
+       "\n",
+       "                                                   related  \\\n",
+       "item_id                                                      \n",
+       "1        {'also_bought': ['B00B1ZPFT4', 'B00A5YDBWK', '...   \n",
+       "2        {'also_bought': ['B006GQTZ8A', 'B006GQEI0A', '...   \n",
+       "3        {'also_bought': ['B0018HPFNG', 'B00BEH4UU4', '...   \n",
+       "4        {'also_bought': ['B0000UTUV0', 'B004ZY1J6G', '...   \n",
+       "5        {'also_bought': ['B0072CTONS', 'B00CYI3RAG', '...   \n",
+       "...                                                    ...   \n",
+       "12097    {'also_bought': ['B00LNVW1IE', 'B00JYKGFWY', '...   \n",
+       "12098    {'also_bought': ['B00IC8JBIE', 'B00IC9AG5A', '...   \n",
+       "12099    {'also_bought': ['B00IC8JBIE', 'B00IC7L3JK', '...   \n",
+       "12100    {'also_viewed': ['B00IC8JBIE', 'B00GYJWL7G', '...   \n",
+       "12101    {'also_bought': ['B00IC7L3JK', 'B00IC9AG5A', '...   \n",
+       "\n",
+       "                              brand  \\\n",
+       "item_id                               \n",
+       "1                               NYX   \n",
+       "2                            Revlon   \n",
+       "3                             M.A.C   \n",
+       "4        Mrs. Meyer&#39;s Clean Day   \n",
+       "5                      Queen Helene   \n",
+       "...                             ...   \n",
+       "12097                           NaN   \n",
+       "12098                           NaN   \n",
+       "12099                           NaN   \n",
+       "12100                           NaN   \n",
+       "12101                           NaN   \n",
+       "\n",
+       "                                             combined_text  \\\n",
+       "item_id                                                      \n",
+       "1        Description: Ten shades of eye shadow that fea...   \n",
+       "2        Description: Color burst lip butter combines b...   \n",
+       "3        Description: Buy MAC Eyeshadows - MAC Eye Shad...   \n",
+       "4        Description: Here's something you simply can't...   \n",
+       "5        Description: Queen Helene has been a leader in...   \n",
+       "...                                                    ...   \n",
+       "12097    Description: empty. Title: Dr Song Rosehip Oil...   \n",
+       "12098    Description: The Best BOTANICAL HYALURONIC ACI...   \n",
+       "12099    Description: Announcing a Dermatologist Grade ...   \n",
+       "12100    Description: Announcing The Ultimate Vitamin C...   \n",
+       "12101    Description: For The Best Natural & Organic In...   \n",
+       "\n",
+       "                                                embeddings  \n",
+       "item_id                                                     \n",
+       "1        [tensor(0.0115), tensor(-0.0265), tensor(-0.00...  \n",
+       "2        [tensor(0.0522), tensor(0.0051), tensor(-0.031...  \n",
+       "3        [tensor(0.0657), tensor(-0.0131), tensor(-0.02...  \n",
+       "4        [tensor(0.0452), tensor(0.0012), tensor(0.0066...  \n",
+       "5        [tensor(0.0141), tensor(-0.0156), tensor(-0.03...  \n",
+       "...                                                    ...  \n",
+       "12097    [tensor(0.0204), tensor(0.0244), tensor(0.0625...  \n",
+       "12098    [tensor(0.0454), tensor(0.0540), tensor(-0.023...  \n",
+       "12099    [tensor(0.0432), tensor(0.0406), tensor(-0.051...  \n",
+       "12100    [tensor(0.0523), tensor(0.0635), tensor(-0.055...  \n",
+       "12101    [tensor(0.0603), tensor(0.0612), tensor(-0.053...  \n",
+       "\n",
+       "[12101 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_index()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 1ef5c9e7..b896fdf8 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -37,6 +37,17 @@ def __init__(
         self._positive_prefix = positive_prefix
 
         self._init_weights(initializer_range)
+        
+        df = torch.load('../data/Beauty/data_full.pt')
+        precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
+        
+        # TODO ask if correct, nans occurs in validation
+        padding_embedding = self._item_embeddings.weight[0].unsqueeze(0)
+        mask_embedding = self._item_embeddings.weight[-1].unsqueeze(0)
+        
+        extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
+        
+        self._item_embeddings.weight.data.copy_(extended_embeddings)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):

From b83529380225f0d97a7107eedb05acf4ce65148f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 26 Jan 2025 11:11:47 +0700
Subject: [PATCH 048/175] update collsion solver & use dedup loss

---
 configs/train/tiger_train_config.json    |  11 +-
 modeling/models/tiger.py                 |  13 +-
 modeling/rqvae_utils/collision_solver.py | 167 +++++++++++++++++------
 3 files changed, 145 insertions(+), 46 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index be57d320..96321efd 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -66,8 +66,15 @@
         "type": "ce",
         "predictions_prefix": "logits",
         "labels_prefix": "semantic.labels",
-        "output_prefix": "downstream_loss",
-        "weight": 1.0
+        "weight": 1.0,
+        "output_prefix": "semantic_loss"
+      },
+      {
+        "type": "ce",
+        "predictions_prefix": "dedup.logits",
+        "labels_prefix": "dedup.labels",
+        "weight": 1.0,
+        "output_prefix": "dedup_loss"
       }
     ],
     "output_prefix": "loss"
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 44a8b624..bd3fae64 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -133,7 +133,7 @@ def create_from_config(cls, config, **kwargs):
             emb_dim=len(rqvae_model.codebook_sizes), 
             codebook_size=rqvae_model.codebook_sizes[0]
         )
-        solver.create_query_candidates_dict(torch.tensor(semantic_ids), residuals)
+        solver.create_query_candidates_dict(torch.tensor(item_ids), torch.tensor(semantic_ids), residuals)
         
         item_id_to_embedding = {
             item_id: embedding for (item_id, embedding) in zip(item_ids, embeddings)
@@ -187,11 +187,20 @@ def forward(self, inputs):
             logits = decoder_prefix_scores.reshape(-1, self._codebook_sizes[0]) # (batch_size * dec_seq_len, _codebook_sizes[0])
             
             semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in label_events.tolist()]) # len(events), len(codebook_sizes)
+            residuals = torch.stack([self._item_id_to_residual[event] for event in label_events.tolist()])
+            info = self._solver.get_scores_batch(semantic_ids, residuals)
+            # TODO use BatchLogSoftmax
+            
             semantic_events = semantic_ids.view(-1)
             
             # TODO batch_logsoftmax don't match shapes (example bert4rec cls)
             
-            return {self._pred_prefix: logits, f"semantic.{self._labels_prefix}.ids": semantic_events}
+            return {
+                self._pred_prefix: logits, 
+                f"semantic.{self._labels_prefix}.ids": semantic_events,
+                "dedup.logits": info["scores"],
+                "dedup.labels.ids": info["dedup_tokens"]
+            }
         else:
             label_events = inputs["{}.ids".format(self._labels_prefix)]
             label_lengths = inputs["{}.length".format(self._labels_prefix)]
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index f81b9454..b4028b23 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -1,3 +1,4 @@
+from collections import defaultdict
 import torch
 
 class CollisionSolver:
@@ -8,15 +9,16 @@ def __init__(self, residual_dim, emb_dim, codebook_size, device: torch.device =
         :param emb_dim: Длина semantic_id (без токена решающего коллизии)
         :param device: Устройство
         """
-        self._semantic_id_dict = None
-        self._unique_ids = None
-        self.counts = None
-        self.residual_dim = residual_dim
-        self.emb_dim = emb_dim
-        self.codebook_size = codebook_size
-        self.device = device
+        self._sem_ids_sparse_tensor = None #тензор группирирующий остатки по semantic_id
+        self.item_ids_sparse_tensor = None #тензор группирируюшщий реальные айди айтемов по semantic_id
+        self.counts_dict = defaultdict(int) #тензор храняющий количество коллизий по semantic_id
+        self.residual_dim = residual_dim #длина остатка
+        self.emb_dim = emb_dim #длина semantic_id
+        self.codebook_size = codebook_size #количество элементов в одном кодбуке
+        self.device = device #девайс
+        self.item_ids_dict = {} #словарь сопостовляющий каждому item_id его semantic_id и токен решающий коллизии
 
-        self.key = torch.tensor([self.codebook_size ** i for i in range(self.emb_dim)], dtype=torch.long, device=self.device)
+        self.key = torch.tensor([self.codebook_size ** i for i in range(self.emb_dim)], dtype=torch.long, device=self.device) #ключ для сопоставления числа каждому semantic_id
 
     def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:
         """
@@ -26,10 +28,11 @@ def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:
             tensor = tensor.to(self.device)
         return tensor
 
-    def create_query_candidates_dict(self, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
+    def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
         """
         Создает разреженный тензор, который содержит сгруппированные по semantic id элементы
 
+        :param item_ids: Реальные айди айтемов (пусть будут больше 0)
         :param semantic_ids: Тензор всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)
         :param residuals: Тензор остатков для каждого semantic_id
         """
@@ -39,9 +42,12 @@ def create_query_candidates_dict(self, semantic_ids: torch.Tensor, residuals: to
         assert residuals_count == semantic_ids_count
         assert semantic_id_length == self.emb_dim
         assert residual_length == self.residual_dim
+        assert item_ids.shape == (residuals_count,)
 
+        item_ids = self._to_device(item_ids)
         residuals = self._to_device(residuals)
         semantic_ids = self._to_device(semantic_ids)
+
         unique_id = torch.einsum('nc,c->n', semantic_ids, self.key)
         unique_ids, inverse_indices = torch.unique(unique_id, return_inverse=True)
         sorted_indices = torch.argsort(inverse_indices)
@@ -51,53 +57,130 @@ def create_query_candidates_dict(self, semantic_ids: torch.Tensor, residuals: to
         row_indices = inverse_indices[sorted_indices]
         col_indices = torch.arange(semantic_ids_count) - offsets[row_indices]
         indices = torch.stack([
-            row_indices,
+            unique_ids[row_indices],
             col_indices
         ], dim=0)
 
-        self._semantic_id_dict = torch.sparse_coo_tensor(indices, residuals[sorted_indices], size=(len(unique_ids), max_residuals_count, self.residual_dim), device=self.device)
-        self._unique_ids = unique_ids
-        self.counts = counts
+        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices], size=(self.codebook_size ** self.emb_dim, max_residuals_count, self.residual_dim), device=self.device)
+        self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))
+
+        item_id_indices = torch.stack((unique_ids[row_indices], col_indices))
+
+        self.item_ids_dict = {
+            item_id.item(): (sem_id_key.item(), dedup_token.item())
+            for item_id, (sem_id_key, dedup_token) in zip(item_ids[sorted_indices], torch.stack((unique_ids[row_indices], col_indices), dim=1))
+        }
+        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(item_id_indices, item_ids[sorted_indices], size=(self.codebook_size ** self.emb_dim, max_residuals_count), device=self.device, dtype=torch.int16)
+
+    def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        assert semantic_ids.shape[1] == self.emb_dim
 
+        semantic_ids = self._to_device(semantic_ids)
+        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+
+        candidates = torch.stack([self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids])
+        counts = torch.tensor([self.counts_dict[key.item()] for key in unique_ids], device=self.device)
+        mask = torch.arange(candidates.shape[1], device=self.device).expand(len(unique_ids), -1) < counts.view(-1, 1)
+
+        return candidates, mask
 
-    def get_residuals_by_semantic_id(self, semantic_id: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    def get_scores_batch(self, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
-        :param semantic_id: [emb_dim] semantic id (без токена решающего коллизии)
+        :param semantic_id: [batch_size, emb_dim] semantic ids (без токена решающего коллизии)
+        :param residuals: [batch_size, residual_dim] остатки
+
+        :return: Словарь с ключами:
+            - 'scores_mask': [batch_size, max_collision_count] маска существующих значений scores
+            - 'scores': [batch_size, max_collision_count] софтмакс для каждого из кандидатов
+            - 'dedup_tokens_mask': [batch_size] маска существующих токенов решающих коллизии
+            - 'dedup_tokens': [batch_size] токены решающие коллизии
+            - 'item_ids': [batch_size] реальные айди айтемов
+        """
+        assert semantic_ids.shape[1] == self.emb_dim
+        assert residuals.shape[1] == self.residual_dim
+        assert semantic_ids.shape[0] == residuals.shape[0]
+
+        semantic_ids = self._to_device(semantic_ids)
+        residuals = self._to_device(residuals)
+
+        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+
+        candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_ids)
+
+        scores = torch.softmax(torch.einsum('njk,nk->nj', candidates, residuals).masked_fill(~mask, float('-inf')), dim=1)
 
-        :return: candidates: [max_residuals_count, residual_dim] список остатков с таким же semantic id
-        :return: mask: [max_residuals_count] маска для списка остатков
+        indices = torch.argmax(scores, dim=1)
+        item_ids = torch.stack([self.item_ids_sparse_tensor[unique_ids[i]][indices[i]] for i in range(semantic_ids.shape[0])])
+
+        return {
+            "scores_mask": mask,
+            "scores": scores,
+            "dedup_tokens_mask": mask.any(dim=1),
+            "dedup_tokens": indices,
+            "item_ids": item_ids
+        }
+
+
+    def get_item_id_info(self, item_id: int) -> dict[str, torch.Tensor]:
         """
-        assert semantic_id.shape == (self.emb_dim,)
+        Возвращает информацию по заданному item_id:
+        - semantic_id
+        - Все элементы с таким же semantic_id
+        - Их item_ids
+        - Их остатки
+        - Токены, решающие коллизии (dedup tokens)
+
+        :param item_id: айди айтема
+
+        :return: Словарь с ключами:
+            - 'semantic_id': [emb_dim] semantic id
+            - 'residuals': [count, residual_dim] остатки
+            - 'item_ids': [count] item ids
+            - 'dedup_tokens': [count] токены решающие коллизии
+        """
+
+        if item_id not in self.item_ids_dict:
+            return {
+                "semantic_id": torch.empty(0, dtype=torch.long, device=self.device),
+                "residuals": torch.empty((0, self.residual_dim), device=self.device),
+                "item_ids": torch.empty(0, dtype=torch.int16, device=self.device),
+                "dedup_tokens": torch.empty(0, dtype=torch.long, device=self.device),
+            }
 
-        semantic_id = self._to_device(semantic_id)
+        semantic_id_key, dedup_token = self.item_ids_dict[item_id]
 
-        unique_id = torch.einsum('c,c->', semantic_id, self.key)
-        target_index = (self._unique_ids == unique_id).nonzero(as_tuple=True)[0]
+        semantic_id = torch.div(semantic_id_key, self.key, rounding_mode='floor') % self.codebook_size
 
-        if len(target_index) == 0:
-            empty_candidates = torch.empty((0, self.residual_dim), device=self.device)
-            empty_mask = torch.empty((0,), dtype=torch.bool, device=self.device)
-            return empty_candidates, empty_mask # Если unique_id отсутствует
+        assert semantic_id.shape == (self.emb_dim,)
 
-        candidates = self._semantic_id_dict[target_index.item()].to_dense()
-        mask = torch.arange(candidates.size(0)) < self.counts[target_index.item()]
+        candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_id[None])
+        residuals = candidates.squeeze(0)[mask.squeeze(0)]
+        item_ids = self.item_ids_sparse_tensor[semantic_id_key].to_dense()[mask.squeeze(0)]
 
-        return candidates, mask
+        dedup_tokens = torch.arange(residuals.shape[0], device=self.device)
 
-    def get_scores(self, semantic_id, residual) -> tuple[torch.Tensor, int]:
+        return {
+            "semantic_id": semantic_id,
+            "residuals": residuals,
+            "item_ids": item_ids,
+            "dedup_tokens": dedup_tokens,
+        }
+
+    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """
-        :param semantic_id: [emb_dim] semantic id (без токена решающего коллизии
-        :param residual: [residual_dim] Остаток
+        :param semantic_id: [batch_size, emb_dim] semantic ids (без токенов решающего коллизии)
+        :param dedup_tokens: [batch_size] токены решающие коллизии
 
-        :return: scores: [residuals_count] Вероятности для остатков
-        :return: index: Индекс наибольшего значения в scores
+        :return: item_ids : [batch_size] реальные айди айтемов
         """
-        assert semantic_id.shape == (self.emb_dim,)
-        assert residual.shape == (self.residual_dim,)
-
-        residual = self._to_device(residual)
-        candidates, mask = self.get_residuals_by_semantic_id(semantic_id)
-        scores = torch.softmax(torch.einsum('jk,k->j', candidates[mask], residual), dim=0)
-        if scores.shape[0] == 0:
-            return torch.empty((0,), device=self.device), torch.empty((0,), device=self.device)
-        return scores, torch.argmax(scores)
\ No newline at end of file
+        assert semantic_ids.shape[1] == self.emb_dim
+        assert dedup_tokens.shape == (semantic_ids.shape[0],)
+
+        semantic_ids = self._to_device(semantic_ids)
+        dedup_tokens = self._to_device(dedup_tokens)
+
+        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+
+        item_ids = torch.stack([self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])
+
+        return item_ids

From dde3835c41aa20d41e68bb1ae90dc7dec6b3ccd0 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 26 Jan 2025 11:55:06 +0700
Subject: [PATCH 049/175] optimize item embs

---
 modeling/main.ipynb      | 447 +--------------------------------------
 modeling/models/tiger.py |  19 +-
 2 files changed, 19 insertions(+), 447 deletions(-)

diff --git a/modeling/main.ipynb b/modeling/main.ipynb
index e0e0b985..b4e92a3d 100644
--- a/modeling/main.ipynb
+++ b/modeling/main.ipynb
@@ -241,457 +241,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 0.0115, -0.0265, -0.0014,  ..., -0.0403, -0.0064, -0.0646],\n",
-       "        [ 0.0522,  0.0051, -0.0317,  ..., -0.0215, -0.0284,  0.0104],\n",
-       "        [ 0.0657, -0.0131, -0.0234,  ..., -0.0370, -0.0462, -0.0503],\n",
-       "        ...,\n",
-       "        [ 0.0432,  0.0406, -0.0517,  ..., -0.0368,  0.0365, -0.0342],\n",
-       "        [ 0.0523,  0.0635, -0.0556,  ..., -0.0422,  0.0264, -0.0267],\n",
-       "        [ 0.0603,  0.0612, -0.0532,  ..., -0.0728,  0.0288, -0.0492]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "torch.stack(df.sort_index().embeddings.tolist())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>raw_user_id</th>\n",
-       "      <th>raw_item_id</th>\n",
-       "      <th>rating</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>user_id</th>\n",
-       "      <th>asin</th>\n",
-       "      <th>description</th>\n",
-       "      <th>title</th>\n",
-       "      <th>imUrl</th>\n",
-       "      <th>salesRank</th>\n",
-       "      <th>categories</th>\n",
-       "      <th>price</th>\n",
-       "      <th>related</th>\n",
-       "      <th>brand</th>\n",
-       "      <th>combined_text</th>\n",
-       "      <th>embeddings</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>item_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>A20NUABVL6KKTV</td>\n",
-       "      <td>B002OVV7F0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1344556800</td>\n",
-       "      <td>651772</td>\n",
-       "      <td>B002OVV7F0</td>\n",
-       "      <td>Ten shades of eye shadow that features bold co...</td>\n",
-       "      <td>NYX Cosmetics Eye Shadow Palette 10 Color, Jaz...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/41PVzq7M...</td>\n",
-       "      <td>{'Beauty': 138235}</td>\n",
-       "      <td>[[Beauty, Makeup, Eyes, Eye Shadow]]</td>\n",
-       "      <td>7.97</td>\n",
-       "      <td>{'also_bought': ['B00B1ZPFT4', 'B00A5YDBWK', '...</td>\n",
-       "      <td>NYX</td>\n",
-       "      <td>Description: Ten shades of eye shadow that fea...</td>\n",
-       "      <td>[tensor(0.0115), tensor(-0.0265), tensor(-0.00...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>A2CNWQEZHQ3K6R</td>\n",
-       "      <td>B006GQPZ8E</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1392940800</td>\n",
-       "      <td>952110</td>\n",
-       "      <td>B006GQPZ8E</td>\n",
-       "      <td>Color burst lip butter combines beautiful colo...</td>\n",
-       "      <td>REVLON Colorburst Lip Butter, Peach Parfait, 0...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/31r1scO3...</td>\n",
-       "      <td>{'Beauty': 12827}</td>\n",
-       "      <td>[[Beauty, Makeup, Lips, Lipstick]]</td>\n",
-       "      <td>6.35</td>\n",
-       "      <td>{'also_bought': ['B006GQTZ8A', 'B006GQEI0A', '...</td>\n",
-       "      <td>Revlon</td>\n",
-       "      <td>Description: Color burst lip butter combines b...</td>\n",
-       "      <td>[tensor(0.0522), tensor(0.0051), tensor(-0.031...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>A1MVAPY2WT4D4M</td>\n",
-       "      <td>B0002DNZAC</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1404086400</td>\n",
-       "      <td>83086</td>\n",
-       "      <td>B0002DNZAC</td>\n",
-       "      <td>Buy MAC Eyeshadows - MAC Eye Shadow Frost Sati...</td>\n",
-       "      <td>MAC Eye Shadow Frost Satin Taupe</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/41PNABCE...</td>\n",
-       "      <td>{'Beauty': 107237}</td>\n",
-       "      <td>[[Beauty, Makeup, Eyes, Eye Shadow]]</td>\n",
-       "      <td>21.19</td>\n",
-       "      <td>{'also_bought': ['B0018HPFNG', 'B00BEH4UU4', '...</td>\n",
-       "      <td>M.A.C</td>\n",
-       "      <td>Description: Buy MAC Eyeshadows - MAC Eye Shad...</td>\n",
-       "      <td>[tensor(0.0657), tensor(-0.0131), tensor(-0.02...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>AO2GZG0N16FCD</td>\n",
-       "      <td>B0000UTUVU</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1384905600</td>\n",
-       "      <td>36346</td>\n",
-       "      <td>B0000UTUVU</td>\n",
-       "      <td>Here's something you simply can't live without...</td>\n",
-       "      <td>Mrs. Meyer's Clean Day Dish Soap, Lavender, 16...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/31H9M36F...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>[[Beauty, Skin Care, Body, Moisturizers, Lotio...</td>\n",
-       "      <td>8.19</td>\n",
-       "      <td>{'also_bought': ['B0000UTUV0', 'B004ZY1J6G', '...</td>\n",
-       "      <td>Mrs. Meyer&amp;#39;s Clean Day</td>\n",
-       "      <td>Description: Here's something you simply can't...</td>\n",
-       "      <td>[tensor(0.0452), tensor(0.0012), tensor(0.0066...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>ASNGEUJ0LCACM</td>\n",
-       "      <td>B000F8HWXU</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1320796800</td>\n",
-       "      <td>27187</td>\n",
-       "      <td>B000F8HWXU</td>\n",
-       "      <td>Queen Helene has been a leader in quality beau...</td>\n",
-       "      <td>Queen Helene Mint Julep Masque, 2 Ounce Travel...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/41YKB8lA...</td>\n",
-       "      <td>{'Beauty': 10453}</td>\n",
-       "      <td>[[Beauty, Skin Care, Face, Treatments &amp; Masks,...</td>\n",
-       "      <td>3.03</td>\n",
-       "      <td>{'also_bought': ['B0072CTONS', 'B00CYI3RAG', '...</td>\n",
-       "      <td>Queen Helene</td>\n",
-       "      <td>Description: Queen Helene has been a leader in...</td>\n",
-       "      <td>[tensor(0.0141), tensor(-0.0156), tensor(-0.03...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12097</th>\n",
-       "      <td>A26ZA5ZV0BPRXK</td>\n",
-       "      <td>B00LCEROA2</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1404259200</td>\n",
-       "      <td>1186930</td>\n",
-       "      <td>B00LCEROA2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Dr Song Rosehip Oil 4oz (4 oz)</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/412qdoPc...</td>\n",
-       "      <td>{'Beauty': 7597}</td>\n",
-       "      <td>[[Beauty, Skin Care, Face, Oils &amp; Serums]]</td>\n",
-       "      <td>19.99</td>\n",
-       "      <td>{'also_bought': ['B00LNVW1IE', 'B00JYKGFWY', '...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Description: empty. Title: Dr Song Rosehip Oil...</td>\n",
-       "      <td>[tensor(0.0204), tensor(0.0244), tensor(0.0625...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12098</th>\n",
-       "      <td>A3DXSM2289U79E</td>\n",
-       "      <td>B00IBMV2ME</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1392768000</td>\n",
-       "      <td>1188037</td>\n",
-       "      <td>B00IBMV2ME</td>\n",
-       "      <td>The Best BOTANICAL HYALURONIC ACID (5.0%) Gel ...</td>\n",
-       "      <td>Best Botanical Hyaluronic Acid Anti Aging Faci...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/4171BmUV...</td>\n",
-       "      <td>{'Beauty': 116649}</td>\n",
-       "      <td>[[Beauty, Skin Care, Face, Oils &amp; Serums]]</td>\n",
-       "      <td>24.50</td>\n",
-       "      <td>{'also_bought': ['B00IC8JBIE', 'B00IC9AG5A', '...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Description: The Best BOTANICAL HYALURONIC ACI...</td>\n",
-       "      <td>[tensor(0.0454), tensor(0.0540), tensor(-0.023...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12099</th>\n",
-       "      <td>A3DXSM2289U79E</td>\n",
-       "      <td>B00IC9AG5A</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1392768000</td>\n",
-       "      <td>1188037</td>\n",
-       "      <td>B00IC9AG5A</td>\n",
-       "      <td>Announcing a Dermatologist Grade Skin Treatmen...</td>\n",
-       "      <td>Anti Aging All In One Facial Treatment (Replac...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/314b-jZn...</td>\n",
-       "      <td>{'Beauty': 84262}</td>\n",
-       "      <td>[[Beauty, Skin Care, Eyes, Combinations]]</td>\n",
-       "      <td>26.50</td>\n",
-       "      <td>{'also_bought': ['B00IC8JBIE', 'B00IC7L3JK', '...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Description: Announcing a Dermatologist Grade ...</td>\n",
-       "      <td>[tensor(0.0432), tensor(0.0406), tensor(-0.051...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12100</th>\n",
-       "      <td>A2BWXFJAQNH8LC</td>\n",
-       "      <td>B00IKKORVU</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1393632000</td>\n",
-       "      <td>1188048</td>\n",
-       "      <td>B00IKKORVU</td>\n",
-       "      <td>Announcing The Ultimate Vitamin C Anti Aging S...</td>\n",
-       "      <td>Best Vitamin C Anti Aging 6 Item System &amp;amp; ...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/51yIcFHj...</td>\n",
-       "      <td>{'Beauty': 87595}</td>\n",
-       "      <td>[[Beauty, Skin Care, Sets &amp; Kits]]</td>\n",
-       "      <td>125.00</td>\n",
-       "      <td>{'also_viewed': ['B00IC8JBIE', 'B00GYJWL7G', '...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Description: Announcing The Ultimate Vitamin C...</td>\n",
-       "      <td>[tensor(0.0523), tensor(0.0635), tensor(-0.055...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12101</th>\n",
-       "      <td>A3DXSM2289U79E</td>\n",
-       "      <td>B00IC8JBIE</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1392768000</td>\n",
-       "      <td>1188037</td>\n",
-       "      <td>B00IC8JBIE</td>\n",
-       "      <td>For The Best Natural &amp; Organic Ingredient Base...</td>\n",
-       "      <td>Best Anti Aging Facial Creme &amp;amp; Face Cream ...</td>\n",
-       "      <td>http://ecx.images-amazon.com/images/I/41EwlMp3...</td>\n",
-       "      <td>{'Beauty': 28766}</td>\n",
-       "      <td>[[Beauty, Skin Care, Face, Creams &amp; Moisturize...</td>\n",
-       "      <td>33.00</td>\n",
-       "      <td>{'also_bought': ['B00IC7L3JK', 'B00IC9AG5A', '...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Description: For The Best Natural &amp; Organic In...</td>\n",
-       "      <td>[tensor(0.0603), tensor(0.0612), tensor(-0.053...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>12101 rows × 16 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            raw_user_id raw_item_id  rating   timestamp  user_id        asin  \\\n",
-       "item_id                                                                        \n",
-       "1        A20NUABVL6KKTV  B002OVV7F0     4.0  1344556800   651772  B002OVV7F0   \n",
-       "2        A2CNWQEZHQ3K6R  B006GQPZ8E     2.0  1392940800   952110  B006GQPZ8E   \n",
-       "3        A1MVAPY2WT4D4M  B0002DNZAC     4.0  1404086400    83086  B0002DNZAC   \n",
-       "4         AO2GZG0N16FCD  B0000UTUVU     5.0  1384905600    36346  B0000UTUVU   \n",
-       "5         ASNGEUJ0LCACM  B000F8HWXU     5.0  1320796800    27187  B000F8HWXU   \n",
-       "...                 ...         ...     ...         ...      ...         ...   \n",
-       "12097    A26ZA5ZV0BPRXK  B00LCEROA2     5.0  1404259200  1186930  B00LCEROA2   \n",
-       "12098    A3DXSM2289U79E  B00IBMV2ME     5.0  1392768000  1188037  B00IBMV2ME   \n",
-       "12099    A3DXSM2289U79E  B00IC9AG5A     5.0  1392768000  1188037  B00IC9AG5A   \n",
-       "12100    A2BWXFJAQNH8LC  B00IKKORVU     5.0  1393632000  1188048  B00IKKORVU   \n",
-       "12101    A3DXSM2289U79E  B00IC8JBIE     5.0  1392768000  1188037  B00IC8JBIE   \n",
-       "\n",
-       "                                               description  \\\n",
-       "item_id                                                      \n",
-       "1        Ten shades of eye shadow that features bold co...   \n",
-       "2        Color burst lip butter combines beautiful colo...   \n",
-       "3        Buy MAC Eyeshadows - MAC Eye Shadow Frost Sati...   \n",
-       "4        Here's something you simply can't live without...   \n",
-       "5        Queen Helene has been a leader in quality beau...   \n",
-       "...                                                    ...   \n",
-       "12097                                                  NaN   \n",
-       "12098    The Best BOTANICAL HYALURONIC ACID (5.0%) Gel ...   \n",
-       "12099    Announcing a Dermatologist Grade Skin Treatmen...   \n",
-       "12100    Announcing The Ultimate Vitamin C Anti Aging S...   \n",
-       "12101    For The Best Natural & Organic Ingredient Base...   \n",
-       "\n",
-       "                                                     title  \\\n",
-       "item_id                                                      \n",
-       "1        NYX Cosmetics Eye Shadow Palette 10 Color, Jaz...   \n",
-       "2        REVLON Colorburst Lip Butter, Peach Parfait, 0...   \n",
-       "3                         MAC Eye Shadow Frost Satin Taupe   \n",
-       "4        Mrs. Meyer's Clean Day Dish Soap, Lavender, 16...   \n",
-       "5        Queen Helene Mint Julep Masque, 2 Ounce Travel...   \n",
-       "...                                                    ...   \n",
-       "12097                       Dr Song Rosehip Oil 4oz (4 oz)   \n",
-       "12098    Best Botanical Hyaluronic Acid Anti Aging Faci...   \n",
-       "12099    Anti Aging All In One Facial Treatment (Replac...   \n",
-       "12100    Best Vitamin C Anti Aging 6 Item System &amp; ...   \n",
-       "12101    Best Anti Aging Facial Creme &amp; Face Cream ...   \n",
-       "\n",
-       "                                                     imUrl  \\\n",
-       "item_id                                                      \n",
-       "1        http://ecx.images-amazon.com/images/I/41PVzq7M...   \n",
-       "2        http://ecx.images-amazon.com/images/I/31r1scO3...   \n",
-       "3        http://ecx.images-amazon.com/images/I/41PNABCE...   \n",
-       "4        http://ecx.images-amazon.com/images/I/31H9M36F...   \n",
-       "5        http://ecx.images-amazon.com/images/I/41YKB8lA...   \n",
-       "...                                                    ...   \n",
-       "12097    http://ecx.images-amazon.com/images/I/412qdoPc...   \n",
-       "12098    http://ecx.images-amazon.com/images/I/4171BmUV...   \n",
-       "12099    http://ecx.images-amazon.com/images/I/314b-jZn...   \n",
-       "12100    http://ecx.images-amazon.com/images/I/51yIcFHj...   \n",
-       "12101    http://ecx.images-amazon.com/images/I/41EwlMp3...   \n",
-       "\n",
-       "                  salesRank  \\\n",
-       "item_id                       \n",
-       "1        {'Beauty': 138235}   \n",
-       "2         {'Beauty': 12827}   \n",
-       "3        {'Beauty': 107237}   \n",
-       "4                       NaN   \n",
-       "5         {'Beauty': 10453}   \n",
-       "...                     ...   \n",
-       "12097      {'Beauty': 7597}   \n",
-       "12098    {'Beauty': 116649}   \n",
-       "12099     {'Beauty': 84262}   \n",
-       "12100     {'Beauty': 87595}   \n",
-       "12101     {'Beauty': 28766}   \n",
-       "\n",
-       "                                                categories   price  \\\n",
-       "item_id                                                              \n",
-       "1                     [[Beauty, Makeup, Eyes, Eye Shadow]]    7.97   \n",
-       "2                       [[Beauty, Makeup, Lips, Lipstick]]    6.35   \n",
-       "3                     [[Beauty, Makeup, Eyes, Eye Shadow]]   21.19   \n",
-       "4        [[Beauty, Skin Care, Body, Moisturizers, Lotio...    8.19   \n",
-       "5        [[Beauty, Skin Care, Face, Treatments & Masks,...    3.03   \n",
-       "...                                                    ...     ...   \n",
-       "12097           [[Beauty, Skin Care, Face, Oils & Serums]]   19.99   \n",
-       "12098           [[Beauty, Skin Care, Face, Oils & Serums]]   24.50   \n",
-       "12099            [[Beauty, Skin Care, Eyes, Combinations]]   26.50   \n",
-       "12100                   [[Beauty, Skin Care, Sets & Kits]]  125.00   \n",
-       "12101    [[Beauty, Skin Care, Face, Creams & Moisturize...   33.00   \n",
-       "\n",
-       "                                                   related  \\\n",
-       "item_id                                                      \n",
-       "1        {'also_bought': ['B00B1ZPFT4', 'B00A5YDBWK', '...   \n",
-       "2        {'also_bought': ['B006GQTZ8A', 'B006GQEI0A', '...   \n",
-       "3        {'also_bought': ['B0018HPFNG', 'B00BEH4UU4', '...   \n",
-       "4        {'also_bought': ['B0000UTUV0', 'B004ZY1J6G', '...   \n",
-       "5        {'also_bought': ['B0072CTONS', 'B00CYI3RAG', '...   \n",
-       "...                                                    ...   \n",
-       "12097    {'also_bought': ['B00LNVW1IE', 'B00JYKGFWY', '...   \n",
-       "12098    {'also_bought': ['B00IC8JBIE', 'B00IC9AG5A', '...   \n",
-       "12099    {'also_bought': ['B00IC8JBIE', 'B00IC7L3JK', '...   \n",
-       "12100    {'also_viewed': ['B00IC8JBIE', 'B00GYJWL7G', '...   \n",
-       "12101    {'also_bought': ['B00IC7L3JK', 'B00IC9AG5A', '...   \n",
-       "\n",
-       "                              brand  \\\n",
-       "item_id                               \n",
-       "1                               NYX   \n",
-       "2                            Revlon   \n",
-       "3                             M.A.C   \n",
-       "4        Mrs. Meyer&#39;s Clean Day   \n",
-       "5                      Queen Helene   \n",
-       "...                             ...   \n",
-       "12097                           NaN   \n",
-       "12098                           NaN   \n",
-       "12099                           NaN   \n",
-       "12100                           NaN   \n",
-       "12101                           NaN   \n",
-       "\n",
-       "                                             combined_text  \\\n",
-       "item_id                                                      \n",
-       "1        Description: Ten shades of eye shadow that fea...   \n",
-       "2        Description: Color burst lip butter combines b...   \n",
-       "3        Description: Buy MAC Eyeshadows - MAC Eye Shad...   \n",
-       "4        Description: Here's something you simply can't...   \n",
-       "5        Description: Queen Helene has been a leader in...   \n",
-       "...                                                    ...   \n",
-       "12097    Description: empty. Title: Dr Song Rosehip Oil...   \n",
-       "12098    Description: The Best BOTANICAL HYALURONIC ACI...   \n",
-       "12099    Description: Announcing a Dermatologist Grade ...   \n",
-       "12100    Description: Announcing The Ultimate Vitamin C...   \n",
-       "12101    Description: For The Best Natural & Organic In...   \n",
-       "\n",
-       "                                                embeddings  \n",
-       "item_id                                                     \n",
-       "1        [tensor(0.0115), tensor(-0.0265), tensor(-0.00...  \n",
-       "2        [tensor(0.0522), tensor(0.0051), tensor(-0.031...  \n",
-       "3        [tensor(0.0657), tensor(-0.0131), tensor(-0.02...  \n",
-       "4        [tensor(0.0452), tensor(0.0012), tensor(0.0066...  \n",
-       "5        [tensor(0.0141), tensor(-0.0156), tensor(-0.03...  \n",
-       "...                                                    ...  \n",
-       "12097    [tensor(0.0204), tensor(0.0244), tensor(0.0625...  \n",
-       "12098    [tensor(0.0454), tensor(0.0540), tensor(-0.023...  \n",
-       "12099    [tensor(0.0432), tensor(0.0406), tensor(-0.051...  \n",
-       "12100    [tensor(0.0523), tensor(0.0635), tensor(-0.055...  \n",
-       "12101    [tensor(0.0603), tensor(0.0612), tensor(-0.053...  \n",
-       "\n",
-       "[12101 rows x 16 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.sort_index()"
    ]
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index bd3fae64..10bba88a 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -74,13 +74,20 @@ def __init__(
         self._solver = solver
         
         self._codebook_sizes = codebook_sizes
+
         self._codebook_item_embeddings_flattened = torch.cat([codebook for codebook in rqvae_model.codebooks], dim=0)
+        self._codebook_item_embeddings_flattened.requires_grad = False # TODO ask if freeze is needed
         self._codebook_item_embeddings_stacked = torch.stack([codebook for codebook in rqvae_model.codebooks])
+        self._codebook_item_embeddings_stacked.requires_grad = False # TODO ask if freeze is needed
         
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
         self._item_id_to_embedding = item_id_to_embedding
         
+        self._item_id_to_semantic_embedding = {
+            item_id: emb for (item_id, emb) in zip(item_id_to_semantic_id.keys(), self.get_init_item_embeddings(list(item_id_to_semantic_id.keys())))
+        }
+        
         self._trie = Trie()
         for item_id, semantic_id in item_id_to_semantic_id.items():
             self._trie.insert(Item(semantic_id, item_id_to_residual[item_id])) # TODO no dedup tokens here
@@ -289,9 +296,13 @@ def _apply_decoder(
 
         return decoder_outputs
     
-    def get_item_embeddings(self, events):
+    def get_item_embeddings(self, events): # TODO freezed embeddings
         events = events.tolist()
-        
+        embs = torch.stack([self._item_id_to_semantic_embedding[event] for event in events])
+        embs = embs.view(-1, self._embedding_dim)
+        return embs
+    
+    def get_init_item_embeddings(self, events): # TODO freezed embeddings
         # convert to semantic ids
         semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
         semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
@@ -303,7 +314,7 @@ def get_item_embeddings(self, events):
         semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
         semantic_embeddings = semantic_embeddings.view(
             len(events), len(self._codebook_sizes), self._embedding_dim
-        )
+        ) # (len(events), len(self._codebook_sizes), embedding_dim)
         
         # get residuals
         text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
@@ -312,7 +323,7 @@ def get_item_embeddings(self, events):
         
         # get true item embeddings
         item_embeddings = torch.cat([semantic_embeddings, residual], dim=1) 
-        item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
+        # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
         
         return item_embeddings
 

From 83e99a297c4c7a4dea37f91c2dd6ae7b9e3b9996 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 26 Jan 2025 12:30:55 +0700
Subject: [PATCH 050/175] add caching in trie

---
 modeling/models/tiger.py     |  2 +-
 modeling/rqvae_utils/trie.py | 39 +++++++++++++++++++-----------------
 2 files changed, 22 insertions(+), 19 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 10bba88a..7ea0d72c 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -100,7 +100,7 @@ def __init__(
             embedding_dim=embedding_dim,
         )
         self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim # TODO
+            num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim
         )  # + 2 for bos token & residual
 
         self._init_weights(initializer_range)
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index e5a94067..ada7bd62 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -1,5 +1,7 @@
+from functools import lru_cache
+
 import torch
-from typing import List, Tuple, Dict, Optional
+
 
 class Item:
     """
@@ -7,7 +9,7 @@ class Item:
       - hierarchical_id: a tuple of ints
       - residual: a torch.Tensor of shape (emb_dim,)
     """
-    def __init__(self, hierarchical_id: Tuple[int, ...], residual: torch.Tensor):
+    def __init__(self, hierarchical_id: tuple[int, ...], residual: torch.Tensor):
         self.hierarchical_id = hierarchical_id
         self.residual = residual
 
@@ -22,8 +24,8 @@ class TrieNode:
       - items: list of Items (only non-empty if this node is the end of some hierarchical_id(s))
     """
     def __init__(self):
-        self.children: Dict[int, TrieNode] = {}
-        self.items: List[Item] = []  # collisions end up here if they share the same path
+        self.children: dict[int, TrieNode] = {}
+        self.items: list[Item] = []  # collisions end up here if they share the same path
 
 
 class Trie:
@@ -34,6 +36,9 @@ class Trie:
     """
     def __init__(self):
         self.root = TrieNode()
+        # Create instance-specific cached methods
+        self._cached_gather_subtree_items = lru_cache(maxsize=1024)(self._gather_subtree_items)
+        self._cached_dot_scores = lru_cache(maxsize=1024)(self._dot_scores)
 
     def insert(self, item: Item) -> None:
         """
@@ -47,7 +52,7 @@ def insert(self, item: Item) -> None:
         # At the leaf, store the item
         current_node.items.append(item)
 
-    def _gather_subtree_items(self, node: TrieNode) -> List[Item]:
+    def _gather_subtree_items(self, node: TrieNode) -> tuple[Item, ...]:
         """
         Collect all items in the sub-tree rooted at 'node'.
         """
@@ -58,26 +63,24 @@ def _gather_subtree_items(self, node: TrieNode) -> List[Item]:
             collected.extend(cur.items)
             for child_node in cur.children.values():
                 stack.append(child_node)
-        return collected
+        return tuple(collected)
 
     def _dot_scores(
         self, 
         query_residual: torch.Tensor, 
-        candidates: List[Item]
-    ) -> List[Tuple[Item, float]]:
+        candidates: tuple[Item, ...]
+    ) -> tuple[tuple[Item, float], ...]:
         """
         Compute dot-product scores between query_residual and each candidate's residual.
-        Return list of (candidate_item, score).
+        Return tuple of (candidate_item, score) pairs.
         """
-        # Note: if your embeddings are large, you may prefer a more efficient approach
-        # (e.g., batched matrix multiplication). For clarity, we do a simple loop here.
         scored = []
         for c in candidates:
             score = torch.dot(query_residual, c.residual).item()
             scored.append((c, score))
-        return scored
+        return tuple(scored)
 
-    def find_closest(self, query_item: Item, n: int) -> List[Item]:
+    def find_closest(self, query_item: Item, n: int) -> list[Item]:
         """
         Find up to n closest items to 'query_item' by the described rules:
           1) Find longest existing matching prefix.
@@ -104,12 +107,12 @@ def find_closest(self, query_item: Item, n: int) -> List[Item]:
         # We'll climb up if needed.
         while path_stack:
             node, parent = path_stack[-1]
-            subtree_items = self._gather_subtree_items(node)
+            subtree_items = self._cached_gather_subtree_items(node)
             if len(subtree_items) >= n:
                 # We can pick the top n by dot product
-                scored = self._dot_scores(query_item.residual, subtree_items)
+                scored = self._cached_dot_scores(query_item.residual, subtree_items)
                 # Sort descending by score
-                scored.sort(key=lambda x: x[1], reverse=True)
+                scored = sorted(scored, key=lambda x: x[1], reverse=True)
                 return [itm for itm, _ in scored[:n]]
             else:
                 # Not enough items: move up one level
@@ -119,8 +122,8 @@ def find_closest(self, query_item: Item, n: int) -> List[Item]:
                     # We are at the root (the last pop).
                     # If still not enough items, we simply return everything we have from the root
                     # or if root has more than n, we pick top n.
-                    scored = self._dot_scores(query_item.residual, subtree_items)
-                    scored.sort(key=lambda x: x[1], reverse=True)
+                    scored = self._cached_dot_scores(query_item.residual, subtree_items)
+                    scored = sorted(scored, key=lambda x: x[1], reverse=True)
                     return [itm for itm, _ in scored[:n]]
                 # Otherwise, continue in the loop (which means gather from the parent's node)
 

From 48172f5aa70d52d10460e90eff525cfd70b98989 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 26 Jan 2025 19:25:00 +0700
Subject: [PATCH 051/175] add autoregressive

---
 .gitignore                            |   1 +
 configs/train/tiger_train_config.json |  93 +----------------
 modeling/loss/base.py                 |  40 +++++++-
 modeling/models/sasrec.py             |   2 +-
 modeling/models/tiger.py              | 137 +++++++++++++++++---------
 modeling/rqvae_utils/trie.py          |   6 +-
 6 files changed, 136 insertions(+), 143 deletions(-)

diff --git a/.gitignore b/.gitignore
index 7980d8a2..51358cc9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,7 @@
 __pycache__
 data/*
 tensorboard_logs/*
+saved_logs/*
 .venv
 papers
 checkpoints/*
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 96321efd..e65ae66d 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,7 +1,6 @@
 {
   "experiment_name": "tiger",
   "train_epochs_num": 10,
-  "best_metric": "validation/ndcg@20",
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",
@@ -63,14 +62,14 @@
     "type": "composite",
     "losses": [
       {
-        "type": "ce",
+        "type": "sample_logsoftmax",
         "predictions_prefix": "logits",
         "labels_prefix": "semantic.labels",
         "weight": 1.0,
         "output_prefix": "semantic_loss"
       },
       {
-        "type": "ce",
+        "type": "sample_logsoftmax",
         "predictions_prefix": "dedup.logits",
         "labels_prefix": "dedup.labels",
         "weight": 1.0,
@@ -86,94 +85,6 @@
         "type": "metric",
         "on_step": 1,
         "loss_prefix": "loss"
-      },
-      {
-        "type": "validation",
-        "on_step": 64,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      },
-      {
-        "type": "eval",
-        "on_step": 64,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
       }
     ]
   }
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 1bbdc186..d9533f2f 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -54,6 +54,44 @@ def forward(self, inputs):
         return total_loss
 
 
+class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
+    def __init__(self, predictions_prefix, labels_prefix):
+        super().__init__()
+        self._predictions_prefix = predictions_prefix
+        self._labels_prefix = labels_prefix
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            predictions_prefix=config.get('predictions_prefix'),
+            labels_prefix=config.get('labels_prefix')
+        )
+
+    def forward(self, inputs):  # use log soft max
+        logits = inputs[self._predictions_prefix]
+        candidates = inputs[self._labels_prefix]
+        
+        assert len(logits.shape) in [2, 3]
+        
+        batch_size = logits.shape[0]
+        seq_len = logits.shape[1]
+        
+        if len(logits.shape) == 3:
+            loss = -torch.gather(
+                torch.log_softmax(logits, dim=-1).view(batch_size * seq_len, logits.shape[-1]),
+                dim=-1, 
+                index=candidates.view(batch_size * seq_len, 1)
+            ).mean()
+        else:
+            loss = -torch.gather(
+                torch.log_softmax(logits, dim=-1),
+                dim=-1, 
+                index=candidates.view(batch_size, 1) # TODO check if this is correct
+            ).mean()
+            
+        return loss
+
+
 class BatchLogSoftmaxLoss(TorchLoss, config_name='batch_logsoftmax'):
 
     def __init__(self, predictions_prefix, candidates_prefix):
@@ -299,7 +337,7 @@ def forward(self, inputs):
         assert positive_scores.shape[0] == negative_scores.shape[0]
 
         positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(dim=-1)  # (x)
-        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores)).sum(dim=-1)  # (x)
+        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores) + 1e-9).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
         loss = positive_loss + negative_loss  # (x)
         loss = -loss.sum()  # (1)
 
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index b896fdf8..28b07e77 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -41,13 +41,13 @@ def __init__(
         df = torch.load('../data/Beauty/data_full.pt')
         precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
         
-        # TODO ask if correct, nans occurs in validation
         padding_embedding = self._item_embeddings.weight[0].unsqueeze(0)
         mask_embedding = self._item_embeddings.weight[-1].unsqueeze(0)
         
         extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
         
         self._item_embeddings.weight.data.copy_(extended_embeddings)
+        self._item_embeddings.weight.requires_grad = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 7ea0d72c..0ddfb393 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -113,7 +113,7 @@ def init_rqvae(cls, config) -> RqVaeModel:
         rqvae_model = RqVaeModel.create_from_config(rqvae_config['model']).to(DEVICE)
         rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
         rqvae_model.eval()
-        for param in rqvae_model.parameters(): # TODO
+        for param in rqvae_model.parameters(): # TODO freezed
             param.requires_grad = False
         
         codebook_sizes = rqvae_model.codebook_sizes
@@ -124,7 +124,7 @@ def init_rqvae(cls, config) -> RqVaeModel:
     @classmethod
     def create_from_config(cls, config, **kwargs):
         rqvae_model = cls.init_rqvae(config)
-        embedding_dim = rqvae_model.encoder.weight.shape[0] # TODO
+        embedding_dim = rqvae_model.encoder.weight.shape[0]
         embs_extractor = torch.load(config['embs_extractor_path'])
         
         item_ids = embs_extractor.index.tolist()
@@ -182,70 +182,64 @@ def forward(self, inputs):
         all_sample_lengths = inputs[
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
+        
+        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
+        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
 
         if self.training:
             label_events = inputs["{}.ids".format(self._positive_prefix)]
             label_lengths = inputs["{}.length".format(self._positive_prefix)]
-        
-            decoder_prefix_scores = self.get_logits(
-                label_events, label_lengths, all_sample_events, all_sample_lengths
-            )  # (batch_size, dec_seq_len, _codebook_sizes[0])
             
-            logits = decoder_prefix_scores.reshape(-1, self._codebook_sizes[0]) # (batch_size * dec_seq_len, _codebook_sizes[0])
+            decoder_outputs = self._apply_decoder(
+                label_events, label_lengths, encoder_embeddings, encoder_mask
+            )  # (batch_size, label_len, embedding_dim)
             
+            decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
+            
+            decoder_output_residual = decoder_outputs[:, -1, :]
+        
             semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in label_events.tolist()]) # len(events), len(codebook_sizes)
-            residuals = torch.stack([self._item_id_to_residual[event] for event in label_events.tolist()])
-            info = self._solver.get_scores_batch(semantic_ids, residuals)
+            
+            true_residuals = torch.stack([self._item_id_to_residual[event] for event in label_events.tolist()])
+            
+            # TODO tensor
+            true_info = self._solver.get_scores_batch(semantic_ids, true_residuals)
+            # batch_size
+            pred_info = self._solver.get_scores_batch(semantic_ids, decoder_output_residual)
+            # batch_size x max_collision_count
             # TODO use BatchLogSoftmax
             
             semantic_events = semantic_ids.view(-1)
             
-            # TODO batch_logsoftmax don't match shapes (example bert4rec cls)
-            
             return {
-                self._pred_prefix: logits, 
-                f"semantic.{self._labels_prefix}.ids": semantic_events,
-                "dedup.logits": info["scores"],
-                "dedup.labels.ids": info["dedup_tokens"]
+                "logits": decoder_prefix_scores, 
+                "semantic.labels": semantic_ids,
+                "dedup.logits": pred_info["scores"],
+                "dedup.labels": true_info["dedup_tokens"]
             }
         else:
-            label_events = inputs["{}.ids".format(self._labels_prefix)]
-            label_lengths = inputs["{}.length".format(self._labels_prefix)]
-            
-            decoder_prefix_scores = self.get_logits(
-                label_events, label_lengths, all_sample_events, all_sample_lengths
-            )  # batch_size, dec_seq_len, emb_dim (_codebook_sizes[0])
+            tgt_embeddings, semanctid_ids = self._apply_decoder_autoregressive(
+                encoder_embeddings, encoder_mask
+            ) # (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim), (batch_size, len(self._codebook_sizes))
 
-            preds = decoder_prefix_scores.argmax(dim=-1)  # (batch_size, dec_seq_len)
-            ids = self._apply_trie(preds)
+            residuals = tgt_embeddings[:, -1, :]
+
+            ids = self._apply_trie(semanctid_ids, residuals)
             return ids
 
-    def _apply_trie(self, preds):
+    def _apply_trie(self, preds: torch.Tensor, residuals: torch.Tensor):
         semantic_ids = [tuple(row.tolist()) for row in preds]
         
         ids = []
-        for semantic_id in tqdm(semantic_ids):
-            item = Item(semantic_id, torch.rand(self._embedding_dim).to(DEVICE)) # TODO use true residuals
+        for semantic_id, residual in tqdm(zip(semantic_ids, residuals), total=len(semantic_ids)):
+            item = Item(semantic_id, residual)
             closest_items = self._trie.find_closest(item, n=20)
             ids.append(closest_items)
         
         return torch.tensor(ids)
-    
-    def get_logits(self, label_events, label_lengths, all_sample_events, all_sample_lengths):
-        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1) # TODO residual gets add as event
-        # TODO encoder_mask sees residual as event (is it correct?)
-        
-        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
-        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
-        
-        decoder_outputs = self._apply_decoder(
-            label_events, label_lengths, encoder_embeddings, encoder_mask
-        )  # (batch_size, label_len, embedding_dim)
-        
-        decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
-        
-        return decoder_prefix_scores
+
 
     def _apply_decoder(
         self, label_events, label_lengths, encoder_embeddings, encoder_mask
@@ -262,11 +256,11 @@ def _apply_decoder(
         batch_size = tgt_embeddings.shape[0]
         bos_embeddings = self._bos_weight.unsqueeze(0).expand(batch_size, 1, -1)  # (batch_size, 1, embedding_dim)
         
-        tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1) # TODO remove residuals (batch_size, dec_seq_len, embedding_dim)
+        tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1)
         
         label_len = tgt_mask.shape[1]
 
-        assert label_len == len(self._codebook_sizes) + 1 # TODO +1 for bos
+        assert label_len == len(self._codebook_sizes) + 1
 
         position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
         assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
@@ -291,15 +285,58 @@ def _apply_decoder(
             memory=encoder_embeddings,
             tgt_mask=~causal_mask,
             memory_key_padding_mask=~encoder_mask,
-            # TODO tgt_key_padding_mask=~tgt_mask,
         )  # (batch_size, dec_seq_len, embedding_dim)
 
         return decoder_outputs
     
+    def _apply_decoder_autoregressive(
+        self, encoder_embeddings, encoder_mask
+    ):
+        batch_size = encoder_embeddings.shape[0]
+        
+        tgt_embeddings = self._bos_weight.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, -1)
+        tgt_mask = torch.ones(batch_size, 1, dtype=torch.bool, device=DEVICE)
+        
+        semantic_ids = torch.tensor([], device=DEVICE)
+        
+        for step in range(len(self._codebook_sizes) + 1): # semantic_id + residual
+            position_embeddings = self._decoder_pos_embeddings(
+                torch.full((batch_size,), tgt_embeddings.shape[1], device=DEVICE),
+                tgt_mask
+            )
+            
+            curr_embeddings = tgt_embeddings + position_embeddings
+            
+            curr_embeddings = self._decoder_layernorm(curr_embeddings)
+            curr_embeddings = self._decoder_dropout(curr_embeddings)
+            
+            decoder_output = self._decoder(
+                tgt=curr_embeddings,
+                memory=encoder_embeddings,
+                memory_key_padding_mask=~encoder_mask,
+            )
+            
+            next_token_embedding = decoder_output[:, -1, :] # batch_size x embedding_dim
+            
+            if step < len(self._codebook_sizes):
+                codebook = self._codebook_item_embeddings_stacked[step] # len(codebook_sizes) x embedding_dim
+                closest_semantic_ids = torch.argmax(
+                    torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
+                ) # batch_size x 1
+                semantic_ids = torch.cat([semantic_ids, closest_semantic_ids.unsqueeze(1)], dim=1) # batch_size x (step + 1)
+                next_token_embedding = codebook[closest_semantic_ids] # batch_size x embedding_dim
+                
+            tgt_embeddings = torch.cat([tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1)
+            tgt_mask = torch.ones(batch_size, tgt_embeddings.shape[1], dtype=torch.bool, device=DEVICE)
+
+        return tgt_embeddings, semantic_ids
+    
     def get_item_embeddings(self, events): # TODO freezed embeddings
         events = events.tolist()
         embs = torch.stack([self._item_id_to_semantic_embedding[event] for event in events])
-        embs = embs.view(-1, self._embedding_dim)
+        # convert to tensor for better performance
+        # 12101 -> 3 semantic, text - sum(dim=1) = res
+        embs = embs.view(len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim)
         return embs
     
     def get_init_item_embeddings(self, events): # TODO freezed embeddings
@@ -322,7 +359,10 @@ def get_init_item_embeddings(self, events): # TODO freezed embeddings
         residual = residual.unsqueeze(1)
         
         # get true item embeddings
-        item_embeddings = torch.cat([semantic_embeddings, residual], dim=1) 
+        item_embeddings = torch.cat(
+            [semantic_embeddings, residual], dim=1
+        ) # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        
         # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
         
         return item_embeddings
@@ -369,7 +409,7 @@ def codebook_lambda(x):
             lengths, mask, codebook_lambda, self._codebook_embeddings
         )
 
-        return position_embeddings + codebook_embeddings
+        return codebook_embeddings + position_embeddings
     
     def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
         batch_size = mask.shape[0]
@@ -383,6 +423,7 @@ def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_lay
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
+        # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 ...
 
         positions = position_lambda(positions)  # (all_batch_events)
         
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index ada7bd62..78cdd0e8 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -37,8 +37,10 @@ class Trie:
     def __init__(self):
         self.root = TrieNode()
         # Create instance-specific cached methods
-        self._cached_gather_subtree_items = lru_cache(maxsize=1024)(self._gather_subtree_items)
-        self._cached_dot_scores = lru_cache(maxsize=1024)(self._dot_scores)
+        self._cached_gather_subtree_items = self._gather_subtree_items
+        self._cached_dot_scores = self._dot_scores
+        # self._cached_gather_subtree_items = lru_cache(maxsize=1024)(self._gather_subtree_items)
+        # self._cached_dot_scores = lru_cache(maxsize=1024)(self._dot_scores)
 
     def insert(self, item: Item) -> None:
         """

From eec5530c68abcb7659ec6447a59e2793ce6cdaf9 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 27 Jan 2025 01:52:17 +0700
Subject: [PATCH 052/175] use collision solver

---
 modeling/models/tiger.py                 | 16 ++-----
 modeling/rqvae_utils/collision_solver.py | 61 ++++++++++++++++--------
 2 files changed, 47 insertions(+), 30 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 0ddfb393..9c9365d3 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -71,7 +71,7 @@ def __init__(
         self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
         self._decoder_dropout = nn.Dropout(dropout)
         
-        self._solver = solver
+        self._solver: CollisionSolver = solver
         
         self._codebook_sizes = codebook_sizes
 
@@ -204,20 +204,14 @@ def forward(self, inputs):
             
             true_residuals = torch.stack([self._item_id_to_residual[event] for event in label_events.tolist()])
             
-            # TODO tensor
-            true_info = self._solver.get_scores_batch(semantic_ids, true_residuals)
-            # batch_size
-            pred_info = self._solver.get_scores_batch(semantic_ids, decoder_output_residual)
-            # batch_size x max_collision_count
-            # TODO use BatchLogSoftmax
-            
-            semantic_events = semantic_ids.view(-1)
+            true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
+            pred_info = self._solver.get_pred_scores(semantic_ids, decoder_output_residual)
             
             return {
                 "logits": decoder_prefix_scores, 
                 "semantic.labels": semantic_ids,
-                "dedup.logits": pred_info["scores"],
-                "dedup.labels": true_info["dedup_tokens"]
+                "dedup.logits": pred_info["pred_scores"],
+                "dedup.labels": true_info["true_dedup_tokens"]
             }
         else:
             tgt_embeddings, semanctid_ids = self._apply_decoder_autoregressive(
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index b4028b23..6821b33c 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -84,40 +84,63 @@ def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tupl
 
         return candidates, mask
 
-    def get_scores_batch(self, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, emb_dim] semantic ids (без токена решающего коллизии)
-        :param residuals: [batch_size, residual_dim] остатки
+        :param pred_residuals: [batch_size, residual_dim] предсказанные остатки
 
         :return: Словарь с ключами:
-            - 'scores_mask': [batch_size, max_collision_count] маска существующих значений scores
-            - 'scores': [batch_size, max_collision_count] софтмакс для каждого из кандидатов
-            - 'dedup_tokens_mask': [batch_size] маска существующих токенов решающих коллизии
-            - 'dedup_tokens': [batch_size] токены решающие коллизии
-            - 'item_ids': [batch_size] реальные айди айтемов
+            - 'pred_scores_mask': [batch_size, max_collision_count] маска существующих значений scores для предсказанных остатков
+            - 'pred_scores': [batch_size, max_collision_count] софтмакс для каждого из кандидатов для предсказанных остатков
+            - 'pred_item_ids': [batch_size] реальные айди айтемов для предсказанных остатков
         """
         assert semantic_ids.shape[1] == self.emb_dim
-        assert residuals.shape[1] == self.residual_dim
-        assert semantic_ids.shape[0] == residuals.shape[0]
+        assert pred_residuals.shape[1] == self.residual_dim
+        assert semantic_ids.shape[0] == pred_residuals.shape[0]
 
         semantic_ids = self._to_device(semantic_ids)
-        residuals = self._to_device(residuals)
+        pred_residuals = self._to_device(pred_residuals)
 
         unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
 
         candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_ids)
 
-        scores = torch.softmax(torch.einsum('njk,nk->nj', candidates, residuals).masked_fill(~mask, float('-inf')), dim=1)
+        pred_scores = torch.einsum('njk,nk->nj', candidates, pred_residuals).masked_fill(~mask, -torch.inf)
+        pred_indices = torch.argmax(pred_scores, dim=1)
+        pred_item_ids = torch.stack([self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]] for i in range(semantic_ids.shape[0])])
+
+        return {
+            "pred_scores_mask": mask,
+            "pred_scores": pred_scores,
+            "pred_item_ids": pred_item_ids
+        }
+
+    def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
+        """
+        :param semantic_id: [batch_size, emb_dim] semantic ids (без токена решающего коллизии)
+        :param true_residuals: [batch_size, residual_dim] реальные остатки
+
+        :return: Словарь с ключами:
+            - 'true_dedup_tokens': [batch_size] токены решающие коллизии для реальных остатков
+        """
+        assert semantic_ids.shape[1] == self.emb_dim
+        assert true_residuals.shape[1] == self.residual_dim
+        assert semantic_ids.shape[0] == true_residuals.shape[0]
+
+        semantic_ids = self._to_device(semantic_ids)
+        true_residuals = self._to_device(true_residuals)
+
+        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+
+        candidates, _ = self.get_residuals_by_semantic_id_batch(semantic_ids)
+
+        matches = torch.all(candidates == true_residuals[:, None, :], dim=2).int()
+        true_dedup_tokens = torch.argmax(matches, dim=1)
 
-        indices = torch.argmax(scores, dim=1)
-        item_ids = torch.stack([self.item_ids_sparse_tensor[unique_ids[i]][indices[i]] for i in range(semantic_ids.shape[0])])
+        assert matches.any(dim=1).all(), "Не у всех батчей есть совпадение"
 
         return {
-            "scores_mask": mask,
-            "scores": scores,
-            "dedup_tokens_mask": mask.any(dim=1),
-            "dedup_tokens": indices,
-            "item_ids": item_ids
+            "true_dedup_tokens": true_dedup_tokens
         }
 
 
@@ -166,7 +189,7 @@ def get_item_id_info(self, item_id: int) -> dict[str, torch.Tensor]:
             "dedup_tokens": dedup_tokens,
         }
 
-    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> torch.Tensor:
         """
         :param semantic_id: [batch_size, emb_dim] semantic ids (без токенов решающего коллизии)
         :param dedup_tokens: [batch_size] токены решающие коллизии

From d155767f700da3d3ed0052105cb1e6a8f2d177dc Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 30 Jan 2025 22:32:16 +0700
Subject: [PATCH 053/175] fix remarks

---
 configs/train/sasrec_train_config.json |   2 +-
 configs/train/tiger_train_config.json  |   4 +-
 modeling/loss/base.py                  |   8 +-
 modeling/main.ipynb                    | 289 -------------------------
 modeling/models/rqvae.py               |   2 +-
 modeling/models/sasrec.py              |   5 +-
 modeling/models/tiger.py               |  74 +++----
 7 files changed, 37 insertions(+), 347 deletions(-)
 delete mode 100644 modeling/main.ipynb

diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 690541d1..aa29a029 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -37,7 +37,7 @@
     "positive_prefix": "positive",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
-    "embedding_dim": 512,
+    "embedding_dim": 64,
     "num_heads": 2,
     "num_layers": 2,
     "dim_feedforward": 256,
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index e65ae66d..60172a9a 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -64,14 +64,14 @@
       {
         "type": "sample_logsoftmax",
         "predictions_prefix": "logits",
-        "labels_prefix": "semantic.labels",
+        "labels": "semantic.labels",
         "weight": 1.0,
         "output_prefix": "semantic_loss"
       },
       {
         "type": "sample_logsoftmax",
         "predictions_prefix": "dedup.logits",
-        "labels_prefix": "dedup.labels",
+        "labels": "dedup.labels",
         "weight": 1.0,
         "output_prefix": "dedup_loss"
       }
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index d9533f2f..17200fcd 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -55,21 +55,21 @@ def forward(self, inputs):
 
 
 class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
-    def __init__(self, predictions_prefix, labels_prefix):
+    def __init__(self, predictions_prefix, labels):
         super().__init__()
         self._predictions_prefix = predictions_prefix
-        self._labels_prefix = labels_prefix
+        self._labels = labels
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
             predictions_prefix=config.get('predictions_prefix'),
-            labels_prefix=config.get('labels_prefix')
+            labels=config.get('labels')
         )
 
     def forward(self, inputs):  # use log soft max
         logits = inputs[self._predictions_prefix]
-        candidates = inputs[self._labels_prefix]
+        candidates = inputs[self._labels]
         
         assert len(logits.shape) in [2, 3]
         
diff --git a/modeling/main.ipynb b/modeling/main.ipynb
deleted file mode 100644
index b4e92a3d..00000000
--- a/modeling/main.ipynb
+++ /dev/null
@@ -1,289 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "from rqvae.rqvae_data import get_data\n",
-    "\n",
-    "df = get_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embs = torch.stack(df[\"embeddings\"].tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from utils import DEVICE\n",
-    "from models.base import BaseModel\n",
-    "\n",
-    "config = json.load(open(\"../configs/train/tiger_train_config.json\"))\n",
-    "\n",
-    "batch_proc_config = config['dataloader']['train']['batch_processor']\n",
-    "\n",
-    "rqvae_train_config = json.load(open(batch_proc_config['rqvae_train_config_path']))\n",
-    "rq_vae_config = rqvae_train_config['model']\n",
-    "rq_vae_config['should_init_codebooks'] = False\n",
-    "\n",
-    "rqvae_model = BaseModel.create_from_config(rq_vae_config).to(DEVICE)\n",
-    "\n",
-    "rqvae_model.load_state_dict(torch.load(batch_proc_config['rqvae_checkpoint_path'], weights_only=True))\n",
-    "rqvae_model.eval()\n",
-    "\n",
-    "ids = df.index.tolist()\n",
-    "\n",
-    "embs_dict = {\"ids\": torch.tensor(ids).to(DEVICE), \"embeddings\": embs.to(DEVICE)}\n",
-    "\n",
-    "semantic_ids = list(rqvae_model.forward(embs_dict))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rqvae.collisions import dedup\n",
-    "\n",
-    "items_with_tuples = list(zip(df[\"asin\"], df[\"title\"].fillna(\"unknown\"), semantic_ids))\n",
-    "items_with_tuples = dedup(items_with_tuples)\n",
-    "\n",
-    "assert len(df) == len(set(item[-1] for item in items_with_tuples))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "items_with_tuples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from trie import Trie\n",
-    "\n",
-    "_trie = Trie()\n",
-    "\n",
-    "for (id, tuple) in zip(df.index, semantic_ids):\n",
-    "    _trie.insert(tuple, id) # todo handle collisions, not overwrite"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle\n",
-    "\n",
-    "with open(\"../data/Beauty/trie.pkl\", 'wb') as f:\n",
-    "    pickle.dump(_trie, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rqvae.rqvae_data import search_similar_items\n",
-    "\n",
-    "\n",
-    "for i in range(5):\n",
-    "    sim = search_similar_items(items_with_tuples, (i,), 5)\n",
-    "    if len(sim) == 0:\n",
-    "        continue\n",
-    "    print(i)\n",
-    "    for asin, item, clust_tuple in sim:\n",
-    "        # if 'shampoo' in item.lower():\n",
-    "        print(f\"{item=} {clust_tuple=}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import Counter\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "plt.hist(Counter(item[-1][:-1] for item in items_with_tuples).values())\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # raw full ids\n",
-    "# full_duplicates = Counter(item[-1][:-1] for item in items_with_tuples).items()\n",
-    "# duplicated = [(semantic_id, amount) for (semantic_id, amount) in full_duplicates if amount > 1]\n",
-    "# duplicated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# collison counters - (how many item have same full semantic id): amount of such sets\n",
-    "vals = Counter(item[-1][:-1] for item in items_with_tuples).values()\n",
-    "Counter(vals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# dedup idxes\n",
-    "Counter(item[-1][4] for item in items_with_tuples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from sklearn import preprocessing\n",
-    "\n",
-    "# labels = df['asin']\n",
-    "\n",
-    "# le = preprocessing.LabelEncoder()\n",
-    "# targets = le.fit_transform(labels)\n",
-    "\n",
-    "# df['asin_numeric'] = targets\n",
-    "\n",
-    "# torch.save(df, './all_data.pt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from utils import create_masked_tensor\n",
-    "\n",
-    "\n",
-    "embeddings = torch.rand((11, 2))\n",
-    "print(embeddings)\n",
-    "\n",
-    "lengths = torch.tensor([3, 1, 2, 5])\n",
-    "\n",
-    "padded_embeddings, mask = create_masked_tensor(embeddings, lengths)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "padded_embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "df = torch.load('../data/Beauty/data_full.pt')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.stack(df.sort_index().embeddings.tolist())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.sort_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "gsrec",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index bb6abb16..82962c84 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -147,7 +147,7 @@ def eval_pass(self, embeddings):
             codebook_vectors = codebook[codebook_indices]
             ind_lists.append(codebook_indices.cpu().numpy())
             remainder = remainder - codebook_vectors
-        return list(zip(*ind_lists)), remainder
+        return torch.tensor(list(zip(*ind_lists))), remainder
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 28b07e77..96e8f839 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -40,11 +40,14 @@ def __init__(
         
         df = torch.load('../data/Beauty/data_full.pt')
         precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
+        self._projector = torch.nn.Linear(precomputed_embeddings.shape[1], embedding_dim) # TODO incorrect projection
+        # TODO infer text embeddings using embedding_dim=64
+        projector_embeddings = self._projector(precomputed_embeddings)
         
         padding_embedding = self._item_embeddings.weight[0].unsqueeze(0)
         mask_embedding = self._item_embeddings.weight[-1].unsqueeze(0)
         
-        extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
+        extended_embeddings = torch.cat([padding_embedding, projector_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
         
         self._item_embeddings.weight.data.copy_(extended_embeddings)
         self._item_embeddings.weight.requires_grad = False
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 9c9365d3..f7db2d43 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -84,21 +84,17 @@ def __init__(
         self._item_id_to_residual = item_id_to_residual
         self._item_id_to_embedding = item_id_to_embedding
         
-        self._item_id_to_semantic_embedding = {
-            item_id: emb for (item_id, emb) in zip(item_id_to_semantic_id.keys(), self.get_init_item_embeddings(list(item_id_to_semantic_id.keys())))
-        }
+        item_ids = torch.tensor(list(range(1, len(item_id_to_semantic_id) + 1)))
+        
+        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         
         self._trie = Trie()
-        for item_id, semantic_id in item_id_to_semantic_id.items():
+        for item_id, semantic_id in enumerate(item_id_to_semantic_id):
             self._trie.insert(Item(semantic_id, item_id_to_residual[item_id])) # TODO no dedup tokens here
 
         self._bos_token_id = codebook_sizes[0]
         self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
         
-        self._decoder_position_embeddings = nn.Embedding(
-            num_embeddings=2,  # bos token + label item id (always 0)
-            embedding_dim=embedding_dim,
-        )
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim
         )  # + 2 for bos token & residual
@@ -127,8 +123,10 @@ def create_from_config(cls, config, **kwargs):
         embedding_dim = rqvae_model.encoder.weight.shape[0]
         embs_extractor = torch.load(config['embs_extractor_path'])
         
+        embs_extractor = embs_extractor.sort_index()
+        
         item_ids = embs_extractor.index.tolist()
-        assert sorted(item_ids) == list(range(1, len(item_ids) + 1))
+        assert item_ids == list(range(1, len(item_ids) + 1))
         
         embeddings = torch.stack(embs_extractor['embeddings'].tolist()).to(DEVICE)
 
@@ -140,23 +138,13 @@ def create_from_config(cls, config, **kwargs):
             emb_dim=len(rqvae_model.codebook_sizes), 
             codebook_size=rqvae_model.codebook_sizes[0]
         )
-        solver.create_query_candidates_dict(torch.tensor(item_ids), torch.tensor(semantic_ids), residuals)
-        
-        item_id_to_embedding = {
-            item_id: embedding for (item_id, embedding) in zip(item_ids, embeddings)
-        }
-        item_id_to_semantic_id = {
-            item_id: torch.tensor(semantic_id, device=DEVICE) for (item_id, semantic_id) in zip(item_ids, semantic_ids)
-        }
-        item_id_to_residual = {
-            item_id: remainder for (item_id, remainder) in zip(item_ids, residuals)
-        }
+        solver.create_query_candidates_dict(torch.tensor(item_ids), semantic_ids, residuals)
 
         return cls(
             rqvae_model=rqvae_model,
-            item_id_to_semantic_id=item_id_to_semantic_id,
-            item_id_to_residual=item_id_to_residual,
-            item_id_to_embedding=item_id_to_embedding,
+            item_id_to_semantic_id=semantic_ids.to(DEVICE),
+            item_id_to_residual=residuals.to(DEVICE),
+            item_id_to_embedding=embeddings.to(DEVICE),
             solver=solver,
             sequence_prefix=config["sequence_prefix"],
             pred_prefix=config["predictions_prefix"],
@@ -192,17 +180,21 @@ def forward(self, inputs):
             label_events = inputs["{}.ids".format(self._positive_prefix)]
             label_lengths = inputs["{}.length".format(self._positive_prefix)]
             
+            label_lengths = label_lengths * (len(self._codebook_sizes) + 1) # TODO bos prepending
+            tgt_embeddings = self.get_item_embeddings( # TODO residual embs
+                label_events
+            )  # (all_batch_events, embedding_dim)
+            
             decoder_outputs = self._apply_decoder(
-                label_events, label_lengths, encoder_embeddings, encoder_mask
+                tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
             )  # (batch_size, label_len, embedding_dim)
             
             decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
             
             decoder_output_residual = decoder_outputs[:, -1, :]
         
-            semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in label_events.tolist()]) # len(events), len(codebook_sizes)
-            
-            true_residuals = torch.stack([self._item_id_to_residual[event] for event in label_events.tolist()])
+            semantic_ids = self._item_id_to_semantic_id[label_events - 1] # len(events), len(codebook_sizes)
+            true_residuals = self._item_id_to_residual[label_events - 1] 
             
             true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
             pred_info = self._solver.get_pred_scores(semantic_ids, decoder_output_residual)
@@ -236,13 +228,8 @@ def _apply_trie(self, preds: torch.Tensor, residuals: torch.Tensor):
 
 
     def _apply_decoder(
-        self, label_events, label_lengths, encoder_embeddings, encoder_mask
+        self, tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
     ):
-        label_lengths = label_lengths * (len(self._codebook_sizes) + 1) # TODO bos prepending
-        tgt_embeddings = self.get_item_embeddings( # TODO residual embs
-            label_events
-        )  # (all_batch_events, embedding_dim)
-        
         tgt_embeddings, tgt_mask = create_masked_tensor(
             data=tgt_embeddings, lengths=label_lengths
         )  # (batch_size, dec_seq_len, embedding_dim), (batch_size, dec_seq_len)
@@ -326,16 +313,12 @@ def _apply_decoder_autoregressive(
         return tgt_embeddings, semantic_ids
     
     def get_item_embeddings(self, events): # TODO freezed embeddings
-        events = events.tolist()
-        embs = torch.stack([self._item_id_to_semantic_embedding[event] for event in events])
-        # convert to tensor for better performance
-        # 12101 -> 3 semantic, text - sum(dim=1) = res
-        embs = embs.view(len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim)
-        return embs
+        embs = self._item_id_to_semantic_embedding[events - 1] # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        return embs.view(len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim)
     
     def get_init_item_embeddings(self, events): # TODO freezed embeddings
         # convert to semantic ids
-        semantic_ids = torch.stack([self._item_id_to_semantic_id[event] for event in events]) # len(events), len(codebook_sizes)
+        semantic_ids = self._item_id_to_semantic_id[events - 1] # len(events), len(codebook_sizes)
         semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
         
         # convert to rqvae embeddings
@@ -348,7 +331,7 @@ def get_init_item_embeddings(self, events): # TODO freezed embeddings
         ) # (len(events), len(self._codebook_sizes), embedding_dim)
         
         # get residuals
-        text_embeddings = torch.stack([self._item_id_to_embedding[event] for event in events])
+        text_embeddings = self._item_id_to_embedding[events - 1]
         residual = text_embeddings - semantic_embeddings.sum(dim=1)
         residual = residual.unsqueeze(1)
         
@@ -387,13 +370,6 @@ def codebook_lambda(x):
         return position_embeddings + codebook_embeddings
 
     def _decoder_pos_embeddings(self, lengths, mask):
-        def position_lambda(x):
-            return x // len(self._codebook_sizes)  #  1 0 0 0 1 0 0 0 ...
-
-        position_embeddings = self._get_position_embeddings(
-            lengths, mask, position_lambda, self._decoder_position_embeddings
-        )
-
         def codebook_lambda(x):
             non_bos = x < len(self._codebook_sizes)
             x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
@@ -403,7 +379,7 @@ def codebook_lambda(x):
             lengths, mask, codebook_lambda, self._codebook_embeddings
         )
 
-        return codebook_embeddings + position_embeddings
+        return codebook_embeddings
     
     def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
         batch_size = mask.shape[0]

From 10aa18850540607dfc4e64a469c3c8f7996fa558 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 30 Jan 2025 23:00:57 +0700
Subject: [PATCH 054/175] fixed flattened

---
 modeling/models/tiger.py | 21 +++++++++------------
 1 file changed, 9 insertions(+), 12 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index f7db2d43..8808c5d0 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -75,8 +75,6 @@ def __init__(
         
         self._codebook_sizes = codebook_sizes
 
-        self._codebook_item_embeddings_flattened = torch.cat([codebook for codebook in rqvae_model.codebooks], dim=0)
-        self._codebook_item_embeddings_flattened.requires_grad = False # TODO ask if freeze is needed
         self._codebook_item_embeddings_stacked = torch.stack([codebook for codebook in rqvae_model.codebooks])
         self._codebook_item_embeddings_stacked.requires_grad = False # TODO ask if freeze is needed
         
@@ -319,16 +317,15 @@ def get_item_embeddings(self, events): # TODO freezed embeddings
     def get_init_item_embeddings(self, events): # TODO freezed embeddings
         # convert to semantic ids
         semantic_ids = self._item_id_to_semantic_id[events - 1] # len(events), len(codebook_sizes)
-        semantic_events = semantic_ids.view(-1) # len(codebook_sizes) * len(events)
-        
-        # convert to rqvae embeddings
-        positions = torch.arange(len(semantic_events), device=DEVICE)
-        codebook_positions = positions % len(self._codebook_sizes)
-        emb_indices = codebook_positions * self._codebook_sizes[0] + semantic_events
-        semantic_embeddings = self._codebook_item_embeddings_flattened[emb_indices]
-        semantic_embeddings = semantic_embeddings.view(
-            len(events), len(self._codebook_sizes), self._embedding_dim
-        ) # (len(events), len(self._codebook_sizes), embedding_dim)
+        
+        result = []
+        for semantic_id in semantic_ids:
+            item_repr = []
+            for codebook_idx, codebook_id in enumerate(semantic_id):
+                item_repr.append(self._codebook_item_embeddings_stacked[codebook_idx][codebook_id])
+            result.append(torch.stack(item_repr))
+        
+        semantic_embeddings = torch.stack(result)
         
         # get residuals
         text_embeddings = self._item_id_to_embedding[events - 1]

From ad3e0ccf112d42ca032f4d4389389acf9966d34b Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 31 Jan 2025 00:48:59 +0700
Subject: [PATCH 055/175] add trie draft

---
 configs/train/rqvae_train_config.json |   2 +-
 configs/train/tiger_train_config.json |  91 +++++++-
 modeling/models/rqvae.py              |   5 +
 modeling/models/tiger.py              |  23 +-
 modeling/rqvae_utils/__init__.py      |   2 +-
 modeling/rqvae_utils/trie.py          | 314 ++++++++++++++------------
 6 files changed, 281 insertions(+), 156 deletions(-)

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index 0d54dba5..225a2fbe 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -33,7 +33,7 @@
     "type": "rqvae",
     "embedding_dim": 512,
     "n_iter": 100,
-    "codebook_sizes": [256, 256, 256, 256],
+    "codebook_sizes": [256, 256, 256],
     "should_init_codebooks": true,
     "should_reinit_unused_clusters": true,
     "initializer_range": 0.02
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 60172a9a..c7cd6cdf 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,6 +1,7 @@
 {
   "experiment_name": "tiger",
-  "train_epochs_num": 10,
+  "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 50,
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",
@@ -85,6 +86,94 @@
         "type": "metric",
         "on_step": 1,
         "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
       }
     ]
   }
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 82962c84..90fd26b4 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,3 +1,4 @@
+import functools
 from models.base import TorchModel
 
 import torch
@@ -156,3 +157,7 @@ def forward(self, inputs):
             return self.train_pass(embeddings)
         else:  # eval mode
             return self.eval_pass(embeddings)
+        
+    @functools.cache
+    def get_single_embedding(self, codebook_idx: int, codebook_id: int):
+        return self.codebooks[codebook_idx][codebook_id]
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 8808c5d0..a57fcaa3 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -3,7 +3,7 @@
 import torch
 from tqdm import tqdm
 from models.base import SequentialTorchModel
-from rqvae_utils import CollisionSolver, Trie, Item
+from rqvae_utils import CollisionSolver, HierarchicalTrie, Item
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
@@ -86,9 +86,9 @@ def __init__(
         
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         
-        self._trie = Trie()
+        self._trie = HierarchicalTrie(rqvae_model)
         for item_id, semantic_id in enumerate(item_id_to_semantic_id):
-            self._trie.insert(Item(semantic_id, item_id_to_residual[item_id])) # TODO no dedup tokens here
+            self._trie.insert(Item(semantic_id, item_id + 1, item_id_to_residual[item_id])) # TODO no dedup tokens here
 
         self._bos_token_id = codebook_sizes[0]
         self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
@@ -204,23 +204,24 @@ def forward(self, inputs):
                 "dedup.labels": true_info["true_dedup_tokens"]
             }
         else:
-            tgt_embeddings, semanctid_ids = self._apply_decoder_autoregressive(
+            semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
                 encoder_embeddings, encoder_mask
-            ) # (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim), (batch_size, len(self._codebook_sizes))
+            ) # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
 
             residuals = tgt_embeddings[:, -1, :]
 
-            ids = self._apply_trie(semanctid_ids, residuals)
+            ids = self._apply_trie(semantic_ids, residuals, n=20)
             return ids
 
-    def _apply_trie(self, preds: torch.Tensor, residuals: torch.Tensor):
+    def _apply_trie(self, preds: torch.Tensor, residuals: torch.Tensor, n: int = 20):
         semantic_ids = [tuple(row.tolist()) for row in preds]
         
         ids = []
         for semantic_id, residual in tqdm(zip(semantic_ids, residuals), total=len(semantic_ids)):
-            item = Item(semantic_id, residual)
-            closest_items = self._trie.find_closest(item, n=20)
-            ids.append(closest_items)
+            item = Item(semantic_id, -999, residual)
+            closest_items = self._trie.find_n_closest(item, n)
+            closest_raw_ids = [item.raw_item_id for item in closest_items]
+            ids.append(closest_raw_ids) # TODO add correct tree inference
         
         return torch.tensor(ids)
 
@@ -308,7 +309,7 @@ def _apply_decoder_autoregressive(
             tgt_embeddings = torch.cat([tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1)
             tgt_mask = torch.ones(batch_size, tgt_embeddings.shape[1], dtype=torch.bool, device=DEVICE)
 
-        return tgt_embeddings, semantic_ids
+        return semantic_ids, tgt_embeddings
     
     def get_item_embeddings(self, events): # TODO freezed embeddings
         embs = self._item_id_to_semantic_embedding[events - 1] # len(events), len(self._codebook_sizes) + 1, embedding_dim
diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
index a25b6189..3f29aee5 100644
--- a/modeling/rqvae_utils/__init__.py
+++ b/modeling/rqvae_utils/__init__.py
@@ -1,2 +1,2 @@
 from .collision_solver import CollisionSolver
-from .trie import Item, Trie
\ No newline at end of file
+from .trie import Item, HierarchicalTrie
\ No newline at end of file
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 78cdd0e8..2b520149 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -1,158 +1,188 @@
-from functools import lru_cache
-
 import torch
+from typing import Optional
 
+from models.rqvae import RqVaeModel
 
 class Item:
-    """
-    Represents an item with:
-      - hierarchical_id: a tuple of ints
-      - residual: a torch.Tensor of shape (emb_dim,)
-    """
-    def __init__(self, hierarchical_id: tuple[int, ...], residual: torch.Tensor):
+    def __init__(
+        self, 
+        hierarchical_id: tuple[int], 
+        raw_item_id: int, 
+        residual: torch.Tensor
+    ):
         self.hierarchical_id = hierarchical_id
+        self.raw_item_id = raw_item_id
         self.residual = residual
 
-    def __repr__(self):
-        return f"Item(hier_id={self.hierarchical_id}, resid_shape={tuple(self.residual.shape)})"
-
-
-class TrieNode:
-    """
-    A node in the trie.
-      - children: dict(int -> TrieNode)
-      - items: list of Items (only non-empty if this node is the end of some hierarchical_id(s))
-    """
-    def __init__(self):
-        self.children: dict[int, TrieNode] = {}
-        self.items: list[Item] = []  # collisions end up here if they share the same path
-
-
-class Trie:
-    """
-    The trie structure that can:
-      1. Insert an Item by its hierarchical_id.
-      2. Find up to n closest items to a query Item by the described prefix-truncation + dot-product logic.
-    """
-    def __init__(self):
-        self.root = TrieNode()
-        # Create instance-specific cached methods
-        self._cached_gather_subtree_items = self._gather_subtree_items
-        self._cached_dot_scores = self._dot_scores
-        # self._cached_gather_subtree_items = lru_cache(maxsize=1024)(self._gather_subtree_items)
-        # self._cached_dot_scores = lru_cache(maxsize=1024)(self._dot_scores)
-
-    def insert(self, item: Item) -> None:
-        """
-        Insert an Item into the trie following item.hierarchical_id.
-        """
-        current_node = self.root
-        for idx in item.hierarchical_id:
-            if idx not in current_node.children:
-                current_node.children[idx] = TrieNode()
-            current_node = current_node.children[idx]
-        # At the leaf, store the item
-        current_node.items.append(item)
-
-    def _gather_subtree_items(self, node: TrieNode) -> tuple[Item, ...]:
-        """
-        Collect all items in the sub-tree rooted at 'node'.
-        """
-        collected = []
-        stack = [node]
-        while stack:
-            cur = stack.pop()
-            collected.extend(cur.items)
-            for child_node in cur.children.values():
-                stack.append(child_node)
-        return tuple(collected)
-
-    def _dot_scores(
+
+class Node:
+    def __init__(self,
+                 codebook_idx: int,
+                 codebook_id: int,
+                 parent: Optional['Node'] = None,
+                 is_leaf: bool = False):
+        self.codebook_idx = codebook_idx
+        self.codebook_id = codebook_id
+        self.children: dict[int, Node] = {}
+        self.leaf_items: list[Item] = []
+        self.is_leaf = is_leaf
+        self.parent = parent
+
+class HierarchicalTrie:
+    def __init__(self, rqvae: RqVaeModel):
+        self.root = Node(codebook_idx=-1, codebook_id=-1, parent=None, is_leaf=False)
+        self.rqvae = rqvae
+    
+    def insert(self, item: Item):
+        current = self.root
+        for level, code_id in enumerate(item.hierarchical_id):
+            if code_id not in current.children:
+                new_node = Node(
+                    codebook_idx=level,
+                    codebook_id=code_id,
+                    parent=current,
+                    is_leaf=False
+                )
+                current.children[code_id] = new_node
+            current = current.children[code_id]
+
+        current.is_leaf = True
+        current.leaf_items.append(item)
+
+    def collect_subtree_items(self, node: Node) -> list[Item]:
+        result = []
+        if node.is_leaf:
+            result.extend(node.leaf_items)
+        for child in node.children.values():
+            result.extend(self.collect_subtree_items(child))
+        return result
+
+    def top_k_by_dot_product(
         self, 
-        query_residual: torch.Tensor, 
-        candidates: tuple[Item, ...]
-    ) -> tuple[tuple[Item, float], ...]:
-        """
-        Compute dot-product scores between query_residual and each candidate's residual.
-        Return tuple of (candidate_item, score) pairs.
-        """
-        scored = []
-        for c in candidates:
-            score = torch.dot(query_residual, c.residual).item()
-            scored.append((c, score))
-        return tuple(scored)
-
-    def find_closest(self, query_item: Item, n: int) -> list[Item]:
-        """
-        Find up to n closest items to 'query_item' by the described rules:
-          1) Find longest existing matching prefix.
-          2) Gather sub-tree items:
-             - If >= n items, pick top n by dot product.
-             - Else, move to parent, gather sub-tree, etc.
-          3) If root is reached, return all if < n, else top n by dot product.
-        """
-        # 1) Descend as far as possible
-        path_stack = [(self.root, None)]  # (node, parent_node) pairs for easy upward climb
-        current_node = self.root
-
-        # Traverse hierarchy while possible
-        for idx in query_item.hierarchical_id:
-            if idx in current_node.children:
-                parent_node = current_node
-                current_node = current_node.children[idx]
-                path_stack.append((current_node, parent_node))
+        query_vec: torch.Tensor, 
+        items: list[Item], 
+        k: int
+    ) -> list[Item]:
+        if not items:
+            return []
+
+        dots = []
+        for it in items:
+            dp = torch.dot(query_vec, it.residual).item()
+            dots.append((dp, it))
+
+        dots.sort(key=lambda x: x[0], reverse=True)
+
+        top = [item for (_, item) in dots[:k]]
+        return top
+
+    def find_n_closest(self, query_item: Item, n: int) -> list[Item]:
+        current = self.root
+        matched_path_length = 0
+
+        for level, code_id in enumerate(query_item.hierarchical_id):
+            if code_id in current.children:
+                current = current.children[code_id]
+                matched_path_length += 1
             else:
-                # Can't go deeper, break
                 break
 
-        # Now path_stack[-1][0] is the deepest node we matched.
-        # We'll climb up if needed.
-        while path_stack:
-            node, parent = path_stack[-1]
-            subtree_items = self._cached_gather_subtree_items(node)
-            if len(subtree_items) >= n:
-                # We can pick the top n by dot product
-                scored = self._cached_dot_scores(query_item.residual, subtree_items)
-                # Sort descending by score
-                scored = sorted(scored, key=lambda x: x[1], reverse=True)
-                return [itm for itm, _ in scored[:n]]
+        node = current
+
+        def try_node(node: Node, query_residual: torch.Tensor, level: int) -> list[Item]:
+            if node.is_leaf:
+                all_items = node.leaf_items
             else:
-                # Not enough items: move up one level
-                # (pop the current node off the stack and keep going)
-                path_stack.pop()
-                if not path_stack:
-                    # We are at the root (the last pop).
-                    # If still not enough items, we simply return everything we have from the root
-                    # or if root has more than n, we pick top n.
-                    scored = self._cached_dot_scores(query_item.residual, subtree_items)
-                    scored = sorted(scored, key=lambda x: x[1], reverse=True)
-                    return [itm for itm, _ in scored[:n]]
-                # Otherwise, continue in the loop (which means gather from the parent's node)
-
-        # Safety net (should never get here logically, but just in case):
-        return []
-
-
-# ---------------------------
-# Usage example (toy):
+                all_items = self.collect_subtree_items(node)
+
+            count_here = len(all_items)
+
+            if count_here == 0:
+                if node.parent is None:
+                    return []
+                return move_up(node, query_residual, level)
+
+            if count_here == n:
+                return all_items
+
+            if count_here > n:
+                top_n_items = self.top_k_by_dot_product(query_residual, all_items, n)
+                return top_n_items
+
+            if node.parent is None:
+                return all_items
+            else:
+                return move_up(node, query_residual, level)
+
+        def move_up(child_node: Node, query_residual: torch.Tensor, level: int) -> list[Item]:
+            parent = child_node.parent
+            if parent is None:
+                return []
+
+            emb = self.rqvae.get_single_embedding(child_node.codebook_idx, child_node.codebook_id)
+            new_query_residual = query_residual + emb
+
+            all_parent_items = self.collect_subtree_items(parent)
+            count_parent = len(all_parent_items)
+
+            if count_parent == 0:
+                if parent.parent is None:
+                    return []
+                return move_up(parent, new_query_residual, level-1)
+
+            if count_parent >= n:
+                adjusted_items = []
+                for it in all_parent_items:
+                    adj_item_res = it.residual + emb if it in child_node.leaf_items else _adjust_residual_up(it, parent, child_node)
+                    new_it = Item(it.hierarchical_id, it.raw_item_id, adj_item_res)
+                    adjusted_items.append(new_it)
+
+                top_n_parent = self.top_k_by_dot_product(new_query_residual, adjusted_items, n)
+                return top_n_parent
+
+            adjusted_items = []
+            for it in all_parent_items:
+                adj_item_res = _adjust_residual_up(it, parent, child_node)
+                new_it = Item(it.hierarchical_id, it.raw_item_id, adj_item_res)
+                adjusted_items.append(new_it)
+
+            # we want to keep them, but also see if we can go further up
+            if parent.parent is None:
+                return adjusted_items
+            else:
+                return move_up(parent, new_query_residual, level-1)
+
+        def _adjust_residual_up(it: Item, parent_node: Node, child_node: Node) -> torch.Tensor:
+            emb = self.rqvae.get_single_embedding(child_node.codebook_idx, child_node.codebook_id)
+            return it.residual + emb
+
+        results = try_node(node, query_item.residual, matched_path_length)
+        return results
+
 if __name__ == "__main__":
-    # Suppose emb_dim = 3 for example
-    trie = Trie()
-
-    # Insert a few items
-    items_to_insert = [
-        Item((1, 2, 3), torch.tensor([1.0, 0.5, 0.2])),
-        Item((1, 2, 3), torch.tensor([1.1, 0.4, 0.0])),  # collision on same path
-        Item((1, 2, 4), torch.tensor([0.9, 0.9, 0.9])),
-        Item((1, 5),     torch.tensor([-0.1, 0.2, 1.0])),
-        Item((2,),       torch.tensor([0.3, 0.3, 0.3])),
+    trie = HierarchicalTrie()
+
+    emb_dim = 8
+
+    torch.manual_seed(42)
+    items = [
+        Item((10, 20, 30, 40),  1, torch.randn(emb_dim)),
+        Item((10, 20, 30, 40),  2, torch.randn(emb_dim)),  # same path => same leaf
+        Item((10, 20, 99, 40),  3, torch.randn(emb_dim)),
+        Item((10, 23, 30, 44),  4, torch.randn(emb_dim)),
+        Item((10, 23, 30, 45),  5, torch.randn(emb_dim)),
+        Item((99,  1,  2,  3),  6, torch.randn(emb_dim))
     ]
-    for it in items_to_insert:
+
+    for it in items:
         trie.insert(it)
 
-    # Query item
-    query = Item((1, 2, 3, 99), torch.tensor([1.0, 1.0, 1.0]))  # (1,2,3,99) partially matches deeper
-    closest = trie.find_closest(query, n=3)
-    print("Closest items:")
-    for c in closest:
-        print(c)
+    query_id = (10, 20, 30, 40)
+    query_residual = torch.randn(emb_dim)
+    query_item = Item(query_id, raw_item_id=-999, residual=query_residual)
+
+    found_items = trie.find_n_closest(query_item, n=3)
+
+    print("Found items:")
+    for fi in found_items:
+        print(f"  raw_item_id={fi.raw_item_id}, hierarchical_id={fi.hierarchical_id}")

From a699b2b2ae6dd3c00d789b32ecd39817205eb704 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 2 Feb 2025 16:00:16 +0700
Subject: [PATCH 056/175] fix sasrec baseline

---
 modeling/models/rqvae.py  |  3 ++-
 modeling/models/sasrec.py | 39 +++++++++++++++++++++++++++++++--------
 modeling/models/tiger.py  | 18 +++++++++++++++---
 3 files changed, 48 insertions(+), 12 deletions(-)

diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 90fd26b4..cfdb0574 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,4 +1,5 @@
 import functools
+from utils import DEVICE
 from models.base import TorchModel
 
 import torch
@@ -148,7 +149,7 @@ def eval_pass(self, embeddings):
             codebook_vectors = codebook[codebook_indices]
             ind_lists.append(codebook_indices.cpu().numpy())
             remainder = remainder - codebook_vectors
-        return torch.tensor(list(zip(*ind_lists))), remainder
+        return torch.tensor(list(zip(*ind_lists))).to(DEVICE), remainder
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 96e8f839..57367e3b 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -40,14 +40,31 @@ def __init__(
         
         df = torch.load('../data/Beauty/data_full.pt')
         precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
-        self._projector = torch.nn.Linear(precomputed_embeddings.shape[1], embedding_dim) # TODO incorrect projection
-        # TODO infer text embeddings using embedding_dim=64
-        projector_embeddings = self._projector(precomputed_embeddings)
         
-        padding_embedding = self._item_embeddings.weight[0].unsqueeze(0)
-        mask_embedding = self._item_embeddings.weight[-1].unsqueeze(0)
+        self._projector = torch.nn.Linear(precomputed_embeddings.shape[1], embedding_dim)
         
-        extended_embeddings = torch.cat([padding_embedding, projector_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
+        padding_embedding = torch.nn.init.trunc_normal_(
+            torch.zeros(1, precomputed_embeddings.shape[1]),
+            std=initializer_range,
+            a=-2 * initializer_range,
+            b=2 * initializer_range
+        )
+
+        mask_embedding = torch.nn.init.trunc_normal_(
+            torch.zeros(1, precomputed_embeddings.shape[1]),
+            std=initializer_range,
+            a=-2 * initializer_range,
+            b=2 * initializer_range
+        )
+        
+        extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
+        
+        self._item_embeddings = torch.nn.Embedding(
+            num_embeddings=num_items + 2,
+            embedding_dim=precomputed_embeddings.shape[1]
+        )
+        
+        # TODO ask about freezed masked & padding tokens
         
         self._item_embeddings.weight.data.copy_(extended_embeddings)
         self._item_embeddings.weight.requires_grad = False
@@ -66,6 +83,12 @@ def create_from_config(cls, config, **kwargs):
             dropout=config.get('dropout', 0.0),
             initializer_range=config.get('initializer_range', 0.02)
         )
+        
+    def get_item_embeddings(self, events):
+        return self._projector(self._item_embeddings(events))
+    
+    def _get_item_embeddings(self):
+        return self._projector(self._item_embeddings.weight)
 
     def forward(self, inputs):
         all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
@@ -80,7 +103,7 @@ def forward(self, inputs):
 
             all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
 
-            all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
+            all_embeddings = self._get_item_embeddings()  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
@@ -121,7 +144,7 @@ def forward(self, inputs):
             candidate_scores = torch.einsum(
                 'bd,nd->bn',
                 last_embeddings,
-                self._item_embeddings.weight
+                self._get_item_embeddings()
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
             candidate_scores[:, self._num_items + 1:] = -torch.inf  # Mask id
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index a57fcaa3..865fd72b 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -140,9 +140,9 @@ def create_from_config(cls, config, **kwargs):
 
         return cls(
             rqvae_model=rqvae_model,
-            item_id_to_semantic_id=semantic_ids.to(DEVICE),
-            item_id_to_residual=residuals.to(DEVICE),
-            item_id_to_embedding=embeddings.to(DEVICE),
+            item_id_to_semantic_id=semantic_ids,
+            item_id_to_residual=residuals,
+            item_id_to_embedding=embeddings,
             solver=solver,
             sequence_prefix=config["sequence_prefix"],
             pred_prefix=config["predictions_prefix"],
@@ -211,6 +211,18 @@ def forward(self, inputs):
             residuals = tgt_embeddings[:, -1, :]
 
             ids = self._apply_trie(semantic_ids, residuals, n=20)
+            # store number of items in tree
+            # take first where >= n
+            # set vector with all trues, then set false from lower level tree
+            # take all true left in upper tree
+            # maybe store items by prefix (not so many memory)?
+            # lex sort by level first, item second
+            # use sparse matrix
+            
+            
+            # store by raw_item_id (semantic_id) uniq key (i0 * 256^3 + i1 * 256^2 + i2 * 256 + i3)
+            # sparse matrix - max_uniq_semantic_ids (for rqvae init items infer (max is 12101, could be less if collisions))
+            # store sem_id -> num
             return ids
 
     def _apply_trie(self, preds: torch.Tensor, residuals: torch.Tensor, n: int = 20):

From fe18a56f89303012faa73a714595e8d49c0810fb Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 2 Feb 2025 19:53:50 +0700
Subject: [PATCH 057/175] create init tree structure

---
 modeling/rqvae_utils/trie.py | 276 ++++++++++++-----------------------
 1 file changed, 90 insertions(+), 186 deletions(-)

diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 2b520149..b842d50c 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -1,188 +1,92 @@
 import torch
-from typing import Optional
-
-from models.rqvae import RqVaeModel
-
-class Item:
-    def __init__(
-        self, 
-        hierarchical_id: tuple[int], 
-        raw_item_id: int, 
-        residual: torch.Tensor
-    ):
-        self.hierarchical_id = hierarchical_id
-        self.raw_item_id = raw_item_id
-        self.residual = residual
-
-
-class Node:
-    def __init__(self,
-                 codebook_idx: int,
-                 codebook_id: int,
-                 parent: Optional['Node'] = None,
-                 is_leaf: bool = False):
-        self.codebook_idx = codebook_idx
-        self.codebook_id = codebook_id
-        self.children: dict[int, Node] = {}
-        self.leaf_items: list[Item] = []
-        self.is_leaf = is_leaf
-        self.parent = parent
-
-class HierarchicalTrie:
-    def __init__(self, rqvae: RqVaeModel):
-        self.root = Node(codebook_idx=-1, codebook_id=-1, parent=None, is_leaf=False)
-        self.rqvae = rqvae
+
+def unique_with_index(x, dim=None):
+    """Unique elements of x and indices of those unique elements
+    https://github.com/pytorch/pytorch/issues/36748#issuecomment-619514810
+
+    e.g.
+
+    unique(tensor([
+        [1, 2, 3],
+        [1, 2, 4],
+        [1, 2, 3],
+        [1, 2, 5]
+    ]), dim=0)
+    => (tensor([[1, 2, 3],
+                [1, 2, 4],
+                [1, 2, 5]]),
+        tensor([0, 1, 3]))
+    """
+    unique, inverse = torch.unique(
+        x, sorted=True, return_inverse=True, dim=dim)
+    perm = torch.arange(inverse.size(0), dtype=inverse.dtype,
+                        device=inverse.device)
+    inverse, perm = inverse.flip([0]), perm.flip([0])
+    return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
+
+def compute_unique_ids(semantic_ids: torch.Tensor):
+    K = semantic_ids.shape[1]
+    exponents = torch.arange(K - 1, -1, -1, dtype=torch.int64)
+    base = 256 ** exponents # TODO don't hardcode 256
+    uniq_ids = torch.einsum('nc,c->n', semantic_ids, base)
+    return uniq_ids
+
+def build_tree_structure(semantic_ids: torch.Tensor, residuals: torch.Tensor, embedding_table: torch.Tensor):
+    bs, K = semantic_ids.shape
+    embedding_dim = residuals.shape[1]
+
+    prefix_counts = torch.zeros(len(semantic_ids), K + 1, dtype=torch.int64) # num_unique x K+1
+    prefix_counts[:, 0] = bs
+
+    for i in range(K):
+        prefix_keys = compute_unique_ids(semantic_ids[:, :i+1]) # bs, semantic_ids order
+        unique_prefixes, inverse_indices_prefix_counts, prefix_counts_at_level = torch.unique(prefix_keys, return_inverse=True, return_counts=True) # num_unique_prefix, num_unique_prefix
+        current_level_same = prefix_counts_at_level[inverse_indices_prefix_counts]
+        prefix_counts[:, i + 1] = current_level_same
+    
+    # TODO print prefix_counts
+
+    residuals_per_level = torch.zeros(len(semantic_ids), K + 1, embedding_dim) # num_unique x K+1 x embedding_dim
+
+    for i in range(K - 1, -1, -1):
+        indices_at_level = semantic_ids[:, i] # bs
+        embeddings_at_level = embedding_table[i, indices_at_level] # bs x embedding_dim
+        residuals_per_level[:, K - i, :] = embeddings_at_level + residuals_per_level[:, K - i - 1, :]
+
+    large_uniq_ids = compute_unique_ids(semantic_ids) # bs, could be collisions
+    _, unique_indicies = unique_with_index(large_uniq_ids) # TODO check with semantic_ids (must be same)
     
-    def insert(self, item: Item):
-        current = self.root
-        for level, code_id in enumerate(item.hierarchical_id):
-            if code_id not in current.children:
-                new_node = Node(
-                    codebook_idx=level,
-                    codebook_id=code_id,
-                    parent=current,
-                    is_leaf=False
-                )
-                current.children[code_id] = new_node
-            current = current.children[code_id]
-
-        current.is_leaf = True
-        current.leaf_items.append(item)
-
-    def collect_subtree_items(self, node: Node) -> list[Item]:
-        result = []
-        if node.is_leaf:
-            result.extend(node.leaf_items)
-        for child in node.children.values():
-            result.extend(self.collect_subtree_items(child))
-        return result
-
-    def top_k_by_dot_product(
-        self, 
-        query_vec: torch.Tensor, 
-        items: list[Item], 
-        k: int
-    ) -> list[Item]:
-        if not items:
-            return []
-
-        dots = []
-        for it in items:
-            dp = torch.dot(query_vec, it.residual).item()
-            dots.append((dp, it))
-
-        dots.sort(key=lambda x: x[0], reverse=True)
-
-        top = [item for (_, item) in dots[:k]]
-        return top
-
-    def find_n_closest(self, query_item: Item, n: int) -> list[Item]:
-        current = self.root
-        matched_path_length = 0
-
-        for level, code_id in enumerate(query_item.hierarchical_id):
-            if code_id in current.children:
-                current = current.children[code_id]
-                matched_path_length += 1
-            else:
-                break
-
-        node = current
-
-        def try_node(node: Node, query_residual: torch.Tensor, level: int) -> list[Item]:
-            if node.is_leaf:
-                all_items = node.leaf_items
-            else:
-                all_items = self.collect_subtree_items(node)
-
-            count_here = len(all_items)
-
-            if count_here == 0:
-                if node.parent is None:
-                    return []
-                return move_up(node, query_residual, level)
-
-            if count_here == n:
-                return all_items
-
-            if count_here > n:
-                top_n_items = self.top_k_by_dot_product(query_residual, all_items, n)
-                return top_n_items
-
-            if node.parent is None:
-                return all_items
-            else:
-                return move_up(node, query_residual, level)
-
-        def move_up(child_node: Node, query_residual: torch.Tensor, level: int) -> list[Item]:
-            parent = child_node.parent
-            if parent is None:
-                return []
-
-            emb = self.rqvae.get_single_embedding(child_node.codebook_idx, child_node.codebook_id)
-            new_query_residual = query_residual + emb
-
-            all_parent_items = self.collect_subtree_items(parent)
-            count_parent = len(all_parent_items)
-
-            if count_parent == 0:
-                if parent.parent is None:
-                    return []
-                return move_up(parent, new_query_residual, level-1)
-
-            if count_parent >= n:
-                adjusted_items = []
-                for it in all_parent_items:
-                    adj_item_res = it.residual + emb if it in child_node.leaf_items else _adjust_residual_up(it, parent, child_node)
-                    new_it = Item(it.hierarchical_id, it.raw_item_id, adj_item_res)
-                    adjusted_items.append(new_it)
-
-                top_n_parent = self.top_k_by_dot_product(new_query_residual, adjusted_items, n)
-                return top_n_parent
-
-            adjusted_items = []
-            for it in all_parent_items:
-                adj_item_res = _adjust_residual_up(it, parent, child_node)
-                new_it = Item(it.hierarchical_id, it.raw_item_id, adj_item_res)
-                adjusted_items.append(new_it)
-
-            # we want to keep them, but also see if we can go further up
-            if parent.parent is None:
-                return adjusted_items
-            else:
-                return move_up(parent, new_query_residual, level-1)
-
-        def _adjust_residual_up(it: Item, parent_node: Node, child_node: Node) -> torch.Tensor:
-            emb = self.rqvae.get_single_embedding(child_node.codebook_idx, child_node.codebook_id)
-            return it.residual + emb
-
-        results = try_node(node, query_item.residual, matched_path_length)
-        return results
-
-if __name__ == "__main__":
-    trie = HierarchicalTrie()
-
-    emb_dim = 8
-
-    torch.manual_seed(42)
-    items = [
-        Item((10, 20, 30, 40),  1, torch.randn(emb_dim)),
-        Item((10, 20, 30, 40),  2, torch.randn(emb_dim)),  # same path => same leaf
-        Item((10, 20, 99, 40),  3, torch.randn(emb_dim)),
-        Item((10, 23, 30, 44),  4, torch.randn(emb_dim)),
-        Item((10, 23, 30, 45),  5, torch.randn(emb_dim)),
-        Item((99,  1,  2,  3),  6, torch.randn(emb_dim))
-    ]
-
-    for it in items:
-        trie.insert(it)
-
-    query_id = (10, 20, 30, 40)
-    query_residual = torch.randn(emb_dim)
-    query_item = Item(query_id, raw_item_id=-999, residual=query_residual)
-
-    found_items = trie.find_n_closest(query_item, n=3)
-
-    print("Found items:")
-    for fi in found_items:
-        print(f"  raw_item_id={fi.raw_item_id}, hierarchical_id={fi.hierarchical_id}")
+    prefix_counts = prefix_counts[unique_indicies]
+    residuals_per_level = residuals_per_level[unique_indicies]
+    unique_ids = large_uniq_ids[unique_indicies]
+
+    sorted_indices = torch.argsort(unique_ids)
+    sorted_uniq_ids = unique_ids[sorted_indices]
+    sorted_prefix_counts = prefix_counts[sorted_indices]
+    sorted_residuals = residuals_per_level[sorted_indices]
+
+    return sorted_uniq_ids, sorted_prefix_counts, sorted_residuals
+
+
+bs = 12101  # Batch size
+K = 4    # Length of semantic_id
+embedding_dim = 512  # Embedding size
+
+# Generate random semantic IDs in range [0, 255]
+semantic_ids = torch.randint(0, 256, (bs, K), dtype=torch.int64)
+
+print(f"{semantic_ids=}")
+
+# Random residuals
+residuals = torch.randn(bs, embedding_dim)
+
+# Example embedding table (num_levels=K, each level has 256 embeddings of size embedding_dim)
+embedding_table = torch.randn(K, 256, embedding_dim)
+
+# Build tree structure
+sorted_uniq_ids, sorted_prefix_counts, sorted_residuals = build_tree_structure(semantic_ids, residuals, embedding_table)
+
+# Print results
+print("Sorted Unique IDs:", sorted_uniq_ids)
+print("Sorted Prefix Counts:", sorted_prefix_counts)
+print("Sorted Residuals:", sorted_residuals)
\ No newline at end of file

From 06f2af3fb9fc413138a4f54fbd12d1511cf203b1 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Tue, 4 Feb 2025 00:11:41 +0700
Subject: [PATCH 058/175] draft trie

---
 modeling/models/tiger.py         |  10 +-
 modeling/rqvae_utils/__init__.py |   2 +-
 modeling/rqvae_utils/trie.py     | 240 ++++++++++++++++++++-----------
 3 files changed, 160 insertions(+), 92 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 865fd72b..f56f0a9f 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -3,7 +3,7 @@
 import torch
 from tqdm import tqdm
 from models.base import SequentialTorchModel
-from rqvae_utils import CollisionSolver, HierarchicalTrie, Item
+from rqvae_utils import CollisionSolver, Trie
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
@@ -86,9 +86,9 @@ def __init__(
         
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         
-        self._trie = HierarchicalTrie(rqvae_model)
-        for item_id, semantic_id in enumerate(item_id_to_semantic_id):
-            self._trie.insert(Item(semantic_id, item_id + 1, item_id_to_residual[item_id])) # TODO no dedup tokens here
+        # self._trie = HierarchicalTrie(rqvae_model)
+        # for item_id, semantic_id in enumerate(item_id_to_semantic_id):
+        #     self._trie.insert(Item(semantic_id, item_id + 1, item_id_to_residual[item_id])) # TODO no dedup tokens here
 
         self._bos_token_id = codebook_sizes[0]
         self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
@@ -209,6 +209,8 @@ def forward(self, inputs):
             ) # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
 
             residuals = tgt_embeddings[:, -1, :]
+            
+            raise Exception("TODO")
 
             ids = self._apply_trie(semantic_ids, residuals, n=20)
             # store number of items in tree
diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
index 3f29aee5..7764a752 100644
--- a/modeling/rqvae_utils/__init__.py
+++ b/modeling/rqvae_utils/__init__.py
@@ -1,2 +1,2 @@
 from .collision_solver import CollisionSolver
-from .trie import Item, HierarchicalTrie
\ No newline at end of file
+from .trie import Trie
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index b842d50c..84a5fba3 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -1,92 +1,158 @@
+import json
 import torch
 
-def unique_with_index(x, dim=None):
-    """Unique elements of x and indices of those unique elements
-    https://github.com/pytorch/pytorch/issues/36748#issuecomment-619514810
-
-    e.g.
-
-    unique(tensor([
-        [1, 2, 3],
-        [1, 2, 4],
-        [1, 2, 3],
-        [1, 2, 5]
-    ]), dim=0)
-    => (tensor([[1, 2, 3],
-                [1, 2, 4],
-                [1, 2, 5]]),
-        tensor([0, 1, 3]))
-    """
-    unique, inverse = torch.unique(
-        x, sorted=True, return_inverse=True, dim=dim)
-    perm = torch.arange(inverse.size(0), dtype=inverse.dtype,
-                        device=inverse.device)
-    inverse, perm = inverse.flip([0]), perm.flip([0])
-    return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
-
-def compute_unique_ids(semantic_ids: torch.Tensor):
-    K = semantic_ids.shape[1]
-    exponents = torch.arange(K - 1, -1, -1, dtype=torch.int64)
-    base = 256 ** exponents # TODO don't hardcode 256
-    uniq_ids = torch.einsum('nc,c->n', semantic_ids, base)
-    return uniq_ids
-
-def build_tree_structure(semantic_ids: torch.Tensor, residuals: torch.Tensor, embedding_table: torch.Tensor):
-    bs, K = semantic_ids.shape
-    embedding_dim = residuals.shape[1]
-
-    prefix_counts = torch.zeros(len(semantic_ids), K + 1, dtype=torch.int64) # num_unique x K+1
-    prefix_counts[:, 0] = bs
-
-    for i in range(K):
-        prefix_keys = compute_unique_ids(semantic_ids[:, :i+1]) # bs, semantic_ids order
-        unique_prefixes, inverse_indices_prefix_counts, prefix_counts_at_level = torch.unique(prefix_keys, return_inverse=True, return_counts=True) # num_unique_prefix, num_unique_prefix
-        current_level_same = prefix_counts_at_level[inverse_indices_prefix_counts]
-        prefix_counts[:, i + 1] = current_level_same
+from models.rqvae import RqVaeModel
+
+
+class Trie:
+    def __init__(self, rqvae_model: RqVaeModel):
+        self.rqvae_model = rqvae_model
+        self.keys = None
+        self.prefix_counts = None
+        self.residuals = None
+        self.raw_item_ids = None
+        
+    def unique_with_index(self, x, dim=None):
+        """Unique elements of x and indices of those unique elements
+        https://github.com/pytorch/pytorch/issues/36748#issuecomment-619514810
+
+        e.g.
+
+        unique(tensor([
+            [1, 2, 3],
+            [1, 2, 4],
+            [1, 2, 3],
+            [1, 2, 5]
+        ]), dim=0)
+        => (tensor([[1, 2, 3],
+                    [1, 2, 4],
+                    [1, 2, 5]]),
+            tensor([0, 1, 3]))
+        """
+        unique, inverse = torch.unique(
+            x, sorted=True, return_inverse=True, dim=dim)
+        perm = torch.arange(inverse.size(0), dtype=inverse.dtype,
+                            device=inverse.device)
+        inverse, perm = inverse.flip([0]), perm.flip([0])
+        return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
+
+    def compute_keys(self, semantic_ids: torch.Tensor):
+        K = semantic_ids.shape[1]
+        exponents = torch.arange(K - 1, -1, -1, dtype=torch.int64)
+        base = self.rqvae_model.codebook_sizes[0] ** exponents # TODO don't hardcode 256
+        uniq_ids = torch.einsum('nc,c->n', semantic_ids, base)
+        return uniq_ids
+
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, raw_item_ids: torch.Tensor):
+        embedding_table = torch.stack([cb for cb in self.rqvae_model.codebooks])
+        
+        print(f"{embedding_table.shape=}")
+        
+        bs, K = semantic_ids.shape
+        embedding_dim = residuals.shape[1]
+
+        prefix_counts = torch.zeros(bs, K + 1, dtype=torch.int64) # bs x K+1
+        prefix_counts[:, 0] = bs
+
+        for i in range(K):
+            truncated_semantic_ids = semantic_ids[:, :i+1]
+            padded_semantic_ids = torch.cat([truncated_semantic_ids, torch.zeros(bs, K - i - 1, dtype=torch.int64)], dim=1)
+            prefix_keys = self.compute_keys(padded_semantic_ids) # bs, semantic_ids order
+            unique_prefixes, inverse_indices_prefix_counts, prefix_counts_at_level = torch.unique(prefix_keys, return_inverse=True, return_counts=True)
+            current_level_same = prefix_counts_at_level[inverse_indices_prefix_counts]
+            prefix_counts[:, i + 1] = current_level_same
+        
+        # TODO print prefix_counts
+
+        residuals_per_level = torch.zeros(bs, K + 1, embedding_dim) # bs x K+1 x embedding_dim
+
+        for i in range(K - 1, -1, -1):
+            indices_at_level = semantic_ids[:, i] # bs
+            embeddings_at_level = embedding_table[i, indices_at_level] # bs x embedding_dim
+            residuals_per_level[:, K - i, :] = embeddings_at_level + residuals_per_level[:, K - i - 1, :]
+
+        keys = self.compute_keys(semantic_ids) # bs, could be collisions
+        # _, unique_indicies = self.unique_with_index(large_uniq_ids) # TODO check with semantic_ids (must be same)
+        
+        # prefix_counts = prefix_counts[unique_indicies]
+        # residuals_per_level = residuals_per_level[unique_indicies]
+        # unique_ids = large_uniq_ids[unique_indicies]
+        
+        self.keys = keys
+        self.prefix_counts = prefix_counts
+        self.residuals = residuals_per_level
+        self.raw_item_ids = raw_item_ids
+
+        # sorted_indices = torch.argsort(keys)
+
+        # self.sorted_keys = keys[sorted_indices]
+        # self.sorted_prefix_counts = prefix_counts[sorted_indices]
+        # self.sorted_residuals = residuals_per_level[sorted_indices]
+        # self.sorted_raw_item_ids = raw_item_ids[sorted_indices]
+        
+    def process_prefixes(self, prefixes: torch.Tensor):
+        bs, prefix_len = prefixes.shape
+        padded_prefix = torch.cat([prefixes, torch.zeros(bs, len(self.rqvae_model.codebook_sizes) - prefix_len, dtype=prefixes.dtype)], dim=1)
+        lower_key = self.compute_keys(padded_prefix) # bs
+        upper_key = lower_key + self.rqvae_model.codebook_sizes[0] ** (len(self.rqvae_model.codebook_sizes) - prefix_len) # bs
+        num_items_in_range = ((self.keys.unsqueeze(0) >= lower_key.unsqueeze(1)) & (self.keys.unsqueeze(0) < upper_key.unsqueeze(1))).sum(dim=1)
+        return num_items_in_range # bs
+        
+        
+    def query(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_to_query: int):
+        bs, K = semantic_ids.shape
+        num_items = torch.stack([self.process_prefixes(semantic_ids[:, :i+1]) for i in range(K)], dim=1)
+        # max idx for each row where num_items > item_to_query
+        forward_mask = (num_items > item_to_query).int()
+        backward_mask = forward_mask.flip(1)
+        outer_level = K - 1 - torch.argmax(backward_mask, dim=1)
+        inner_level = outer_level + 1
+        
+        taken_outer_prefixes = semantic_ids * (torch.arange(K).expand(bs, K) < outer_level.unsqueeze(1))
+        taken_inner_prefixes = semantic_ids * (torch.arange(K).expand(bs, K) < inner_level.unsqueeze(1))
+        
+        outer_lower_prefix_keys = self.compute_keys(taken_outer_prefixes)
+        outer_upper_prefix_keys = outer_lower_prefix_keys + self.rqvae_model.codebook_sizes[0] ** (K - 1 - outer_level)
+        
+        print(outer_lower_prefix_keys)
+        
+        outer_mask = (self.keys.unsqueeze(0) >= outer_lower_prefix_keys.unsqueeze(1)) & (self.keys.unsqueeze(0) < outer_upper_prefix_keys.unsqueeze(1))
+        
+        inner_lower_prefix_keys = self.compute_keys(taken_inner_prefixes)
+        inner_upper_prefix_keys = inner_lower_prefix_keys + self.rqvae_model.codebook_sizes[0] ** (K - 1 - inner_level)
+        
+        inner_mask = (self.keys.unsqueeze(0) >= inner_lower_prefix_keys.unsqueeze(1)) & (self.keys.unsqueeze(0) < inner_upper_prefix_keys.unsqueeze(1))
+        
+        assert (inner_mask <= outer_mask).all() # TODO fix this
+        
+        return outer_mask, inner_mask
+
+
+if __name__ == "__main__":
+    embedding_dim = 512  # Embedding size
+    config = json.load(open("../configs/train/tiger_train_config.json"))
+    config = config["model"]
+    rqvae_config = json.load(open(config["rqvae_train_config_path"]))
+    rqvae_config["model"]["should_init_codebooks"] = False
+    rqvae_model = RqVaeModel.create_from_config(rqvae_config['model'])
+    rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
+    rqvae_model.eval()
     
-    # TODO print prefix_counts
-
-    residuals_per_level = torch.zeros(len(semantic_ids), K + 1, embedding_dim) # num_unique x K+1 x embedding_dim
-
-    for i in range(K - 1, -1, -1):
-        indices_at_level = semantic_ids[:, i] # bs
-        embeddings_at_level = embedding_table[i, indices_at_level] # bs x embedding_dim
-        residuals_per_level[:, K - i, :] = embeddings_at_level + residuals_per_level[:, K - i - 1, :]
-
-    large_uniq_ids = compute_unique_ids(semantic_ids) # bs, could be collisions
-    _, unique_indicies = unique_with_index(large_uniq_ids) # TODO check with semantic_ids (must be same)
+    trie = Trie(rqvae_model)
     
-    prefix_counts = prefix_counts[unique_indicies]
-    residuals_per_level = residuals_per_level[unique_indicies]
-    unique_ids = large_uniq_ids[unique_indicies]
-
-    sorted_indices = torch.argsort(unique_ids)
-    sorted_uniq_ids = unique_ids[sorted_indices]
-    sorted_prefix_counts = prefix_counts[sorted_indices]
-    sorted_residuals = residuals_per_level[sorted_indices]
-
-    return sorted_uniq_ids, sorted_prefix_counts, sorted_residuals
-
-
-bs = 12101  # Batch size
-K = 4    # Length of semantic_id
-embedding_dim = 512  # Embedding size
 
-# Generate random semantic IDs in range [0, 255]
-semantic_ids = torch.randint(0, 256, (bs, K), dtype=torch.int64)
-
-print(f"{semantic_ids=}")
-
-# Random residuals
-residuals = torch.randn(bs, embedding_dim)
-
-# Example embedding table (num_levels=K, each level has 256 embeddings of size embedding_dim)
-embedding_table = torch.randn(K, 256, embedding_dim)
-
-# Build tree structure
-sorted_uniq_ids, sorted_prefix_counts, sorted_residuals = build_tree_structure(semantic_ids, residuals, embedding_table)
-
-# Print results
-print("Sorted Unique IDs:", sorted_uniq_ids)
-print("Sorted Prefix Counts:", sorted_prefix_counts)
-print("Sorted Residuals:", sorted_residuals)
\ No newline at end of file
+    N = 100
+    K = 3
+    semantic_ids = torch.randint(0, 4, (N, K), dtype=torch.int64)
+    residuals = torch.randn(N, embedding_dim)
+    trie.build_tree_structure(semantic_ids, residuals, torch.arange(N))
+    
+    query_num = 10
+    q_semantic_ids = torch.randint(0, 4, (query_num, K), dtype=torch.int64)
+    q_residuals = torch.randn(query_num, embedding_dim)
+
+    a, b  = trie.query(q_semantic_ids, q_residuals, 10)
+    # print(f"{a=}")
+    # print(f"{b=}")
+    print(f"{a.shape=}")
+    print(f"{b.shape=}")

From db3aaf0a508cf557071c00b2dba62af48468822e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Feb 2025 17:04:40 +0700
Subject: [PATCH 059/175] working trie

---
 modeling/rqvae_utils/trie.py | 423 +++++++++++++++++++++++++++--------
 1 file changed, 324 insertions(+), 99 deletions(-)

diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 84a5fba3..2b46567a 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -1,6 +1,7 @@
 import json
-import torch
+import time
 
+import torch
 from models.rqvae import RqVaeModel
 
 
@@ -9,9 +10,14 @@ def __init__(self, rqvae_model: RqVaeModel):
         self.rqvae_model = rqvae_model
         self.keys = None
         self.prefix_counts = None
-        self.residuals = None
+        self.residuals_per_level = None
         self.raw_item_ids = None
-        
+        self.K = len(self.rqvae_model.codebook_sizes)
+        self.total_items = None
+        self.embedding_table = torch.stack(
+            [cb for cb in self.rqvae_model.codebooks]
+        )  # K x codebook_size x embedding_dim
+
     def unique_with_index(self, x, dim=None):
         """Unique elements of x and indices of those unique elements
         https://github.com/pytorch/pytorch/issues/36748#issuecomment-619514810
@@ -29,103 +35,320 @@ def unique_with_index(self, x, dim=None):
                     [1, 2, 5]]),
             tensor([0, 1, 3]))
         """
-        unique, inverse = torch.unique(
-            x, sorted=True, return_inverse=True, dim=dim)
-        perm = torch.arange(inverse.size(0), dtype=inverse.dtype,
-                            device=inverse.device)
+        unique, inverse = torch.unique(x, sorted=True, return_inverse=True, dim=dim)
+        perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
         inverse, perm = inverse.flip([0]), perm.flip([0])
         return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
 
     def compute_keys(self, semantic_ids: torch.Tensor):
-        K = semantic_ids.shape[1]
-        exponents = torch.arange(K - 1, -1, -1, dtype=torch.int64)
-        base = self.rqvae_model.codebook_sizes[0] ** exponents # TODO don't hardcode 256
-        uniq_ids = torch.einsum('nc,c->n', semantic_ids, base)
+        exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
+        base = self.rqvae_model.codebook_sizes[0] ** exponents
+        uniq_ids = torch.einsum("nc,c->n", semantic_ids, base)
         return uniq_ids
 
-    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, raw_item_ids: torch.Tensor):
-        embedding_table = torch.stack([cb for cb in self.rqvae_model.codebooks])
-        
-        print(f"{embedding_table.shape=}")
-        
-        bs, K = semantic_ids.shape
-        embedding_dim = residuals.shape[1]
+    def pad_semantic_ids(self, semantic_ids: torch.Tensor):
+        return torch.cat(
+            [
+                semantic_ids,
+                torch.zeros(
+                    semantic_ids.shape[0],
+                    self.K - semantic_ids.shape[1],
+                    dtype=semantic_ids.dtype,
+                ),
+            ],
+            dim=1,
+        )
+
+    def build_tree_structure(
+        self,
+        semantic_ids: torch.Tensor,
+        residuals: torch.Tensor,
+        raw_item_ids: torch.Tensor,
+    ):
+        """
+        Order of semantic ids, residuals, raw_item_ids must be the same (corresponding to same item)
+        """
+        bs = semantic_ids.shape[0]
 
-        prefix_counts = torch.zeros(bs, K + 1, dtype=torch.int64) # bs x K+1
+        prefix_counts = torch.zeros(bs, self.K + 1, dtype=torch.int64)  # bs x K+1
         prefix_counts[:, 0] = bs
 
-        for i in range(K):
-            truncated_semantic_ids = semantic_ids[:, :i+1]
-            padded_semantic_ids = torch.cat([truncated_semantic_ids, torch.zeros(bs, K - i - 1, dtype=torch.int64)], dim=1)
-            prefix_keys = self.compute_keys(padded_semantic_ids) # bs, semantic_ids order
-            unique_prefixes, inverse_indices_prefix_counts, prefix_counts_at_level = torch.unique(prefix_keys, return_inverse=True, return_counts=True)
-            current_level_same = prefix_counts_at_level[inverse_indices_prefix_counts]
+        for i in range(self.K):
+            truncated_semantic_ids = semantic_ids[:, : i + 1]
+            padded_semantic_ids = self.pad_semantic_ids(truncated_semantic_ids)
+            prefix_keys = self.compute_keys(
+                padded_semantic_ids
+            )  # bs, semantic_ids order
+            unique_prefixes, inverse_indices_prefix_counts, prefix_counts_at_level = (
+                torch.unique(prefix_keys, return_inverse=True, return_counts=True)
+            )  # [1 2 3 3 2] -> [1 2 3] [0 1 2 2 1] [1 2 2]
+            current_level_same = prefix_counts_at_level[
+                inverse_indices_prefix_counts
+            ]  # [1 2 2 2 2]
             prefix_counts[:, i + 1] = current_level_same
-        
-        # TODO print prefix_counts
-
-        residuals_per_level = torch.zeros(bs, K + 1, embedding_dim) # bs x K+1 x embedding_dim
-
-        for i in range(K - 1, -1, -1):
-            indices_at_level = semantic_ids[:, i] # bs
-            embeddings_at_level = embedding_table[i, indices_at_level] # bs x embedding_dim
-            residuals_per_level[:, K - i, :] = embeddings_at_level + residuals_per_level[:, K - i - 1, :]
-
-        keys = self.compute_keys(semantic_ids) # bs, could be collisions
-        # _, unique_indicies = self.unique_with_index(large_uniq_ids) # TODO check with semantic_ids (must be same)
-        
-        # prefix_counts = prefix_counts[unique_indicies]
-        # residuals_per_level = residuals_per_level[unique_indicies]
-        # unique_ids = large_uniq_ids[unique_indicies]
-        
+
+        residuals_per_level = self.get_residuals_per_level(
+            semantic_ids, residuals
+        )  # total_items x K + 2 x embedding_dim
+
+        keys = self.compute_keys(semantic_ids)  # bs, could be collisions
+
         self.keys = keys
         self.prefix_counts = prefix_counts
-        self.residuals = residuals_per_level
+        self.residuals_per_level = residuals_per_level
         self.raw_item_ids = raw_item_ids
+        self.total_items = len(keys)
+
+    def get_residuals_per_level(
+        self, semantic_ids: torch.Tensor, residuals: torch.Tensor
+    ):
+        bs = semantic_ids.shape[0]
+        embedding_dim = residuals.shape[1]
+        residuals_per_level = torch.zeros(
+            bs, self.K + 1, embedding_dim
+        )  # bs x K + 1 x embedding_dim
+
+        # TODO think if reverse is needed here
+        # i = 3, 2, 1, 0
+        for i in range(self.K - 1, -1, -1):
+            indices_at_level = semantic_ids[:, i]  # bs
+            embeddings_at_level = self.embedding_table[
+                i, indices_at_level
+            ]  # bs x embedding_dim
+            # 1 2 3 4
+            residuals_per_level[:, self.K - i, :] = (
+                embeddings_at_level + residuals_per_level[:, self.K - i - 1, :]
+            )  # [0 first_cumul_emb, second, ..., full_emb]
+
+        # TODO check that residuals_per_level equal at last layer to full embedding of semantic id
 
-        # sorted_indices = torch.argsort(keys)
+        residuals_per_level[:, 0, :] = residuals
+
+        residuals_per_level = torch.cat(
+            [
+                torch.zeros(bs, 1, embedding_dim),
+                residuals_per_level,
+            ],
+            dim=1,
+        )
+
+        return residuals_per_level  # bs x K + 2 x embedding_dim
+
+    def get_mask_by_prefix(self, prefixes: torch.Tensor, taken_lens: torch.Tensor):
+        bs = prefixes.shape[0]
+        padded_prefix = self.pad_semantic_ids(prefixes)
+        lower_key = self.compute_keys(padded_prefix)  # bs
+        upper_key = lower_key + self.rqvae_model.codebook_sizes[0] ** (
+            self.K - taken_lens
+        )  # bs
+
+        # self.K = 4, prefix_len = 3 => 256 ^ 3 + 256 ^ 2 + 256 ^ 1 + 256 ^ 0
+        # need to add 256 ^ 1 to get exclusive upper bound
+        # self.K = 4, prefix_len = 2 => 256 ^ 3 + 256 ^ 2 + 256 ^ 1 + 256 ^ 0
+        # need to add 256 ^ 2 to get exclusive upper bound
+        # self.K = 4, prefix_len = 1 => 256 ^ 3 + 256 ^ 2 + 256 ^ 1 + 256 ^ 0
+        # need to add 256 ^ 3 to get exclusive upper bound
+        # self.keys.shape = bs, lower_key.shape = bs, upper_key.shape = bs
+
+        assert lower_key.shape[0] == upper_key.shape[0] == bs
+        assert self.keys.shape[0] == self.total_items
+
+        mask = (
+            (
+                self.keys.unsqueeze(0) >= lower_key.unsqueeze(1)
+            )  # including prefix [1, 2, 0, 0]
+            & (
+                self.keys.unsqueeze(0) <= upper_key.unsqueeze(1)
+            )  # excluding [1, 3, 0, 0], last [1, 2, 256, 256]
+        )
+
+        return mask
 
-        # self.sorted_keys = keys[sorted_indices]
-        # self.sorted_prefix_counts = prefix_counts[sorted_indices]
-        # self.sorted_residuals = residuals_per_level[sorted_indices]
-        # self.sorted_raw_item_ids = raw_item_ids[sorted_indices]
-        
     def process_prefixes(self, prefixes: torch.Tensor):
         bs, prefix_len = prefixes.shape
-        padded_prefix = torch.cat([prefixes, torch.zeros(bs, len(self.rqvae_model.codebook_sizes) - prefix_len, dtype=prefixes.dtype)], dim=1)
-        lower_key = self.compute_keys(padded_prefix) # bs
-        upper_key = lower_key + self.rqvae_model.codebook_sizes[0] ** (len(self.rqvae_model.codebook_sizes) - prefix_len) # bs
-        num_items_in_range = ((self.keys.unsqueeze(0) >= lower_key.unsqueeze(1)) & (self.keys.unsqueeze(0) < upper_key.unsqueeze(1))).sum(dim=1)
-        return num_items_in_range # bs
-        
-        
-    def query(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_to_query: int):
+        taken_len = torch.full((bs,), prefix_len)
+        mask = self.get_mask_by_prefix(prefixes, taken_len)
+        # self.keys.unsqueeze(0) = 1 x bs
+        # lower_key.unsqueeze(1), upper_key.unsqueeze(1) = bs x 1
+        num_items_in_range = (mask).sum(dim=1)
+        return num_items_in_range  # bs
+
+    def get_outer_inner_levels(self, semantic_ids: torch.Tensor, items_to_query: int):
         bs, K = semantic_ids.shape
-        num_items = torch.stack([self.process_prefixes(semantic_ids[:, :i+1]) for i in range(K)], dim=1)
-        # max idx for each row where num_items > item_to_query
-        forward_mask = (num_items > item_to_query).int()
-        backward_mask = forward_mask.flip(1)
-        outer_level = K - 1 - torch.argmax(backward_mask, dim=1)
-        inner_level = outer_level + 1
-        
-        taken_outer_prefixes = semantic_ids * (torch.arange(K).expand(bs, K) < outer_level.unsqueeze(1))
-        taken_inner_prefixes = semantic_ids * (torch.arange(K).expand(bs, K) < inner_level.unsqueeze(1))
-        
-        outer_lower_prefix_keys = self.compute_keys(taken_outer_prefixes)
-        outer_upper_prefix_keys = outer_lower_prefix_keys + self.rqvae_model.codebook_sizes[0] ** (K - 1 - outer_level)
-        
-        print(outer_lower_prefix_keys)
-        
-        outer_mask = (self.keys.unsqueeze(0) >= outer_lower_prefix_keys.unsqueeze(1)) & (self.keys.unsqueeze(0) < outer_upper_prefix_keys.unsqueeze(1))
-        
-        inner_lower_prefix_keys = self.compute_keys(taken_inner_prefixes)
-        inner_upper_prefix_keys = inner_lower_prefix_keys + self.rqvae_model.codebook_sizes[0] ** (K - 1 - inner_level)
-        
-        inner_mask = (self.keys.unsqueeze(0) >= inner_lower_prefix_keys.unsqueeze(1)) & (self.keys.unsqueeze(0) < inner_upper_prefix_keys.unsqueeze(1))
-        
-        assert (inner_mask <= outer_mask).all() # TODO fix this
-        
-        return outer_mask, inner_mask
+        num_items = torch.stack(
+            [self.process_prefixes(semantic_ids[:, : i + 1]) for i in range(K)], dim=1
+        )
+        num_items: torch.Tensor = torch.cat(
+            [
+                torch.full(
+                    (bs, 1),
+                    self.total_items,
+                ),
+                num_items,
+            ],
+            dim=1,
+        )
+
+        # first idx from end where it > items_to_query
+
+        forward_mask = (num_items > items_to_query).int()  # bs x K + 1
+        backward_mask = forward_mask.flip(1)  # bs x K + 1
+        outer_level = K - torch.argmax(backward_mask, dim=1)  # bs
+        inner_level = outer_level + 1  # bs
+
+        # ol & il - how long prefix take => get (> items_to_query & <= items_to_query) items
+
+        assert (outer_level <= K).all()
+
+        return num_items, outer_level, inner_level  # bs, bs
+
+    def get_closest_single(
+        self,
+        guaranteed_raw_item_ids,  # guaranteed_len
+        guaranteed_stored_residuals,  # guaranteed_len x embedding_dim
+        guaranteed_query_residual,  # embedding_dim
+        left_raw_item_ids,  # left_len
+        left_stored_residuals,  # left_len x embedding_dim
+        left_query_residual,  # embedding_dim
+    ):
+        guaranteed_scores = torch.matmul(
+            guaranteed_stored_residuals, guaranteed_query_residual
+        )  # (guaranteed_len,)
+
+        # Compute scores for left items
+        left_scores = torch.matmul(
+            left_stored_residuals, left_query_residual
+        )  # (left_len,)
+
+        # Sort guaranteed items by score (descending)
+        guaranteed_sorted_indices = torch.argsort(guaranteed_scores, descending=True)
+        guaranteed_sorted_ids = guaranteed_raw_item_ids[guaranteed_sorted_indices]
+
+        # Sort left items by score (descending)
+        left_sorted_indices = torch.argsort(left_scores, descending=True)
+        left_sorted_ids = left_raw_item_ids[left_sorted_indices]
+
+        # Concatenate the sorted lists
+        result_ids = torch.cat((guaranteed_sorted_ids, left_sorted_ids))
+
+        return result_ids
+
+    def get_closest(
+        self,
+        outer_masks,
+        inner_masks,
+        outer_levels,
+        inner_levels,
+        query_residuals_per_level,
+        items_to_query,
+    ):
+        # K = 3
+        # self.residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 2
+        # query_residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 2
+        # outer_level = 3 # TODO if K (get by dot only from outer)
+        # inner_level = 4 # TODO if K + 1 (get by dot only from outer)
+        # outer_mask.shape = total_items
+        # inner_mask.shape = total_items
+
+        raw_item_ids = []
+
+        for (
+            outer_mask,
+            inner_mask,
+            outer_level,
+            inner_level,
+            query_residual_per_level,
+        ) in zip(
+            outer_masks,
+            inner_masks,
+            outer_levels,
+            inner_levels,
+            query_residuals_per_level,
+        ):
+            guaranteed = inner_mask  # guaranteed_len
+            left = outer_mask & ~inner_mask  # left_len
+
+            # self.residuals_per_level.shape = total_items, K + 2, embedding_dim
+            guaranteed_raw_item_ids = self.raw_item_ids[guaranteed]  # guaranteed_len
+            guaranteed_stored_residuals = self.residuals_per_level[guaranteed][
+                :, -(inner_level + 1)
+            ]  # guaranteed_len x embedding_dim
+            guaranteed_query_residual = query_residual_per_level[
+                -(inner_level + 1)
+            ]  # embedding_dim
+
+            left_raw_item_ids = self.raw_item_ids[left]  # left_len
+            left_stored_residuals = self.residuals_per_level[left][
+                :, -outer_level
+            ]  # left_len x embedding_dim
+            left_query_residual = query_residual_per_level[
+                -outer_level
+            ]  # embedding_dim
+
+            result_ids = self.get_closest_single(
+                guaranteed_raw_item_ids,
+                guaranteed_stored_residuals,
+                guaranteed_query_residual,
+                left_raw_item_ids,
+                left_stored_residuals,
+                left_query_residual,
+            )
+
+            raw_item_ids.append(result_ids[:items_to_query])
+
+        return torch.stack(raw_item_ids)
+
+    def query(
+        self, semantic_ids: torch.Tensor, residuals: torch.Tensor, items_to_query: int
+    ):
+        bs, K = semantic_ids.shape
+
+        assert K == self.K, "Semantic ids must have same number of levels as the trie"
+
+        num_items, outer_levels, inner_levels = self.get_outer_inner_levels(
+            semantic_ids, items_to_query
+        )  # bs, bs
+
+        taken_outer_prefixes = semantic_ids * (
+            torch.arange(K).expand(bs, K) < outer_levels.unsqueeze(1)
+        )
+        taken_inner_prefixes = semantic_ids * (
+            torch.arange(K).expand(bs, K) < inner_levels.unsqueeze(1)
+        )
+
+        outer_masks = self.get_mask_by_prefix(
+            taken_outer_prefixes, outer_levels
+        )  # bs, total_items
+        inner_masks = self.get_mask_by_prefix(
+            taken_inner_prefixes, inner_levels
+        )  # bs, total_items
+
+        assert (
+            num_items[torch.arange(bs), outer_levels] == outer_masks.sum(dim=1)
+        ).all()
+        assert (
+            num_items[torch.arange(bs), inner_levels] == inner_masks.sum(dim=1)
+        ).all()
+
+        assert (outer_masks.sum(dim=1) > items_to_query).all()
+        assert (inner_masks.sum(dim=1) <= items_to_query).all()
+
+        assert (inner_masks <= outer_masks).all()
+
+        query_residuals_per_level = self.get_residuals_per_level(
+            semantic_ids, residuals
+        )
+
+        raw_item_ids = self.get_closest(
+            outer_masks,
+            inner_masks,
+            outer_levels,
+            inner_levels,
+            query_residuals_per_level,
+            items_to_query,
+        )
+
+        return raw_item_ids
 
 
 if __name__ == "__main__":
@@ -134,25 +357,27 @@ def query(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_to_que
     config = config["model"]
     rqvae_config = json.load(open(config["rqvae_train_config_path"]))
     rqvae_config["model"]["should_init_codebooks"] = False
-    rqvae_model = RqVaeModel.create_from_config(rqvae_config['model'])
-    rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
+    rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"])
+    rqvae_model.load_state_dict(
+        torch.load(config["rqvae_checkpoint_path"], weights_only=True)
+    )
     rqvae_model.eval()
-    
+
     trie = Trie(rqvae_model)
-    
+    alphabet_size = rqvae_model.codebook_sizes[0]
 
-    N = 100
-    K = 3
-    semantic_ids = torch.randint(0, 4, (N, K), dtype=torch.int64)
+    N = 12101
+    K = len(rqvae_model.codebook_sizes)
+    semantic_ids = torch.randint(0, alphabet_size, (N, K), dtype=torch.int64)
     residuals = torch.randn(N, embedding_dim)
     trie.build_tree_structure(semantic_ids, residuals, torch.arange(N))
-    
-    query_num = 10
-    q_semantic_ids = torch.randint(0, 4, (query_num, K), dtype=torch.int64)
-    q_residuals = torch.randn(query_num, embedding_dim)
-
-    a, b  = trie.query(q_semantic_ids, q_residuals, 10)
-    # print(f"{a=}")
-    # print(f"{b=}")
-    print(f"{a.shape=}")
-    print(f"{b.shape=}")
+
+    items_to_query = 20
+    batch_size = 256
+    q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64)
+    q_residuals = torch.randn(batch_size, embedding_dim)
+
+    for i in range(100):
+        now = time.time()
+        item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
+        print(time.time() - now)

From 3bbafa9b2fc0c1eb6bf14e55db1d05d206bd0999 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Feb 2025 17:58:09 +0700
Subject: [PATCH 060/175] working tiger (1 min / eval)

---
 modeling/callbacks/base.py   |   5 +-
 modeling/models/tiger.py     | 279 +++++++++++++++++++----------------
 modeling/rqvae_utils/trie.py |  45 +++---
 review.md                    |   6 +
 4 files changed, 184 insertions(+), 151 deletions(-)

diff --git a/modeling/callbacks/base.py b/modeling/callbacks/base.py
index d959dd4f..c4b2e0ef 100644
--- a/modeling/callbacks/base.py
+++ b/modeling/callbacks/base.py
@@ -1,3 +1,4 @@
+from tqdm import tqdm
 from metric import BaseMetric, StatefullMetric
 
 import utils
@@ -202,8 +203,8 @@ def __call__(self, inputs, step_num):
 
             self._model.eval()
             with torch.no_grad():
-                for batch in self._get_dataloader():
-
+                total = len(self._get_dataloader())
+                for batch in tqdm(self._get_dataloader(), total=total):
                     for key, value in batch.items():
                         batch[key] = value.to(utils.DEVICE)
 
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index f56f0a9f..147b8ea4 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,17 +1,16 @@
 import json
 
 import torch
-from tqdm import tqdm
 from models.base import SequentialTorchModel
 from rqvae_utils import CollisionSolver, Trie
 from torch import nn
+from tqdm import tqdm
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
 from .rqvae import RqVaeModel
 
 
 class TigerModel(SequentialTorchModel, config_name="tiger"):
-
     def __init__(
         self,
         rqvae_model,
@@ -63,80 +62,92 @@ def __init__(
             layer_norm_eps=layer_norm_eps,
             batch_first=True,
         )
-        
+
         self._decoder = nn.TransformerDecoder(
             transformer_decoder_layer, num_decoder_layers
         )
-    
+
         self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
         self._decoder_dropout = nn.Dropout(dropout)
-        
+
         self._solver: CollisionSolver = solver
-        
+
         self._codebook_sizes = codebook_sizes
 
-        self._codebook_item_embeddings_stacked = torch.stack([codebook for codebook in rqvae_model.codebooks])
-        self._codebook_item_embeddings_stacked.requires_grad = False # TODO ask if freeze is needed
-        
+        self._codebook_item_embeddings_stacked = torch.stack(
+            [codebook for codebook in rqvae_model.codebooks]
+        )
+        self._codebook_item_embeddings_stacked.requires_grad = (
+            False  # TODO ask if freeze is needed
+        )
+
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
         self._item_id_to_embedding = item_id_to_embedding
-        
+
         item_ids = torch.tensor(list(range(1, len(item_id_to_semantic_id) + 1)))
-        
+
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
-        
-        # self._trie = HierarchicalTrie(rqvae_model)
-        # for item_id, semantic_id in enumerate(item_id_to_semantic_id):
-        #     self._trie.insert(Item(semantic_id, item_id + 1, item_id_to_residual[item_id])) # TODO no dedup tokens here
+
+        self._trie = Trie(rqvae_model)
+
+        self._trie.build_tree_structure(
+            item_id_to_semantic_id.to("cpu"),
+            item_id_to_residual.to("cpu"),
+            torch.arange(1, len(item_id_to_semantic_id) + 1),
+        )
 
         self._bos_token_id = codebook_sizes[0]
         self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
-        
+
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim
         )  # + 2 for bos token & residual
 
         self._init_weights(initializer_range)
-        
+
     @classmethod
     def init_rqvae(cls, config) -> RqVaeModel:
         rqvae_config = json.load(open(config["rqvae_train_config_path"]))
         rqvae_config["model"]["should_init_codebooks"] = False
-        
-        rqvae_model = RqVaeModel.create_from_config(rqvae_config['model']).to(DEVICE)
-        rqvae_model.load_state_dict(torch.load(config['rqvae_checkpoint_path'], weights_only=True))
+
+        rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
+        rqvae_model.load_state_dict(
+            torch.load(config["rqvae_checkpoint_path"], weights_only=True)
+        )
         rqvae_model.eval()
-        for param in rqvae_model.parameters(): # TODO freezed
+        for param in rqvae_model.parameters():  # TODO freezed
             param.requires_grad = False
-        
+
         codebook_sizes = rqvae_model.codebook_sizes
         assert all([book_size == codebook_sizes[0] for book_size in codebook_sizes])
-        
+
         return rqvae_model
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         rqvae_model = cls.init_rqvae(config)
         embedding_dim = rqvae_model.encoder.weight.shape[0]
-        embs_extractor = torch.load(config['embs_extractor_path'])
-        
+        embs_extractor = torch.load(config["embs_extractor_path"])
+
         embs_extractor = embs_extractor.sort_index()
-        
+
         item_ids = embs_extractor.index.tolist()
         assert item_ids == list(range(1, len(item_ids) + 1))
-        
-        embeddings = torch.stack(embs_extractor['embeddings'].tolist()).to(DEVICE)
+
+        embeddings = torch.stack(embs_extractor["embeddings"].tolist()).to(DEVICE)
 
         semantic_ids, residuals = rqvae_model({"embeddings": embeddings})
         assert embedding_dim == residuals.shape[1]
-        
+
         solver = CollisionSolver(
-            residual_dim=residuals.shape[1], 
-            emb_dim=len(rqvae_model.codebook_sizes), 
-            codebook_size=rqvae_model.codebook_sizes[0]
+            residual_dim=residuals.shape[1],
+            emb_dim=len(rqvae_model.codebook_sizes),
+            codebook_size=rqvae_model.codebook_sizes[0],
+        )
+        solver.create_query_candidates_dict(
+            torch.tensor(item_ids), semantic_ids, residuals
         )
-        solver.create_query_candidates_dict(torch.tensor(item_ids), semantic_ids, residuals)
 
         return cls(
             rqvae_model=rqvae_model,
@@ -148,7 +159,7 @@ def create_from_config(cls, config, **kwargs):
             pred_prefix=config["predictions_prefix"],
             positive_prefix=config["positive_prefix"],
             labels_prefix=config["labels_prefix"],
-            num_items=rqvae_model.codebook_sizes[0], # unused
+            num_items=rqvae_model.codebook_sizes[0],  # unused
             max_sequence_length=kwargs["max_sequence_length"],
             embedding_dim=embedding_dim,
             num_heads=config.get("num_heads", int(embedding_dim // 64)),
@@ -168,7 +179,7 @@ def forward(self, inputs):
         all_sample_lengths = inputs[
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
-        
+
         all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
         encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
@@ -177,68 +188,55 @@ def forward(self, inputs):
         if self.training:
             label_events = inputs["{}.ids".format(self._positive_prefix)]
             label_lengths = inputs["{}.length".format(self._positive_prefix)]
-            
-            label_lengths = label_lengths * (len(self._codebook_sizes) + 1) # TODO bos prepending
-            tgt_embeddings = self.get_item_embeddings( # TODO residual embs
+
+            label_lengths = label_lengths * (
+                len(self._codebook_sizes) + 1
+            )  # TODO bos prepending
+            tgt_embeddings = self.get_item_embeddings(  # TODO residual embs
                 label_events
             )  # (all_batch_events, embedding_dim)
-            
+
             decoder_outputs = self._apply_decoder(
                 tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
             )  # (batch_size, label_len, embedding_dim)
-            
-            decoder_prefix_scores = torch.einsum("bsd,scd->bsc", decoder_outputs[:, :-1, :], self._codebook_item_embeddings_stacked)
-            
+
+            decoder_prefix_scores = torch.einsum(
+                "bsd,scd->bsc",
+                decoder_outputs[:, :-1, :],
+                self._codebook_item_embeddings_stacked,
+            )
+
             decoder_output_residual = decoder_outputs[:, -1, :]
-        
-            semantic_ids = self._item_id_to_semantic_id[label_events - 1] # len(events), len(codebook_sizes)
-            true_residuals = self._item_id_to_residual[label_events - 1] 
-            
+
+            semantic_ids = self._item_id_to_semantic_id[
+                label_events - 1
+            ]  # len(events), len(codebook_sizes)
+            true_residuals = self._item_id_to_residual[label_events - 1]
+
             true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
-            pred_info = self._solver.get_pred_scores(semantic_ids, decoder_output_residual)
-            
+            pred_info = self._solver.get_pred_scores(
+                semantic_ids, decoder_output_residual
+            )
+
             return {
-                "logits": decoder_prefix_scores, 
+                "logits": decoder_prefix_scores,
                 "semantic.labels": semantic_ids,
                 "dedup.logits": pred_info["pred_scores"],
-                "dedup.labels": true_info["true_dedup_tokens"]
+                "dedup.labels": true_info["true_dedup_tokens"],
             }
         else:
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
                 encoder_embeddings, encoder_mask
-            ) # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
+            )  # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
 
             residuals = tgt_embeddings[:, -1, :]
-            
-            raise Exception("TODO")
-
-            ids = self._apply_trie(semantic_ids, residuals, n=20)
-            # store number of items in tree
-            # take first where >= n
-            # set vector with all trues, then set false from lower level tree
-            # take all true left in upper tree
-            # maybe store items by prefix (not so many memory)?
-            # lex sort by level first, item second
-            # use sparse matrix
-            
-            
-            # store by raw_item_id (semantic_id) uniq key (i0 * 256^3 + i1 * 256^2 + i2 * 256 + i3)
-            # sparse matrix - max_uniq_semantic_ids (for rqvae init items infer (max is 12101, could be less if collisions))
-            # store sem_id -> num
-            return ids
-
-    def _apply_trie(self, preds: torch.Tensor, residuals: torch.Tensor, n: int = 20):
-        semantic_ids = [tuple(row.tolist()) for row in preds]
-        
-        ids = []
-        for semantic_id, residual in tqdm(zip(semantic_ids, residuals), total=len(semantic_ids)):
-            item = Item(semantic_id, -999, residual)
-            closest_items = self._trie.find_n_closest(item, n)
-            closest_raw_ids = [item.raw_item_id for item in closest_items]
-            ids.append(closest_raw_ids) # TODO add correct tree inference
-        
-        return torch.tensor(ids)
+            semantic_ids = semantic_ids.to(torch.int64)
+
+            item_ids = self._trie.query(
+                semantic_ids.to("cpu"), residuals.to("cpu"), items_to_query=20
+            )
 
+            return item_ids
 
     def _apply_decoder(
         self, tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
@@ -246,12 +244,14 @@ def _apply_decoder(
         tgt_embeddings, tgt_mask = create_masked_tensor(
             data=tgt_embeddings, lengths=label_lengths
         )  # (batch_size, dec_seq_len, embedding_dim), (batch_size, dec_seq_len)
-        
+
         batch_size = tgt_embeddings.shape[0]
-        bos_embeddings = self._bos_weight.unsqueeze(0).expand(batch_size, 1, -1)  # (batch_size, 1, embedding_dim)
-        
+        bos_embeddings = self._bos_weight.unsqueeze(0).expand(
+            batch_size, 1, -1
+        )  # (batch_size, 1, embedding_dim)
+
         tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1)
-        
+
         label_len = tgt_mask.shape[1]
 
         assert label_len == len(self._codebook_sizes) + 1
@@ -273,7 +273,7 @@ def _apply_decoder(
         causal_mask = (
             torch.tril(torch.ones(label_len, label_len)).bool().to(DEVICE)
         )  # (dec_seq_len, dec_seq_len)
-        
+
         decoder_outputs = self._decoder(
             tgt=tgt_embeddings,
             memory=encoder_embeddings,
@@ -282,89 +282,110 @@ def _apply_decoder(
         )  # (batch_size, dec_seq_len, embedding_dim)
 
         return decoder_outputs
-    
-    def _apply_decoder_autoregressive(
-        self, encoder_embeddings, encoder_mask
-    ):
+
+    def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
         batch_size = encoder_embeddings.shape[0]
-        
-        tgt_embeddings = self._bos_weight.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, -1)
+
+        tgt_embeddings = (
+            self._bos_weight.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, -1)
+        )
         tgt_mask = torch.ones(batch_size, 1, dtype=torch.bool, device=DEVICE)
-        
+
         semantic_ids = torch.tensor([], device=DEVICE)
-        
-        for step in range(len(self._codebook_sizes) + 1): # semantic_id + residual
+
+        for step in range(len(self._codebook_sizes) + 1):  # semantic_id + residual
             position_embeddings = self._decoder_pos_embeddings(
                 torch.full((batch_size,), tgt_embeddings.shape[1], device=DEVICE),
-                tgt_mask
+                tgt_mask,
             )
-            
+
             curr_embeddings = tgt_embeddings + position_embeddings
-            
+
             curr_embeddings = self._decoder_layernorm(curr_embeddings)
             curr_embeddings = self._decoder_dropout(curr_embeddings)
-            
+
             decoder_output = self._decoder(
                 tgt=curr_embeddings,
                 memory=encoder_embeddings,
                 memory_key_padding_mask=~encoder_mask,
             )
-            
-            next_token_embedding = decoder_output[:, -1, :] # batch_size x embedding_dim
-            
+
+            next_token_embedding = decoder_output[
+                :, -1, :
+            ]  # batch_size x embedding_dim
+
             if step < len(self._codebook_sizes):
-                codebook = self._codebook_item_embeddings_stacked[step] # len(codebook_sizes) x embedding_dim
+                codebook = self._codebook_item_embeddings_stacked[
+                    step
+                ]  # len(codebook_sizes) x embedding_dim
                 closest_semantic_ids = torch.argmax(
                     torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
-                ) # batch_size x 1
-                semantic_ids = torch.cat([semantic_ids, closest_semantic_ids.unsqueeze(1)], dim=1) # batch_size x (step + 1)
-                next_token_embedding = codebook[closest_semantic_ids] # batch_size x embedding_dim
-                
-            tgt_embeddings = torch.cat([tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1)
-            tgt_mask = torch.ones(batch_size, tgt_embeddings.shape[1], dtype=torch.bool, device=DEVICE)
+                )  # batch_size x 1
+                semantic_ids = torch.cat(
+                    [semantic_ids, closest_semantic_ids.unsqueeze(1)], dim=1
+                )  # batch_size x (step + 1)
+                next_token_embedding = codebook[
+                    closest_semantic_ids
+                ]  # batch_size x embedding_dim
+
+            tgt_embeddings = torch.cat(
+                [tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1
+            )
+            tgt_mask = torch.ones(
+                batch_size, tgt_embeddings.shape[1], dtype=torch.bool, device=DEVICE
+            )
 
         return semantic_ids, tgt_embeddings
-    
-    def get_item_embeddings(self, events): # TODO freezed embeddings
-        embs = self._item_id_to_semantic_embedding[events - 1] # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.view(len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim)
-    
-    def get_init_item_embeddings(self, events): # TODO freezed embeddings
+
+    def get_item_embeddings(self, events):  # TODO freezed embeddings
+        embs = self._item_id_to_semantic_embedding[
+            events - 1
+        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        return embs.view(
+            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
+        )
+
+    def get_init_item_embeddings(self, events):  # TODO freezed embeddings
         # convert to semantic ids
-        semantic_ids = self._item_id_to_semantic_id[events - 1] # len(events), len(codebook_sizes)
-        
+        semantic_ids = self._item_id_to_semantic_id[
+            events - 1
+        ]  # len(events), len(codebook_sizes)
+
         result = []
         for semantic_id in semantic_ids:
             item_repr = []
             for codebook_idx, codebook_id in enumerate(semantic_id):
-                item_repr.append(self._codebook_item_embeddings_stacked[codebook_idx][codebook_id])
+                item_repr.append(
+                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
+                )
             result.append(torch.stack(item_repr))
-        
+
         semantic_embeddings = torch.stack(result)
-        
+
         # get residuals
         text_embeddings = self._item_id_to_embedding[events - 1]
         residual = text_embeddings - semantic_embeddings.sum(dim=1)
         residual = residual.unsqueeze(1)
-        
+
         # get true item embeddings
         item_embeddings = torch.cat(
             [semantic_embeddings, residual], dim=1
-        ) # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        
+        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+
         # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
-        
+
         return item_embeddings
 
     def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
             return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
+
         # TODO +1 for residual embedding
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._position_embeddings
         )
-        
+
         # print(f"{position_embeddings.isnan().any()=}") # TODO fix NaN in pos_embs
 
         def codebook_lambda(x):
@@ -392,7 +413,7 @@ def codebook_lambda(x):
         )
 
         return codebook_embeddings
-    
+
     def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
@@ -408,10 +429,12 @@ def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_lay
         # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 ...
 
         positions = position_lambda(positions)  # (all_batch_events)
-        
+
         # print(f"{positions.tolist()[:20]=}")
-        
-        assert (positions >= 0).all() and (positions < embedding_layer.num_embeddings).all()
+
+        assert (positions >= 0).all() and (
+            positions < embedding_layer.num_embeddings
+        ).all()
 
         position_embeddings = embedding_layer(
             positions
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 2b46567a..3c5462f5 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -16,7 +16,7 @@ def __init__(self, rqvae_model: RqVaeModel):
         self.total_items = None
         self.embedding_table = torch.stack(
             [cb for cb in self.rqvae_model.codebooks]
-        )  # K x codebook_size x embedding_dim
+        ).to("cpu")  # K x codebook_size x embedding_dim
 
     def unique_with_index(self, x, dim=None):
         """Unique elements of x and indices of those unique elements
@@ -89,7 +89,7 @@ def build_tree_structure(
 
         residuals_per_level = self.get_residuals_per_level(
             semantic_ids, residuals
-        )  # total_items x K + 2 x embedding_dim
+        )  # total_items x K + 1 x embedding_dim
 
         keys = self.compute_keys(semantic_ids)  # bs, could be collisions
 
@@ -124,15 +124,7 @@ def get_residuals_per_level(
 
         residuals_per_level[:, 0, :] = residuals
 
-        residuals_per_level = torch.cat(
-            [
-                torch.zeros(bs, 1, embedding_dim),
-                residuals_per_level,
-            ],
-            dim=1,
-        )
-
-        return residuals_per_level  # bs x K + 2 x embedding_dim
+        return residuals_per_level  # bs x K + 1 x embedding_dim
 
     def get_mask_by_prefix(self, prefixes: torch.Tensor, taken_lens: torch.Tensor):
         bs = prefixes.shape[0]
@@ -243,10 +235,9 @@ def get_closest(
         items_to_query,
     ):
         # K = 3
-        # self.residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 2
-        # query_residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 2
-        # outer_level = 3 # TODO if K (get by dot only from outer)
-        # inner_level = 4 # TODO if K + 1 (get by dot only from outer)
+        # self.residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 1
+        # query_residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 1
+        # outer_level = 3 # if K (get by dot only from outer) => inner_mask = outer_mask
         # outer_mask.shape = total_items
         # inner_mask.shape = total_items
 
@@ -268,7 +259,9 @@ def get_closest(
             guaranteed = inner_mask  # guaranteed_len
             left = outer_mask & ~inner_mask  # left_len
 
-            # self.residuals_per_level.shape = total_items, K + 2, embedding_dim
+            assert guaranteed.sum() + left.sum() == outer_mask.sum()
+
+            # self.residuals_per_level.shape = total_items, K + 1, embedding_dim
             guaranteed_raw_item_ids = self.raw_item_ids[guaranteed]  # guaranteed_len
             guaranteed_stored_residuals = self.residuals_per_level[guaranteed][
                 :, -(inner_level + 1)
@@ -323,6 +316,10 @@ def query(
             taken_inner_prefixes, inner_levels
         )  # bs, total_items
 
+        inner_levels_max_mask = inner_levels == self.K + 1
+        inner_levels[inner_levels_max_mask] = self.K
+        inner_masks[inner_levels_max_mask] = outer_masks[inner_levels_max_mask]
+
         assert (
             num_items[torch.arange(bs), outer_levels] == outer_masks.sum(dim=1)
         ).all()
@@ -331,7 +328,7 @@ def query(
         ).all()
 
         assert (outer_masks.sum(dim=1) > items_to_query).all()
-        assert (inner_masks.sum(dim=1) <= items_to_query).all()
+        # assert (inner_masks.sum(dim=1) <= items_to_query).all() # can be false if collisions
 
         assert (inner_masks <= outer_masks).all()
 
@@ -364,10 +361,10 @@ def query(
     rqvae_model.eval()
 
     trie = Trie(rqvae_model)
-    alphabet_size = rqvae_model.codebook_sizes[0]
+    alphabet_size = 6
 
     N = 12101
-    K = len(rqvae_model.codebook_sizes)
+    K = 3
     semantic_ids = torch.randint(0, alphabet_size, (N, K), dtype=torch.int64)
     residuals = torch.randn(N, embedding_dim)
     trie.build_tree_structure(semantic_ids, residuals, torch.arange(N))
@@ -377,7 +374,13 @@ def query(
     q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64)
     q_residuals = torch.randn(batch_size, embedding_dim)
 
-    for i in range(100):
+    total_time = 0
+    n_exps = 100
+
+    for i in range(n_exps):
         now = time.time()
         item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
-        print(time.time() - now)
+        assert item_ids.shape == (batch_size, items_to_query)
+        total_time += time.time() - now
+
+    print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
diff --git a/review.md b/review.md
index 8904976e..21b625a5 100644
--- a/review.md
+++ b/review.md
@@ -95,3 +95,9 @@ decoder: (bos, 1, 2, 3) -> (1, 2, 3, 4) # causal mask so (bos -> 1), (bos, 1 ->
 - user_id & codebook_ids -> repr ???
 - add last 'sequence' prediction, now only last item is supported
 - dataloader (semantic ids lens)
+
+## TODO
+
+1) Tiger
+2) SasRec
+3) SasRec freezed (all on single board)

From 8ddf8f2c0cdde4db30e5bdf2f87d9bb109156a62 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Feb 2025 18:50:46 +0700
Subject: [PATCH 061/175] add sasrec freezed model

---
 configs/train/rqvae_train_config.json         |   5 +-
 .../train/sasrec_freezed_train_config.json    | 167 ++++++++++++++++++
 configs/train/tiger_train_config.json         |   9 +-
 modeling/callbacks/base.py                    |   4 +-
 modeling/models/__init__.py                   |   1 +
 modeling/models/rqvae.py                      |   2 +-
 modeling/models/sasrec.py                     |  43 +----
 modeling/models/sasrec_freezed.py             | 157 ++++++++++++++++
 modeling/models/tiger.py                      |  21 +--
 9 files changed, 347 insertions(+), 62 deletions(-)
 create mode 100644 configs/train/sasrec_freezed_train_config.json
 create mode 100644 modeling/models/sasrec_freezed.py

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index 225a2fbe..d5db90e1 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "train_steps_num": 512,
+  "train_epochs_num": 20,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
@@ -31,7 +31,8 @@
   },
   "model": {
     "type": "rqvae",
-    "embedding_dim": 512,
+    "text_embedding_dim": 512,
+    "embedding_dim": 64,
     "n_iter": 100,
     "codebook_sizes": [256, 256, 256],
     "should_init_codebooks": true,
diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
new file mode 100644
index 00000000..58153797
--- /dev/null
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -0,0 +1,167 @@
+{
+  "experiment_name": "sasrec_beauty",
+  "best_metric": "validation/ndcg@20",
+  "dataset": {
+    "type": "sequence",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "max_sequence_length": 50,
+    "samplers": {
+      "type": "next_item_prediction",
+      "negative_sampler_type": "random"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": true,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "sasrec_freezed",
+    "sequence_prefix": "item",
+    "positive_prefix": "positive",
+    "negative_prefix": "negative",
+    "candidate_prefix": "candidates",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
+    "activation": "gelu",
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 0.001
+    },
+    "clip_grad_threshold": 5.0
+  },
+  "loss": {
+    "type": "composite",
+    "losses": [
+      {
+        "type": "sasrec",
+        "positive_prefix": "positive_scores",
+        "negative_prefix": "negative_scores",
+        "output_prefix": "downstream_loss"
+      }
+    ],
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 64,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index c7cd6cdf..a70ec211 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -42,11 +42,12 @@
     "predictions_prefix": "logits",
     "positive_prefix": "labels",
     "labels_prefix": "labels",
+    "embedding_dim": 64,
     "num_heads": 2,
     "num_encoder_layers": 2,
     "num_decoder_layers": 2,
-    "dim_feedforward": 128,
-    "dropout": 0.2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
     "activation": "gelu",
     "layer_norm_eps": 1e-9,
     "initializer_range": 0.02
@@ -89,7 +90,7 @@
       },
       {
         "type": "validation",
-        "on_step": 256,
+        "on_step": 64,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -133,7 +134,7 @@
       },
       {
         "type": "eval",
-        "on_step": 256,
+        "on_step": 64,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/modeling/callbacks/base.py b/modeling/callbacks/base.py
index c4b2e0ef..1ae26267 100644
--- a/modeling/callbacks/base.py
+++ b/modeling/callbacks/base.py
@@ -1,4 +1,3 @@
-from tqdm import tqdm
 from metric import BaseMetric, StatefullMetric
 
 import utils
@@ -203,8 +202,7 @@ def __call__(self, inputs, step_num):
 
             self._model.eval()
             with torch.no_grad():
-                total = len(self._get_dataloader())
-                for batch in tqdm(self._get_dataloader(), total=total):
+                for batch in self._get_dataloader():
                     for key, value in batch.items():
                         batch[key] = value.to(utils.DEVICE)
 
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index a169d41e..b9678e2e 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -13,6 +13,7 @@
 from .pure_mf import PureMF
 from .random import RandomModel
 from .sasrec import SasRecModel, SasRecInBatchModel
+from .sasrec_freezed import SasRecFreezedModel
 from .sasrec_ce import SasRecCeModel
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index cfdb0574..53f1fece 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -56,7 +56,7 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         return cls(
             train_sampler=kwargs.get('train_sampler'),
-            input_dim=config['embedding_dim'],
+            input_dim=config['text_embedding_dim'],
             hidden_dim=config['embedding_dim'],
             n_iter=config['n_iter'],
             codebook_sizes=config['codebook_sizes'],
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 57367e3b..cd36b544 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -37,37 +37,6 @@ def __init__(
         self._positive_prefix = positive_prefix
 
         self._init_weights(initializer_range)
-        
-        df = torch.load('../data/Beauty/data_full.pt')
-        precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
-        
-        self._projector = torch.nn.Linear(precomputed_embeddings.shape[1], embedding_dim)
-        
-        padding_embedding = torch.nn.init.trunc_normal_(
-            torch.zeros(1, precomputed_embeddings.shape[1]),
-            std=initializer_range,
-            a=-2 * initializer_range,
-            b=2 * initializer_range
-        )
-
-        mask_embedding = torch.nn.init.trunc_normal_(
-            torch.zeros(1, precomputed_embeddings.shape[1]),
-            std=initializer_range,
-            a=-2 * initializer_range,
-            b=2 * initializer_range
-        )
-        
-        extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
-        
-        self._item_embeddings = torch.nn.Embedding(
-            num_embeddings=num_items + 2,
-            embedding_dim=precomputed_embeddings.shape[1]
-        )
-        
-        # TODO ask about freezed masked & padding tokens
-        
-        self._item_embeddings.weight.data.copy_(extended_embeddings)
-        self._item_embeddings.weight.requires_grad = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
@@ -83,12 +52,6 @@ def create_from_config(cls, config, **kwargs):
             dropout=config.get('dropout', 0.0),
             initializer_range=config.get('initializer_range', 0.02)
         )
-        
-    def get_item_embeddings(self, events):
-        return self._projector(self._item_embeddings(events))
-    
-    def _get_item_embeddings(self):
-        return self._projector(self._item_embeddings.weight)
 
     def forward(self, inputs):
         all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
@@ -103,7 +66,7 @@ def forward(self, inputs):
 
             all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
 
-            all_embeddings = self._get_item_embeddings()  # (num_items + 2, embedding_dim)
+            all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
@@ -144,7 +107,7 @@ def forward(self, inputs):
             candidate_scores = torch.einsum(
                 'bd,nd->bn',
                 last_embeddings,
-                self._get_item_embeddings()
+                self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
             candidate_scores[:, self._num_items + 1:] = -torch.inf  # Mask id
@@ -253,4 +216,4 @@ def forward(self, inputs):
                 k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
+            return indices
\ No newline at end of file
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
new file mode 100644
index 00000000..167ae468
--- /dev/null
+++ b/modeling/models/sasrec_freezed.py
@@ -0,0 +1,157 @@
+from models import SequentialTorchModel
+from utils import create_masked_tensor
+
+import torch
+
+
+class SasRecFreezedModel(SequentialTorchModel, config_name='sasrec_freezed'):
+
+    def __init__(
+            self,
+            sequence_prefix,
+            positive_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True
+        )
+        self._sequence_prefix = sequence_prefix
+        self._positive_prefix = positive_prefix
+
+        self._init_weights(initializer_range)
+        
+        df = torch.load('../data/Beauty/data_full.pt')
+        precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
+        
+        self._projector = torch.nn.Linear(precomputed_embeddings.shape[1], embedding_dim)
+        
+        padding_embedding = torch.nn.init.trunc_normal_(
+            torch.zeros(1, precomputed_embeddings.shape[1]),
+            std=initializer_range,
+            a=-2 * initializer_range,
+            b=2 * initializer_range
+        )
+
+        mask_embedding = torch.nn.init.trunc_normal_(
+            torch.zeros(1, precomputed_embeddings.shape[1]),
+            std=initializer_range,
+            a=-2 * initializer_range,
+            b=2 * initializer_range
+        )
+        
+        extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
+        
+        self._item_embeddings = torch.nn.Embedding(
+            num_embeddings=num_items + 2,
+            embedding_dim=precomputed_embeddings.shape[1]
+        )
+        
+        # TODO ask about freezed masked & padding tokens
+        
+        self._item_embeddings.weight.data.copy_(extended_embeddings)
+        self._item_embeddings.weight.requires_grad = False
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
+        )
+        
+    def get_item_embeddings(self, events): # TODO refactor (single projection)
+        return self._projector(self._item_embeddings(events))
+    
+    def _get_item_embeddings(self):
+        return self._projector(self._item_embeddings.weight)
+
+    def forward(self, inputs):
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+
+        embeddings, mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        if self.training:  # training mode
+            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+
+            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+
+            all_embeddings = self._get_item_embeddings()  # (num_items + 2, embedding_dim)
+
+            # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
+            all_scores = torch.einsum(
+                'ad,nd->an',
+                all_sample_embeddings,
+                all_embeddings
+            )  # (all_batch_events, num_items + 2)
+
+            positive_scores = torch.gather(
+                input=all_scores,
+                dim=1,
+                index=all_positive_sample_events[..., None]
+            )  # (all_batch_items, 1)
+
+            sample_ids, _ = create_masked_tensor(
+                data=all_sample_events,
+                lengths=all_sample_lengths
+            )  # (batch_size, seq_len)
+
+            sample_ids = torch.repeat_interleave(sample_ids, all_sample_lengths, dim=0)  # (all_batch_events, seq_len)
+
+            negative_scores = torch.scatter(
+                input=all_scores,
+                dim=1,
+                index=sample_ids,
+                src=torch.ones_like(sample_ids) * (-torch.inf)
+            )  # (all_batch_events, num_items + 2)
+            negative_scores[:, 0] = -torch.inf  # Padding idx
+            negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
+
+            return {
+                'positive_scores': positive_scores,
+                'negative_scores': negative_scores
+            }
+        else:  # eval mode
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            # b - batch_size, n - num_candidates, d - embedding_dim
+            candidate_scores = torch.einsum(
+                'bd,nd->bn',
+                last_embeddings,
+                self._get_item_embeddings()
+            )  # (batch_size, num_items + 2)
+            candidate_scores[:, 0] = -torch.inf  # Padding id
+            candidate_scores[:, self._num_items + 1:] = -torch.inf  # Mask id
+
+            _, indices = torch.topk(
+                candidate_scores,
+                k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 147b8ea4..78b46ba2 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -4,7 +4,6 @@
 from models.base import SequentialTorchModel
 from rqvae_utils import CollisionSolver, Trie
 from torch import nn
-from tqdm import tqdm
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
 from .rqvae import RqVaeModel
@@ -16,7 +15,7 @@ def __init__(
         rqvae_model,
         item_id_to_semantic_id,
         item_id_to_residual,
-        item_id_to_embedding,
+        item_id_to_text_embedding,
         solver,
         sequence_prefix,
         pred_prefix,
@@ -83,7 +82,7 @@ def __init__(
 
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
-        self._item_id_to_embedding = item_id_to_embedding
+        self._item_id_to_text_embedding = item_id_to_text_embedding
 
         item_ids = torch.tensor(list(range(1, len(item_id_to_semantic_id) + 1)))
 
@@ -127,7 +126,6 @@ def init_rqvae(cls, config) -> RqVaeModel:
     @classmethod
     def create_from_config(cls, config, **kwargs):
         rqvae_model = cls.init_rqvae(config)
-        embedding_dim = rqvae_model.encoder.weight.shape[0]
         embs_extractor = torch.load(config["embs_extractor_path"])
 
         embs_extractor = embs_extractor.sort_index()
@@ -135,10 +133,9 @@ def create_from_config(cls, config, **kwargs):
         item_ids = embs_extractor.index.tolist()
         assert item_ids == list(range(1, len(item_ids) + 1))
 
-        embeddings = torch.stack(embs_extractor["embeddings"].tolist()).to(DEVICE)
+        text_embeddings = torch.stack(embs_extractor["embeddings"].tolist()).to(DEVICE)
 
-        semantic_ids, residuals = rqvae_model({"embeddings": embeddings})
-        assert embedding_dim == residuals.shape[1]
+        semantic_ids, residuals = rqvae_model({"embeddings": text_embeddings})
 
         solver = CollisionSolver(
             residual_dim=residuals.shape[1],
@@ -153,7 +150,7 @@ def create_from_config(cls, config, **kwargs):
             rqvae_model=rqvae_model,
             item_id_to_semantic_id=semantic_ids,
             item_id_to_residual=residuals,
-            item_id_to_embedding=embeddings,
+            item_id_to_text_embedding=text_embeddings,
             solver=solver,
             sequence_prefix=config["sequence_prefix"],
             pred_prefix=config["predictions_prefix"],
@@ -161,11 +158,11 @@ def create_from_config(cls, config, **kwargs):
             labels_prefix=config["labels_prefix"],
             num_items=rqvae_model.codebook_sizes[0],  # unused
             max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=embedding_dim,
-            num_heads=config.get("num_heads", int(embedding_dim // 64)),
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
             num_encoder_layers=config["num_encoder_layers"],
             num_decoder_layers=config["num_decoder_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * embedding_dim),
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
             codebook_sizes=rqvae_model.codebook_sizes,
             dropout=config.get("dropout", 0.0),
             initializer_range=config.get("initializer_range", 0.02),
@@ -363,7 +360,7 @@ def get_init_item_embeddings(self, events):  # TODO freezed embeddings
         semantic_embeddings = torch.stack(result)
 
         # get residuals
-        text_embeddings = self._item_id_to_embedding[events - 1]
+        text_embeddings = self._item_id_to_text_embedding[events - 1]
         residual = text_embeddings - semantic_embeddings.sum(dim=1)
         residual = residual.unsqueeze(1)
 

From 705bad660376c861ab5763508960e049202c5463 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Feb 2025 18:54:20 +0700
Subject: [PATCH 062/175] small_fixes + todo

---
 configs/train/sasrec_freezed_train_config.json | 2 +-
 review.md                                      | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index 58153797..f458cb49 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_beauty",
+  "experiment_name": "sasrec_freezed_beauty",
   "best_metric": "validation/ndcg@20",
   "dataset": {
     "type": "sequence",
diff --git a/review.md b/review.md
index 21b625a5..1a862f85 100644
--- a/review.md
+++ b/review.md
@@ -101,3 +101,5 @@ decoder: (bos, 1, 2, 3) -> (1, 2, 3, 4) # causal mask so (bos -> 1), (bos, 1 ->
 1) Tiger
 2) SasRec
 3) SasRec freezed (all on single board)
+4) Tiger batched inference
+5) Tiger honest embedding_dim

From 098982b7d76a27137abe010bb1f792bbc7acef1c Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 9 Feb 2025 14:58:18 +0700
Subject: [PATCH 063/175] TODO: add projector to tiger

---
 configs/train/rqvae_train_config.json | 14 ++++----------
 modeling/models/rqvae.py              |  2 +-
 modeling/models/tiger.py              |  6 ++++++
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index d5db90e1..ee6ef333 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "train_epochs_num": 20,
+  "train_epochs_num": 50,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
@@ -31,8 +31,7 @@
   },
   "model": {
     "type": "rqvae",
-    "text_embedding_dim": 512,
-    "embedding_dim": 64,
+    "embedding_dim": 512,
     "n_iter": 100,
     "codebook_sizes": [256, 256, 256],
     "should_init_codebooks": true,
@@ -43,14 +42,9 @@
     "type": "basic",
     "optimizer": {
       "type": "adam",
-      "lr": 1e-4
+      "lr": 5e-5
     },
-    "clip_grad_threshold": 5.0,
-    "scheduler": {
-      "type": "step",
-      "step_size": 100, 
-      "gamma": 0.96
-    }
+    "clip_grad_threshold": 5.0
   },
   "loss": {
     "type": "rqvae_loss",
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 53f1fece..cfdb0574 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -56,7 +56,7 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         return cls(
             train_sampler=kwargs.get('train_sampler'),
-            input_dim=config['text_embedding_dim'],
+            input_dim=config['embedding_dim'],
             hidden_dim=config['embedding_dim'],
             n_iter=config['n_iter'],
             codebook_sizes=config['codebook_sizes'],
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 78b46ba2..af527d2b 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -84,6 +84,8 @@ def __init__(
         self._item_id_to_residual = item_id_to_residual
         self._item_id_to_text_embedding = item_id_to_text_embedding
 
+        self._projector = nn.Linear(item_id_to_text_embedding.shape[1], embedding_dim)
+
         item_ids = torch.tensor(list(range(1, len(item_id_to_semantic_id) + 1)))
 
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
@@ -197,6 +199,9 @@ def forward(self, inputs):
                 tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
             )  # (batch_size, label_len, embedding_dim)
 
+            print(decoder_outputs.shape)
+            print(self._codebook_item_embeddings_stacked.shape)
+
             decoder_prefix_scores = torch.einsum(
                 "bsd,scd->bsc",
                 decoder_outputs[:, :-1, :],
@@ -338,6 +343,7 @@ def get_item_embeddings(self, events):  # TODO freezed embeddings
         embs = self._item_id_to_semantic_embedding[
             events - 1
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        embs = self._projector(embs)
         return embs.view(
             len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
         )

From cebe016f04121b16adc84eda335de818e2dc2bba Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 13 Feb 2025 19:14:49 +0700
Subject: [PATCH 064/175] TODO ask about last / next item predicition

---
 .../train/sasrec_freezed_train_config.json    |  1 +
 configs/train/sasrec_train_config.json        |  1 +
 configs/train/tiger_train_config.json         |  7 +++---
 modeling/models/sasrec_freezed.py             |  6 ++---
 modeling/rqvae_utils/trie.py                  | 24 +++++++++++++------
 5 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index f458cb49..7d9d557d 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -1,6 +1,7 @@
 {
   "experiment_name": "sasrec_freezed_beauty",
   "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index aa29a029..4bffd95d 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -1,6 +1,7 @@
 {
   "experiment_name": "sasrec_beauty",
   "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index a70ec211..04adc1d2 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,14 +1,14 @@
 {
   "experiment_name": "tiger",
   "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 50,
+  "train_epochs_num": 100,
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "last_item_prediction",
+      "type": "next_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -34,7 +34,6 @@
   },
   "model": {
     "type": "tiger",
-    "trie": "../data/Beauty/rqvae/trie.pkl",
     "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
     "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
     "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
@@ -134,7 +133,7 @@
       },
       {
         "type": "eval",
-        "on_step": 64,
+        "on_step": 256,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 167ae468..c6fab7f6 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -87,7 +87,7 @@ def create_from_config(cls, config, **kwargs):
     def get_item_embeddings(self, events): # TODO refactor (single projection)
         return self._projector(self._item_embeddings(events))
     
-    def _get_item_embeddings(self):
+    def get_last_item_embeddings(self):
         return self._projector(self._item_embeddings.weight)
 
     def forward(self, inputs):
@@ -103,7 +103,7 @@ def forward(self, inputs):
 
             all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
 
-            all_embeddings = self._get_item_embeddings()  # (num_items + 2, embedding_dim)
+            all_embeddings = self.get_last_item_embeddings()  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
@@ -144,7 +144,7 @@ def forward(self, inputs):
             candidate_scores = torch.einsum(
                 'bd,nd->bn',
                 last_embeddings,
-                self._get_item_embeddings()
+                self.get_last_item_embeddings()
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
             candidate_scores[:, self._num_items + 1:] = -torch.inf  # Mask id
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 3c5462f5..60d1c286 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -365,21 +365,31 @@ def query(
 
     N = 12101
     K = 3
-    semantic_ids = torch.randint(0, alphabet_size, (N, K), dtype=torch.int64)
-    residuals = torch.randn(N, embedding_dim)
-    trie.build_tree_structure(semantic_ids, residuals, torch.arange(N))
+    # make tensor of size N x K
+    # of ([1, 2, 3], [1, 2, 3], [1, 2, 3], ...)
+    a = torch.arange(K).repeat(20, 1)
+    b = torch.arange(K + 1, K + K + 1).repeat(20, 1)
+    semantic_ids = torch.cat([a, b], dim=0)
+    residuals = torch.randn(semantic_ids.shape[0], embedding_dim)
+    trie.build_tree_structure(
+        semantic_ids, residuals, torch.arange(semantic_ids.shape[0])
+    )
 
-    items_to_query = 20
-    batch_size = 256
-    q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64)
+    items_to_query = 5
+    batch_size = 1
+    q_semantic_ids = semantic_ids[0].repeat(batch_size, 1)
+    # q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64)
     q_residuals = torch.randn(batch_size, embedding_dim)
 
     total_time = 0
-    n_exps = 100
+    n_exps = 1
 
     for i in range(n_exps):
         now = time.time()
         item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
+        print(semantic_ids[item_ids].shape)
+        print(q_semantic_ids.shape)
+        print(semantic_ids[item_ids] == q_semantic_ids)
         assert item_ids.shape == (batch_size, items_to_query)
         total_time += time.time() - now
 

From e5fe9782e1716260ee2c069afc35cbcdcfb6bcc8 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 13 Feb 2025 19:20:46 +0700
Subject: [PATCH 065/175] use last item prediciton

---
 configs/train/sasrec_freezed_train_config.json | 2 +-
 configs/train/sasrec_train_config.json         | 2 +-
 configs/train/tiger_train_config.json          | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index 7d9d557d..5f383018 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 4bffd95d..44079ace 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 04adc1d2..672591d0 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },

From 91b564938a2f70cec1b83dfce43292b65bbd0a8c Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 13 Feb 2025 21:26:10 +0700
Subject: [PATCH 066/175] fix collision solver

---
 configs/train/tiger_train_config.json |  7 -----
 modeling/models/tiger.py              | 40 +++++++++++++-----------
 modeling/rqvae_utils/trie.py          | 45 +++++++++++++++++----------
 3 files changed, 50 insertions(+), 42 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 672591d0..fd80a1c0 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -68,13 +68,6 @@
         "labels": "semantic.labels",
         "weight": 1.0,
         "output_prefix": "semantic_loss"
-      },
-      {
-        "type": "sample_logsoftmax",
-        "predictions_prefix": "dedup.logits",
-        "labels": "dedup.labels",
-        "weight": 1.0,
-        "output_prefix": "dedup_loss"
       }
     ],
     "output_prefix": "loss"
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index af527d2b..41ad2479 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -77,7 +77,7 @@ def __init__(
             [codebook for codebook in rqvae_model.codebooks]
         )
         self._codebook_item_embeddings_stacked.requires_grad = (
-            False  # TODO ask if freeze is needed
+            False  # TODO maybe unfreeeze later
         )
 
         self._item_id_to_semantic_id = item_id_to_semantic_id
@@ -90,16 +90,23 @@ def __init__(
 
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
 
-        self._trie = Trie(rqvae_model)
+        self._trie = Trie(rqvae_model, self._projector)
 
         self._trie.build_tree_structure(
-            item_id_to_semantic_id.to("cpu"),
-            item_id_to_residual.to("cpu"),
+            item_id_to_semantic_id,
+            item_id_to_residual,
             torch.arange(1, len(item_id_to_semantic_id) + 1),
         )
 
         self._bos_token_id = codebook_sizes[0]
-        self._bos_weight = nn.Parameter(torch.randn(embedding_dim))
+        self._bos_weight = nn.Parameter(
+            torch.nn.init.trunc_normal_(
+                torch.zeros(embedding_dim),
+                std=initializer_range,
+                a=-2 * initializer_range,
+                b=2 * initializer_range,
+            )
+        )
 
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim
@@ -117,7 +124,7 @@ def init_rqvae(cls, config) -> RqVaeModel:
             torch.load(config["rqvae_checkpoint_path"], weights_only=True)
         )
         rqvae_model.eval()
-        for param in rqvae_model.parameters():  # TODO freezed
+        for param in rqvae_model.parameters():
             param.requires_grad = False
 
         codebook_sizes = rqvae_model.codebook_sizes
@@ -199,13 +206,12 @@ def forward(self, inputs):
                 tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
             )  # (batch_size, label_len, embedding_dim)
 
-            print(decoder_outputs.shape)
-            print(self._codebook_item_embeddings_stacked.shape)
+            projected_stacked = self._projector(self._codebook_item_embeddings_stacked)
 
             decoder_prefix_scores = torch.einsum(
                 "bsd,scd->bsc",
                 decoder_outputs[:, :-1, :],
-                self._codebook_item_embeddings_stacked,
+                projected_stacked,
             )
 
             decoder_output_residual = decoder_outputs[:, -1, :]
@@ -215,16 +221,14 @@ def forward(self, inputs):
             ]  # len(events), len(codebook_sizes)
             true_residuals = self._item_id_to_residual[label_events - 1]
 
-            true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
-            pred_info = self._solver.get_pred_scores(
-                semantic_ids, decoder_output_residual
-            )
+            # true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
+            # pred_info = self._solver.get_pred_scores(
+            #     semantic_ids, decoder_output_residual
+            # ) TODO shapes don't match (solver init with 512, here 64)
 
             return {
                 "logits": decoder_prefix_scores,
                 "semantic.labels": semantic_ids,
-                "dedup.logits": pred_info["pred_scores"],
-                "dedup.labels": true_info["true_dedup_tokens"],
             }
         else:
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
@@ -316,10 +320,10 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                 :, -1, :
             ]  # batch_size x embedding_dim
 
+            projected = self._projector(self._codebook_item_embeddings_stacked)
+
             if step < len(self._codebook_sizes):
-                codebook = self._codebook_item_embeddings_stacked[
-                    step
-                ]  # len(codebook_sizes) x embedding_dim
+                codebook = projected[step]  # len(codebook_sizes) x embedding_dim
                 closest_semantic_ids = torch.argmax(
                     torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
                 )  # batch_size x 1
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 60d1c286..308d999a 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -2,11 +2,12 @@
 import time
 
 import torch
+from utils import DEVICE
 from models.rqvae import RqVaeModel
 
 
 class Trie:
-    def __init__(self, rqvae_model: RqVaeModel):
+    def __init__(self, rqvae_model: RqVaeModel, projector: torch.nn.Linear):
         self.rqvae_model = rqvae_model
         self.keys = None
         self.prefix_counts = None
@@ -16,7 +17,8 @@ def __init__(self, rqvae_model: RqVaeModel):
         self.total_items = None
         self.embedding_table = torch.stack(
             [cb for cb in self.rqvae_model.codebooks]
-        ).to("cpu")  # K x codebook_size x embedding_dim
+        )  # K x codebook_size x embedding_dim
+        self.projector = projector
 
     def unique_with_index(self, x, dim=None):
         """Unique elements of x and indices of those unique elements
@@ -43,7 +45,7 @@ def unique_with_index(self, x, dim=None):
     def compute_keys(self, semantic_ids: torch.Tensor):
         exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
         base = self.rqvae_model.codebook_sizes[0] ** exponents
-        uniq_ids = torch.einsum("nc,c->n", semantic_ids, base)
+        uniq_ids = semantic_ids.to("cpu") @ base  # TODO fix device
         return uniq_ids
 
     def pad_semantic_ids(self, semantic_ids: torch.Tensor):
@@ -54,7 +56,7 @@ def pad_semantic_ids(self, semantic_ids: torch.Tensor):
                     semantic_ids.shape[0],
                     self.K - semantic_ids.shape[1],
                     dtype=semantic_ids.dtype,
-                ),
+                ).to(semantic_ids.device),
             ],
             dim=1,
         )
@@ -88,7 +90,7 @@ def build_tree_structure(
             prefix_counts[:, i + 1] = current_level_same
 
         residuals_per_level = self.get_residuals_per_level(
-            semantic_ids, residuals
+            self.embedding_table.to(residuals.device), semantic_ids, residuals
         )  # total_items x K + 1 x embedding_dim
 
         keys = self.compute_keys(semantic_ids)  # bs, could be collisions
@@ -100,19 +102,22 @@ def build_tree_structure(
         self.total_items = len(keys)
 
     def get_residuals_per_level(
-        self, semantic_ids: torch.Tensor, residuals: torch.Tensor
+        self,
+        embedding_table: torch.Tensor,
+        semantic_ids: torch.Tensor,
+        residuals: torch.Tensor,
     ):
         bs = semantic_ids.shape[0]
         embedding_dim = residuals.shape[1]
         residuals_per_level = torch.zeros(
-            bs, self.K + 1, embedding_dim
+            bs, self.K + 1, embedding_dim, device=embedding_table.device
         )  # bs x K + 1 x embedding_dim
 
         # TODO think if reverse is needed here
         # i = 3, 2, 1, 0
         for i in range(self.K - 1, -1, -1):
             indices_at_level = semantic_ids[:, i]  # bs
-            embeddings_at_level = self.embedding_table[
+            embeddings_at_level = embedding_table[
                 i, indices_at_level
             ]  # bs x embedding_dim
             # 1 2 3 4
@@ -214,11 +219,15 @@ def get_closest_single(
 
         # Sort guaranteed items by score (descending)
         guaranteed_sorted_indices = torch.argsort(guaranteed_scores, descending=True)
-        guaranteed_sorted_ids = guaranteed_raw_item_ids[guaranteed_sorted_indices]
+        guaranteed_sorted_ids = guaranteed_raw_item_ids[
+            guaranteed_sorted_indices.to(guaranteed_raw_item_ids.device)
+        ]
 
         # Sort left items by score (descending)
         left_sorted_indices = torch.argsort(left_scores, descending=True)
-        left_sorted_ids = left_raw_item_ids[left_sorted_indices]
+        left_sorted_ids = left_raw_item_ids[
+            left_sorted_indices.to(left_raw_item_ids.device)
+        ]
 
         # Concatenate the sorted lists
         result_ids = torch.cat((guaranteed_sorted_ids, left_sorted_ids))
@@ -263,17 +272,17 @@ def get_closest(
 
             # self.residuals_per_level.shape = total_items, K + 1, embedding_dim
             guaranteed_raw_item_ids = self.raw_item_ids[guaranteed]  # guaranteed_len
-            guaranteed_stored_residuals = self.residuals_per_level[guaranteed][
-                :, -(inner_level + 1)
-            ]  # guaranteed_len x embedding_dim
+            guaranteed_stored_residuals = self.projector(
+                self.residuals_per_level[guaranteed][:, -(inner_level + 1)].to(DEVICE)
+            )  # guaranteed_len x embedding_dim
             guaranteed_query_residual = query_residual_per_level[
                 -(inner_level + 1)
             ]  # embedding_dim
 
             left_raw_item_ids = self.raw_item_ids[left]  # left_len
-            left_stored_residuals = self.residuals_per_level[left][
-                :, -outer_level
-            ]  # left_len x embedding_dim
+            left_stored_residuals = self.projector(
+                self.residuals_per_level[left][:, -outer_level].to(DEVICE)
+            )  # left_len x embedding_dim
             left_query_residual = query_residual_per_level[
                 -outer_level
             ]  # embedding_dim
@@ -332,8 +341,10 @@ def query(
 
         assert (inner_masks <= outer_masks).all()
 
+        projected_embedding_table = self.projector(self.embedding_table)
+
         query_residuals_per_level = self.get_residuals_per_level(
-            semantic_ids, residuals
+            projected_embedding_table, semantic_ids, residuals
         )
 
         raw_item_ids = self.get_closest(

From 5943aa5a9bb5f507070ed1ec06d293b25113458b Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 13 Feb 2025 22:16:35 +0700
Subject: [PATCH 067/175] fix if inner tree is empty

---
 modeling/rqvae_utils/trie.py | 51 ++++++++++++++++++++----------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 308d999a..655f53f3 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -197,7 +197,15 @@ def get_outer_inner_levels(self, semantic_ids: torch.Tensor, items_to_query: int
 
         assert (outer_level <= K).all()
 
-        return num_items, outer_level, inner_level  # bs, bs
+        return num_items, outer_level, inner_level  # bs x K + 1, bs, bs
+
+    def get_sorted_ids(self, stored_residuals, query_residual, raw_item_ids):
+        scores = torch.matmul(stored_residuals, query_residual)  # (guaranteed_len,)
+        # Sort guaranteed items by score (descending)
+        sorted_indices = torch.argsort(scores, descending=True)
+        sorted_ids = raw_item_ids[sorted_indices.to(raw_item_ids.device)]
+
+        return sorted_ids
 
     def get_closest_single(
         self,
@@ -208,26 +216,18 @@ def get_closest_single(
         left_stored_residuals,  # left_len x embedding_dim
         left_query_residual,  # embedding_dim
     ):
-        guaranteed_scores = torch.matmul(
-            guaranteed_stored_residuals, guaranteed_query_residual
-        )  # (guaranteed_len,)
-
-        # Compute scores for left items
-        left_scores = torch.matmul(
-            left_stored_residuals, left_query_residual
-        )  # (left_len,)
-
-        # Sort guaranteed items by score (descending)
-        guaranteed_sorted_indices = torch.argsort(guaranteed_scores, descending=True)
-        guaranteed_sorted_ids = guaranteed_raw_item_ids[
-            guaranteed_sorted_indices.to(guaranteed_raw_item_ids.device)
-        ]
+        if guaranteed_raw_item_ids.shape[0] == 0:
+            guaranteed_sorted_ids = torch.tensor([])
+        else:
+            guaranteed_sorted_ids = self.get_sorted_ids(
+                guaranteed_stored_residuals,
+                guaranteed_query_residual,
+                guaranteed_raw_item_ids,
+            )
 
-        # Sort left items by score (descending)
-        left_sorted_indices = torch.argsort(left_scores, descending=True)
-        left_sorted_ids = left_raw_item_ids[
-            left_sorted_indices.to(left_raw_item_ids.device)
-        ]
+        left_sorted_ids = self.get_sorted_ids(
+            left_stored_residuals, left_query_residual, left_raw_item_ids
+        )
 
         # Concatenate the sorted lists
         result_ids = torch.cat((guaranteed_sorted_ids, left_sorted_ids))
@@ -309,13 +309,17 @@ def query(
 
         num_items, outer_levels, inner_levels = self.get_outer_inner_levels(
             semantic_ids, items_to_query
-        )  # bs, bs
+        )  # bs x K + 1, bs, bs
+
+        # print(num_items.shape, outer_levels.shape, inner_levels.shape)
+        # print(num_items, outer_levels, inner_levels)
 
         taken_outer_prefixes = semantic_ids * (
             torch.arange(K).expand(bs, K) < outer_levels.unsqueeze(1)
         )
         taken_inner_prefixes = semantic_ids * (
-            torch.arange(K).expand(bs, K) < inner_levels.unsqueeze(1)
+            torch.arange(K).expand(bs, K) < inner_levels
+            .unsqueeze(1)
         )
 
         outer_masks = self.get_mask_by_prefix(
@@ -325,6 +329,9 @@ def query(
             taken_inner_prefixes, inner_levels
         )  # bs, total_items
 
+        # print(inner_masks.shape, outer_masks.shape)
+        # print(inner_masks, outer_masks)
+
         inner_levels_max_mask = inner_levels == self.K + 1
         inner_levels[inner_levels_max_mask] = self.K
         inner_masks[inner_levels_max_mask] = outer_masks[inner_levels_max_mask]

From 11937a3148c3fd10d2acbeeb890826a7b41a46ec Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Thu, 13 Feb 2025 22:43:12 +0700
Subject: [PATCH 068/175] use int in infer after training (why?)

---
 modeling/models/tiger.py     | 4 ++--
 modeling/rqvae_utils/trie.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 41ad2479..9a8611cf 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -86,7 +86,7 @@ def __init__(
 
         self._projector = nn.Linear(item_id_to_text_embedding.shape[1], embedding_dim)
 
-        item_ids = torch.tensor(list(range(1, len(item_id_to_semantic_id) + 1)))
+        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
 
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
 
@@ -95,7 +95,7 @@ def __init__(
         self._trie.build_tree_structure(
             item_id_to_semantic_id,
             item_id_to_residual,
-            torch.arange(1, len(item_id_to_semantic_id) + 1),
+            item_ids,
         )
 
         self._bos_token_id = codebook_sizes[0]
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 655f53f3..20338fff 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -298,7 +298,7 @@ def get_closest(
 
             raw_item_ids.append(result_ids[:items_to_query])
 
-        return torch.stack(raw_item_ids)
+        return torch.stack(raw_item_ids).int() # TODO
 
     def query(
         self, semantic_ids: torch.Tensor, residuals: torch.Tensor, items_to_query: int

From 032b5baea9569c75a936ac42680b850a7ac84e3f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:07:49 +0300
Subject: [PATCH 069/175] add sasrec for last

---
 configs/train/sasrec_train_config.json        |   5 +-
 .../train/sasrec_train_config_last_pred.json  | 168 ++++++++++++++++++
 2 files changed, 170 insertions(+), 3 deletions(-)
 create mode 100644 configs/train/sasrec_train_config_last_pred.json

diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 44079ace..de95097b 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -1,14 +1,13 @@
 {
   "experiment_name": "sasrec_beauty",
   "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 100,
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "last_item_prediction",
+      "type": "next_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -165,4 +164,4 @@
       }
     ]
   }
-}
+}
\ No newline at end of file
diff --git a/configs/train/sasrec_train_config_last_pred.json b/configs/train/sasrec_train_config_last_pred.json
new file mode 100644
index 00000000..6326dc96
--- /dev/null
+++ b/configs/train/sasrec_train_config_last_pred.json
@@ -0,0 +1,168 @@
+{
+  "experiment_name": "sasrec_beauty_last",
+  "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
+  "dataset": {
+    "type": "sequence",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "max_sequence_length": 50,
+    "samplers": {
+      "type": "last_item_prediction",
+      "negative_sampler_type": "random"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": true,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "sasrec",
+    "sequence_prefix": "item",
+    "positive_prefix": "positive",
+    "negative_prefix": "negative",
+    "candidate_prefix": "candidates",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
+    "activation": "gelu",
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 0.001
+    },
+    "clip_grad_threshold": 5.0
+  },
+  "loss": {
+    "type": "composite",
+    "losses": [
+      {
+        "type": "sasrec",
+        "positive_prefix": "positive_scores",
+        "negative_prefix": "negative_scores",
+        "output_prefix": "downstream_loss"
+      }
+    ],
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 64,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      }
+    ]
+  }
+}

From 31449be362b5ad22177556491d6e923865fcbdb5 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:10:32 +0300
Subject: [PATCH 070/175] fix for last

---
 configs/train/sasrec_train_config_last_pred.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_train_config_last_pred.json b/configs/train/sasrec_train_config_last_pred.json
index 6326dc96..def78931 100644
--- a/configs/train/sasrec_train_config_last_pred.json
+++ b/configs/train/sasrec_train_config_last_pred.json
@@ -35,7 +35,7 @@
   "model": {
     "type": "sasrec",
     "sequence_prefix": "item",
-    "positive_prefix": "positive",
+    "positive_prefix": "labels",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
     "embedding_dim": 64,
@@ -165,4 +165,4 @@
       }
     ]
   }
-}
+}
\ No newline at end of file

From e40c8ae58f93052a6032509aff571360baaf9600 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:12:16 +0300
Subject: [PATCH 071/175] return to next_item_pred for sasrec

---
 configs/train/sasrec_freezed_train_config.json | 2 +-
 configs/train/sasrec_train_config.json         | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index 5f383018..7d9d557d 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "last_item_prediction",
+      "type": "next_item_prediction",
       "negative_sampler_type": "random"
     }
   },
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index de95097b..f1711326 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -1,6 +1,7 @@
 {
   "experiment_name": "sasrec_beauty",
   "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
   "dataset": {
     "type": "sequence",
     "path_to_data_dir": "../data",

From 01370469da5355470ad6d2323906f194e2b6fa3d Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:12:50 +0300
Subject: [PATCH 072/175] remove last_pred sasrec

---
 .../train/sasrec_train_config_last_pred.json  | 168 ------------------
 1 file changed, 168 deletions(-)
 delete mode 100644 configs/train/sasrec_train_config_last_pred.json

diff --git a/configs/train/sasrec_train_config_last_pred.json b/configs/train/sasrec_train_config_last_pred.json
deleted file mode 100644
index def78931..00000000
--- a/configs/train/sasrec_train_config_last_pred.json
+++ /dev/null
@@ -1,168 +0,0 @@
-{
-  "experiment_name": "sasrec_beauty_last",
-  "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 100,
-  "dataset": {
-    "type": "sequence",
-    "path_to_data_dir": "../data",
-    "name": "Beauty",
-    "max_sequence_length": 50,
-    "samplers": {
-      "type": "last_item_prediction",
-      "negative_sampler_type": "random"
-    }
-  },
-  "dataloader": {
-    "train": {
-      "type": "torch",
-      "batch_size": 256,
-      "batch_processor": {
-        "type": "basic"
-      },
-      "drop_last": true,
-      "shuffle": true
-    },
-    "validation": {
-      "type": "torch",
-      "batch_size": 256,
-      "batch_processor": {
-        "type": "basic"
-      },
-      "drop_last": false,
-      "shuffle": false
-    }
-  },
-  "model": {
-    "type": "sasrec",
-    "sequence_prefix": "item",
-    "positive_prefix": "labels",
-    "negative_prefix": "negative",
-    "candidate_prefix": "candidates",
-    "embedding_dim": 64,
-    "num_heads": 2,
-    "num_layers": 2,
-    "dim_feedforward": 256,
-    "dropout": 0.3,
-    "activation": "gelu",
-    "layer_norm_eps": 1e-9,
-    "initializer_range": 0.02
-  },
-  "optimizer": {
-    "type": "basic",
-    "optimizer": {
-      "type": "adam",
-      "lr": 0.001
-    },
-    "clip_grad_threshold": 5.0
-  },
-  "loss": {
-    "type": "composite",
-    "losses": [
-      {
-        "type": "sasrec",
-        "positive_prefix": "positive_scores",
-        "negative_prefix": "negative_scores",
-        "output_prefix": "downstream_loss"
-      }
-    ],
-    "output_prefix": "loss"
-  },
-  "callback": {
-    "type": "composite",
-    "callbacks": [
-      {
-        "type": "metric",
-        "on_step": 1,
-        "loss_prefix": "loss"
-      },
-      {
-        "type": "validation",
-        "on_step": 64,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      },
-      {
-        "type": "eval",
-        "on_step": 256,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      }
-    ]
-  }
-}
\ No newline at end of file

From c25e49da3f89cbe7436cc9d293932f83905d7014 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:16:15 +0300
Subject: [PATCH 073/175] fix for sasrec weight only

---
 modeling/models/sasrec_freezed.py | 147 ++++++++++++++++--------------
 1 file changed, 77 insertions(+), 70 deletions(-)

diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index c6fab7f6..5d9eff91 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -1,25 +1,23 @@
+import torch
 from models import SequentialTorchModel
 from utils import create_masked_tensor
 
-import torch
-
-
-class SasRecFreezedModel(SequentialTorchModel, config_name='sasrec_freezed'):
 
+class SasRecFreezedModel(SequentialTorchModel, config_name="sasrec_freezed"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -31,127 +29,136 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
 
         self._init_weights(initializer_range)
-        
-        df = torch.load('../data/Beauty/data_full.pt')
+
+        df = torch.load("../data/Beauty/data_full.pt", weights_only=False)
         precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
-        
-        self._projector = torch.nn.Linear(precomputed_embeddings.shape[1], embedding_dim)
-        
+
+        self._projector = torch.nn.Linear(
+            precomputed_embeddings.shape[1], embedding_dim
+        )
+
         padding_embedding = torch.nn.init.trunc_normal_(
             torch.zeros(1, precomputed_embeddings.shape[1]),
             std=initializer_range,
             a=-2 * initializer_range,
-            b=2 * initializer_range
+            b=2 * initializer_range,
         )
 
         mask_embedding = torch.nn.init.trunc_normal_(
             torch.zeros(1, precomputed_embeddings.shape[1]),
             std=initializer_range,
             a=-2 * initializer_range,
-            b=2 * initializer_range
+            b=2 * initializer_range,
         )
-        
-        extended_embeddings = torch.cat([padding_embedding, precomputed_embeddings, mask_embedding], dim=0)  # Shape: (num_items + 2, embedding_dim)
-        
+
+        extended_embeddings = torch.cat(
+            [padding_embedding, precomputed_embeddings, mask_embedding], dim=0
+        )  # Shape: (num_items + 2, embedding_dim)
+
         self._item_embeddings = torch.nn.Embedding(
-            num_embeddings=num_items + 2,
-            embedding_dim=precomputed_embeddings.shape[1]
+            num_embeddings=num_items + 2, embedding_dim=precomputed_embeddings.shape[1]
         )
-        
+
         # TODO ask about freezed masked & padding tokens
-        
+
         self._item_embeddings.weight.data.copy_(extended_embeddings)
         self._item_embeddings.weight.requires_grad = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
-        
-    def get_item_embeddings(self, events): # TODO refactor (single projection)
+
+    def get_item_embeddings(self, events):  # TODO refactor (single projection)
         return self._projector(self._item_embeddings(events))
-    
+
     def get_last_item_embeddings(self):
         return self._projector(self._item_embeddings.weight)
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         if self.training:  # training mode
-            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_positive_sample_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
 
-            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            all_sample_embeddings = embeddings[
+                mask
+            ]  # (all_batch_events, embedding_dim)
 
-            all_embeddings = self.get_last_item_embeddings()  # (num_items + 2, embedding_dim)
+            all_embeddings = (
+                self.get_last_item_embeddings()
+            )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
-                'ad,nd->an',
-                all_sample_embeddings,
-                all_embeddings
+                "ad,nd->an", all_sample_embeddings, all_embeddings
             )  # (all_batch_events, num_items + 2)
 
             positive_scores = torch.gather(
-                input=all_scores,
-                dim=1,
-                index=all_positive_sample_events[..., None]
+                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
             )  # (all_batch_items, 1)
 
             sample_ids, _ = create_masked_tensor(
-                data=all_sample_events,
-                lengths=all_sample_lengths
+                data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-            sample_ids = torch.repeat_interleave(sample_ids, all_sample_lengths, dim=0)  # (all_batch_events, seq_len)
+            sample_ids = torch.repeat_interleave(
+                sample_ids, all_sample_lengths, dim=0
+            )  # (all_batch_events, seq_len)
 
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
                 index=sample_ids,
-                src=torch.ones_like(sample_ids) * (-torch.inf)
+                src=torch.ones_like(sample_ids) * (-torch.inf),
             )  # (all_batch_events, num_items + 2)
             negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
+            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
             return {
-                'positive_scores': positive_scores,
-                'negative_scores': negative_scores
+                "positive_scores": positive_scores,
+                "negative_scores": negative_scores,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self.get_last_item_embeddings()
+                "bd,nd->bn", last_embeddings, self.get_last_item_embeddings()
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
-            candidate_scores[:, self._num_items + 1:] = -torch.inf  # Mask id
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices

From b62e11d0100cced203f42b3c97d2206d1b56e5ec Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:28:14 +0300
Subject: [PATCH 074/175] fix trie device

---
 modeling/models/tiger.py     | 4 ++--
 modeling/rqvae_utils/trie.py | 9 ++++-----
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 9a8611cf..75eb944a 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -343,7 +343,7 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
 
         return semantic_ids, tgt_embeddings
 
-    def get_item_embeddings(self, events):  # TODO freezed embeddings
+    def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
             events - 1
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
@@ -352,7 +352,7 @@ def get_item_embeddings(self, events):  # TODO freezed embeddings
             len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
         )
 
-    def get_init_item_embeddings(self, events):  # TODO freezed embeddings
+    def get_init_item_embeddings(self, events):
         # convert to semantic ids
         semantic_ids = self._item_id_to_semantic_id[
             events - 1
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 20338fff..5e297438 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -43,9 +43,9 @@ def unique_with_index(self, x, dim=None):
         return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
 
     def compute_keys(self, semantic_ids: torch.Tensor):
-        exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
+        exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64, device=DEVICE)
         base = self.rqvae_model.codebook_sizes[0] ** exponents
-        uniq_ids = semantic_ids.to("cpu") @ base  # TODO fix device
+        uniq_ids = semantic_ids @ base
         return uniq_ids
 
     def pad_semantic_ids(self, semantic_ids: torch.Tensor):
@@ -298,7 +298,7 @@ def get_closest(
 
             raw_item_ids.append(result_ids[:items_to_query])
 
-        return torch.stack(raw_item_ids).int() # TODO
+        return torch.stack(raw_item_ids).int()  # TODO
 
     def query(
         self, semantic_ids: torch.Tensor, residuals: torch.Tensor, items_to_query: int
@@ -318,8 +318,7 @@ def query(
             torch.arange(K).expand(bs, K) < outer_levels.unsqueeze(1)
         )
         taken_inner_prefixes = semantic_ids * (
-            torch.arange(K).expand(bs, K) < inner_levels
-            .unsqueeze(1)
+            torch.arange(K).expand(bs, K) < inner_levels.unsqueeze(1)
         )
 
         outer_masks = self.get_mask_by_prefix(

From a70fc7834c94c45c12212f394e04c4026e18c4ce Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:28:54 +0300
Subject: [PATCH 075/175] fix tiger weights only

---
 modeling/models/tiger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 75eb944a..2ed9e58f 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -135,7 +135,7 @@ def init_rqvae(cls, config) -> RqVaeModel:
     @classmethod
     def create_from_config(cls, config, **kwargs):
         rqvae_model = cls.init_rqvae(config)
-        embs_extractor = torch.load(config["embs_extractor_path"])
+        embs_extractor = torch.load(config["embs_extractor_path"], weights_only=False)
 
         embs_extractor = embs_extractor.sort_index()
 

From e21e5170baf4e1202c54edbc2929b58dddce771a Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 10:29:36 +0300
Subject: [PATCH 076/175] revert trie device

---
 modeling/rqvae_utils/trie.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 5e297438..f16412d3 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -2,8 +2,8 @@
 import time
 
 import torch
-from utils import DEVICE
 from models.rqvae import RqVaeModel
+from utils import DEVICE
 
 
 class Trie:
@@ -43,9 +43,9 @@ def unique_with_index(self, x, dim=None):
         return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
 
     def compute_keys(self, semantic_ids: torch.Tensor):
-        exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64, device=DEVICE)
+        exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
         base = self.rqvae_model.codebook_sizes[0] ** exponents
-        uniq_ids = semantic_ids @ base
+        uniq_ids = semantic_ids.to("cpu") @ base
         return uniq_ids
 
     def pad_semantic_ids(self, semantic_ids: torch.Tensor):

From 6c69111997cdb873bb16fe091c4932cd3c65005e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 14 Feb 2025 12:50:49 +0300
Subject: [PATCH 077/175] return collion solver

---
 configs/train/tiger_train_config.json |  7 +++++++
 modeling/models/tiger.py              | 10 ++++++----
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index fd80a1c0..672591d0 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -68,6 +68,13 @@
         "labels": "semantic.labels",
         "weight": 1.0,
         "output_prefix": "semantic_loss"
+      },
+      {
+        "type": "sample_logsoftmax",
+        "predictions_prefix": "dedup.logits",
+        "labels": "dedup.labels",
+        "weight": 1.0,
+        "output_prefix": "dedup_loss"
       }
     ],
     "output_prefix": "loss"
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 2ed9e58f..2eefb4f7 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -221,14 +221,16 @@ def forward(self, inputs):
             ]  # len(events), len(codebook_sizes)
             true_residuals = self._item_id_to_residual[label_events - 1]
 
-            # true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
-            # pred_info = self._solver.get_pred_scores(
-            #     semantic_ids, decoder_output_residual
-            # ) TODO shapes don't match (solver init with 512, here 64)
+            true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
+            pred_info = self._solver.get_pred_scores(
+                semantic_ids, decoder_output_residual
+            )
 
             return {
                 "logits": decoder_prefix_scores,
                 "semantic.labels": semantic_ids,
+                "dedup.logits": pred_info["pred_scores"],
+                "dedup.labels": true_info["true_dedup_tokens"],
             }
         else:
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(

From 0ad40461e3eb9741c6b02d4075f692b0d9218841 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Feb 2025 13:37:08 +0300
Subject: [PATCH 078/175] add todos & remove projector

---
 configs/train/rqvae_train_config.json |  1 +
 modeling/models/rqvae.py              |  2 +-
 modeling/models/sasrec.py             |  4 ++++
 modeling/models/tiger.py              | 20 ++++++++------------
 modeling/rqvae_utils/trie.py          | 24 ++++++++++--------------
 5 files changed, 24 insertions(+), 27 deletions(-)

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index ee6ef333..5531c552 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -32,6 +32,7 @@
   "model": {
     "type": "rqvae",
     "embedding_dim": 512,
+    "hidden_dim": 64,
     "n_iter": 100,
     "codebook_sizes": [256, 256, 256],
     "should_init_codebooks": true,
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index cfdb0574..e8362665 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -57,7 +57,7 @@ def create_from_config(cls, config, **kwargs):
         return cls(
             train_sampler=kwargs.get('train_sampler'),
             input_dim=config['embedding_dim'],
-            hidden_dim=config['embedding_dim'],
+            hidden_dim=config['hidden_dim'],
             n_iter=config['n_iter'],
             codebook_sizes=config['codebook_sizes'],
             should_init_codebooks=config.get('should_init_codebooks', False),
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index cd36b544..bc0dbbcd 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -65,6 +65,10 @@ def forward(self, inputs):
             all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
 
             all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            # TODO last item for each event in batch
+            # TODO compare rqvae embs and textual
+            # TODO 4 embs (codebook vectors + residuals) for item intoo sasrec + positional embeddings
+            # rqvae EmbedingDim = TransformerEmbeddingDim
 
             all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
 
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 2eefb4f7..c1030a44 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -84,13 +84,11 @@ def __init__(
         self._item_id_to_residual = item_id_to_residual
         self._item_id_to_text_embedding = item_id_to_text_embedding
 
-        self._projector = nn.Linear(item_id_to_text_embedding.shape[1], embedding_dim)
-
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
 
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
 
-        self._trie = Trie(rqvae_model, self._projector)
+        self._trie = Trie(rqvae_model)
 
         self._trie.build_tree_structure(
             item_id_to_semantic_id,
@@ -206,12 +204,10 @@ def forward(self, inputs):
                 tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
             )  # (batch_size, label_len, embedding_dim)
 
-            projected_stacked = self._projector(self._codebook_item_embeddings_stacked)
-
             decoder_prefix_scores = torch.einsum(
                 "bsd,scd->bsc",
                 decoder_outputs[:, :-1, :],
-                projected_stacked,
+                self._codebook_item_embeddings_stacked,
             )
 
             decoder_output_residual = decoder_outputs[:, -1, :]
@@ -322,10 +318,10 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                 :, -1, :
             ]  # batch_size x embedding_dim
 
-            projected = self._projector(self._codebook_item_embeddings_stacked)
-
             if step < len(self._codebook_sizes):
-                codebook = projected[step]  # len(codebook_sizes) x embedding_dim
+                codebook = self._codebook_item_embeddings_stacked[
+                    step
+                ]  # len(codebook_sizes) x embedding_dim
                 closest_semantic_ids = torch.argmax(
                     torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
                 )  # batch_size x 1
@@ -349,7 +345,6 @@ def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
             events - 1
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        embs = self._projector(embs)
         return embs.view(
             len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
         )
@@ -372,8 +367,9 @@ def get_init_item_embeddings(self, events):
         semantic_embeddings = torch.stack(result)
 
         # get residuals
-        text_embeddings = self._item_id_to_text_embedding[events - 1]
-        residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = self._item_id_to_residual[events - 1]
+        # text_embeddings = self._item_id_to_text_embedding[events - 1]
+        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
         residual = residual.unsqueeze(1)
 
         # get true item embeddings
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index f16412d3..78a91117 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -7,7 +7,7 @@
 
 
 class Trie:
-    def __init__(self, rqvae_model: RqVaeModel, projector: torch.nn.Linear):
+    def __init__(self, rqvae_model: RqVaeModel):
         self.rqvae_model = rqvae_model
         self.keys = None
         self.prefix_counts = None
@@ -18,7 +18,6 @@ def __init__(self, rqvae_model: RqVaeModel, projector: torch.nn.Linear):
         self.embedding_table = torch.stack(
             [cb for cb in self.rqvae_model.codebooks]
         )  # K x codebook_size x embedding_dim
-        self.projector = projector
 
     def unique_with_index(self, x, dim=None):
         """Unique elements of x and indices of those unique elements
@@ -90,7 +89,7 @@ def build_tree_structure(
             prefix_counts[:, i + 1] = current_level_same
 
         residuals_per_level = self.get_residuals_per_level(
-            self.embedding_table.to(residuals.device), semantic_ids, residuals
+            semantic_ids, residuals
         )  # total_items x K + 1 x embedding_dim
 
         keys = self.compute_keys(semantic_ids)  # bs, could be collisions
@@ -103,21 +102,20 @@ def build_tree_structure(
 
     def get_residuals_per_level(
         self,
-        embedding_table: torch.Tensor,
         semantic_ids: torch.Tensor,
         residuals: torch.Tensor,
     ):
         bs = semantic_ids.shape[0]
         embedding_dim = residuals.shape[1]
         residuals_per_level = torch.zeros(
-            bs, self.K + 1, embedding_dim, device=embedding_table.device
+            bs, self.K + 1, embedding_dim, device=self.embedding_table.device
         )  # bs x K + 1 x embedding_dim
 
         # TODO think if reverse is needed here
         # i = 3, 2, 1, 0
         for i in range(self.K - 1, -1, -1):
             indices_at_level = semantic_ids[:, i]  # bs
-            embeddings_at_level = embedding_table[
+            embeddings_at_level = self.embedding_table[
                 i, indices_at_level
             ]  # bs x embedding_dim
             # 1 2 3 4
@@ -272,16 +270,16 @@ def get_closest(
 
             # self.residuals_per_level.shape = total_items, K + 1, embedding_dim
             guaranteed_raw_item_ids = self.raw_item_ids[guaranteed]  # guaranteed_len
-            guaranteed_stored_residuals = self.projector(
-                self.residuals_per_level[guaranteed][:, -(inner_level + 1)].to(DEVICE)
-            )  # guaranteed_len x embedding_dim
+            guaranteed_stored_residuals = self.residuals_per_level[guaranteed][
+                :, -(inner_level + 1)
+            ].to(DEVICE)  # guaranteed_len x embedding_dim
             guaranteed_query_residual = query_residual_per_level[
                 -(inner_level + 1)
             ]  # embedding_dim
 
             left_raw_item_ids = self.raw_item_ids[left]  # left_len
-            left_stored_residuals = self.projector(
-                self.residuals_per_level[left][:, -outer_level].to(DEVICE)
+            left_stored_residuals = self.residuals_per_level[left][:, -outer_level].to(
+                DEVICE
             )  # left_len x embedding_dim
             left_query_residual = query_residual_per_level[
                 -outer_level
@@ -347,10 +345,8 @@ def query(
 
         assert (inner_masks <= outer_masks).all()
 
-        projected_embedding_table = self.projector(self.embedding_table)
-
         query_residuals_per_level = self.get_residuals_per_level(
-            projected_embedding_table, semantic_ids, residuals
+            semantic_ids, residuals
         )
 
         raw_item_ids = self.get_closest(

From 8c07876bc8508360250409d9f8530c5de069571a Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Feb 2025 13:42:17 +0300
Subject: [PATCH 079/175] gpu

---
 modeling/models/rqvae.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index e8362665..7e150421 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -84,7 +84,7 @@ def init_codebooks(self, embeddings):
                     d=embeddings_np.shape[1],
                     k=n_clusters,
                     niter=self.n_iter,
-                    gpu=torch.cuda.is_available(),
+                    gpu=1,
                 )
                 kmeans.train(embeddings_np)
 

From 30ce91c7030e1f5f8ef3b2dfccce5cf0e853fd9c Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Feb 2025 13:47:34 +0300
Subject: [PATCH 080/175] move trie

---
 modeling/models/tiger.py     | 2 +-
 modeling/rqvae_utils/trie.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index c1030a44..49a83c33 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -237,7 +237,7 @@ def forward(self, inputs):
             semantic_ids = semantic_ids.to(torch.int64)
 
             item_ids = self._trie.query(
-                semantic_ids.to("cpu"), residuals.to("cpu"), items_to_query=20
+                semantic_ids, residuals, items_to_query=20
             )
 
             return item_ids
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 78a91117..6168d305 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -44,7 +44,7 @@ def unique_with_index(self, x, dim=None):
     def compute_keys(self, semantic_ids: torch.Tensor):
         exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
         base = self.rqvae_model.codebook_sizes[0] ** exponents
-        uniq_ids = semantic_ids.to("cpu") @ base
+        uniq_ids = semantic_ids @ base.to(DEVICE)
         return uniq_ids
 
     def pad_semantic_ids(self, semantic_ids: torch.Tensor):

From 9591183295f3ccb4bc15e9570dcefae3f2cd6bde Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Feb 2025 13:49:09 +0300
Subject: [PATCH 081/175] debug

---
 modeling/rqvae_utils/trie.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 6168d305..f197c835 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -44,6 +44,8 @@ def unique_with_index(self, x, dim=None):
     def compute_keys(self, semantic_ids: torch.Tensor):
         exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
         base = self.rqvae_model.codebook_sizes[0] ** exponents
+        print(semantic_ids.device)
+        print(base.device)
         uniq_ids = semantic_ids @ base.to(DEVICE)
         return uniq_ids
 

From 640dadae382d731050d485c3c424c24b474cd123 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Feb 2025 14:58:46 +0300
Subject: [PATCH 082/175] fix for sasrec last_item

---
 configs/train/sasrec_freezed_train_config.json | 4 ++--
 configs/train/sasrec_train_config.json         | 4 ++--
 modeling/models/sasrec.py                      | 4 +++-
 3 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index 7d9d557d..00f9d7ef 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -35,7 +35,7 @@
   "model": {
     "type": "sasrec_freezed",
     "sequence_prefix": "item",
-    "positive_prefix": "positive",
+    "positive_prefix": "labels",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
     "embedding_dim": 64,
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index f1711326..2c12a4e3 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -8,7 +8,7 @@
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -35,7 +35,7 @@
   "model": {
     "type": "sasrec",
     "sequence_prefix": "item",
-    "positive_prefix": "positive",
+    "positive_prefix": "labels",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
     "embedding_dim": 64,
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index bc0dbbcd..fbfbc02d 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -101,9 +101,11 @@ def forward(self, inputs):
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
 
+            last_item_mask = torch.cumsum(mask.sum(dim=1), dim=0) - 1 # TODO ask if correct (mask, last True in eahch row, index as only Trues appeared)
+
             return {
                 'positive_scores': positive_scores,
-                'negative_scores': negative_scores
+                'negative_scores': negative_scores[last_item_mask]
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)

From b461af9b4dd1b49ba5f04d0bf95ad985345c43d8 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Feb 2025 15:13:36 +0300
Subject: [PATCH 083/175] move trie to device

---
 modeling/models/tiger.py     |  6 +++---
 modeling/rqvae_utils/trie.py | 28 +++++++++++++---------------
 2 files changed, 16 insertions(+), 18 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 49a83c33..8e1ae9ff 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -91,9 +91,9 @@ def __init__(
         self._trie = Trie(rqvae_model)
 
         self._trie.build_tree_structure(
-            item_id_to_semantic_id,
-            item_id_to_residual,
-            item_ids,
+            item_id_to_semantic_id.to(DEVICE),
+            item_id_to_residual.to(DEVICE),
+            item_ids.to(DEVICE),
         )
 
         self._bos_token_id = codebook_sizes[0]
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index f197c835..89833c91 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -42,12 +42,10 @@ def unique_with_index(self, x, dim=None):
         return unique, inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
 
     def compute_keys(self, semantic_ids: torch.Tensor):
-        exponents = torch.arange(self.K - 1, -1, -1, dtype=torch.int64)
+        exponents = torch.arange(self.K - 1, -1, -1, device=DEVICE).float()
         base = self.rqvae_model.codebook_sizes[0] ** exponents
-        print(semantic_ids.device)
-        print(base.device)
-        uniq_ids = semantic_ids @ base.to(DEVICE)
-        return uniq_ids
+        uniq_ids = semantic_ids.float() @ base
+        return uniq_ids.int()
 
     def pad_semantic_ids(self, semantic_ids: torch.Tensor):
         return torch.cat(
@@ -57,7 +55,8 @@ def pad_semantic_ids(self, semantic_ids: torch.Tensor):
                     semantic_ids.shape[0],
                     self.K - semantic_ids.shape[1],
                     dtype=semantic_ids.dtype,
-                ).to(semantic_ids.device),
+                    device=semantic_ids.device,
+                ),
             ],
             dim=1,
         )
@@ -163,7 +162,7 @@ def get_mask_by_prefix(self, prefixes: torch.Tensor, taken_lens: torch.Tensor):
 
     def process_prefixes(self, prefixes: torch.Tensor):
         bs, prefix_len = prefixes.shape
-        taken_len = torch.full((bs,), prefix_len)
+        taken_len = torch.full((bs,), prefix_len, device=DEVICE)
         mask = self.get_mask_by_prefix(prefixes, taken_len)
         # self.keys.unsqueeze(0) = 1 x bs
         # lower_key.unsqueeze(1), upper_key.unsqueeze(1) = bs x 1
@@ -180,6 +179,7 @@ def get_outer_inner_levels(self, semantic_ids: torch.Tensor, items_to_query: int
                 torch.full(
                     (bs, 1),
                     self.total_items,
+                    device=DEVICE,
                 ),
                 num_items,
             ],
@@ -203,7 +203,7 @@ def get_sorted_ids(self, stored_residuals, query_residual, raw_item_ids):
         scores = torch.matmul(stored_residuals, query_residual)  # (guaranteed_len,)
         # Sort guaranteed items by score (descending)
         sorted_indices = torch.argsort(scores, descending=True)
-        sorted_ids = raw_item_ids[sorted_indices.to(raw_item_ids.device)]
+        sorted_ids = raw_item_ids[sorted_indices]
 
         return sorted_ids
 
@@ -217,7 +217,7 @@ def get_closest_single(
         left_query_residual,  # embedding_dim
     ):
         if guaranteed_raw_item_ids.shape[0] == 0:
-            guaranteed_sorted_ids = torch.tensor([])
+            guaranteed_sorted_ids = torch.tensor([], device=DEVICE)
         else:
             guaranteed_sorted_ids = self.get_sorted_ids(
                 guaranteed_stored_residuals,
@@ -274,15 +274,13 @@ def get_closest(
             guaranteed_raw_item_ids = self.raw_item_ids[guaranteed]  # guaranteed_len
             guaranteed_stored_residuals = self.residuals_per_level[guaranteed][
                 :, -(inner_level + 1)
-            ].to(DEVICE)  # guaranteed_len x embedding_dim
+            ]  # guaranteed_len x embedding_dim
             guaranteed_query_residual = query_residual_per_level[
                 -(inner_level + 1)
             ]  # embedding_dim
 
             left_raw_item_ids = self.raw_item_ids[left]  # left_len
-            left_stored_residuals = self.residuals_per_level[left][:, -outer_level].to(
-                DEVICE
-            )  # left_len x embedding_dim
+            left_stored_residuals = self.residuals_per_level[left][:, -outer_level]  # left_len x embedding_dim
             left_query_residual = query_residual_per_level[
                 -outer_level
             ]  # embedding_dim
@@ -315,10 +313,10 @@ def query(
         # print(num_items, outer_levels, inner_levels)
 
         taken_outer_prefixes = semantic_ids * (
-            torch.arange(K).expand(bs, K) < outer_levels.unsqueeze(1)
+            torch.arange(K, device=DEVICE).expand(bs, K) < outer_levels.unsqueeze(1)
         )
         taken_inner_prefixes = semantic_ids * (
-            torch.arange(K).expand(bs, K) < inner_levels.unsqueeze(1)
+            torch.arange(K, device=DEVICE).expand(bs, K) < inner_levels.unsqueeze(1)
         )
 
         outer_masks = self.get_mask_by_prefix(

From e31a976b563526ac286fcd64f0953da1032a9a95 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 12:28:01 +0300
Subject: [PATCH 084/175] add sequence full dataset

---
 .../train/sasrec_freezed_train_config.json    |   2 +-
 configs/train/sasrec_train_config.json        |   2 +-
 configs/train/tiger_train_config.json         |   2 +-
 modeling/dataset/base.py                      | 119 ++++++++++++++++++
 4 files changed, 122 insertions(+), 3 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index 00f9d7ef..a53ddf0b 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -3,7 +3,7 @@
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 2c12a4e3..8c64df89 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -3,7 +3,7 @@
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 672591d0..2d3369c5 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -3,7 +3,7 @@
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 1cb01562..e375f6ac 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -196,7 +196,126 @@ def meta(self):
             'num_items': self.num_items,
             'max_sequence_length': self.max_sequence_length
         }
+        
+        
+class SequenceFullDataset(SequenceDataset, config_name='sequence_full'):
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
+
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='train',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
+        )
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='valid',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
+        )
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='test',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
+        )
+
+        max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
+        max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
+        max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
+
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_seq_len))
+
+        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
+        valid_interactions = len(validation_dataset)  # each new interaction as a sample
+        test_interactions = len(test_dataset) # each new interaction as a sample
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
+        ))
+
+        train_sampler = TrainSampler.create_from_config(
+            config['samplers'],
+            dataset=train_dataset,
+            num_users=max_user_id,
+            num_items=max_item_id
+        )
+        validation_sampler = EvalSampler.create_from_config(
+            config['samplers'],
+            dataset=validation_dataset,
+            num_users=max_user_id,
+            num_items=max_item_id
+        )
+        test_sampler = EvalSampler.create_from_config(
+            config['samplers'],
+            dataset=test_dataset,
+            num_users=max_user_id,
+            num_items=max_item_id
+        )
+
+        return cls(
+            train_sampler=train_sampler,
+            validation_sampler=validation_sampler,
+            test_sampler=test_sampler,
+            num_users=max_user_id,
+            num_items=max_item_id,
+            max_sequence_length=max_seq_len
+        )
+        
+    @classmethod
+    def flatten_item_sequence(cls, item_ids):
+        min_history_length = 3 # TODO: make this configurable
+        histories = []
+        for i in range(min_history_length-1, len(item_ids)):
+            histories.append(item_ids[:i+1])
+        return histories
+    
+    @classmethod
+    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
+        max_user_id = 0
+        max_item_id = 0
+        max_sequence_len = 0
+
+        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
+            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
 
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
+        else:
+            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
+            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
+
+            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
+            with open(dataset_path, 'r') as f:
+                data = f.readlines()
+
+            sequence_info = cls._create_sequences(data, max_sequence_length)
+            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
+
+            dataset = []
+            for user_id, item_ids in zip(user_sequences, item_sequences):
+                flattened_item_ids = cls.flatten_item_sequence(item_ids)
+                for seq in flattened_item_ids:
+                    dataset.append({
+                        'user.ids': [user_id], 'user.length': 1,
+                        'item.ids': seq, 'item.length': len(seq)
+                    })
+
+            logger.info('{} dataset size: {}'.format(part, len(dataset)))
+            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
+
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
+                pickle.dump(
+                    (dataset, max_user_id, max_item_id, max_sequence_len),
+                    dataset_file
+                )
+
+        return dataset, max_user_id, max_item_id, max_sequence_len
 
 class GraphDataset(BaseDataset, config_name='graph'):
 

From a30432f94852ebf4195147872999055deb64915a Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 12:39:35 +0300
Subject: [PATCH 085/175] sasrec semantic

---
 modeling/models/sasrec.py          |   4 -
 modeling/models/sasrec_semantic.py | 124 +++++++++++++++++++++++++++++
 modeling/models/tiger.py           |   6 +-
 3 files changed, 125 insertions(+), 9 deletions(-)
 create mode 100644 modeling/models/sasrec_semantic.py

diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index fbfbc02d..e94e95fb 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -65,10 +65,6 @@ def forward(self, inputs):
             all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
 
             all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
-            # TODO last item for each event in batch
-            # TODO compare rqvae embs and textual
-            # TODO 4 embs (codebook vectors + residuals) for item intoo sasrec + positional embeddings
-            # rqvae EmbedingDim = TransformerEmbeddingDim
 
             all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
 
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
new file mode 100644
index 00000000..7c9a16d2
--- /dev/null
+++ b/modeling/models/sasrec_semantic.py
@@ -0,0 +1,124 @@
+import torch
+from models import SequentialTorchModel
+from utils import create_masked_tensor
+
+
+class SasRecSemanticModel(SequentialTorchModel, config_name="sasrec_semantic"):
+    def __init__(
+            self,
+            sequence_prefix,
+            positive_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True
+        )
+        self._sequence_prefix = sequence_prefix
+        self._positive_prefix = positive_prefix
+
+        self._init_weights(initializer_range)
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
+        )
+
+    def forward(self, inputs):
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+
+        embeddings, mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        if self.training:  # training mode
+            all_positive_sample_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+
+            all_sample_embeddings = embeddings[
+                mask
+            ]  # (all_batch_events, embedding_dim)
+
+            all_embeddings = (
+                self.get_last_item_embeddings()
+            )  # (num_items + 2, embedding_dim)
+
+            # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
+            all_scores = torch.einsum(
+                "ad,nd->an", all_sample_embeddings, all_embeddings
+            )  # (all_batch_events, num_items + 2)
+
+            positive_scores = torch.gather(
+                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
+            )  # (all_batch_items, 1)
+
+            sample_ids, _ = create_masked_tensor(
+                data=all_sample_events, lengths=all_sample_lengths
+            )  # (batch_size, seq_len)
+
+            sample_ids = torch.repeat_interleave(
+                sample_ids, all_sample_lengths, dim=0
+            )  # (all_batch_events, seq_len)
+
+            negative_scores = torch.scatter(
+                input=all_scores,
+                dim=1,
+                index=sample_ids,
+                src=torch.ones_like(sample_ids) * (-torch.inf),
+            )  # (all_batch_events, num_items + 2)
+            negative_scores[:, 0] = -torch.inf  # Padding idx
+            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
+
+            return {
+                "positive_scores": positive_scores,
+                "negative_scores": negative_scores,
+            }
+        else:  # eval mode
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
+            # b - batch_size, n - num_candidates, d - embedding_dim
+            candidate_scores = torch.einsum(
+                "bd,nd->bn", last_embeddings, self.get_last_item_embeddings()
+            )  # (batch_size, num_items + 2)
+            candidate_scores[:, 0] = -torch.inf  # Padding id
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
+
+            _, indices = torch.topk(
+                candidate_scores, k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 8e1ae9ff..09902184 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -391,8 +391,6 @@ def position_lambda(x):
             lengths, mask, position_lambda, self._position_embeddings
         )
 
-        # print(f"{position_embeddings.isnan().any()=}") # TODO fix NaN in pos_embs
-
         def codebook_lambda(x):
             x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
             x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
@@ -403,8 +401,6 @@ def codebook_lambda(x):
             lengths, mask, codebook_lambda, self._codebook_embeddings
         )
 
-        # print(f"{codebook_embeddings.isnan().any()=}") # TODO fix NaN in pos_embs
-
         return position_embeddings + codebook_embeddings
 
     def _decoder_pos_embeddings(self, lengths, mask):
@@ -444,7 +440,7 @@ def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_lay
         position_embeddings = embedding_layer(
             positions
         )  # (all_batch_events, embedding_dim)
-        # print(f"{position_embeddings.isnan().any()=}") # TODO without embeddings also NaN
+
         position_embeddings, _ = create_masked_tensor(
             data=position_embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)

From 5da9423cf3975b6095cbc6d92ef14007e737c8fc Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 12:51:57 +0300
Subject: [PATCH 086/175] fix sasrec

---
 modeling/models/sasrec.py          |  4 +---
 modeling/models/sasrec_freezed.py  | 20 +++++++++-------
 modeling/models/sasrec_semantic.py | 38 +++++++++++++++++-------------
 3 files changed, 34 insertions(+), 28 deletions(-)

diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index e94e95fb..cd36b544 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -97,11 +97,9 @@ def forward(self, inputs):
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
 
-            last_item_mask = torch.cumsum(mask.sum(dim=1), dim=0) - 1 # TODO ask if correct (mask, last True in eahch row, index as only Trues appeared)
-
             return {
                 'positive_scores': positive_scores,
-                'negative_scores': negative_scores[last_item_mask]
+                'negative_scores': negative_scores
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 5d9eff91..6b9710ad 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -85,11 +85,11 @@ def create_from_config(cls, config, **kwargs):
             initializer_range=config.get("initializer_range", 0.02),
         )
 
-    def get_item_embeddings(self, events):  # TODO refactor (single projection)
-        return self._projector(self._item_embeddings(events))
-
-    def get_last_item_embeddings(self):
-        return self._projector(self._item_embeddings.weight)
+    def get_item_embeddings(self, events=None):  # TODO refactor (single projection)
+        if events is None:
+            return self._projector(self._item_embeddings.weight)
+        else:
+            return self._projector(self._item_embeddings(events))
 
     def forward(self, inputs):
         all_sample_events = inputs[
@@ -113,7 +113,7 @@ def forward(self, inputs):
             ]  # (all_batch_events, embedding_dim)
 
             all_embeddings = (
-                self.get_last_item_embeddings()
+                self.get_item_embeddings()
             )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
@@ -142,9 +142,13 @@ def forward(self, inputs):
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
+            last_item_mask = (
+                torch.cumsum(mask.sum(dim=1), dim=0) - 1
+            )  # TODO ask if correct (mask, last True in each row, index as only Trues appeared)
+
             return {
                 "positive_scores": positive_scores,
-                "negative_scores": negative_scores,
+                "negative_scores": negative_scores[last_item_mask],
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(
@@ -152,7 +156,7 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self.get_last_item_embeddings()
+                "bd,nd->bn", last_embeddings, self.get_item_embeddings()
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
             candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 7c9a16d2..6703f8e8 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -5,19 +5,19 @@
 
 class SasRecSemanticModel(SequentialTorchModel, config_name="sasrec_semantic"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -29,7 +29,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -73,7 +73,7 @@ def forward(self, inputs):
             ]  # (all_batch_events, embedding_dim)
 
             all_embeddings = (
-                self.get_last_item_embeddings()
+                self._item_embeddings.weight
             )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
@@ -102,9 +102,13 @@ def forward(self, inputs):
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
+            last_item_mask = (
+                torch.cumsum(mask.sum(dim=1), dim=0) - 1
+            )  # TODO ask if correct (mask, last True in each row, index as only Trues appeared)
+
             return {
                 "positive_scores": positive_scores,
-                "negative_scores": negative_scores,
+                "negative_scores": negative_scores[last_item_mask],
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(
@@ -112,7 +116,7 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self.get_last_item_embeddings()
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
             candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id

From 83a97c2311525053bcabf78dadd760486c49fa1b Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 13:13:57 +0300
Subject: [PATCH 087/175] sasrec semantic draft

---
 .../train/sasrec_semantic_train_config.json   | 168 ++++++++++++++++++
 modeling/models/sasrec_semantic.py            |  64 +++++++
 modeling/models/tiger.py                      |   8 +-
 3 files changed, 235 insertions(+), 5 deletions(-)
 create mode 100644 configs/train/sasrec_semantic_train_config.json

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
new file mode 100644
index 00000000..47c1a9eb
--- /dev/null
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -0,0 +1,168 @@
+{
+  "experiment_name": "sasrec_semantic_beauty",
+  "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
+  "dataset": {
+    "type": "sequence_full",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "max_sequence_length": 50,
+    "samplers": {
+      "type": "last_item_prediction",
+      "negative_sampler_type": "random"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": true,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "sasrec_semantic",
+    "sequence_prefix": "item",
+    "positive_prefix": "labels",
+    "negative_prefix": "negative",
+    "candidate_prefix": "candidates",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
+    "activation": "gelu",
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 0.001
+    },
+    "clip_grad_threshold": 5.0
+  },
+  "loss": {
+    "type": "composite",
+    "losses": [
+      {
+        "type": "sasrec",
+        "positive_prefix": "positive_scores",
+        "negative_prefix": "negative_scores",
+        "output_prefix": "downstream_loss"
+      }
+    ],
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 64,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 6703f8e8..ee80b14f 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -50,6 +50,47 @@ def create_from_config(cls, config, **kwargs):
             dropout=config.get("dropout", 0.0),
             initializer_range=config.get("initializer_range", 0.02),
         )
+        
+    
+    def get_item_embeddings(self, events):
+        embs = self._item_id_to_semantic_embedding[
+            events - 1
+        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        return embs.view(
+            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
+        )
+
+    def get_init_item_embeddings(self, events):
+        # convert to semantic ids
+        semantic_ids = self._item_id_to_semantic_id[
+            events - 1
+        ]  # len(events), len(codebook_sizes)
+
+        result = []
+        for semantic_id in semantic_ids:
+            item_repr = []
+            for codebook_idx, codebook_id in enumerate(semantic_id):
+                item_repr.append(
+                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
+                )
+            result.append(torch.stack(item_repr))
+
+        semantic_embeddings = torch.stack(result)
+
+        # get residuals
+        residual = self._item_id_to_residual[events - 1]
+        # text_embeddings = self._item_id_to_text_embedding[events - 1]
+        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = residual.unsqueeze(1)
+
+        # get true item embeddings
+        item_embeddings = torch.cat(
+            [semantic_embeddings, residual], dim=1
+        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+
+        # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
+
+        return item_embeddings
 
     def forward(self, inputs):
         all_sample_events = inputs[
@@ -59,6 +100,7 @@ def forward(self, inputs):
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
+        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
@@ -126,3 +168,25 @@ def forward(self, inputs):
             )  # (batch_size, 20)
 
             return indices
+        
+    def _encoder_pos_embeddings(self, lengths, mask):
+        def position_lambda(x):
+            return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
+
+        # TODO +1 for residual embedding
+
+        position_embeddings = self._get_position_embeddings(
+            lengths, mask, position_lambda, self._position_embeddings
+        )
+
+        def codebook_lambda(x):
+            x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
+            x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
+            # 0 1 2 3 5 0 1 2 3 5 ... # len(self._codebook_sizes) for bos, len(self._codebook_sizes) + 1 for residual
+            return x
+
+        codebook_embeddings = self._get_position_embeddings(
+            lengths, mask, codebook_lambda, self._codebook_embeddings
+        )
+        
+        return position_embeddings + codebook_embeddings
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 09902184..6b3a4fde 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -28,7 +28,6 @@ def __init__(
         num_encoder_layers,
         num_decoder_layers,
         dim_feedforward,
-        codebook_sizes,
         dropout=0.0,
         activation="relu",
         layer_norm_eps=1e-9,
@@ -71,7 +70,7 @@ def __init__(
 
         self._solver: CollisionSolver = solver
 
-        self._codebook_sizes = codebook_sizes
+        self._codebook_sizes = rqvae_model.codebook_sizes
 
         self._codebook_item_embeddings_stacked = torch.stack(
             [codebook for codebook in rqvae_model.codebooks]
@@ -96,7 +95,7 @@ def __init__(
             item_ids.to(DEVICE),
         )
 
-        self._bos_token_id = codebook_sizes[0]
+        self._bos_token_id = self._codebook_sizes[0]
         self._bos_weight = nn.Parameter(
             torch.nn.init.trunc_normal_(
                 torch.zeros(embedding_dim),
@@ -107,7 +106,7 @@ def __init__(
         )
 
         self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(codebook_sizes) + 2, embedding_dim=embedding_dim
+            num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
         )  # + 2 for bos token & residual
 
         self._init_weights(initializer_range)
@@ -170,7 +169,6 @@ def create_from_config(cls, config, **kwargs):
             num_encoder_layers=config["num_encoder_layers"],
             num_decoder_layers=config["num_decoder_layers"],
             dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            codebook_sizes=rqvae_model.codebook_sizes,
             dropout=config.get("dropout", 0.0),
             initializer_range=config.get("initializer_range", 0.02),
         )

From 926e0c478e1ee06cb60facba1da6732df01c4443 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 17:22:01 +0300
Subject: [PATCH 088/175] fix sasrec

---
 modeling/models/sasrec_freezed.py  | 21 +++++++--------------
 modeling/models/sasrec_semantic.py | 23 ++++++++---------------
 2 files changed, 15 insertions(+), 29 deletions(-)

diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 6b9710ad..3334c33b 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -108,9 +108,9 @@ def forward(self, inputs):
                 "{}.ids".format(self._positive_prefix)
             ]  # (all_batch_events)
 
-            all_sample_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
             all_embeddings = (
                 self.get_item_embeddings()
@@ -118,20 +118,17 @@ def forward(self, inputs):
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
-                "ad,nd->an", all_sample_embeddings, all_embeddings
-            )  # (all_batch_events, num_items + 2)
+                "ad,nd->an", last_embeddings, all_embeddings
+            )  # (batch_size, num_items + 2)
 
             positive_scores = torch.gather(
                 input=all_scores, dim=1, index=all_positive_sample_events[..., None]
-            )  # (all_batch_items, 1)
+            )  # (batch_size, 1)
 
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-            sample_ids = torch.repeat_interleave(
-                sample_ids, all_sample_lengths, dim=0
-            )  # (all_batch_events, seq_len)
 
             negative_scores = torch.scatter(
                 input=all_scores,
@@ -142,13 +139,9 @@ def forward(self, inputs):
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
-            last_item_mask = (
-                torch.cumsum(mask.sum(dim=1), dim=0) - 1
-            )  # TODO ask if correct (mask, last True in each row, index as only Trues appeared)
-
             return {
                 "positive_scores": positive_scores,
-                "negative_scores": negative_scores[last_item_mask],
+                "negative_scores": negative_scores,
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index ee80b14f..88e08890 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -110,30 +110,27 @@ def forward(self, inputs):
                 "{}.ids".format(self._positive_prefix)
             ]  # (all_batch_events)
 
-            all_sample_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
             all_embeddings = (
-                self._item_embeddings.weight
+                self.get_item_embeddings()
             )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
-                "ad,nd->an", all_sample_embeddings, all_embeddings
-            )  # (all_batch_events, num_items + 2)
+                "ad,nd->an", last_embeddings, all_embeddings
+            )  # (batch_size, num_items + 2)
 
             positive_scores = torch.gather(
                 input=all_scores, dim=1, index=all_positive_sample_events[..., None]
-            )  # (all_batch_items, 1)
+            )  # (batch_size, 1)
 
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-            sample_ids = torch.repeat_interleave(
-                sample_ids, all_sample_lengths, dim=0
-            )  # (all_batch_events, seq_len)
 
             negative_scores = torch.scatter(
                 input=all_scores,
@@ -144,13 +141,9 @@ def forward(self, inputs):
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
-            last_item_mask = (
-                torch.cumsum(mask.sum(dim=1), dim=0) - 1
-            )  # TODO ask if correct (mask, last True in each row, index as only Trues appeared)
-
             return {
                 "positive_scores": positive_scores,
-                "negative_scores": negative_scores[last_item_mask],
+                "negative_scores": negative_scores,
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(

From 491a940d1210ce2388611ad59ee27d9b945cc05f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 17:29:20 +0300
Subject: [PATCH 089/175] fix sasrec native

---
 modeling/models/sasrec.py          | 16 +++++++---------
 modeling/models/sasrec_semantic.py |  1 -
 2 files changed, 7 insertions(+), 10 deletions(-)

diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index cd36b544..06dee724 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -64,36 +64,34 @@ def forward(self, inputs):
         if self.training:  # training mode
             all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
 
-            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
             all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
-                'ad,nd->an',
-                all_sample_embeddings,
-                all_embeddings
-            )  # (all_batch_events, num_items + 2)
+                "ad,nd->an", last_embeddings, all_embeddings
+            )  # (batch_size, num_items + 2)
 
             positive_scores = torch.gather(
                 input=all_scores,
                 dim=1,
                 index=all_positive_sample_events[..., None]
-            )  # (all_batch_items, 1)
+            )  # (batch_size, 1)
 
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events,
                 lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-            sample_ids = torch.repeat_interleave(sample_ids, all_sample_lengths, dim=0)  # (all_batch_events, seq_len)
-
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
                 index=sample_ids,
                 src=torch.ones_like(sample_ids) * (-torch.inf)
-            )  # (all_batch_events, num_items + 2)
+            )  # (batch_size, num_items + 2)
             negative_scores[:, 0] = -torch.inf  # Padding idx
             negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
 
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 88e08890..8553aced 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -131,7 +131,6 @@ def forward(self, inputs):
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,

From d6e9138a2a195754614965c5eb1ad30117e4b5ae Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 19:39:51 +0300
Subject: [PATCH 090/175] better trie query

---
 modeling/rqvae_utils/trie.py | 148 ++++++++++++++---------------------
 1 file changed, 58 insertions(+), 90 deletions(-)

diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 89833c91..d80da941 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -199,104 +199,72 @@ def get_outer_inner_levels(self, semantic_ids: torch.Tensor, items_to_query: int
 
         return num_items, outer_level, inner_level  # bs x K + 1, bs, bs
 
-    def get_sorted_ids(self, stored_residuals, query_residual, raw_item_ids):
-        scores = torch.matmul(stored_residuals, query_residual)  # (guaranteed_len,)
-        # Sort guaranteed items by score (descending)
-        sorted_indices = torch.argsort(scores, descending=True)
-        sorted_ids = raw_item_ids[sorted_indices]
+    def get_scores(self, item_indices, idx, query_residuals_per_level):
+        bs = idx.shape[0]  # batch_size
 
-        return sorted_ids
+        # stored[n, i, :] = self.residuals_per_level[item_indices[n,i], idx_expanded[n,i], :]
+        stored = self.residuals_per_level[item_indices[None, :], idx[:, None], :]
 
-    def get_closest_single(
-        self,
-        guaranteed_raw_item_ids,  # guaranteed_len
-        guaranteed_stored_residuals,  # guaranteed_len x embedding_dim
-        guaranteed_query_residual,  # embedding_dim
-        left_raw_item_ids,  # left_len
-        left_stored_residuals,  # left_len x embedding_dim
-        left_query_residual,  # embedding_dim
-    ):
-        if guaranteed_raw_item_ids.shape[0] == 0:
-            guaranteed_sorted_ids = torch.tensor([], device=DEVICE)
-        else:
-            guaranteed_sorted_ids = self.get_sorted_ids(
-                guaranteed_stored_residuals,
-                guaranteed_query_residual,
-                guaranteed_raw_item_ids,
-            )
-
-        left_sorted_ids = self.get_sorted_ids(
-            left_stored_residuals, left_query_residual, left_raw_item_ids
-        )
+        # Gather the corresponding query vectors for each row:
+        # query[n, :] = query_residuals_per_level[n, idx[n], :]
+        query = query_residuals_per_level[
+            torch.arange(bs, device=item_indices.device), idx, :
+        ]  # Shape [batch_size, D]
 
-        # Concatenate the sorted lists
-        result_ids = torch.cat((guaranteed_sorted_ids, left_sorted_ids))
+        # Dot products => shape [batch_size, total_items]
+        scores = torch.einsum("bnd,bd->bn", stored, query)
 
-        return result_ids
+        return scores
 
-    def get_closest(
+    def get_closest_vectorized(
         self,
-        outer_masks,
-        inner_masks,
-        outer_levels,
-        inner_levels,
-        query_residuals_per_level,
+        outer_masks,  # shape: [batch_size, total_items] (boolean)
+        inner_masks,  # shape: [batch_size, total_items] (boolean)
+        outer_levels,  # shape: [batch_size]
+        inner_levels,  # shape: [batch_size]
+        query_residuals_per_level,  # shape: [batch_size, K+1, embedding_dim]
         items_to_query,
     ):
-        # K = 3
-        # self.residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 1
-        # query_residuals_per_level = 0, res, res+emb_2, res+emb_2+emb_1, res+emb_2+emb_1+emb_0 # K + 1
-        # outer_level = 3 # if K (get by dot only from outer) => inner_mask = outer_mask
-        # outer_mask.shape = total_items
-        # inner_mask.shape = total_items
-
-        raw_item_ids = []
-
-        for (
-            outer_mask,
-            inner_mask,
-            outer_level,
-            inner_level,
-            query_residual_per_level,
-        ) in zip(
-            outer_masks,
-            inner_masks,
-            outer_levels,
-            inner_levels,
+        device = outer_masks.device
+        bs, total_items = outer_masks.shape
+
+        item_indices = torch.arange(total_items, device=device)
+
+        guaranteed_scores = self.get_scores(
+            item_indices,
+            -(inner_levels + 1),
             query_residuals_per_level,
-        ):
-            guaranteed = inner_mask  # guaranteed_len
-            left = outer_mask & ~inner_mask  # left_len
-
-            assert guaranteed.sum() + left.sum() == outer_mask.sum()
-
-            # self.residuals_per_level.shape = total_items, K + 1, embedding_dim
-            guaranteed_raw_item_ids = self.raw_item_ids[guaranteed]  # guaranteed_len
-            guaranteed_stored_residuals = self.residuals_per_level[guaranteed][
-                :, -(inner_level + 1)
-            ]  # guaranteed_len x embedding_dim
-            guaranteed_query_residual = query_residual_per_level[
-                -(inner_level + 1)
-            ]  # embedding_dim
-
-            left_raw_item_ids = self.raw_item_ids[left]  # left_len
-            left_stored_residuals = self.residuals_per_level[left][:, -outer_level]  # left_len x embedding_dim
-            left_query_residual = query_residual_per_level[
-                -outer_level
-            ]  # embedding_dim
-
-            result_ids = self.get_closest_single(
-                guaranteed_raw_item_ids,
-                guaranteed_stored_residuals,
-                guaranteed_query_residual,
-                left_raw_item_ids,
-                left_stored_residuals,
-                left_query_residual,
-            )
-
-            raw_item_ids.append(result_ids[:items_to_query])
-
-        return torch.stack(raw_item_ids).int()  # TODO
+        )
+        guaranteed_scores = torch.where(
+            inner_masks, guaranteed_scores, torch.tensor(float("-inf"), device=device)
+        )  # [batch_size, total_items]
+
+        left_scores = self.get_scores(
+            item_indices,
+            -outer_levels,
+            query_residuals_per_level,
+        )
+        left_masks = outer_masks & ~inner_masks
+        left_scores = torch.where(
+            left_masks, left_scores, torch.tensor(float("-inf"), device=device)
+        )  # [batch_size, total_items]
+
+        _, guaranteed_indices = torch.topk(
+            guaranteed_scores, items_to_query, dim=1
+        )  # [batch_size, items_to_query]
+        _, left_indices = torch.topk(
+            left_scores, items_to_query, dim=1
+        )  # [batch_size, items_to_query]
+
+        indices = torch.cat(
+            [guaranteed_indices, left_indices], dim=1
+        )  # [batch_size, 2 * items_to_query]
+
+        top_ids = self.raw_item_ids[indices][
+            :, :items_to_query
+        ]  # [batch_size, items_to_query]
+
+        return top_ids
 
     def query(
         self, semantic_ids: torch.Tensor, residuals: torch.Tensor, items_to_query: int
@@ -349,7 +317,7 @@ def query(
             semantic_ids, residuals
         )
 
-        raw_item_ids = self.get_closest(
+        raw_item_ids = self.get_closest_vectorized(
             outer_masks,
             inner_masks,
             outer_levels,

From 62924ecc8bbadadcabfdbb347d786380c4d5b049 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 19:51:44 +0300
Subject: [PATCH 091/175] rename todopk

---
 modeling/dataset/base.py           | 2 +-
 modeling/loss/base.py              | 2 +-
 modeling/models/sasrec_freezed.py  | 4 ++--
 modeling/models/sasrec_semantic.py | 2 +-
 modeling/models/tiger.py           | 8 ++++----
 modeling/rqvae_utils/trie.py       | 4 ++--
 6 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index e375f6ac..38a704c1 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -269,7 +269,7 @@ def create_from_config(cls, config, **kwargs):
         
     @classmethod
     def flatten_item_sequence(cls, item_ids):
-        min_history_length = 3 # TODO: make this configurable
+        min_history_length = 3 # TODOPK make this configurable
         histories = []
         for i in range(min_history_length-1, len(item_ids)):
             histories.append(item_ids[:i+1])
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 17200fcd..10f9e390 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -86,7 +86,7 @@ def forward(self, inputs):  # use log soft max
             loss = -torch.gather(
                 torch.log_softmax(logits, dim=-1),
                 dim=-1, 
-                index=candidates.view(batch_size, 1) # TODO check if this is correct
+                index=candidates.view(batch_size, 1) # TODOPK check if this is correct
             ).mean()
             
         return loss
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 3334c33b..6c12caf2 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -65,7 +65,7 @@ def __init__(
             num_embeddings=num_items + 2, embedding_dim=precomputed_embeddings.shape[1]
         )
 
-        # TODO ask about freezed masked & padding tokens
+        # TODOPK ask about freezed masked & padding tokens
 
         self._item_embeddings.weight.data.copy_(extended_embeddings)
         self._item_embeddings.weight.requires_grad = False
@@ -85,7 +85,7 @@ def create_from_config(cls, config, **kwargs):
             initializer_range=config.get("initializer_range", 0.02),
         )
 
-    def get_item_embeddings(self, events=None):  # TODO refactor (single projection)
+    def get_item_embeddings(self, events=None):
         if events is None:
             return self._projector(self._item_embeddings.weight)
         else:
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 8553aced..57b0c546 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -165,7 +165,7 @@ def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
             return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
 
-        # TODO +1 for residual embedding
+        # TODOPK +1 for residual embedding
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._position_embeddings
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 6b3a4fde..6fe9a910 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -76,7 +76,7 @@ def __init__(
             [codebook for codebook in rqvae_model.codebooks]
         )
         self._codebook_item_embeddings_stacked.requires_grad = (
-            False  # TODO maybe unfreeeze later
+            False  # TODOPK maybe unfreeeze later
         )
 
         self._item_id_to_semantic_id = item_id_to_semantic_id
@@ -193,8 +193,8 @@ def forward(self, inputs):
 
             label_lengths = label_lengths * (
                 len(self._codebook_sizes) + 1
-            )  # TODO bos prepending
-            tgt_embeddings = self.get_item_embeddings(  # TODO residual embs
+            )  # TODOPK bos prepending
+            tgt_embeddings = self.get_item_embeddings(
                 label_events
             )  # (all_batch_events, embedding_dim)
 
@@ -383,7 +383,7 @@ def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
             return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
 
-        # TODO +1 for residual embedding
+        # TODOPK +1 for residual embedding
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._position_embeddings
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index d80da941..b7971e86 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -112,7 +112,7 @@ def get_residuals_per_level(
             bs, self.K + 1, embedding_dim, device=self.embedding_table.device
         )  # bs x K + 1 x embedding_dim
 
-        # TODO think if reverse is needed here
+        # TODOPK think if reverse is needed here
         # i = 3, 2, 1, 0
         for i in range(self.K - 1, -1, -1):
             indices_at_level = semantic_ids[:, i]  # bs
@@ -124,7 +124,7 @@ def get_residuals_per_level(
                 embeddings_at_level + residuals_per_level[:, self.K - i - 1, :]
             )  # [0 first_cumul_emb, second, ..., full_emb]
 
-        # TODO check that residuals_per_level equal at last layer to full embedding of semantic id
+        # TODOPK check that residuals_per_level equal at last layer to full embedding of semantic id
 
         residuals_per_level[:, 0, :] = residuals
 

From de106df15d07dad2a5c11dceb0bff4b36f741f13 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 21:15:21 +0300
Subject: [PATCH 092/175] fix decoder codebook

---
 modeling/models/tiger.py | 54 +++++++++++++++++++++++++---------------
 1 file changed, 34 insertions(+), 20 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 6fe9a910..4c8c0374 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -234,9 +234,7 @@ def forward(self, inputs):
             residuals = tgt_embeddings[:, -1, :]
             semantic_ids = semantic_ids.to(torch.int64)
 
-            item_ids = self._trie.query(
-                semantic_ids, residuals, items_to_query=20
-            )
+            item_ids = self._trie.query(semantic_ids, residuals, items_to_query=20)
 
             return item_ids
 
@@ -252,7 +250,9 @@ def _apply_decoder(
             batch_size, 1, -1
         )  # (batch_size, 1, embedding_dim)
 
-        tgt_embeddings = torch.cat([bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1)
+        tgt_embeddings = torch.cat(
+            [bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1
+        )  # remove residual by using :-1
 
         label_len = tgt_mask.shape[1]
 
@@ -287,21 +287,32 @@ def _apply_decoder(
 
     def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
         batch_size = encoder_embeddings.shape[0]
+        embedding_dim = encoder_embeddings.shape[2]
 
         tgt_embeddings = (
-            self._bos_weight.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, -1)
+            self._bos_weight.unsqueeze(0)
+            .unsqueeze(0)
+            .expand(batch_size, 1, embedding_dim)
         )
-        tgt_mask = torch.ones(batch_size, 1, dtype=torch.bool, device=DEVICE)
 
         semantic_ids = torch.tensor([], device=DEVICE)
 
-        for step in range(len(self._codebook_sizes) + 1):  # semantic_id + residual
-            position_embeddings = self._decoder_pos_embeddings(
-                torch.full((batch_size,), tgt_embeddings.shape[1], device=DEVICE),
-                tgt_mask,
-            )
+        for step in range(len(self._codebook_sizes) + 1):  # semantic_id_seq + residual
+            if step == 0:
+                indexes = torch.full(
+                    (batch_size, 1), len(self._codebook_sizes), device=DEVICE
+                )  # len(self._codebook_sizes) for bos
+            else:
+                indexes = torch.cat(
+                    [indexes, torch.full((batch_size, 1), step - 1, device=DEVICE)],
+                    dim=1,
+                )
 
-            curr_embeddings = tgt_embeddings + position_embeddings
+            position_embeddings = self._codebook_embeddings(indexes.view(-1))
+
+            curr_embeddings = tgt_embeddings + position_embeddings.view(
+                batch_size, step + 1, embedding_dim
+            )
 
             curr_embeddings = self._decoder_layernorm(curr_embeddings)
             curr_embeddings = self._decoder_dropout(curr_embeddings)
@@ -316,6 +327,12 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                 :, -1, :
             ]  # batch_size x embedding_dim
 
+            # TODOPK ask if we take last
+            # torch.Size([256, 1, 64])
+            # torch.Size([256, 2, 64])
+            # torch.Size([256, 3, 64])
+            # torch.Size([256, 4, 64])
+
             if step < len(self._codebook_sizes):
                 codebook = self._codebook_item_embeddings_stacked[
                     step
@@ -333,9 +350,6 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             tgt_embeddings = torch.cat(
                 [tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1
             )
-            tgt_mask = torch.ones(
-                batch_size, tgt_embeddings.shape[1], dtype=torch.bool, device=DEVICE
-            )
 
         return semantic_ids, tgt_embeddings
 
@@ -381,9 +395,9 @@ def get_init_item_embeddings(self, events):
 
     def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
-            return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
-
-        # TODOPK +1 for residual embedding
+            return x // (
+                len(self._codebook_sizes) + 1
+            )  # 5 5 5 5 4 4 4 4 ..., +1 for residual
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._position_embeddings
@@ -392,7 +406,7 @@ def position_lambda(x):
         def codebook_lambda(x):
             x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
             x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
-            # 0 1 2 3 5 0 1 2 3 5 ... # len(self._codebook_sizes) for bos, len(self._codebook_sizes) + 1 for residual
+            # 0 1 2 4 0 1 2 4 ... # len(self._codebook_sizes) + 1 = 4 for residual
             return x
 
         codebook_embeddings = self._get_position_embeddings(
@@ -405,7 +419,7 @@ def _decoder_pos_embeddings(self, lengths, mask):
         def codebook_lambda(x):
             non_bos = x < len(self._codebook_sizes)
             x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
-            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ...
+            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ... len(self._codebook_sizes) = 3 for bos
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings

From e1975a20877e090a414e8c9a98e306fb5e23bb63 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Sat, 15 Feb 2025 20:30:17 +0300
Subject: [PATCH 093/175] =?UTF-8?q?=D1=82=D0=B5=D1=81=D1=82=20rqvae?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 configs/train/rqvae_train_config.json |  2 +-
 modeling/rqvae_utils/rqvae_test.py    | 51 +++++++++++++++++++++++++++
 modeling/utils/__init__.py            |  2 +-
 3 files changed, 53 insertions(+), 2 deletions(-)
 create mode 100644 modeling/rqvae_utils/rqvae_test.py

diff --git a/configs/train/rqvae_train_config.json b/configs/train/rqvae_train_config.json
index 5531c552..8b7d8bd9 100644
--- a/configs/train/rqvae_train_config.json
+++ b/configs/train/rqvae_train_config.json
@@ -1,6 +1,6 @@
 {
   "experiment_name": "rqvae_beauty",
-  "train_epochs_num": 50,
+  "train_epochs_num": 200,
   "dataset": {
     "type": "rqvae",
     "path_to_data_dir": "../data",
diff --git a/modeling/rqvae_utils/rqvae_test.py b/modeling/rqvae_utils/rqvae_test.py
new file mode 100644
index 00000000..611e7739
--- /dev/null
+++ b/modeling/rqvae_utils/rqvae_test.py
@@ -0,0 +1,51 @@
+import json
+
+import numpy as np
+import torch
+
+from models import RqVaeModel
+from utils import DEVICE
+
+def test(a, b):
+    cos_sim = torch.nn.functional.cosine_similarity(a, b, dim=0)
+    norm_a = torch.norm(a, p=2)
+    norm_b = torch.norm(b, p=2)
+    l2_dist = torch.norm(a - b, p=2) / (norm_a + norm_b + 1e-8)
+    return cos_sim, l2_dist
+
+if __name__ == "__main__":
+    config = json.load(open("../configs/train/tiger_train_config.json"))
+    config = config["model"]
+    rqvae_config = json.load(open(config["rqvae_train_config_path"]))
+    rqvae_config["model"]["should_init_codebooks"] = False
+    rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
+    rqvae_model.load_state_dict(
+        torch.load(config["rqvae_checkpoint_path"], weights_only=True)
+    )
+    df = torch.load(config["embs_extractor_path"], weights_only=False)
+    embeddings_array = np.stack(df["embeddings"].values)
+    tensor_embeddings = torch.tensor(embeddings_array, dtype=torch.float32, device=DEVICE)
+    inputs = {'embeddings': tensor_embeddings}
+
+    rqvae_model.eval()
+    sem_ids, residuals = rqvae_model.forward(inputs)
+    scores = residuals.detach()
+    print(torch.norm(residuals, p=2, dim=1).median())
+    for (i, codebook) in enumerate(rqvae_model.codebooks):
+        scores += codebook[sem_ids[:, i]].detach()
+    decoder_output = rqvae_model.decoder(scores.detach()).detach()
+
+    a = tensor_embeddings[0]
+    b = decoder_output[0]
+    cos_sim, l2_dist = test(a, b)
+    print("косинусное расстояние", cos_sim)
+    print("евклидово расстояние", l2_dist)
+
+    cos_sim = torch.nn.functional.cosine_similarity(tensor_embeddings, decoder_output, dim=1)
+    print("косинусное расстояние", cos_sim.mean(), cos_sim.min(), cos_sim.max())
+
+    norm_a = torch.norm(tensor_embeddings, p=2, dim = 1)
+    norm_b = torch.norm(decoder_output, p=2, dim = 1)
+    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim = 1) / (norm_a + norm_b + 1e-8)
+    print("евклидово расстояние",l2_dist.median(), l2_dist.min(), l2_dist.max())
+
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 5cdc705a..59101ca4 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -13,7 +13,7 @@
 if torch.cuda.is_available():
     DEVICE = torch.device('cuda')
 elif torch.backends.mps.is_available():
-    DEVICE = torch.device("mps")
+    DEVICE = torch.device("mps:0")
 else:
     DEVICE = torch.device('cpu')
 

From f568d3c483cfca6fbfeb71486ddab738b58ae934 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Sun, 16 Feb 2025 20:45:38 +0300
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---
 modeling/rqvae_utils/__init__.py         |   2 +
 modeling/rqvae_utils/collision_solver.py | 125 +++-----------
 modeling/rqvae_utils/simplified_tree.py  |  48 ++++++
 modeling/rqvae_utils/tree.py             | 204 +++++++++++++++++++++++
 modeling/rqvae_utils/tree_comparing.py   | 109 ++++++++++++
 5 files changed, 387 insertions(+), 101 deletions(-)
 create mode 100644 modeling/rqvae_utils/simplified_tree.py
 create mode 100644 modeling/rqvae_utils/tree.py
 create mode 100644 modeling/rqvae_utils/tree_comparing.py

diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
index 7764a752..2722515a 100644
--- a/modeling/rqvae_utils/__init__.py
+++ b/modeling/rqvae_utils/__init__.py
@@ -1,2 +1,4 @@
 from .collision_solver import CollisionSolver
 from .trie import Trie
+from .tree import Tree
+from .simplified_tree import SimplifiedTree
\ No newline at end of file
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index 6821b33c..506de2ee 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -1,6 +1,8 @@
 from collections import defaultdict
+
 import torch
 
+
 class CollisionSolver:
     def __init__(self, residual_dim, emb_dim, codebook_size, device: torch.device = torch.device('cpu')):
         """
@@ -9,24 +11,15 @@ def __init__(self, residual_dim, emb_dim, codebook_size, device: torch.device =
         :param emb_dim: Длина semantic_id (без токена решающего коллизии)
         :param device: Устройство
         """
-        self._sem_ids_sparse_tensor = None #тензор группирирующий остатки по semantic_id
-        self.item_ids_sparse_tensor = None #тензор группирируюшщий реальные айди айтемов по semantic_id
-        self.counts_dict = defaultdict(int) #тензор храняющий количество коллизий по semantic_id
-        self.residual_dim = residual_dim #длина остатка
-        self.emb_dim = emb_dim #длина semantic_id
-        self.codebook_size = codebook_size #количество элементов в одном кодбуке
-        self.device = device #девайс
-        self.item_ids_dict = {} #словарь сопостовляющий каждому item_id его semantic_id и токен решающий коллизии
-
-        self.key = torch.tensor([self.codebook_size ** i for i in range(self.emb_dim)], dtype=torch.long, device=self.device) #ключ для сопоставления числа каждому semantic_id
-
-    def _to_device(self, tensor: torch.Tensor) -> torch.Tensor:
-        """
-        Перенос тензора на устройство
-        """
-        if tensor.device != self.device:
-            tensor = tensor.to(self.device)
-        return tensor
+        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0)) #тензор группирирующий остатки по semantic_id
+        self.item_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0)) #тензор группирируюшщий реальные айди айтемов по semantic_id
+        self.counts_dict: dict[int, int] = defaultdict(int) #тензор храняющий количество коллизий по semantic_id
+        self.residual_dim: int = residual_dim #длина остатка
+        self.emb_dim: int = emb_dim #длина semantic_id
+        self.codebook_size: int = codebook_size #количество элементов в одном кодбуке
+        self.device: torch.device = device #девайс
+
+        self.key: torch.Tensor = torch.tensor([self.codebook_size ** i for i in range(self.emb_dim)], dtype=torch.long, device=self.device) #ключ для сопоставления числа каждому semantic_id
 
     def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
         """
@@ -44,15 +37,16 @@ def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: tor
         assert residual_length == self.residual_dim
         assert item_ids.shape == (residuals_count,)
 
-        item_ids = self._to_device(item_ids)
-        residuals = self._to_device(residuals)
-        semantic_ids = self._to_device(semantic_ids)
+        item_ids = item_ids.to(self.device)
+        residuals = residuals.to(self.device)
+        semantic_ids = semantic_ids.to(self.device)
 
         unique_id = torch.einsum('nc,c->n', semantic_ids, self.key)
         unique_ids, inverse_indices = torch.unique(unique_id, return_inverse=True)
         sorted_indices = torch.argsort(inverse_indices)
         counts = torch.bincount(inverse_indices)
-        max_residuals_count = counts.max().item()
+        max_residuals_count = int(counts.max().item())
+        max_sid = int(self.codebook_size ** self.emb_dim)
         offsets = torch.cumsum(torch.cat((torch.tensor([0], dtype=torch.long, device=self.device), counts[:-1])), dim=0)
         row_indices = inverse_indices[sorted_indices]
         col_indices = torch.arange(semantic_ids_count) - offsets[row_indices]
@@ -61,21 +55,17 @@ def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: tor
             col_indices
         ], dim=0)
 
-        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices], size=(self.codebook_size ** self.emb_dim, max_residuals_count, self.residual_dim), device=self.device)
+        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices], size=(max_sid, max_residuals_count, self.residual_dim), device=self.device)
         self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))
 
-        item_id_indices = torch.stack((unique_ids[row_indices], col_indices))
+        item_id_indices: torch.Tensor = torch.stack((unique_ids[row_indices], col_indices))
 
-        self.item_ids_dict = {
-            item_id.item(): (sem_id_key.item(), dedup_token.item())
-            for item_id, (sem_id_key, dedup_token) in zip(item_ids[sorted_indices], torch.stack((unique_ids[row_indices], col_indices), dim=1))
-        }
-        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(item_id_indices, item_ids[sorted_indices], size=(self.codebook_size ** self.emb_dim, max_residuals_count), device=self.device, dtype=torch.int16)
+        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(item_id_indices, item_ids[sorted_indices], size=(max_sid, max_residuals_count), device=self.device, dtype=torch.int16)
 
     def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         assert semantic_ids.shape[1] == self.emb_dim
 
-        semantic_ids = self._to_device(semantic_ids)
+        semantic_ids = semantic_ids.to(self.device)
         unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
 
         candidates = torch.stack([self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids])
@@ -98,8 +88,8 @@ def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tens
         assert pred_residuals.shape[1] == self.residual_dim
         assert semantic_ids.shape[0] == pred_residuals.shape[0]
 
-        semantic_ids = self._to_device(semantic_ids)
-        pred_residuals = self._to_device(pred_residuals)
+        semantic_ids = semantic_ids.to(self.device)
+        pred_residuals = pred_residuals.to(self.device)
 
         unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
 
@@ -127,10 +117,8 @@ def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torc
         assert true_residuals.shape[1] == self.residual_dim
         assert semantic_ids.shape[0] == true_residuals.shape[0]
 
-        semantic_ids = self._to_device(semantic_ids)
-        true_residuals = self._to_device(true_residuals)
-
-        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+        semantic_ids = semantic_ids.to(self.device)
+        true_residuals = true_residuals.to(self.device)
 
         candidates, _ = self.get_residuals_by_semantic_id_batch(semantic_ids)
 
@@ -142,68 +130,3 @@ def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torc
         return {
             "true_dedup_tokens": true_dedup_tokens
         }
-
-
-    def get_item_id_info(self, item_id: int) -> dict[str, torch.Tensor]:
-        """
-        Возвращает информацию по заданному item_id:
-        - semantic_id
-        - Все элементы с таким же semantic_id
-        - Их item_ids
-        - Их остатки
-        - Токены, решающие коллизии (dedup tokens)
-
-        :param item_id: айди айтема
-
-        :return: Словарь с ключами:
-            - 'semantic_id': [emb_dim] semantic id
-            - 'residuals': [count, residual_dim] остатки
-            - 'item_ids': [count] item ids
-            - 'dedup_tokens': [count] токены решающие коллизии
-        """
-
-        if item_id not in self.item_ids_dict:
-            return {
-                "semantic_id": torch.empty(0, dtype=torch.long, device=self.device),
-                "residuals": torch.empty((0, self.residual_dim), device=self.device),
-                "item_ids": torch.empty(0, dtype=torch.int16, device=self.device),
-                "dedup_tokens": torch.empty(0, dtype=torch.long, device=self.device),
-            }
-
-        semantic_id_key, dedup_token = self.item_ids_dict[item_id]
-
-        semantic_id = torch.div(semantic_id_key, self.key, rounding_mode='floor') % self.codebook_size
-
-        assert semantic_id.shape == (self.emb_dim,)
-
-        candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_id[None])
-        residuals = candidates.squeeze(0)[mask.squeeze(0)]
-        item_ids = self.item_ids_sparse_tensor[semantic_id_key].to_dense()[mask.squeeze(0)]
-
-        dedup_tokens = torch.arange(residuals.shape[0], device=self.device)
-
-        return {
-            "semantic_id": semantic_id,
-            "residuals": residuals,
-            "item_ids": item_ids,
-            "dedup_tokens": dedup_tokens,
-        }
-
-    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> torch.Tensor:
-        """
-        :param semantic_id: [batch_size, emb_dim] semantic ids (без токенов решающего коллизии)
-        :param dedup_tokens: [batch_size] токены решающие коллизии
-
-        :return: item_ids : [batch_size] реальные айди айтемов
-        """
-        assert semantic_ids.shape[1] == self.emb_dim
-        assert dedup_tokens.shape == (semantic_ids.shape[0],)
-
-        semantic_ids = self._to_device(semantic_ids)
-        dedup_tokens = self._to_device(dedup_tokens)
-
-        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
-
-        item_ids = torch.stack([self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])
-
-        return item_ids
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
new file mode 100644
index 00000000..9c19e3c4
--- /dev/null
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -0,0 +1,48 @@
+import torch
+
+
+class SimplifiedTree:
+    def __init__(self, embedding_table: torch.Tensor, device: torch.device = torch.device('cpu')):
+        """
+        :param embedding_table: Тензор из RQ-VAE # (semantic_id_len, codebook_size, emb_dim)
+        :param device: Устройство
+        """
+        self.embedding_table: torch.Tensor = embedding_table.to(device)  # (semantic_id_len, codebook_size, emb_dim)
+        self.sem_id_len, self.codebook_size, self.emb_dim = embedding_table.shape
+        self.device: torch.device = device
+        self.sem_ids_count: int = 0
+        self.full_embeddings: torch.Tensor = torch.empty((0, 0))
+
+    def init_tree(self, embeddings: torch.Tensor) -> None:
+        """
+        :param embeddings: тензор эмбеддингов для каждого из semantic ids (sem_ids_count, emb_dim)
+        """
+        assert embeddings.shape[1] == self.emb_dim
+        self.full_embeddings = embeddings.to(self.device)  # (sem_ids_count, emb_dim)
+        self.sem_ids_count = embeddings.shape[0]
+
+    def get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
+        """
+        :param request_sem_ids: батч из sem ids (batch_size, sem_id_len)
+        :param k: количество ближайших элементов которые нужно взять (int)
+        :return: тензор индексов ближайших k элементов из всех semantic_ids для каждого sem_id из батча (batch_size, k)
+        """
+        assert request_sem_ids.shape[1] == self.sem_id_len
+        assert 0 < k <= self.sem_ids_count
+        request_sem_ids = request_sem_ids.to(self.device)
+
+        expanded_emb_table = (self.embedding_table.unsqueeze(0)
+                              .expand(request_sem_ids.shape[0], -1, -1,
+                                      -1))  # (batch_size, sem_id_len, codebook_size, emb_dim)
+
+        index = (request_sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (batch_size, sem_id_len, 1, emb_dim)
+
+        request_embeddings = (torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1)
+                              .expand(-1, self.sem_ids_count, -1))  # (batch_size, sem_ids_count, emb_dim)
+
+        diff_norm = torch.norm(self.full_embeddings - request_embeddings, p=2, dim=2)  # (batch_size, sem_ids_count)
+
+        indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :k]  # (batch_size, k)
+        return indices
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
new file mode 100644
index 00000000..928533e5
--- /dev/null
+++ b/modeling/rqvae_utils/tree.py
@@ -0,0 +1,204 @@
+import numpy as np
+import torch
+
+from utils import DEVICE
+
+
+class Tree:
+    def __init__(self, embedding_table, device: torch.device = DEVICE):
+        """
+        :param embedding_table: Тензор из RQ-VAE # (semantic_id_len, codebook_size, emb_dim)
+        :param device: Устройство
+        """
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
+        self.sem_id_len, self.codebook_size, self.emb_dim = embedding_table.shape
+        self.device: torch.device = device
+        self.key: torch.Tensor = torch.empty((0, 0))
+        self.A: torch.Tensor = torch.empty((0, 0))  # будет (max_sem_id, )
+        self.sem_ids_count: int = -1
+        self.sem_ids_embs: torch.Tensor = torch.empty((0, 0))
+        self.sids: torch.Tensor = torch.empty((0, 0))  # будет (sem_id_len, )
+
+    def init_tree(self, semantic_ids, residuals):
+        """
+        :param semantic_ids: (sem_ids_count, sem_id_len)
+        :param residuals: (sem_ids_count, emb_dim)
+        """
+
+        assert semantic_ids.shape[0] == residuals.shape[0]
+        assert semantic_ids.shape[1] == self.sem_id_len
+        assert residuals.shape[1] == self.emb_dim
+
+        self.sem_ids_count = semantic_ids.shape[0]
+        self.key = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len - 1, -1, -1)],
+                                dtype=torch.long, device=self.device)
+        self.sids = self.get_sids(semantic_ids.float())  # (sem_id_len, )
+        self.sem_ids_embs = self.calculate_full(semantic_ids, residuals)
+
+        result = torch.full(size=[self.codebook_size ** self.sem_id_len], fill_value=0, dtype=torch.int64,
+                            device=self.device)
+        temp_unique_id = self.sids * self.codebook_size
+        temp_sem_ids = torch.concat([semantic_ids, torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1)],
+                                    dim=-1)
+
+        for i in range(0, self.sem_id_len + 1):
+            temp_unique_id = temp_unique_id - (self.codebook_size ** i) * temp_sem_ids[:, self.sem_id_len - i]
+            temp_unique_ids, temp_inverse_indices = torch.unique(temp_unique_id, return_inverse=True)
+            temp_counts = torch.bincount(temp_inverse_indices)
+            truncated_ids = torch.floor_divide(input=temp_unique_id, other=(self.codebook_size ** (i + 1))).long()
+            result[truncated_ids] = temp_counts[temp_inverse_indices]
+
+        self.A = result
+
+    def get_counts(self, sem_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param sem_ids: (batch_size, sem_id_len)
+        :return: префиксы всех длин sem_ids, количество sem_id на каждой глубине дерева
+        """
+        assert sem_ids.shape[1] == self.sem_id_len
+        assert sem_ids.device == self.device
+
+        offsets = torch.arange(self.sem_id_len + 1, device=self.device)
+        i = torch.arange(self.sem_id_len, device=self.device)
+
+        mask_sem = (i < (self.sem_id_len - offsets.unsqueeze(1))).long()  # (sem_id_len + 1, sem_id_len)
+        divs = torch.pow(self.codebook_size, offsets)  # (sem_id_len + 1,)
+
+        C = (sem_ids.unsqueeze(1) * mask_sem.unsqueeze(0) * self.key.unsqueeze(0).unsqueeze(1)).sum(dim=-1)
+        B = C // divs.unsqueeze(0)
+
+        return C, self.A[B]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
+
+    def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
+        """
+        :param sem_ids: (sem_id_count, sem_id_len)
+        :return: хэши sem_ids (sem_id_count,)
+        """
+        assert sem_ids.shape[1] == self.sem_id_len
+        return torch.einsum('nc,c->n', sem_ids, self.key.float())  # (sem_ids_count,)
+
+    def calc_ol(self, batch_ids: torch.Tensor, k: int) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        :param batch_ids: (batch_size, sem_id_len)
+        :param k: int
+        :return: тензор глубин на которые нужно подняться (batch_size,), маска для sem_id для нужной глубины (batch_size, sem_ids_count)
+        """
+        assert batch_ids.shape[1] == self.sem_id_len
+        assert k < self.sem_ids_count  # корректный сценарий когда тензор не пустой
+
+        c, a = self.get_counts(batch_ids)
+        ol = torch.argmax((a > k).long(), dim=-1)  # (bs,)
+        gather_ol = torch.gather(c, dim=1, index=ol.unsqueeze(1)).squeeze()  # (bs,)
+
+        mask_ol = (gather_ol.unsqueeze(-1) <= self.sids) & (
+                self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1))
+        return ol, mask_ol  # (bs,) (bs, sem_ids_count)
+
+    def calc_il(self, batch_ids, k):
+        """
+        :param batch_ids: (batch_size, sem_id_len)
+        :param k: int
+        :return: тензор глубин на которые нужно подняться (batch_size,), маска для sem_id для нужной глубины (batch_size, sem_ids_count)
+        """
+        assert batch_ids.shape[1] == self.sem_id_len
+        assert k < self.sem_ids_count  # корректный сценарий когда тензор не пустой
+
+        batch_dim = batch_ids.shape[0]
+        c, a = self.get_counts(batch_ids)
+        extended_c = torch.concat([torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c], dim=1)
+
+        il = torch.argmax((a > k).long(), dim=-1) - 1  # (bs,)
+        gather_il = torch.gather(extended_c, dim=1, index=(il + 1).unsqueeze(1)).squeeze()  # (bs,)
+
+        mask_il = (gather_il.unsqueeze(-1) <= self.sids) & (
+                self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1))
+        return il, mask_il  # (bs,) (bs, sem_ids_count)
+
+    def get_repeated_sids(self, k: int) -> torch.Tensor:
+        return self.sids.repeat(k, 1)  # (k, sem_ids_count)
+
+    def get_request_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
+        """
+        :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
+        :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
+        :return: эмбеддинги sem_id для нужных глубин (count, emb_dim)
+        """
+        assert decomposed_embeddings.shape[1:] == (self.sem_id_len + 1, self.emb_dim)
+        assert levels.shape == (decomposed_embeddings.shape[0],)
+
+        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(self.sem_id_len + 2, 0, -1,
+                                                                                        device=self.device).unsqueeze(1)
+        return torch.sum(decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1)  # (bs, emb_dim)
+
+    def calculate_full(self, sem_ids: torch.Tensor, residuals: torch.Tensor) -> torch.Tensor:
+        """
+        :param sem_ids: sem_ids (count, sem_id_len)
+        :param residuals: остатки для каждого sem_id (count, emb_dim)
+        :return: полные эмбеддинги для каждого айтема (count, emb_dim)
+        """
+        assert sem_ids.shape[1] == self.sem_id_len
+        assert residuals.shape[1] == self.emb_dim
+        assert residuals.shape[0] == sem_ids.shape[0]
+
+        count = residuals.shape[0]
+        index = sem_ids.view(count, -1, 1, 1).expand(-1, -1, -1, self.emb_dim)
+        embs = torch.gather(input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1), dim=2,
+                            index=index)  # expand бесплатный по памяти
+        decomposed_embs = torch.concat([embs.squeeze(2), residuals.unsqueeze(1)], dim=1)  # (sem_ids_count, emb_dim)
+
+        assert decomposed_embs.shape == (sem_ids.shape[0], self.sem_id_len + 1, self.emb_dim)
+        return decomposed_embs
+
+    def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
+        """
+        :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
+        :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
+        :return: эмбеддинги для всех sem_ids для нужных глубин (batch_size, sem_ids_count, emb_dim)
+        """
+        assert decomposed_embeddings.shape == (self.sem_ids_count, self.sem_id_len + 1, self.emb_dim)
+
+        mask = (torch.arange(1, self.sem_id_len + 2, device=self.device) >=
+                torch.arange(self.sem_id_len + 2, 0, -1,device=self.device).unsqueeze(1)).float()
+        sids_mask = mask[levels + 1].unsqueeze(-1)  # (batch_size, sem_id_len + 1, 1)
+        return torch.einsum('nld,bld->bnd', decomposed_embeddings, sids_mask)  # (batch_size, sem_ids_count, emb_dim)
+
+    def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+        return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
+
+    def get_ids(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor, k: int) -> torch.Tensor:
+        """
+        :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
+        :param request_residuals: батч из остатков (batch_size, emb_dim)
+        :param k: количество ближайших элементов которые нужно взять int
+        :return: тензор индексов ближайших k элементов из всех semantic_ids для каждого sem_id из батча (batch_size, k)
+        """
+        assert request_sem_ids.shape[0] == request_residuals.shape[0]
+        assert request_sem_ids.shape[1] == self.sem_id_len
+        assert request_residuals.shape[1] == self.emb_dim
+        assert 0 <= k < self.sem_ids_count
+
+        ol, ol_mask = self.calc_ol(request_sem_ids, k)
+        il, il_mask = self.calc_il(request_sem_ids, k)
+
+        il_mask = il_mask.detach().cpu()
+        ol_mask = ol_mask.detach().cpu()
+
+        ol_mask = ol_mask & ~il_mask
+
+        request_embs = self.calculate_full(request_sem_ids, request_residuals)
+
+        ol_sids_embeddings = self.calculate_level_embeddings(self.sem_ids_embs, ol)
+        il_sids_embeddings = self.calculate_level_embeddings(self.sem_ids_embs, il)
+
+        ol_request_embeddings = self.get_request_embeddings(request_embs, ol)
+        il_request_embeddings = self.get_request_embeddings(request_embs, il)
+
+        ol_scores = torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
+
+        il_scores = torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
+
+        ids = np.lexsort(keys=(-torch.cat([il_scores, ol_scores], dim=1),
+                               ~torch.cat([torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1),
+                               ~torch.cat([il_mask, ol_mask], dim=1)))
+
+        return (ids % self.sem_ids_count)[:, :self.sem_ids_count][:, :k]  # (batch_size, k)
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
new file mode 100644
index 00000000..9a9b3822
--- /dev/null
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -0,0 +1,109 @@
+import json
+import os
+import time
+
+import psutil
+import torch
+
+from models.rqvae import RqVaeModel
+from rqvae_utils import Trie, SimplifiedTree, Tree
+from utils import DEVICE
+
+
+def memory_stats(k):
+    process = psutil.Process(os.getpid())
+    memory_usage = process.memory_info().rss / 1024 ** 2
+    print(f"{k}. Использование памяти: {memory_usage:.2f} MB")
+
+
+def calc_sid(sid, codebook_size):
+    res = sid[-1]
+    for i in range(1, sid.shape[0]):
+        res += sid[-i - 1] * (codebook_size ** i)
+    return res
+
+
+def stats(query_sem_id, codebook_size, sids, item_ids):
+    for sem_id, ids in zip(query_sem_id.tolist(), item_ids.tolist()):
+        print(calc_sid(torch.tensor(sem_id), codebook_size))
+        print(sids[torch.tensor(ids)])
+
+
+if __name__ == "__main__":
+    embedding_dim = 64  # Embedding size
+    config = json.load(open("../configs/train/tiger_train_config.json"))
+    config = config["model"]
+    rqvae_config = json.load(open(config["rqvae_train_config_path"]))
+    rqvae_config["model"]["should_init_codebooks"] = False
+    rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
+    rqvae_model.load_state_dict(
+        torch.load(config["rqvae_checkpoint_path"], weights_only=True)
+    )
+    rqvae_model.eval()
+
+    emb_table = torch.stack(
+        [cb for cb in rqvae_model.codebooks]
+    ).to(DEVICE)
+
+    trie = Trie(rqvae_model)
+    tree = Tree(emb_table, DEVICE)
+    simplified_tree = SimplifiedTree(emb_table, DEVICE)
+    alphabet_size = 10
+
+    N = 12101
+    K = 3
+
+    semantic_ids = torch.randint(0, alphabet_size, (N, K), dtype=torch.int64).to(DEVICE)
+    residuals = torch.randn(N, embedding_dim).to(DEVICE)
+
+    now = time.time()
+    trie.build_tree_structure(semantic_ids, residuals, torch.arange(N).to(DEVICE))
+    print(f"Time for trie init: {(time.time() - now) * 1000:.2f} ms")
+
+    now = time.time()
+    tree.init_tree(semantic_ids, residuals)
+    print(f"Time for tree init: {(time.time() - now) * 1000:.2f} ms")
+
+    now = time.time()
+    full_embeddings = tree.calculate_full(semantic_ids, torch.zeros_like(residuals)).sum(1)
+    simplified_tree.init_tree(full_embeddings)
+    print(f"Time for simplified tree init: {(time.time() - now) * 1000:.2f} ms")
+
+    items_to_query = 20
+    batch_size = 256
+    q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE)
+    q_residuals = torch.randn(batch_size, embedding_dim).to(DEVICE)
+
+    total_time = 0
+    n_exps = 1
+
+    memory_stats(1)
+    for i in range(n_exps):
+        now = time.time()
+        item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
+        total_time += time.time() - now
+        stats(q_semantic_ids[:3], 256, tree.sids, item_ids[:3])
+
+    print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
+
+    memory_stats(2)
+
+    for i in range(n_exps):
+        now = time.time()
+        simplified_tree_ids = simplified_tree.get_ids(q_semantic_ids, items_to_query)
+        total_time += time.time() - now
+        stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
+
+    print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
+
+    memory_stats(3)
+
+    for i in range(n_exps):
+        now = time.time()
+        tree_ids = tree.get_ids(q_semantic_ids, q_residuals, items_to_query)
+        total_time += time.time() - now
+        stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
+
+    print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
+
+    memory_stats(4)

From c97e0a92e78ea282251bffc8a9c509d6fd97aa18 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 21:43:27 +0300
Subject: [PATCH 095/175] add sasrec_semantic

---
 .../train/sasrec_semantic_train_config.json   |   3 +
 modeling/models/__init__.py                   |   1 +
 modeling/models/sasrec_semantic.py            | 179 ++++++++++++------
 modeling/models/tiger.py                      |  18 +-
 4 files changed, 128 insertions(+), 73 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 47c1a9eb..1cdf6762 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -34,6 +34,9 @@
   },
   "model": {
     "type": "sasrec_semantic",
+    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+    "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+    "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
     "sequence_prefix": "item",
     "positive_prefix": "labels",
     "negative_prefix": "negative",
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index b9678e2e..e03dc42d 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -14,6 +14,7 @@
 from .random import RandomModel
 from .sasrec import SasRecModel, SasRecInBatchModel
 from .sasrec_freezed import SasRecFreezedModel
+from .sasrec_semantic import SasRecSemanticModel
 from .sasrec_ce import SasRecCeModel
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 57b0c546..a36513de 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -1,11 +1,16 @@
 import torch
+from .tiger import TigerModel
 from models import SequentialTorchModel
-from utils import create_masked_tensor
+from utils import DEVICE, create_masked_tensor
+from torch import nn
 
 
 class SasRecSemanticModel(SequentialTorchModel, config_name="sasrec_semantic"):
     def __init__(
         self,
+        rqvae_model,
+        item_id_to_semantic_id,
+        item_id_to_residual,
         sequence_prefix,
         positive_prefix,
         num_items,
@@ -34,11 +39,37 @@ def __init__(
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
 
+        self._codebook_sizes = rqvae_model.codebook_sizes
+        self._codebook_item_embeddings_stacked = torch.stack(
+            [codebook for codebook in rqvae_model.codebooks]
+        )
+        self._codebook_item_embeddings_stacked.requires_grad = (
+            False  # TODOPK maybe unfreeeze later
+        )
+
+        self._item_id_to_semantic_id = item_id_to_semantic_id
+        self._item_id_to_residual = item_id_to_residual
+
+        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
+
+        self._item_id_to_semantic_embedding, self._item_id_to_full_embedding = (
+            self.get_init_item_embeddings(item_ids)
+        )
+
+        self._codebook_embeddings = nn.Embedding(
+            num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
+        )  # + 2 for bos token & residual
+
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
+        rqvae_model, semantic_ids, residuals, _ = TigerModel.init_rqvae(config)
+
         return cls(
+            rqvae_model=rqvae_model,
+            item_id_to_semantic_id=semantic_ids,
+            item_id_to_residual=residuals,
             sequence_prefix=config["sequence_prefix"],
             positive_prefix=config["positive_prefix"],
             num_items=kwargs["num_items"],
@@ -50,47 +81,6 @@ def create_from_config(cls, config, **kwargs):
             dropout=config.get("dropout", 0.0),
             initializer_range=config.get("initializer_range", 0.02),
         )
-        
-    
-    def get_item_embeddings(self, events):
-        embs = self._item_id_to_semantic_embedding[
-            events - 1
-        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.view(
-            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
-        )
-
-    def get_init_item_embeddings(self, events):
-        # convert to semantic ids
-        semantic_ids = self._item_id_to_semantic_id[
-            events - 1
-        ]  # len(events), len(codebook_sizes)
-
-        result = []
-        for semantic_id in semantic_ids:
-            item_repr = []
-            for codebook_idx, codebook_id in enumerate(semantic_id):
-                item_repr.append(
-                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
-                )
-            result.append(torch.stack(item_repr))
-
-        semantic_embeddings = torch.stack(result)
-
-        # get residuals
-        residual = self._item_id_to_residual[events - 1]
-        # text_embeddings = self._item_id_to_text_embedding[events - 1]
-        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
-        residual = residual.unsqueeze(1)
-
-        # get true item embeddings
-        item_embeddings = torch.cat(
-            [semantic_embeddings, residual], dim=1
-        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-
-        # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
-
-        return item_embeddings
 
     def forward(self, inputs):
         all_sample_events = inputs[
@@ -100,9 +90,8 @@ def forward(self, inputs):
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
-        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
         embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
+            all_sample_events, all_sample_lengths * (len(self._codebook_sizes) + 1)
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         if self.training:  # training mode
@@ -115,13 +104,13 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             all_embeddings = (
-                self.get_item_embeddings()
-            )  # (num_items + 2, embedding_dim)
+                self._item_id_to_full_embedding
+            )  # (num_items, embedding_dim)
 
-            # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
+            # a -- all_batch_events, n -- num_items, d -- embedding_dim
             all_scores = torch.einsum(
                 "ad,nd->an", last_embeddings, all_embeddings
-            )  # (batch_size, num_items + 2)
+            )  # (batch_size, num_items)
 
             positive_scores = torch.gather(
                 input=all_scores, dim=1, index=all_positive_sample_events[..., None]
@@ -136,9 +125,7 @@ def forward(self, inputs):
                 dim=1,
                 index=sample_ids,
                 src=torch.ones_like(sample_ids) * (-torch.inf),
-            )  # (all_batch_events, num_items + 2)
-            negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
+            )  # (all_batch_events, num_items)
 
             return {
                 "positive_scores": positive_scores,
@@ -150,22 +137,60 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
-            )  # (batch_size, num_items + 2)
-            candidate_scores[:, 0] = -torch.inf  # Padding id
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
+                "bd,nd->bn", last_embeddings, self._item_id_to_full_embedding
+            )  # (batch_size, num_items)
 
             _, indices = torch.topk(
                 candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
-        
+
+    def get_item_embeddings(self, events):
+        embs = self._item_id_to_semantic_embedding[
+            events - 1
+        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        return embs.view(
+            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
+        )
+
+    def get_init_item_embeddings(self, events):
+        # convert to semantic ids
+        semantic_ids = self._item_id_to_semantic_id[
+            events - 1
+        ]  # len(events), len(codebook_sizes)
+
+        result = []
+        for semantic_id in semantic_ids:
+            item_repr = []
+            for codebook_idx, codebook_id in enumerate(semantic_id):
+                item_repr.append(
+                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
+                )
+            result.append(torch.stack(item_repr))
+
+        semantic_embeddings = torch.stack(result)
+
+        # get residuals
+        residual = self._item_id_to_residual[events - 1]
+        # text_embeddings = self._item_id_to_text_embedding[events - 1]
+        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = residual.unsqueeze(1)
+
+        # get true item embeddings
+        item_embeddings = torch.cat(
+            [semantic_embeddings, residual], dim=1
+        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+
+        full_embeddings = item_embeddings.sum(dim=1)
+
+        return item_embeddings, full_embeddings
+
     def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):
-            return x // (len(self._codebook_sizes) + 1)  # 5 5 5 4 4 4 3 3 3 ...
-
-        # TODOPK +1 for residual embedding
+            return x // (
+                len(self._codebook_sizes) + 1
+            )  # 5 5 5 5 4 4 4 4 ..., +1 for residual
 
         position_embeddings = self._get_position_embeddings(
             lengths, mask, position_lambda, self._position_embeddings
@@ -174,11 +199,43 @@ def position_lambda(x):
         def codebook_lambda(x):
             x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
             x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
-            # 0 1 2 3 5 0 1 2 3 5 ... # len(self._codebook_sizes) for bos, len(self._codebook_sizes) + 1 for residual
+            # 0 1 2 4 0 1 2 4 ... # len(self._codebook_sizes) + 1 = 4 for residual
             return x
 
         codebook_embeddings = self._get_position_embeddings(
             lengths, mask, codebook_lambda, self._codebook_embeddings
         )
-        
+
         return position_embeddings + codebook_embeddings
+
+    def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
+        batch_size = mask.shape[0]
+        seq_len = mask.shape[1]
+
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=DEVICE)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
+        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
+
+        positions = positions[positions_mask]  # (all_batch_events)
+        # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 ...
+
+        positions = position_lambda(positions)  # (all_batch_events)
+
+        # print(f"{positions.tolist()[:20]=}")
+
+        assert (positions >= 0).all() and (
+            positions < embedding_layer.num_embeddings
+        ).all()
+
+        position_embeddings = embedding_layer(
+            positions
+        )  # (all_batch_events, embedding_dim)
+
+        position_embeddings, _ = create_masked_tensor(
+            data=position_embeddings, lengths=lengths
+        )  # (batch_size, seq_len, embedding_dim)
+
+        return position_embeddings
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 4c8c0374..49ab6074 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -15,7 +15,6 @@ def __init__(
         rqvae_model,
         item_id_to_semantic_id,
         item_id_to_residual,
-        item_id_to_text_embedding,
         solver,
         sequence_prefix,
         pred_prefix,
@@ -81,7 +80,6 @@ def __init__(
 
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
-        self._item_id_to_text_embedding = item_id_to_text_embedding
 
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
 
@@ -127,11 +125,6 @@ def init_rqvae(cls, config) -> RqVaeModel:
         codebook_sizes = rqvae_model.codebook_sizes
         assert all([book_size == codebook_sizes[0] for book_size in codebook_sizes])
 
-        return rqvae_model
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        rqvae_model = cls.init_rqvae(config)
         embs_extractor = torch.load(config["embs_extractor_path"], weights_only=False)
 
         embs_extractor = embs_extractor.sort_index()
@@ -143,6 +136,12 @@ def create_from_config(cls, config, **kwargs):
 
         semantic_ids, residuals = rqvae_model({"embeddings": text_embeddings})
 
+        return rqvae_model, semantic_ids, residuals, item_ids
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_model, semantic_ids, residuals, item_ids = cls.init_rqvae(config)
+
         solver = CollisionSolver(
             residual_dim=residuals.shape[1],
             emb_dim=len(rqvae_model.codebook_sizes),
@@ -156,7 +155,6 @@ def create_from_config(cls, config, **kwargs):
             rqvae_model=rqvae_model,
             item_id_to_semantic_id=semantic_ids,
             item_id_to_residual=residuals,
-            item_id_to_text_embedding=text_embeddings,
             solver=solver,
             sequence_prefix=config["sequence_prefix"],
             pred_prefix=config["predictions_prefix"],
@@ -380,8 +378,6 @@ def get_init_item_embeddings(self, events):
 
         # get residuals
         residual = self._item_id_to_residual[events - 1]
-        # text_embeddings = self._item_id_to_text_embedding[events - 1]
-        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
         residual = residual.unsqueeze(1)
 
         # get true item embeddings
@@ -389,8 +385,6 @@ def get_init_item_embeddings(self, events):
             [semantic_embeddings, residual], dim=1
         )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
 
-        # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
-
         return item_embeddings
 
     def _encoder_pos_embeddings(self, lengths, mask):

From dd77cdc152594df717155c7e4264eb2299bd4141 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 16 Feb 2025 23:12:08 +0300
Subject: [PATCH 096/175] unfreeze embs

---
 modeling/models/sasrec_freezed.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 6c12caf2..0c910e5d 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -68,7 +68,6 @@ def __init__(
         # TODOPK ask about freezed masked & padding tokens
 
         self._item_embeddings.weight.data.copy_(extended_embeddings)
-        self._item_embeddings.weight.requires_grad = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):

From ac5f1238d1cf0e555fefcd79efa7bc3f4b3c8573 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Sun, 16 Feb 2025 23:36:57 +0300
Subject: [PATCH 097/175] =?UTF-8?q?=D0=BF=D0=BE=D0=BF=D1=8B=D1=82=D0=BA?=
 =?UTF-8?q?=D0=B0=20=D1=83=D1=81=D0=BA=D0=BE=D1=80=D0=B8=D1=82=D1=8C=20Sim?=
 =?UTF-8?q?plifiedTree=20(=5Fget=5Fids=20=D0=BE=D0=BA=D0=B0=D0=B7=D0=B0?=
 =?UTF-8?q?=D0=BB=D1=81=D1=8F=20=D0=BC=D0=B5=D0=B4=D0=BB=D0=B5=D0=BD=D0=BD?=
 =?UTF-8?q?=D0=B5=D0=B9)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/rqvae_utils/simplified_tree.py | 31 +++++++++++++++++++++++--
 modeling/rqvae_utils/tree_comparing.py  | 20 ++++++++++++----
 2 files changed, 44 insertions(+), 7 deletions(-)

diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 9c19e3c4..6ef16357 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -18,12 +18,12 @@ def init_tree(self, embeddings: torch.Tensor) -> None:
         :param embeddings: тензор эмбеддингов для каждого из semantic ids (sem_ids_count, emb_dim)
         """
         assert embeddings.shape[1] == self.emb_dim
-        self.full_embeddings = embeddings.to(self.device)  # (sem_ids_count, emb_dim)
+        self.full_embeddings = embeddings.to(self.device).float()  # (sem_ids_count, emb_dim)
         self.sem_ids_count = embeddings.shape[0]
 
     def get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         """
-        :param request_sem_ids: батч из sem ids (batch_size, sem_id_len)
+        :param request_sem_ids: батч sem ids (batch_size, sem_id_len)
         :param k: количество ближайших элементов которые нужно взять (int)
         :return: тензор индексов ближайших k элементов из всех semantic_ids для каждого sem_id из батча (batch_size, k)
         """
@@ -46,3 +46,30 @@ def get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
 
         indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :k]  # (batch_size, k)
         return indices
+
+    def _get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
+        """
+        Альтернатива get_ids, попытка ускорить
+        :param request_sem_ids: батч sem ids (batch_size, sem_id_len)
+        :param k: количество ближайших элементов которые нужно взять (int)
+        :return: тензор индексов ближайших k элементов из всех semantic_ids для каждого sem_id из батча (batch_size, k)
+        """
+        assert request_sem_ids.shape[1] == self.sem_id_len
+        assert 0 < k <= self.sem_ids_count
+        request_sem_ids = request_sem_ids.to(self.device)
+
+        index = (request_sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (batch_size, sem_id_len, 1, emb_dim)
+
+        request_embeddings = torch.gather(
+            input=self.embedding_table.unsqueeze(0).expand(request_sem_ids.shape[0], -1, -1, -1),
+            dim=2,
+            index=index
+        ).sum(1)  # (batch_size, emb_dim)
+
+        diff_norm = torch.cdist(self.full_embeddings, request_embeddings.unsqueeze(1), p=2).squeeze(
+            1)  # (batch_size, sem_ids_count)
+
+        _, indices = torch.topk(diff_norm, k=k, dim=1, largest=False)  # (batch_size, k)
+        return indices.squeeze(-1)
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index 9a9b3822..3ad36f15 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -75,14 +75,14 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     q_residuals = torch.randn(batch_size, embedding_dim).to(DEVICE)
 
     total_time = 0
-    n_exps = 1
+    n_exps = 20
 
     memory_stats(1)
     for i in range(n_exps):
         now = time.time()
         item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, item_ids[:3])
+        #stats(q_semantic_ids[:3], 256, tree.sids, item_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -92,7 +92,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         simplified_tree_ids = simplified_tree.get_ids(q_semantic_ids, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
+        #stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -100,10 +100,20 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     for i in range(n_exps):
         now = time.time()
-        tree_ids = tree.get_ids(q_semantic_ids, q_residuals, items_to_query)
+        simplified_tree_ids = simplified_tree._get_ids(q_semantic_ids, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
+        #stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
     memory_stats(4)
+
+    for i in range(n_exps):
+        now = time.time()
+        tree_ids = tree.get_ids(q_semantic_ids, q_residuals, items_to_query)
+        total_time += time.time() - now
+        #stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
+
+    print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
+
+    memory_stats(5)

From 228edbdf902243cf567f57c39252c4d22b142865 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Sun, 16 Feb 2025 23:38:27 +0300
Subject: [PATCH 098/175] =?UTF-8?q?=D1=83=D0=B1=D1=80=D0=B0=D0=BB=20=D0=BB?=
 =?UTF-8?q?=D0=B8=D1=88=D0=BD=D0=B8=D0=B9=20assert?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/rqvae_utils/tree.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index 928533e5..9180325a 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -56,7 +56,6 @@ def get_counts(self, sem_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]
         :return: префиксы всех длин sem_ids, количество sem_id на каждой глубине дерева
         """
         assert sem_ids.shape[1] == self.sem_id_len
-        assert sem_ids.device == self.device
 
         offsets = torch.arange(self.sem_id_len + 1, device=self.device)
         i = torch.arange(self.sem_id_len, device=self.device)

From 5a5d039e8153100cc03cc7b56e94fad751c77402 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Thu, 20 Feb 2025 00:10:14 +0300
Subject: [PATCH 099/175] =?UTF-8?q?=D0=BF=D1=80=D0=B8=D0=B2=D0=B5=D0=BB=20?=
 =?UTF-8?q?=D0=BA=20=D0=BE=D0=B1=D1=89=D0=B5=D0=BC=D1=83=20=D0=B2=D0=B8?=
 =?UTF-8?q?=D0=B4=D1=83=20=D0=B4=D0=B5=D1=80=D0=B5=D0=B2=D1=8C=D1=8F=20?=
 =?UTF-8?q?=D0=BF=D0=BB=D1=8E=D1=81=20=D0=BE=D0=BF=D0=B8=D1=81=D0=B0=D0=BD?=
 =?UTF-8?q?=D0=B8=D0=B5=20=D0=BA=20=D1=81=D0=BE=D0=BB=D0=B2=D0=B5=D1=80?=
 =?UTF-8?q?=D1=83?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/models/tiger.py                 |   4 +-
 modeling/rqvae_utils/collision_solver.py | 133 +++++++++++++++--------
 modeling/rqvae_utils/simplified_tree.py  |  48 +++++---
 modeling/rqvae_utils/tree.py             |   4 +-
 modeling/rqvae_utils/tree_comparing.py   |  14 ++-
 5 files changed, 133 insertions(+), 70 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 4c8c0374..9522e9ad 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -144,8 +144,8 @@ def create_from_config(cls, config, **kwargs):
         semantic_ids, residuals = rqvae_model({"embeddings": text_embeddings})
 
         solver = CollisionSolver(
-            residual_dim=residuals.shape[1],
-            emb_dim=len(rqvae_model.codebook_sizes),
+            emb_dim=residuals.shape[1],
+            sem_id_len=len(rqvae_model.codebook_sizes),
             codebook_size=rqvae_model.codebook_sizes[0],
         )
         solver.create_query_candidates_dict(
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index 506de2ee..fd71d0fa 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -4,66 +4,89 @@
 
 
 class CollisionSolver:
-    def __init__(self, residual_dim, emb_dim, codebook_size, device: torch.device = torch.device('cpu')):
+    def __init__(self,
+                 emb_dim: int,
+                 sem_id_len: int,
+                 codebook_size: int,
+                 device: torch.device = torch.device('cpu')):
         """
-        :param residual_dim: Длина остатка
+        :param emb_dim: Длина остатка
         :param codebook_size: Количество элементов в одном кодбуке
-        :param emb_dim: Длина semantic_id (без токена решающего коллизии)
+        :param sem_id_len: Длина semantic_id (без токена решающего коллизии)
         :param device: Устройство
         """
-        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0)) #тензор группирирующий остатки по semantic_id
-        self.item_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0)) #тензор группирируюшщий реальные айди айтемов по semantic_id
-        self.counts_dict: dict[int, int] = defaultdict(int) #тензор храняющий количество коллизий по semantic_id
-        self.residual_dim: int = residual_dim #длина остатка
-        self.emb_dim: int = emb_dim #длина semantic_id
-        self.codebook_size: int = codebook_size #количество элементов в одном кодбуке
-        self.device: torch.device = device #девайс
-
-        self.key: torch.Tensor = torch.tensor([self.codebook_size ** i for i in range(self.emb_dim)], dtype=torch.long, device=self.device) #ключ для сопоставления числа каждому semantic_id
-
-    def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
+        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0))  # тензор группирирующий остатки по semantic_id
+        self.item_ids_sparse_tensor: torch.Tensor = torch.empty(
+            (0, 0))  # тензор группирирующий реальные айди айтемов по semantic_id
+        self.counts_dict: dict[int, int] = defaultdict(int)  # тензор храняющий количество коллизий по semantic_id
+        self.emb_dim: int = emb_dim  # длина остатка
+        self.sem_id_len: int = sem_id_len  # длина semantic_id
+        self.codebook_size: int = codebook_size  # количество элементов в одном кодбуке
+        self.device: torch.device = device  # девайс
+
+        self.key: torch.Tensor = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len)],
+                                              dtype=torch.long,
+                                              device=self.device)  # ключ для сопоставления числа каждому semantic_id
+
+    def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor,
+                                     residuals: torch.Tensor) -> None:
         """
         Создает разреженный тензор, который содержит сгруппированные по semantic id элементы
 
-        :param item_ids: Реальные айди айтемов (пусть будут больше 0)
-        :param semantic_ids: Тензор всех semantic_id, полученных из rq-vae (без токенов решающих коллизии)
-        :param residuals: Тензор остатков для каждого semantic_id
+        :param item_ids: Реальные айди айтемов (пусть будут больше 0) (count,)
+        :param semantic_ids: Тензор всех semantic_id, полученных из rq-vae (без токенов решающих коллизии) (count, sem_id_len)
+        :param residuals: Тензор остатков для каждого semantic_id (count, emb_dim)
         """
         residuals_count, residual_length = residuals.shape
         semantic_ids_count, semantic_id_length = semantic_ids.shape
 
         assert residuals_count == semantic_ids_count
-        assert semantic_id_length == self.emb_dim
-        assert residual_length == self.residual_dim
+        assert semantic_id_length == self.sem_id_len
+        assert residual_length == self.emb_dim
         assert item_ids.shape == (residuals_count,)
 
         item_ids = item_ids.to(self.device)
         residuals = residuals.to(self.device)
         semantic_ids = semantic_ids.to(self.device)
 
-        unique_id = torch.einsum('nc,c->n', semantic_ids, self.key)
-        unique_ids, inverse_indices = torch.unique(unique_id, return_inverse=True)
-        sorted_indices = torch.argsort(inverse_indices)
-        counts = torch.bincount(inverse_indices)
-        max_residuals_count = int(counts.max().item())
-        max_sid = int(self.codebook_size ** self.emb_dim)
-        offsets = torch.cumsum(torch.cat((torch.tensor([0], dtype=torch.long, device=self.device), counts[:-1])), dim=0)
-        row_indices = inverse_indices[sorted_indices]
-        col_indices = torch.arange(semantic_ids_count) - offsets[row_indices]
+        unique_id = torch.einsum('nc,c->n', semantic_ids, self.key)  # хэши
+        unique_ids, inverse_indices, counts = torch.unique(unique_id, return_inverse=True, return_counts=True)
+        sorted_indices = torch.argsort(inverse_indices)  # сортированные индексы чтобы совпадающие хэши шли подряд
+
+        row_indices = inverse_indices[sorted_indices]  # отсортированные хэши
+
+        offsets = torch.cumsum(counts, dim=0) - counts
+        col_indices = (torch.arange(semantic_ids_count)
+                       - offsets[row_indices])  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
+
         indices = torch.stack([
             unique_ids[row_indices],
             col_indices
-        ], dim=0)
+        ],
+            dim=0)  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
 
-        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices], size=(max_sid, max_residuals_count, self.residual_dim), device=self.device)
-        self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))
+        max_residuals_count = int(counts.max().item())  # максимальное количество коллизий для одного sem_id
+        max_sid = int(self.codebook_size ** self.sem_id_len)  # максимальный хэш sem_id который может быть
 
-        item_id_indices: torch.Tensor = torch.stack((unique_ids[row_indices], col_indices))
+        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices],
+                                                              size=(max_sid, max_residuals_count, self.emb_dim),
+                                                              device=self.device)  # (max_sid, max_residuals_count, emb_dim)
 
-        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(item_id_indices, item_ids[sorted_indices], size=(max_sid, max_residuals_count), device=self.device, dtype=torch.int16)
+        self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))  # sid -> collision count
+
+        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(indices, item_ids[sorted_indices],
+                                                              size=(max_sid, max_residuals_count), device=self.device,
+                                                              dtype=torch.int32)  # (max_sid, max_residuals_count)
 
     def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-        assert semantic_ids.shape[1] == self.emb_dim
+        """
+        :param semantic_ids батч из semantic ids (batch_size, sem_id_len)
+
+        :return:
+            Возвращает тензор эмбеддингов для батча semantic_ids, размерность (batch_size, max_residuals_count, emb_dim)
+            Возвращает маску для этого тензора, размерность (batch_size, max_residuals_count, emb_dim)
+        """
+        assert semantic_ids.shape[1] == self.sem_id_len
 
         semantic_ids = semantic_ids.to(self.device)
         unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
@@ -76,16 +99,16 @@ def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tupl
 
     def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
         """
-        :param semantic_id: [batch_size, emb_dim] semantic ids (без токена решающего коллизии)
-        :param pred_residuals: [batch_size, residual_dim] предсказанные остатки
+        :param semantic_ids: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
+        :param pred_residuals: [batch_size, emb_dim] предсказанные остатки
 
         :return: Словарь с ключами:
             - 'pred_scores_mask': [batch_size, max_collision_count] маска существующих значений scores для предсказанных остатков
             - 'pred_scores': [batch_size, max_collision_count] софтмакс для каждого из кандидатов для предсказанных остатков
             - 'pred_item_ids': [batch_size] реальные айди айтемов для предсказанных остатков
         """
-        assert semantic_ids.shape[1] == self.emb_dim
-        assert pred_residuals.shape[1] == self.residual_dim
+        assert semantic_ids.shape[1] == self.sem_id_len
+        assert pred_residuals.shape[1] == self.emb_dim
         assert semantic_ids.shape[0] == pred_residuals.shape[0]
 
         semantic_ids = semantic_ids.to(self.device)
@@ -97,7 +120,8 @@ def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tens
 
         pred_scores = torch.einsum('njk,nk->nj', candidates, pred_residuals).masked_fill(~mask, -torch.inf)
         pred_indices = torch.argmax(pred_scores, dim=1)
-        pred_item_ids = torch.stack([self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]] for i in range(semantic_ids.shape[0])])
+        pred_item_ids = torch.stack(
+            [self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]] for i in range(semantic_ids.shape[0])])
 
         return {
             "pred_scores_mask": mask,
@@ -105,16 +129,17 @@ def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tens
             "pred_item_ids": pred_item_ids
         }
 
-    def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
+    def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[
+        str, torch.Tensor]:
         """
-        :param semantic_id: [batch_size, emb_dim] semantic ids (без токена решающего коллизии)
-        :param true_residuals: [batch_size, residual_dim] реальные остатки
+        :param semantic_ids: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
+        :param true_residuals: [batch_size, emb_dim] реальные остатки
 
         :return: Словарь с ключами:
             - 'true_dedup_tokens': [batch_size] токены решающие коллизии для реальных остатков
         """
-        assert semantic_ids.shape[1] == self.emb_dim
-        assert true_residuals.shape[1] == self.residual_dim
+        assert semantic_ids.shape[1] == self.sem_id_len
+        assert true_residuals.shape[1] == self.emb_dim
         assert semantic_ids.shape[0] == true_residuals.shape[0]
 
         semantic_ids = semantic_ids.to(self.device)
@@ -130,3 +155,23 @@ def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torc
         return {
             "true_dedup_tokens": true_dedup_tokens
         }
+
+    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> torch.Tensor:
+        """
+        :param semantic_id: [batch_size, sem_id_len] semantic ids (без токенов решающего коллизии)
+        :param dedup_tokens: [batch_size] токены решающие коллизии
+
+        :return: item_ids : [batch_size] реальные айди айтемов
+        """
+        assert semantic_ids.shape[1] == self.sem_id_len
+        assert dedup_tokens.shape == (semantic_ids.shape[0],)
+
+        semantic_ids = semantic_ids.to(self.device)
+        dedup_tokens = dedup_tokens.to(self.device)
+
+        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+
+        item_ids = torch.stack(
+            [self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])
+
+        return item_ids
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 6ef16357..2fb7a627 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -13,15 +13,37 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = torch.d
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
 
-    def init_tree(self, embeddings: torch.Tensor) -> None:
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
         """
-        :param embeddings: тензор эмбеддингов для каждого из semantic ids (sem_ids_count, emb_dim)
+        :param semantic_ids: (sem_ids_count, sem_id_len)
+        :param residuals: (sem_ids_count, emb_dim)
         """
-        assert embeddings.shape[1] == self.emb_dim
-        self.full_embeddings = embeddings.to(self.device).float()  # (sem_ids_count, emb_dim)
-        self.sem_ids_count = embeddings.shape[0]
+        self.sem_ids_count = semantic_ids.shape[0]
+        assert residuals.shape == (self.sem_ids_count, self.emb_dim)
+        assert semantic_ids.shape == (self.sem_ids_count, self.sem_id_len)
 
-    def get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
+        semantic_ids = semantic_ids.to(self.device)
+        residuals = residuals.to(self.device).float()
+        self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
+
+    def calculate_full(self, sem_ids: torch.Tensor) -> torch.Tensor:
+        """
+        :param sem_ids: набор из sem ids (count, sem_id_len)
+        :return: эмбеддинг для каждого sem_id из набора (count, emb_dim)
+        """
+        assert sem_ids.shape[1] == self.sem_id_len
+        sem_ids = sem_ids.to(self.device)
+
+        expanded_emb_table = (self.embedding_table.unsqueeze(0)
+                              .expand(sem_ids.shape[0], -1, -1, -1))  # (count, sem_id_len, codebook_size, emb_dim)
+
+        index = (sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (count, sem_id_len, 1, emb_dim)
+
+        return torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)  # (count, emb_dim)
+
+    def query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч sem ids (batch_size, sem_id_len)
         :param k: количество ближайших элементов которые нужно взять (int)
@@ -29,17 +51,11 @@ def get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         """
         assert request_sem_ids.shape[1] == self.sem_id_len
         assert 0 < k <= self.sem_ids_count
-        request_sem_ids = request_sem_ids.to(self.device)
-
-        expanded_emb_table = (self.embedding_table.unsqueeze(0)
-                              .expand(request_sem_ids.shape[0], -1, -1,
-                                      -1))  # (batch_size, sem_id_len, codebook_size, emb_dim)
 
-        index = (request_sem_ids.unsqueeze(-1)
-                 .expand(-1, -1, self.emb_dim)
-                 .unsqueeze(2))  # (batch_size, sem_id_len, 1, emb_dim)
+        request_sem_ids = request_sem_ids.to(self.device)
+        request_embeddings = self.calculate_full(request_sem_ids)  # (batch_size, emb_dim)
 
-        request_embeddings = (torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1)
+        request_embeddings = (request_embeddings.unsqueeze(1)
                               .expand(-1, self.sem_ids_count, -1))  # (batch_size, sem_ids_count, emb_dim)
 
         diff_norm = torch.norm(self.full_embeddings - request_embeddings, p=2, dim=2)  # (batch_size, sem_ids_count)
@@ -47,7 +63,7 @@ def get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :k]  # (batch_size, k)
         return indices
 
-    def _get_ids(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
+    def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         """
         Альтернатива get_ids, попытка ускорить
         :param request_sem_ids: батч sem ids (batch_size, sem_id_len)
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index 9180325a..fe175f9b 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -19,7 +19,7 @@ def __init__(self, embedding_table, device: torch.device = DEVICE):
         self.sem_ids_embs: torch.Tensor = torch.empty((0, 0))
         self.sids: torch.Tensor = torch.empty((0, 0))  # будет (sem_id_len, )
 
-    def init_tree(self, semantic_ids, residuals):
+    def build_tree_structure(self, semantic_ids, residuals):
         """
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
@@ -164,7 +164,7 @@ def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels
     def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
         return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
 
-    def get_ids(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor, k: int) -> torch.Tensor:
+    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor, k: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
         :param request_residuals: батч из остатков (batch_size, emb_dim)
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index 3ad36f15..07e1f102 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -61,14 +61,16 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     print(f"Time for trie init: {(time.time() - now) * 1000:.2f} ms")
 
     now = time.time()
-    tree.init_tree(semantic_ids, residuals)
+    tree.build_tree_structure(semantic_ids, residuals)
     print(f"Time for tree init: {(time.time() - now) * 1000:.2f} ms")
 
     now = time.time()
-    full_embeddings = tree.calculate_full(semantic_ids, torch.zeros_like(residuals)).sum(1)
-    simplified_tree.init_tree(full_embeddings)
+    simplified_tree.build_tree_structure(semantic_ids, residuals)
     print(f"Time for simplified tree init: {(time.time() - now) * 1000:.2f} ms")
 
+    full_embeddings = tree.calculate_full(semantic_ids, residuals).sum(1)
+    print(torch.all((full_embeddings == simplified_tree.full_embeddings) == True))
+
     items_to_query = 20
     batch_size = 256
     q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE)
@@ -90,7 +92,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     for i in range(n_exps):
         now = time.time()
-        simplified_tree_ids = simplified_tree.get_ids(q_semantic_ids, items_to_query)
+        simplified_tree_ids = simplified_tree.query(q_semantic_ids, items_to_query)
         total_time += time.time() - now
         #stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
 
@@ -100,7 +102,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     for i in range(n_exps):
         now = time.time()
-        simplified_tree_ids = simplified_tree._get_ids(q_semantic_ids, items_to_query)
+        simplified_tree_ids = simplified_tree._query(q_semantic_ids, items_to_query)
         total_time += time.time() - now
         #stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
 
@@ -110,7 +112,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     for i in range(n_exps):
         now = time.time()
-        tree_ids = tree.get_ids(q_semantic_ids, q_residuals, items_to_query)
+        tree_ids = tree.query(q_semantic_ids, q_residuals, items_to_query)
         total_time += time.time() - now
         #stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
 

From c9a096060d0f92ef87041ef39b15342d17c080fc Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Thu, 20 Feb 2025 00:32:35 +0300
Subject: [PATCH 100/175] =?UTF-8?q?=D0=BF=D0=BE=D0=BC=D0=B5=D0=BD=D1=8F?=
 =?UTF-8?q?=D0=BB=20trie=20=D0=BD=D0=B0=20simplified=20tree?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/models/tiger.py                |  4 ++--
 modeling/rqvae_utils/simplified_tree.py | 18 +++++++++++-------
 modeling/rqvae_utils/tree.py            | 21 +++++++++++++--------
 modeling/rqvae_utils/tree_comparing.py  | 19 ++++++++++---------
 4 files changed, 36 insertions(+), 26 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 9522e9ad..40fb36e9 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -2,7 +2,7 @@
 
 import torch
 from models.base import SequentialTorchModel
-from rqvae_utils import CollisionSolver, Trie
+from rqvae_utils import CollisionSolver, SimplifiedTree
 from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
@@ -87,7 +87,7 @@ def __init__(
 
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
 
-        self._trie = Trie(rqvae_model)
+        self._trie = SimplifiedTree(rqvae_model)
 
         self._trie.build_tree_structure(
             item_id_to_semantic_id.to(DEVICE),
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 2fb7a627..76f75f2f 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -12,19 +12,23 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = torch.d
         self.device: torch.device = device
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
+        self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor) -> None:
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor) -> None:
         """
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
+        :param item_ids: (sem_ids_count,)
         """
         self.sem_ids_count = semantic_ids.shape[0]
         assert residuals.shape == (self.sem_ids_count, self.emb_dim)
         assert semantic_ids.shape == (self.sem_ids_count, self.sem_id_len)
+        assert item_ids.shape == (self.sem_ids_count,)
 
         semantic_ids = semantic_ids.to(self.device)
         residuals = residuals.to(self.device).float()
         self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
+        self.item_ids = item_ids
 
     def calculate_full(self, sem_ids: torch.Tensor) -> torch.Tensor:
         """
@@ -43,14 +47,14 @@ def calculate_full(self, sem_ids: torch.Tensor) -> torch.Tensor:
 
         return torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)  # (count, emb_dim)
 
-    def query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
+    def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч sem ids (batch_size, sem_id_len)
-        :param k: количество ближайших элементов которые нужно взять (int)
+        :param items_to_query: количество ближайших элементов которые нужно взять (int)
         :return: тензор индексов ближайших k элементов из всех semantic_ids для каждого sem_id из батча (batch_size, k)
         """
         assert request_sem_ids.shape[1] == self.sem_id_len
-        assert 0 < k <= self.sem_ids_count
+        assert 0 < items_to_query <= self.sem_ids_count
 
         request_sem_ids = request_sem_ids.to(self.device)
         request_embeddings = self.calculate_full(request_sem_ids)  # (batch_size, emb_dim)
@@ -60,8 +64,8 @@ def query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
 
         diff_norm = torch.norm(self.full_embeddings - request_embeddings, p=2, dim=2)  # (batch_size, sem_ids_count)
 
-        indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :k]  # (batch_size, k)
-        return indices
+        indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :items_to_query]  # (batch_size, k)
+        return self.item_ids[indices]
 
     def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         """
@@ -88,4 +92,4 @@ def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
             1)  # (batch_size, sem_ids_count)
 
         _, indices = torch.topk(diff_norm, k=k, dim=1, largest=False)  # (batch_size, k)
-        return indices.squeeze(-1)
+        return self.item_ids[indices.squeeze(-1)]
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index fe175f9b..5f125012 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -18,18 +18,22 @@ def __init__(self, embedding_table, device: torch.device = DEVICE):
         self.sem_ids_count: int = -1
         self.sem_ids_embs: torch.Tensor = torch.empty((0, 0))
         self.sids: torch.Tensor = torch.empty((0, 0))  # будет (sem_id_len, )
+        self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(self, semantic_ids, residuals):
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor):
         """
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
+        :param item_ids: (sem_ids_count,)
         """
+        self.sem_ids_count = semantic_ids.shape[0]
 
         assert semantic_ids.shape[0] == residuals.shape[0]
         assert semantic_ids.shape[1] == self.sem_id_len
         assert residuals.shape[1] == self.emb_dim
+        assert item_ids.shape == (self.sem_ids_count,)
 
-        self.sem_ids_count = semantic_ids.shape[0]
+        self.item_ids = item_ids
         self.key = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len - 1, -1, -1)],
                                 dtype=torch.long, device=self.device)
         self.sids = self.get_sids(semantic_ids.float())  # (sem_id_len, )
@@ -164,20 +168,20 @@ def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels
     def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
         return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
 
-    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor, k: int) -> torch.Tensor:
+    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor, items_to_query: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
         :param request_residuals: батч из остатков (batch_size, emb_dim)
-        :param k: количество ближайших элементов которые нужно взять int
+        :param items_to_query: количество ближайших элементов которые нужно взять int
         :return: тензор индексов ближайших k элементов из всех semantic_ids для каждого sem_id из батча (batch_size, k)
         """
         assert request_sem_ids.shape[0] == request_residuals.shape[0]
         assert request_sem_ids.shape[1] == self.sem_id_len
         assert request_residuals.shape[1] == self.emb_dim
-        assert 0 <= k < self.sem_ids_count
+        assert 0 <= items_to_query < self.sem_ids_count
 
-        ol, ol_mask = self.calc_ol(request_sem_ids, k)
-        il, il_mask = self.calc_il(request_sem_ids, k)
+        ol, ol_mask = self.calc_ol(request_sem_ids, items_to_query)
+        il, il_mask = self.calc_il(request_sem_ids, items_to_query)
 
         il_mask = il_mask.detach().cpu()
         ol_mask = ol_mask.detach().cpu()
@@ -200,4 +204,5 @@ def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor,
                                ~torch.cat([torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1),
                                ~torch.cat([il_mask, ol_mask], dim=1)))
 
-        return (ids % self.sem_ids_count)[:, :self.sem_ids_count][:, :k]  # (batch_size, k)
+        ids = (ids % self.sem_ids_count)[:, :self.sem_ids_count][:, :items_to_query]  # (batch_size, k)
+        return self.item_ids[ids]
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index 07e1f102..f18c73a7 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -54,18 +54,19 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     K = 3
 
     semantic_ids = torch.randint(0, alphabet_size, (N, K), dtype=torch.int64).to(DEVICE)
-    residuals = torch.randn(N, embedding_dim).to(DEVICE)
+    residuals = torch.zeros_like(torch.randn(N, embedding_dim)).to(DEVICE)
+    item_ids = torch.arange(5, N + 5).to(DEVICE)
 
     now = time.time()
-    trie.build_tree_structure(semantic_ids, residuals, torch.arange(N).to(DEVICE))
+    trie.build_tree_structure(semantic_ids, residuals, item_ids)
     print(f"Time for trie init: {(time.time() - now) * 1000:.2f} ms")
 
     now = time.time()
-    tree.build_tree_structure(semantic_ids, residuals)
+    tree.build_tree_structure(semantic_ids, residuals, item_ids)
     print(f"Time for tree init: {(time.time() - now) * 1000:.2f} ms")
 
     now = time.time()
-    simplified_tree.build_tree_structure(semantic_ids, residuals)
+    simplified_tree.build_tree_structure(semantic_ids, residuals, item_ids)
     print(f"Time for simplified tree init: {(time.time() - now) * 1000:.2f} ms")
 
     full_embeddings = tree.calculate_full(semantic_ids, residuals).sum(1)
@@ -77,14 +78,14 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     q_residuals = torch.randn(batch_size, embedding_dim).to(DEVICE)
 
     total_time = 0
-    n_exps = 20
+    n_exps = 1
 
     memory_stats(1)
     for i in range(n_exps):
         now = time.time()
         item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
         total_time += time.time() - now
-        #stats(q_semantic_ids[:3], 256, tree.sids, item_ids[:3])
+        stats(q_semantic_ids[:3], 256, tree.sids, item_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -94,7 +95,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         simplified_tree_ids = simplified_tree.query(q_semantic_ids, items_to_query)
         total_time += time.time() - now
-        #stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
+        stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -104,7 +105,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         simplified_tree_ids = simplified_tree._query(q_semantic_ids, items_to_query)
         total_time += time.time() - now
-        #stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
+        stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -114,7 +115,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         tree_ids = tree.query(q_semantic_ids, q_residuals, items_to_query)
         total_time += time.time() - now
-        #stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
+        stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 

From 407be2bd80829016e719d1a9cffdc3e796838652 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Thu, 20 Feb 2025 00:54:49 +0300
Subject: [PATCH 101/175] =?UTF-8?q?=D0=B2=D0=BE=D0=B7=D0=BC=D0=BE=D0=B6?=
 =?UTF-8?q?=D0=BD=D0=BE=D1=81=D1=82=D1=8C=20=D0=BD=D0=B5=20=D1=83=D1=87?=
 =?UTF-8?q?=D0=B8=D1=82=D1=8B=D0=B2=D0=B0=D1=82=D1=8C=20=D0=BE=D1=81=D1=82?=
 =?UTF-8?q?=D0=B0=D1=82=D0=BA=D0=B8?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/models/tiger.py                |  3 ++-
 modeling/rqvae_utils/simplified_tree.py |  5 +++--
 modeling/rqvae_utils/tree_comparing.py  | 20 +++++++++++++-------
 3 files changed, 18 insertions(+), 10 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 40fb36e9..feed89a7 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -93,6 +93,7 @@ def __init__(
             item_id_to_semantic_id.to(DEVICE),
             item_id_to_residual.to(DEVICE),
             item_ids.to(DEVICE),
+            sum_with_residuals=False
         )
 
         self._bos_token_id = self._codebook_sizes[0]
@@ -234,7 +235,7 @@ def forward(self, inputs):
             residuals = tgt_embeddings[:, -1, :]
             semantic_ids = semantic_ids.to(torch.int64)
 
-            item_ids = self._trie.query(semantic_ids, residuals, items_to_query=20)
+            item_ids = self._trie.query(semantic_ids, items_to_query=20)
 
             return item_ids
 
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 76f75f2f..34a0b69e 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -14,8 +14,9 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = torch.d
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor) -> None:
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor, sum_with_residuals: bool = True) -> None:
         """
+        :param sum_with_residuals: флаг, отвечающий за то учитывать ли остатки при выборе кандидатов
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
         :param item_ids: (sem_ids_count,)
@@ -26,7 +27,7 @@ def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tens
         assert item_ids.shape == (self.sem_ids_count,)
 
         semantic_ids = semantic_ids.to(self.device)
-        residuals = residuals.to(self.device).float()
+        residuals = residuals.to(self.device).float() if sum_with_residuals else torch.zeros_like(residuals, device=self.device, dtype=torch.float)
         self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
         self.item_ids = item_ids
 
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index f18c73a7..2dbcabd4 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -26,7 +26,7 @@ def calc_sid(sid, codebook_size):
 def stats(query_sem_id, codebook_size, sids, item_ids):
     for sem_id, ids in zip(query_sem_id.tolist(), item_ids.tolist()):
         print(calc_sid(torch.tensor(sem_id), codebook_size))
-        print(sids[torch.tensor(ids)])
+        print(sids[torch.tensor(ids)][:10])
 
 
 if __name__ == "__main__":
@@ -48,14 +48,16 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     trie = Trie(rqvae_model)
     tree = Tree(emb_table, DEVICE)
     simplified_tree = SimplifiedTree(emb_table, DEVICE)
+    simplified_tree_wr = SimplifiedTree(emb_table, DEVICE)
     alphabet_size = 10
 
     N = 12101
     K = 3
 
     semantic_ids = torch.randint(0, alphabet_size, (N, K), dtype=torch.int64).to(DEVICE)
-    residuals = torch.zeros_like(torch.randn(N, embedding_dim)).to(DEVICE)
+    residuals = torch.randn(N, embedding_dim).to(DEVICE)
     item_ids = torch.arange(5, N + 5).to(DEVICE)
+    print(residuals[0])
 
     now = time.time()
     trie.build_tree_structure(semantic_ids, residuals, item_ids)
@@ -69,6 +71,10 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     simplified_tree.build_tree_structure(semantic_ids, residuals, item_ids)
     print(f"Time for simplified tree init: {(time.time() - now) * 1000:.2f} ms")
 
+    now = time.time()
+    simplified_tree_wr.build_tree_structure(semantic_ids, residuals, item_ids, False)
+    print(f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms")
+
     full_embeddings = tree.calculate_full(semantic_ids, residuals).sum(1)
     print(torch.all((full_embeddings == simplified_tree.full_embeddings) == True))
 
@@ -85,7 +91,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         item_ids = trie.query(q_semantic_ids, q_residuals, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, item_ids[:3])
+        stats(q_semantic_ids[:1], 256, tree.sids, item_ids[:1])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -95,7 +101,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         simplified_tree_ids = simplified_tree.query(q_semantic_ids, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
+        stats(q_semantic_ids[:1], 256, tree.sids, simplified_tree_ids[:1])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -103,9 +109,9 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     for i in range(n_exps):
         now = time.time()
-        simplified_tree_ids = simplified_tree._query(q_semantic_ids, items_to_query)
+        simplified_tree_ids = simplified_tree_wr.query(q_semantic_ids, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, simplified_tree_ids[:3])
+        stats(q_semantic_ids[:1], 256, tree.sids, simplified_tree_ids[:1])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 
@@ -115,7 +121,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
         now = time.time()
         tree_ids = tree.query(q_semantic_ids, q_residuals, items_to_query)
         total_time += time.time() - now
-        stats(q_semantic_ids[:3], 256, tree.sids, tree_ids[:3])
+        stats(q_semantic_ids[:1], 256, tree.sids, tree_ids[:1])
 
     print(f"Time per query: {total_time / n_exps * 1000:.2f} ms")
 

From fd89947acb21e2b0a68a853345f7741a0208f456 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Thu, 20 Feb 2025 01:08:08 +0300
Subject: [PATCH 102/175] =?UTF-8?q?=D1=82=D0=B5=D0=BF=D0=B5=D1=80=D1=8C=20?=
 =?UTF-8?q?=D0=B2=20=D0=B8=D0=BD=D0=B8=D1=82=D0=B5=20=D0=B4=D0=B5=D1=80?=
 =?UTF-8?q?=D0=B5=D0=B2=D1=8C=D0=B5=D0=B2=20rqvae=20=D0=B0=20=D0=BD=D0=B5?=
 =?UTF-8?q?=20emb=20table?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/rqvae_utils/simplified_tree.py | 22 +++++++++++++++-------
 modeling/rqvae_utils/tree.py            | 18 +++++++++++-------
 modeling/rqvae_utils/tree_comparing.py  |  6 +++---
 3 files changed, 29 insertions(+), 17 deletions(-)

diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 34a0b69e..77ddb764 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -1,20 +1,26 @@
 import torch
 
+from models import RqVaeModel
+from utils import DEVICE
+
 
 class SimplifiedTree:
-    def __init__(self, embedding_table: torch.Tensor, device: torch.device = torch.device('cpu')):
+    def __init__(self, rqvae_model: RqVaeModel, device: torch.device = DEVICE):
         """
-        :param embedding_table: Тензор из RQ-VAE # (semantic_id_len, codebook_size, emb_dim)
-        :param device: Устройство
+        :param rqvae_model: обученная модель rq-vae
+        :param device: устройство
         """
-        self.embedding_table: torch.Tensor = embedding_table.to(device)  # (semantic_id_len, codebook_size, emb_dim)
-        self.sem_id_len, self.codebook_size, self.emb_dim = embedding_table.shape
         self.device: torch.device = device
+        self.embedding_table: torch.Tensor = torch.stack(
+            [cb for cb in rqvae_model.codebooks]
+        ).to(self.device)  # (semantic_id_len, codebook_size, emb_dim
+        self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor, sum_with_residuals: bool = True) -> None:
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor,
+                             sum_with_residuals: bool = True) -> None:
         """
         :param sum_with_residuals: флаг, отвечающий за то учитывать ли остатки при выборе кандидатов
         :param semantic_ids: (sem_ids_count, sem_id_len)
@@ -27,7 +33,9 @@ def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tens
         assert item_ids.shape == (self.sem_ids_count,)
 
         semantic_ids = semantic_ids.to(self.device)
-        residuals = residuals.to(self.device).float() if sum_with_residuals else torch.zeros_like(residuals, device=self.device, dtype=torch.float)
+        residuals = residuals.to(self.device).float() if sum_with_residuals else torch.zeros_like(residuals,
+                                                                                                  device=self.device,
+                                                                                                  dtype=torch.float)
         self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
         self.item_ids = item_ids
 
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index 5f125012..433c870e 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -1,18 +1,21 @@
 import numpy as np
 import torch
 
+from models import RqVaeModel
 from utils import DEVICE
 
 
 class Tree:
-    def __init__(self, embedding_table, device: torch.device = DEVICE):
+    def __init__(self, rqvae_model: RqVaeModel, device: torch.device = DEVICE):
         """
-        :param embedding_table: Тензор из RQ-VAE # (semantic_id_len, codebook_size, emb_dim)
-        :param device: Устройство
+        :param rqvae_model: обученная модель rq-vae
+        :param device: устройство
         """
-        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
-        self.sem_id_len, self.codebook_size, self.emb_dim = embedding_table.shape
         self.device: torch.device = device
+        self.embedding_table: torch.Tensor = torch.stack(
+            [cb for cb in rqvae_model.codebooks]
+        ).to(self.device)  # (semantic_id_len, codebook_size, emb_dim)
+        self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.key: torch.Tensor = torch.empty((0, 0))
         self.A: torch.Tensor = torch.empty((0, 0))  # будет (max_sem_id, )
         self.sem_ids_count: int = -1
@@ -161,14 +164,15 @@ def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels
         assert decomposed_embeddings.shape == (self.sem_ids_count, self.sem_id_len + 1, self.emb_dim)
 
         mask = (torch.arange(1, self.sem_id_len + 2, device=self.device) >=
-                torch.arange(self.sem_id_len + 2, 0, -1,device=self.device).unsqueeze(1)).float()
+                torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)).float()
         sids_mask = mask[levels + 1].unsqueeze(-1)  # (batch_size, sem_id_len + 1, 1)
         return torch.einsum('nld,bld->bnd', decomposed_embeddings, sids_mask)  # (batch_size, sem_ids_count, emb_dim)
 
     def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
         return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
 
-    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor, items_to_query: int) -> torch.Tensor:
+    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor,
+              items_to_query: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
         :param request_residuals: батч из остатков (batch_size, emb_dim)
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index 2dbcabd4..3f1d3f38 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -46,9 +46,9 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     ).to(DEVICE)
 
     trie = Trie(rqvae_model)
-    tree = Tree(emb_table, DEVICE)
-    simplified_tree = SimplifiedTree(emb_table, DEVICE)
-    simplified_tree_wr = SimplifiedTree(emb_table, DEVICE)
+    tree = Tree(rqvae_model, DEVICE)
+    simplified_tree = SimplifiedTree(rqvae_model, DEVICE)
+    simplified_tree_wr = SimplifiedTree(rqvae_model, DEVICE)
     alphabet_size = 10
 
     N = 12101

From 1e13ae3e5f8ad1ac91f16fc45d7ac71466d0c030 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Thu, 20 Feb 2025 13:35:00 +0300
Subject: [PATCH 103/175] =?UTF-8?q?=D1=82=D0=B5=D1=81=D1=82=20=D1=81=20?=
 =?UTF-8?q?=D0=B4=D1=80=D1=83=D0=B3=D0=B8=D0=BC=20=D0=B4=D0=B5=D1=80=D0=B5?=
 =?UTF-8?q?=D0=B2=D0=BE=D0=BC?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 configs/train/tiger_train_config.json     |   2 +-
 configs/train/tiger_wat_train_config.json | 180 +++++++++
 modeling/models/__init__.py               |   1 +
 modeling/models/tiger.py                  |   2 +-
 modeling/models/tigerWithAnotherTree.py   | 460 ++++++++++++++++++++++
 5 files changed, 643 insertions(+), 2 deletions(-)
 create mode 100644 configs/train/tiger_wat_train_config.json
 create mode 100644 modeling/models/tigerWithAnotherTree.py

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 2d3369c5..e357b294 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,7 +1,7 @@
 {
   "experiment_name": "tiger",
   "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 100,
+  "train_epochs_num": 50,
   "dataset": {
     "type": "sequence_full",
     "path_to_data_dir": "../data",
diff --git a/configs/train/tiger_wat_train_config.json b/configs/train/tiger_wat_train_config.json
new file mode 100644
index 00000000..a7eabbac
--- /dev/null
+++ b/configs/train/tiger_wat_train_config.json
@@ -0,0 +1,180 @@
+{
+  "experiment_name": "tigerWithAnotherTree",
+  "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 50,
+  "dataset": {
+    "type": "sequence_full",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "max_sequence_length": 50,
+    "samplers": {
+      "type": "last_item_prediction",
+      "negative_sampler_type": "random"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": true,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "tigerWithAnotherTree",
+    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+    "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+    "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
+    "sequence_prefix": "item",
+    "predictions_prefix": "logits",
+    "positive_prefix": "labels",
+    "labels_prefix": "labels",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_encoder_layers": 2,
+    "num_decoder_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
+    "activation": "gelu",
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 0.001
+    },
+    "clip_grad_threshold": 5.0
+  },
+  "loss": {
+    "type": "composite",
+    "losses": [
+      {
+        "type": "sample_logsoftmax",
+        "predictions_prefix": "logits",
+        "labels": "semantic.labels",
+        "weight": 1.0,
+        "output_prefix": "semantic_loss"
+      },
+      {
+        "type": "sample_logsoftmax",
+        "predictions_prefix": "dedup.logits",
+        "labels": "dedup.labels",
+        "weight": 1.0,
+        "output_prefix": "dedup_loss"
+      }
+    ],
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 64,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 256,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      }
+    ]
+  }
+}
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index b9678e2e..c5dc42e2 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -18,3 +18,4 @@
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
 from .tiger import TigerModel
+from .tigerWithAnotherTree import TigerModel2
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index feed89a7..49d51706 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -93,7 +93,7 @@ def __init__(
             item_id_to_semantic_id.to(DEVICE),
             item_id_to_residual.to(DEVICE),
             item_ids.to(DEVICE),
-            sum_with_residuals=False
+            sum_with_residuals=True
         )
 
         self._bos_token_id = self._codebook_sizes[0]
diff --git a/modeling/models/tigerWithAnotherTree.py b/modeling/models/tigerWithAnotherTree.py
new file mode 100644
index 00000000..1883b5b9
--- /dev/null
+++ b/modeling/models/tigerWithAnotherTree.py
@@ -0,0 +1,460 @@
+import json
+
+import torch
+from models.base import SequentialTorchModel
+from rqvae_utils import CollisionSolver, Tree
+from torch import nn
+from utils import DEVICE, create_masked_tensor, get_activation_function
+
+from .rqvae import RqVaeModel
+
+
+class TigerModel2(SequentialTorchModel, config_name="tigerWithAnotherTree"):
+    def __init__(
+        self,
+        rqvae_model,
+        item_id_to_semantic_id,
+        item_id_to_residual,
+        item_id_to_text_embedding,
+        solver,
+        sequence_prefix,
+        pred_prefix,
+        positive_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_encoder_layers,
+        num_decoder_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_encoder_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True,
+        )
+
+        self._sequence_prefix = sequence_prefix
+        self._pred_prefix = pred_prefix
+        self._positive_prefix = positive_prefix
+        self._labels_prefix = labels_prefix
+
+        transformer_decoder_layer = nn.TransformerDecoderLayer(
+            d_model=embedding_dim,
+            nhead=num_heads,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=get_activation_function(activation),
+            layer_norm_eps=layer_norm_eps,
+            batch_first=True,
+        )
+
+        self._decoder = nn.TransformerDecoder(
+            transformer_decoder_layer, num_decoder_layers
+        )
+
+        self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
+        self._decoder_dropout = nn.Dropout(dropout)
+
+        self._solver: CollisionSolver = solver
+
+        self._codebook_sizes = rqvae_model.codebook_sizes
+
+        self._codebook_item_embeddings_stacked = torch.stack(
+            [codebook for codebook in rqvae_model.codebooks]
+        )
+        self._codebook_item_embeddings_stacked.requires_grad = (
+            False  # TODOPK maybe unfreeeze later
+        )
+
+        self._item_id_to_semantic_id = item_id_to_semantic_id
+        self._item_id_to_residual = item_id_to_residual
+        self._item_id_to_text_embedding = item_id_to_text_embedding
+
+        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
+
+        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
+
+        self._trie = Tree(rqvae_model)
+
+        self._trie.build_tree_structure(
+            item_id_to_semantic_id.to(DEVICE),
+            item_id_to_residual.to(DEVICE),
+            item_ids.to(DEVICE)
+        )
+
+        self._bos_token_id = self._codebook_sizes[0]
+        self._bos_weight = nn.Parameter(
+            torch.nn.init.trunc_normal_(
+                torch.zeros(embedding_dim),
+                std=initializer_range,
+                a=-2 * initializer_range,
+                b=2 * initializer_range,
+            )
+        )
+
+        self._codebook_embeddings = nn.Embedding(
+            num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
+        )  # + 2 for bos token & residual
+
+        self._init_weights(initializer_range)
+
+    @classmethod
+    def init_rqvae(cls, config) -> RqVaeModel:
+        rqvae_config = json.load(open(config["rqvae_train_config_path"]))
+        rqvae_config["model"]["should_init_codebooks"] = False
+
+        rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
+        rqvae_model.load_state_dict(
+            torch.load(config["rqvae_checkpoint_path"], weights_only=True)
+        )
+        rqvae_model.eval()
+        for param in rqvae_model.parameters():
+            param.requires_grad = False
+
+        codebook_sizes = rqvae_model.codebook_sizes
+        assert all([book_size == codebook_sizes[0] for book_size in codebook_sizes])
+
+        return rqvae_model
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        rqvae_model = cls.init_rqvae(config)
+        embs_extractor = torch.load(config["embs_extractor_path"], weights_only=False)
+
+        embs_extractor = embs_extractor.sort_index()
+
+        item_ids = embs_extractor.index.tolist()
+        assert item_ids == list(range(1, len(item_ids) + 1))
+
+        text_embeddings = torch.stack(embs_extractor["embeddings"].tolist()).to(DEVICE)
+
+        semantic_ids, residuals = rqvae_model({"embeddings": text_embeddings})
+
+        solver = CollisionSolver(
+            emb_dim=residuals.shape[1],
+            sem_id_len=len(rqvae_model.codebook_sizes),
+            codebook_size=rqvae_model.codebook_sizes[0],
+        )
+        solver.create_query_candidates_dict(
+            torch.tensor(item_ids), semantic_ids, residuals
+        )
+
+        return cls(
+            rqvae_model=rqvae_model,
+            item_id_to_semantic_id=semantic_ids,
+            item_id_to_residual=residuals,
+            item_id_to_text_embedding=text_embeddings,
+            solver=solver,
+            sequence_prefix=config["sequence_prefix"],
+            pred_prefix=config["predictions_prefix"],
+            positive_prefix=config["positive_prefix"],
+            labels_prefix=config["labels_prefix"],
+            num_items=rqvae_model.codebook_sizes[0],  # unused
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_encoder_layers=config["num_encoder_layers"],
+            num_decoder_layers=config["num_decoder_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
+        )
+
+    # semantic ids come with dedup token
+    def forward(self, inputs):
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+
+        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
+        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
+
+        if self.training:
+            label_events = inputs["{}.ids".format(self._positive_prefix)]
+            label_lengths = inputs["{}.length".format(self._positive_prefix)]
+
+            label_lengths = label_lengths * (
+                len(self._codebook_sizes) + 1
+            )  # TODOPK bos prepending
+            tgt_embeddings = self.get_item_embeddings(
+                label_events
+            )  # (all_batch_events, embedding_dim)
+
+            decoder_outputs = self._apply_decoder(
+                tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
+            )  # (batch_size, label_len, embedding_dim)
+
+            decoder_prefix_scores = torch.einsum(
+                "bsd,scd->bsc",
+                decoder_outputs[:, :-1, :],
+                self._codebook_item_embeddings_stacked,
+            )
+
+            decoder_output_residual = decoder_outputs[:, -1, :]
+
+            semantic_ids = self._item_id_to_semantic_id[
+                label_events - 1
+            ]  # len(events), len(codebook_sizes)
+            true_residuals = self._item_id_to_residual[label_events - 1]
+
+            true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
+            pred_info = self._solver.get_pred_scores(
+                semantic_ids, decoder_output_residual
+            )
+
+            return {
+                "logits": decoder_prefix_scores,
+                "semantic.labels": semantic_ids,
+                "dedup.logits": pred_info["pred_scores"],
+                "dedup.labels": true_info["true_dedup_tokens"],
+            }
+        else:
+            semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
+                encoder_embeddings, encoder_mask
+            )  # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
+
+            residuals = tgt_embeddings[:, -1, :]
+            semantic_ids = semantic_ids.to(torch.int64)
+
+            item_ids = self._trie.query(semantic_ids, residuals, items_to_query=20)
+
+            return item_ids
+
+    def _apply_decoder(
+        self, tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
+    ):
+        tgt_embeddings, tgt_mask = create_masked_tensor(
+            data=tgt_embeddings, lengths=label_lengths
+        )  # (batch_size, dec_seq_len, embedding_dim), (batch_size, dec_seq_len)
+
+        batch_size = tgt_embeddings.shape[0]
+        bos_embeddings = self._bos_weight.unsqueeze(0).expand(
+            batch_size, 1, -1
+        )  # (batch_size, 1, embedding_dim)
+
+        tgt_embeddings = torch.cat(
+            [bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1
+        )  # remove residual by using :-1
+
+        label_len = tgt_mask.shape[1]
+
+        assert label_len == len(self._codebook_sizes) + 1
+
+        position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
+        assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
+
+        tgt_embeddings = tgt_embeddings + position_embeddings
+
+        tgt_embeddings = self._decoder_layernorm(
+            tgt_embeddings
+        )  # (batch_size, dec_seq_len, embedding_dim)
+        tgt_embeddings = self._decoder_dropout(
+            tgt_embeddings
+        )  # (batch_size, dec_seq_len, embedding_dim)
+
+        tgt_embeddings[~tgt_mask] = 0
+
+        causal_mask = (
+            torch.tril(torch.ones(label_len, label_len)).bool().to(DEVICE)
+        )  # (dec_seq_len, dec_seq_len)
+
+        decoder_outputs = self._decoder(
+            tgt=tgt_embeddings,
+            memory=encoder_embeddings,
+            tgt_mask=~causal_mask,
+            memory_key_padding_mask=~encoder_mask,
+        )  # (batch_size, dec_seq_len, embedding_dim)
+
+        return decoder_outputs
+
+    def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
+        batch_size = encoder_embeddings.shape[0]
+        embedding_dim = encoder_embeddings.shape[2]
+
+        tgt_embeddings = (
+            self._bos_weight.unsqueeze(0)
+            .unsqueeze(0)
+            .expand(batch_size, 1, embedding_dim)
+        )
+
+        semantic_ids = torch.tensor([], device=DEVICE)
+
+        for step in range(len(self._codebook_sizes) + 1):  # semantic_id_seq + residual
+            if step == 0:
+                indexes = torch.full(
+                    (batch_size, 1), len(self._codebook_sizes), device=DEVICE
+                )  # len(self._codebook_sizes) for bos
+            else:
+                indexes = torch.cat(
+                    [indexes, torch.full((batch_size, 1), step - 1, device=DEVICE)],
+                    dim=1,
+                )
+
+            position_embeddings = self._codebook_embeddings(indexes.view(-1))
+
+            curr_embeddings = tgt_embeddings + position_embeddings.view(
+                batch_size, step + 1, embedding_dim
+            )
+
+            curr_embeddings = self._decoder_layernorm(curr_embeddings)
+            curr_embeddings = self._decoder_dropout(curr_embeddings)
+
+            decoder_output = self._decoder(
+                tgt=curr_embeddings,
+                memory=encoder_embeddings,
+                memory_key_padding_mask=~encoder_mask,
+            )
+
+            next_token_embedding = decoder_output[
+                :, -1, :
+            ]  # batch_size x embedding_dim
+
+            # TODOPK ask if we take last
+            # torch.Size([256, 1, 64])
+            # torch.Size([256, 2, 64])
+            # torch.Size([256, 3, 64])
+            # torch.Size([256, 4, 64])
+
+            if step < len(self._codebook_sizes):
+                codebook = self._codebook_item_embeddings_stacked[
+                    step
+                ]  # len(codebook_sizes) x embedding_dim
+                closest_semantic_ids = torch.argmax(
+                    torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
+                )  # batch_size x 1
+                semantic_ids = torch.cat(
+                    [semantic_ids, closest_semantic_ids.unsqueeze(1)], dim=1
+                )  # batch_size x (step + 1)
+                next_token_embedding = codebook[
+                    closest_semantic_ids
+                ]  # batch_size x embedding_dim
+
+            tgt_embeddings = torch.cat(
+                [tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1
+            )
+
+        return semantic_ids, tgt_embeddings
+
+    def get_item_embeddings(self, events):
+        embs = self._item_id_to_semantic_embedding[
+            events - 1
+        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        return embs.view(
+            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
+        )
+
+    def get_init_item_embeddings(self, events):
+        # convert to semantic ids
+        semantic_ids = self._item_id_to_semantic_id[
+            events - 1
+        ]  # len(events), len(codebook_sizes)
+
+        result = []
+        for semantic_id in semantic_ids:
+            item_repr = []
+            for codebook_idx, codebook_id in enumerate(semantic_id):
+                item_repr.append(
+                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
+                )
+            result.append(torch.stack(item_repr))
+
+        semantic_embeddings = torch.stack(result)
+
+        # get residuals
+        residual = self._item_id_to_residual[events - 1]
+        # text_embeddings = self._item_id_to_text_embedding[events - 1]
+        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
+        residual = residual.unsqueeze(1)
+
+        # get true item embeddings
+        item_embeddings = torch.cat(
+            [semantic_embeddings, residual], dim=1
+        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+
+        # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
+
+        return item_embeddings
+
+    def _encoder_pos_embeddings(self, lengths, mask):
+        def position_lambda(x):
+            return x // (
+                len(self._codebook_sizes) + 1
+            )  # 5 5 5 5 4 4 4 4 ..., +1 for residual
+
+        position_embeddings = self._get_position_embeddings(
+            lengths, mask, position_lambda, self._position_embeddings
+        )
+
+        def codebook_lambda(x):
+            x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
+            x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
+            # 0 1 2 4 0 1 2 4 ... # len(self._codebook_sizes) + 1 = 4 for residual
+            return x
+
+        codebook_embeddings = self._get_position_embeddings(
+            lengths, mask, codebook_lambda, self._codebook_embeddings
+        )
+
+        return position_embeddings + codebook_embeddings
+
+    def _decoder_pos_embeddings(self, lengths, mask):
+        def codebook_lambda(x):
+            non_bos = x < len(self._codebook_sizes)
+            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
+            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ... len(self._codebook_sizes) = 3 for bos
+
+        codebook_embeddings = self._get_position_embeddings(
+            lengths, mask, codebook_lambda, self._codebook_embeddings
+        )
+
+        return codebook_embeddings
+
+    def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
+        batch_size = mask.shape[0]
+        seq_len = mask.shape[1]
+
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=DEVICE)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
+        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
+
+        positions = positions[positions_mask]  # (all_batch_events)
+        # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 ...
+
+        positions = position_lambda(positions)  # (all_batch_events)
+
+        # print(f"{positions.tolist()[:20]=}")
+
+        assert (positions >= 0).all() and (
+            positions < embedding_layer.num_embeddings
+        ).all()
+
+        position_embeddings = embedding_layer(
+            positions
+        )  # (all_batch_events, embedding_dim)
+
+        position_embeddings, _ = create_masked_tensor(
+            data=position_embeddings, lengths=lengths
+        )  # (batch_size, seq_len, embedding_dim)
+
+        return position_embeddings

From 6250cbe0a0cc9b84e28df6d18035f46b861b2711 Mon Sep 17 00:00:00 2001
From: iskbaga <112892889+iskbaga@users.noreply.github.com>
Date: Thu, 20 Feb 2025 20:21:33 +0300
Subject: [PATCH 104/175] =?UTF-8?q?CollisionSolver=20=D1=82=D0=B5=D0=BF?=
 =?UTF-8?q?=D0=B5=D1=80=D1=8C=20=D0=BD=D0=B0=20cuda?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 modeling/rqvae_utils/collision_solver.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index fd71d0fa..f673c6dd 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -2,13 +2,15 @@
 
 import torch
 
+from utils import DEVICE
+
 
 class CollisionSolver:
     def __init__(self,
                  emb_dim: int,
                  sem_id_len: int,
                  codebook_size: int,
-                 device: torch.device = torch.device('cpu')):
+                 device: torch.device=DEVICE):
         """
         :param emb_dim: Длина остатка
         :param codebook_size: Количество элементов в одном кодбуке
@@ -49,15 +51,15 @@ def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: tor
         residuals = residuals.to(self.device)
         semantic_ids = semantic_ids.to(self.device)
 
-        unique_id = torch.einsum('nc,c->n', semantic_ids, self.key)  # хэши
+        unique_id = (semantic_ids * self.key).sum(dim=1)  # хэши
         unique_ids, inverse_indices, counts = torch.unique(unique_id, return_inverse=True, return_counts=True)
         sorted_indices = torch.argsort(inverse_indices)  # сортированные индексы чтобы совпадающие хэши шли подряд
 
         row_indices = inverse_indices[sorted_indices]  # отсортированные хэши
 
         offsets = torch.cumsum(counts, dim=0) - counts
-        col_indices = (torch.arange(semantic_ids_count)
-                       - offsets[row_indices])  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
+        col_indices = torch.arange(semantic_ids_count, device=self.device) - offsets[
+            row_indices]  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
 
         indices = torch.stack([
             unique_ids[row_indices],
@@ -89,7 +91,7 @@ def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tupl
         assert semantic_ids.shape[1] == self.sem_id_len
 
         semantic_ids = semantic_ids.to(self.device)
-        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+        unique_ids = (semantic_ids * self.key).sum(dim=1)
 
         candidates = torch.stack([self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids])
         counts = torch.tensor([self.counts_dict[key.item()] for key in unique_ids], device=self.device)
@@ -99,7 +101,7 @@ def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tupl
 
     def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
         """
-        :param semantic_ids: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
+        :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param pred_residuals: [batch_size, emb_dim] предсказанные остатки
 
         :return: Словарь с ключами:
@@ -114,7 +116,7 @@ def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tens
         semantic_ids = semantic_ids.to(self.device)
         pred_residuals = pred_residuals.to(self.device)
 
-        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+        unique_ids = (semantic_ids * self.key).sum(dim=1)
 
         candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_ids)
 
@@ -132,7 +134,7 @@ def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tens
     def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[
         str, torch.Tensor]:
         """
-        :param semantic_ids: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
+        :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param true_residuals: [batch_size, emb_dim] реальные остатки
 
         :return: Словарь с ключами:
@@ -169,7 +171,7 @@ def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Ten
         semantic_ids = semantic_ids.to(self.device)
         dedup_tokens = dedup_tokens.to(self.device)
 
-        unique_ids = torch.einsum('nc,c->n', semantic_ids, self.key)
+        unique_ids = (semantic_ids * self.key).sum(dim=1)
 
         item_ids = torch.stack(
             [self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])

From ec80d50bcb33baaefd56e9ed2d59c9aed9f186b3 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 21 Feb 2025 17:06:33 +0300
Subject: [PATCH 105/175] use single codebook for training & query

---
 configs/train/tiger_train_config.json   |  4 ++--
 modeling/models/tiger.py                | 32 +++++++++++--------------
 modeling/rqvae_utils/simplified_tree.py |  9 +++----
 modeling/rqvae_utils/tree.py            |  9 +++----
 4 files changed, 22 insertions(+), 32 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index e357b294..c44e10a6 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,7 +1,7 @@
 {
-  "experiment_name": "tiger",
+  "experiment_name": "tiger_simplified_no_residuals_freezed",
   "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 50,
+  "train_epochs_num": 100,
   "dataset": {
     "type": "sequence_full",
     "path_to_data_dir": "../data",
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index b1e8a340..00303a29 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -69,15 +69,6 @@ def __init__(
 
         self._solver: CollisionSolver = solver
 
-        self._codebook_sizes = rqvae_model.codebook_sizes
-
-        self._codebook_item_embeddings_stacked = torch.stack(
-            [codebook for codebook in rqvae_model.codebooks]
-        )
-        self._codebook_item_embeddings_stacked.requires_grad = (
-            False  # TODOPK maybe unfreeeze later
-        )
-
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
 
@@ -85,15 +76,7 @@ def __init__(
 
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
 
-        self._trie = SimplifiedTree(rqvae_model)
-
-        self._trie.build_tree_structure(
-            item_id_to_semantic_id.to(DEVICE),
-            item_id_to_residual.to(DEVICE),
-            item_ids.to(DEVICE),
-            sum_with_residuals=True
-        )
-
+        self._codebook_sizes = rqvae_model.codebook_sizes
         self._bos_token_id = self._codebook_sizes[0]
         self._bos_weight = nn.Parameter(
             torch.nn.init.trunc_normal_(
@@ -110,6 +93,19 @@ def __init__(
 
         self._init_weights(initializer_range)
 
+        self._codebook_item_embeddings_stacked = nn.Parameter(torch.stack(
+            [codebook for codebook in rqvae_model.codebooks]
+        ), requires_grad=False)
+        
+        self._trie = SimplifiedTree(self._codebook_item_embeddings_stacked)
+
+        self._trie.build_tree_structure(
+            item_id_to_semantic_id.to(DEVICE),
+            item_id_to_residual.to(DEVICE),
+            item_ids.to(DEVICE),
+            sum_with_residuals=False
+        )
+
     @classmethod
     def init_rqvae(cls, config) -> RqVaeModel:
         rqvae_config = json.load(open(config["rqvae_train_config_path"]))
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 77ddb764..5a09dcb4 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -1,19 +1,16 @@
 import torch
 
-from models import RqVaeModel
 from utils import DEVICE
 
 
 class SimplifiedTree:
-    def __init__(self, rqvae_model: RqVaeModel, device: torch.device = DEVICE):
+    def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE):
         """
-        :param rqvae_model: обученная модель rq-vae
+        :param embedding_table: обученные эмбеддинги
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = torch.stack(
-            [cb for cb in rqvae_model.codebooks]
-        ).to(self.device)  # (semantic_id_len, codebook_size, emb_dim
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index 433c870e..b09cd049 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -1,20 +1,17 @@
 import numpy as np
 import torch
 
-from models import RqVaeModel
 from utils import DEVICE
 
 
 class Tree:
-    def __init__(self, rqvae_model: RqVaeModel, device: torch.device = DEVICE):
+    def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE):
         """
-        :param rqvae_model: обученная модель rq-vae
+        :param embedding_table: обученные эмбеддинги
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = torch.stack(
-            [cb for cb in rqvae_model.codebooks]
-        ).to(self.device)  # (semantic_id_len, codebook_size, emb_dim)
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.key: torch.Tensor = torch.empty((0, 0))
         self.A: torch.Tensor = torch.empty((0, 0))  # будет (max_sem_id, )

From 6f8d757935177d99ec71ff7775bb0f601f0f42ce Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 21 Feb 2025 17:10:26 +0300
Subject: [PATCH 106/175] move init

---
 modeling/models/tiger.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 00303a29..5721fe19 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -74,8 +74,6 @@ def __init__(
 
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
 
-        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
-
         self._codebook_sizes = rqvae_model.codebook_sizes
         self._bos_token_id = self._codebook_sizes[0]
         self._bos_weight = nn.Parameter(
@@ -97,6 +95,8 @@ def __init__(
             [codebook for codebook in rqvae_model.codebooks]
         ), requires_grad=False)
         
+        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
+        
         self._trie = SimplifiedTree(self._codebook_item_embeddings_stacked)
 
         self._trie.build_tree_structure(

From 42cbf1ce0c22046c6b81aded7ba00581fe2bc564 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Fri, 21 Feb 2025 17:43:13 +0300
Subject: [PATCH 107/175] unfreeze

---
 configs/train/tiger_train_config.json | 2 +-
 modeling/models/tiger.py              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index c44e10a6..f5173ce1 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "tiger_simplified_no_residuals_freezed",
+  "experiment_name": "tiger_simplified_no_residuals_unfreezed",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 5721fe19..0fa67f4c 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -93,7 +93,7 @@ def __init__(
 
         self._codebook_item_embeddings_stacked = nn.Parameter(torch.stack(
             [codebook for codebook in rqvae_model.codebooks]
-        ), requires_grad=False)
+        ))
         
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         

From b2e0520d7c66b5f2af29e2876ebc8ceb87156d2e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 12:35:27 +0300
Subject: [PATCH 108/175] add parametera to semantic sasrec

---
 modeling/models/sasrec_semantic.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index a36513de..e836f6b6 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -40,6 +40,16 @@ def __init__(
         self._positive_prefix = positive_prefix
 
         self._codebook_sizes = rqvae_model.codebook_sizes
+
+        self._item_id_to_semantic_id = item_id_to_semantic_id
+        self._item_id_to_residual = item_id_to_residual
+
+        self._codebook_embeddings = nn.Embedding(
+            num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
+        )  # + 2 for bos token & residual
+
+        self._init_weights(initializer_range)
+
         self._codebook_item_embeddings_stacked = torch.stack(
             [codebook for codebook in rqvae_model.codebooks]
         )
@@ -47,20 +57,18 @@ def __init__(
             False  # TODOPK maybe unfreeeze later
         )
 
-        self._item_id_to_semantic_id = item_id_to_semantic_id
-        self._item_id_to_residual = item_id_to_residual
-
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
 
         self._item_id_to_semantic_embedding, self._item_id_to_full_embedding = (
             self.get_init_item_embeddings(item_ids)
         )
 
-        self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
-        )  # + 2 for bos token & residual
-
-        self._init_weights(initializer_range)
+        self._item_id_to_semantic_embedding = nn.Parameter(
+            self._item_id_to_semantic_embedding, requires_grad=False
+        )
+        self._item_id_to_full_embedding = nn.Parameter(
+            self._item_id_to_full_embedding, requires_grad=False
+        )
 
     @classmethod
     def create_from_config(cls, config, **kwargs):

From 3fd32b6b4a337ff730b5dc9336e99073dfea60f7 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:39:21 +0300
Subject: [PATCH 109/175] fix sasrec

---
 modeling/models/sasrec_semantic.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index e836f6b6..628f6e46 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -121,7 +121,7 @@ def forward(self, inputs):
             )  # (batch_size, num_items)
 
             positive_scores = torch.gather(
-                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
+                input=all_scores, dim=1, index=all_positive_sample_events[..., None] - 1
             )  # (batch_size, 1)
 
             sample_ids, _ = create_masked_tensor(
@@ -131,7 +131,7 @@ def forward(self, inputs):
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
-                index=sample_ids,
+                index=sample_ids - 1,
                 src=torch.ones_like(sample_ids) * (-torch.inf),
             )  # (all_batch_events, num_items)
 
@@ -152,14 +152,14 @@ def forward(self, inputs):
                 candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
+            return indices + 1
 
     def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
             events - 1
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.view(
-            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
+        return embs.reshape(
+            -1, self._embedding_dim
         )
 
     def get_init_item_embeddings(self, events):

From 59c760f1189afa82883e0d3b5a23a5265cda397f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:41:59 +0300
Subject: [PATCH 110/175] fix sasrec semantic

---
 modeling/models/sasrec_semantic.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 628f6e46..98770aa5 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -132,7 +132,7 @@ def forward(self, inputs):
                 input=all_scores,
                 dim=1,
                 index=sample_ids - 1,
-                src=torch.ones_like(sample_ids) * (-torch.inf),
+                src=torch.ones_like(sample_ids - 1) * (-torch.inf),
             )  # (all_batch_events, num_items)
 
             return {
@@ -158,9 +158,7 @@ def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
             events - 1
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.reshape(
-            -1, self._embedding_dim
-        )
+        return embs.reshape(-1, self._embedding_dim)
 
     def get_init_item_embeddings(self, events):
         # convert to semantic ids

From b0ac4d1f08b5ac9a7b0306e869d86aeabfed2548 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:47:56 +0300
Subject: [PATCH 111/175] add debugs

---
 modeling/models/sasrec_semantic.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 98770aa5..10e44b62 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -119,15 +119,21 @@ def forward(self, inputs):
             all_scores = torch.einsum(
                 "ad,nd->an", last_embeddings, all_embeddings
             )  # (batch_size, num_items)
+            
+            print("before gather")
 
             positive_scores = torch.gather(
                 input=all_scores, dim=1, index=all_positive_sample_events[..., None] - 1
             )  # (batch_size, 1)
 
+            print("after gather")
+
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
+            print("before scatter")
+
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
@@ -135,6 +141,8 @@ def forward(self, inputs):
                 src=torch.ones_like(sample_ids - 1) * (-torch.inf),
             )  # (all_batch_events, num_items)
 
+            print("after scatter")
+
             return {
                 "positive_scores": positive_scores,
                 "negative_scores": negative_scores,

From 5f5adeb353cec731189e6d7d6b08be98d2f8849d Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:50:09 +0300
Subject: [PATCH 112/175] remove debugging

---
 modeling/models/sasrec_semantic.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 10e44b62..98770aa5 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -119,21 +119,15 @@ def forward(self, inputs):
             all_scores = torch.einsum(
                 "ad,nd->an", last_embeddings, all_embeddings
             )  # (batch_size, num_items)
-            
-            print("before gather")
 
             positive_scores = torch.gather(
                 input=all_scores, dim=1, index=all_positive_sample_events[..., None] - 1
             )  # (batch_size, 1)
 
-            print("after gather")
-
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-            print("before scatter")
-
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
@@ -141,8 +135,6 @@ def forward(self, inputs):
                 src=torch.ones_like(sample_ids - 1) * (-torch.inf),
             )  # (all_batch_events, num_items)
 
-            print("after scatter")
-
             return {
                 "positive_scores": positive_scores,
                 "negative_scores": negative_scores,

From 26030d1755bd38937d8aa1dd6519f32f81eb0774 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:54:55 +0300
Subject: [PATCH 113/175] debug

---
 modeling/models/sasrec_semantic.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 98770aa5..b3cbb143 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -127,6 +127,8 @@ def forward(self, inputs):
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
+            
+            print(f"{sample_ids.max()=} {sample_ids.min()=}")
 
             negative_scores = torch.scatter(
                 input=all_scores,

From df7a445b5cbf4f30bdf04dd7de69400daf450b3d Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:57:00 +0300
Subject: [PATCH 114/175] debug

---
 modeling/models/sasrec_semantic.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index b3cbb143..870d984b 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -97,6 +97,8 @@ def forward(self, inputs):
         all_sample_lengths = inputs[
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
+        
+        print(f"a {all_sample_events.max()=} {all_sample_events.min()=}")
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths * (len(self._codebook_sizes) + 1)

From cca0d7c099a011086efe2a8374b93a127aa8549f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 14:59:52 +0300
Subject: [PATCH 115/175] fixes

---
 modeling/models/sasrec_semantic.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 870d984b..b0f3b547 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -116,14 +116,22 @@ def forward(self, inputs):
             all_embeddings = (
                 self._item_id_to_full_embedding
             )  # (num_items, embedding_dim)
+            
+            all_embeddings = torch.cat(
+                [
+                    torch.zeros(1, self._embedding_dim, device=DEVICE),
+                    all_embeddings
+                ],
+                dim=0
+            )
 
             # a -- all_batch_events, n -- num_items, d -- embedding_dim
             all_scores = torch.einsum(
                 "ad,nd->an", last_embeddings, all_embeddings
-            )  # (batch_size, num_items)
+            )  # (batch_size, num_items + 1)
 
             positive_scores = torch.gather(
-                input=all_scores, dim=1, index=all_positive_sample_events[..., None] - 1
+                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
             )  # (batch_size, 1)
 
             sample_ids, _ = create_masked_tensor(
@@ -135,8 +143,8 @@ def forward(self, inputs):
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
-                index=sample_ids - 1,
-                src=torch.ones_like(sample_ids - 1) * (-torch.inf),
+                index=sample_ids,
+                src=torch.ones_like(sample_ids) * (-torch.inf),
             )  # (all_batch_events, num_items)
 
             return {

From a97acc219e30666a618e66eee8e9505a76daffce Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 15:52:43 +0300
Subject: [PATCH 116/175] semantic last refactors (cat passes grads)

---
 modeling/models/sasrec_semantic.py | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index b0f3b547..0ea42d7e 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -97,8 +97,6 @@ def forward(self, inputs):
         all_sample_lengths = inputs[
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
-        
-        print(f"a {all_sample_events.max()=} {all_sample_events.min()=}")
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths * (len(self._codebook_sizes) + 1)
@@ -116,14 +114,11 @@ def forward(self, inputs):
             all_embeddings = (
                 self._item_id_to_full_embedding
             )  # (num_items, embedding_dim)
-            
+
             all_embeddings = torch.cat(
-                [
-                    torch.zeros(1, self._embedding_dim, device=DEVICE),
-                    all_embeddings
-                ],
-                dim=0
-            )
+                [torch.zeros(1, self._embedding_dim, device=DEVICE), all_embeddings],
+                dim=0,
+            )  # (num_items + 1, embedding_dim)
 
             # a -- all_batch_events, n -- num_items, d -- embedding_dim
             all_scores = torch.einsum(
@@ -137,15 +132,13 @@ def forward(self, inputs):
             sample_ids, _ = create_masked_tensor(
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
-            
-            print(f"{sample_ids.max()=} {sample_ids.min()=}")
 
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
                 index=sample_ids,
                 src=torch.ones_like(sample_ids) * (-torch.inf),
-            )  # (all_batch_events, num_items)
+            )  # (all_batch_events, num_items + 1)
 
             return {
                 "positive_scores": positive_scores,
@@ -187,7 +180,9 @@ def get_init_item_embeddings(self, events):
                 )
             result.append(torch.stack(item_repr))
 
-        semantic_embeddings = torch.stack(result)
+        semantic_embeddings = torch.stack(
+            result
+        )  # len(events), len(codebook_sizes), embedding_dim
 
         # get residuals
         residual = self._item_id_to_residual[events - 1]

From cf30973bc9a10c10c4080be4b4d48e2b99e96df8 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 15:54:05 +0300
Subject: [PATCH 117/175] TODOPK + loss fixes

---
 modeling/loss/base.py             |  8 ++--
 modeling/models/sasrec_freezed.py |  1 +
 modeling/models/tiger.py          | 71 ++++++++++++++++++++++++++-----
 3 files changed, 66 insertions(+), 14 deletions(-)

diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 10f9e390..1863600f 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -53,7 +53,7 @@ def forward(self, inputs):
 
         return total_loss
 
-
+# TODOPK CrossEntropy loss (logits, labels)
 class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
     def __init__(self, predictions_prefix, labels):
         super().__init__()
@@ -78,15 +78,15 @@ def forward(self, inputs):  # use log soft max
         
         if len(logits.shape) == 3:
             loss = -torch.gather(
-                torch.log_softmax(logits, dim=-1).view(batch_size * seq_len, logits.shape[-1]),
+                torch.log_softmax(logits, dim=-1).reshape(batch_size * seq_len, logits.shape[-1]),
                 dim=-1, 
-                index=candidates.view(batch_size * seq_len, 1)
+                index=candidates.reshape(batch_size * seq_len, 1)
             ).mean()
         else:
             loss = -torch.gather(
                 torch.log_softmax(logits, dim=-1),
                 dim=-1, 
-                index=candidates.view(batch_size, 1) # TODOPK check if this is correct
+                index=candidates.reshape(batch_size, 1)
             ).mean()
             
         return loss
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 0c910e5d..bbc20ab9 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -66,6 +66,7 @@ def __init__(
         )
 
         # TODOPK ask about freezed masked & padding tokens
+        # TODOPK use rqvae embeddings instead of text embeddings (freeze / unfreeze)
 
         self._item_embeddings.weight.data.copy_(extended_embeddings)
 
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 0fa67f4c..66fbcea5 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -224,7 +224,13 @@ def forward(self, inputs):
         else:
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
                 encoder_embeddings, encoder_mask
-            )  # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
+            )  # (batch_size, len(self._codebook_sizes) (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
+
+            
+            # 1 4 6 -> lookup -> sum = emb (last embedding) # bs, embedding_dim
+            # take all embedings (from stacked) # all_items, embedding_dim
+            # take from sasrec eval (indices + 1)
+            # guarantee that all items are in correct order
 
             residuals = tgt_embeddings[:, -1, :]
             semantic_ids = semantic_ids.to(torch.int64)
@@ -232,6 +238,22 @@ def forward(self, inputs):
             item_ids = self._trie.query(semantic_ids, items_to_query=20)
 
             return item_ids
+        
+        # else:  # eval mode
+        #     last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+        #     # b - batch_size, n - num_candidates, d - embedding_dim
+        #     candidate_scores = torch.einsum(
+        #         'bd,nd->bn',
+        #         last_embeddings,
+        #         self._item_embeddings.weight
+        #     )  # (batch_size, num_items + 2)
+
+        #     _, indices = torch.topk(
+        #         candidate_scores,
+        #         k=20, dim=-1, largest=True
+        #     )  # (batch_size, 20)
+
+        #     return indices + 1
 
     def _apply_decoder(
         self, tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
@@ -258,12 +280,13 @@ def _apply_decoder(
 
         tgt_embeddings = tgt_embeddings + position_embeddings
 
-        tgt_embeddings = self._decoder_layernorm(
-            tgt_embeddings
-        )  # (batch_size, dec_seq_len, embedding_dim)
-        tgt_embeddings = self._decoder_dropout(
-            tgt_embeddings
-        )  # (batch_size, dec_seq_len, embedding_dim)
+        # TODOPK remove layernorm & dropout (for inference)
+        # tgt_embeddings = self._decoder_layernorm(
+        #     tgt_embeddings
+        # )  # (batch_size, dec_seq_len, embedding_dim)
+        # tgt_embeddings = self._decoder_dropout(
+        #     tgt_embeddings
+        # )  # (batch_size, dec_seq_len, embedding_dim)
 
         tgt_embeddings[~tgt_mask] = 0
 
@@ -289,6 +312,10 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             .unsqueeze(0)
             .expand(batch_size, 1, embedding_dim)
         )
+        
+        # curr_target_emb = () # <- layernorm, dropout & positional
+        # concat target <- [tgt_embs, cutt_tgt_emb]
+        # decoder(concat target) 
 
         semantic_ids = torch.tensor([], device=DEVICE)
 
@@ -303,20 +330,44 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                     dim=1,
                 )
 
-            position_embeddings = self._codebook_embeddings(indexes.view(-1))
+            # 3
+            # 3 0
+            # 3 0 1
+            # 3 0 1 2
+            # 3 0 1 2 4
+
+            # bos_token = len(self._codebook_sizes)
+            # residual_token = len(self._codebook_sizes) + 1
+
+            # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
+            # 5 5 5 5 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1
+            # 0 1 2 4 0 1 2 4 0 1 2 4 0 1 2 4 0 1 2 4
+
+            position_embeddings = self._codebook_embeddings(indexes.view(-1)) # cb + 2
 
             curr_embeddings = tgt_embeddings + position_embeddings.view(
                 batch_size, step + 1, embedding_dim
             )
+            
+            # TODOPK += last
+            # curr_embeddings = tgt_embeddings[:, -1] += position_embeddings.view(
+            #     batch_size, step + 1, embedding_dim
+            # )
+
+            # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings)
+            # curr_embeddings[:, -1, :] = self._decoder_dropout(curr_embeddings)
 
-            curr_embeddings = self._decoder_layernorm(curr_embeddings)
-            curr_embeddings = self._decoder_dropout(curr_embeddings)
 
+            # TODOPK KV caching
             decoder_output = self._decoder(
                 tgt=curr_embeddings,
                 memory=encoder_embeddings,
                 memory_key_padding_mask=~encoder_mask,
             )
+            
+            # TODOPK
+            # assert that prelast items don't change
+            # assert decoder changes only last index in dim = 1
 
             next_token_embedding = decoder_output[
                 :, -1, :

From 769c51472b0e1e567602ce5302d78f310e552899 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 16:50:50 +0300
Subject: [PATCH 118/175] cross entropy loss in Tiger, remove mps

---
 configs/train/tiger_train_config.json     |  8 ++++----
 configs/train/tiger_wat_train_config.json |  8 ++++----
 modeling/models/tiger.py                  | 13 +++----------
 modeling/models/tigerWithAnotherTree.py   | 12 +++---------
 modeling/utils/__init__.py                |  4 ++--
 5 files changed, 16 insertions(+), 29 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index f5173ce1..119337d1 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -63,16 +63,16 @@
     "type": "composite",
     "losses": [
       {
-        "type": "sample_logsoftmax",
+        "type": "ce",
         "predictions_prefix": "logits",
-        "labels": "semantic.labels",
+        "labels_prefix": "semantic.labels",
         "weight": 1.0,
         "output_prefix": "semantic_loss"
       },
       {
-        "type": "sample_logsoftmax",
+        "type": "ce",
         "predictions_prefix": "dedup.logits",
-        "labels": "dedup.labels",
+        "labels_prefix": "dedup.labels",
         "weight": 1.0,
         "output_prefix": "dedup_loss"
       }
diff --git a/configs/train/tiger_wat_train_config.json b/configs/train/tiger_wat_train_config.json
index a7eabbac..70422448 100644
--- a/configs/train/tiger_wat_train_config.json
+++ b/configs/train/tiger_wat_train_config.json
@@ -63,16 +63,16 @@
     "type": "composite",
     "losses": [
       {
-        "type": "sample_logsoftmax",
+        "type": "ce",
         "predictions_prefix": "logits",
-        "labels": "semantic.labels",
+        "labels_prefix": "semantic.labels",
         "weight": 1.0,
         "output_prefix": "semantic_loss"
       },
       {
-        "type": "sample_logsoftmax",
+        "type": "ce",
         "predictions_prefix": "dedup.logits",
-        "labels": "dedup.labels",
+        "labels_prefix": "dedup.labels",
         "weight": 1.0,
         "output_prefix": "dedup_loss"
       }
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 66fbcea5..df992dc2 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -216,10 +216,10 @@ def forward(self, inputs):
             )
 
             return {
-                "logits": decoder_prefix_scores,
-                "semantic.labels": semantic_ids,
+                "logits": decoder_prefix_scores.reshape(-1, decoder_prefix_scores.shape[2]),
+                "semantic.labels.ids": semantic_ids.reshape(-1),
                 "dedup.logits": pred_info["pred_scores"],
-                "dedup.labels": true_info["true_dedup_tokens"],
+                "dedup.labels.ids": true_info["true_dedup_tokens"],
             }
         else:
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
@@ -358,7 +358,6 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             # curr_embeddings[:, -1, :] = self._decoder_dropout(curr_embeddings)
 
 
-            # TODOPK KV caching
             decoder_output = self._decoder(
                 tgt=curr_embeddings,
                 memory=encoder_embeddings,
@@ -373,12 +372,6 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                 :, -1, :
             ]  # batch_size x embedding_dim
 
-            # TODOPK ask if we take last
-            # torch.Size([256, 1, 64])
-            # torch.Size([256, 2, 64])
-            # torch.Size([256, 3, 64])
-            # torch.Size([256, 4, 64])
-
             if step < len(self._codebook_sizes):
                 codebook = self._codebook_item_embeddings_stacked[
                     step
diff --git a/modeling/models/tigerWithAnotherTree.py b/modeling/models/tigerWithAnotherTree.py
index 1883b5b9..58ac0a6f 100644
--- a/modeling/models/tigerWithAnotherTree.py
+++ b/modeling/models/tigerWithAnotherTree.py
@@ -221,10 +221,10 @@ def forward(self, inputs):
             )
 
             return {
-                "logits": decoder_prefix_scores,
-                "semantic.labels": semantic_ids,
+                "logits": decoder_prefix_scores.reshape(-1, decoder_prefix_scores.shape[2]),
+                "semantic.labels.ids": semantic_ids.reshape(-1),
                 "dedup.logits": pred_info["pred_scores"],
-                "dedup.labels": true_info["true_dedup_tokens"],
+                "dedup.labels.ids": true_info["true_dedup_tokens"],
             }
         else:
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
@@ -327,12 +327,6 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                 :, -1, :
             ]  # batch_size x embedding_dim
 
-            # TODOPK ask if we take last
-            # torch.Size([256, 1, 64])
-            # torch.Size([256, 2, 64])
-            # torch.Size([256, 3, 64])
-            # torch.Size([256, 4, 64])
-
             if step < len(self._codebook_sizes):
                 codebook = self._codebook_item_embeddings_stacked[
                     step
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 59101ca4..8db8f069 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -12,8 +12,8 @@
 
 if torch.cuda.is_available():
     DEVICE = torch.device('cuda')
-elif torch.backends.mps.is_available():
-    DEVICE = torch.device("mps:0")
+# elif torch.backends.mps.is_available():
+#     DEVICE = torch.device("mps:0")
 else:
     DEVICE = torch.device('cpu')
 

From b3193ea1563ddb4accdafb1d1b2961d08b7b2c71 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 22 Feb 2025 17:28:35 +0300
Subject: [PATCH 119/175] fix decoder

---
 configs/train/tiger_wat_train_config.json     | 180 -------
 modeling/loss/base.py                         |   1 -
 modeling/models/__init__.py                   |   1 -
 modeling/models/tiger.py                      |  89 ++--
 modeling/models/tigerWithAnotherTree.py       | 454 ------------------
 ...ts.out.tfevents.1739517568.laplas.230979.0 | Bin 0 -> 343437 bytes
 ...ts.out.tfevents.1739620895.laplas.473812.0 | Bin 0 -> 191451 bytes
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 ...s.out.tfevents.1740149051.laplas.2323452.0 | Bin 0 -> 900416 bytes
 33 files changed, 34 insertions(+), 691 deletions(-)
 delete mode 100644 configs/train/tiger_wat_train_config.json
 delete mode 100644 modeling/models/tigerWithAnotherTree.py
 create mode 100644 tensorboard_logs_laplas/sasrec_beauty_2025-02-14T10:19/events.out.tfevents.1739517568.laplas.230979.0
 create mode 100644 tensorboard_logs_laplas/sasrec_beauty_2025-02-15T15:01/events.out.tfevents.1739620895.laplas.473812.0
 create mode 100644 tensorboard_logs_laplas/sasrec_beauty_2025-02-16T17:24/events.out.tfevents.1739715883.laplas.236512.0
 create mode 100644 tensorboard_logs_laplas/sasrec_beauty_2025-02-16T17:29/events.out.tfevents.1739716174.laplas.237976.0
 create mode 100644 tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-14T10:19/events.out.tfevents.1739517574.laplas.231017.0
 create mode 100644 tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-16T17:30/events.out.tfevents.1739716254.laplas.238391.0
 create mode 100644 tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-16T23:14/events.out.tfevents.1739736855.laplas.341454.0
 create mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-16T21:43/events.out.tfevents.1739731427.laplas.311641.0
 create mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-16T21:59/events.out.tfevents.1739732357.laplas.319889.0
 create mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-16T22:06/events.out.tfevents.1739732803.laplas.322005.0
 create mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-22T12:36/events.out.tfevents.1740217007.laplas.2652164.0
 create mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-22T15:00/events.out.tfevents.1740225602.laplas.2693646.0
 create mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-22T15:00/sasrec_semantic_beauty_2025-02-22T15:00/events.out.tfevents.1740225602.laplas.2693646.0
 create mode 100644 tensorboard_logs_laplas/tiger_2025-02-14T10:28/events.out.tfevents.1739518113.laplas.232484.0
 create mode 100644 tensorboard_logs_laplas/tiger_2025-02-14T10:29/events.out.tfevents.1739518142.laplas.232572.0
 create mode 100644 tensorboard_logs_laplas/tiger_2025-02-14T10:29/events.out.tfevents.1739518184.laplas.232702.0
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 create mode 100644 tensorboard_logs_laplas/tiger_simplified_no_residuals_freezed_2025-02-21T17:08/events.out.tfevents.1740146925.laplas.2313159.0
 create mode 100644 tensorboard_logs_laplas/tiger_simplified_no_residuals_freezed_2025-02-21T17:10/events.out.tfevents.1740147036.laplas.2313784.0
 create mode 100644 tensorboard_logs_laplas/tiger_simplified_no_residuals_unfreezed_2025-02-21T17:44/events.out.tfevents.1740149051.laplas.2323452.0

diff --git a/configs/train/tiger_wat_train_config.json b/configs/train/tiger_wat_train_config.json
deleted file mode 100644
index 70422448..00000000
--- a/configs/train/tiger_wat_train_config.json
+++ /dev/null
@@ -1,180 +0,0 @@
-{
-  "experiment_name": "tigerWithAnotherTree",
-  "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 50,
-  "dataset": {
-    "type": "sequence_full",
-    "path_to_data_dir": "../data",
-    "name": "Beauty",
-    "max_sequence_length": 50,
-    "samplers": {
-      "type": "last_item_prediction",
-      "negative_sampler_type": "random"
-    }
-  },
-  "dataloader": {
-    "train": {
-      "type": "torch",
-      "batch_size": 256,
-      "batch_processor": {
-        "type": "basic"
-      },
-      "drop_last": true,
-      "shuffle": true
-    },
-    "validation": {
-      "type": "torch",
-      "batch_size": 256,
-      "batch_processor": {
-        "type": "basic"
-      },
-      "drop_last": false,
-      "shuffle": false
-    }
-  },
-  "model": {
-    "type": "tigerWithAnotherTree",
-    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-    "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
-    "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
-    "sequence_prefix": "item",
-    "predictions_prefix": "logits",
-    "positive_prefix": "labels",
-    "labels_prefix": "labels",
-    "embedding_dim": 64,
-    "num_heads": 2,
-    "num_encoder_layers": 2,
-    "num_decoder_layers": 2,
-    "dim_feedforward": 256,
-    "dropout": 0.3,
-    "activation": "gelu",
-    "layer_norm_eps": 1e-9,
-    "initializer_range": 0.02
-  },
-  "optimizer": {
-    "type": "basic",
-    "optimizer": {
-      "type": "adam",
-      "lr": 0.001
-    },
-    "clip_grad_threshold": 5.0
-  },
-  "loss": {
-    "type": "composite",
-    "losses": [
-      {
-        "type": "ce",
-        "predictions_prefix": "logits",
-        "labels_prefix": "semantic.labels",
-        "weight": 1.0,
-        "output_prefix": "semantic_loss"
-      },
-      {
-        "type": "ce",
-        "predictions_prefix": "dedup.logits",
-        "labels_prefix": "dedup.labels",
-        "weight": 1.0,
-        "output_prefix": "dedup_loss"
-      }
-    ],
-    "output_prefix": "loss"
-  },
-  "callback": {
-    "type": "composite",
-    "callbacks": [
-      {
-        "type": "metric",
-        "on_step": 1,
-        "loss_prefix": "loss"
-      },
-      {
-        "type": "validation",
-        "on_step": 64,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      },
-      {
-        "type": "eval",
-        "on_step": 256,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      }
-    ]
-  }
-}
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 1863600f..80ca20d8 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -53,7 +53,6 @@ def forward(self, inputs):
 
         return total_loss
 
-# TODOPK CrossEntropy loss (logits, labels)
 class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
     def __init__(self, predictions_prefix, labels):
         super().__init__()
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index 7f0247a0..e03dc42d 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -19,4 +19,3 @@
 from .s3rec import S3RecModel
 from .rqvae import RqVaeModel
 from .tiger import TigerModel
-from .tigerWithAnotherTree import TigerModel2
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index df992dc2..7f24f733 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -82,7 +82,8 @@ def __init__(
                 std=initializer_range,
                 a=-2 * initializer_range,
                 b=2 * initializer_range,
-            )
+            ),
+            requires_grad=True, # TODOPK added for bos
         )
 
         self._codebook_embeddings = nn.Embedding(
@@ -91,19 +92,19 @@ def __init__(
 
         self._init_weights(initializer_range)
 
-        self._codebook_item_embeddings_stacked = nn.Parameter(torch.stack(
-            [codebook for codebook in rqvae_model.codebooks]
-        ))
-        
+        self._codebook_item_embeddings_stacked = nn.Parameter(
+            torch.stack([codebook for codebook in rqvae_model.codebooks])
+        )
+
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
-        
+
         self._trie = SimplifiedTree(self._codebook_item_embeddings_stacked)
 
         self._trie.build_tree_structure(
             item_id_to_semantic_id.to(DEVICE),
             item_id_to_residual.to(DEVICE),
             item_ids.to(DEVICE),
-            sum_with_residuals=False
+            sum_with_residuals=False,
         )
 
     @classmethod
@@ -186,15 +187,15 @@ def forward(self, inputs):
             label_events = inputs["{}.ids".format(self._positive_prefix)]
             label_lengths = inputs["{}.length".format(self._positive_prefix)]
 
-            label_lengths = label_lengths * (
-                len(self._codebook_sizes) + 1
-            )  # TODOPK bos prepending
             tgt_embeddings = self.get_item_embeddings(
                 label_events
             )  # (all_batch_events, embedding_dim)
 
             decoder_outputs = self._apply_decoder(
-                tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
+                tgt_embeddings,
+                label_lengths * (len(self._codebook_sizes) + 1),
+                encoder_embeddings,
+                encoder_mask,
             )  # (batch_size, label_len, embedding_dim)
 
             decoder_prefix_scores = torch.einsum(
@@ -216,7 +217,9 @@ def forward(self, inputs):
             )
 
             return {
-                "logits": decoder_prefix_scores.reshape(-1, decoder_prefix_scores.shape[2]),
+                "logits": decoder_prefix_scores.reshape(
+                    -1, decoder_prefix_scores.shape[2]
+                ),
                 "semantic.labels.ids": semantic_ids.reshape(-1),
                 "dedup.logits": pred_info["pred_scores"],
                 "dedup.labels.ids": true_info["true_dedup_tokens"],
@@ -226,7 +229,6 @@ def forward(self, inputs):
                 encoder_embeddings, encoder_mask
             )  # (batch_size, len(self._codebook_sizes) (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
 
-            
             # 1 4 6 -> lookup -> sum = emb (last embedding) # bs, embedding_dim
             # take all embedings (from stacked) # all_items, embedding_dim
             # take from sasrec eval (indices + 1)
@@ -238,7 +240,7 @@ def forward(self, inputs):
             item_ids = self._trie.query(semantic_ids, items_to_query=20)
 
             return item_ids
-        
+
         # else:  # eval mode
         #     last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
         #     # b - batch_size, n - num_candidates, d - embedding_dim
@@ -312,59 +314,36 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             .unsqueeze(0)
             .expand(batch_size, 1, embedding_dim)
         )
-        
-        # curr_target_emb = () # <- layernorm, dropout & positional
-        # concat target <- [tgt_embs, cutt_tgt_emb]
-        # decoder(concat target) 
 
         semantic_ids = torch.tensor([], device=DEVICE)
 
         for step in range(len(self._codebook_sizes) + 1):  # semantic_id_seq + residual
-            if step == 0:
-                indexes = torch.full(
-                    (batch_size, 1), len(self._codebook_sizes), device=DEVICE
-                )  # len(self._codebook_sizes) for bos
-            else:
-                indexes = torch.cat(
-                    [indexes, torch.full((batch_size, 1), step - 1, device=DEVICE)],
-                    dim=1,
-                )
-
-            # 3
-            # 3 0
-            # 3 0 1
-            # 3 0 1 2
-            # 3 0 1 2 4
-
-            # bos_token = len(self._codebook_sizes)
-            # residual_token = len(self._codebook_sizes) + 1
+            index = len(self._codebook_sizes) if step == 0 else step - 1
 
-            # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0
-            # 5 5 5 5 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1
-            # 0 1 2 4 0 1 2 4 0 1 2 4 0 1 2 4 0 1 2 4
+            last_position_embedding = self._codebook_embeddings(
+                torch.full((batch_size,), index, device=DEVICE)
+            )
 
-            position_embeddings = self._codebook_embeddings(indexes.view(-1)) # cb + 2
+            assert last_position_embedding.shape == tgt_embeddings[:, -1, :].shape
+            assert tgt_embeddings.shape == torch.Size([batch_size, step + 1, embedding_dim])
 
-            curr_embeddings = tgt_embeddings + position_embeddings.view(
-                batch_size, step + 1, embedding_dim
+            curr_step_embeddings = tgt_embeddings.clone()
+            curr_step_embeddings[:, -1, :] = (
+                tgt_embeddings[:, -1, :] + last_position_embedding
             )
-            
-            # TODOPK += last
-            # curr_embeddings = tgt_embeddings[:, -1] += position_embeddings.view(
-            #     batch_size, step + 1, embedding_dim
-            # )
-
-            # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings)
-            # curr_embeddings[:, -1, :] = self._decoder_dropout(curr_embeddings)
+            assert torch.allclose(tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :])
+            tgt_embeddings = curr_step_embeddings
 
+            # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings[:, -1, :])
+            # curr_embeddings[:, -1, :] = self._decoder_dropout(curr_embeddings[:, -1, :])
 
             decoder_output = self._decoder(
-                tgt=curr_embeddings,
+                tgt=tgt_embeddings,
                 memory=encoder_embeddings,
                 memory_key_padding_mask=~encoder_mask,
             )
-            
-            # TODOPK
+
+            # TODOPK ASK it is not true?
             # assert that prelast items don't change
             # assert decoder changes only last index in dim = 1
 
@@ -375,10 +354,10 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             if step < len(self._codebook_sizes):
                 codebook = self._codebook_item_embeddings_stacked[
                     step
-                ]  # len(codebook_sizes) x embedding_dim
+                ]  # codebook_size x embedding_dim
                 closest_semantic_ids = torch.argmax(
                     torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
-                )  # batch_size x 1
+                )  # batch_size
                 semantic_ids = torch.cat(
                     [semantic_ids, closest_semantic_ids.unsqueeze(1)], dim=1
                 )  # batch_size x (step + 1)
diff --git a/modeling/models/tigerWithAnotherTree.py b/modeling/models/tigerWithAnotherTree.py
deleted file mode 100644
index 58ac0a6f..00000000
--- a/modeling/models/tigerWithAnotherTree.py
+++ /dev/null
@@ -1,454 +0,0 @@
-import json
-
-import torch
-from models.base import SequentialTorchModel
-from rqvae_utils import CollisionSolver, Tree
-from torch import nn
-from utils import DEVICE, create_masked_tensor, get_activation_function
-
-from .rqvae import RqVaeModel
-
-
-class TigerModel2(SequentialTorchModel, config_name="tigerWithAnotherTree"):
-    def __init__(
-        self,
-        rqvae_model,
-        item_id_to_semantic_id,
-        item_id_to_residual,
-        item_id_to_text_embedding,
-        solver,
-        sequence_prefix,
-        pred_prefix,
-        positive_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_encoder_layers,
-        num_decoder_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-    ):
-        super().__init__(
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-            embedding_dim=embedding_dim,
-            num_heads=num_heads,
-            num_layers=num_encoder_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=activation,
-            layer_norm_eps=layer_norm_eps,
-            is_causal=True,
-        )
-
-        self._sequence_prefix = sequence_prefix
-        self._pred_prefix = pred_prefix
-        self._positive_prefix = positive_prefix
-        self._labels_prefix = labels_prefix
-
-        transformer_decoder_layer = nn.TransformerDecoderLayer(
-            d_model=embedding_dim,
-            nhead=num_heads,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=get_activation_function(activation),
-            layer_norm_eps=layer_norm_eps,
-            batch_first=True,
-        )
-
-        self._decoder = nn.TransformerDecoder(
-            transformer_decoder_layer, num_decoder_layers
-        )
-
-        self._decoder_layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
-        self._decoder_dropout = nn.Dropout(dropout)
-
-        self._solver: CollisionSolver = solver
-
-        self._codebook_sizes = rqvae_model.codebook_sizes
-
-        self._codebook_item_embeddings_stacked = torch.stack(
-            [codebook for codebook in rqvae_model.codebooks]
-        )
-        self._codebook_item_embeddings_stacked.requires_grad = (
-            False  # TODOPK maybe unfreeeze later
-        )
-
-        self._item_id_to_semantic_id = item_id_to_semantic_id
-        self._item_id_to_residual = item_id_to_residual
-        self._item_id_to_text_embedding = item_id_to_text_embedding
-
-        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
-
-        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
-
-        self._trie = Tree(rqvae_model)
-
-        self._trie.build_tree_structure(
-            item_id_to_semantic_id.to(DEVICE),
-            item_id_to_residual.to(DEVICE),
-            item_ids.to(DEVICE)
-        )
-
-        self._bos_token_id = self._codebook_sizes[0]
-        self._bos_weight = nn.Parameter(
-            torch.nn.init.trunc_normal_(
-                torch.zeros(embedding_dim),
-                std=initializer_range,
-                a=-2 * initializer_range,
-                b=2 * initializer_range,
-            )
-        )
-
-        self._codebook_embeddings = nn.Embedding(
-            num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
-        )  # + 2 for bos token & residual
-
-        self._init_weights(initializer_range)
-
-    @classmethod
-    def init_rqvae(cls, config) -> RqVaeModel:
-        rqvae_config = json.load(open(config["rqvae_train_config_path"]))
-        rqvae_config["model"]["should_init_codebooks"] = False
-
-        rqvae_model = RqVaeModel.create_from_config(rqvae_config["model"]).to(DEVICE)
-        rqvae_model.load_state_dict(
-            torch.load(config["rqvae_checkpoint_path"], weights_only=True)
-        )
-        rqvae_model.eval()
-        for param in rqvae_model.parameters():
-            param.requires_grad = False
-
-        codebook_sizes = rqvae_model.codebook_sizes
-        assert all([book_size == codebook_sizes[0] for book_size in codebook_sizes])
-
-        return rqvae_model
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        rqvae_model = cls.init_rqvae(config)
-        embs_extractor = torch.load(config["embs_extractor_path"], weights_only=False)
-
-        embs_extractor = embs_extractor.sort_index()
-
-        item_ids = embs_extractor.index.tolist()
-        assert item_ids == list(range(1, len(item_ids) + 1))
-
-        text_embeddings = torch.stack(embs_extractor["embeddings"].tolist()).to(DEVICE)
-
-        semantic_ids, residuals = rqvae_model({"embeddings": text_embeddings})
-
-        solver = CollisionSolver(
-            emb_dim=residuals.shape[1],
-            sem_id_len=len(rqvae_model.codebook_sizes),
-            codebook_size=rqvae_model.codebook_sizes[0],
-        )
-        solver.create_query_candidates_dict(
-            torch.tensor(item_ids), semantic_ids, residuals
-        )
-
-        return cls(
-            rqvae_model=rqvae_model,
-            item_id_to_semantic_id=semantic_ids,
-            item_id_to_residual=residuals,
-            item_id_to_text_embedding=text_embeddings,
-            solver=solver,
-            sequence_prefix=config["sequence_prefix"],
-            pred_prefix=config["predictions_prefix"],
-            positive_prefix=config["positive_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=rqvae_model.codebook_sizes[0],  # unused
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_encoder_layers=config["num_encoder_layers"],
-            num_decoder_layers=config["num_decoder_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-        )
-
-    # semantic ids come with dedup token
-    def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-
-        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
-        encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
-        )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
-
-        if self.training:
-            label_events = inputs["{}.ids".format(self._positive_prefix)]
-            label_lengths = inputs["{}.length".format(self._positive_prefix)]
-
-            label_lengths = label_lengths * (
-                len(self._codebook_sizes) + 1
-            )  # TODOPK bos prepending
-            tgt_embeddings = self.get_item_embeddings(
-                label_events
-            )  # (all_batch_events, embedding_dim)
-
-            decoder_outputs = self._apply_decoder(
-                tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
-            )  # (batch_size, label_len, embedding_dim)
-
-            decoder_prefix_scores = torch.einsum(
-                "bsd,scd->bsc",
-                decoder_outputs[:, :-1, :],
-                self._codebook_item_embeddings_stacked,
-            )
-
-            decoder_output_residual = decoder_outputs[:, -1, :]
-
-            semantic_ids = self._item_id_to_semantic_id[
-                label_events - 1
-            ]  # len(events), len(codebook_sizes)
-            true_residuals = self._item_id_to_residual[label_events - 1]
-
-            true_info = self._solver.get_true_dedup_tokens(semantic_ids, true_residuals)
-            pred_info = self._solver.get_pred_scores(
-                semantic_ids, decoder_output_residual
-            )
-
-            return {
-                "logits": decoder_prefix_scores.reshape(-1, decoder_prefix_scores.shape[2]),
-                "semantic.labels.ids": semantic_ids.reshape(-1),
-                "dedup.logits": pred_info["pred_scores"],
-                "dedup.labels.ids": true_info["true_dedup_tokens"],
-            }
-        else:
-            semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
-                encoder_embeddings, encoder_mask
-            )  # (batch_size, len(self._codebook_sizes) + 2 (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
-
-            residuals = tgt_embeddings[:, -1, :]
-            semantic_ids = semantic_ids.to(torch.int64)
-
-            item_ids = self._trie.query(semantic_ids, residuals, items_to_query=20)
-
-            return item_ids
-
-    def _apply_decoder(
-        self, tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
-    ):
-        tgt_embeddings, tgt_mask = create_masked_tensor(
-            data=tgt_embeddings, lengths=label_lengths
-        )  # (batch_size, dec_seq_len, embedding_dim), (batch_size, dec_seq_len)
-
-        batch_size = tgt_embeddings.shape[0]
-        bos_embeddings = self._bos_weight.unsqueeze(0).expand(
-            batch_size, 1, -1
-        )  # (batch_size, 1, embedding_dim)
-
-        tgt_embeddings = torch.cat(
-            [bos_embeddings, tgt_embeddings[:, :-1, :]], dim=1
-        )  # remove residual by using :-1
-
-        label_len = tgt_mask.shape[1]
-
-        assert label_len == len(self._codebook_sizes) + 1
-
-        position_embeddings = self._decoder_pos_embeddings(label_lengths, tgt_mask)
-        assert torch.allclose(position_embeddings[~tgt_mask], tgt_embeddings[~tgt_mask])
-
-        tgt_embeddings = tgt_embeddings + position_embeddings
-
-        tgt_embeddings = self._decoder_layernorm(
-            tgt_embeddings
-        )  # (batch_size, dec_seq_len, embedding_dim)
-        tgt_embeddings = self._decoder_dropout(
-            tgt_embeddings
-        )  # (batch_size, dec_seq_len, embedding_dim)
-
-        tgt_embeddings[~tgt_mask] = 0
-
-        causal_mask = (
-            torch.tril(torch.ones(label_len, label_len)).bool().to(DEVICE)
-        )  # (dec_seq_len, dec_seq_len)
-
-        decoder_outputs = self._decoder(
-            tgt=tgt_embeddings,
-            memory=encoder_embeddings,
-            tgt_mask=~causal_mask,
-            memory_key_padding_mask=~encoder_mask,
-        )  # (batch_size, dec_seq_len, embedding_dim)
-
-        return decoder_outputs
-
-    def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
-        batch_size = encoder_embeddings.shape[0]
-        embedding_dim = encoder_embeddings.shape[2]
-
-        tgt_embeddings = (
-            self._bos_weight.unsqueeze(0)
-            .unsqueeze(0)
-            .expand(batch_size, 1, embedding_dim)
-        )
-
-        semantic_ids = torch.tensor([], device=DEVICE)
-
-        for step in range(len(self._codebook_sizes) + 1):  # semantic_id_seq + residual
-            if step == 0:
-                indexes = torch.full(
-                    (batch_size, 1), len(self._codebook_sizes), device=DEVICE
-                )  # len(self._codebook_sizes) for bos
-            else:
-                indexes = torch.cat(
-                    [indexes, torch.full((batch_size, 1), step - 1, device=DEVICE)],
-                    dim=1,
-                )
-
-            position_embeddings = self._codebook_embeddings(indexes.view(-1))
-
-            curr_embeddings = tgt_embeddings + position_embeddings.view(
-                batch_size, step + 1, embedding_dim
-            )
-
-            curr_embeddings = self._decoder_layernorm(curr_embeddings)
-            curr_embeddings = self._decoder_dropout(curr_embeddings)
-
-            decoder_output = self._decoder(
-                tgt=curr_embeddings,
-                memory=encoder_embeddings,
-                memory_key_padding_mask=~encoder_mask,
-            )
-
-            next_token_embedding = decoder_output[
-                :, -1, :
-            ]  # batch_size x embedding_dim
-
-            if step < len(self._codebook_sizes):
-                codebook = self._codebook_item_embeddings_stacked[
-                    step
-                ]  # len(codebook_sizes) x embedding_dim
-                closest_semantic_ids = torch.argmax(
-                    torch.einsum("bd,cd->bc", next_token_embedding, codebook), dim=1
-                )  # batch_size x 1
-                semantic_ids = torch.cat(
-                    [semantic_ids, closest_semantic_ids.unsqueeze(1)], dim=1
-                )  # batch_size x (step + 1)
-                next_token_embedding = codebook[
-                    closest_semantic_ids
-                ]  # batch_size x embedding_dim
-
-            tgt_embeddings = torch.cat(
-                [tgt_embeddings, next_token_embedding.unsqueeze(1)], dim=1
-            )
-
-        return semantic_ids, tgt_embeddings
-
-    def get_item_embeddings(self, events):
-        embs = self._item_id_to_semantic_embedding[
-            events - 1
-        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.view(
-            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
-        )
-
-    def get_init_item_embeddings(self, events):
-        # convert to semantic ids
-        semantic_ids = self._item_id_to_semantic_id[
-            events - 1
-        ]  # len(events), len(codebook_sizes)
-
-        result = []
-        for semantic_id in semantic_ids:
-            item_repr = []
-            for codebook_idx, codebook_id in enumerate(semantic_id):
-                item_repr.append(
-                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
-                )
-            result.append(torch.stack(item_repr))
-
-        semantic_embeddings = torch.stack(result)
-
-        # get residuals
-        residual = self._item_id_to_residual[events - 1]
-        # text_embeddings = self._item_id_to_text_embedding[events - 1]
-        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
-        residual = residual.unsqueeze(1)
-
-        # get true item embeddings
-        item_embeddings = torch.cat(
-            [semantic_embeddings, residual], dim=1
-        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-
-        # item_embeddings = item_embeddings.view(-1, self._embedding_dim) # (all_batch_events, embedding_dim)
-
-        return item_embeddings
-
-    def _encoder_pos_embeddings(self, lengths, mask):
-        def position_lambda(x):
-            return x // (
-                len(self._codebook_sizes) + 1
-            )  # 5 5 5 5 4 4 4 4 ..., +1 for residual
-
-        position_embeddings = self._get_position_embeddings(
-            lengths, mask, position_lambda, self._position_embeddings
-        )
-
-        def codebook_lambda(x):
-            x = len(self._codebook_sizes) - x % (len(self._codebook_sizes) + 1)
-            x[x == len(self._codebook_sizes)] = len(self._codebook_sizes) + 1
-            # 0 1 2 4 0 1 2 4 ... # len(self._codebook_sizes) + 1 = 4 for residual
-            return x
-
-        codebook_embeddings = self._get_position_embeddings(
-            lengths, mask, codebook_lambda, self._codebook_embeddings
-        )
-
-        return position_embeddings + codebook_embeddings
-
-    def _decoder_pos_embeddings(self, lengths, mask):
-        def codebook_lambda(x):
-            non_bos = x < len(self._codebook_sizes)
-            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
-            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ... len(self._codebook_sizes) = 3 for bos
-
-        codebook_embeddings = self._get_position_embeddings(
-            lengths, mask, codebook_lambda, self._codebook_embeddings
-        )
-
-        return codebook_embeddings
-
-    def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
-        batch_size = mask.shape[0]
-        seq_len = mask.shape[1]
-
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=DEVICE)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
-        positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
-
-        positions = positions[positions_mask]  # (all_batch_events)
-        # 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 7 6 5 4 3 2 1 0 ...
-
-        positions = position_lambda(positions)  # (all_batch_events)
-
-        # print(f"{positions.tolist()[:20]=}")
-
-        assert (positions >= 0).all() and (
-            positions < embedding_layer.num_embeddings
-        ).all()
-
-        position_embeddings = embedding_layer(
-            positions
-        )  # (all_batch_events, embedding_dim)
-
-        position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
-        )  # (batch_size, seq_len, embedding_dim)
-
-        return position_embeddings
diff --git a/tensorboard_logs_laplas/sasrec_beauty_2025-02-14T10:19/events.out.tfevents.1739517568.laplas.230979.0 b/tensorboard_logs_laplas/sasrec_beauty_2025-02-14T10:19/events.out.tfevents.1739517568.laplas.230979.0
new file mode 100644
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diff --git a/tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-14T10:19/events.out.tfevents.1739517574.laplas.231017.0 b/tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-14T10:19/events.out.tfevents.1739517574.laplas.231017.0
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diff --git a/tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-16T23:14/events.out.tfevents.1739736855.laplas.341454.0 b/tensorboard_logs_laplas/sasrec_freezed_beauty_2025-02-16T23:14/events.out.tfevents.1739736855.laplas.341454.0
new file mode 100644
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diff --git a/tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-16T21:43/events.out.tfevents.1739731427.laplas.311641.0 b/tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-16T21:43/events.out.tfevents.1739731427.laplas.311641.0
new file mode 100644
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HcmV?d00001


From c7a3e93a9b862284997210746c65806c5f936939 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Feb 2025 15:02:15 +0300
Subject: [PATCH 120/175] honest sasrec

---
 modeling/loss/base.py                         |  18 +++++++++++++++++
 modeling/models/sasrec.py                     |   3 ++-
 modeling/models/sasrec_freezed.py             |   1 +
 modeling/models/sasrec_semantic.py            |  19 ++++++++++--------
 ...s.out.tfevents.1740225602.laplas.2693646.0 | Bin 140105 -> 1072545 bytes
 ...s.out.tfevents.1740225602.laplas.2693646.0 | Bin 170512 -> 0 bytes
 6 files changed, 32 insertions(+), 9 deletions(-)
 delete mode 100644 tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-22T15:00/sasrec_semantic_beauty_2025-02-22T15:00/events.out.tfevents.1740225602.laplas.2693646.0

diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 80ca20d8..7827a7f7 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -333,6 +333,24 @@ def __init__(
     def forward(self, inputs):
         positive_scores = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_scores = inputs[self._negative_prefix]  # (x, embedding_dim)
+        sample_ids = inputs["sample_ids"]
+        
+        num_items = negative_scores.shape[1] - 2
+        
+        possible_indices = torch.arange(1, num_items + 1, device=negative_scores.device) # 1, 2, ... num_items
+        mask = torch.ones_like(possible_indices, dtype=torch.bool) # True, True, ... True
+        mask[sample_ids - 1] = False # True, False, ... False, True, ... True
+        valid_indices = possible_indices[mask] # 1, 2, ... num_items, except sample_ids
+        
+        rand_idx = torch.randint(0, len(valid_indices), size=(negative_scores.shape[0], 1), device=negative_scores.device)
+        index = valid_indices[rand_idx]
+        
+        negative_scores = torch.gather(
+            input=negative_scores,
+            dim=1,
+            index=index,
+        )
+        
         assert positive_scores.shape[0] == negative_scores.shape[0]
 
         positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(dim=-1)  # (x)
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 06dee724..4bf6dac3 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -97,7 +97,8 @@ def forward(self, inputs):
 
             return {
                 'positive_scores': positive_scores,
-                'negative_scores': negative_scores
+                'negative_scores': negative_scores,
+                "sample_ids": sample_ids,
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index bbc20ab9..42194c1d 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -142,6 +142,7 @@ def forward(self, inputs):
             return {
                 "positive_scores": positive_scores,
                 "negative_scores": negative_scores,
+                "sample_ids": sample_ids,
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 0ea42d7e..e347018b 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -111,19 +111,19 @@ def forward(self, inputs):
                 embeddings, mask
             )  # (batch_size, embedding_dim)
 
-            all_embeddings = (
-                self._item_id_to_full_embedding
-            )  # (num_items, embedding_dim)
-
             all_embeddings = torch.cat(
-                [torch.zeros(1, self._embedding_dim, device=DEVICE), all_embeddings],
+                [
+                    torch.zeros(1, self._embedding_dim, device=DEVICE),
+                    self._item_id_to_full_embedding,
+                    torch.zeros(1, self._embedding_dim, device=DEVICE),
+                ],
                 dim=0,
-            )  # (num_items + 1, embedding_dim)
+            )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items, d -- embedding_dim
             all_scores = torch.einsum(
                 "ad,nd->an", last_embeddings, all_embeddings
-            )  # (batch_size, num_items + 1)
+            )  # (batch_size, num_items + 2)
 
             positive_scores = torch.gather(
                 input=all_scores, dim=1, index=all_positive_sample_events[..., None]
@@ -138,11 +138,14 @@ def forward(self, inputs):
                 dim=1,
                 index=sample_ids,
                 src=torch.ones_like(sample_ids) * (-torch.inf),
-            )  # (all_batch_events, num_items + 1)
+            )  # (all_batch_events, num_items + 2)
+            negative_scores[:, 0] = -torch.inf  # Padding idx
+            negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
 
             return {
                 "positive_scores": positive_scores,
                 "negative_scores": negative_scores,
+                "sample_ids": sample_ids,
             }
         else:  # eval mode
             last_embeddings = self._get_last_embedding(
diff --git a/tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-22T15:00/events.out.tfevents.1740225602.laplas.2693646.0 b/tensorboard_logs_laplas/sasrec_semantic_beauty_2025-02-22T15:00/events.out.tfevents.1740225602.laplas.2693646.0
index 637b808a56a236557c37b76a5957c889dcae35b3..491325ba4688f634481874617804a01a007a10ff 100644
GIT binary patch
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J-hRl%{|Akj-r4{F


From 3c861150f07a2b3967b966ea8d89f0cbc1f0789f Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Feb 2025 15:24:24 +0300
Subject: [PATCH 121/175] fix sasrec freezed

---
 .../train/sasrec_freezed_train_config.json    |  3 +
 modeling/models/sasrec_freezed.py             | 96 +++++++++++--------
 modeling/models/tiger.py                      | 10 +-
 3 files changed, 68 insertions(+), 41 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index a53ddf0b..209a5671 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -34,6 +34,9 @@
   },
   "model": {
     "type": "sasrec_freezed",
+    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+    "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+    "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
     "sequence_prefix": "item",
     "positive_prefix": "labels",
     "negative_prefix": "negative",
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 42194c1d..6ade4326 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -1,11 +1,15 @@
 import torch
+from models.tiger import TigerModel
 from models import SequentialTorchModel
-from utils import create_masked_tensor
+from utils import DEVICE, create_masked_tensor
 
 
 class SasRecFreezedModel(SequentialTorchModel, config_name="sasrec_freezed"):
     def __init__(
         self,
+        rqvae_model,
+        item_id_to_semantic_id,
+        item_id_to_residual,
         sequence_prefix,
         positive_prefix,
         num_items,
@@ -36,43 +40,55 @@ def __init__(
 
         self._init_weights(initializer_range)
 
-        df = torch.load("../data/Beauty/data_full.pt", weights_only=False)
-        precomputed_embeddings = torch.stack(df.sort_index().embeddings.tolist())
-
-        self._projector = torch.nn.Linear(
-            precomputed_embeddings.shape[1], embedding_dim
-        )
-
-        padding_embedding = torch.nn.init.trunc_normal_(
-            torch.zeros(1, precomputed_embeddings.shape[1]),
-            std=initializer_range,
-            a=-2 * initializer_range,
-            b=2 * initializer_range,
-        )
-
-        mask_embedding = torch.nn.init.trunc_normal_(
-            torch.zeros(1, precomputed_embeddings.shape[1]),
-            std=initializer_range,
-            a=-2 * initializer_range,
-            b=2 * initializer_range,
-        )
-
-        extended_embeddings = torch.cat(
-            [padding_embedding, precomputed_embeddings, mask_embedding], dim=0
-        )  # Shape: (num_items + 2, embedding_dim)
-
-        self._item_embeddings = torch.nn.Embedding(
-            num_embeddings=num_items + 2, embedding_dim=precomputed_embeddings.shape[1]
+        self._codebook_item_embeddings_stacked = torch.nn.Parameter(
+            torch.stack([codebook for codebook in rqvae_model.codebooks]),
+            requires_grad=False,  # TODOPK compare with unfrozen codebooks
         )
-
-        # TODOPK ask about freezed masked & padding tokens
-        # TODOPK use rqvae embeddings instead of text embeddings (freeze / unfreeze)
-
-        self._item_embeddings.weight.data.copy_(extended_embeddings)
+        self._item_id_to_semantic_id = item_id_to_semantic_id
+        self._item_id_to_residual = item_id_to_residual
+
+        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
+        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
+        self._item_id_to_semantic_embedding = torch.nn.Parameter(
+            self._item_id_to_semantic_embedding.sum(dim=1), requires_grad=False
+        ) # len(events), embedding_dim
+
+    def get_init_item_embeddings(self, events):
+        # convert to semantic ids
+        semantic_ids = self._item_id_to_semantic_id[
+            events - 1
+        ]  # len(events), len(codebook_sizes)
+
+        result = []
+        for semantic_id in semantic_ids:
+            item_repr = []
+            for codebook_idx, codebook_id in enumerate(semantic_id):
+                item_repr.append(
+                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
+                )
+            result.append(torch.stack(item_repr))
+
+        semantic_embeddings = torch.stack(result)
+
+        # get residuals
+        residual = self._item_id_to_residual[events - 1]
+        residual = residual.unsqueeze(1)
+
+        # get true item embeddings
+        item_embeddings = torch.cat(
+            [semantic_embeddings, residual], dim=1
+        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+
+        return item_embeddings
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
+        rqvae_model, semantic_ids, residuals, item_ids = TigerModel.init_rqvae(config)
+
         return cls(
+            rqvae_model=rqvae_model,
+            item_id_to_semantic_id=semantic_ids,
+            item_id_to_residual=residuals,
             sequence_prefix=config["sequence_prefix"],
             positive_prefix=config["positive_prefix"],
             num_items=kwargs["num_items"],
@@ -87,9 +103,9 @@ def create_from_config(cls, config, **kwargs):
 
     def get_item_embeddings(self, events=None):
         if events is None:
-            return self._projector(self._item_embeddings.weight)
+            return self._item_id_to_semantic_embedding
         else:
-            return self._projector(self._item_embeddings(events))
+            return self._item_id_to_semantic_embedding[events - 1]
 
     def forward(self, inputs):
         all_sample_events = inputs[
@@ -112,8 +128,13 @@ def forward(self, inputs):
                 embeddings, mask
             )  # (batch_size, embedding_dim)
 
-            all_embeddings = (
-                self.get_item_embeddings()
+            all_embeddings = torch.cat(
+                [
+                    torch.zeros(1, self._embedding_dim, device=DEVICE),
+                    self._item_id_to_semantic_embedding,
+                    torch.zeros(1, self._embedding_dim, device=DEVICE),
+                ],
+                dim=0,
             )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
@@ -129,7 +150,6 @@ def forward(self, inputs):
                 data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
-
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 7f24f733..1035f009 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -72,8 +72,6 @@ def __init__(
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
 
-        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
-
         self._codebook_sizes = rqvae_model.codebook_sizes
         self._bos_token_id = self._codebook_sizes[0]
         self._bos_weight = nn.Parameter(
@@ -93,10 +91,15 @@ def __init__(
         self._init_weights(initializer_range)
 
         self._codebook_item_embeddings_stacked = nn.Parameter(
-            torch.stack([codebook for codebook in rqvae_model.codebooks])
+            torch.stack([codebook for codebook in rqvae_model.codebooks]),
+            requires_grad=False # TODOPK compare with unfrozen codebooks
         )
 
+        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
+        self._item_id_to_semantic_embedding = nn.Parameter(
+            self._item_id_to_semantic_embedding, requires_grad=False # TODOPK compare with unfrozen codebooks
+        )
 
         self._trie = SimplifiedTree(self._codebook_item_embeddings_stacked)
 
@@ -241,6 +244,7 @@ def forward(self, inputs):
 
             return item_ids
 
+        # TODOPK (decompose tree)
         # else:  # eval mode
         #     last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
         #     # b - batch_size, n - num_candidates, d - embedding_dim

From 632045f4dd732c85f5bd8f86514073691e017f4a Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Feb 2025 15:30:44 +0300
Subject: [PATCH 122/175] sasrec freezed learnable

---
 configs/train/sasrec_freezed_train_config.json | 2 +-
 modeling/models/sasrec_freezed.py              | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index 209a5671..cbc9af5c 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_freezed_beauty",
+  "experiment_name": "sasrec_freezed_beauty_learnable",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index 6ade4326..f8244228 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -42,7 +42,7 @@ def __init__(
 
         self._codebook_item_embeddings_stacked = torch.nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
-            requires_grad=False,  # TODOPK compare with unfrozen codebooks
+            requires_grad=True,  # TODOPK compare with unfrozen codebooks
         )
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
@@ -50,8 +50,8 @@ def __init__(
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         self._item_id_to_semantic_embedding = torch.nn.Parameter(
-            self._item_id_to_semantic_embedding.sum(dim=1), requires_grad=False
-        ) # len(events), embedding_dim
+            self._item_id_to_semantic_embedding.sum(dim=1), requires_grad=True
+        )  # len(events), embedding_dim
 
     def get_init_item_embeddings(self, events):
         # convert to semantic ids

From 8e96a9bdb3cbed652cb1c596e20bd681f61b41ed Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Feb 2025 15:33:17 +0300
Subject: [PATCH 123/175] empty cuda

---
 modeling/utils/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 8db8f069..0fcd5873 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -11,7 +11,7 @@
 import torch
 
 if torch.cuda.is_available():
-    DEVICE = torch.device('cuda')
+    DEVICE = torch.device('cuda:1')
 # elif torch.backends.mps.is_available():
 #     DEVICE = torch.device("mps:0")
 else:

From 95e816c39a0e665f10c647417c96219ad6d727d3 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 24 Feb 2025 18:07:31 +0300
Subject: [PATCH 124/175] freeze sasrec

---
 configs/train/sasrec_freezed_train_config.json | 2 +-
 modeling/models/sasrec_freezed.py              | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
index cbc9af5c..209a5671 100644
--- a/configs/train/sasrec_freezed_train_config.json
+++ b/configs/train/sasrec_freezed_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_freezed_beauty_learnable",
+  "experiment_name": "sasrec_freezed_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index f8244228..dcaea8ff 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -42,7 +42,7 @@ def __init__(
 
         self._codebook_item_embeddings_stacked = torch.nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
-            requires_grad=True,  # TODOPK compare with unfrozen codebooks
+            requires_grad=False,  # TODOPK compare with unfrozen codebooks
         )
         self._item_id_to_semantic_id = item_id_to_semantic_id
         self._item_id_to_residual = item_id_to_residual
@@ -50,7 +50,7 @@ def __init__(
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         self._item_id_to_semantic_embedding = torch.nn.Parameter(
-            self._item_id_to_semantic_embedding.sum(dim=1), requires_grad=True
+            self._item_id_to_semantic_embedding.sum(dim=1), requires_grad=False
         )  # len(events), embedding_dim
 
     def get_init_item_embeddings(self, events):

From 61b5c54afea0aee5ff3e4146d4f5c9ce2bcff223 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 10:53:12 +0300
Subject: [PATCH 125/175] fix cuda ordinal

---
 modeling/utils/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 0fcd5873..8b96ac52 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -11,7 +11,7 @@
 import torch
 
 if torch.cuda.is_available():
-    DEVICE = torch.device('cuda:1')
+    DEVICE = torch.device('cuda:0')
 # elif torch.backends.mps.is_available():
 #     DEVICE = torch.device("mps:0")
 else:

From 6bad6ce0a57bcb6ce3d33f05150f0f565196c4b7 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 11:02:54 +0300
Subject: [PATCH 126/175] remove laplas logs

---
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 ...ts.out.tfevents.1739620895.laplas.473812.0 | Bin 191451 -> 0 bytes
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 27 files changed, 0 insertions(+), 0 deletions(-)
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diff --git a/tensorboard_logs_laplas/sasrec_beauty_2025-02-14T10:19/events.out.tfevents.1739517568.laplas.230979.0 b/tensorboard_logs_laplas/sasrec_beauty_2025-02-14T10:19/events.out.tfevents.1739517568.laplas.230979.0
deleted file mode 100644
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From 4077b0579fa1ee0a938e635ee8e7e0ad1603a7bc Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 12:17:42 +0300
Subject: [PATCH 127/175] ruff format

---
 modeling/callbacks/__init__.py                |   8 +-
 modeling/callbacks/base.py                    | 293 ++++----
 modeling/dataloader/base.py                   |  24 +-
 modeling/dataloader/batch_processors.py       |  34 +-
 modeling/dataset/base.py                      | 656 ++++++++++--------
 .../dataset/negative_samplers/__init__.py     |   6 +-
 modeling/dataset/negative_samplers/base.py    |  12 +-
 modeling/dataset/negative_samplers/popular.py |  28 +-
 modeling/dataset/negative_samplers/random.py  |  14 +-
 modeling/dataset/samplers/__init__.py         |  29 +-
 modeling/dataset/samplers/base.py             |  24 +-
 modeling/dataset/samplers/cl4srec.py          | 110 +--
 modeling/dataset/samplers/duorec.py           |  44 +-
 modeling/dataset/samplers/identity.py         |  19 +-
 .../dataset/samplers/last_item_prediction.py  |  38 +-
 .../samplers/masked_item_prediction.py        |  59 +-
 modeling/dataset/samplers/mclsr.py            |  45 +-
 .../dataset/samplers/next_item_prediction.py  |  69 +-
 modeling/dataset/samplers/pop.py              |  52 +-
 modeling/dataset/samplers/s3rec.py            |  92 +--
 modeling/infer.py                             |  69 +-
 modeling/loss/base.py                         | 405 ++++++-----
 modeling/metric/base.py                       |  65 +-
 modeling/models/__init__.py                   |   8 +-
 modeling/models/base.py                       | 117 ++--
 modeling/models/bert4rec.py                   |  94 +--
 modeling/models/bert4rec_cls.py               |  91 ++-
 modeling/models/cl4srec.py                    | 127 ++--
 modeling/models/duorec.py                     | 123 ++--
 modeling/models/graph_seq_rec.py              | 225 +++---
 modeling/models/gru4rec.py                    | 172 ++---
 modeling/models/gtorec.py                     | 475 ++++++++-----
 modeling/models/lightgcn.py                   | 129 ++--
 modeling/models/mclsr.py                      | 274 ++++----
 modeling/models/mrgsrec.py                    | 115 +--
 modeling/models/ngcf.py                       | 133 ++--
 modeling/models/pop.py                        |  36 +-
 modeling/models/pure_mf.py                    | 107 +--
 modeling/models/pure_svd.py                   |   6 +-
 modeling/models/random.py                     |  29 +-
 modeling/models/rqvae.py                      |  81 ++-
 modeling/models/s3rec.py                      | 214 +++---
 modeling/models/sasrec.py                     | 187 ++---
 modeling/models/sasrec_ce.py                  |  85 +--
 modeling/models/sasrec_freezed.py             |   3 +-
 modeling/models/sasrec_semantic.py            |   8 +-
 modeling/models/tiger.py                      |  18 +-
 modeling/optimizer/base.py                    |  41 +-
 modeling/pretrain.py                          |  65 +-
 modeling/rqvae_utils/__init__.py              |   4 +-
 modeling/rqvae_utils/collision_solver.py      | 156 +++--
 modeling/rqvae_utils/rqvae_data.py            |  13 +-
 modeling/rqvae_utils/rqvae_test.py            |  25 +-
 modeling/rqvae_utils/simplified_tree.py       |  70 +-
 modeling/rqvae_utils/tree.py                  | 191 +++--
 modeling/rqvae_utils/tree_comparing.py        |  18 +-
 modeling/rqvae_utils/trie.py                  |   1 +
 modeling/train.py                             | 106 +--
 modeling/train_multiple.py                    | 103 +--
 modeling/utils/__init__.py                    |  93 +--
 modeling/utils/grid_search.py                 |  25 +-
 modeling/utils/registry.py                    |  31 +-
 modeling/utils/tensorboards/__init__.py       |   2 +-
 .../utils/tensorboards/tensorboard_writers.py |  11 +-
 64 files changed, 3309 insertions(+), 2698 deletions(-)

diff --git a/modeling/callbacks/__init__.py b/modeling/callbacks/__init__.py
index a23d5ba3..81d5054e 100644
--- a/modeling/callbacks/__init__.py
+++ b/modeling/callbacks/__init__.py
@@ -1 +1,7 @@
-from .base import BaseCallback, CompositeCallback, EvalCallback, InferenceCallback, ValidationCallback
+from .base import (
+    BaseCallback,
+    CompositeCallback,
+    EvalCallback,
+    InferenceCallback,
+    ValidationCallback,
+)
diff --git a/modeling/callbacks/base.py b/modeling/callbacks/base.py
index 1ae26267..900b492e 100644
--- a/modeling/callbacks/base.py
+++ b/modeling/callbacks/base.py
@@ -1,25 +1,19 @@
-from metric import BaseMetric, StatefullMetric
-
-import utils
-from utils import MetaParent, create_logger
+import os
+from pathlib import Path
 
 import numpy as np
-import os
 import torch
-from pathlib import Path
+
+import utils
+from metric import BaseMetric, StatefullMetric
+from utils import MetaParent, create_logger
 
 logger = create_logger(name=__name__)
 
 
 class BaseCallback(metaclass=MetaParent):
-
     def __init__(
-            self,
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer
+        self, model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
     ):
         self._model = model
         self._train_dataloader = train_dataloader
@@ -31,25 +25,20 @@ def __call__(self, inputs, step_num):
         raise NotImplementedError
 
 
-class MetricCallback(BaseCallback, config_name='metric'):
-
+class MetricCallback(BaseCallback, config_name="metric"):
     def __init__(
-            self,
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer,
-            on_step,
-            metrics,
-            loss_prefix
+        self,
+        model,
+        train_dataloader,
+        validation_dataloader,
+        eval_dataloader,
+        optimizer,
+        on_step,
+        metrics,
+        loss_prefix,
     ):
         super().__init__(
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer
+            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
         )
         self._on_step = on_step
         self._loss_prefix = loss_prefix
@@ -58,113 +47,103 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs['model'],
-            train_dataloader=kwargs['train_dataloader'],
-            validation_dataloader=kwargs['validation_dataloader'],
-            eval_dataloader=kwargs['eval_dataloader'],
-            optimizer=kwargs['optimizer'],
-            on_step=config['on_step'],
-            metrics=config.get('metrics', None),
-            loss_prefix=config['loss_prefix']
+            model=kwargs["model"],
+            train_dataloader=kwargs["train_dataloader"],
+            validation_dataloader=kwargs["validation_dataloader"],
+            eval_dataloader=kwargs["eval_dataloader"],
+            optimizer=kwargs["optimizer"],
+            on_step=config["on_step"],
+            metrics=config.get("metrics", None),
+            loss_prefix=config["loss_prefix"],
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:
             for metric_name, metric_function in self._metrics.items():
                 metric_value = metric_function(
-                    ground_truth=inputs[self._model.schema['ground_truth_prefix']],
-                    predictions=inputs[self._model.schema['predictions_prefix']]
+                    ground_truth=inputs[self._model.schema["ground_truth_prefix"]],
+                    predictions=inputs[self._model.schema["predictions_prefix"]],
                 )
 
                 utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                    'train/{}'.format(metric_name),
-                    metric_value,
-                    step_num
+                    "train/{}".format(metric_name), metric_value, step_num
                 )
 
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                'train/{}'.format(self._loss_prefix),
+                "train/{}".format(self._loss_prefix),
                 inputs[self._loss_prefix],
-                step_num
+                step_num,
             )
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.flush()
 
 
-class CheckpointCallback(BaseCallback, config_name='checkpoint'):
-
+class CheckpointCallback(BaseCallback, config_name="checkpoint"):
     def __init__(
-            self,
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer,
-            on_step,
-            save_path,
-            model_name
+        self,
+        model,
+        train_dataloader,
+        validation_dataloader,
+        eval_dataloader,
+        optimizer,
+        on_step,
+        save_path,
+        model_name,
     ):
         super().__init__(
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer
+            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
         )
         self._on_step = on_step
         self._save_path = Path(os.path.join(save_path, model_name))
         if self._save_path.exists():
-            logger.warning('Checkpoint path `{}` is already exists!'.format(self._save_path))
+            logger.warning(
+                "Checkpoint path `{}` is already exists!".format(self._save_path)
+            )
         else:
             self._save_path.mkdir(parents=True, exist_ok=True)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs['model'],
-            train_dataloader=kwargs['train_dataloader'],
-            validation_dataloader=kwargs['validation_dataloader'],
-            eval_dataloader=kwargs['eval_dataloader'],
-            optimizer=kwargs['optimizer'],
-            on_step=config['on_step'],
-            save_path=config['save_path'],
-            model_name=config['model_name']
+            model=kwargs["model"],
+            train_dataloader=kwargs["train_dataloader"],
+            validation_dataloader=kwargs["validation_dataloader"],
+            eval_dataloader=kwargs["eval_dataloader"],
+            optimizer=kwargs["optimizer"],
+            on_step=config["on_step"],
+            save_path=config["save_path"],
+            model_name=config["model_name"],
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:
-            logger.debug('Saving model state on step {}...'.format(step_num))
+            logger.debug("Saving model state on step {}...".format(step_num))
             torch.save(
                 {
-                    'step_num': step_num,
-                    'model_state_dict': self._model.state_dict(),
-                    'optimizer_state_dict': self._optimizer.state_dict(),
+                    "step_num": step_num,
+                    "model_state_dict": self._model.state_dict(),
+                    "optimizer_state_dict": self._optimizer.state_dict(),
                 },
-                os.path.join(self._save_path, 'checkpoint_{}.pth'.format(step_num))
+                os.path.join(self._save_path, "checkpoint_{}.pth".format(step_num)),
             )
-            logger.debug('Saving done!')
+            logger.debug("Saving done!")
 
 
 class InferenceCallback(BaseCallback):
-
     def __init__(
-            self,
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer,
-            on_step,
-            pred_prefix,
-            labels_prefix,
-            metrics=None,
-            loss_prefix=None,
+        self,
+        model,
+        train_dataloader,
+        validation_dataloader,
+        eval_dataloader,
+        optimizer,
+        on_step,
+        pred_prefix,
+        labels_prefix,
+        metrics=None,
+        loss_prefix=None,
     ):
         super().__init__(
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer
+            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
         )
         self._on_step = on_step
         self._metrics = metrics if metrics is not None else {}
@@ -176,24 +155,24 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config['metrics'].items()
+            for metric_name, metric_cfg in config["metrics"].items()
         }
 
         return cls(
-            model=kwargs['model'],
-            train_dataloader=kwargs['train_dataloader'],
-            validation_dataloader=kwargs['validation_dataloader'],
-            eval_dataloader=kwargs['eval_dataloader'],
-            optimizer=kwargs['optimizer'],
-            on_step=config['on_step'],
+            model=kwargs["model"],
+            train_dataloader=kwargs["train_dataloader"],
+            validation_dataloader=kwargs["validation_dataloader"],
+            eval_dataloader=kwargs["eval_dataloader"],
+            optimizer=kwargs["optimizer"],
+            on_step=config["on_step"],
             metrics=metrics,
-            pred_prefix=config['pred_prefix'],
-            labels_prefix=config['labels_prefix']
+            pred_prefix=config["pred_prefix"],
+            labels_prefix=config["labels_prefix"],
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:  # TODO Add time monitoring
-            logger.debug(f'Running {self._get_name()} on step {step_num}...')
+            logger.debug(f"Running {self._get_name()} on step {step_num}...")
             running_params = {}
             for metric_name, metric_function in self._metrics.items():
                 running_params[metric_name] = []
@@ -212,30 +191,34 @@ def __call__(self, inputs, step_num):
                         batch[key] = values.cpu()
 
                     for metric_name, metric_function in self._metrics.items():
-                        running_params[metric_name].extend(metric_function(
-                            inputs=batch,
-                            pred_prefix=self._pred_prefix,
-                            labels_prefix=self._labels_prefix,
-                        ))
+                        running_params[metric_name].extend(
+                            metric_function(
+                                inputs=batch,
+                                pred_prefix=self._pred_prefix,
+                                labels_prefix=self._labels_prefix,
+                            )
+                        )
 
                     if self._loss_prefix is not None:
-                        running_params[self._loss_prefix] += batch[self._loss_prefix].item()
-            
+                        running_params[self._loss_prefix] += batch[
+                            self._loss_prefix
+                        ].item()
+
             for metric_name, metric_function in self._metrics.items():
                 if isinstance(metric_function, StatefullMetric):
-                    running_params[metric_name] = metric_function.reduce(running_params[metric_name])
+                    running_params[metric_name] = metric_function.reduce(
+                        running_params[metric_name]
+                    )
 
             for label, value in running_params.items():
-                inputs[f'{self._get_name()}/{label}'] = np.mean(value)
+                inputs[f"{self._get_name()}/{label}"] = np.mean(value)
                 utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                    f'{self._get_name()}/{label}',
-                    np.mean(value),
-                    step_num
+                    f"{self._get_name()}/{label}", np.mean(value), step_num
                 )
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.flush()
 
-            logger.debug(f'Running {self._get_name()} on step {step_num} is done!')
-        
+            logger.debug(f"Running {self._get_name()} on step {step_num} is done!")
+
     def _get_name(self):
         return self.config_name
 
@@ -243,87 +226,81 @@ def _get_dataloader(self):
         raise NotImplementedError
 
 
-class ValidationCallback(InferenceCallback, config_name='validation'):
-
+class ValidationCallback(InferenceCallback, config_name="validation"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config['metrics'].items()
+            for metric_name, metric_cfg in config["metrics"].items()
         }
 
         return cls(
-            model=kwargs['model'],
-            train_dataloader=kwargs['train_dataloader'],
-            validation_dataloader=kwargs['validation_dataloader'],
-            eval_dataloader=kwargs['eval_dataloader'],
-            optimizer=kwargs['optimizer'],
-            on_step=config['on_step'],
+            model=kwargs["model"],
+            train_dataloader=kwargs["train_dataloader"],
+            validation_dataloader=kwargs["validation_dataloader"],
+            eval_dataloader=kwargs["eval_dataloader"],
+            optimizer=kwargs["optimizer"],
+            on_step=config["on_step"],
             metrics=metrics,
-            pred_prefix=config['pred_prefix'],
-            labels_prefix=config['labels_prefix']
+            pred_prefix=config["pred_prefix"],
+            labels_prefix=config["labels_prefix"],
         )
 
     def _get_dataloader(self):
         return self._validation_dataloader
 
 
-class EvalCallback(InferenceCallback, config_name='eval'):
-
+class EvalCallback(InferenceCallback, config_name="eval"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config['metrics'].items()
+            for metric_name, metric_cfg in config["metrics"].items()
         }
 
         return cls(
-            model=kwargs['model'],
-            train_dataloader=kwargs['train_dataloader'],
-            validation_dataloader=kwargs['validation_dataloader'],
-            eval_dataloader=kwargs['eval_dataloader'],
-            optimizer=kwargs['optimizer'],
-            on_step=config['on_step'],
+            model=kwargs["model"],
+            train_dataloader=kwargs["train_dataloader"],
+            validation_dataloader=kwargs["validation_dataloader"],
+            eval_dataloader=kwargs["eval_dataloader"],
+            optimizer=kwargs["optimizer"],
+            on_step=config["on_step"],
             metrics=metrics,
-            pred_prefix=config['pred_prefix'],
-            labels_prefix=config['labels_prefix']
+            pred_prefix=config["pred_prefix"],
+            labels_prefix=config["labels_prefix"],
         )
 
     def _get_dataloader(self):
         return self._eval_dataloader
 
 
-class CompositeCallback(BaseCallback, config_name='composite'):
+class CompositeCallback(BaseCallback, config_name="composite"):
     def __init__(
-            self,
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer,
-            callbacks
+        self,
+        model,
+        train_dataloader,
+        validation_dataloader,
+        eval_dataloader,
+        optimizer,
+        callbacks,
     ):
         super().__init__(
-            model,
-            train_dataloader,
-            validation_dataloader,
-            eval_dataloader,
-            optimizer
+            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
         )
         self._callbacks = callbacks
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs['model'],
-            train_dataloader=kwargs['train_dataloader'],
-            validation_dataloader=kwargs['validation_dataloader'],
-            eval_dataloader=kwargs['eval_dataloader'],
-            optimizer=kwargs['optimizer'],
+            model=kwargs["model"],
+            train_dataloader=kwargs["train_dataloader"],
+            validation_dataloader=kwargs["validation_dataloader"],
+            eval_dataloader=kwargs["eval_dataloader"],
+            optimizer=kwargs["optimizer"],
             callbacks=[
                 BaseCallback.create_from_config(cfg, **kwargs)
-                for cfg in config['callbacks']
-            ]
+                for cfg in config["callbacks"]
+            ],
         )
 
     def __call__(self, inputs, step_num):
diff --git a/modeling/dataloader/base.py b/modeling/dataloader/base.py
index 2e23876d..71e7f6e7 100644
--- a/modeling/dataloader/base.py
+++ b/modeling/dataloader/base.py
@@ -1,12 +1,13 @@
 import copy
-
-from utils import MetaParent
-from .batch_processors import BaseBatchProcessor
-
 import logging
+
 import numpy as np
 from torch.utils.data import DataLoader, random_split
 
+from utils import MetaParent
+
+from .batch_processors import BaseBatchProcessor
+
 logger = logging.getLogger(__name__)
 
 
@@ -14,8 +15,7 @@ class BaseDataloader(metaclass=MetaParent):
     pass
 
 
-class TorchDataloader(BaseDataloader, config_name='torch'):
-
+class TorchDataloader(BaseDataloader, config_name="torch"):
     def __init__(self, dataloader):
         self._dataloader = dataloader
 
@@ -29,7 +29,13 @@ def __len__(self):
     def create_from_config(cls, config, **kwargs):
         create_config = copy.deepcopy(config)
         batch_processor = BaseBatchProcessor.create_from_config(
-            create_config.pop('batch_processor') if 'batch_processor' in create_config else {'type': 'identity'}
+            create_config.pop("batch_processor")
+            if "batch_processor" in create_config
+            else {"type": "identity"}
+        )
+        create_config.pop("type")  # For passing as **config in torch DataLoader
+        return cls(
+            dataloader=DataLoader(
+                kwargs["dataset"], collate_fn=batch_processor, **create_config
+            )
         )
-        create_config.pop('type')  # For passing as **config in torch DataLoader
-        return cls(dataloader=DataLoader(kwargs['dataset'], collate_fn=batch_processor, **create_config))
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 9991a073..bd45c86c 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,43 +1,43 @@
 import torch
+
 from utils import MetaParent
 
 
 class BaseBatchProcessor(metaclass=MetaParent):
-
     def __call__(self, batch):
         raise NotImplementedError
 
 
-class IdentityBatchProcessor(BaseBatchProcessor, config_name='identity'):
-
+class IdentityBatchProcessor(BaseBatchProcessor, config_name="identity"):
     def __call__(self, batch):
         return torch.tensor(batch)
-    
-class EmbedBatchProcessor(BaseBatchProcessor, config_name='embed'):
 
+
+class EmbedBatchProcessor(BaseBatchProcessor, config_name="embed"):
     def __call__(self, batch):
-        ids = torch.tensor([entry['item.id'] for entry in batch])
-        embeds = torch.stack([entry['item.embed'] for entry in batch])
-        
-        return {'ids': ids, 'embeddings': embeds}
+        ids = torch.tensor([entry["item.id"] for entry in batch])
+        embeds = torch.stack([entry["item.embed"] for entry in batch])
 
+        return {"ids": ids, "embeddings": embeds}
 
-class BasicBatchProcessor(BaseBatchProcessor, config_name='basic'):
 
+class BasicBatchProcessor(BaseBatchProcessor, config_name="basic"):
     def __call__(self, batch):
         processed_batch = {}
 
         for key in batch[0].keys():
-            if key.endswith('.ids'):
-                prefix = key.split('.')[0]
-                assert '{}.length'.format(prefix) in batch[0]
+            if key.endswith(".ids"):
+                prefix = key.split(".")[0]
+                assert "{}.length".format(prefix) in batch[0]
 
-                processed_batch[f'{prefix}.ids'] = []
-                processed_batch[f'{prefix}.length'] = []
+                processed_batch[f"{prefix}.ids"] = []
+                processed_batch[f"{prefix}.length"] = []
 
                 for sample in batch:
-                    processed_batch[f'{prefix}.ids'].extend(sample[f'{prefix}.ids'])
-                    processed_batch[f'{prefix}.length'].append(sample[f'{prefix}.length'])
+                    processed_batch[f"{prefix}.ids"].extend(sample[f"{prefix}.ids"])
+                    processed_batch[f"{prefix}.length"].append(
+                        sample[f"{prefix}.length"]
+                    )
 
         for part, values in processed_batch.items():
             processed_batch[part] = torch.tensor(values, dtype=torch.long)
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 38a704c1..4e0aea5b 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -1,40 +1,35 @@
-from collections import defaultdict
 import json
-
-from tqdm import tqdm
-
-from dataset.samplers import TrainSampler, EvalSampler
-
-from utils import MetaParent, DEVICE
-
+import logging
+import os
 import pickle
-import torch
+from collections import defaultdict
+
 import numpy as np
 import scipy.sparse as sp
+import torch
 from scipy.sparse import csr_matrix
+from tqdm import tqdm
 
-import os
-import logging
+from dataset.samplers import EvalSampler, TrainSampler
+from utils import DEVICE, MetaParent
 
 logger = logging.getLogger(__name__)
 
 
 class BaseDataset(metaclass=MetaParent):
-
     def get_samplers(self):
         raise NotImplementedError
 
 
-class SequenceDataset(BaseDataset, config_name='sequence'):
-
+class SequenceDataset(BaseDataset, config_name="sequence"):
     def __init__(
-            self,
-            train_sampler,
-            validation_sampler,
-            test_sampler,
-            num_users,
-            num_items,
-            max_sequence_length
+        self,
+        train_sampler,
+        validation_sampler,
+        test_sampler,
+        num_users,
+        num_items,
+        max_sequence_length,
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -45,61 +40,74 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
-
-        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
-            dir_path=data_dir_path,
-            part='train',
-            max_sequence_length=config['max_sequence_length'],
-            use_cached=config.get('use_cached', False)
+        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = (
+            cls._create_dataset(
+                dir_path=data_dir_path,
+                part="train",
+                max_sequence_length=config["max_sequence_length"],
+                use_cached=config.get("use_cached", False),
+            )
         )
-        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
-            dir_path=data_dir_path,
-            part='valid',
-            max_sequence_length=config['max_sequence_length'],
-            use_cached=config.get('use_cached', False)
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = (
+            cls._create_dataset(
+                dir_path=data_dir_path,
+                part="valid",
+                max_sequence_length=config["max_sequence_length"],
+                use_cached=config.get("use_cached", False),
+            )
         )
-        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
-            dir_path=data_dir_path,
-            part='test',
-            max_sequence_length=config['max_sequence_length'],
-            use_cached=config.get('use_cached', False)
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = (
+            cls._create_dataset(
+                dir_path=data_dir_path,
+                part="test",
+                max_sequence_length=config["max_sequence_length"],
+                use_cached=config.get("use_cached", False),
+            )
         )
 
         max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
         max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
-        logger.info('Train dataset size: {}'.format(len(train_dataset)))
-        logger.info('Test dataset size: {}'.format(len(test_dataset)))
-        logger.info('Max user id: {}'.format(max_user_id))
-        logger.info('Max item id: {}'.format(max_item_id))
-        logger.info('Max sequence length: {}'.format(max_seq_len))
+        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Test dataset size: {}".format(len(test_dataset)))
+        logger.info("Max user id: {}".format(max_user_id))
+        logger.info("Max item id: {}".format(max_item_id))
+        logger.info("Max sequence length: {}".format(max_seq_len))
 
-        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
+        train_interactions = sum(
+            list(map(lambda x: len(x), train_dataset))
+        )  # whole user history as a sample
         valid_interactions = len(validation_dataset)  # each new interaction as a sample
-        test_interactions = len(test_dataset) # each new interaction as a sample
-        logger.info('{} dataset sparsity: {}'.format(
-            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
-        ))
+        test_interactions = len(test_dataset)  # each new interaction as a sample
+        logger.info(
+            "{} dataset sparsity: {}".format(
+                config["name"],
+                (train_interactions + valid_interactions + test_interactions)
+                / max_user_id
+                / max_item_id,
+            )
+        )
 
         train_sampler = TrainSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
         validation_sampler = EvalSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
         test_sampler = EvalSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
 
         return cls(
@@ -108,45 +116,72 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_seq_len
+            max_sequence_length=max_seq_len,
         )
 
     @classmethod
-    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
+    def _create_dataset(
+        cls, dir_path, part, max_sequence_length=None, use_cached=False
+    ):
         max_user_id = 0
         max_item_id = 0
         max_sequence_len = 0
 
-        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
-            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
+        if use_cached and os.path.exists(os.path.join(dir_path, "{}.pkl".format(part))):
+            logger.info(
+                f"Take cached dataset from {os.path.join(dir_path, '{}.pkl'.format(part))}"
+            )
 
-            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
-                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
+            with open(
+                os.path.join(dir_path, "{}.pkl".format(part)), "rb"
+            ) as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(
+                    dataset_file
+                )
         else:
-            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
-            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
+            logger.info(
+                "Cache is forecefully ignored."
+                if not use_cached
+                else "No cached dataset has been found."
+            )
+            logger.info(
+                f"Creating a dataset {os.path.join(dir_path, '{}.txt'.format(part))}..."
+            )
 
-            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
-            with open(dataset_path, 'r') as f:
+            dataset_path = os.path.join(dir_path, "{}.txt".format(part))
+            with open(dataset_path, "r") as f:
                 data = f.readlines()
 
             sequence_info = cls._create_sequences(data, max_sequence_length)
-            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
+            (
+                user_sequences,
+                item_sequences,
+                max_user_id,
+                max_item_id,
+                max_sequence_len,
+            ) = sequence_info
 
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
-                dataset.append({
-                    'user.ids': [user_id], 'user.length': 1,
-                    'item.ids': item_ids, 'item.length': len(item_ids)
-                })
+                dataset.append(
+                    {
+                        "user.ids": [user_id],
+                        "user.length": 1,
+                        "item.ids": item_ids,
+                        "item.length": len(item_ids),
+                    }
+                )
 
-            logger.info('{} dataset size: {}'.format(part, len(dataset)))
-            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
+            logger.info("{} dataset size: {}".format(part, len(dataset)))
+            logger.info(
+                "{} dataset max sequence length: {}".format(part, max_sequence_len)
+            )
 
-            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
+            with open(
+                os.path.join(dir_path, "{}.pkl".format(part)), "wb"
+            ) as dataset_file:
                 pickle.dump(
-                    (dataset, max_user_id, max_item_id, max_sequence_len),
-                    dataset_file
+                    (dataset, max_user_id, max_item_id, max_sequence_len), dataset_file
                 )
 
         return dataset, max_user_id, max_item_id, max_sequence_len
@@ -161,7 +196,7 @@ def _create_sequences(data, max_sample_len):
         max_sequence_length = 0
 
         for sample in data:
-            sample = sample.strip('\n').split(' ')
+            sample = sample.strip("\n").split(" ")
             item_ids = [int(item_id) for item_id in sample[1:]][-max_sample_len:]
             user_id = int(sample[0])
 
@@ -172,7 +207,13 @@ def _create_sequences(data, max_sample_len):
             user_sequences.append(user_id)
             item_sequences.append(item_ids)
 
-        return user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_length
+        return (
+            user_sequences,
+            item_sequences,
+            max_user_id,
+            max_item_id,
+            max_sequence_length,
+        )
 
     def get_samplers(self):
         return self._train_sampler, self._validation_sampler, self._test_sampler
@@ -192,70 +233,83 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            'num_users': self.num_users,
-            'num_items': self.num_items,
-            'max_sequence_length': self.max_sequence_length
+            "num_users": self.num_users,
+            "num_items": self.num_items,
+            "max_sequence_length": self.max_sequence_length,
         }
-        
-        
-class SequenceFullDataset(SequenceDataset, config_name='sequence_full'):
+
+
+class SequenceFullDataset(SequenceDataset, config_name="sequence_full"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
-
-        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
-            dir_path=data_dir_path,
-            part='train',
-            max_sequence_length=config['max_sequence_length'],
-            use_cached=config.get('use_cached', False)
+        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = (
+            cls._create_dataset(
+                dir_path=data_dir_path,
+                part="train",
+                max_sequence_length=config["max_sequence_length"],
+                use_cached=config.get("use_cached", False),
+            )
         )
-        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
-            dir_path=data_dir_path,
-            part='valid',
-            max_sequence_length=config['max_sequence_length'],
-            use_cached=config.get('use_cached', False)
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = (
+            cls._create_dataset(
+                dir_path=data_dir_path,
+                part="valid",
+                max_sequence_length=config["max_sequence_length"],
+                use_cached=config.get("use_cached", False),
+            )
         )
-        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
-            dir_path=data_dir_path,
-            part='test',
-            max_sequence_length=config['max_sequence_length'],
-            use_cached=config.get('use_cached', False)
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = (
+            cls._create_dataset(
+                dir_path=data_dir_path,
+                part="test",
+                max_sequence_length=config["max_sequence_length"],
+                use_cached=config.get("use_cached", False),
+            )
         )
 
         max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
         max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
-        logger.info('Train dataset size: {}'.format(len(train_dataset)))
-        logger.info('Test dataset size: {}'.format(len(test_dataset)))
-        logger.info('Max user id: {}'.format(max_user_id))
-        logger.info('Max item id: {}'.format(max_item_id))
-        logger.info('Max sequence length: {}'.format(max_seq_len))
+        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Test dataset size: {}".format(len(test_dataset)))
+        logger.info("Max user id: {}".format(max_user_id))
+        logger.info("Max item id: {}".format(max_item_id))
+        logger.info("Max sequence length: {}".format(max_seq_len))
 
-        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
+        train_interactions = sum(
+            list(map(lambda x: len(x), train_dataset))
+        )  # whole user history as a sample
         valid_interactions = len(validation_dataset)  # each new interaction as a sample
-        test_interactions = len(test_dataset) # each new interaction as a sample
-        logger.info('{} dataset sparsity: {}'.format(
-            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
-        ))
+        test_interactions = len(test_dataset)  # each new interaction as a sample
+        logger.info(
+            "{} dataset sparsity: {}".format(
+                config["name"],
+                (train_interactions + valid_interactions + test_interactions)
+                / max_user_id
+                / max_item_id,
+            )
+        )
 
         train_sampler = TrainSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
         validation_sampler = EvalSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
         test_sampler = EvalSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
 
         return cls(
@@ -264,68 +318,95 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_seq_len
+            max_sequence_length=max_seq_len,
         )
-        
+
     @classmethod
     def flatten_item_sequence(cls, item_ids):
-        min_history_length = 3 # TODOPK make this configurable
+        min_history_length = 3  # TODOPK make this configurable
         histories = []
-        for i in range(min_history_length-1, len(item_ids)):
-            histories.append(item_ids[:i+1])
+        for i in range(min_history_length - 1, len(item_ids)):
+            histories.append(item_ids[: i + 1])
         return histories
-    
+
     @classmethod
-    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
+    def _create_dataset(
+        cls, dir_path, part, max_sequence_length=None, use_cached=False
+    ):
         max_user_id = 0
         max_item_id = 0
         max_sequence_len = 0
 
-        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
-            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
+        if use_cached and os.path.exists(os.path.join(dir_path, "{}.pkl".format(part))):
+            logger.info(
+                f"Take cached dataset from {os.path.join(dir_path, '{}.pkl'.format(part))}"
+            )
 
-            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
-                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
+            with open(
+                os.path.join(dir_path, "{}.pkl".format(part)), "rb"
+            ) as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(
+                    dataset_file
+                )
         else:
-            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
-            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
+            logger.info(
+                "Cache is forecefully ignored."
+                if not use_cached
+                else "No cached dataset has been found."
+            )
+            logger.info(
+                f"Creating a dataset {os.path.join(dir_path, '{}.txt'.format(part))}..."
+            )
 
-            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
-            with open(dataset_path, 'r') as f:
+            dataset_path = os.path.join(dir_path, "{}.txt".format(part))
+            with open(dataset_path, "r") as f:
                 data = f.readlines()
 
             sequence_info = cls._create_sequences(data, max_sequence_length)
-            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
+            (
+                user_sequences,
+                item_sequences,
+                max_user_id,
+                max_item_id,
+                max_sequence_len,
+            ) = sequence_info
 
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
                 flattened_item_ids = cls.flatten_item_sequence(item_ids)
                 for seq in flattened_item_ids:
-                    dataset.append({
-                        'user.ids': [user_id], 'user.length': 1,
-                        'item.ids': seq, 'item.length': len(seq)
-                    })
-
-            logger.info('{} dataset size: {}'.format(part, len(dataset)))
-            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
+                    dataset.append(
+                        {
+                            "user.ids": [user_id],
+                            "user.length": 1,
+                            "item.ids": seq,
+                            "item.length": len(seq),
+                        }
+                    )
+
+            logger.info("{} dataset size: {}".format(part, len(dataset)))
+            logger.info(
+                "{} dataset max sequence length: {}".format(part, max_sequence_len)
+            )
 
-            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
+            with open(
+                os.path.join(dir_path, "{}.pkl".format(part)), "wb"
+            ) as dataset_file:
                 pickle.dump(
-                    (dataset, max_user_id, max_item_id, max_sequence_len),
-                    dataset_file
+                    (dataset, max_user_id, max_item_id, max_sequence_len), dataset_file
                 )
 
         return dataset, max_user_id, max_item_id, max_sequence_len
 
-class GraphDataset(BaseDataset, config_name='graph'):
 
+class GraphDataset(BaseDataset, config_name="graph"):
     def __init__(
-            self,
-            dataset,
-            graph_dir_path,
-            use_train_data_only=True,
-            use_user_graph=False,
-            use_item_graph=False
+        self,
+        dataset,
+        graph_dir_path,
+        use_train_data_only=True,
+        use_user_graph=False,
+        use_item_graph=False,
     ):
         self._dataset = dataset
         self._graph_dir_path = graph_dir_path
@@ -338,15 +419,19 @@ def __init__(
 
         train_sampler, validation_sampler, test_sampler = dataset.get_samplers()
 
-        train_interactions, train_user_interactions, train_item_interactions = [], [], []
+        train_interactions, train_user_interactions, train_item_interactions = (
+            [],
+            [],
+            [],
+        )
 
         train_user_2_items = defaultdict(set)
         train_item_2_users = defaultdict(set)
         visited_user_item_pairs = set()
 
         for sample in train_sampler.dataset:
-            user_id = sample['user.ids'][0]
-            item_ids = sample['item.ids']
+            user_id = sample["user.ids"][0]
+            item_ids = sample["item.ids"]
 
             for item_id in item_ids:
                 if (user_id, item_id) not in visited_user_item_pairs:
@@ -362,8 +447,8 @@ def __init__(
         # TODO create separated function
         if not self._use_train_data_only:
             for sample in validation_sampler.dataset:
-                user_id = sample['user.ids'][0]
-                item_ids = sample['item.ids']
+                user_id = sample["user.ids"][0]
+                item_ids = sample["item.ids"]
 
                 for item_id in item_ids:
                     if (user_id, item_id) not in visited_user_item_pairs:
@@ -377,8 +462,8 @@ def __init__(
                         visited_user_item_pairs.add((user_id, item_id))
 
             for sample in test_sampler.dataset:
-                user_id = sample['user.ids'][0]
-                item_ids = sample['item.ids']
+                user_id = sample["user.ids"][0]
+                item_ids = sample["item.ids"]
 
                 for item_id in item_ids:
                     if (user_id, item_id) not in visited_user_item_pairs:
@@ -395,27 +480,32 @@ def __init__(
         self._train_user_interactions = np.array(train_user_interactions)
         self._train_item_interactions = np.array(train_item_interactions)
 
-        path_to_graph = os.path.join(graph_dir_path, 'general_graph.npz')
+        path_to_graph = os.path.join(graph_dir_path, "general_graph.npz")
         if os.path.exists(path_to_graph):
             self._graph = sp.load_npz(path_to_graph)
         else:
             # place ones only when co-occurrence happens
             user2item_connections = csr_matrix(
-                (np.ones(len(train_user_interactions)), (train_user_interactions, train_item_interactions)),
-                shape=(self._num_users + 2, self._num_items + 2)
+                (
+                    np.ones(len(train_user_interactions)),
+                    (train_user_interactions, train_item_interactions),
+                ),
+                shape=(self._num_users + 2, self._num_items + 2),
             )  # (num_users + 2, num_items + 2), bipartite graph
             self._graph = self.get_sparse_graph_layer(
                 user2item_connections,
                 self._num_users + 2,
                 self._num_items + 2,
-                biparite=True
+                biparite=True,
             )
             sp.save_npz(path_to_graph, self._graph)
 
-        self._graph = self._convert_sp_mat_to_sp_tensor(self._graph).coalesce().to(DEVICE)
+        self._graph = (
+            self._convert_sp_mat_to_sp_tensor(self._graph).coalesce().to(DEVICE)
+        )
 
         if self._use_user_graph:
-            path_to_user_graph = os.path.join(graph_dir_path, 'user_graph.npz')
+            path_to_user_graph = os.path.join(graph_dir_path, "user_graph.npz")
             if os.path.exists(path_to_user_graph):
                 self._user_graph = sp.load_npz(path_to_user_graph)
             else:
@@ -424,13 +514,18 @@ def __init__(
                 visited_user_item_pairs = set()
                 visited_user_user_pairs = set()
 
-                for user_id, item_id in tqdm(zip(self._train_user_interactions, self._train_item_interactions)):
+                for user_id, item_id in tqdm(
+                    zip(self._train_user_interactions, self._train_item_interactions)
+                ):
                     if (user_id, item_id) in visited_user_item_pairs:
                         continue  # process (user, item) pair only once
                     visited_user_item_pairs.add((user_id, item_id))
 
                     for connected_user_id in train_item_2_users[item_id]:
-                        if (user_id, connected_user_id) in visited_user_user_pairs or user_id == connected_user_id:
+                        if (
+                            user_id,
+                            connected_user_id,
+                        ) in visited_user_user_pairs or user_id == connected_user_id:
                             continue  # add (user, user) to graph connections pair only once
                         visited_user_user_pairs.add((user_id, connected_user_id))
 
@@ -440,24 +535,30 @@ def __init__(
                 # (user, user) graph
                 user2user_connections = csr_matrix(
                     (
-                    np.ones(len(user2user_interactions_fst)), (user2user_interactions_fst, user2user_interactions_snd)),
-                    shape=(self._num_users + 2, self._num_users + 2)
+                        np.ones(len(user2user_interactions_fst)),
+                        (user2user_interactions_fst, user2user_interactions_snd),
+                    ),
+                    shape=(self._num_users + 2, self._num_users + 2),
                 )
 
                 self._user_graph = self.get_sparse_graph_layer(
                     user2user_connections,
                     self._num_users + 2,
                     self._num_users + 2,
-                    biparite=False
+                    biparite=False,
                 )
                 sp.save_npz(path_to_user_graph, self._user_graph)
 
-            self._user_graph = self._convert_sp_mat_to_sp_tensor(self._user_graph).coalesce().to(DEVICE)
+            self._user_graph = (
+                self._convert_sp_mat_to_sp_tensor(self._user_graph)
+                .coalesce()
+                .to(DEVICE)
+            )
         else:
             self._user_graph = None
 
         if self._use_item_graph:
-            path_to_item_graph = os.path.join(graph_dir_path, 'item_graph.npz')
+            path_to_item_graph = os.path.join(graph_dir_path, "item_graph.npz")
             if os.path.exists(path_to_item_graph):
                 self._item_graph = sp.load_npz(path_to_item_graph)
             else:
@@ -466,13 +567,18 @@ def __init__(
                 visited_user_item_pairs = set()
                 visited_item_item_pairs = set()
 
-                for user_id, item_id in tqdm(zip(self._train_user_interactions, self._train_item_interactions)):
+                for user_id, item_id in tqdm(
+                    zip(self._train_user_interactions, self._train_item_interactions)
+                ):
                     if (user_id, item_id) in visited_user_item_pairs:
                         continue  # process (user, item) pair only once
                     visited_user_item_pairs.add((user_id, item_id))
 
                     for connected_item_id in train_user_2_items[user_id]:
-                        if (item_id, connected_item_id) in visited_item_item_pairs or item_id == connected_item_id:
+                        if (
+                            item_id,
+                            connected_item_id,
+                        ) in visited_item_item_pairs or item_id == connected_item_id:
                             continue  # add (item, item) to graph connections pair only once
                         visited_item_item_pairs.add((item_id, connected_item_id))
 
@@ -482,39 +588,42 @@ def __init__(
                 # (item, item) graph
                 item2item_connections = csr_matrix(
                     (
-                    np.ones(len(item2item_interactions_fst)), (item2item_interactions_fst, item2item_interactions_snd)),
-                    shape=(self._num_items + 2, self._num_items + 2)
+                        np.ones(len(item2item_interactions_fst)),
+                        (item2item_interactions_fst, item2item_interactions_snd),
+                    ),
+                    shape=(self._num_items + 2, self._num_items + 2),
                 )
                 self._item_graph = self.get_sparse_graph_layer(
                     item2item_connections,
                     self._num_items + 2,
                     self._num_items + 2,
-                    biparite=False
+                    biparite=False,
                 )
                 sp.save_npz(path_to_item_graph, self._item_graph)
 
-            self._item_graph = self._convert_sp_mat_to_sp_tensor(self._item_graph).coalesce().to(DEVICE)
+            self._item_graph = (
+                self._convert_sp_mat_to_sp_tensor(self._item_graph)
+                .coalesce()
+                .to(DEVICE)
+            )
         else:
             self._item_graph = None
 
     @classmethod
     def create_from_config(cls, config):
-        dataset = BaseDataset.create_from_config(config['dataset'])
+        dataset = BaseDataset.create_from_config(config["dataset"])
         return cls(
             dataset=dataset,
-            graph_dir_path=config['graph_dir_path'],
-            use_user_graph=config.get('use_user_graph', False),
-            use_item_graph=config.get('use_item_graph', False)
+            graph_dir_path=config["graph_dir_path"],
+            use_user_graph=config.get("use_user_graph", False),
+            use_item_graph=config.get("use_item_graph", False),
         )
 
     @staticmethod
     def get_sparse_graph_layer(sparse_matrix, fst_dim, snd_dim, biparite=False):
         mat_dim_size = fst_dim + snd_dim if biparite else fst_dim
 
-        adj_mat = sp.dok_matrix(
-            (mat_dim_size, mat_dim_size),
-            dtype=np.float32
-        )
+        adj_mat = sp.dok_matrix((mat_dim_size, mat_dim_size), dtype=np.float32)
         adj_mat = adj_mat.tolil()
 
         R = sparse_matrix.tolil()  # list of lists (fst_dim, snd_dim)
@@ -532,11 +641,11 @@ def get_sparse_graph_layer(sparse_matrix, fst_dim, snd_dim, biparite=False):
 
         rowsum = np.array(adj_mat.sum(1))
         d_inv = np.power(rowsum, -1).flatten()
-        d_inv[np.isinf(d_inv)] = 0.
+        d_inv[np.isinf(d_inv)] = 0.0
         d_mat_inv = sp.diags(d_inv)
 
         d_inv = np.power(edges_degree, -0.5).flatten()  # D^(-0.5)
-        d_inv[np.isinf(d_inv)] = 0.  # fix NaNs in case if row with zero connections
+        d_inv[np.isinf(d_inv)] = 0.0  # fix NaNs in case if row with zero connections
         d_mat = sp.diags(d_inv)  # make it square matrix
 
         # D^(-0.5) @ A @ D^(-0.5)
@@ -567,16 +676,15 @@ def get_samplers(self):
     @property
     def meta(self):
         meta = {
-            'user_graph': self._user_graph,
-            'item_graph': self._item_graph,
-            'graph': self._graph,
-            **self._dataset.meta
+            "user_graph": self._user_graph,
+            "item_graph": self._item_graph,
+            "graph": self._graph,
+            **self._dataset.meta,
         }
         return meta
 
 
-class DuorecDataset(BaseDataset, config_name='duorec'):
-
+class DuorecDataset(BaseDataset, config_name="duorec"):
     def __init__(self, dataset):
         self._dataset = dataset
         self._num_users = dataset.num_users
@@ -586,7 +694,7 @@ def __init__(self, dataset):
 
         target_2_sequences = defaultdict(list)
         for sample in train_sampler.dataset:
-            item_ids = sample['item.ids']
+            item_ids = sample["item.ids"]
 
             target_item = item_ids[-1]
             semantic_similar_item_ids = item_ids[:-1]
@@ -597,7 +705,7 @@ def __init__(self, dataset):
 
     @classmethod
     def create_from_config(cls, config):
-        dataset = BaseDataset.create_from_config(config['dataset'])
+        dataset = BaseDataset.create_from_config(config["dataset"])
         return cls(dataset)
 
     @property
@@ -616,16 +724,15 @@ def meta(self):
         return self._dataset.meta
 
 
-class ScientificDataset(BaseDataset, config_name='scientific'):
-
+class ScientificDataset(BaseDataset, config_name="scientific"):
     def __init__(
-            self,
-            train_sampler,
-            validation_sampler,
-            test_sampler,
-            num_users,
-            num_items,
-            max_sequence_length
+        self,
+        train_sampler,
+        validation_sampler,
+        test_sampler,
+        num_users,
+        num_items,
+        max_sequence_length,
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -636,17 +743,17 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
-        max_sequence_length = config['max_sequence_length']
+        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        max_sequence_length = config["max_sequence_length"]
         max_user_id, max_item_id = 0, 0
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, '{}.txt'.format('all_data'))
-        with open(dataset_path, 'r') as f:
+        dataset_path = os.path.join(data_dir_path, "{}.txt".format("all_data"))
+        with open(dataset_path, "r") as f:
             data = f.readlines()
 
         for sample in data:
-            sample = sample.strip('\n').split(' ')
+            sample = sample.strip("\n").split(" ")
             user_id = int(sample[0])
             item_ids = [int(item_id) for item_id in sample[1:]]
 
@@ -655,54 +762,69 @@ def create_from_config(cls, config, **kwargs):
 
             assert len(item_ids) >= 5
 
-            train_dataset.append({
-                'user.ids': [user_id],
-                'user.length': 1,
-                'item.ids': item_ids[:-2][-max_sequence_length:],
-                'item.length': len(item_ids[:-2][-max_sequence_length:])
-            })
-            assert len(item_ids[:-2][-max_sequence_length:]) == len(set(item_ids[:-2][-max_sequence_length:]))
-            validation_dataset.append({
-                'user.ids': [user_id],
-                'user.length': 1,
-                'item.ids': item_ids[:-1][-max_sequence_length:],
-                'item.length': len(item_ids[:-1][-max_sequence_length:])
-            })
-            assert len(item_ids[:-1][-max_sequence_length:]) == len(set(item_ids[:-1][-max_sequence_length:]))
-            test_dataset.append({
-                'user.ids': [user_id],
-                'user.length': 1,
-                'item.ids': item_ids[-max_sequence_length:],
-                'item.length': len(item_ids[-max_sequence_length:])
-            })
-            assert len(item_ids[-max_sequence_length:]) == len(set(item_ids[-max_sequence_length:]))
-
-        logger.info('Train dataset size: {}'.format(len(train_dataset)))
-        logger.info('Test dataset size: {}'.format(len(test_dataset)))
-        logger.info('Max user id: {}'.format(max_user_id))
-        logger.info('Max item id: {}'.format(max_item_id))
-        logger.info('Max sequence length: {}'.format(max_sequence_length))
-        logger.info('{} dataset sparsity: {}'.format(
-            config['name'], (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id
-        ))
+            train_dataset.append(
+                {
+                    "user.ids": [user_id],
+                    "user.length": 1,
+                    "item.ids": item_ids[:-2][-max_sequence_length:],
+                    "item.length": len(item_ids[:-2][-max_sequence_length:]),
+                }
+            )
+            assert len(item_ids[:-2][-max_sequence_length:]) == len(
+                set(item_ids[:-2][-max_sequence_length:])
+            )
+            validation_dataset.append(
+                {
+                    "user.ids": [user_id],
+                    "user.length": 1,
+                    "item.ids": item_ids[:-1][-max_sequence_length:],
+                    "item.length": len(item_ids[:-1][-max_sequence_length:]),
+                }
+            )
+            assert len(item_ids[:-1][-max_sequence_length:]) == len(
+                set(item_ids[:-1][-max_sequence_length:])
+            )
+            test_dataset.append(
+                {
+                    "user.ids": [user_id],
+                    "user.length": 1,
+                    "item.ids": item_ids[-max_sequence_length:],
+                    "item.length": len(item_ids[-max_sequence_length:]),
+                }
+            )
+            assert len(item_ids[-max_sequence_length:]) == len(
+                set(item_ids[-max_sequence_length:])
+            )
+
+        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Test dataset size: {}".format(len(test_dataset)))
+        logger.info("Max user id: {}".format(max_user_id))
+        logger.info("Max item id: {}".format(max_item_id))
+        logger.info("Max sequence length: {}".format(max_sequence_length))
+        logger.info(
+            "{} dataset sparsity: {}".format(
+                config["name"],
+                (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id,
+            )
+        )
 
         train_sampler = TrainSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
         validation_sampler = EvalSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
         test_sampler = EvalSampler.create_from_config(
-            config['samplers'],
+            config["samplers"],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id
+            num_items=max_item_id,
         )
 
         return cls(
@@ -711,7 +833,7 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_sequence_length
+            max_sequence_length=max_sequence_length,
         )
 
     def get_samplers(self):
@@ -732,21 +854,14 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            'num_users': self.num_users,
-            'num_items': self.num_items,
-            'max_sequence_length': self.max_sequence_length
+            "num_users": self.num_users,
+            "num_items": self.num_items,
+            "max_sequence_length": self.max_sequence_length,
         }
 
 
-class RqVaeDataset(BaseDataset, config_name='rqvae'):
-
-    def __init__(
-            self,
-            train_sampler,
-            validation_sampler,
-            test_sampler,
-            num_items
-    ):
+class RqVaeDataset(BaseDataset, config_name="rqvae"):
+    def __init__(self, train_sampler, validation_sampler, test_sampler, num_items):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
         self._test_sampler = test_sampler
@@ -754,39 +869,33 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
+        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, '{}.pt'.format('data_full'))
+        dataset_path = os.path.join(data_dir_path, "{}.pt".format("data_full"))
         df = torch.load(dataset_path, weights_only=False)
 
         for idx, sample in df.iterrows():
-            train_dataset.append({
-                'item.id': idx,
-                'item.embed': sample["embeddings"]
-            })
-            
-        logger.info('Train dataset size: {}'.format(len(train_dataset)))
-        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+            train_dataset.append({"item.id": idx, "item.embed": sample["embeddings"]})
+
+        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Test dataset size: {}".format(len(test_dataset)))
 
         train_sampler = TrainSampler.create_from_config(
-            config['samplers'],
-            dataset=train_dataset
+            config["samplers"], dataset=train_dataset
         )
         validation_sampler = EvalSampler.create_from_config(
-            config['samplers'],
-            dataset=validation_dataset
+            config["samplers"], dataset=validation_dataset
         )
         test_sampler = EvalSampler.create_from_config(
-            config['samplers'],
-            dataset=test_dataset
+            config["samplers"], dataset=test_dataset
         )
 
         return cls(
             train_sampler=train_sampler,
             validation_sampler=validation_sampler,
             test_sampler=test_sampler,
-            num_items=len(df)
+            num_items=len(df),
         )
 
     def get_samplers(self):
@@ -802,7 +911,4 @@ def max_sequence_length(self):
 
     @property
     def meta(self):
-        return {
-            'num_items': self.num_items,
-            'train_sampler': self._train_sampler
-        }
+        return {"num_items": self.num_items, "train_sampler": self._train_sampler}
diff --git a/modeling/dataset/negative_samplers/__init__.py b/modeling/dataset/negative_samplers/__init__.py
index 498de21f..6cac99af 100644
--- a/modeling/dataset/negative_samplers/__init__.py
+++ b/modeling/dataset/negative_samplers/__init__.py
@@ -2,8 +2,4 @@
 from .popular import PopularNegativeSampler
 from .random import RandomNegativeSampler
 
-__all__ = [
-    'BaseNegativeSampler',
-    'PopularNegativeSampler',
-    'RandomNegativeSampler'
-]
+__all__ = ["BaseNegativeSampler", "PopularNegativeSampler", "RandomNegativeSampler"]
diff --git a/modeling/dataset/negative_samplers/base.py b/modeling/dataset/negative_samplers/base.py
index 5483bd29..ebaef60c 100644
--- a/modeling/dataset/negative_samplers/base.py
+++ b/modeling/dataset/negative_samplers/base.py
@@ -4,21 +4,15 @@
 
 
 class BaseNegativeSampler(metaclass=MetaParent):
-
-    def __init__(
-            self,
-            dataset,
-            num_users,
-            num_items
-    ):
+    def __init__(self, dataset, num_users, num_items):
         self._dataset = dataset
         self._num_users = num_users
         self._num_items = num_items
 
         self._seen_items = defaultdict(set)
         for sample in self._dataset:
-            user_id = sample['user.ids'][0]
-            items = list(sample['item.ids'])
+            user_id = sample["user.ids"][0]
+            items = list(sample["item.ids"])
             self._seen_items[user_id].update(items)
 
     def generate_negative_samples(self, sample, num_negatives):
diff --git a/modeling/dataset/negative_samplers/popular.py b/modeling/dataset/negative_samplers/popular.py
index 68476651..6e478e0c 100644
--- a/modeling/dataset/negative_samplers/popular.py
+++ b/modeling/dataset/negative_samplers/popular.py
@@ -1,44 +1,34 @@
-from dataset.negative_samplers.base import BaseNegativeSampler
-
 from collections import Counter
 
+from dataset.negative_samplers.base import BaseNegativeSampler
 
-class PopularNegativeSampler(BaseNegativeSampler, config_name='popular'):
 
-    def __init__(
-            self,
-            dataset,
-            num_users,
-            num_items
-    ):
-        super().__init__(
-            dataset=dataset,
-            num_users=num_users,
-            num_items=num_items
-        )
+class PopularNegativeSampler(BaseNegativeSampler, config_name="popular"):
+    def __init__(self, dataset, num_users, num_items):
+        super().__init__(dataset=dataset, num_users=num_users, num_items=num_items)
 
         self._popular_items = self._items_by_popularity()
 
     @classmethod
     def create_from_config(cls, _, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def _items_by_popularity(self):
         popularity = Counter()
 
         for sample in self._dataset:
-            for item_id in sample['item.ids']:
+            for item_id in sample["item.ids"]:
                 popularity[item_id] += 1
 
         popular_items = sorted(popularity, key=popularity.get, reverse=True)
         return popular_items
 
     def generate_negative_samples(self, sample, num_negatives):
-        user_id = sample['user.ids'][0]
+        user_id = sample["user.ids"][0]
         popularity_idx = 0
         negatives = []
         while len(negatives) < num_negatives:
diff --git a/modeling/dataset/negative_samplers/random.py b/modeling/dataset/negative_samplers/random.py
index b83042b0..b4f44f8c 100644
--- a/modeling/dataset/negative_samplers/random.py
+++ b/modeling/dataset/negative_samplers/random.py
@@ -1,24 +1,22 @@
 from collections import defaultdict
 
+import numpy as np
 from tqdm import tqdm
 
 from dataset.negative_samplers.base import BaseNegativeSampler
 
-import numpy as np
-
-
-class RandomNegativeSampler(BaseNegativeSampler, config_name='random'):
 
+class RandomNegativeSampler(BaseNegativeSampler, config_name="random"):
     @classmethod
     def create_from_config(cls, _, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def generate_negative_samples(self, sample, num_negatives):
-        user_id = sample['user.ids'][0]
+        user_id = sample["user.ids"][0]
         all_items = list(range(1, self._num_items + 1))
         np.random.shuffle(all_items)
 
diff --git a/modeling/dataset/samplers/__init__.py b/modeling/dataset/samplers/__init__.py
index 6ed31eed..40ffd454 100644
--- a/modeling/dataset/samplers/__init__.py
+++ b/modeling/dataset/samplers/__init__.py
@@ -1,10 +1,19 @@
-from .base import TrainSampler, EvalSampler
-from .cl4srec import Cl4SRecTrainSampler, Cl4SRecEvalSampler
-from .duorec import DuorecTrainSampler, DuoRecEvalSampler
-from .next_item_prediction import NextItemPredictionTrainSampler, NextItemPredictionEvalSampler
-from .last_item_prediction import LastItemPredictionTrainSampler, LastItemPredictionEvalSampler
-from .masked_item_prediction import MaskedItemPredictionTrainSampler, MaskedItemPredictionEvalSampler
-from .mclsr import MCLSRTrainSampler, MCLSRPredictionEvalSampler
-from .pop import PopTrainSampler, PopEvalSampler
-from .s3rec import S3RecPretrainTrainSampler, S3RecPretrainEvalSampler
-from .identity import IdentityTrainSampler, IdentityEvalSampler
+from .base import EvalSampler, TrainSampler
+from .cl4srec import Cl4SRecEvalSampler, Cl4SRecTrainSampler
+from .duorec import DuoRecEvalSampler, DuorecTrainSampler
+from .identity import IdentityEvalSampler, IdentityTrainSampler
+from .last_item_prediction import (
+    LastItemPredictionEvalSampler,
+    LastItemPredictionTrainSampler,
+)
+from .masked_item_prediction import (
+    MaskedItemPredictionEvalSampler,
+    MaskedItemPredictionTrainSampler,
+)
+from .mclsr import MCLSRPredictionEvalSampler, MCLSRTrainSampler
+from .next_item_prediction import (
+    NextItemPredictionEvalSampler,
+    NextItemPredictionTrainSampler,
+)
+from .pop import PopEvalSampler, PopTrainSampler
+from .s3rec import S3RecPretrainEvalSampler, S3RecPretrainTrainSampler
diff --git a/modeling/dataset/samplers/base.py b/modeling/dataset/samplers/base.py
index 2dbfa705..723abb0a 100644
--- a/modeling/dataset/samplers/base.py
+++ b/modeling/dataset/samplers/base.py
@@ -1,10 +1,9 @@
-from utils import MetaParent
-
 import copy
 
+from utils import MetaParent
 
-class TrainSampler(metaclass=MetaParent):
 
+class TrainSampler(metaclass=MetaParent):
     def __init__(self):
         self._dataset = None
 
@@ -20,7 +19,6 @@ def __getitem__(self, index):
 
 
 class EvalSampler(metaclass=MetaParent):
-
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -37,16 +35,14 @@ def __len__(self):
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids'][:-1]
-        next_item = sample['item.ids'][-1]
+        item_sequence = sample["item.ids"][:-1]
+        next_item = sample["item.ids"][-1]
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': item_sequence,
-            'item.length': len(item_sequence),
-
-            'labels.ids': [next_item],
-            'labels.length': 1
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": item_sequence,
+            "item.length": len(item_sequence),
+            "labels.ids": [next_item],
+            "labels.length": 1,
         }
diff --git a/modeling/dataset/samplers/cl4srec.py b/modeling/dataset/samplers/cl4srec.py
index 8aba1de1..6fbcd143 100644
--- a/modeling/dataset/samplers/cl4srec.py
+++ b/modeling/dataset/samplers/cl4srec.py
@@ -1,20 +1,19 @@
-import numpy as np
-
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
 
+import numpy as np
+
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class Cl4SRecTrainSampler(TrainSampler, config_name='cl4srec'):
 
+class Cl4SRecTrainSampler(TrainSampler, config_name="cl4srec"):
     def __init__(
-            self,
-            dataset,
-            num_users,
-            num_items,
-            item_crop_portion,
-            item_mask_portion,
-            item_reorder_portion
+        self,
+        dataset,
+        num_users,
+        num_items,
+        item_crop_portion,
+        item_mask_portion,
+        item_reorder_portion,
     ):
         super().__init__()
         self._dataset = dataset
@@ -28,19 +27,23 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
-            item_crop_portion=config['item_crop_portion'],
-            item_mask_portion=config['item_mask_portion'],
-            item_reorder_portion=config['item_reorder_portion']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+            item_crop_portion=config["item_crop_portion"],
+            item_mask_portion=config["item_mask_portion"],
+            item_reorder_portion=config["item_reorder_portion"],
         )
 
     def _apply_crop_augmentation(self, item_sequence):
         num_elements_to_crop = max(1, int(self._item_crop_portion * len(item_sequence)))
-        crop_start_index = np.random.randint(low=0, high=len(item_sequence) - num_elements_to_crop + 1)
+        crop_start_index = np.random.randint(
+            low=0, high=len(item_sequence) - num_elements_to_crop + 1
+        )
         assert 0 <= crop_start_index <= len(item_sequence) - num_elements_to_crop
-        item_sequence = item_sequence[crop_start_index: crop_start_index + num_elements_to_crop]
+        item_sequence = item_sequence[
+            crop_start_index : crop_start_index + num_elements_to_crop
+        ]
         return item_sequence
 
     def _apply_mask_augmentation(self, item_sequence):
@@ -65,22 +68,28 @@ def _apply_mask_augmentation(self, item_sequence):
 
     def _apply_reorder_augmentation(self, item_sequence):
         num_elements_to_reorder = int(self._item_reorder_portion * len(item_sequence))
-        reorder_start_index = np.random.randint(low=0, high=len(item_sequence) - num_elements_to_reorder + 1)
+        reorder_start_index = np.random.randint(
+            low=0, high=len(item_sequence) - num_elements_to_reorder + 1
+        )
         assert 0 <= reorder_start_index <= len(item_sequence) - num_elements_to_reorder
-        reordered_subsequence = item_sequence[reorder_start_index: reorder_start_index + num_elements_to_reorder]
+        reordered_subsequence = item_sequence[
+            reorder_start_index : reorder_start_index + num_elements_to_reorder
+        ]
         np.random.shuffle(reordered_subsequence)  # This works in-place
-        item_sequence = item_sequence[:reorder_start_index] \
-                        + reordered_subsequence \
-                        + item_sequence[reorder_start_index + num_elements_to_reorder:]
+        item_sequence = (
+            item_sequence[:reorder_start_index]
+            + reordered_subsequence
+            + item_sequence[reorder_start_index + num_elements_to_reorder :]
+        )
         return item_sequence
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids'][:-1]
-        next_item = sample['item.ids'][-1]
+        item_sequence = sample["item.ids"][:-1]
+        next_item = sample["item.ids"][-1]
 
-        sample_items = set(sample['item.ids'])
+        sample_items = set(sample["item.ids"])
         negative = np.random.randint(low=1, high=self._num_items + 1)
         while negative in sample_items:
             negative = np.random.randint(low=1, high=self._num_items + 1)
@@ -88,7 +97,7 @@ def __getitem__(self, index):
         augmentation_list = [
             self._apply_crop_augmentation,
             self._apply_mask_augmentation,
-            self._apply_reorder_augmentation
+            self._apply_reorder_augmentation,
         ]
 
         fst_augmentation = np.random.choice(augmentation_list)
@@ -98,35 +107,28 @@ def __getitem__(self, index):
         snd_augmented_sequence = snd_augmentation(item_sequence)
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': item_sequence,
-            'item.length': len(item_sequence),
-
-            'fst_augmented_item.ids': fst_augmented_sequence,
-            'fst_augmented_item.length': len(fst_augmented_sequence),
-
-            'snd_augmented_item.ids': snd_augmented_sequence,
-            'snd_augmented_item.length': len(snd_augmented_sequence),
-
-            'labels.ids': [next_item],
-            'labels.length': 1,
-
-            'positive.ids': [next_item],
-            'positive.length': 1,
-
-            'negative.ids': [negative],
-            'negative.length': 1
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": item_sequence,
+            "item.length": len(item_sequence),
+            "fst_augmented_item.ids": fst_augmented_sequence,
+            "fst_augmented_item.length": len(fst_augmented_sequence),
+            "snd_augmented_item.ids": snd_augmented_sequence,
+            "snd_augmented_item.length": len(snd_augmented_sequence),
+            "labels.ids": [next_item],
+            "labels.length": 1,
+            "positive.ids": [next_item],
+            "positive.length": 1,
+            "negative.ids": [negative],
+            "negative.length": 1,
         }
 
 
-class Cl4SRecEvalSampler(EvalSampler, config_name='cl4srec'):
-
+class Cl4SRecEvalSampler(EvalSampler, config_name="cl4srec"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
diff --git a/modeling/dataset/samplers/duorec.py b/modeling/dataset/samplers/duorec.py
index a3067fdd..f45ae100 100644
--- a/modeling/dataset/samplers/duorec.py
+++ b/modeling/dataset/samplers/duorec.py
@@ -1,12 +1,10 @@
-import random
-
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
+import random
 
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class DuorecTrainSampler(TrainSampler, config_name='duorec'):
 
+class DuorecTrainSampler(TrainSampler, config_name="duorec"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -16,15 +14,15 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids']
+        item_sequence = sample["item.ids"]
 
         target_item = item_sequence[-1]
         item_sequence = item_sequence[:-1]
@@ -33,26 +31,22 @@ def __getitem__(self, index):
         semantic_similar_sequence = random.choice(self._target_2_sequences[target_item])
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': item_sequence,
-            'item.length': len(item_sequence),
-
-            'labels.ids': [target_item],
-            'labels.length': 1,
-
-            'semantic_similar_item.ids': semantic_similar_sequence,
-            'semantic_similar_item.length': len(semantic_similar_sequence)
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": item_sequence,
+            "item.length": len(item_sequence),
+            "labels.ids": [target_item],
+            "labels.length": 1,
+            "semantic_similar_item.ids": semantic_similar_sequence,
+            "semantic_similar_item.length": len(semantic_similar_sequence),
         }
 
 
-class DuoRecEvalSampler(EvalSampler, config_name='duorec'):
-
+class DuoRecEvalSampler(EvalSampler, config_name="duorec"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
diff --git a/modeling/dataset/samplers/identity.py b/modeling/dataset/samplers/identity.py
index ffe01e23..4b34b03e 100644
--- a/modeling/dataset/samplers/identity.py
+++ b/modeling/dataset/samplers/identity.py
@@ -1,35 +1,30 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
 
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class IdentityTrainSampler(TrainSampler, config_name='identity'):
 
+class IdentityTrainSampler(TrainSampler, config_name="identity"):
     def __init__(self, dataset):
         super().__init__()
         self._dataset = dataset
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(
-            dataset=kwargs['dataset']
-        )
+        return cls(dataset=kwargs["dataset"])
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
         return sample
 
 
-class IdentityEvalSampler(EvalSampler, config_name='identity'):
+class IdentityEvalSampler(EvalSampler, config_name="identity"):
     def __init__(self, dataset):
         self._dataset = dataset
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(
-            dataset=kwargs['dataset']
-        )
-    
+        return cls(dataset=kwargs["dataset"])
+
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        return sample
\ No newline at end of file
+        return sample
diff --git a/modeling/dataset/samplers/last_item_prediction.py b/modeling/dataset/samplers/last_item_prediction.py
index c0a93212..22eeed51 100644
--- a/modeling/dataset/samplers/last_item_prediction.py
+++ b/modeling/dataset/samplers/last_item_prediction.py
@@ -1,10 +1,9 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
 
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class LastItemPredictionTrainSampler(TrainSampler, config_name='last_item_prediction'):
 
+class LastItemPredictionTrainSampler(TrainSampler, config_name="last_item_prediction"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -14,35 +13,32 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids'][:-1]
-        last_item = sample['item.ids'][-1]
+        item_sequence = sample["item.ids"][:-1]
+        last_item = sample["item.ids"][-1]
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': item_sequence,
-            'item.length': len(item_sequence),
-
-            'labels.ids': [last_item],
-            'labels.length': 1,
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": item_sequence,
+            "item.length": len(item_sequence),
+            "labels.ids": [last_item],
+            "labels.length": 1,
         }
 
 
-class LastItemPredictionEvalSampler(EvalSampler, config_name='last_item_prediction'):
-
+class LastItemPredictionEvalSampler(EvalSampler, config_name="last_item_prediction"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
diff --git a/modeling/dataset/samplers/masked_item_prediction.py b/modeling/dataset/samplers/masked_item_prediction.py
index 0100f731..2243e7ca 100644
--- a/modeling/dataset/samplers/masked_item_prediction.py
+++ b/modeling/dataset/samplers/masked_item_prediction.py
@@ -1,11 +1,13 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
+
 import numpy as np
 
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class MaskedItemPredictionTrainSampler(TrainSampler, config_name='masked_item_prediction'):
 
+class MaskedItemPredictionTrainSampler(
+    TrainSampler, config_name="masked_item_prediction"
+):
     def __init__(self, dataset, num_users, num_items, mask_prob=0.0):
         super().__init__()
         self._dataset = dataset
@@ -17,16 +19,16 @@ def __init__(self, dataset, num_users, num_items, mask_prob=0.0):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
-            mask_prob=config.get('mask_prob', 0.0)
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+            mask_prob=config.get("mask_prob", 0.0),
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids']
+        item_sequence = sample["item.ids"]
 
         masked_sequence = []
         labels = []
@@ -54,19 +56,18 @@ def __getitem__(self, index):
         labels[-1] = item_sequence[-1]
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': masked_sequence,
-            'item.length': len(masked_sequence),
-
-            'labels.ids': labels,
-            'labels.length': len(labels)
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": masked_sequence,
+            "item.length": len(masked_sequence),
+            "labels.ids": labels,
+            "labels.length": len(labels),
         }
 
 
-class MaskedItemPredictionEvalSampler(EvalSampler, config_name='masked_item_prediction'):
-
+class MaskedItemPredictionEvalSampler(
+    EvalSampler, config_name="masked_item_prediction"
+):
     def __init__(self, dataset, num_users, num_items):
         super().__init__(dataset, num_users, num_items)
         self._mask_item_idx = self._num_items + 1
@@ -74,24 +75,22 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        item_sequence = sample['item.ids']
+        item_sequence = sample["item.ids"]
         labels = [item_sequence[-1]]
         sequence = item_sequence[:-1] + [self._mask_item_idx]
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': sequence,
-            'item.length': len(sequence),
-
-            'labels.ids': labels,
-            'labels.length': len(labels)
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": sequence,
+            "item.length": len(sequence),
+            "labels.ids": labels,
+            "labels.length": len(labels),
         }
diff --git a/modeling/dataset/samplers/mclsr.py b/modeling/dataset/samplers/mclsr.py
index 2da0986c..e3610547 100644
--- a/modeling/dataset/samplers/mclsr.py
+++ b/modeling/dataset/samplers/mclsr.py
@@ -1,10 +1,9 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
 
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class MCLSRTrainSampler(TrainSampler, config_name='mclsr'):
 
+class MCLSRTrainSampler(TrainSampler, config_name="mclsr"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -14,39 +13,35 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids'][:-1]
-        next_item = sample['item.ids'][-1]
-        next_item_sequence = sample['item.ids'][1:]
+        item_sequence = sample["item.ids"][:-1]
+        next_item = sample["item.ids"][-1]
+        next_item_sequence = sample["item.ids"][1:]
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': item_sequence,
-            'item.length': len(item_sequence),
-
-            'positive.ids': next_item_sequence,
-            'positive.length': len(next_item_sequence),
-
-            'labels.ids': [next_item],
-            'labels.length': 1
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": item_sequence,
+            "item.length": len(item_sequence),
+            "positive.ids": next_item_sequence,
+            "positive.length": len(next_item_sequence),
+            "labels.ids": [next_item],
+            "labels.length": 1,
         }
 
 
-class MCLSRPredictionEvalSampler(EvalSampler, config_name='mclsr'):
-
+class MCLSRPredictionEvalSampler(EvalSampler, config_name="mclsr"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
diff --git a/modeling/dataset/samplers/next_item_prediction.py b/modeling/dataset/samplers/next_item_prediction.py
index c141b065..cf9321ad 100644
--- a/modeling/dataset/samplers/next_item_prediction.py
+++ b/modeling/dataset/samplers/next_item_prediction.py
@@ -1,12 +1,13 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-from dataset.negative_samplers.base import BaseNegativeSampler
-
 import copy
 
+from dataset.negative_samplers.base import BaseNegativeSampler
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class NextItemPredictionTrainSampler(TrainSampler, config_name='next_item_prediction'):
 
-    def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives=0):
+class NextItemPredictionTrainSampler(TrainSampler, config_name="next_item_prediction"):
+    def __init__(
+        self, dataset, num_users, num_items, negative_sampler, num_negatives=0
+    ):
         super().__init__()
         self._dataset = dataset
         self._num_users = num_users
@@ -16,32 +17,32 @@ def __init__(self, dataset, num_users, num_items, negative_sampler, num_negative
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        negative_sampler = BaseNegativeSampler.create_from_config({'type': config['negative_sampler_type']}, **kwargs)
+        negative_sampler = BaseNegativeSampler.create_from_config(
+            {"type": config["negative_sampler_type"]}, **kwargs
+        )
 
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
             negative_sampler=negative_sampler,
-            num_negatives=config.get('num_negatives_train', 0)
+            num_negatives=config.get("num_negatives_train", 0),
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids'][:-1]
-        next_item_sequence = sample['item.ids'][1:]
+        item_sequence = sample["item.ids"][:-1]
+        next_item_sequence = sample["item.ids"][1:]
 
         if self._num_negatives == 0:
             return {
-                'user.ids': sample['user.ids'],
-                'user.length': sample['user.length'],
-
-                'item.ids': item_sequence,
-                'item.length': len(item_sequence),
-
-                'positive.ids': next_item_sequence,
-                'positive.length': len(next_item_sequence)
+                "user.ids": sample["user.ids"],
+                "user.length": sample["user.length"],
+                "item.ids": item_sequence,
+                "item.length": len(item_sequence),
+                "positive.ids": next_item_sequence,
+                "positive.length": len(next_item_sequence),
             }
         else:
             negative_sequence = self._negative_sampler.generate_negative_samples(
@@ -49,26 +50,22 @@ def __getitem__(self, index):
             )
 
             return {
-                'user.ids': sample['user.ids'],
-                'user.length': sample['user.length'],
-
-                'item.ids': item_sequence,
-                'item.length': len(item_sequence),
-
-                'positive.ids': next_item_sequence,
-                'positive.length': len(next_item_sequence),
-
-                'negative.ids': negative_sequence,
-                'negative.length': len(negative_sequence)
+                "user.ids": sample["user.ids"],
+                "user.length": sample["user.length"],
+                "item.ids": item_sequence,
+                "item.length": len(item_sequence),
+                "positive.ids": next_item_sequence,
+                "positive.length": len(next_item_sequence),
+                "negative.ids": negative_sequence,
+                "negative.length": len(negative_sequence),
             }
 
 
-class NextItemPredictionEvalSampler(EvalSampler, config_name='next_item_prediction'):
-
+class NextItemPredictionEvalSampler(EvalSampler, config_name="next_item_prediction"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
diff --git a/modeling/dataset/samplers/pop.py b/modeling/dataset/samplers/pop.py
index afd8b208..1d3ace34 100644
--- a/modeling/dataset/samplers/pop.py
+++ b/modeling/dataset/samplers/pop.py
@@ -1,15 +1,12 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
-
 from collections import Counter
 
+from dataset.samplers.base import EvalSampler, TrainSampler
 
 CANDIDATE_COUNTS = None
 
 
-class PopTrainSampler(TrainSampler, config_name='pop'):
-
+class PopTrainSampler(TrainSampler, config_name="pop"):
     def __init__(self, dataset, num_items):
         super().__init__()
 
@@ -19,49 +16,48 @@ def __init__(self, dataset, num_items):
             item_2_count = Counter()
 
             for sample in dataset:
-                items = sample['item.ids']
+                items = sample["item.ids"]
                 for item in items:
                     item_2_count[item] += 1
 
-            CANDIDATE_COUNTS = [0] + [
-                self._item_2_count[item_id] for item_id in range(1, self._num_items + 1)
-            ] + [0]  # Mask + items + padding
-        
+            CANDIDATE_COUNTS = (
+                [0]
+                + [
+                    self._item_2_count[item_id]
+                    for item_id in range(1, self._num_items + 1)
+                ]
+                + [0]
+            )  # Mask + items + padding
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(
-            dataset=kwargs['dataset'],
-            num_items=kwargs['num_items']
-        )
-
+        return cls(dataset=kwargs["dataset"], num_items=kwargs["num_items"])
 
-class PopEvalSampler(EvalSampler, config_name='pop'):
 
+class PopEvalSampler(EvalSampler, config_name="pop"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__(dataset, num_users, num_items)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        labels = [sample['item.ids'][-1]]
+        labels = [sample["item.ids"][-1]]
 
         global CANDIDATE_COUNTS
         assert CANDIDATE_COUNTS is not None
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'labels.ids': labels,
-            'labels.length': len(labels),
-
-            'candidates_counts.ids': CANDIDATE_COUNTS,
-            'candidates_counts.length': len(CANDIDATE_COUNTS)
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "labels.ids": labels,
+            "labels.length": len(labels),
+            "candidates_counts.ids": CANDIDATE_COUNTS,
+            "candidates_counts.length": len(CANDIDATE_COUNTS),
         }
diff --git a/modeling/dataset/samplers/s3rec.py b/modeling/dataset/samplers/s3rec.py
index d1b424cf..73bf9ff4 100644
--- a/modeling/dataset/samplers/s3rec.py
+++ b/modeling/dataset/samplers/s3rec.py
@@ -1,12 +1,12 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-from dataset.negative_samplers.base import BaseNegativeSampler
-
 import copy
+
 import numpy as np
 
+from dataset.negative_samplers.base import BaseNegativeSampler
+from dataset.samplers.base import EvalSampler, TrainSampler
 
-class S3RecPretrainTrainSampler(TrainSampler, config_name='s3rec_pretrain'):
 
+class S3RecPretrainTrainSampler(TrainSampler, config_name="s3rec_pretrain"):
     def __init__(self, dataset, num_users, num_items, negative_sampler, mask_prob=0.0):
         super().__init__()
         self._dataset = dataset
@@ -18,27 +18,29 @@ def __init__(self, dataset, num_users, num_items, negative_sampler, mask_prob=0.
 
         self._long_sequence = []
         for sample in self._dataset:
-            self._long_sequence.extend(copy.deepcopy(sample['item.ids']))
+            self._long_sequence.extend(copy.deepcopy(sample["item.ids"]))
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        negative_sampler = BaseNegativeSampler.create_from_config({'type': config['negative_sampler_type']}, **kwargs)
+        negative_sampler = BaseNegativeSampler.create_from_config(
+            {"type": config["negative_sampler_type"]}, **kwargs
+        )
 
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
             negative_sampler=negative_sampler,
-            mask_prob=config.get('mask_prob', 0.0)
+            mask_prob=config.get("mask_prob", 0.0),
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample['item.ids']
+        item_sequence = sample["item.ids"]
 
         if len(item_sequence) < 3:
-            assert False, 'Something strange is happening'
+            assert False, "Something strange is happening"
 
         # Masked Item Prediction
         masked_sequence = []
@@ -63,48 +65,56 @@ def __getitem__(self, index):
         # Mask last item
         masked_sequence[-1] = self._mask_item_idx
         positive_sequence[-1] = item_sequence[-1]
-        negative_sequence[-1] = self._negative_sampler.generate_negative_samples(sample, 1)[0]
-        assert len(positive_sequence) == len(negative_sequence) == len(masked_sequence) == len(item_sequence)
+        negative_sequence[-1] = self._negative_sampler.generate_negative_samples(
+            sample, 1
+        )[0]
+        assert (
+            len(positive_sequence)
+            == len(negative_sequence)
+            == len(masked_sequence)
+            == len(item_sequence)
+        )
 
         # Segment Prediction
         sample_length = np.random.randint(1, (len(item_sequence) + 1) // 2)
         start_id = np.random.randint(0, len(item_sequence) - sample_length)
-        negative_start_id = np.random.randint(0, len(self._long_sequence) - sample_length)
-        masked_segment_sequence = item_sequence[:start_id] + [self._mask_item_idx] * sample_length + item_sequence[start_id + sample_length:]
-        positive_segment = item_sequence[start_id: start_id + sample_length]
-        negative_segment = self._long_sequence[negative_start_id:negative_start_id + sample_length]
+        negative_start_id = np.random.randint(
+            0, len(self._long_sequence) - sample_length
+        )
+        masked_segment_sequence = (
+            item_sequence[:start_id]
+            + [self._mask_item_idx] * sample_length
+            + item_sequence[start_id + sample_length :]
+        )
+        positive_segment = item_sequence[start_id : start_id + sample_length]
+        negative_segment = self._long_sequence[
+            negative_start_id : negative_start_id + sample_length
+        ]
         assert len(positive_segment) == len(negative_segment) == sample_length
 
         return {
-            'user.ids': sample['user.ids'],
-            'user.length': sample['user.length'],
-
-            'item.ids': masked_sequence,
-            'item.length': len(masked_sequence),
-
-            'positive.ids': item_sequence,
-            'positive.length': len(item_sequence),
-
-            'negative.ids': negative_sequence,
-            'negative.length': len(negative_sequence),
-
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": masked_sequence,
+            "item.length": len(masked_sequence),
+            "positive.ids": item_sequence,
+            "positive.length": len(item_sequence),
+            "negative.ids": negative_sequence,
+            "negative.length": len(negative_sequence),
             "item_segment.ids": masked_segment_sequence,
             "item_segment.length": len(masked_segment_sequence),
-
-            'positive_segment.ids': positive_segment,
-            'positive_segment.length': len(positive_segment),
-
-            'negative_segment.ids': negative_segment,
-            'negative_segment.length': len(negative_segment)
+            "positive_segment.ids": positive_segment,
+            "positive_segment.length": len(positive_segment),
+            "negative_segment.ids": negative_segment,
+            "negative_segment.length": len(negative_segment),
         }
 
 
-class S3RecPretrainEvalSampler(EvalSampler, config_name='s3rec_pretrain'):
-
+class S3RecPretrainEvalSampler(EvalSampler, config_name="s3rec_pretrain"):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs['dataset'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items']
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
         )
diff --git a/modeling/infer.py b/modeling/infer.py
index c3abe16c..5bd4664e 100644
--- a/modeling/infer.py
+++ b/modeling/infer.py
@@ -1,15 +1,14 @@
-from utils import parse_args, create_logger, fix_random_seed, DEVICE
-
-from dataset import BaseDataset
-from dataloader import BaseDataloader
-from models import BaseModel, TorchModel
-from metric import BaseMetric, StatefullMetric
-
+import datetime
 import json
+
 import numpy as np
 import torch
-import datetime
 
+from dataloader import BaseDataloader
+from dataset import BaseDataset
+from metric import BaseMetric, StatefullMetric
+from models import BaseModel, TorchModel
+from utils import DEVICE, create_logger, fix_random_seed, parse_args
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -25,7 +24,6 @@ def inference(dataloader, model, metrics, pred_prefix, labels_prefix):
 
     with torch.no_grad():
         for idx, batch in enumerate(dataloader):
-
             for key, value in batch.items():
                 batch[key] = value.to(DEVICE)
             batch[pred_prefix] = model(batch)
@@ -34,57 +32,62 @@ def inference(dataloader, model, metrics, pred_prefix, labels_prefix):
                 batch[key] = values.cpu()
 
             for metric_name, metric_function in metrics.items():
-                running_metrics[metric_name].extend(metric_function(
-                    inputs=batch,
-                    pred_prefix=pred_prefix,
-                    labels_prefix=labels_prefix,
-                ))
-            
+                running_metrics[metric_name].extend(
+                    metric_function(
+                        inputs=batch,
+                        pred_prefix=pred_prefix,
+                        labels_prefix=labels_prefix,
+                    )
+                )
+
         for metric_name, metric_function in metrics.items():
             if isinstance(metric_function, StatefullMetric):
-                running_metrics[metric_name] = metric_function.reduce(running_metrics[metric_name])
+                running_metrics[metric_name] = metric_function.reduce(
+                    running_metrics[metric_name]
+                )
 
-    logger.debug('Inference procedure has been finished!')
-    logger.debug('Metrics are the following:')
+    logger.debug("Inference procedure has been finished!")
+    logger.debug("Metrics are the following:")
     for metric_name, metric_value in running_metrics.items():
-        logger.info('{}: {}'.format(metric_name, np.mean(metric_value)))
+        logger.info("{}: {}".format(metric_name, np.mean(metric_value)))
 
 
 def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    logger.debug('Inference config: \n{}'.format(json.dumps(config, indent=2)))
+    logger.debug("Inference config: \n{}".format(json.dumps(config, indent=2)))
 
-    dataset = BaseDataset.create_from_config(config['dataset'])
+    dataset = BaseDataset.create_from_config(config["dataset"])
 
     _, _, eval_dataset = dataset.get_samplers()
 
     eval_dataloader = BaseDataloader.create_from_config(
-        config['dataloader']['validation'],
-        dataset=eval_dataset
+        config["dataloader"]["validation"], dataset=eval_dataset
     )
 
-    model = BaseModel.create_from_config(config['model'], **dataset.meta)
+    model = BaseModel.create_from_config(config["model"], **dataset.meta)
 
     if isinstance(model, TorchModel):
         model = model.to(DEVICE)
-        checkpoint_path = '../checkpoints/{}_final_state.pth'.format(config['experiment_name'])
+        checkpoint_path = "../checkpoints/{}_final_state.pth".format(
+            config["experiment_name"]
+        )
         model.load_state_dict(torch.load(checkpoint_path))
 
     metrics = {
-        metric_name: BaseMetric.create_from_config(metric_cfg , **dataset.meta)
-        for metric_name, metric_cfg in config['metrics'].items()
+        metric_name: BaseMetric.create_from_config(metric_cfg, **dataset.meta)
+        for metric_name, metric_cfg in config["metrics"].items()
     }
 
     _ = inference(
-        dataloader=eval_dataloader, 
-        model=model, 
-        metrics=metrics, 
-        pred_prefix=config['pred_prefix'], 
-        labels_prefix=config['label_prefix']
+        dataloader=eval_dataloader,
+        model=model,
+        metrics=metrics,
+        pred_prefix=config["pred_prefix"],
+        labels_prefix=config["label_prefix"],
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 7827a7f7..d39fff6b 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -1,11 +1,11 @@
 import copy
 
-from utils import MetaParent, get_activation_function, maybe_to_list, DEVICE
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from utils import DEVICE, MetaParent, get_activation_function, maybe_to_list
+
 
 class BaseLoss(metaclass=MetaParent):
     pass
@@ -15,14 +15,12 @@ class TorchLoss(BaseLoss, nn.Module):
     pass
 
 
-class IdentityLoss(BaseLoss, config_name='identity'):
-
+class IdentityLoss(BaseLoss, config_name="identity"):
     def __call__(self, inputs):
         return inputs
 
 
-class CompositeLoss(TorchLoss, config_name='composite'):
-
+class CompositeLoss(TorchLoss, config_name="composite"):
     def __init__(self, losses, weights=None, output_prefix=None):
         super().__init__()
         self._losses = losses
@@ -34,14 +32,16 @@ def create_from_config(cls, config, **kwargs):
         losses = []
         weights = []
 
-        for loss_cfg in copy.deepcopy(config)['losses']:
-            weight = loss_cfg.pop('weight') if 'weight' in loss_cfg else 1.0
+        for loss_cfg in copy.deepcopy(config)["losses"]:
+            weight = loss_cfg.pop("weight") if "weight" in loss_cfg else 1.0
             loss_function = BaseLoss.create_from_config(loss_cfg)
 
             weights.append(weight)
             losses.append(loss_function)
 
-        return cls(losses=losses, weights=weights, output_prefix=config.get('output_prefix'))
+        return cls(
+            losses=losses, weights=weights, output_prefix=config.get("output_prefix")
+        )
 
     def forward(self, inputs):
         total_loss = 0.0
@@ -53,7 +53,8 @@ def forward(self, inputs):
 
         return total_loss
 
-class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
+
+class SampleLogSoftmaxLoss(TorchLoss, config_name="sample_logsoftmax"):
     def __init__(self, predictions_prefix, labels):
         super().__init__()
         self._predictions_prefix = predictions_prefix
@@ -62,37 +63,38 @@ def __init__(self, predictions_prefix, labels):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            predictions_prefix=config.get('predictions_prefix'),
-            labels=config.get('labels')
+            predictions_prefix=config.get("predictions_prefix"),
+            labels=config.get("labels"),
         )
 
     def forward(self, inputs):  # use log soft max
         logits = inputs[self._predictions_prefix]
         candidates = inputs[self._labels]
-        
+
         assert len(logits.shape) in [2, 3]
-        
+
         batch_size = logits.shape[0]
         seq_len = logits.shape[1]
-        
+
         if len(logits.shape) == 3:
             loss = -torch.gather(
-                torch.log_softmax(logits, dim=-1).reshape(batch_size * seq_len, logits.shape[-1]),
-                dim=-1, 
-                index=candidates.reshape(batch_size * seq_len, 1)
+                torch.log_softmax(logits, dim=-1).reshape(
+                    batch_size * seq_len, logits.shape[-1]
+                ),
+                dim=-1,
+                index=candidates.reshape(batch_size * seq_len, 1),
             ).mean()
         else:
             loss = -torch.gather(
                 torch.log_softmax(logits, dim=-1),
-                dim=-1, 
-                index=candidates.reshape(batch_size, 1)
+                dim=-1,
+                index=candidates.reshape(batch_size, 1),
             ).mean()
-            
-        return loss
 
+        return loss
 
-class BatchLogSoftmaxLoss(TorchLoss, config_name='batch_logsoftmax'):
 
+class BatchLogSoftmaxLoss(TorchLoss, config_name="batch_logsoftmax"):
     def __init__(self, predictions_prefix, candidates_prefix):
         super().__init__()
         self._predictions_prefix = predictions_prefix
@@ -101,8 +103,8 @@ def __init__(self, predictions_prefix, candidates_prefix):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            predictions_prefix=config.get('predictions_prefix'),
-            candidates_prefix=config.get('candidates_prefix')
+            predictions_prefix=config.get("predictions_prefix"),
+            candidates_prefix=config.get("candidates_prefix"),
         )
 
     def forward(self, inputs):  # use log soft max
@@ -121,8 +123,7 @@ def forward(self, inputs):  # use log soft max
         return loss
 
 
-class CrossEntropyLoss(TorchLoss, config_name='ce'):
-
+class CrossEntropyLoss(TorchLoss, config_name="ce"):
     def __init__(self, predictions_prefix, labels_prefix, output_prefix=None):
         super().__init__()
         self._pred_prefix = predictions_prefix
@@ -133,7 +134,7 @@ def __init__(self, predictions_prefix, labels_prefix, output_prefix=None):
 
     def forward(self, inputs):
         all_logits = inputs[self._pred_prefix]  # (all_items, num_classes)
-        all_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_items)
+        all_labels = inputs["{}.ids".format(self._labels_prefix)]  # (all_items)
         assert all_logits.shape[0] == all_labels.shape[0]
 
         loss = self._loss(all_logits, all_labels)  # (1)
@@ -141,55 +142,48 @@ def forward(self, inputs):
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
-    
-class RqVaeLoss(TorchLoss, config_name='rqvae_loss'):
 
+
+class RqVaeLoss(TorchLoss, config_name="rqvae_loss"):
     def __init__(self, beta, output_prefix=None):
         super().__init__()
         self.beta = beta
         self._output_prefix = output_prefix
-        
+
         self._loss = nn.MSELoss()
-        
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         # 0.25 is default Beta in paper
         return cls(
-            beta = config.get('beta', 0.25),
-            output_prefix = config['output_prefix'],
+            beta=config.get("beta", 0.25),
+            output_prefix=config["output_prefix"],
         )
-                    
+
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
         embeddings_restored = inputs["embeddings_restored"]
         remainders = inputs["remainders"]
         codebooks_vectors = inputs["codebooks_vectors"]
-        
+
         rqvae_loss = 0
-        
+
         for remainder, codebook_vectors in zip(remainders, codebooks_vectors):
-            rqvae_loss += self.beta * self._loss(
-                remainder, codebook_vectors.detach()
-            )
+            rqvae_loss += self.beta * self._loss(remainder, codebook_vectors.detach())
             rqvae_loss += self._loss(codebook_vectors, remainder.detach())
-            
+
         recon_loss = self._loss(embeddings_restored, embeddings)
         loss = (recon_loss + rqvae_loss).mean(dim=0)
-        
+
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
-        
-        return loss
 
+        return loss
 
-class BinaryCrossEntropyLoss(TorchLoss, config_name='bce'):
 
+class BinaryCrossEntropyLoss(TorchLoss, config_name="bce"):
     def __init__(
-            self,
-            predictions_prefix,
-            labels_prefix,
-            with_logits=True,
-            output_prefix=None
+        self, predictions_prefix, labels_prefix, with_logits=True, output_prefix=None
     ):
         super().__init__()
         self._pred_prefix = predictions_prefix
@@ -213,14 +207,8 @@ def forward(self, inputs):
         return loss
 
 
-class BPRLoss(TorchLoss, config_name='bpr'):
-
-    def __init__(
-            self,
-            positive_prefix,
-            negative_prefix,
-            output_prefix=None
-    ):
+class BPRLoss(TorchLoss, config_name="bpr"):
+    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -237,8 +225,7 @@ def forward(self, inputs):
         return loss
 
 
-class RegularizationLoss(TorchLoss, config_name='regularization'):
-
+class RegularizationLoss(TorchLoss, config_name="regularization"):
     def __init__(self, prefix, output_prefix=None):
         super().__init__()
         self._prefix = maybe_to_list(prefix)
@@ -247,7 +234,7 @@ def __init__(self, prefix, output_prefix=None):
     def forward(self, inputs):
         loss = 0.0
         for prefix in self._prefix:
-            loss += (1/2) * inputs[prefix].pow(2).mean()
+            loss += (1 / 2) * inputs[prefix].pow(2).mean()
 
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
@@ -255,22 +242,23 @@ def forward(self, inputs):
         return loss
 
 
-class FpsLoss(TorchLoss, config_name='fps'):
-
+class FpsLoss(TorchLoss, config_name="fps"):
     def __init__(
-            self,
-            fst_embeddings_prefix,
-            snd_embeddings_prefix,
-            tau=1.0,
-            normalize_embeddings=False,
-            use_mean=True,
-            output_prefix=None
+        self,
+        fst_embeddings_prefix,
+        snd_embeddings_prefix,
+        tau=1.0,
+        normalize_embeddings=False,
+        use_mean=True,
+        output_prefix=None,
     ):
         super().__init__()
         self._fst_embeddings_prefix = fst_embeddings_prefix
         self._snd_embeddings_prefix = snd_embeddings_prefix
         self._tau = tau
-        self._loss_function = nn.CrossEntropyLoss(reduction='mean' if use_mean else 'sum')
+        self._loss_function = nn.CrossEntropyLoss(
+            reduction="mean" if use_mean else "sum"
+        )
         self._normalize_embeddings = normalize_embeddings
         self._output_prefix = output_prefix
 
@@ -280,34 +268,49 @@ def forward(self, inputs):
 
         batch_size = fst_embeddings.shape[0]
 
-        combined_embeddings = torch.cat((fst_embeddings, snd_embeddings), dim=0)  # (2 * x, embedding_dim)
+        combined_embeddings = torch.cat(
+            (fst_embeddings, snd_embeddings), dim=0
+        )  # (2 * x, embedding_dim)
 
         if self._normalize_embeddings:
             combined_embeddings = torch.nn.functional.normalize(
                 combined_embeddings, p=2, dim=-1, eps=1e-6
             )  # (2 * x, embedding_dim)
 
-        similarity_scores = torch.mm(
-            combined_embeddings,
-            combined_embeddings.T
-        ) / self._tau  # (2 * x, 2 * x)
+        similarity_scores = (
+            torch.mm(combined_embeddings, combined_embeddings.T) / self._tau
+        )  # (2 * x, 2 * x)
 
         positive_samples = torch.cat(
-            (torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size)),
-            dim=0
+            (
+                torch.diag(similarity_scores, batch_size),
+                torch.diag(similarity_scores, -batch_size),
+            ),
+            dim=0,
         ).reshape(2 * batch_size, 1)  # (2 * x, 1)
-        assert torch.allclose(torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size))
+        assert torch.allclose(
+            torch.diag(similarity_scores, batch_size),
+            torch.diag(similarity_scores, -batch_size),
+        )
 
-        mask = torch.ones(2 * batch_size, 2 * batch_size, dtype=torch.bool)  # (2 * x, 2 * x)
+        mask = torch.ones(
+            2 * batch_size, 2 * batch_size, dtype=torch.bool
+        )  # (2 * x, 2 * x)
         mask = mask.fill_diagonal_(0)  # Remove equal embeddings scores
         for i in range(batch_size):  # Remove positives
             mask[i, batch_size + i] = 0
             mask[batch_size + i, i] = 0
 
-        negative_samples = similarity_scores[mask].reshape(2 * batch_size, -1)  # (2 * x, 2 * x - 2)
+        negative_samples = similarity_scores[mask].reshape(
+            2 * batch_size, -1
+        )  # (2 * x, 2 * x - 2)
 
-        labels = torch.zeros(2 * batch_size).to(positive_samples.device).long()  # (2 * x)
-        logits = torch.cat((positive_samples, negative_samples), dim=1)  # (2 * x, 2 * x - 1)
+        labels = (
+            torch.zeros(2 * batch_size).to(positive_samples.device).long()
+        )  # (2 * x)
+        logits = torch.cat(
+            (positive_samples, negative_samples), dim=1
+        )  # (2 * x, 2 * x - 1)
 
         loss = self._loss_function(logits, labels) / 2  # (1)
 
@@ -317,14 +320,8 @@ def forward(self, inputs):
         return loss
 
 
-class SASRecLoss(TorchLoss, config_name='sasrec'):
-
-    def __init__(
-            self,
-            positive_prefix,
-            negative_prefix,
-            output_prefix=None
-    ):
+class SASRecLoss(TorchLoss, config_name="sasrec"):
+    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -334,27 +331,40 @@ def forward(self, inputs):
         positive_scores = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_scores = inputs[self._negative_prefix]  # (x, embedding_dim)
         sample_ids = inputs["sample_ids"]
-        
+
         num_items = negative_scores.shape[1] - 2
-        
-        possible_indices = torch.arange(1, num_items + 1, device=negative_scores.device) # 1, 2, ... num_items
-        mask = torch.ones_like(possible_indices, dtype=torch.bool) # True, True, ... True
-        mask[sample_ids - 1] = False # True, False, ... False, True, ... True
-        valid_indices = possible_indices[mask] # 1, 2, ... num_items, except sample_ids
-        
-        rand_idx = torch.randint(0, len(valid_indices), size=(negative_scores.shape[0], 1), device=negative_scores.device)
+
+        possible_indices = torch.arange(
+            1, num_items + 1, device=negative_scores.device
+        )  # 1, 2, ... num_items
+        mask = torch.ones_like(
+            possible_indices, dtype=torch.bool
+        )  # True, True, ... True
+        mask[sample_ids - 1] = False  # True, False, ... False, True, ... True
+        valid_indices = possible_indices[mask]  # 1, 2, ... num_items, except sample_ids
+
+        rand_idx = torch.randint(
+            0,
+            len(valid_indices),
+            size=(negative_scores.shape[0], 1),
+            device=negative_scores.device,
+        )
         index = valid_indices[rand_idx]
-        
+
         negative_scores = torch.gather(
             input=negative_scores,
             dim=1,
             index=index,
         )
-        
+
         assert positive_scores.shape[0] == negative_scores.shape[0]
 
-        positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(dim=-1)  # (x)
-        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores) + 1e-9).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
+        positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(
+            dim=-1
+        )  # (x)
+        negative_loss = torch.log(
+            1.0 - nn.functional.sigmoid(negative_scores) + 1e-9
+        ).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
         loss = positive_loss + negative_loss  # (x)
         loss = -loss.sum()  # (1)
 
@@ -364,14 +374,9 @@ def forward(self, inputs):
         return loss
 
 
-class SamplesSoftmaxLoss(TorchLoss, config_name='sampled_softmax'):
-
+class SamplesSoftmaxLoss(TorchLoss, config_name="sampled_softmax"):
     def __init__(
-            self,
-            queries_prefix,
-            positive_prefix,
-            negative_prefix,
-            output_prefix=None
+        self, queries_prefix, positive_prefix, negative_prefix, output_prefix=None
     ):
         super().__init__()
         self._queries_prefix = queries_prefix
@@ -381,32 +386,34 @@ def __init__(
 
     def forward(self, inputs):
         queries_embeddings = inputs[self._queries_prefix]  # (batch_size, embedding_dim)
-        positive_embeddings = inputs[self._positive_prefix]  # (batch_size, embedding_dim)
-        negative_embeddings = inputs[self._negative_prefix]  # (num_negatives, embedding_dim) or (batch_size, num_negatives, embedding_dim)
+        positive_embeddings = inputs[
+            self._positive_prefix
+        ]  # (batch_size, embedding_dim)
+        negative_embeddings = inputs[
+            self._negative_prefix
+        ]  # (num_negatives, embedding_dim) or (batch_size, num_negatives, embedding_dim)
 
         # b -- batch_size, d -- embedding_dim
         positive_scores = torch.einsum(
-            'bd,bd->b',
-            queries_embeddings,
-            positive_embeddings
+            "bd,bd->b", queries_embeddings, positive_embeddings
         ).unsqueeze(-1)  # (batch_size, 1)
 
         if negative_embeddings.dim() == 2:  # (num_negatives, embedding_dim)
             # b -- batch_size, n -- num_negatives, d -- embedding_dim
             negative_scores = torch.einsum(
-                'bd,nd->bn',
-                queries_embeddings,
-                negative_embeddings
+                "bd,nd->bn", queries_embeddings, negative_embeddings
             )  # (batch_size, num_negatives)
         else:
-            assert negative_embeddings.dim() == 3  # (batch_size, num_negatives, embedding_dim)
+            assert (
+                negative_embeddings.dim() == 3
+            )  # (batch_size, num_negatives, embedding_dim)
             # b -- batch_size, n -- num_negatives, d -- embedding_dim
             negative_scores = torch.einsum(
-                'bd,bnd->bn',
-                queries_embeddings,
-                negative_embeddings
+                "bd,bnd->bn", queries_embeddings, negative_embeddings
             )  # (batch_size, num_negatives)
-        all_scores = torch.cat([positive_scores, negative_scores], dim=1)  # (batch_size, 1 + num_negatives)
+        all_scores = torch.cat(
+            [positive_scores, negative_scores], dim=1
+        )  # (batch_size, 1 + num_negatives)
 
         logits = torch.log_softmax(all_scores, dim=1)  # (batch_size, 1 + num_negatives)
         loss = (-logits)[:, 0]  # (batch_size)
@@ -418,14 +425,13 @@ def forward(self, inputs):
         return loss
 
 
-class S3RecPretrainLoss(TorchLoss, config_name='s3rec_pretrain'):
-
+class S3RecPretrainLoss(TorchLoss, config_name="s3rec_pretrain"):
     def __init__(
-            self,
-            positive_prefix,
-            negative_prefix,
-            representation_prefix,
-            output_prefix=None
+        self,
+        positive_prefix,
+        negative_prefix,
+        representation_prefix,
+        output_prefix=None,
     ):
         super().__init__()
         self._positive_prefix = positive_prefix
@@ -438,36 +444,39 @@ def forward(self, inputs):
         positive_embeddings = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_embeddings = inputs[self._negative_prefix]  # (x, embedding_dim)
         current_embeddings = inputs[self._representation_prefix]  # (x, embedding_dim)
-        assert positive_embeddings.shape[0] == negative_embeddings.shape[0] == current_embeddings.shape[0]
+        assert (
+            positive_embeddings.shape[0]
+            == negative_embeddings.shape[0]
+            == current_embeddings.shape[0]
+        )
 
         positive_scores = torch.einsum(
-            'bd,bd->b',
-            positive_embeddings,
-            current_embeddings
+            "bd,bd->b", positive_embeddings, current_embeddings
         )  # (x)
 
         negative_scores = torch.einsum(
-            'bd,bd->b',
-            negative_embeddings,
-            current_embeddings
+            "bd,bd->b", negative_embeddings, current_embeddings
         )  # (x)
 
-        distance = torch.sigmoid(positive_scores) - torch.sigmoid(negative_scores)  # (x)
-        loss = torch.sum(self._criterion(distance, torch.ones_like(distance, dtype=torch.float32)))  # (1)
+        distance = torch.sigmoid(positive_scores) - torch.sigmoid(
+            negative_scores
+        )  # (x)
+        loss = torch.sum(
+            self._criterion(distance, torch.ones_like(distance, dtype=torch.float32))
+        )  # (1)
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
 
 
-class Cl4sRecLoss(TorchLoss, config_name='cl4srec'):
-
+class Cl4sRecLoss(TorchLoss, config_name="cl4srec"):
     def __init__(
-            self,
-            current_representation,
-            all_items_representation,
-            tau=1.0,
-            output_prefix=None
+        self,
+        current_representation,
+        all_items_representation,
+        tau=1.0,
+        output_prefix=None,
     ):
         super().__init__()
         self._current_representation = current_representation
@@ -477,7 +486,9 @@ def __init__(
         self._output_prefix = output_prefix
 
     def forward(self, inputs):
-        current_representation = inputs[self._current_representation]  # (batch_size, embedding_dim)
+        current_representation = inputs[
+            self._current_representation
+        ]  # (batch_size, embedding_dim)
         all_items_representation = inputs[
             self._all_items_representation
         ]  # (batch_size, num_negatives + 1, embedding_dim)
@@ -485,9 +496,7 @@ def forward(self, inputs):
         batch_size = current_representation.shape[0]
 
         logits = torch.einsum(
-            'bnd,bd->bn',
-            all_items_representation,
-            current_representation
+            "bnd,bd->bn", all_items_representation, current_representation
         )  # (batch_size, num_negatives + 1)
         labels = logits.new_zeros(batch_size)  # (batch_size)
 
@@ -499,16 +508,15 @@ def forward(self, inputs):
         return loss
 
 
-class DuorecSSLLoss(TorchLoss, config_name='duorec_ssl'):
-
+class DuorecSSLLoss(TorchLoss, config_name="duorec_ssl"):
     def __init__(
-            self,
-            original_embedding_prefix,
-            dropout_embedding_prefix,
-            similar_embedding_prefix,
-            normalize_embeddings=False,
-            tau=1.0,
-            output_prefix=None
+        self,
+        original_embedding_prefix,
+        dropout_embedding_prefix,
+        similar_embedding_prefix,
+        normalize_embeddings=False,
+        tau=1.0,
+        output_prefix=None,
     ):
         super().__init__()
         self._original_embedding_prefix = original_embedding_prefix
@@ -517,14 +525,13 @@ def __init__(
         self._normalize_embeddings = normalize_embeddings
         self._output_prefix = output_prefix
         self._tau = tau
-        self._loss_function = nn.CrossEntropyLoss(reduction='mean')
+        self._loss_function = nn.CrossEntropyLoss(reduction="mean")
 
     def _compute_partial_loss(self, fst_embeddings, snd_embeddings):
         batch_size = fst_embeddings.shape[0]
 
         combined_embeddings = torch.cat(
-            (fst_embeddings, snd_embeddings),
-            dim=0
+            (fst_embeddings, snd_embeddings), dim=0
         )  # (2 * x, embedding_dim)
 
         if self._normalize_embeddings:
@@ -532,59 +539,76 @@ def _compute_partial_loss(self, fst_embeddings, snd_embeddings):
                 combined_embeddings, p=2, dim=-1, eps=1e-6
             )
 
-        similarity_scores = torch.mm(
-            combined_embeddings,
-            combined_embeddings.T
-        ) / self._tau  # (2 * x, 2 * x)
+        similarity_scores = (
+            torch.mm(combined_embeddings, combined_embeddings.T) / self._tau
+        )  # (2 * x, 2 * x)
 
         positive_samples = torch.cat(
-            (torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size)),
-            dim=0
+            (
+                torch.diag(similarity_scores, batch_size),
+                torch.diag(similarity_scores, -batch_size),
+            ),
+            dim=0,
         ).reshape(2 * batch_size, 1)  # (2 * x, 1)
 
         # TODO optimize
-        mask = torch.ones(2 * batch_size, 2 * batch_size, dtype=torch.bool)  # (2 * x, 2 * x)
+        mask = torch.ones(
+            2 * batch_size, 2 * batch_size, dtype=torch.bool
+        )  # (2 * x, 2 * x)
         mask = mask.fill_diagonal_(0)  # Remove equal embeddings scores
         for i in range(batch_size):  # Remove positives
             mask[i, batch_size + i] = 0
             mask[batch_size + i, i] = 0
 
-        negative_samples = similarity_scores[mask].reshape(2 * batch_size, -1)  # (2 * x, 2 * x - 2)
+        negative_samples = similarity_scores[mask].reshape(
+            2 * batch_size, -1
+        )  # (2 * x, 2 * x - 2)
 
-        labels = torch.zeros(2 * batch_size).to(positive_samples.device).long()  # (2 * x)
-        logits = torch.cat((positive_samples, negative_samples), dim=1)  # (2 * x, 2 * x - 1)
+        labels = (
+            torch.zeros(2 * batch_size).to(positive_samples.device).long()
+        )  # (2 * x)
+        logits = torch.cat(
+            (positive_samples, negative_samples), dim=1
+        )  # (2 * x, 2 * x - 1)
 
         loss = self._loss_function(logits, labels) / 2  # (1)
 
         return loss
 
     def forward(self, inputs):
-        original_embeddings = inputs[self._original_embedding_prefix]  # (x, embedding_dim)
-        dropout_embeddings = inputs[self._dropout_embedding_prefix]  # (x, embedding_dim)
-        similar_embeddings = inputs[self._similar_embedding_prefix]  # (x, embedding_dim)
-
-        dropout_loss = self._compute_partial_loss(original_embeddings, dropout_embeddings)
+        original_embeddings = inputs[
+            self._original_embedding_prefix
+        ]  # (x, embedding_dim)
+        dropout_embeddings = inputs[
+            self._dropout_embedding_prefix
+        ]  # (x, embedding_dim)
+        similar_embeddings = inputs[
+            self._similar_embedding_prefix
+        ]  # (x, embedding_dim)
+
+        dropout_loss = self._compute_partial_loss(
+            original_embeddings, dropout_embeddings
+        )
         ssl_loss = self._compute_partial_loss(original_embeddings, similar_embeddings)
 
         loss = dropout_loss + ssl_loss
 
         if self._output_prefix is not None:
-            inputs[f'{self._output_prefix}_dropout'] = dropout_loss.cpu().item()
-            inputs[f'{self._output_prefix}_ssl'] = ssl_loss.cpu().item()
+            inputs[f"{self._output_prefix}_dropout"] = dropout_loss.cpu().item()
+            inputs[f"{self._output_prefix}_ssl"] = ssl_loss.cpu().item()
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
 
 
-class MCLSRLoss(TorchLoss, config_name='mclsr'):
-
+class MCLSRLoss(TorchLoss, config_name="mclsr"):
     def __init__(
-            self,
-            all_scores_prefix,
-            mask_prefix,
-            normalize_embeddings=False,
-            tau=1.0,
-            output_prefix=None
+        self,
+        all_scores_prefix,
+        mask_prefix,
+        normalize_embeddings=False,
+        tau=1.0,
+        output_prefix=None,
     ):
         super().__init__()
         self._all_scores_prefix = all_scores_prefix
@@ -594,7 +618,9 @@ def __init__(
         self._tau = tau
 
     def forward(self, inputs):
-        all_scores = inputs[self._all_scores_prefix]  # (batch_size, batch_size, seq_len)
+        all_scores = inputs[
+            self._all_scores_prefix
+        ]  # (batch_size, batch_size, seq_len)
         mask = inputs[self._mask_prefix]  # (batch_size)
 
         batch_size = mask.shape[0]
@@ -604,8 +630,7 @@ def forward(self, inputs):
 
         positive_scores = all_scores[positive_mask]  # (batch_size, seq_len)
         negative_scores = torch.reshape(
-            all_scores[~positive_mask],
-            shape=(batch_size, batch_size - 1, seq_len)
+            all_scores[~positive_mask], shape=(batch_size, batch_size - 1, seq_len)
         )  # (batch_size, batch_size - 1, seq_len)
         assert torch.allclose(all_scores[0, 1], negative_scores[0, 0])
         assert torch.allclose(all_scores[-1, -2], negative_scores[-1, -1])
diff --git a/modeling/metric/base.py b/modeling/metric/base.py
index 677f9086..e22c1d20 100644
--- a/modeling/metric/base.py
+++ b/modeling/metric/base.py
@@ -1,19 +1,18 @@
-from utils import MetaParent
-
 import torch
 
+from utils import MetaParent
+
 
 class BaseMetric(metaclass=MetaParent):
     pass
 
 
 class StatefullMetric(BaseMetric):
-
     def reduce(self):
         raise NotImplementedError
 
 
-class StaticMetric(BaseMetric, config_name='dummy'):
+class StaticMetric(BaseMetric, config_name="dummy"):
     def __init__(self, name, value):
         self._name = name
         self._value = value
@@ -24,17 +23,15 @@ def __call__(self, inputs):
         return inputs
 
 
-class CompositeMetric(BaseMetric, config_name='composite'):
-
+class CompositeMetric(BaseMetric, config_name="composite"):
     def __init__(self, metrics):
         self._metrics = metrics
 
     @classmethod
     def create_from_config(cls, config):
-        return cls(metrics=[
-            BaseMetric.create_from_config(cfg)
-            for cfg in config['metrics']
-        ])
+        return cls(
+            metrics=[BaseMetric.create_from_config(cfg) for cfg in config["metrics"]]
+        )
 
     def __call__(self, inputs):
         for metric in self._metrics:
@@ -42,57 +39,63 @@ def __call__(self, inputs):
         return inputs
 
 
-class NDCGMetric(BaseMetric, config_name='ndcg'):
-
+class NDCGMetric(BaseMetric, config_name="ndcg"):
     def __init__(self, k):
         self._k = k
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
-        labels = inputs['{}.ids'.format(labels_prefix)].float()  # (batch_size)
+        predictions = inputs[pred_prefix][
+            :, : self._k
+        ].float()  # (batch_size, top_k_indices)
+        labels = inputs["{}.ids".format(labels_prefix)].float()  # (batch_size)
 
         assert labels.shape[0] == predictions.shape[0]
 
-        hits = torch.eq(predictions, labels[..., None]).float()  # (batch_size, top_k_indices)
-        discount_factor = 1 / torch.log2(torch.arange(1, self._k + 1, 1).float() + 1.).to(hits.device)  # (k)
-        dcg = torch.einsum('bk,k->b', hits, discount_factor)  # (batch_size)
+        hits = torch.eq(
+            predictions, labels[..., None]
+        ).float()  # (batch_size, top_k_indices)
+        discount_factor = 1 / torch.log2(
+            torch.arange(1, self._k + 1, 1).float() + 1.0
+        ).to(hits.device)  # (k)
+        dcg = torch.einsum("bk,k->b", hits, discount_factor)  # (batch_size)
 
         return dcg.cpu().tolist()
 
 
-class RecallMetric(BaseMetric, config_name='recall'):
-
+class RecallMetric(BaseMetric, config_name="recall"):
     def __init__(self, k):
         self._k = k
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
-        labels = inputs['{}.ids'.format(labels_prefix)].float()  # (batch_size)
+        predictions = inputs[pred_prefix][
+            :, : self._k
+        ].float()  # (batch_size, top_k_indices)
+        labels = inputs["{}.ids".format(labels_prefix)].float()  # (batch_size)
 
         assert labels.shape[0] == predictions.shape[0]
 
-        hits = torch.eq(predictions, labels[..., None]).float()  # (batch_size, top_k_indices)
+        hits = torch.eq(
+            predictions, labels[..., None]
+        ).float()  # (batch_size, top_k_indices)
         recall = hits.sum(dim=-1)  # (batch_size)
 
         return recall.cpu().tolist()
 
 
-class CoverageMetric(StatefullMetric, config_name='coverage'):
-
+class CoverageMetric(StatefullMetric, config_name="coverage"):
     def __init__(self, k, num_items):
         self._k = k
         self._num_items = num_items
-    
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(
-            k=config['k'],
-            num_items=kwargs['num_items']
-        )
+        return cls(k=config["k"], num_items=kwargs["num_items"])
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
+        predictions = inputs[pred_prefix][
+            :, : self._k
+        ].float()  # (batch_size, top_k_indices)
         return predictions.view(-1).long().cpu().detach().tolist()  # (batch_size * k)
-    
+
     def reduce(self, values):
         return len(set(values)) / self._num_items
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index e03dc42d..8f8e2039 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -12,10 +12,10 @@
 from .pop import PopModel
 from .pure_mf import PureMF
 from .random import RandomModel
-from .sasrec import SasRecModel, SasRecInBatchModel
+from .rqvae import RqVaeModel
+from .s3rec import S3RecModel
+from .sasrec import SasRecInBatchModel, SasRecModel
+from .sasrec_ce import SasRecCeModel
 from .sasrec_freezed import SasRecFreezedModel
 from .sasrec_semantic import SasRecSemanticModel
-from .sasrec_ce import SasRecCeModel
-from .s3rec import S3RecModel
-from .rqvae import RqVaeModel
 from .tiger import TigerModel
diff --git a/modeling/models/base.py b/modeling/models/base.py
index b3abef1e..b4dd440a 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -1,5 +1,6 @@
 import torch
 import torch.nn as nn
+
 from utils import DEVICE, MetaParent, create_masked_tensor, get_activation_function
 
 
@@ -8,39 +9,42 @@ class BaseModel(metaclass=MetaParent):
 
 
 class TorchModel(nn.Module, BaseModel):
-
     @torch.no_grad()
     def _init_weights(self, initializer_range):
         for key, value in self.named_parameters():
-            if 'weight' in key:
-                if 'norm' in key:
+            if "weight" in key:
+                if "norm" in key:
                     nn.init.ones_(value.data)
                 else:
                     nn.init.trunc_normal_(
                         value.data,
                         std=initializer_range,
                         a=-2 * initializer_range,
-                        b=2 * initializer_range
+                        b=2 * initializer_range,
                     )
-            elif 'bias' in key:
+            elif "bias" in key:
                 nn.init.zeros_(value.data)
-            elif 'codebook' in key:
+            elif "codebook" in key:
                 nn.init.trunc_normal_(
                     value.data,
                     std=initializer_range,
                     a=-2 * initializer_range,
-                    b=2 * initializer_range
+                    b=2 * initializer_range,
                 )
             else:
-                raise ValueError(f'Unknown transformer weight: {key}')
+                raise ValueError(f"Unknown transformer weight: {key}")
 
     @staticmethod
     def _get_last_embedding(embeddings, mask):
         lengths = torch.sum(mask, dim=-1)  # (batch_size)
-        lengths = (lengths - 1)  # (batch_size)
+        lengths = lengths - 1  # (batch_size)
         last_masks = mask.gather(dim=1, index=lengths[:, None])  # (batch_size, 1)
-        lengths = torch.tile(lengths[:, None, None], (1, 1, embeddings.shape[-1]))  # (batch_size, 1, emb_dim)
-        last_embeddings = embeddings.gather(dim=1, index=lengths)  # (batch_size, 1, emb_dim)
+        lengths = torch.tile(
+            lengths[:, None, None], (1, 1, embeddings.shape[-1])
+        )  # (batch_size, 1, emb_dim)
+        last_embeddings = embeddings.gather(
+            dim=1, index=lengths
+        )  # (batch_size, 1, emb_dim)
         last_embeddings = last_embeddings[last_masks]  # (batch_size, emb_dim)
         if not torch.allclose(embeddings[mask][-1], last_embeddings[-1]):
             print(embeddings)
@@ -52,19 +56,18 @@ def _get_last_embedding(embeddings, mask):
 
 
 class SequentialTorchModel(TorchModel):
-
     def __init__(
-            self,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-5,
-            is_causal=True
+        self,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        is_causal=True,
     ):
         super().__init__()
         self._is_causal = is_causal
@@ -74,11 +77,12 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim
+            embedding_dim=embedding_dim,
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim
+            num_embeddings=max_sequence_length
+            + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim,
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -91,21 +95,22 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True
+            batch_first=True,
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
-    
+
     def get_item_embeddings(self, events):
         return self._item_embeddings(events)
 
     def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
-        embeddings = self.get_item_embeddings(events)  # (all_batch_events, embedding_dim)
-        
+        embeddings = self.get_item_embeddings(
+            events
+        )  # (all_batch_events, embedding_dim)
+
         assert embeddings.shape[0] == sum(lengths)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings,
-            lengths=lengths
+            data=embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
@@ -114,7 +119,9 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
         position_embeddings = self._encoder_pos_embeddings(lengths, mask)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        embeddings = (
+            embeddings + position_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -125,19 +132,21 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
             cls_token_tensor = self._cls_token.unsqueeze(0).unsqueeze(0)
             cls_token_expanded = torch.tile(cls_token_tensor, (batch_size, 1, 1))
             embeddings = torch.cat((cls_token_expanded, embeddings), dim=1)
-            mask = torch.cat((torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask), dim=1)
+            mask = torch.cat(
+                (torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask),
+                dim=1,
+            )
 
         if self._is_causal:
-            causal_mask = torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)  # (seq_len, seq_len)
+            causal_mask = (
+                torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)
+            )  # (seq_len, seq_len)
             embeddings = self._encoder(
-                src=embeddings,
-                mask=~causal_mask,
-                src_key_padding_mask=~mask
+                src=embeddings, mask=~causal_mask, src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
         else:
             embeddings = self._encoder(
-                src=embeddings,
-                src_key_padding_mask=~mask
+                src=embeddings, src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
 
         return embeddings, mask
@@ -146,16 +155,19 @@ def _encoder_pos_embeddings(self, lengths, mask):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = torch.arange(
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(
+            positions
+        )  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=lengths
+            data=position_embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         return position_embeddings
 
@@ -165,17 +177,18 @@ def _add_cls_token(items, lengths, cls_token_id=0):
         batch_size = lengths.shape[0]
         num_new_items = num_items + batch_size
 
-        new_items = torch.ones(
-            num_new_items,
-            dtype=items.dtype,
-            device=items.device
-        ) * cls_token_id  # (num_new_items)
+        new_items = (
+            torch.ones(num_new_items, dtype=items.dtype, device=items.device)
+            * cls_token_id
+        )  # (num_new_items)
 
         old_items_mask = torch.zeros_like(new_items).bool()  # (num_new_items)
         old_items_mask = ~old_items_mask.scatter(
             src=torch.ones_like(lengths).bool(),
             dim=0,
-            index=torch.cat([torch.LongTensor([0]).to(DEVICE), lengths + 1]).cumsum(dim=0)[:-1]
+            index=torch.cat([torch.LongTensor([0]).to(DEVICE), lengths + 1]).cumsum(
+                dim=0
+            )[:-1],
         )  # (num_new_items)
         new_items[old_items_mask] = items
         new_length = lengths + 1
diff --git a/modeling/models/bert4rec.py b/modeling/models/bert4rec.py
index 40f1d331..69794fb6 100644
--- a/modeling/models/bert4rec.py
+++ b/modeling/models/bert4rec.py
@@ -1,25 +1,24 @@
-from models.base import SequentialTorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import SequentialTorchModel
 
-class Bert4RecModel(SequentialTorchModel, config_name='bert4rec'):
 
+class Bert4RecModel(SequentialTorchModel, config_name="bert4rec"):
     def __init__(
-            self,
-            sequence_prefix,
-            labels_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='gelu',
-            layer_norm_eps=1e-5,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="gelu",
+        layer_norm_eps=1e-5,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -31,70 +30,77 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=False
+            is_causal=False,
         )
         self._sequence_prefix = sequence_prefix
         self._labels_prefix = labels_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim,
-            out_features=embedding_dim
+            in_features=embedding_dim, out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(
-            data=torch.zeros(num_items + 2),
-            requires_grad=True
-        )
+        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
 
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            labels_prefix=config['labels_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            labels_prefix=config["labels_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
-        embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(
+            embeddings
+        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = torch.nn.functional.gelu(
+            embeddings
+        )  # (batch_size, seq_len, embedding_dim)
         embeddings = torch.einsum(
-            'bsd,nd->bsn', embeddings, self._item_embeddings.weight
+            "bsd,nd->bsn", embeddings, self._item_embeddings.weight
         )  # (batch_size, seq_len, num_items)
         embeddings += self._bias[None, None, :]  # (batch_size, seq_len, num_items)
 
         if self.training:  # training mode
-            all_sample_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_batch_events)
+            all_sample_labels = inputs[
+                "{}.ids".format(self._labels_prefix)
+            ]  # (all_batch_events)
             embeddings = embeddings[mask]  # (all_batch_events, num_items)
             labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events)
 
             needed_logits = embeddings[labels_mask]  # (non_zero_events, num_items)
             needed_labels = all_sample_labels[labels_mask]  # (non_zero_events)
 
-            return {'logits': needed_logits, 'labels.ids': needed_labels}
+            return {"logits": needed_logits, "labels.ids": needed_labels}
         else:  # eval mode
-            candidate_scores = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items)
+            candidate_scores = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/bert4rec_cls.py b/modeling/models/bert4rec_cls.py
index d6e59b0a..c0bbc61a 100644
--- a/modeling/models/bert4rec_cls.py
+++ b/modeling/models/bert4rec_cls.py
@@ -1,25 +1,24 @@
-from models.base import SequentialTorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import SequentialTorchModel
 
-class Bert4RecModelCLS(SequentialTorchModel, config_name='bert4rec_cls'):
 
+class Bert4RecModelCLS(SequentialTorchModel, config_name="bert4rec_cls"):
     def __init__(
-            self,
-            sequence_prefix,
-            labels_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='gelu',
-            layer_norm_eps=1e-5,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="gelu",
+        layer_norm_eps=1e-5,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -31,20 +30,16 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=False
+            is_causal=False,
         )
         self._sequence_prefix = sequence_prefix
         self._labels_prefix = labels_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim,
-            out_features=embedding_dim
+            in_features=embedding_dim, out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(
-            data=torch.zeros(num_items + 2),
-            requires_grad=True
-        )
+        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
 
         self._init_weights(initializer_range)
 
@@ -53,48 +48,50 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            labels_prefix=config['labels_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            labels_prefix=config["labels_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
-            events=all_sample_events,
-            lengths=all_sample_lengths,
-            add_cls_token=True
+            events=all_sample_events, lengths=all_sample_lengths, add_cls_token=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(
+            embeddings
+        )  # (batch_size, seq_len, embedding_dim)
         predictions = embeddings[:, 0, :]  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             candidates = self._item_embeddings(
-                inputs['{}.ids'.format(self._labels_prefix)])  # (batch_size, embedding_dim)
+                inputs["{}.ids".format(self._labels_prefix)]
+            )  # (batch_size, embedding_dim)
 
-            return {'predictions': predictions, 'candidates': candidates}
+            return {"predictions": predictions, "candidates": candidates}
         else:  # eval mode
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                predictions,
-                self._item_embeddings.weight
+                "bd,nd->bn", predictions, self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/cl4srec.py b/modeling/models/cl4srec.py
index 945c2a92..e1bf7152 100644
--- a/modeling/models/cl4srec.py
+++ b/modeling/models/cl4srec.py
@@ -1,28 +1,27 @@
-from models.base import SequentialTorchModel
-
 import torch
 
+from models.base import SequentialTorchModel
 
-class Cl4SRecModel(SequentialTorchModel, config_name='cl4srec'):
 
+class Cl4SRecModel(SequentialTorchModel, config_name="cl4srec"):
     def __init__(
-            self,
-            sequence_prefix,
-            fst_augmented_sequence_prefix,
-            snd_augmented_sequence_prefix,
-            positive_prefix,
-            negative_prefix,
-            labels_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-5,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        fst_augmented_sequence_prefix,
+        snd_augmented_sequence_prefix,
+        positive_prefix,
+        negative_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -34,7 +33,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._fst_augmented_sequence_prefix = fst_augmented_sequence_prefix
@@ -47,51 +46,56 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            fst_augmented_sequence_prefix=config['fst_augmented_sequence_prefix'],
-            snd_augmented_sequence_prefix=config['snd_augmented_sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            labels_prefix=config['labels_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            num_layers=config['num_layers'],
-            num_heads=config['num_heads'],
-            embedding_dim=config['embedding_dim'],
-            dim_feedforward=config['dim_feedforward'],
-            dropout=config['dropout'],
-            activation=config['activation'],
-            layer_norm_eps=config['layer_norm_eps'],
-            initializer_range=config['initializer_range']
+            sequence_prefix=config["sequence_prefix"],
+            fst_augmented_sequence_prefix=config["fst_augmented_sequence_prefix"],
+            snd_augmented_sequence_prefix=config["snd_augmented_sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            labels_prefix=config["labels_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            num_layers=config["num_layers"],
+            num_heads=config["num_heads"],
+            embedding_dim=config["embedding_dim"],
+            dim_feedforward=config["dim_feedforward"],
+            dropout=config["dropout"],
+            activation=config["activation"],
+            layer_norm_eps=config["layer_norm_eps"],
+            initializer_range=config["initializer_range"],
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             items_logits = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items)
 
             # TODO remove this check
-            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
+            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
             assert torch.allclose(
-                self._item_embeddings(labels),
-                self._item_embeddings.weight[labels]
+                self._item_embeddings(labels), self._item_embeddings.weight[labels]
             )
 
             all_fst_aug_sample_events = inputs[
-                '{}.ids'.format(self._fst_augmented_sequence_prefix)
+                "{}.ids".format(self._fst_augmented_sequence_prefix)
             ]  # (all_batch_events)
-            all_fst_aug_sample_lengths = inputs['{}.length'.format(self._fst_augmented_sequence_prefix)]  # (batch_size)
+            all_fst_aug_sample_lengths = inputs[
+                "{}.length".format(self._fst_augmented_sequence_prefix)
+            ]  # (batch_size)
             fst_aug_embeddings, fst_aug_mask = self._apply_sequential_encoder(
                 all_fst_aug_sample_events, all_fst_aug_sample_lengths
             )  # (batch_size, fst_aug_seq_len, embedding_dim), (batch_size, fst_aug_seq_len)
@@ -100,9 +104,11 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             all_snd_aug_sample_events = inputs[
-                '{}.ids'.format(self._snd_augmented_sequence_prefix)
+                "{}.ids".format(self._snd_augmented_sequence_prefix)
             ]  # (all_batch_events)
-            all_snd_aug_sample_lengths = inputs['{}.length'.format(self._snd_augmented_sequence_prefix)]  # (batch_size)
+            all_snd_aug_sample_lengths = inputs[
+                "{}.length".format(self._snd_augmented_sequence_prefix)
+            ]  # (batch_size)
             snd_aug_embeddings, snd_aug_mask = self._apply_sequential_encoder(
                 all_snd_aug_sample_events, all_snd_aug_sample_lengths
             )  # (batch_size, snd_aug_seq_len, embedding_dim), (batch_size, snd_aug_seq_len)
@@ -111,24 +117,23 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             return {
-                'logits': items_logits,
-                'sequence_representation': last_embeddings,
-                'fst_aug_sequence_representation': last_fst_aug_embeddings,
-                'snd_aug_sequence_representation': last_snd_aug_embeddings
+                "logits": items_logits,
+                "sequence_representation": last_embeddings,
+                "fst_aug_sequence_representation": last_fst_aug_embeddings,
+                "snd_aug_sequence_representation": last_snd_aug_embeddings,
             }
         else:  # eval mode
-            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
+            candidate_embeddings = (
+                self._item_embeddings.weight
+            )  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                candidate_embeddings
+                "bd,nd->bn", last_embeddings, candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/duorec.py b/modeling/models/duorec.py
index 380a6675..68f9df33 100644
--- a/modeling/models/duorec.py
+++ b/modeling/models/duorec.py
@@ -1,29 +1,27 @@
-from models.base import SequentialTorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import SequentialTorchModel
 from utils import create_masked_tensor
 
 
-class DuoRecModel(SequentialTorchModel, config_name='duorec'):
-
+class DuoRecModel(SequentialTorchModel, config_name="duorec"):
     def __init__(
-            self,
-            sequence_prefix,
-            augmented_sequence_prefix,
-            labels_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-5,
-            initializer_range=0.02,
-            is_causal=True
+        self,
+        sequence_prefix,
+        augmented_sequence_prefix,
+        labels_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        initializer_range=0.02,
+        is_causal=True,
     ):
         super().__init__(
             num_items=num_items,
@@ -35,7 +33,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=is_causal
+            is_causal=is_causal,
         )
         self._sequence_prefix = sequence_prefix
         self._augmented_sequence_prefix = augmented_sequence_prefix
@@ -49,19 +47,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            augmented_sequence_prefix=config['augmented_sequence_prefix'],
-            labels_prefix=config['labels_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            num_layers=config['num_layers'],
-            num_heads=config['num_heads'],
-            embedding_dim=config['embedding_dim'],
-            dim_feedforward=config['dim_feedforward'],
-            dropout=config['dropout'],
-            activation=config['activation'],
-            layer_norm_eps=config['layer_norm_eps'],
-            initializer_range=config['initializer_range']
+            sequence_prefix=config["sequence_prefix"],
+            augmented_sequence_prefix=config["augmented_sequence_prefix"],
+            labels_prefix=config["labels_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            num_layers=config["num_layers"],
+            num_heads=config["num_heads"],
+            embedding_dim=config["embedding_dim"],
+            dim_feedforward=config["dim_feedforward"],
+            dropout=config["dropout"],
+            activation=config["activation"],
+            layer_norm_eps=config["layer_norm_eps"],
+            initializer_range=config["initializer_range"],
         )
 
     # TODO taken from duorec github
@@ -77,47 +75,53 @@ def _init_weights(self, module):
             module.bias.data.zero_()
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             items_logits = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items)
             training_output = {
-                'logits': items_logits,
-                'sequence_representation': last_embeddings
+                "logits": items_logits,
+                "sequence_representation": last_embeddings,
             }
 
             # TODO remove this check
-            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
+            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
             assert torch.allclose(
-                self._item_embeddings(labels),
-                self._item_embeddings.weight[labels]
+                self._item_embeddings(labels), self._item_embeddings.weight[labels]
             )
 
             # Unsupervised Augmentation
             embeddings_, mask_ = self._apply_sequential_encoder(
                 all_sample_events, all_sample_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-            last_embeddings_ = self._get_last_embedding(embeddings_, mask_)  # (batch_size, embedding_dim)
-            training_output['similar_sequence_representation'] = last_embeddings_
-            assert not torch.allclose(last_embeddings, last_embeddings_), \
-                'Embedding must be different because of dropout'
+            last_embeddings_ = self._get_last_embedding(
+                embeddings_, mask_
+            )  # (batch_size, embedding_dim)
+            training_output["similar_sequence_representation"] = last_embeddings_
+            assert not torch.allclose(last_embeddings, last_embeddings_), (
+                "Embedding must be different because of dropout"
+            )
 
             # Semantic Similarity
             all_sample_augmented_events = inputs[
-                '{}.ids'.format(self._augmented_sequence_prefix)
+                "{}.ids".format(self._augmented_sequence_prefix)
             ]  # (all_batch_events)
             all_sample_augmented_lengths = inputs[
-                '{}.length'.format(self._augmented_sequence_prefix)
+                "{}.length".format(self._augmented_sequence_prefix)
             ]  # (batch_size)
 
             augmented_embeddings, augmented_mask = self._apply_sequential_encoder(
@@ -126,22 +130,23 @@ def forward(self, inputs):
             last_augmented_embeddings = self._get_last_embedding(
                 augmented_embeddings, augmented_mask
             )  # (batch_size, embedding_dim)
-            training_output['augmented_sequence_representation'] = last_augmented_embeddings
+            training_output["augmented_sequence_representation"] = (
+                last_augmented_embeddings
+            )
 
             return training_output
         else:  # eval mode
-            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
+            candidate_embeddings = (
+                self._item_embeddings.weight
+            )  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                candidate_embeddings
+                "bd,nd->bn", last_embeddings, candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
\ No newline at end of file
+            return indices
diff --git a/modeling/models/graph_seq_rec.py b/modeling/models/graph_seq_rec.py
index b227f184..caa7e2e0 100644
--- a/modeling/models/graph_seq_rec.py
+++ b/modeling/models/graph_seq_rec.py
@@ -1,35 +1,33 @@
-from models.base import SequentialTorchModel
-
-from utils import create_masked_tensor, DEVICE
-
 import torch
 import torch.nn as nn
 
+from models.base import SequentialTorchModel
+from utils import DEVICE, create_masked_tensor
 
-class GraphSeqRecModel(SequentialTorchModel, config_name='graph_seq_rec'):
 
+class GraphSeqRecModel(SequentialTorchModel, config_name="graph_seq_rec"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            negative_prefix,
-            candidate_prefix,
-            common_graph,
-            user_graph,
-            item_graph,
-            num_hops,
-            graph_dropout,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            use_ce=False,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        negative_prefix,
+        candidate_prefix,
+        common_graph,
+        user_graph,
+        item_graph,
+        num_hops,
+        graph_dropout,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        use_ce=False,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -41,7 +39,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -57,38 +55,34 @@ def __init__(
         self._graph_dropout = graph_dropout
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim,
-            out_features=embedding_dim
+            in_features=embedding_dim, out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(
-            data=torch.zeros(num_items + 2),
-            requires_grad=True
-        )
+        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
 
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            candidate_prefix=config['candidate_prefix'],
-            common_graph=kwargs['graph'],
-            user_graph=kwargs['user_graph'],
-            item_graph=kwargs['item_graph'],
-            num_hops=config['num_hops'],
-            graph_dropout=config['graph_dropout'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            use_ce=config.get('use_ce', False),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            candidate_prefix=config["candidate_prefix"],
+            common_graph=kwargs["graph"],
+            user_graph=kwargs["user_graph"],
+            item_graph=kwargs["item_graph"],
+            num_hops=config["num_hops"],
+            graph_dropout=config["graph_dropout"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            use_ce=config.get("use_ce", False),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def _apply_graph_encoder(self, embeddings, graph):
@@ -110,38 +104,45 @@ def _apply_graph_encoder(self, embeddings, graph):
         return embeddings
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        lengths = inputs["{}.length".format(self._sequence_prefix)]  # (batch_size)
 
         common_graph_embeddings = self._apply_graph_encoder(
-            embeddings=self._item_embeddings.weight,
-            graph=self._item_graph
+            embeddings=self._item_embeddings.weight, graph=self._item_graph
         )  # (num_items + 2, embedding_dim)
 
-        embeddings = common_graph_embeddings[all_sample_events]  # (all_batch_events, embedding_dim)
+        embeddings = common_graph_embeddings[
+            all_sample_events
+        ]  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings,
-            lengths=lengths
+            data=embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = torch.arange(
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(
+            positions
+        )  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=lengths
+            data=position_embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        embeddings = (
+            embeddings + position_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -149,35 +150,52 @@ def forward(self, inputs):
         embeddings[~mask] = 0
 
         if self._is_causal:
-            causal_mask = torch.tril(torch.tile(mask.unsqueeze(1), dims=[self._num_heads, seq_len, 1])).bool().to(DEVICE)  # (seq_len, seq_len)
+            causal_mask = (
+                torch.tril(
+                    torch.tile(mask.unsqueeze(1), dims=[self._num_heads, seq_len, 1])
+                )
+                .bool()
+                .to(DEVICE)
+            )  # (seq_len, seq_len)
             embeddings = self._encoder(
                 src=embeddings,
                 mask=~causal_mask,
             )  # (batch_size, seq_len, embedding_dim)
         else:
             embeddings = self._encoder(
-                src=embeddings,
-                src_key_padding_mask=~mask
+                src=embeddings, src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
 
         if self._use_ce:
-            embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
-            embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
+            embeddings = self._output_projection(
+                embeddings
+            )  # (batch_size, seq_len, embedding_dim)
+            embeddings = torch.nn.functional.gelu(
+                embeddings
+            )  # (batch_size, seq_len, embedding_dim)
             embeddings = torch.einsum(
-                'bsd,nd->bsn', embeddings, self._item_embeddings.weight
+                "bsd,nd->bsn", embeddings, self._item_embeddings.weight
             )  # (batch_size, seq_len, num_items)
             embeddings += self._bias[None, None, :]  # (batch_size, seq_len, num_items)
         else:
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             if self._use_ce:
-                return {'logits': embeddings[mask]}
+                return {"logits": embeddings[mask]}
             else:
-                all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-                all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
-
-                all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+                all_positive_sample_events = inputs[
+                    "{}.ids".format(self._positive_prefix)
+                ]  # (all_batch_events)
+                all_negative_sample_events = inputs[
+                    "{}.ids".format(self._negative_prefix)
+                ]  # (all_batch_events)
+
+                all_sample_embeddings = embeddings[
+                    mask
+                ]  # (all_batch_events, embedding_dim)
                 all_positive_sample_embeddings = self._item_embeddings(
                     all_positive_sample_events
                 )  # (all_batch_events, embedding_dim)
@@ -186,21 +204,27 @@ def forward(self, inputs):
                 )  # (all_batch_events, embedding_dim)
 
                 return {
-                    'current_embeddings': all_sample_embeddings,
-                    'positive_embeddings': all_positive_sample_embeddings,
-                    'negative_embeddings': all_negative_sample_embeddings
+                    "current_embeddings": all_sample_embeddings,
+                    "positive_embeddings": all_positive_sample_embeddings,
+                    "negative_embeddings": all_negative_sample_embeddings,
                 }
         else:  # eval mode
             if self._use_ce:
-                last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items)
-
-                if '{}.ids'.format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
-                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
+                last_embeddings = self._get_last_embedding(
+                    embeddings, mask
+                )  # (batch_size, num_items)
+
+                if "{}.ids".format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs[
+                        "{}.ids".format(self._candidate_prefix)
+                    ]  # (all_batch_candidates)
+                    candidate_lengths = inputs[
+                        "{}.length".format(self._candidate_prefix)
+                    ]  # (batch_size)
 
                     candidate_ids = torch.reshape(
                         candidate_events,
-                        (candidate_lengths.shape[0], candidate_lengths[0])
+                        (candidate_lengths.shape[0], candidate_lengths[0]),
                     )  # (batch_size, num_candidates)
                     candidate_scores = last_embeddings.gather(
                         dim=1, index=candidate_ids
@@ -208,34 +232,35 @@ def forward(self, inputs):
                 else:
                     candidate_scores = last_embeddings  # (batch_size, num_items + 2)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1:] = -torch.inf
+                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
             else:
-                if '{}.ids'.format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
-                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
+                if "{}.ids".format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs[
+                        "{}.ids".format(self._candidate_prefix)
+                    ]  # (all_batch_candidates)
+                    candidate_lengths = inputs[
+                        "{}.length".format(self._candidate_prefix)
+                    ]  # (batch_size)
 
                     candidate_embeddings = self._item_embeddings(
                         candidate_events
                     )  # (all_batch_candidates, embedding_dim)
 
                     candidate_embeddings, _ = create_masked_tensor(
-                        data=candidate_embeddings,
-                        lengths=candidate_lengths
+                        data=candidate_embeddings, lengths=candidate_lengths
                     )  # (batch_size, num_candidates, embedding_dim)
 
                     candidate_scores = torch.einsum(
-                        'bd,bnd->bn',
-                        last_embeddings,
-                        candidate_embeddings
+                        "bd,bnd->bn", last_embeddings, candidate_embeddings
                     )  # (batch_size, num_candidates)
                 else:
-                    candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
+                    candidate_embeddings = (
+                        self._item_embeddings.weight
+                    )  # (num_items, embedding_dim)
                     candidate_scores = torch.einsum(
-                        'bd,nd->bn',
-                        last_embeddings,
-                        candidate_embeddings
+                        "bd,nd->bn", last_embeddings, candidate_embeddings
                     )  # (batch_size, num_items)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1:] = -torch.inf
+                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             return candidate_scores
diff --git a/modeling/models/gru4rec.py b/modeling/models/gru4rec.py
index 927445ac..474743df 100644
--- a/modeling/models/gru4rec.py
+++ b/modeling/models/gru4rec.py
@@ -1,22 +1,20 @@
-from models.base import TorchModel
-
-from utils import create_masked_tensor, get_activation_function
-
 import torch
 from torch import nn
 
+from models.base import TorchModel
+from utils import create_masked_tensor, get_activation_function
 
-class GRUModel(TorchModel):
 
+class GRUModel(TorchModel):
     def __init__(
-            self,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_layers,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-5
+        self,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_layers,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-5,
     ):
         super().__init__()
         self._num_items = num_items
@@ -25,11 +23,12 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim
+            embedding_dim=embedding_dim,
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim
+            num_embeddings=max_sequence_length
+            + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim,
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -41,7 +40,7 @@ def __init__(
             num_layers=num_layers,
             batch_first=True,
             dropout=dropout,
-            bidirectional=False
+            bidirectional=False,
         )
 
         self._hidden_to_output_projection = nn.Linear(embedding_dim, num_items)
@@ -51,27 +50,31 @@ def _apply_sequential_encoder(self, events, lengths):
         embeddings = self._item_embeddings(events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings,
-            lengths=lengths
+            data=embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = torch.arange(
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(
+            positions
+        )  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=lengths
+            data=position_embeddings, lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        embeddings = (
+            embeddings + position_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -82,14 +85,16 @@ def _apply_sequential_encoder(self, events, lengths):
             input=embeddings,
             lengths=lengths.cpu(),
             batch_first=True,
-            enforce_sorted=False
+            enforce_sorted=False,
         )
 
         hidden = torch.zeros(
-            self._num_layers, batch_size, self._embedding_dim,
+            self._num_layers,
+            batch_size,
+            self._embedding_dim,
             dtype=embeddings.dtype,
             device=embeddings.device,
-            requires_grad=True
+            requires_grad=True,
         )  # (num_layers, batch_size, embedding_dim)
         out, hidden = self._encoder(packed_embeddings, hidden)
         embeddings, embedding_lengths = torch.nn.utils.rnn.pad_packed_sequence(
@@ -102,21 +107,20 @@ def _apply_sequential_encoder(self, events, lengths):
         return embeddings, mask
 
 
-class GRU4RecModel(GRUModel, config_name='gru4rec'):
-
+class GRU4RecModel(GRUModel, config_name="gru4rec"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            negative_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_layers,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-5,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        negative_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_layers,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -125,7 +129,7 @@ def __init__(
             num_layers=num_layers,
             dropout=dropout,
             activation=activation,
-            layer_norm_eps=layer_norm_eps
+            layer_norm_eps=layer_norm_eps,
         )
 
         self._sequence_prefix = sequence_prefix
@@ -136,35 +140,42 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_layers=config['num_layers'],
-            dropout=config.get('dropout', 0.0),
-            activation=config.get('activation', 'tanh'),
-            layer_norm_eps=config.get('layer_norm_eps', 1e-5),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_layers=config["num_layers"],
+            dropout=config.get("dropout", 0.0),
+            activation=config.get("activation", "tanh"),
+            layer_norm_eps=config.get("layer_norm_eps", 1e-5),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
-            events=all_sample_events,
-            lengths=all_sample_lengths
+            events=all_sample_events, lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
 
         if self.training:  # training mode
-            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_positive_sample_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
             all_positive_sample_embeddings = self._item_embeddings(
                 all_positive_sample_events
             )  # (all_batch_events, embedding_dim)
 
-            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            all_sample_embeddings = embeddings[
+                mask
+            ]  # (all_batch_events, embedding_dim)
 
             sample_end_idx = torch.cumsum(all_sample_lengths, dim=0)  # (batch_size)
             sample_begin_idx = sample_end_idx - all_sample_lengths  # (batch_size)
@@ -173,44 +184,47 @@ def forward(self, inputs):
             sample_begin_idx = sample_begin_idx[:, None]  # (batch_size, 1)
 
             negative_indices = torch.tile(
-                torch.arange(start=0, end=all_positive_sample_events.shape[0], device=all_sample_lengths.device).long()[None],
-                dims=[all_sample_lengths.shape[0], 1]
+                torch.arange(
+                    start=0,
+                    end=all_positive_sample_events.shape[0],
+                    device=all_sample_lengths.device,
+                ).long()[None],
+                dims=[all_sample_lengths.shape[0], 1],
             )  # (batch_size, all_batch_events)
 
-            negative_mask = (negative_indices >= sample_begin_idx) & (negative_indices < sample_end_idx)
+            negative_mask = (negative_indices >= sample_begin_idx) & (
+                negative_indices < sample_end_idx
+            )
             negative_mask = torch.repeat_interleave(
                 negative_mask, all_sample_lengths, dim=0
             )
 
             negative_scores = torch.einsum(
-                'ad,bd->ab',
+                "ad,bd->ab",
                 all_sample_embeddings,
-                self._item_embeddings(all_sample_events)
+                self._item_embeddings(all_sample_events),
             )  # (all_batch_events, all_batch_events)
-            
+
             positive_scores = torch.einsum(
-                'ad,ad->a',
-                all_sample_embeddings,
-                all_positive_sample_embeddings
+                "ad,ad->a", all_sample_embeddings, all_positive_sample_embeddings
             )  # (all_batch_events)
 
             return {
-                'positive_scores': positive_scores[..., None],
-                'negative_scores': negative_scores,
+                "positive_scores": positive_scores[..., None],
+                "negative_scores": negative_scores,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/gtorec.py b/modeling/models/gtorec.py
index dc29435f..d30e4616 100644
--- a/modeling/models/gtorec.py
+++ b/modeling/models/gtorec.py
@@ -1,40 +1,39 @@
-from models.base import SequentialTorchModel, TorchModel
-
-from utils import create_masked_tensor, get_activation_function
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from models.base import SequentialTorchModel, TorchModel
+from utils import create_masked_tensor, get_activation_function
+
 
-class GTOModel(SequentialTorchModel, config_name='gtorec'):
+class GTOModel(SequentialTorchModel, config_name="gtorec"):
     def __init__(
-            self,
-            # sequential params 
-            sequence_prefix, # =item_prefix
-            positive_prefix, 
-            negative_prefix, 
-            candidate_prefix,
-            source_domain, 
-            num_users,  
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            # graph params
-            user_prefix,
-            graph,
-            graph_embedding_dim,
-            graph_num_layers,
-            # params with default values
-            dropout=0.0,
-            graph_dropout=0.0,
-            activation='relu', 
-            layer_norm_eps=1e-9, 
-            initializer_range=0.02,
-            norm_first=True
+        self,
+        # sequential params
+        sequence_prefix,  # =item_prefix
+        positive_prefix,
+        negative_prefix,
+        candidate_prefix,
+        source_domain,
+        num_users,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        # graph params
+        user_prefix,
+        graph,
+        graph_embedding_dim,
+        graph_num_layers,
+        # params with default values
+        dropout=0.0,
+        graph_dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
+        norm_first=True,
     ):
         super().__init__(
             num_items=num_items,
@@ -46,9 +45,9 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True 
+            is_causal=True,
         )
-        # sequential part 
+        # sequential part
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -56,13 +55,9 @@ def __init__(
         self._source_domain = source_domain
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim,
-            out_features=embedding_dim
-        )
-        self._bias = nn.Parameter(
-            data=torch.zeros(num_items + 2),
-            requires_grad=True
+            in_features=embedding_dim, out_features=embedding_dim
         )
+        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
 
         # graph part
         self._user_prefix = user_prefix
@@ -73,15 +68,13 @@ def __init__(
         self._graph_dropout = graph_dropout
 
         self._graph_user_embeddings = nn.Embedding(
-            num_embeddings=num_users + 2,
-            embedding_dim=self._graph_embedding_dim
+            num_embeddings=num_users + 2, embedding_dim=self._graph_embedding_dim
         )
         self._graph_item_embeddings = nn.Embedding(
-            num_embeddings=num_items + 2,
-            embedding_dim=self._graph_embedding_dim
+            num_embeddings=num_items + 2, embedding_dim=self._graph_embedding_dim
         )
 
-        # cross_attention part 
+        # cross_attention part
         self._mha = nn.MultiheadAttention(
             embed_dim=embedding_dim,
             num_heads=num_heads,
@@ -107,36 +100,39 @@ def __init__(
             in_features=2 * embedding_dim,
             out_features=embedding_dim,
         )
-        
+
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
             # sequential part
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            candidate_prefix=config['candidate_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02),
-            norm_first=config.get('norm_first', True),
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            candidate_prefix=config["candidate_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
+            norm_first=config.get("norm_first", True),
             # graph part
-            user_prefix=config['user_prefix'],
-            num_users=kwargs['num_users'],
+            user_prefix=config["user_prefix"],
+            num_users=kwargs["num_users"],
             graph_embedding_dim=config["graph_embedding_dim"],
             graph_num_layers=config["graph_num_layers"],
-            graph_dropout=config.get("graph_dropout", 0.0)
+            graph_dropout=config.get("graph_dropout", 0.0),
         )
-    
+
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat((self._graph_user_embeddings.weight, self._graph_item_embeddings.weight), dim=0)
+        ego_embeddings = torch.cat(
+            (self._graph_user_embeddings.weight, self._graph_item_embeddings.weight),
+            dim=0,
+        )
         all_embeddings = [ego_embeddings]
 
         if self._graph_dropout > 0:  # drop some edges
@@ -167,8 +163,8 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def _get_graph_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs['{}.ids'.format(prefix)]  # (batch_size)
-        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
+        ids = inputs["{}.ids".format(prefix)]  # (batch_size)
+        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (batch_size, emb_dim)
         ego_embeddings = ego_embeddings(ids)  # (batch_size, emb_dim)
@@ -179,13 +175,15 @@ def _get_graph_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings
         assert torch.all(mask == ego_mask)
 
         return padded_embeddings, padded_ego_embeddings, mask
-    
+
     def _ca_block(self, q, k, v, attn_mask=None, key_padding_mask=None):
         x = self._mha(
-            q, k, v,
+            q,
+            k,
+            v,
             attn_mask=attn_mask,
             key_padding_mask=key_padding_mask,
-            need_weights=False
+            need_weights=False,
         )[0]  # (batch_size, seq_len, embedding_dim)
         return self.dropout1(x)  # (batch_size, seq_len, embedding_dim)
 
@@ -195,11 +193,19 @@ def _ff_block(self, x):
 
     def forward(self, inputs):
         # target domain item sequence
-        all_sample_events_target = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths_target = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events_target = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths_target = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
         # source domain item sequence
-        all_sample_events_source = inputs['{}.{}.ids'.format(self._sequence_prefix, self._source_domain)]  # (all_batch_events)
-        all_sample_lengths_source = inputs['{}.{}.length'.format(self._sequence_prefix, self._source_domain)]  # (batch_size)
+        all_sample_events_source = inputs[
+            "{}.{}.ids".format(self._sequence_prefix, self._source_domain)
+        ]  # (all_batch_events)
+        all_sample_lengths_source = inputs[
+            "{}.{}.length".format(self._sequence_prefix, self._source_domain)
+        ]  # (batch_size)
 
         # sequential model encoder and target domain items embeddings from sequential model
         seq_embeddings_target, seq_mask_target = self._apply_sequential_encoder(
@@ -207,81 +213,137 @@ def forward(self, inputs):
         )  # (batch_size, target_seq_len, embedding_dim), (batch_size, target_seq_len)
 
         # target domain items encoder for graph model
-        all_final_user_embeddings_target, all_final_item_embeddings_target = \
-            self._apply_graph_encoder(all_sample_events_target, all_sample_lengths_target)  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings_target, all_final_item_embeddings_target = (
+            self._apply_graph_encoder(
+                all_sample_events_target, all_sample_lengths_target
+            )
+        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
         # source domain items encoder for graph model
-        all_final_user_embeddings_source, all_final_item_embeddings_source = \
-            self._apply_graph_encoder(all_sample_events_source, all_sample_lengths_source)  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
-        
+        all_final_user_embeddings_source, all_final_item_embeddings_source = (
+            self._apply_graph_encoder(
+                all_sample_events_source, all_sample_lengths_source
+            )
+        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+
         # target domain items embeddings from graph model
-        graph_embeddings_target, graph_item_ego_embeddings_target, graph_item_mask_target = self._get_graph_embeddings(
-            inputs, self._sequence_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
+        (
+            graph_embeddings_target,
+            graph_item_ego_embeddings_target,
+            graph_item_mask_target,
+        ) = self._get_graph_embeddings(
+            inputs,
+            self._sequence_prefix,
+            self._graph_item_embeddings,
+            all_final_item_embeddings_target,
         )
-        graph_item_embeddings_target = graph_embeddings_target[graph_item_mask_target]  # (batch_size, target_seq_len, embedding_dim) 
+        graph_item_embeddings_target = graph_embeddings_target[
+            graph_item_mask_target
+        ]  # (batch_size, target_seq_len, embedding_dim)
         # source domain items embeddings from graph model
-        graph_embeddings_source, graph_item_ego_embeddings_source, graph_item_mask_source = self._get_graph_embeddings(
-            inputs, self._sequence_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
+        (
+            graph_embeddings_source,
+            graph_item_ego_embeddings_source,
+            graph_item_mask_source,
+        ) = self._get_graph_embeddings(
+            inputs,
+            self._sequence_prefix,
+            self._graph_item_embeddings,
+            all_final_item_embeddings_source,
         )
-        graph_item_embeddings_source = graph_embeddings_source[graph_item_mask_source]  # (batch_size, source_seq_len, embedding_dim) 
-        
+        graph_item_embeddings_source = graph_embeddings_source[
+            graph_item_mask_source
+        ]  # (batch_size, source_seq_len, embedding_dim)
+
         # embeddings + graph_embeddings_target -> cross-attention
         # query   = embeddings
         # keys    = graph_embeddings_target
         # values  = graph_embeddings_target
-        if self.norm_first: 
-            graph_embeddings_target = graph_embeddings_target + self.norm1(self._ca_block(
-                q=seq_embeddings_target,
-                k=graph_embeddings_target,
-                v=graph_embeddings_target,
-                attn_mask=None,
-                key_padding_mask=~graph_item_mask_target
-            ))  # (batch_size, target_seq_len, embedding_dim)
-            graph_embeddings_target = graph_embeddings_target + self.norm2(self._ff_block(graph_embeddings_target))
+        if self.norm_first:
+            graph_embeddings_target = graph_embeddings_target + self.norm1(
+                self._ca_block(
+                    q=seq_embeddings_target,
+                    k=graph_embeddings_target,
+                    v=graph_embeddings_target,
+                    attn_mask=None,
+                    key_padding_mask=~graph_item_mask_target,
+                )
+            )  # (batch_size, target_seq_len, embedding_dim)
+            graph_embeddings_target = graph_embeddings_target + self.norm2(
+                self._ff_block(graph_embeddings_target)
+            )
         else:
-            graph_embeddings_target = self.norm1(graph_embeddings_target + self._ca_block(
-                q=seq_embeddings_target,
-                k=graph_embeddings_target,
-                v=graph_embeddings_target,
-                attn_mask=None,
-                key_padding_mask=~graph_item_mask_target
-            ))  # (batch_size, target_seq_len, embedding_dim)
-            graph_embeddings_target = self.norm2(graph_embeddings_target + self._ff_block(graph_embeddings_target))
+            graph_embeddings_target = self.norm1(
+                graph_embeddings_target
+                + self._ca_block(
+                    q=seq_embeddings_target,
+                    k=graph_embeddings_target,
+                    v=graph_embeddings_target,
+                    attn_mask=None,
+                    key_padding_mask=~graph_item_mask_target,
+                )
+            )  # (batch_size, target_seq_len, embedding_dim)
+            graph_embeddings_target = self.norm2(
+                graph_embeddings_target + self._ff_block(graph_embeddings_target)
+            )
         # target-target cross-attention result
-        mha_embeddings_target = torch.cat([seq_embeddings_target, graph_embeddings_target], dim=-1)
-        mha_embeddings_target = self._mha_output_projection(mha_embeddings_target)  # (batch_size, target_seq_len, embedding_dim)
+        mha_embeddings_target = torch.cat(
+            [seq_embeddings_target, graph_embeddings_target], dim=-1
+        )
+        mha_embeddings_target = self._mha_output_projection(
+            mha_embeddings_target
+        )  # (batch_size, target_seq_len, embedding_dim)
 
         # embeddings + graph_embeddings_source -> cross-attention
         # query   = embeddings
         # keys    = graph_embeddings_source
         # values  = graph_embeddings_source
-        if self.norm_first: 
-            graph_embeddings_source = graph_embeddings_source + self.norm1(self._ca_block(
-                q=seq_embeddings_target,
-                k=graph_embeddings_source,
-                v=graph_embeddings_source,
-                attn_mask=None,
-                key_padding_mask=~graph_item_mask_source
-            ))  # (batch_size, seq_len, embedding_dim)
-            graph_embeddings_source = graph_embeddings_source + self.norm2(self._ff_block(graph_embeddings_source))
+        if self.norm_first:
+            graph_embeddings_source = graph_embeddings_source + self.norm1(
+                self._ca_block(
+                    q=seq_embeddings_target,
+                    k=graph_embeddings_source,
+                    v=graph_embeddings_source,
+                    attn_mask=None,
+                    key_padding_mask=~graph_item_mask_source,
+                )
+            )  # (batch_size, seq_len, embedding_dim)
+            graph_embeddings_source = graph_embeddings_source + self.norm2(
+                self._ff_block(graph_embeddings_source)
+            )
         else:
-            graph_embeddings_source = self.norm1(graph_embeddings_source + self._ca_block(
-                q=seq_embeddings_target,
-                k=graph_embeddings_source,
-                v=graph_embeddings_source,
-                attn_mask=None,
-                key_padding_mask=~graph_item_mask_source
-            ))  # (batch_size, seq_len, embedding_dim)
-            graph_embeddings_source = self.norm2(graph_embeddings_source + self._ff_block(graph_embeddings_source))
+            graph_embeddings_source = self.norm1(
+                graph_embeddings_source
+                + self._ca_block(
+                    q=seq_embeddings_target,
+                    k=graph_embeddings_source,
+                    v=graph_embeddings_source,
+                    attn_mask=None,
+                    key_padding_mask=~graph_item_mask_source,
+                )
+            )  # (batch_size, seq_len, embedding_dim)
+            graph_embeddings_source = self.norm2(
+                graph_embeddings_source + self._ff_block(graph_embeddings_source)
+            )
         # source-target cross-attention result
-        mha_embeddings_source = torch.cat([seq_embeddings_target, graph_embeddings_source], dim=-1)
-        mha_embeddings_source = self._mha_output_projection(mha_embeddings_source)  # (batch_size, seq_len, embedding_dim)
+        mha_embeddings_source = torch.cat(
+            [seq_embeddings_target, graph_embeddings_source], dim=-1
+        )
+        mha_embeddings_source = self._mha_output_projection(
+            mha_embeddings_source
+        )  # (batch_size, seq_len, embedding_dim)
 
         if self.training:  # training mode
             # sequential part
-            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-            all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
-
-            all_sample_embeddings = seq_embeddings_target[seq_mask_target]  # (all_batch_events, embedding_dim)
+            all_positive_sample_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+            all_negative_sample_events = inputs[
+                "{}.ids".format(self._negative_prefix)
+            ]  # (all_batch_events)
+
+            all_sample_embeddings = seq_embeddings_target[
+                seq_mask_target
+            ]  # (all_batch_events, embedding_dim)
             all_positive_sample_embeddings = self._item_embeddings(
                 all_positive_sample_events
             )  # (all_batch_events, embedding_dim)
@@ -290,96 +352,150 @@ def forward(self, inputs):
             )  # (all_batch_events, embedding_dim)
 
             # graph part, target domain item embeddings
-            graph_positive_embeddings_target, _, graph_positive_mask_target = self._get_graph_embeddings(
-                inputs, self._positive_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
+            graph_positive_embeddings_target, _, graph_positive_mask_target = (
+                self._get_graph_embeddings(
+                    inputs,
+                    self._positive_prefix,
+                    self._graph_item_embeddings,
+                    all_final_item_embeddings_target,
+                )
             )
-            graph_negative_embeddings_target, _, graph_negative_mask_target = self._get_graph_embeddings(
-                inputs, self._negative_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
+            graph_negative_embeddings_target, _, graph_negative_mask_target = (
+                self._get_graph_embeddings(
+                    inputs,
+                    self._negative_prefix,
+                    self._graph_item_embeddings,
+                    all_final_item_embeddings_target,
+                )
             )
             # b - batch_size, s - seq_len, d - embedding_dim
             graph_positive_scores_target = torch.einsum(
-                'bd,bsd->bs', graph_item_embeddings_target, graph_positive_embeddings_target
+                "bd,bsd->bs",
+                graph_item_embeddings_target,
+                graph_positive_embeddings_target,
             )  # (batch_size, target_seq_len)
             graph_negative_scores_target = torch.einsum(
-                'bd,bsd->bs', graph_item_embeddings_target, graph_negative_embeddings_target
+                "bd,bsd->bs",
+                graph_item_embeddings_target,
+                graph_negative_embeddings_target,
             )  # (batch_size, target_seq_len)
-            graph_positive_scores_target = graph_positive_scores_target[graph_positive_mask_target]  # (all_batch_events)
-            graph_negative_scores_target = graph_negative_scores_target[graph_negative_mask_target]  # (all_batch_events)
+            graph_positive_scores_target = graph_positive_scores_target[
+                graph_positive_mask_target
+            ]  # (all_batch_events)
+            graph_negative_scores_target = graph_negative_scores_target[
+                graph_negative_mask_target
+            ]  # (all_batch_events)
 
             # graph part, source domain item embeddings
-            graph_positive_embeddings_source, _, graph_positive_mask_source = self._get_graph_embeddings(
-                inputs, self._positive_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
+            graph_positive_embeddings_source, _, graph_positive_mask_source = (
+                self._get_graph_embeddings(
+                    inputs,
+                    self._positive_prefix,
+                    self._graph_item_embeddings,
+                    all_final_item_embeddings_source,
+                )
             )
-            graph_negative_embeddings_source, _, graph_negative_mask_source = self._get_graph_embeddings(
-                inputs, self._negative_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
+            graph_negative_embeddings_source, _, graph_negative_mask_source = (
+                self._get_graph_embeddings(
+                    inputs,
+                    self._negative_prefix,
+                    self._graph_item_embeddings,
+                    all_final_item_embeddings_source,
+                )
             )
             # b - batch_size, s - seq_len, d - embedding_dim
             graph_positive_scores_source = torch.einsum(
-                'bd,bsd->bs', graph_item_embeddings_source, graph_positive_embeddings_source
+                "bd,bsd->bs",
+                graph_item_embeddings_source,
+                graph_positive_embeddings_source,
             )  # (batch_size, source_seq_len)
             graph_negative_scores_source = torch.einsum(
-                'bd,bsd->bs', graph_item_embeddings_source, graph_negative_embeddings_source
+                "bd,bsd->bs",
+                graph_item_embeddings_source,
+                graph_negative_embeddings_source,
             )  # (batch_size, source_seq_len)
-            graph_positive_scores_source = graph_positive_scores_source[graph_positive_mask_source]  # (all_batch_events)
-            graph_negative_scores_source = graph_negative_scores_source[graph_negative_mask_source]  # (all_batch_events)
+            graph_positive_scores_source = graph_positive_scores_source[
+                graph_positive_mask_source
+            ]  # (all_batch_events)
+            graph_negative_scores_source = graph_negative_scores_source[
+                graph_negative_mask_source
+            ]  # (all_batch_events)
 
             # mha part
-            mha_all_sample_embeddings_target = mha_embeddings_target[seq_mask_target]  # (all_batch_events, embedding_dim)
-            mha_all_sample_embeddings_source = mha_embeddings_source[seq_mask_target]  # (all_batch_events, embedding_dim)
+            mha_all_sample_embeddings_target = mha_embeddings_target[
+                seq_mask_target
+            ]  # (all_batch_events, embedding_dim)
+            mha_all_sample_embeddings_source = mha_embeddings_source[
+                seq_mask_target
+            ]  # (all_batch_events, embedding_dim)
 
             return {
                 # sequential part
                 # target domain item embeddings
-                'current_embeddings': all_sample_embeddings, 
-                'positive_embeddings': all_positive_sample_embeddings,
-                'negative_embeddings': all_negative_sample_embeddings,
-                    
+                "current_embeddings": all_sample_embeddings,
+                "positive_embeddings": all_positive_sample_embeddings,
+                "negative_embeddings": all_negative_sample_embeddings,
                 # graph part
                 # target domain item embeddings
-                'graph_positive_embeddings_target': graph_positive_embeddings_target[graph_positive_mask_target],
-                'graph_negative_embeddings_target': graph_negative_embeddings_target[graph_negative_mask_target],
-                'graph_positive_scores_target': graph_positive_scores_target,
-                'graph_negative_scores_target': graph_negative_scores_target,
-                'graph_item_embeddings_target': graph_item_embeddings_target,
+                "graph_positive_embeddings_target": graph_positive_embeddings_target[
+                    graph_positive_mask_target
+                ],
+                "graph_negative_embeddings_target": graph_negative_embeddings_target[
+                    graph_negative_mask_target
+                ],
+                "graph_positive_scores_target": graph_positive_scores_target,
+                "graph_negative_scores_target": graph_negative_scores_target,
+                "graph_item_embeddings_target": graph_item_embeddings_target,
                 # source domain item embeddings
-                'graph_positive_embeddings_source': graph_positive_embeddings_source[graph_positive_mask_source],
-                'graph_negative_embeddings_source': graph_negative_embeddings_source[graph_negative_mask_source],
-                'graph_positive_scores_source': graph_positive_scores_source,
-                'graph_negative_scores_source': graph_negative_scores_source,
-                'graph_item_embeddings_source': graph_item_embeddings_source,
-
+                "graph_positive_embeddings_source": graph_positive_embeddings_source[
+                    graph_positive_mask_source
+                ],
+                "graph_negative_embeddings_source": graph_negative_embeddings_source[
+                    graph_negative_mask_source
+                ],
+                "graph_positive_scores_source": graph_positive_scores_source,
+                "graph_negative_scores_source": graph_negative_scores_source,
+                "graph_item_embeddings_source": graph_item_embeddings_source,
                 # mha part
                 # target domain item embeddings
-                'mha_embeddings_target': mha_all_sample_embeddings_target, 
-                'mha_positive_embeddings_target': all_positive_sample_embeddings,
-                'mha_negative_embeddings_target': all_negative_sample_embeddings,
+                "mha_embeddings_target": mha_all_sample_embeddings_target,
+                "mha_positive_embeddings_target": all_positive_sample_embeddings,
+                "mha_negative_embeddings_target": all_negative_sample_embeddings,
                 # source domain item embeddings
-                'mha_embeddings_source': mha_all_sample_embeddings_source, 
-                'mha_positive_embeddings_source': all_positive_sample_embeddings,
-                'mha_negative_embeddings_source': all_negative_sample_embeddings
+                "mha_embeddings_source": mha_all_sample_embeddings_source,
+                "mha_positive_embeddings_source": all_positive_sample_embeddings,
+                "mha_negative_embeddings_source": all_negative_sample_embeddings,
             }
         else:  # eval mode
-            seq_last_embeddings_target = self._get_last_embedding(seq_embeddings_target, seq_mask_target)  # (batch_size, embedding_dim)
-            mha_last_embeddings_target = self._get_last_embedding(mha_embeddings_target, seq_mask_target)  # (batch_size, embedding_dim)
-            mha_last_embeddings_source = self._get_last_embedding(mha_embeddings_source, seq_mask_target)  # (batch_size, embedding_dim)
+            seq_last_embeddings_target = self._get_last_embedding(
+                seq_embeddings_target, seq_mask_target
+            )  # (batch_size, embedding_dim)
+            mha_last_embeddings_target = self._get_last_embedding(
+                mha_embeddings_target, seq_mask_target
+            )  # (batch_size, embedding_dim)
+            mha_last_embeddings_source = self._get_last_embedding(
+                mha_embeddings_source, seq_mask_target
+            )  # (batch_size, embedding_dim)
 
             aggregated_last_embeddings = torch.maximum(
-                seq_last_embeddings_target, 
-                torch.maximum(mha_last_embeddings_target, mha_last_embeddings_source)
+                seq_last_embeddings_target,
+                torch.maximum(mha_last_embeddings_target, mha_last_embeddings_source),
             )  # (batch_size, embedding_dim)
 
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                aggregated_last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", aggregated_last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
-            if '{}.ids'.format(self._candidate_prefix) in inputs:
-                candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
-                candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
+            if "{}.ids".format(self._candidate_prefix) in inputs:
+                candidate_events = inputs[
+                    "{}.ids".format(self._candidate_prefix)
+                ]  # (all_batch_candidates)
+                candidate_lengths = inputs[
+                    "{}.length".format(self._candidate_prefix)
+                ]  # (batch_size)
 
                 batch_size = candidate_lengths.shape[0]
                 num_candidates = candidate_lengths[0]
@@ -387,12 +503,11 @@ def forward(self, inputs):
                 candidate_scores = torch.gather(
                     input=candidate_scores,
                     dim=1,
-                    index=torch.reshape(candidate_events, [batch_size, num_candidates])
+                    index=torch.reshape(candidate_events, [batch_size, num_candidates]),
                 )  # (batch_size, num_candidates)
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20), (batch_size, 20)
 
             return indices
diff --git a/modeling/models/lightgcn.py b/modeling/models/lightgcn.py
index 79e5f788..19d79b80 100644
--- a/modeling/models/lightgcn.py
+++ b/modeling/models/lightgcn.py
@@ -1,25 +1,23 @@
-from models.base import TorchModel
-
-from utils import create_masked_tensor, DEVICE
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from models.base import TorchModel
+from utils import DEVICE, create_masked_tensor
 
-class LightGCNModel(TorchModel, config_name='light_gcn'):
 
+class LightGCNModel(TorchModel, config_name="light_gcn"):
     def __init__(
-            self,
-            user_prefix,
-            positive_prefix,
-            graph,
-            num_users,
-            num_items,
-            embedding_dim,
-            num_layers,
-            dropout=0.0,
-            initializer_range=0.02
+        self,
+        user_prefix,
+        positive_prefix,
+        graph,
+        num_users,
+        num_items,
+        embedding_dim,
+        num_layers,
+        dropout=0.0,
+        initializer_range=0.02,
     ):
         super().__init__()
         self._user_prefix = user_prefix
@@ -32,13 +30,11 @@ def __init__(
         self._dropout_rate = dropout
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2,
-            embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2,
-            embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -46,19 +42,21 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config['user_prefix'],
-            positive_prefix=config['positive_prefix'],
-            graph=kwargs['graph'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
-            embedding_dim=config['embedding_dim'],
-            num_layers=config['num_layers'],
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            user_prefix=config["user_prefix"],
+            positive_prefix=config["positive_prefix"],
+            graph=kwargs["graph"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+            embedding_dim=config["embedding_dim"],
+            num_layers=config["num_layers"],
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
+        ego_embeddings = torch.cat(
+            (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
+        )
         all_embeddings = [ego_embeddings]
 
         if self._dropout_rate > 0:  # drop some edges
@@ -89,8 +87,8 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs['{}.ids'.format(prefix)]  # (all_batch_events)
-        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
+        ids = inputs["{}.ids".format(prefix)]  # (all_batch_events)
+        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (all_batch_events, embedding_dim)
         ego_embeddings = ego_embeddings(ids)  # (all_batch_events, embedding_dim)
@@ -108,8 +106,9 @@ def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
         return padded_embeddings, padded_ego_embeddings, mask
 
     def forward(self, inputs):
-        all_final_user_embeddings, all_final_item_embeddings = \
-            self._apply_graph_encoder()  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings, all_final_item_embeddings = (
+            self._apply_graph_encoder()
+        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
         user_embeddings, user_ego_embeddings, user_mask = self._get_embeddings(
             inputs, self._user_prefix, self._user_embeddings, all_final_user_embeddings
@@ -117,63 +116,69 @@ def forward(self, inputs):
         user_embeddings = user_embeddings[user_mask]  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
-            positive_item_ids = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-            positive_item_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
+            positive_item_ids = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+            positive_item_lengths = inputs[
+                "{}.length".format(self._positive_prefix)
+            ]  # (batch_size)
 
             batch_size = positive_item_lengths.shape[0]
             max_sequence_length = positive_item_lengths.max().item()
 
-            mask = torch.arange(
-                end=max_sequence_length,
-                device=DEVICE
-            )[None].tile([batch_size, 1]) < positive_item_lengths[:, None]  # (batch_size, max_seq_len)
-
-            positive_user_ids = torch.arange(
-                batch_size,
-                device=DEVICE
-            )[None].tile([max_sequence_length, 1]).T  # (batch_size, max_seq_len)
+            mask = (
+                torch.arange(end=max_sequence_length, device=DEVICE)[None].tile(
+                    [batch_size, 1]
+                )
+                < positive_item_lengths[:, None]
+            )  # (batch_size, max_seq_len)
+
+            positive_user_ids = (
+                torch.arange(batch_size, device=DEVICE)[None]
+                .tile([max_sequence_length, 1])
+                .T
+            )  # (batch_size, max_seq_len)
             positive_user_ids = positive_user_ids[mask]  # (all_batch_items)
-            user_embeddings = user_embeddings[positive_user_ids]  # (all_batch_items, embedding_dim)
+            user_embeddings = user_embeddings[
+                positive_user_ids
+            ]  # (all_batch_items, embedding_dim)
 
             all_scores = torch.einsum(
-                'ad,nd->an',
-                user_embeddings,
-                all_final_item_embeddings
+                "ad,nd->an", user_embeddings, all_final_item_embeddings
             )  # (all_batch_items, num_items + 2)
 
-            negative_mask = torch.zeros(self._num_items + 2, dtype=torch.bool, device=DEVICE)  # (num_items + 2)
+            negative_mask = torch.zeros(
+                self._num_items + 2, dtype=torch.bool, device=DEVICE
+            )  # (num_items + 2)
             negative_mask[positive_item_ids] = 1
 
             positive_scores = torch.gather(
-                input=all_scores,
-                dim=1,
-                index=positive_item_ids[..., None]
+                input=all_scores, dim=1, index=positive_item_ids[..., None]
             )  # (all_batch_items, 1)
 
             all_scores = torch.scatter_add(
                 input=all_scores,
                 dim=1,
                 index=positive_item_ids[..., None],
-                src=torch.ones_like(positive_item_ids[..., None]).float()
+                src=torch.ones_like(positive_item_ids[..., None]).float(),
             )  # (all_batch_items, num_items + 2)
 
             return {
-                'positive_scores': positive_scores,
-                'negative_scores': all_scores,
-                'item_embeddings': torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
+                "positive_scores": positive_scores,
+                "negative_scores": all_scores,
+                "item_embeddings": torch.cat(
+                    (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
+                ),
             }
         else:  # eval mode
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                user_embeddings,
-                all_final_item_embeddings
+                "bd,nd->bn", user_embeddings, all_final_item_embeddings
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/mclsr.py b/modeling/models/mclsr.py
index 8f78cda4..c7fefd3c 100644
--- a/modeling/models/mclsr.py
+++ b/modeling/models/mclsr.py
@@ -1,32 +1,30 @@
-from models.base import TorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import TorchModel
 from utils import create_masked_tensor
 
 
-class MCLSRModel(TorchModel, config_name='mclsr'):
-
+class MCLSRModel(TorchModel, config_name="mclsr"):
     def __init__(
-            self,
-            sequence_prefix,
-            user_prefix,
-            labels_prefix,
-            candidate_prefix,
-            num_users,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_graph_layers,
-            common_graph,
-            user_graph,
-            item_graph,
-            dropout=0.0,
-            layer_norm_eps=1e-5,
-            graph_dropout=0.0,
-            alpha=0.5,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        user_prefix,
+        labels_prefix,
+        candidate_prefix,
+        num_users,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_graph_layers,
+        common_graph,
+        user_graph,
+        item_graph,
+        dropout=0.0,
+        layer_norm_eps=1e-5,
+        graph_dropout=0.0,
+        alpha=0.5,
+        initializer_range=0.02,
     ):
         super().__init__()
         self._sequence_prefix = sequence_prefix
@@ -50,16 +48,17 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim
+            embedding_dim=embedding_dim,
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim
+            num_embeddings=max_sequence_length
+            + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim,
         )
 
         self._user_embeddings = nn.Embedding(
             num_embeddings=num_users + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim
+            embedding_dim=embedding_dim,
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -67,39 +66,43 @@ def __init__(
 
         # Current interest learning
         self._current_interest_learning_encoder = nn.Sequential(
-            nn.Linear(in_features=embedding_dim, out_features=4 * embedding_dim, bias=False),
+            nn.Linear(
+                in_features=embedding_dim, out_features=4 * embedding_dim, bias=False
+            ),
             nn.Tanh(),
-            nn.Linear(in_features=4 * embedding_dim, out_features=1, bias=False)
+            nn.Linear(in_features=4 * embedding_dim, out_features=1, bias=False),
         )
 
         # General interest learning
         self._general_interest_learning_encoder = nn.Sequential(
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=False),
-            nn.Tanh()
+            nn.Linear(
+                in_features=embedding_dim, out_features=embedding_dim, bias=False
+            ),
+            nn.Tanh(),
         )
 
         # Cross-view contrastive learning
         self._sequential_projector = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
         )
         self._graph_projector = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
         )
 
         self._user_projection = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
         )
 
         self._item_projection = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
         )
 
         self._init_weights(initializer_range)
@@ -107,22 +110,22 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            user_prefix=config['user_prefix'],
-            labels_prefix=config['labels_prefix'],
-            candidate_prefix=config['candidate_prefix'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_graph_layers=config['num_graph_layers'],
-            common_graph=kwargs['graph'],
-            user_graph=kwargs['user_graph'],
-            item_graph=kwargs['item_graph'],
-            dropout=config.get('dropout', 0.0),
-            layer_norm_eps=config.get('layer_norm_eps', 1e-5),
-            graph_dropout=config.get('graph_dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            user_prefix=config["user_prefix"],
+            labels_prefix=config["labels_prefix"],
+            candidate_prefix=config["candidate_prefix"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_graph_layers=config["num_graph_layers"],
+            common_graph=kwargs["graph"],
+            user_graph=kwargs["user_graph"],
+            item_graph=kwargs["item_graph"],
+            dropout=config.get("dropout", 0.0),
+            layer_norm_eps=config.get("layer_norm_eps", 1e-5),
+            graph_dropout=config.get("graph_dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def _apply_graph_encoder(self, embeddings, graph, use_mean=False):
@@ -149,14 +152,19 @@ def _apply_graph_encoder(self, embeddings, graph, use_mean=False):
             return all_embeddings[-1]
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
-        user_ids = inputs['{}.ids'.format(self._user_prefix)]  # (batch_size)
-
-        embeddings = self._item_embeddings(all_sample_events)  # (all_batch_events, embedding_dim)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+        user_ids = inputs["{}.ids".format(self._user_prefix)]  # (batch_size)
+
+        embeddings = self._item_embeddings(
+            all_sample_events
+        )  # (all_batch_events, embedding_dim)
         embeddings, mask = create_masked_tensor(
-            data=embeddings,
-            lengths=all_sample_lengths
+            data=embeddings, lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
 
         batch_size = mask.shape[0]
@@ -164,88 +172,108 @@ def forward(self, inputs):
 
         # Current interest learning
         # 1) get embeddings with positions
-        positions = torch.arange(
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
-        positions_mask = positions < all_sample_lengths[:, None]  # (batch_size, max_seq_len)
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
+        positions_mask = (
+            positions < all_sample_lengths[:, None]
+        )  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(
+            positions
+        )  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=all_sample_lengths
+            data=position_embeddings, lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        positioned_embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
-        positioned_embeddings = self._layernorm(positioned_embeddings)  # (batch_size, seq_len, embedding_dim)
-        positioned_embeddings = self._dropout(positioned_embeddings)  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = (
+            embeddings + position_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = self._layernorm(
+            positioned_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = self._dropout(
+            positioned_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
         positioned_embeddings[~mask] = 0
 
         sequential_attention_matrix = self._current_interest_learning_encoder(
             positioned_embeddings
         ).squeeze()  # (batch_size, seq_len)
         sequential_attention_matrix[~mask] = -torch.inf
-        sequential_attention_matrix = torch.softmax(sequential_attention_matrix, dim=1)  # (batch_size, seq_len)
+        sequential_attention_matrix = torch.softmax(
+            sequential_attention_matrix, dim=1
+        )  # (batch_size, seq_len)
         sequential_representation = torch.einsum(
-            'bs,bsd->bd', sequential_attention_matrix, embeddings
+            "bs,bsd->bd", sequential_attention_matrix, embeddings
         )  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             # General interest learning
             all_embeddings = torch.cat(
-                [self._user_embeddings.weight, self._item_embeddings.weight],
-                dim=0
+                [self._user_embeddings.weight, self._item_embeddings.weight], dim=0
             )  # (num_users + 2 + num_items + 2, embedding_dim)
             common_graph_embeddings = self._apply_graph_encoder(
-                embeddings=all_embeddings,
-                graph=self._graph
+                embeddings=all_embeddings, graph=self._graph
             )  # (num_users + 2 + num_items + 2, embedding_dim)
             common_graph_user_embeddings, common_graph_item_embeddings = torch.split(
-                common_graph_embeddings,
-                [self._num_users + 2, self._num_items + 2]
+                common_graph_embeddings, [self._num_users + 2, self._num_items + 2]
             )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
-            common_graph_user_embeddings = common_graph_user_embeddings[user_ids]  # (batch_size, embedding_dim)
-            common_graph_item_embeddings = common_graph_item_embeddings[all_sample_events]  # (all_batch_events, embedding_dim)
+            common_graph_user_embeddings = common_graph_user_embeddings[
+                user_ids
+            ]  # (batch_size, embedding_dim)
+            common_graph_item_embeddings = common_graph_item_embeddings[
+                all_sample_events
+            ]  # (all_batch_events, embedding_dim)
             common_graph_item_embeddings, _ = create_masked_tensor(
-                data=common_graph_item_embeddings,
-                lengths=all_sample_lengths
+                data=common_graph_item_embeddings, lengths=all_sample_lengths
             )  # (batch_size, seq_len, embedding_dim)
 
             graph_attention_matrix = torch.einsum(
-                'bd,bsd->bs',
+                "bd,bsd->bs",
                 self._general_interest_learning_encoder(common_graph_user_embeddings),
-                common_graph_item_embeddings
+                common_graph_item_embeddings,
             )  # (batch_size, seq_len)
             graph_attention_matrix[~mask] = -torch.inf
-            graph_attention_matrix = torch.softmax(graph_attention_matrix, dim=1)  # (batch_size, seq_len)
+            graph_attention_matrix = torch.softmax(
+                graph_attention_matrix, dim=1
+            )  # (batch_size, seq_len)
             graph_representation = torch.einsum(
-                'bs,bsd->bd', graph_attention_matrix, common_graph_item_embeddings
+                "bs,bsd->bd", graph_attention_matrix, common_graph_item_embeddings
             )  # (batch_size, embedding_dim)
 
             # Get final representation
-            combined_representation = \
-                self._alpha * sequential_representation + \
-                (1 - self._alpha) * graph_representation  # (batch_size, embedding_dim)
+            combined_representation = (
+                self._alpha * sequential_representation
+                + (1 - self._alpha) * graph_representation
+            )  # (batch_size, embedding_dim)
 
-            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
-            labels_embeddings = self._item_embeddings(labels)  # (batch_size, embedding_dim)
+            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
+            labels_embeddings = self._item_embeddings(
+                labels
+            )  # (batch_size, embedding_dim)
 
             # Cross-view contrastive learning
             sequential_representation = self._sequential_projector(
-                sequential_representation)  # (batch_size, embedding_dim)
-            graph_representation = self._graph_projector(graph_representation)  # (batch_size, embedding_dim)
+                sequential_representation
+            )  # (batch_size, embedding_dim)
+            graph_representation = self._graph_projector(
+                graph_representation
+            )  # (batch_size, embedding_dim)
 
             # Feature-level Contrastive Learning
             user_graph_user_embeddings = self._apply_graph_encoder(
-                embeddings=self._user_embeddings.weight,
-                graph=self._user_graph
+                embeddings=self._user_embeddings.weight, graph=self._user_graph
             )  # (num_users + 2, embedding_dim)
             user_graph_user_embeddings = torch.gather(
                 user_graph_user_embeddings,
                 dim=0,
-                index=user_ids[..., None].tile(1, self._embedding_dim)
+                index=user_ids[..., None].tile(1, self._embedding_dim),
             )  # (batch_size, embedding_dim)
 
             user_graph_user_embeddings = self._user_projection(
@@ -256,13 +284,12 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             item_graph_item_embeddings = self._apply_graph_encoder(
-                embeddings=self._item_embeddings.weight,
-                graph=self._item_graph
+                embeddings=self._item_embeddings.weight, graph=self._item_graph
             )  # (num_items + 2, embedding_dim)
             item_graph_item_embeddings = torch.gather(
                 item_graph_item_embeddings,
                 dim=0,
-                index=all_sample_events[..., None].tile(1, self._embedding_dim)
+                index=all_sample_events[..., None].tile(1, self._embedding_dim),
             )  # (all_sample_events, embedding_dim)
 
             item_graph_item_embeddings = self._item_projection(
@@ -274,51 +301,50 @@ def forward(self, inputs):
 
             return {
                 # Downstream task
-                'combined_representation': combined_representation,
-                'label_representation': labels_embeddings,
-
+                "combined_representation": combined_representation,
+                "label_representation": labels_embeddings,
                 # Interest-level Contrastive Learning
-                'sequential_representation': sequential_representation,
-                'graph_representation': graph_representation,
-
+                "sequential_representation": sequential_representation,
+                "graph_representation": graph_representation,
                 # Feature-level Contrastive Learning (users)
-                'user_graph_user_embeddings': user_graph_user_embeddings,
-                'common_graph_user_embeddings': common_graph_user_embeddings,
-
+                "user_graph_user_embeddings": user_graph_user_embeddings,
+                "common_graph_user_embeddings": common_graph_user_embeddings,
                 # Feature-level Contrastive Learning (items)
-                'item_graph_item_embeddings': item_graph_item_embeddings,
-                'common_graph_item_embeddings': common_graph_item_embeddings
+                "item_graph_item_embeddings": item_graph_item_embeddings,
+                "common_graph_item_embeddings": common_graph_item_embeddings,
             }
         else:  # eval mode
-            if '{}.ids'.format(self._candidate_prefix) in inputs:
-                candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
-                candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
-
-                candidate_embeddings = self._item_embeddings(candidate_events)  # (all_batch_candidates, embedding_dim)
+            if "{}.ids".format(self._candidate_prefix) in inputs:
+                candidate_events = inputs[
+                    "{}.ids".format(self._candidate_prefix)
+                ]  # (all_batch_candidates)
+                candidate_lengths = inputs[
+                    "{}.length".format(self._candidate_prefix)
+                ]  # (batch_size)
+
+                candidate_embeddings = self._item_embeddings(
+                    candidate_events
+                )  # (all_batch_candidates, embedding_dim)
 
                 candidate_embeddings, _ = create_masked_tensor(
-                    data=candidate_embeddings,
-                    lengths=candidate_lengths
+                    data=candidate_embeddings, lengths=candidate_lengths
                 )  # (batch_size, num_candidates, embedding_dim)
 
                 candidate_scores = torch.einsum(
-                    'bd,bnd->bn',
-                    sequential_representation,
-                    candidate_embeddings
+                    "bd,bnd->bn", sequential_representation, candidate_embeddings
                 )  # (batch_size, num_candidates)
             else:
-                candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
+                candidate_embeddings = (
+                    self._item_embeddings.weight
+                )  # (num_items, embedding_dim)
                 candidate_scores = torch.einsum(
-                    'bd,nd->bn',
-                    sequential_representation,
-                    candidate_embeddings
+                    "bd,nd->bn", sequential_representation, candidate_embeddings
                 )  # (batch_size, num_items)
                 candidate_scores[:, 0] = -torch.inf
-                candidate_scores[:, self._num_items + 1:] = -torch.inf
+                candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             values, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 100), (batch_size, 100)
 
             return indices
diff --git a/modeling/models/mrgsrec.py b/modeling/models/mrgsrec.py
index 488c580a..f53861a0 100644
--- a/modeling/models/mrgsrec.py
+++ b/modeling/models/mrgsrec.py
@@ -1,32 +1,29 @@
+import torch
+import torch.nn as nn
 from torch.nn import MultiheadAttention
 
 from models.base import TorchModel
-
 from utils import create_masked_tensor, get_activation_function
 
-import torch
-import torch.nn as nn
-
-
-class MRGSRecModel(TorchModel, config_name='mrgsrec'):
 
+class MRGSRecModel(TorchModel, config_name="mrgsrec"):
     def __init__(
-            self,
-            sequence_prefix,
-            user_prefix,
-            positive_prefix,
-            negative_prefix,
-            candidate_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        user_prefix,
+        positive_prefix,
+        negative_prefix,
+        candidate_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__()
         self._sequence_prefix = sequence_prefix
@@ -40,11 +37,12 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim
+            embedding_dim=embedding_dim,
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim
+            num_embeddings=max_sequence_length
+            + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim,
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -57,60 +55,69 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True
+            batch_first=True,
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            candidate_prefix=config['candidate_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            candidate_prefix=config["candidate_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
-        embeddings = self._item_embeddings(all_sample_events)  # (all_batch_events, embedding_dim)
+        embeddings = self._item_embeddings(
+            all_sample_events
+        )  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings,
-            lengths=all_sample_lengths
+            data=embeddings, lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = torch.arange(
-            start=seq_len - 1, end=-1, step=-1, device=mask.device
-        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
-        positions_mask = positions < all_sample_lengths[:, None]  # (batch_size, max_seq_len)
+        positions = (
+            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
+            .tile([batch_size, 1])
+            .long()
+        )  # (batch_size, seq_len)
+        positions_mask = (
+            positions < all_sample_lengths[:, None]
+        )  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(
+            positions
+        )  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings,
-            lengths=all_sample_lengths
+            data=position_embeddings, lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        embeddings = (
+            embeddings + position_embeddings
+        )  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
 
         embeddings[~mask] = 0
-
-
-
diff --git a/modeling/models/ngcf.py b/modeling/models/ngcf.py
index 2fcc9081..8df917eb 100644
--- a/modeling/models/ngcf.py
+++ b/modeling/models/ngcf.py
@@ -1,25 +1,23 @@
-from models.base import TorchModel
-
-from utils import create_masked_tensor, DEVICE
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+from models.base import TorchModel
+from utils import DEVICE, create_masked_tensor
 
-class NgcfModel(TorchModel, config_name='ngcf'):
 
+class NgcfModel(TorchModel, config_name="ngcf"):
     def __init__(
-            self,
-            user_prefix,
-            positive_prefix,
-            graph,
-            num_users,
-            num_items,
-            embedding_dim,
-            num_layers,
-            dropout=0.0,
-            initializer_range=0.02
+        self,
+        user_prefix,
+        positive_prefix,
+        graph,
+        num_users,
+        num_items,
+        embedding_dim,
+        num_layers,
+        dropout=0.0,
+        initializer_range=0.02,
     ):
         super().__init__()
         self._user_prefix = user_prefix
@@ -40,13 +38,11 @@ def __init__(
             self.Bi_Linear_list.append(nn.Linear(embedding_dim, embedding_dim))
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2,
-            embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2,
-            embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -54,20 +50,20 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config['user_prefix'],
-            positive_prefix=config['positive_prefix'],
-            graph=kwargs['graph'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
-            embedding_dim=config['embedding_dim'],
-            num_layers=config['num_layers'],
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            user_prefix=config["user_prefix"],
+            positive_prefix=config["positive_prefix"],
+            graph=kwargs["graph"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+            embedding_dim=config["embedding_dim"],
+            num_layers=config["num_layers"],
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs['{}.ids'.format(prefix)]  # (all_batch_events)
-        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
+        ids = inputs["{}.ids".format(prefix)]  # (all_batch_events)
+        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (all_batch_events, embedding_dim)
         ego_embeddings = ego_embeddings(ids)  # (all_batch_events, embedding_dim)
@@ -85,7 +81,9 @@ def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
         return padded_embeddings, padded_ego_embeddings, mask
 
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
+        ego_embeddings = torch.cat(
+            (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
+        )
         all_embeddings = [ego_embeddings]
 
         if self._dropout_rate > 0:  # drop some edges
@@ -122,73 +120,82 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def forward(self, inputs):
-        all_final_user_embeddings, all_final_item_embeddings = \
-            self._apply_graph_encoder()  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings, all_final_item_embeddings = (
+            self._apply_graph_encoder()
+        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
         user_embeddings, user_ego_embeddings, user_mask = self._get_embeddings(
             inputs, self._user_prefix, self._user_embeddings, all_final_user_embeddings
         )
-        user_embeddings = user_embeddings[user_mask]  # (all_batch_events, embedding_dim)
+        user_embeddings = user_embeddings[
+            user_mask
+        ]  # (all_batch_events, embedding_dim)
 
         if self.training:  # training mode
-            positive_item_ids = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-            positive_item_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
+            positive_item_ids = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+            positive_item_lengths = inputs[
+                "{}.length".format(self._positive_prefix)
+            ]  # (batch_size)
 
             batch_size = positive_item_lengths.shape[0]
             max_sequence_length = positive_item_lengths.max().item()
 
-            mask = torch.arange(
-                end=max_sequence_length,
-                device=DEVICE
-            )[None].tile([batch_size, 1]) < positive_item_lengths[:, None]  # (batch_size, max_seq_len)
-
-            positive_user_ids = torch.arange(
-                batch_size,
-                device=DEVICE
-            )[None].tile([max_sequence_length, 1]).T  # (batch_size, max_seq_len)
+            mask = (
+                torch.arange(end=max_sequence_length, device=DEVICE)[None].tile(
+                    [batch_size, 1]
+                )
+                < positive_item_lengths[:, None]
+            )  # (batch_size, max_seq_len)
+
+            positive_user_ids = (
+                torch.arange(batch_size, device=DEVICE)[None]
+                .tile([max_sequence_length, 1])
+                .T
+            )  # (batch_size, max_seq_len)
             positive_user_ids = positive_user_ids[mask]  # (all_batch_items)
-            user_embeddings = user_embeddings[positive_user_ids]  # (all_batch_items, embedding_dim)
+            user_embeddings = user_embeddings[
+                positive_user_ids
+            ]  # (all_batch_items, embedding_dim)
 
             all_scores = torch.einsum(
-                'ad,nd->an',
-                user_embeddings,
-                all_final_item_embeddings
+                "ad,nd->an", user_embeddings, all_final_item_embeddings
             )  # (all_batch_items, num_items + 2)
 
-            negative_mask = torch.zeros(self._num_items + 2, dtype=torch.bool, device=DEVICE)  # (num_items + 2)
+            negative_mask = torch.zeros(
+                self._num_items + 2, dtype=torch.bool, device=DEVICE
+            )  # (num_items + 2)
             negative_mask[positive_item_ids] = 1
 
             positive_scores = torch.gather(
-                input=all_scores,
-                dim=1,
-                index=positive_item_ids[..., None]
+                input=all_scores, dim=1, index=positive_item_ids[..., None]
             )  # (all_batch_items, 1)
 
             all_scores = torch.scatter_add(
                 input=all_scores,
                 dim=1,
                 index=positive_item_ids[..., None],
-                src=torch.ones_like(positive_item_ids[..., None]).float()
+                src=torch.ones_like(positive_item_ids[..., None]).float(),
             )  # (all_batch_items, num_items + 2)
 
             return {
-                'positive_scores': positive_scores,
-                'negative_scores': all_scores,
-                'item_embeddings': torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
+                "positive_scores": positive_scores,
+                "negative_scores": all_scores,
+                "item_embeddings": torch.cat(
+                    (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
+                ),
             }
         else:  # eval mode
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                user_embeddings,
-                all_final_item_embeddings
+                "bd,nd->bn", user_embeddings, all_final_item_embeddings
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/pop.py b/modeling/models/pop.py
index bb0b6774..ddf26881 100644
--- a/modeling/models/pop.py
+++ b/modeling/models/pop.py
@@ -1,16 +1,10 @@
-from models.base import BaseModel
-
 import torch
 
+from models.base import BaseModel
 
-class PopModel(BaseModel, config_name='pop'):
 
-    def __init__(
-            self,
-            label_prefix,
-            counts_prefix,
-            num_items
-    ):
+class PopModel(BaseModel, config_name="pop"):
+    def __init__(self, label_prefix, counts_prefix, num_items):
         self._label_prefix = label_prefix
         self._counts_prefix = counts_prefix
         self._num_items = num_items
@@ -18,26 +12,30 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            label_prefix=config['label_prefix'],
-            counts_prefix=config['counts_prefix'],
-            num_items=kwargs['num_items']
+            label_prefix=config["label_prefix"],
+            counts_prefix=config["counts_prefix"],
+            num_items=kwargs["num_items"],
         )
 
     def __call__(self, inputs):
-        candidate_counts = inputs['{}.ids'.format(self._counts_prefix)]  # (all_batch_candidates)
-        candidate_counts_lengths = inputs['{}.length'.format(self._counts_prefix)]  # (batch_size)
+        candidate_counts = inputs[
+            "{}.ids".format(self._counts_prefix)
+        ]  # (all_batch_candidates)
+        candidate_counts_lengths = inputs[
+            "{}.length".format(self._counts_prefix)
+        ]  # (batch_size)
         batch_size = candidate_counts_lengths.shape[0]
 
         candidate_scores = torch.reshape(
-            candidate_counts,
-            shape=(batch_size, self._num_items + 2)
+            candidate_counts, shape=(batch_size, self._num_items + 2)
         ).float()  # (batch_size, num_items)
         candidate_scores[:, 0] = -torch.inf  # zero (padding) token
-        candidate_scores[:, self._num_items + 1:] = -torch.inf  # all not real items-related things
+        candidate_scores[
+            :, self._num_items + 1 :
+        ] = -torch.inf  # all not real items-related things
 
         _, indices = torch.topk(
-            candidate_scores,
-            k=20, dim=-1, largest=True
+            candidate_scores, k=20, dim=-1, largest=True
         )  # (batch_size, 20)
 
         return indices
diff --git a/modeling/models/pure_mf.py b/modeling/models/pure_mf.py
index 6cbe8dc5..dc0b691c 100644
--- a/modeling/models/pure_mf.py
+++ b/modeling/models/pure_mf.py
@@ -1,22 +1,20 @@
-from models.base import TorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import TorchModel
 from utils import create_masked_tensor
 
 
-class PureMF(TorchModel, config_name='pure_mf'):
-
+class PureMF(TorchModel, config_name="pure_mf"):
     def __init__(
-            self,
-            user_prefix,
-            positive_prefix,
-            negative_prefix,
-            num_users,
-            num_items,
-            embedding_dim,
-            initializer_range
+        self,
+        user_prefix,
+        positive_prefix,
+        negative_prefix,
+        num_users,
+        num_items,
+        embedding_dim,
+        initializer_range,
     ):
         super().__init__()
 
@@ -29,13 +27,11 @@ def __init__(
         self._embedding_dim = embedding_dim
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2,
-            embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2,
-            embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -43,54 +39,73 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config['user_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            num_users=kwargs['num_users'],
-            num_items=kwargs['num_items'],
-            embedding_dim=config['embedding_dim'],
-            initializer_range=config.get('initializer_range', 0.02)
+            user_prefix=config["user_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+            embedding_dim=config["embedding_dim"],
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        user_ids = inputs['{}.ids'.format(self._user_prefix)]  # (batch_size)
+        user_ids = inputs["{}.ids".format(self._user_prefix)]  # (batch_size)
         user_embeddings = self._user_embeddings(user_ids)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
-            all_positive = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-            all_positive_embeddings = self._item_embeddings(all_positive)  # (all_batch_events, embedding_dim)
-            positive_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
-
-            all_negative = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
-            all_negative_embeddings = self._item_embeddings(all_negative)  # (all_batch_events, embedding_dim)
-            negative_lengths = inputs['{}.length'.format(self._negative_prefix)]  # (batch_size)
-
-            positive_embeddings, positive_mask = create_masked_tensor(all_positive_embeddings, positive_lengths)
-            negative_embeddings, negative_mask = create_masked_tensor(all_negative_embeddings, negative_lengths)
-
-            positive_scores = torch.einsum('bd,bsd->bs', user_embeddings, positive_embeddings)  # (batch_size, seq_len)
-            negative_scores = torch.einsum('bd,bsd->bs', user_embeddings, negative_embeddings)  # (batch_size, seq_len)
+            all_positive = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+            all_positive_embeddings = self._item_embeddings(
+                all_positive
+            )  # (all_batch_events, embedding_dim)
+            positive_lengths = inputs[
+                "{}.length".format(self._positive_prefix)
+            ]  # (batch_size)
+
+            all_negative = inputs[
+                "{}.ids".format(self._negative_prefix)
+            ]  # (all_batch_events)
+            all_negative_embeddings = self._item_embeddings(
+                all_negative
+            )  # (all_batch_events, embedding_dim)
+            negative_lengths = inputs[
+                "{}.length".format(self._negative_prefix)
+            ]  # (batch_size)
+
+            positive_embeddings, positive_mask = create_masked_tensor(
+                all_positive_embeddings, positive_lengths
+            )
+            negative_embeddings, negative_mask = create_masked_tensor(
+                all_negative_embeddings, negative_lengths
+            )
+
+            positive_scores = torch.einsum(
+                "bd,bsd->bs", user_embeddings, positive_embeddings
+            )  # (batch_size, seq_len)
+            negative_scores = torch.einsum(
+                "bd,bsd->bs", user_embeddings, negative_embeddings
+            )  # (batch_size, seq_len)
 
             positive_scores = positive_scores[positive_mask]  # (all_batch_events)
             negative_scores = negative_scores[negative_mask]  # (all_batch_events)
 
             return {
-                'positive_scores': positive_scores,
-                'negative_scores': negative_scores
+                "positive_scores": positive_scores,
+                "negative_scores": negative_scores,
             }
         else:
-            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
+            candidate_embeddings = (
+                self._item_embeddings.weight
+            )  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                user_embeddings,
-                candidate_embeddings
+                "bd,nd->bn", user_embeddings, candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/pure_svd.py b/modeling/models/pure_svd.py
index f705543b..fcb55a49 100644
--- a/modeling/models/pure_svd.py
+++ b/modeling/models/pure_svd.py
@@ -1,16 +1,14 @@
 from models.base import BaseModel
 
 
-class SVDModel(BaseModel, config_name='pure_svd'):
-
+class SVDModel(BaseModel, config_name="pure_svd"):
     def __init__(self, rank):
         super().__init__()
 
         self._rank = rank
-        self._method = 'PureSVD'
+        self._method = "PureSVD"
         self._factors = {}
 
     @property
     def rank(self):
         return self._rank
-
diff --git a/modeling/models/random.py b/modeling/models/random.py
index d5fae7c5..cfa8d5fa 100644
--- a/modeling/models/random.py
+++ b/modeling/models/random.py
@@ -1,36 +1,31 @@
-from models.base import BaseModel
-
 import torch
 
+from models.base import BaseModel
 
-class RandomModel(BaseModel, config_name='random'):
 
-    def __init__(
-            self,
-            label_prefix,
-            num_items
-    ):
+class RandomModel(BaseModel, config_name="random"):
+    def __init__(self, label_prefix, num_items):
         self._label_prefix = label_prefix
         self._num_items = num_items
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(
-            label_prefix=config['label_prefix'],
-            num_items=kwargs['num_items']
-        )
+        return cls(label_prefix=config["label_prefix"], num_items=kwargs["num_items"])
 
     def __call__(self, inputs):
-        labels_lengths = inputs['{}.length'.format(self._label_prefix)]  # (batch_size)
+        labels_lengths = inputs["{}.length".format(self._label_prefix)]  # (batch_size)
         batch_size = labels_lengths.shape[0]
 
-        candidate_scores = torch.rand(batch_size, self._num_items + 1)  # (batch_size, num_items)
+        candidate_scores = torch.rand(
+            batch_size, self._num_items + 1
+        )  # (batch_size, num_items)
         candidate_scores[:, 0] = -torch.inf  # zero (padding) token
-        candidate_scores[:, self._num_items + 1:] = -torch.inf  # all not real items-related things
+        candidate_scores[
+            :, self._num_items + 1 :
+        ] = -torch.inf  # all not real items-related things
 
         _, indices = torch.topk(
-            candidate_scores,
-            k=20, dim=-1, largest=True
+            candidate_scores, k=20, dim=-1, largest=True
         )  # (batch_size, 20)
 
         return indices
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 7e150421..d7d1606e 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,22 +1,23 @@
 import functools
-from utils import DEVICE
-from models.base import TorchModel
 
-import torch
 import faiss
+import torch
 
-class RqVaeModel(TorchModel, config_name='rqvae'):
+from models.base import TorchModel
+from utils import DEVICE
 
+
+class RqVaeModel(TorchModel, config_name="rqvae"):
     def __init__(
-            self,
-            train_sampler,
-            input_dim: int,
-            hidden_dim: int,
-            n_iter: int,
-            codebook_sizes: list[int],
-            should_init_codebooks,
-            should_reinit_unused_clusters,
-            initializer_range
+        self,
+        train_sampler,
+        input_dim: int,
+        hidden_dim: int,
+        n_iter: int,
+        codebook_sizes: list[int],
+        should_init_codebooks,
+        should_reinit_unused_clusters,
+        initializer_range,
     ):
         super().__init__()
 
@@ -34,35 +35,39 @@ def __init__(
 
         # Default initialization of codebook
         self.codebooks = torch.nn.ParameterList()
-        
+
         self.codebook_sizes = codebook_sizes
-        
+
         for codebook_size in codebook_sizes:
             cb = torch.FloatTensor(codebook_size, hidden_dim)
             self.codebooks.append(cb)
-         
+
         self._init_weights(initializer_range)
-        
+
         if self.should_init_codebooks:
             if train_sampler is None:
                 raise AttributeError("Train sampler is None")
-            
-            embeddings = torch.stack([entry['item.embed'] for entry in train_sampler._dataset])
+
+            embeddings = torch.stack(
+                [entry["item.embed"] for entry in train_sampler._dataset]
+            )
             self.init_codebooks(embeddings)
-            print('Codebooks initialized with Faiss Kmeans')
+            print("Codebooks initialized with Faiss Kmeans")
             self.should_init_codebooks = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            train_sampler=kwargs.get('train_sampler'),
-            input_dim=config['embedding_dim'],
-            hidden_dim=config['hidden_dim'],
-            n_iter=config['n_iter'],
-            codebook_sizes=config['codebook_sizes'],
-            should_init_codebooks=config.get('should_init_codebooks', False),
-            should_reinit_unused_clusters=config.get('should_reinit_unused_clusters', False),
-            initializer_range=config.get('initializer_range', 0.02)
+            train_sampler=kwargs.get("train_sampler"),
+            input_dim=config["embedding_dim"],
+            hidden_dim=config["hidden_dim"],
+            n_iter=config["n_iter"],
+            codebook_sizes=config["codebook_sizes"],
+            should_init_codebooks=config.get("should_init_codebooks", False),
+            should_reinit_unused_clusters=config.get(
+                "should_reinit_unused_clusters", False
+            ),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def make_encoding_tower(self, d1: int, d2: int):
@@ -102,21 +107,21 @@ def reinit_unused_clusters(remainder, codebook, codebook_indices):
             is_used[unique_indices] = True
             rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(),))
             codebook[~is_used] = remainder[rand_input]
-    
+
     def train_pass(self, embeddings):
         latent_vector = self.encoder(embeddings)
 
         latent_restored = 0
-        
+
         num_unique_clusters = []
         remainder = latent_vector
-        
+
         remainders = []
         codebooks_vectors = []
-        
+
         for codebook in self.codebooks:
             remainders.append(remainder)
-            
+
             codebook_indices = self.get_codebook_indices(remainder, codebook)
             codebook_vectors = codebook[codebook_indices]
 
@@ -124,7 +129,7 @@ def train_pass(self, embeddings):
                 self.reinit_unused_clusters(remainder, codebook, codebook_indices)
 
             num_unique_clusters.append(codebook_indices.unique().shape[0])
-            
+
             codebooks_vectors.append(codebook_vectors)
 
             latent_restored = latent_restored + codebook_vectors
@@ -138,9 +143,9 @@ def train_pass(self, embeddings):
             "embeddings": embeddings,
             "embeddings_restored": embeddings_restored,
             "remainders": remainders,
-            "codebooks_vectors": codebooks_vectors
+            "codebooks_vectors": codebooks_vectors,
         }
-        
+
     def eval_pass(self, embeddings):
         ind_lists = []
         remainder = self.encoder(embeddings)
@@ -153,12 +158,12 @@ def eval_pass(self, embeddings):
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
-        
+
         if self.training:  # training mode
             return self.train_pass(embeddings)
         else:  # eval mode
             return self.eval_pass(embeddings)
-        
+
     @functools.cache
     def get_single_embedding(self, codebook_idx: int, codebook_id: int):
         return self.codebooks[codebook_idx][codebook_id]
diff --git a/modeling/models/s3rec.py b/modeling/models/s3rec.py
index e1fcffb4..f9f361d4 100644
--- a/modeling/models/s3rec.py
+++ b/modeling/models/s3rec.py
@@ -1,33 +1,31 @@
-from models.base import SequentialTorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import SequentialTorchModel
 from utils import create_masked_tensor
 
 
-class S3RecModel(SequentialTorchModel, config_name='s3rec'):
-
+class S3RecModel(SequentialTorchModel, config_name="s3rec"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            negative_prefix,
-            sequence_segment_prefix,
-            positive_segment_prefix,
-            negative_segment_prefix,
-            candidate_prefix,
-            num_items,
-            max_sequence_length,
-            is_training,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-5,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        negative_prefix,
+        sequence_segment_prefix,
+        positive_segment_prefix,
+        negative_segment_prefix,
+        candidate_prefix,
+        num_items,
+        max_sequence_length,
+        is_training,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-5,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -39,7 +37,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=is_training
+            is_causal=is_training,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -61,50 +59,60 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            sequence_segment_prefix=config['sequence_segment_prefix'],
-            positive_segment_prefix=config['positive_segment_prefix'],
-            negative_segment_prefix=config['negative_segment_prefix'],
-            candidate_prefix=config['candidate_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            is_training=config['is_training'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            sequence_segment_prefix=config["sequence_segment_prefix"],
+            positive_segment_prefix=config["positive_segment_prefix"],
+            negative_segment_prefix=config["negative_segment_prefix"],
+            candidate_prefix=config["candidate_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            is_training=config["is_training"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def masked_item_prediction(self, sequence_embeddings, sequence_mask, target_item):
         all_items = sequence_embeddings[sequence_mask]  # (all_batch_items, emb_dim)
-        score = torch.einsum(
-            'ad,ad->a', all_items, target_item
-        )  # (all_batch_items)
+        score = torch.einsum("ad,ad->a", all_items, target_item)  # (all_batch_items)
         return torch.sigmoid(score)  # (all_batch_items)
 
     def segment_prediction(self, context, segment):
-        score = torch.einsum('bd,bd->b', self.sp_norm(context), segment)  # (batch_size)
+        score = torch.einsum("bd,bd->b", self.sp_norm(context), segment)  # (batch_size)
         return torch.sigmoid(score)  # (batch_size)
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
 
         if self._is_training:
             if self.training:  # training mode
-                all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
-                all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
-
-                all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+                all_positive_sample_events = inputs[
+                    "{}.ids".format(self._positive_prefix)
+                ]  # (all_batch_events)
+                all_negative_sample_events = inputs[
+                    "{}.ids".format(self._negative_prefix)
+                ]  # (all_batch_events)
+
+                all_sample_embeddings = embeddings[
+                    mask
+                ]  # (all_batch_events, embedding_dim)
                 all_positive_sample_embeddings = self._item_embeddings(
                     all_positive_sample_events
                 )  # (all_batch_events, embedding_dim)
@@ -113,53 +121,68 @@ def forward(self, inputs):
                 )  # (all_batch_events, embedding_dim)
 
                 return {
-                    'current_embeddings': all_sample_embeddings,
-                    'positive_embeddings': all_positive_sample_embeddings,
-                    'negative_embeddings': all_negative_sample_embeddings
+                    "current_embeddings": all_sample_embeddings,
+                    "positive_embeddings": all_positive_sample_embeddings,
+                    "negative_embeddings": all_negative_sample_embeddings,
                 }
             else:  # eval mode
-                if '{}.ids'.format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
-                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
+                if "{}.ids".format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs[
+                        "{}.ids".format(self._candidate_prefix)
+                    ]  # (all_batch_candidates)
+                    candidate_lengths = inputs[
+                        "{}.length".format(self._candidate_prefix)
+                    ]  # (batch_size)
 
                     candidate_embeddings = self._item_embeddings(
                         candidate_events
                     )  # (all_batch_candidates, embedding_dim)
 
                     candidate_embeddings, _ = create_masked_tensor(
-                        data=candidate_embeddings,
-                        lengths=candidate_lengths
+                        data=candidate_embeddings, lengths=candidate_lengths
                     )  # (batch_size, num_candidates, embedding_dim)
 
                     candidate_scores = torch.einsum(
-                        'bd,bnd->bn',
-                        last_embeddings,
-                        candidate_embeddings
+                        "bd,bnd->bn", last_embeddings, candidate_embeddings
                     )  # (batch_size, num_candidates)
                 else:
-                    candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
+                    candidate_embeddings = (
+                        self._item_embeddings.weight
+                    )  # (num_items, embedding_dim)
                     candidate_scores = torch.einsum(
-                        'bd,nd->bn',
-                        last_embeddings,
-                        candidate_embeddings
+                        "bd,nd->bn", last_embeddings, candidate_embeddings
                     )  # (batch_size, num_items)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1:] = -torch.inf
+                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
                 return candidate_scores
         else:
             # Masked Item Prediction
-            mip_mask = (all_sample_events == self._mask_item_idx).bool()  # (all_batch_events)
+            mip_mask = (
+                all_sample_events == self._mask_item_idx
+            ).bool()  # (all_batch_events)
             embeddings = embeddings[mask][mip_mask]  # (all_batch_events, embedding_dim)
-            positive_item_events = inputs['{}.ids'.format(self._positive_prefix)][mip_mask]  # (all_batch_events)
-            negative_item_events = inputs['{}.ids'.format(self._negative_prefix)][mip_mask]  # (all_batch_events)
-
-            positive_item_embeddings = self._item_embeddings(positive_item_events)  # (all_batch_events, embedding_dim)
-            negative_item_embeddings = self._item_embeddings(negative_item_events)  # (all_batch_events, embedding_dim)
+            positive_item_events = inputs["{}.ids".format(self._positive_prefix)][
+                mip_mask
+            ]  # (all_batch_events)
+            negative_item_events = inputs["{}.ids".format(self._negative_prefix)][
+                mip_mask
+            ]  # (all_batch_events)
+
+            positive_item_embeddings = self._item_embeddings(
+                positive_item_events
+            )  # (all_batch_events, embedding_dim)
+            negative_item_embeddings = self._item_embeddings(
+                negative_item_events
+            )  # (all_batch_events, embedding_dim)
 
             # Sequence Prediction
-            all_segment_events = inputs['{}.ids'.format(self._sequence_segment_prefix)]  # (all_batch_events)
-            all_segment_lengths = inputs['{}.length'.format(self._sequence_segment_prefix)]  # (batch_size)
+            all_segment_events = inputs[
+                "{}.ids".format(self._sequence_segment_prefix)
+            ]  # (all_batch_events)
+            all_segment_lengths = inputs[
+                "{}.length".format(self._sequence_segment_prefix)
+            ]  # (batch_size)
             segment_embeddings, segment_mask = self._apply_sequential_encoder(
                 all_segment_events, all_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
@@ -167,30 +190,41 @@ def forward(self, inputs):
                 segment_embeddings, segment_mask
             )  # (batch_size, embedding_dim)
 
-            positive_segment_events = inputs['{}.ids'.format(self._positive_segment_prefix)]  # (all_batch_events)
-            positive_segment_lengths = inputs['{}.length'.format(self._positive_segment_prefix)]  # (batch_size)
-            positive_segment_embeddings, positive_segment_mask = self._apply_sequential_encoder(
-                positive_segment_events, positive_segment_lengths
+            positive_segment_events = inputs[
+                "{}.ids".format(self._positive_segment_prefix)
+            ]  # (all_batch_events)
+            positive_segment_lengths = inputs[
+                "{}.length".format(self._positive_segment_prefix)
+            ]  # (batch_size)
+            positive_segment_embeddings, positive_segment_mask = (
+                self._apply_sequential_encoder(
+                    positive_segment_events, positive_segment_lengths
+                )
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
             last_positive_segment_embeddings = self._get_last_embedding(
                 positive_segment_embeddings, positive_segment_mask
             )  # (batch_size, embedding_dim)
 
-            negative_segment_events = inputs['{}.ids'.format(self._negative_segment_prefix)]  # (all_batch_events)
-            negative_segment_lengths = inputs['{}.length'.format(self._negative_segment_prefix)]  # (batch_size)
-            negative_segment_embeddings, negative_segment_mask = self._apply_sequential_encoder(
-                negative_segment_events, negative_segment_lengths
+            negative_segment_events = inputs[
+                "{}.ids".format(self._negative_segment_prefix)
+            ]  # (all_batch_events)
+            negative_segment_lengths = inputs[
+                "{}.length".format(self._negative_segment_prefix)
+            ]  # (batch_size)
+            negative_segment_embeddings, negative_segment_mask = (
+                self._apply_sequential_encoder(
+                    negative_segment_events, negative_segment_lengths
+                )
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
             last_negative_segment_embeddings = self._get_last_embedding(
                 negative_segment_embeddings, negative_segment_mask
             )  # (batch_size, embedding_dim)
 
             return {
-                'positive_representation': positive_item_embeddings,
-                'negative_representation': negative_item_embeddings,
-                'current_representation': embeddings,
-
-                'positive_segment_representation': last_positive_segment_embeddings,
-                'negative_segment_representation': last_negative_segment_embeddings,
-                'current_segment_representation': last_segment_embeddings
+                "positive_representation": positive_item_embeddings,
+                "negative_representation": negative_item_embeddings,
+                "current_representation": embeddings,
+                "positive_segment_representation": last_positive_segment_embeddings,
+                "negative_segment_representation": last_negative_segment_embeddings,
+                "current_segment_representation": last_segment_embeddings,
             }
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
index 4bf6dac3..b1d7044f 100644
--- a/modeling/models/sasrec.py
+++ b/modeling/models/sasrec.py
@@ -1,25 +1,24 @@
-from models import SequentialTorchModel
-from utils import create_masked_tensor
-
 import torch
 
+from models import SequentialTorchModel
+from utils import create_masked_tensor
 
-class SasRecModel(SequentialTorchModel, config_name='sasrec'):
 
+class SasRecModel(SequentialTorchModel, config_name="sasrec"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -31,7 +30,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -41,34 +40,42 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         if self.training:  # training mode
-            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_positive_sample_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
 
             last_embeddings = self._get_last_embedding(
                 embeddings, mask
             )  # (batch_size, embedding_dim)
 
-            all_embeddings = self._item_embeddings.weight  # (num_items + 2, embedding_dim)
+            all_embeddings = (
+                self._item_embeddings.weight
+            )  # (num_items + 2, embedding_dim)
 
             # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
             all_scores = torch.einsum(
@@ -76,65 +83,60 @@ def forward(self, inputs):
             )  # (batch_size, num_items + 2)
 
             positive_scores = torch.gather(
-                input=all_scores,
-                dim=1,
-                index=all_positive_sample_events[..., None]
+                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
             )  # (batch_size, 1)
 
             sample_ids, _ = create_masked_tensor(
-                data=all_sample_events,
-                lengths=all_sample_lengths
+                data=all_sample_events, lengths=all_sample_lengths
             )  # (batch_size, seq_len)
 
             negative_scores = torch.scatter(
                 input=all_scores,
                 dim=1,
                 index=sample_ids,
-                src=torch.ones_like(sample_ids) * (-torch.inf)
+                src=torch.ones_like(sample_ids) * (-torch.inf),
             )  # (batch_size, num_items + 2)
             negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
+            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
             return {
-                'positive_scores': positive_scores,
-                'negative_scores': negative_scores,
+                "positive_scores": positive_scores,
+                "negative_scores": negative_scores,
                 "sample_ids": sample_ids,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf  # Padding id
-            candidate_scores[:, self._num_items + 1:] = -torch.inf  # Mask id
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
 
 
-class SasRecInBatchModel(SasRecModel, config_name='sasrec_in_batch'):
-
+class SasRecInBatchModel(SasRecModel, config_name="sasrec_in_batch"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             sequence_prefix=sequence_prefix,
@@ -148,27 +150,31 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            initializer_range=initializer_range
+            initializer_range=initializer_range,
         )
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
@@ -176,10 +182,14 @@ def forward(self, inputs):
 
         if self.training:  # training mode
             # queries
-            in_batch_queries_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            in_batch_queries_embeddings = embeddings[
+                mask
+            ]  # (all_batch_events, embedding_dim)
 
             # positives
-            in_batch_positive_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            in_batch_positive_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
             in_batch_positive_embeddings = self._item_embeddings(
                 in_batch_positive_events
             )  # (all_batch_events, embedding_dim)
@@ -194,25 +204,24 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             return {
-                'query_embeddings': in_batch_queries_embeddings,
-                'positive_embeddings': in_batch_positive_embeddings,
-                'negative_embeddings': in_batch_negative_embeddings
+                "query_embeddings": in_batch_queries_embeddings,
+                "positive_embeddings": in_batch_positive_embeddings,
+                "negative_embeddings": in_batch_negative_embeddings,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
\ No newline at end of file
+            return indices
diff --git a/modeling/models/sasrec_ce.py b/modeling/models/sasrec_ce.py
index a0316853..d9eb882b 100644
--- a/modeling/models/sasrec_ce.py
+++ b/modeling/models/sasrec_ce.py
@@ -1,25 +1,24 @@
-from models.base import SequentialTorchModel
-
 import torch
 import torch.nn as nn
 
+from models.base import SequentialTorchModel
 
-class SasRecCeModel(SequentialTorchModel, config_name='sasrec_ce'):
 
+class SasRecCeModel(SequentialTorchModel, config_name="sasrec_ce"):
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -31,14 +30,13 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim,
-            out_features=embedding_dim
+            in_features=embedding_dim, out_features=embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -46,42 +44,51 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
-        embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(
+            embeddings
+        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = torch.nn.functional.gelu(
+            embeddings
+        )  # (batch_size, seq_len, embedding_dim)
         embeddings = torch.einsum(
-            'bsd,nd->bsn', embeddings, self._item_embeddings.weight
+            "bsd,nd->bsn", embeddings, self._item_embeddings.weight
         )  # (batch_size, seq_len, num_items + 2)
 
         if self.training:  # training mode
-            return {'logits': embeddings[mask]}
+            return {"logits": embeddings[mask]}
         else:  # eval mode
-            candidate_scores = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items + 2)
+            candidate_scores = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
index dcaea8ff..9f3b5b9e 100644
--- a/modeling/models/sasrec_freezed.py
+++ b/modeling/models/sasrec_freezed.py
@@ -1,6 +1,7 @@
 import torch
-from models.tiger import TigerModel
+
 from models import SequentialTorchModel
+from models.tiger import TigerModel
 from utils import DEVICE, create_masked_tensor
 
 
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index e347018b..68af3035 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -1,8 +1,10 @@
 import torch
-from .tiger import TigerModel
+from torch import nn
+
 from models import SequentialTorchModel
 from utils import DEVICE, create_masked_tensor
-from torch import nn
+
+from .tiger import TigerModel
 
 
 class SasRecSemanticModel(SequentialTorchModel, config_name="sasrec_semantic"):
@@ -140,7 +142,7 @@ def forward(self, inputs):
                 src=torch.ones_like(sample_ids) * (-torch.inf),
             )  # (all_batch_events, num_items + 2)
             negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1:] = -torch.inf  # Mask idx
+            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
 
             return {
                 "positive_scores": positive_scores,
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 1035f009..f62203cf 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,9 +1,10 @@
 import json
 
 import torch
+from torch import nn
+
 from models.base import SequentialTorchModel
 from rqvae_utils import CollisionSolver, SimplifiedTree
-from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
 from .rqvae import RqVaeModel
@@ -81,7 +82,7 @@ def __init__(
                 a=-2 * initializer_range,
                 b=2 * initializer_range,
             ),
-            requires_grad=True, # TODOPK added for bos
+            requires_grad=True,  # TODOPK added for bos
         )
 
         self._codebook_embeddings = nn.Embedding(
@@ -92,13 +93,14 @@ def __init__(
 
         self._codebook_item_embeddings_stacked = nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
-            requires_grad=False # TODOPK compare with unfrozen codebooks
+            requires_grad=False,  # TODOPK compare with unfrozen codebooks
         )
 
         item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
         self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         self._item_id_to_semantic_embedding = nn.Parameter(
-            self._item_id_to_semantic_embedding, requires_grad=False # TODOPK compare with unfrozen codebooks
+            self._item_id_to_semantic_embedding,
+            requires_grad=False,  # TODOPK compare with unfrozen codebooks
         )
 
         self._trie = SimplifiedTree(self._codebook_item_embeddings_stacked)
@@ -329,13 +331,17 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             )
 
             assert last_position_embedding.shape == tgt_embeddings[:, -1, :].shape
-            assert tgt_embeddings.shape == torch.Size([batch_size, step + 1, embedding_dim])
+            assert tgt_embeddings.shape == torch.Size(
+                [batch_size, step + 1, embedding_dim]
+            )
 
             curr_step_embeddings = tgt_embeddings.clone()
             curr_step_embeddings[:, -1, :] = (
                 tgt_embeddings[:, -1, :] + last_position_embedding
             )
-            assert torch.allclose(tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :])
+            assert torch.allclose(
+                tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :]
+            )
             tgt_embeddings = curr_step_embeddings
 
             # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings[:, -1, :])
diff --git a/modeling/optimizer/base.py b/modeling/optimizer/base.py
index 86c63537..0e85cab8 100644
--- a/modeling/optimizer/base.py
+++ b/modeling/optimizer/base.py
@@ -1,17 +1,18 @@
 import copy
 
 import torch
+
 from utils import MetaParent
 
 OPTIMIZERS = {
-    'sgd': torch.optim.SGD,
-    'adam': torch.optim.Adam,
-    'adamw': torch.optim.AdamW
+    "sgd": torch.optim.SGD,
+    "adam": torch.optim.Adam,
+    "adamw": torch.optim.AdamW,
 }
 
 SCHEDULERS = {
-    'step': torch.optim.lr_scheduler.StepLR,
-    'cyclic': torch.optim.lr_scheduler.CyclicLR
+    "step": torch.optim.lr_scheduler.StepLR,
+    "cyclic": torch.optim.lr_scheduler.CyclicLR,
 }
 
 
@@ -19,7 +20,7 @@ class BaseOptimizer(metaclass=MetaParent):
     pass
 
 
-class BasicOptimizer(BaseOptimizer, config_name='basic'):
+class BasicOptimizer(BaseOptimizer, config_name="basic"):
     def __init__(self, model, optimizer, scheduler=None, clip_grad_threshold=None):
         self._model = model
         self._optimizer = optimizer
@@ -28,26 +29,24 @@ def __init__(self, model, optimizer, scheduler=None, clip_grad_threshold=None):
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        optimizer_cfg = copy.deepcopy(config['optimizer'])
-        optimizer = OPTIMIZERS[optimizer_cfg.pop('type')](
-            kwargs['model'].parameters(),
-            **optimizer_cfg
+        optimizer_cfg = copy.deepcopy(config["optimizer"])
+        optimizer = OPTIMIZERS[optimizer_cfg.pop("type")](
+            kwargs["model"].parameters(), **optimizer_cfg
         )
 
-        if 'scheduler' in config:
-            scheduler_cfg = copy.deepcopy(config['scheduler'])
-            scheduler = SCHEDULERS[scheduler_cfg.pop('type')](
-                optimizer,
-                **scheduler_cfg
+        if "scheduler" in config:
+            scheduler_cfg = copy.deepcopy(config["scheduler"])
+            scheduler = SCHEDULERS[scheduler_cfg.pop("type")](
+                optimizer, **scheduler_cfg
             )
         else:
             scheduler = None
 
         return cls(
-            model=kwargs['model'],
+            model=kwargs["model"],
             optimizer=optimizer,
             scheduler=scheduler,
-            clip_grad_threshold=config.get('clip_grad_threshold', None)
+            clip_grad_threshold=config.get("clip_grad_threshold", None),
         )
 
     def step(self, loss):
@@ -55,14 +54,16 @@ def step(self, loss):
         loss.backward()
 
         if self._clip_grad_threshold is not None:
-            torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._clip_grad_threshold)
+            torch.nn.utils.clip_grad_norm_(
+                self._model.parameters(), self._clip_grad_threshold
+            )
 
         self._optimizer.step()
         if self._scheduler is not None:
             self._scheduler.step()
 
     def state_dict(self):
-        state_dict = {'optimizer': self._optimizer.state_dict()}
+        state_dict = {"optimizer": self._optimizer.state_dict()}
         if self._scheduler is not None:
-            state_dict.update({'scheduler': self._scheduler.state_dict()})
+            state_dict.update({"scheduler": self._scheduler.state_dict()})
         return state_dict
diff --git a/modeling/pretrain.py b/modeling/pretrain.py
index 4597e3e8..4221e62c 100644
--- a/modeling/pretrain.py
+++ b/modeling/pretrain.py
@@ -1,16 +1,16 @@
-import utils
-from utils import parse_args, create_logger, fix_random_seed, DEVICE
+import copy
+import json
 
-from dataset import BaseDataset
+import torch
+
+import utils
+from callbacks import BaseCallback
 from dataloader import BaseDataloader
+from dataset import BaseDataset
+from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
-from loss import BaseLoss
-from callbacks import BaseCallback
-
-import copy
-import json
-import torch
+from utils import DEVICE, create_logger, fix_random_seed, parse_args
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -20,10 +20,10 @@ def pretrain(dataloader, model, optimizer, loss_function, callback, epoch_cnt):
     step_num = 0
     best_checkpoint = None
 
-    logger.debug('Start pretraining...')
+    logger.debug("Start pretraining...")
 
     for epoch in range(epoch_cnt):
-        logger.debug(f'Start epoch {epoch}')
+        logger.debug(f"Start epoch {epoch}")
         for step, batch in enumerate(dataloader):
             model.train()
 
@@ -39,7 +39,7 @@ def pretrain(dataloader, model, optimizer, loss_function, callback, epoch_cnt):
 
             best_checkpoint = copy.deepcopy(model.state_dict())
 
-    logger.debug('Pretraining procedure has been finished!')
+    logger.debug("Pretraining procedure has been finished!")
     return best_checkpoint
 
 
@@ -47,41 +47,38 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = \
-        utils.tensorboards.TensorboardWriter(config['experiment_name'])
+    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(
+        config["experiment_name"]
+    )
 
-    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
+    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
 
-    dataset = BaseDataset.create_from_config(config['dataset'])
+    dataset = BaseDataset.create_from_config(config["dataset"])
 
     train_sampler, test_sampler = dataset.get_samplers()
 
     train_dataloader = BaseDataloader.create_from_config(
-        config['dataloader']['train'],
-        dataset=train_sampler,
-        **dataset.meta
+        config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
     )
 
     validation_dataloader = BaseDataloader.create_from_config(
-        config['dataloader']['validation'],
-        dataset=test_sampler,
-        **dataset.meta
+        config["dataloader"]["validation"], dataset=test_sampler, **dataset.meta
     )
 
-    model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
+    model = BaseModel.create_from_config(config["model"], **dataset.meta).to(DEVICE)
 
-    loss_function = BaseLoss.create_from_config(config['loss'])
+    loss_function = BaseLoss.create_from_config(config["loss"])
 
-    optimizer = BaseOptimizer.create_from_config(config['optimizer'], model=model)
+    optimizer = BaseOptimizer.create_from_config(config["optimizer"], model=model)
 
     callback = BaseCallback.create_from_config(
-        config['callback'],
+        config["callback"],
         model=model,
         dataloader=validation_dataloader,
-        optimizer=optimizer
+        optimizer=optimizer,
     )
 
-    logger.debug('Everything is ready for pretraining process!')
+    logger.debug("Everything is ready for pretraining process!")
 
     # Pretrain process
     pretrain(
@@ -90,14 +87,16 @@ def main():
         optimizer=optimizer,
         loss_function=loss_function,
         callback=callback,
-        epoch_cnt=config['pretrain_epochs_num']
+        epoch_cnt=config["pretrain_epochs_num"],
     )
 
-    logger.debug('Saving model...')
-    checkpoint_path = '../checkpoints/pretrain_{}_final_state.pth'.format(config['experiment_name'])
+    logger.debug("Saving model...")
+    checkpoint_path = "../checkpoints/pretrain_{}_final_state.pth".format(
+        config["experiment_name"]
+    )
     torch.save(model.state_dict(), checkpoint_path)
-    logger.debug('Saved model as {}'.format(checkpoint_path))
+    logger.debug("Saved model as {}".format(checkpoint_path))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
index 2722515a..33c649b9 100644
--- a/modeling/rqvae_utils/__init__.py
+++ b/modeling/rqvae_utils/__init__.py
@@ -1,4 +1,4 @@
 from .collision_solver import CollisionSolver
-from .trie import Trie
+from .simplified_tree import SimplifiedTree
 from .tree import Tree
-from .simplified_tree import SimplifiedTree
\ No newline at end of file
+from .trie import Trie
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index f673c6dd..426cf1bf 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -6,32 +6,45 @@
 
 
 class CollisionSolver:
-    def __init__(self,
-                 emb_dim: int,
-                 sem_id_len: int,
-                 codebook_size: int,
-                 device: torch.device=DEVICE):
+    def __init__(
+        self,
+        emb_dim: int,
+        sem_id_len: int,
+        codebook_size: int,
+        device: torch.device = DEVICE,
+    ):
         """
         :param emb_dim: Длина остатка
         :param codebook_size: Количество элементов в одном кодбуке
         :param sem_id_len: Длина semantic_id (без токена решающего коллизии)
         :param device: Устройство
         """
-        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0))  # тензор группирирующий остатки по semantic_id
+        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty(
+            (0, 0)
+        )  # тензор группирирующий остатки по semantic_id
         self.item_ids_sparse_tensor: torch.Tensor = torch.empty(
-            (0, 0))  # тензор группирирующий реальные айди айтемов по semantic_id
-        self.counts_dict: dict[int, int] = defaultdict(int)  # тензор храняющий количество коллизий по semantic_id
+            (0, 0)
+        )  # тензор группирирующий реальные айди айтемов по semantic_id
+        self.counts_dict: dict[int, int] = defaultdict(
+            int
+        )  # тензор храняющий количество коллизий по semantic_id
         self.emb_dim: int = emb_dim  # длина остатка
         self.sem_id_len: int = sem_id_len  # длина semantic_id
         self.codebook_size: int = codebook_size  # количество элементов в одном кодбуке
         self.device: torch.device = device  # девайс
 
-        self.key: torch.Tensor = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len)],
-                                              dtype=torch.long,
-                                              device=self.device)  # ключ для сопоставления числа каждому semantic_id
-
-    def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor,
-                                     residuals: torch.Tensor) -> None:
+        self.key: torch.Tensor = torch.tensor(
+            [self.codebook_size**i for i in range(self.sem_id_len)],
+            dtype=torch.long,
+            device=self.device,
+        )  # ключ для сопоставления числа каждому semantic_id
+
+    def create_query_candidates_dict(
+        self,
+        item_ids: torch.Tensor,
+        semantic_ids: torch.Tensor,
+        residuals: torch.Tensor,
+    ) -> None:
         """
         Создает разреженный тензор, который содержит сгруппированные по semantic id элементы
 
@@ -52,35 +65,53 @@ def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: tor
         semantic_ids = semantic_ids.to(self.device)
 
         unique_id = (semantic_ids * self.key).sum(dim=1)  # хэши
-        unique_ids, inverse_indices, counts = torch.unique(unique_id, return_inverse=True, return_counts=True)
-        sorted_indices = torch.argsort(inverse_indices)  # сортированные индексы чтобы совпадающие хэши шли подряд
+        unique_ids, inverse_indices, counts = torch.unique(
+            unique_id, return_inverse=True, return_counts=True
+        )
+        sorted_indices = torch.argsort(
+            inverse_indices
+        )  # сортированные индексы чтобы совпадающие хэши шли подряд
 
         row_indices = inverse_indices[sorted_indices]  # отсортированные хэши
 
         offsets = torch.cumsum(counts, dim=0) - counts
-        col_indices = torch.arange(semantic_ids_count, device=self.device) - offsets[
-            row_indices]  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
-
-        indices = torch.stack([
-            unique_ids[row_indices],
-            col_indices
-        ],
-            dim=0)  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
-
-        max_residuals_count = int(counts.max().item())  # максимальное количество коллизий для одного sem_id
-        max_sid = int(self.codebook_size ** self.sem_id_len)  # максимальный хэш sem_id который может быть
-
-        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices],
-                                                              size=(max_sid, max_residuals_count, self.emb_dim),
-                                                              device=self.device)  # (max_sid, max_residuals_count, emb_dim)
-
-        self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))  # sid -> collision count
-
-        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(indices, item_ids[sorted_indices],
-                                                              size=(max_sid, max_residuals_count), device=self.device,
-                                                              dtype=torch.int32)  # (max_sid, max_residuals_count)
-
-    def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        col_indices = (
+            torch.arange(semantic_ids_count, device=self.device) - offsets[row_indices]
+        )  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
+
+        indices = torch.stack(
+            [unique_ids[row_indices], col_indices], dim=0
+        )  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
+
+        max_residuals_count = int(
+            counts.max().item()
+        )  # максимальное количество коллизий для одного sem_id
+        max_sid = int(
+            self.codebook_size**self.sem_id_len
+        )  # максимальный хэш sem_id который может быть
+
+        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(
+            indices,
+            residuals[sorted_indices],
+            size=(max_sid, max_residuals_count, self.emb_dim),
+            device=self.device,
+        )  # (max_sid, max_residuals_count, emb_dim)
+
+        self.counts_dict = defaultdict(
+            int, zip(unique_ids.tolist(), counts.tolist())
+        )  # sid -> collision count
+
+        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(
+            indices,
+            item_ids[sorted_indices],
+            size=(max_sid, max_residuals_count),
+            device=self.device,
+            dtype=torch.int32,
+        )  # (max_sid, max_residuals_count)
+
+    def get_residuals_by_semantic_id_batch(
+        self, semantic_ids: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         :param semantic_ids батч из semantic ids (batch_size, sem_id_len)
 
@@ -93,13 +124,21 @@ def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tupl
         semantic_ids = semantic_ids.to(self.device)
         unique_ids = (semantic_ids * self.key).sum(dim=1)
 
-        candidates = torch.stack([self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids])
-        counts = torch.tensor([self.counts_dict[key.item()] for key in unique_ids], device=self.device)
-        mask = torch.arange(candidates.shape[1], device=self.device).expand(len(unique_ids), -1) < counts.view(-1, 1)
+        candidates = torch.stack(
+            [self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids]
+        )
+        counts = torch.tensor(
+            [self.counts_dict[key.item()] for key in unique_ids], device=self.device
+        )
+        mask = torch.arange(candidates.shape[1], device=self.device).expand(
+            len(unique_ids), -1
+        ) < counts.view(-1, 1)
 
         return candidates, mask
 
-    def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
+    def get_pred_scores(
+        self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor
+    ) -> dict[str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param pred_residuals: [batch_size, emb_dim] предсказанные остатки
@@ -120,19 +159,26 @@ def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tens
 
         candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_ids)
 
-        pred_scores = torch.einsum('njk,nk->nj', candidates, pred_residuals).masked_fill(~mask, -torch.inf)
+        pred_scores = torch.einsum(
+            "njk,nk->nj", candidates, pred_residuals
+        ).masked_fill(~mask, -torch.inf)
         pred_indices = torch.argmax(pred_scores, dim=1)
         pred_item_ids = torch.stack(
-            [self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]] for i in range(semantic_ids.shape[0])])
+            [
+                self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]]
+                for i in range(semantic_ids.shape[0])
+            ]
+        )
 
         return {
             "pred_scores_mask": mask,
             "pred_scores": pred_scores,
-            "pred_item_ids": pred_item_ids
+            "pred_item_ids": pred_item_ids,
         }
 
-    def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[
-        str, torch.Tensor]:
+    def get_true_dedup_tokens(
+        self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor
+    ) -> dict[str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param true_residuals: [batch_size, emb_dim] реальные остатки
@@ -154,11 +200,11 @@ def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torc
 
         assert matches.any(dim=1).all(), "Не у всех батчей есть совпадение"
 
-        return {
-            "true_dedup_tokens": true_dedup_tokens
-        }
+        return {"true_dedup_tokens": true_dedup_tokens}
 
-    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> torch.Tensor:
+    def get_item_ids_batch(
+        self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor
+    ) -> torch.Tensor:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токенов решающего коллизии)
         :param dedup_tokens: [batch_size] токены решающие коллизии
@@ -174,6 +220,10 @@ def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Ten
         unique_ids = (semantic_ids * self.key).sum(dim=1)
 
         item_ids = torch.stack(
-            [self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])
+            [
+                self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]]
+                for i in range(semantic_ids.shape[0])
+            ]
+        )
 
         return item_ids
diff --git a/modeling/rqvae_utils/rqvae_data.py b/modeling/rqvae_utils/rqvae_data.py
index 47692cae..5ffec08e 100644
--- a/modeling/rqvae_utils/rqvae_data.py
+++ b/modeling/rqvae_utils/rqvae_data.py
@@ -1,11 +1,11 @@
-import pandas as pd
-import json
 import gzip
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
+import json
 import random
 
+import pandas as pd
+import torch
 from tqdm import tqdm
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 tqdm.pandas()
 
@@ -76,7 +76,7 @@ def get_data(cached=True):
             df["embeddings"] = df["combined_text"].progress_apply(encode_text)
     else:
         df = torch.load("../data/Beauty/data_full.pt", weights_only=False)
-        
+
     return df
 
 
@@ -91,6 +91,3 @@ def search_similar_items(items_with_tuples, clust2search, max_cnt=5):
         if cnt >= max_cnt:
             return similars
     return similars
-
-
-
diff --git a/modeling/rqvae_utils/rqvae_test.py b/modeling/rqvae_utils/rqvae_test.py
index 611e7739..03f8e3e8 100644
--- a/modeling/rqvae_utils/rqvae_test.py
+++ b/modeling/rqvae_utils/rqvae_test.py
@@ -6,6 +6,7 @@
 from models import RqVaeModel
 from utils import DEVICE
 
+
 def test(a, b):
     cos_sim = torch.nn.functional.cosine_similarity(a, b, dim=0)
     norm_a = torch.norm(a, p=2)
@@ -13,6 +14,7 @@ def test(a, b):
     l2_dist = torch.norm(a - b, p=2) / (norm_a + norm_b + 1e-8)
     return cos_sim, l2_dist
 
+
 if __name__ == "__main__":
     config = json.load(open("../configs/train/tiger_train_config.json"))
     config = config["model"]
@@ -24,14 +26,16 @@ def test(a, b):
     )
     df = torch.load(config["embs_extractor_path"], weights_only=False)
     embeddings_array = np.stack(df["embeddings"].values)
-    tensor_embeddings = torch.tensor(embeddings_array, dtype=torch.float32, device=DEVICE)
-    inputs = {'embeddings': tensor_embeddings}
+    tensor_embeddings = torch.tensor(
+        embeddings_array, dtype=torch.float32, device=DEVICE
+    )
+    inputs = {"embeddings": tensor_embeddings}
 
     rqvae_model.eval()
     sem_ids, residuals = rqvae_model.forward(inputs)
     scores = residuals.detach()
     print(torch.norm(residuals, p=2, dim=1).median())
-    for (i, codebook) in enumerate(rqvae_model.codebooks):
+    for i, codebook in enumerate(rqvae_model.codebooks):
         scores += codebook[sem_ids[:, i]].detach()
     decoder_output = rqvae_model.decoder(scores.detach()).detach()
 
@@ -41,11 +45,14 @@ def test(a, b):
     print("косинусное расстояние", cos_sim)
     print("евклидово расстояние", l2_dist)
 
-    cos_sim = torch.nn.functional.cosine_similarity(tensor_embeddings, decoder_output, dim=1)
+    cos_sim = torch.nn.functional.cosine_similarity(
+        tensor_embeddings, decoder_output, dim=1
+    )
     print("косинусное расстояние", cos_sim.mean(), cos_sim.min(), cos_sim.max())
 
-    norm_a = torch.norm(tensor_embeddings, p=2, dim = 1)
-    norm_b = torch.norm(decoder_output, p=2, dim = 1)
-    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim = 1) / (norm_a + norm_b + 1e-8)
-    print("евклидово расстояние",l2_dist.median(), l2_dist.min(), l2_dist.max())
-
+    norm_a = torch.norm(tensor_embeddings, p=2, dim=1)
+    norm_b = torch.norm(decoder_output, p=2, dim=1)
+    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim=1) / (
+        norm_a + norm_b + 1e-8
+    )
+    print("евклидово расстояние", l2_dist.median(), l2_dist.min(), l2_dist.max())
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 5a09dcb4..120f9a80 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -10,14 +10,21 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
+        self.embedding_table: torch.Tensor = (
+            embedding_table  # (semantic_id_len, codebook_size, emb_dim)
+        )
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor,
-                             sum_with_residuals: bool = True) -> None:
+    def build_tree_structure(
+        self,
+        semantic_ids: torch.Tensor,
+        residuals: torch.Tensor,
+        item_ids: torch.Tensor,
+        sum_with_residuals: bool = True,
+    ) -> None:
         """
         :param sum_with_residuals: флаг, отвечающий за то учитывать ли остатки при выборе кандидатов
         :param semantic_ids: (sem_ids_count, sem_id_len)
@@ -30,9 +37,11 @@ def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tens
         assert item_ids.shape == (self.sem_ids_count,)
 
         semantic_ids = semantic_ids.to(self.device)
-        residuals = residuals.to(self.device).float() if sum_with_residuals else torch.zeros_like(residuals,
-                                                                                                  device=self.device,
-                                                                                                  dtype=torch.float)
+        residuals = (
+            residuals.to(self.device).float()
+            if sum_with_residuals
+            else torch.zeros_like(residuals, device=self.device, dtype=torch.float)
+        )
         self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
         self.item_ids = item_ids
 
@@ -44,14 +53,17 @@ def calculate_full(self, sem_ids: torch.Tensor) -> torch.Tensor:
         assert sem_ids.shape[1] == self.sem_id_len
         sem_ids = sem_ids.to(self.device)
 
-        expanded_emb_table = (self.embedding_table.unsqueeze(0)
-                              .expand(sem_ids.shape[0], -1, -1, -1))  # (count, sem_id_len, codebook_size, emb_dim)
+        expanded_emb_table = self.embedding_table.unsqueeze(0).expand(
+            sem_ids.shape[0], -1, -1, -1
+        )  # (count, sem_id_len, codebook_size, emb_dim)
 
-        index = (sem_ids.unsqueeze(-1)
-                 .expand(-1, -1, self.emb_dim)
-                 .unsqueeze(2))  # (count, sem_id_len, 1, emb_dim)
+        index = (
+            sem_ids.unsqueeze(-1).expand(-1, -1, self.emb_dim).unsqueeze(2)
+        )  # (count, sem_id_len, 1, emb_dim)
 
-        return torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)  # (count, emb_dim)
+        return (
+            torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)
+        )  # (count, emb_dim)
 
     def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Tensor:
         """
@@ -63,14 +75,21 @@ def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Ten
         assert 0 < items_to_query <= self.sem_ids_count
 
         request_sem_ids = request_sem_ids.to(self.device)
-        request_embeddings = self.calculate_full(request_sem_ids)  # (batch_size, emb_dim)
+        request_embeddings = self.calculate_full(
+            request_sem_ids
+        )  # (batch_size, emb_dim)
 
-        request_embeddings = (request_embeddings.unsqueeze(1)
-                              .expand(-1, self.sem_ids_count, -1))  # (batch_size, sem_ids_count, emb_dim)
+        request_embeddings = request_embeddings.unsqueeze(1).expand(
+            -1, self.sem_ids_count, -1
+        )  # (batch_size, sem_ids_count, emb_dim)
 
-        diff_norm = torch.norm(self.full_embeddings - request_embeddings, p=2, dim=2)  # (batch_size, sem_ids_count)
+        diff_norm = torch.norm(
+            self.full_embeddings - request_embeddings, p=2, dim=2
+        )  # (batch_size, sem_ids_count)
 
-        indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :items_to_query]  # (batch_size, k)
+        indices = torch.argsort(diff_norm, descending=False, dim=1)[
+            :, :items_to_query
+        ]  # (batch_size, k)
         return self.item_ids[indices]
 
     def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
@@ -84,18 +103,21 @@ def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         assert 0 < k <= self.sem_ids_count
         request_sem_ids = request_sem_ids.to(self.device)
 
-        index = (request_sem_ids.unsqueeze(-1)
-                 .expand(-1, -1, self.emb_dim)
-                 .unsqueeze(2))  # (batch_size, sem_id_len, 1, emb_dim)
+        index = (
+            request_sem_ids.unsqueeze(-1).expand(-1, -1, self.emb_dim).unsqueeze(2)
+        )  # (batch_size, sem_id_len, 1, emb_dim)
 
         request_embeddings = torch.gather(
-            input=self.embedding_table.unsqueeze(0).expand(request_sem_ids.shape[0], -1, -1, -1),
+            input=self.embedding_table.unsqueeze(0).expand(
+                request_sem_ids.shape[0], -1, -1, -1
+            ),
             dim=2,
-            index=index
+            index=index,
         ).sum(1)  # (batch_size, emb_dim)
 
-        diff_norm = torch.cdist(self.full_embeddings, request_embeddings.unsqueeze(1), p=2).squeeze(
-            1)  # (batch_size, sem_ids_count)
+        diff_norm = torch.cdist(
+            self.full_embeddings, request_embeddings.unsqueeze(1), p=2
+        ).squeeze(1)  # (batch_size, sem_ids_count)
 
         _, indices = torch.topk(diff_norm, k=k, dim=1, largest=False)  # (batch_size, k)
         return self.item_ids[indices.squeeze(-1)]
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index b09cd049..336f6dbe 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -11,7 +11,9 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
+        self.embedding_table: torch.Tensor = (
+            embedding_table  # (semantic_id_len, codebook_size, emb_dim)
+        )
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.key: torch.Tensor = torch.empty((0, 0))
         self.A: torch.Tensor = torch.empty((0, 0))  # будет (max_sem_id, )
@@ -20,7 +22,12 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         self.sids: torch.Tensor = torch.empty((0, 0))  # будет (sem_id_len, )
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor):
+    def build_tree_structure(
+        self,
+        semantic_ids: torch.Tensor,
+        residuals: torch.Tensor,
+        item_ids: torch.Tensor,
+    ):
         """
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
@@ -34,22 +41,41 @@ def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tens
         assert item_ids.shape == (self.sem_ids_count,)
 
         self.item_ids = item_ids
-        self.key = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len - 1, -1, -1)],
-                                dtype=torch.long, device=self.device)
+        self.key = torch.tensor(
+            [self.codebook_size**i for i in range(self.sem_id_len - 1, -1, -1)],
+            dtype=torch.long,
+            device=self.device,
+        )
         self.sids = self.get_sids(semantic_ids.float())  # (sem_id_len, )
         self.sem_ids_embs = self.calculate_full(semantic_ids, residuals)
 
-        result = torch.full(size=[self.codebook_size ** self.sem_id_len], fill_value=0, dtype=torch.int64,
-                            device=self.device)
+        result = torch.full(
+            size=[self.codebook_size**self.sem_id_len],
+            fill_value=0,
+            dtype=torch.int64,
+            device=self.device,
+        )
         temp_unique_id = self.sids * self.codebook_size
-        temp_sem_ids = torch.concat([semantic_ids, torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1)],
-                                    dim=-1)
+        temp_sem_ids = torch.concat(
+            [
+                semantic_ids,
+                torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1),
+            ],
+            dim=-1,
+        )
 
         for i in range(0, self.sem_id_len + 1):
-            temp_unique_id = temp_unique_id - (self.codebook_size ** i) * temp_sem_ids[:, self.sem_id_len - i]
-            temp_unique_ids, temp_inverse_indices = torch.unique(temp_unique_id, return_inverse=True)
+            temp_unique_id = (
+                temp_unique_id
+                - (self.codebook_size**i) * temp_sem_ids[:, self.sem_id_len - i]
+            )
+            temp_unique_ids, temp_inverse_indices = torch.unique(
+                temp_unique_id, return_inverse=True
+            )
             temp_counts = torch.bincount(temp_inverse_indices)
-            truncated_ids = torch.floor_divide(input=temp_unique_id, other=(self.codebook_size ** (i + 1))).long()
+            truncated_ids = torch.floor_divide(
+                input=temp_unique_id, other=(self.codebook_size ** (i + 1))
+            ).long()
             result[truncated_ids] = temp_counts[temp_inverse_indices]
 
         self.A = result
@@ -64,13 +90,21 @@ def get_counts(self, sem_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]
         offsets = torch.arange(self.sem_id_len + 1, device=self.device)
         i = torch.arange(self.sem_id_len, device=self.device)
 
-        mask_sem = (i < (self.sem_id_len - offsets.unsqueeze(1))).long()  # (sem_id_len + 1, sem_id_len)
+        mask_sem = (
+            i < (self.sem_id_len - offsets.unsqueeze(1))
+        ).long()  # (sem_id_len + 1, sem_id_len)
         divs = torch.pow(self.codebook_size, offsets)  # (sem_id_len + 1,)
 
-        C = (sem_ids.unsqueeze(1) * mask_sem.unsqueeze(0) * self.key.unsqueeze(0).unsqueeze(1)).sum(dim=-1)
+        C = (
+            sem_ids.unsqueeze(1)
+            * mask_sem.unsqueeze(0)
+            * self.key.unsqueeze(0).unsqueeze(1)
+        ).sum(dim=-1)
         B = C // divs.unsqueeze(0)
 
-        return C, self.A[B]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
+        return C, self.A[
+            B
+        ]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
 
     def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
         """
@@ -78,9 +112,11 @@ def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
         :return: хэши sem_ids (sem_id_count,)
         """
         assert sem_ids.shape[1] == self.sem_id_len
-        return torch.einsum('nc,c->n', sem_ids, self.key.float())  # (sem_ids_count,)
+        return torch.einsum("nc,c->n", sem_ids, self.key.float())  # (sem_ids_count,)
 
-    def calc_ol(self, batch_ids: torch.Tensor, k: int) -> tuple[torch.Tensor, torch.Tensor]:
+    def calc_ol(
+        self, batch_ids: torch.Tensor, k: int
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         """
         :param batch_ids: (batch_size, sem_id_len)
         :param k: int
@@ -94,7 +130,8 @@ def calc_ol(self, batch_ids: torch.Tensor, k: int) -> tuple[torch.Tensor, torch.
         gather_ol = torch.gather(c, dim=1, index=ol.unsqueeze(1)).squeeze()  # (bs,)
 
         mask_ol = (gather_ol.unsqueeze(-1) <= self.sids) & (
-                self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1))
+            self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1)
+        )
         return ol, mask_ol  # (bs,) (bs, sem_ids_count)
 
     def calc_il(self, batch_ids, k):
@@ -108,19 +145,27 @@ def calc_il(self, batch_ids, k):
 
         batch_dim = batch_ids.shape[0]
         c, a = self.get_counts(batch_ids)
-        extended_c = torch.concat([torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c], dim=1)
+        extended_c = torch.concat(
+            [torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c],
+            dim=1,
+        )
 
         il = torch.argmax((a > k).long(), dim=-1) - 1  # (bs,)
-        gather_il = torch.gather(extended_c, dim=1, index=(il + 1).unsqueeze(1)).squeeze()  # (bs,)
+        gather_il = torch.gather(
+            extended_c, dim=1, index=(il + 1).unsqueeze(1)
+        ).squeeze()  # (bs,)
 
         mask_il = (gather_il.unsqueeze(-1) <= self.sids) & (
-                self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1))
+            self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1)
+        )
         return il, mask_il  # (bs,) (bs, sem_ids_count)
 
     def get_repeated_sids(self, k: int) -> torch.Tensor:
         return self.sids.repeat(k, 1)  # (k, sem_ids_count)
 
-    def get_request_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
+    def get_request_embeddings(
+        self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor
+    ) -> torch.Tensor:
         """
         :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
         :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
@@ -129,11 +174,16 @@ def get_request_embeddings(self, decomposed_embeddings: torch.Tensor, levels: to
         assert decomposed_embeddings.shape[1:] == (self.sem_id_len + 1, self.emb_dim)
         assert levels.shape == (decomposed_embeddings.shape[0],)
 
-        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(self.sem_id_len + 2, 0, -1,
-                                                                                        device=self.device).unsqueeze(1)
-        return torch.sum(decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1)  # (bs, emb_dim)
+        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(
+            self.sem_id_len + 2, 0, -1, device=self.device
+        ).unsqueeze(1)
+        return torch.sum(
+            decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1
+        )  # (bs, emb_dim)
 
-    def calculate_full(self, sem_ids: torch.Tensor, residuals: torch.Tensor) -> torch.Tensor:
+    def calculate_full(
+        self, sem_ids: torch.Tensor, residuals: torch.Tensor
+    ) -> torch.Tensor:
         """
         :param sem_ids: sem_ids (count, sem_id_len)
         :param residuals: остатки для каждого sem_id (count, emb_dim)
@@ -145,31 +195,56 @@ def calculate_full(self, sem_ids: torch.Tensor, residuals: torch.Tensor) -> torc
 
         count = residuals.shape[0]
         index = sem_ids.view(count, -1, 1, 1).expand(-1, -1, -1, self.emb_dim)
-        embs = torch.gather(input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1), dim=2,
-                            index=index)  # expand бесплатный по памяти
-        decomposed_embs = torch.concat([embs.squeeze(2), residuals.unsqueeze(1)], dim=1)  # (sem_ids_count, emb_dim)
-
-        assert decomposed_embs.shape == (sem_ids.shape[0], self.sem_id_len + 1, self.emb_dim)
+        embs = torch.gather(
+            input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1),
+            dim=2,
+            index=index,
+        )  # expand бесплатный по памяти
+        decomposed_embs = torch.concat(
+            [embs.squeeze(2), residuals.unsqueeze(1)], dim=1
+        )  # (sem_ids_count, emb_dim)
+
+        assert decomposed_embs.shape == (
+            sem_ids.shape[0],
+            self.sem_id_len + 1,
+            self.emb_dim,
+        )
         return decomposed_embs
 
-    def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
+    def calculate_level_embeddings(
+        self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor
+    ) -> torch.Tensor:
         """
         :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
         :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
         :return: эмбеддинги для всех sem_ids для нужных глубин (batch_size, sem_ids_count, emb_dim)
         """
-        assert decomposed_embeddings.shape == (self.sem_ids_count, self.sem_id_len + 1, self.emb_dim)
-
-        mask = (torch.arange(1, self.sem_id_len + 2, device=self.device) >=
-                torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)).float()
+        assert decomposed_embeddings.shape == (
+            self.sem_ids_count,
+            self.sem_id_len + 1,
+            self.emb_dim,
+        )
+
+        mask = (
+            torch.arange(1, self.sem_id_len + 2, device=self.device)
+            >= torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)
+        ).float()
         sids_mask = mask[levels + 1].unsqueeze(-1)  # (batch_size, sem_id_len + 1, 1)
-        return torch.einsum('nld,bld->bnd', decomposed_embeddings, sids_mask)  # (batch_size, sem_ids_count, emb_dim)
+        return torch.einsum(
+            "nld,bld->bnd", decomposed_embeddings, sids_mask
+        )  # (batch_size, sem_ids_count, emb_dim)
 
     def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
-
-    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor,
-              items_to_query: int) -> torch.Tensor:
+        return torch.where(
+            mask, result, torch.tensor(float("-inf"), device=self.device)
+        )
+
+    def query(
+        self,
+        request_sem_ids: torch.Tensor,
+        request_residuals: torch.Tensor,
+        items_to_query: int,
+    ) -> torch.Tensor:
         """
         :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
         :param request_residuals: батч из остатков (batch_size, emb_dim)
@@ -197,13 +272,31 @@ def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor,
         ol_request_embeddings = self.get_request_embeddings(request_embs, ol)
         il_request_embeddings = self.get_request_embeddings(request_embs, il)
 
-        ol_scores = torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
-
-        il_scores = torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
-
-        ids = np.lexsort(keys=(-torch.cat([il_scores, ol_scores], dim=1),
-                               ~torch.cat([torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1),
-                               ~torch.cat([il_mask, ol_mask], dim=1)))
-
-        ids = (ids % self.sem_ids_count)[:, :self.sem_ids_count][:, :items_to_query]  # (batch_size, k)
+        ol_scores = (
+            torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1))
+            .squeeze(-1)
+            .detach()
+            .cpu()
+        )
+
+        il_scores = (
+            torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1))
+            .squeeze(-1)
+            .detach()
+            .cpu()
+        )
+
+        ids = np.lexsort(
+            keys=(
+                -torch.cat([il_scores, ol_scores], dim=1),
+                ~torch.cat(
+                    [torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1
+                ),
+                ~torch.cat([il_mask, ol_mask], dim=1),
+            )
+        )
+
+        ids = (ids % self.sem_ids_count)[:, : self.sem_ids_count][
+            :, :items_to_query
+        ]  # (batch_size, k)
         return self.item_ids[ids]
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index 3f1d3f38..f73db7c6 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -6,20 +6,20 @@
 import torch
 
 from models.rqvae import RqVaeModel
-from rqvae_utils import Trie, SimplifiedTree, Tree
+from rqvae_utils import SimplifiedTree, Tree, Trie
 from utils import DEVICE
 
 
 def memory_stats(k):
     process = psutil.Process(os.getpid())
-    memory_usage = process.memory_info().rss / 1024 ** 2
+    memory_usage = process.memory_info().rss / 1024**2
     print(f"{k}. Использование памяти: {memory_usage:.2f} MB")
 
 
 def calc_sid(sid, codebook_size):
     res = sid[-1]
     for i in range(1, sid.shape[0]):
-        res += sid[-i - 1] * (codebook_size ** i)
+        res += sid[-i - 1] * (codebook_size**i)
     return res
 
 
@@ -41,9 +41,7 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     )
     rqvae_model.eval()
 
-    emb_table = torch.stack(
-        [cb for cb in rqvae_model.codebooks]
-    ).to(DEVICE)
+    emb_table = torch.stack([cb for cb in rqvae_model.codebooks]).to(DEVICE)
 
     trie = Trie(rqvae_model)
     tree = Tree(rqvae_model, DEVICE)
@@ -73,14 +71,18 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     now = time.time()
     simplified_tree_wr.build_tree_structure(semantic_ids, residuals, item_ids, False)
-    print(f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms")
+    print(
+        f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms"
+    )
 
     full_embeddings = tree.calculate_full(semantic_ids, residuals).sum(1)
     print(torch.all((full_embeddings == simplified_tree.full_embeddings) == True))
 
     items_to_query = 20
     batch_size = 256
-    q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE)
+    q_semantic_ids = torch.randint(
+        0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE
+    )
     q_residuals = torch.randn(batch_size, embedding_dim).to(DEVICE)
 
     total_time = 0
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index b7971e86..4a4ca5b7 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -2,6 +2,7 @@
 import time
 
 import torch
+
 from models.rqvae import RqVaeModel
 from utils import DEVICE
 
diff --git a/modeling/train.py b/modeling/train.py
index d865620d..4919be6f 100644
--- a/modeling/train.py
+++ b/modeling/train.py
@@ -1,23 +1,32 @@
-import utils
-from utils import parse_args, create_logger, DEVICE, fix_random_seed
+import copy
+import json
+import os
 
+import torch
+
+import utils
 from callbacks import BaseCallback
-from dataset import BaseDataset
 from dataloader import BaseDataloader
+from dataset import BaseDataset
 from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
-
-import copy
-import json
-import os
-import torch
+from utils import DEVICE, create_logger, fix_random_seed, parse_args
 
 logger = create_logger(name=__name__)
 seed_val = 42
 
 
-def train(dataloader, model, optimizer, loss_function, callback, epoch_cnt=None, step_cnt=None, best_metric=None):
+def train(
+    dataloader,
+    model,
+    optimizer,
+    loss_function,
+    callback,
+    epoch_cnt=None,
+    step_cnt=None,
+    best_metric=None,
+):
     step_num = 0
     epoch_num = 0
     current_metric = 0
@@ -27,14 +36,20 @@ def train(dataloader, model, optimizer, loss_function, callback, epoch_cnt=None,
     best_epoch = 0
     best_checkpoint = None
 
-    logger.debug('Start training...')
+    logger.debug("Start training...")
 
-    while (epoch_cnt is None or epoch_num < epoch_cnt) and (step_cnt is None or step_num < step_cnt):
+    while (epoch_cnt is None or epoch_num < epoch_cnt) and (
+        step_cnt is None or step_num < step_cnt
+    ):
         if best_epoch + epochs_threshold < epoch_num:
-            logger.debug('There is no progress during {} epochs. Finish training'.format(epochs_threshold))
+            logger.debug(
+                "There is no progress during {} epochs. Finish training".format(
+                    epochs_threshold
+                )
+            )
             break
 
-        logger.debug(f'Start epoch {epoch_num}')
+        logger.debug(f"Start epoch {epoch_num}")
         for step, batch in enumerate(dataloader):
             batch_ = copy.deepcopy(batch)
 
@@ -54,14 +69,18 @@ def train(dataloader, model, optimizer, loss_function, callback, epoch_cnt=None,
                 # Take the last model
                 best_checkpoint = copy.deepcopy(model.state_dict())
                 best_epoch = epoch_num
-            elif best_checkpoint is None or best_metric in batch_ and current_metric <= batch_[best_metric]:
+            elif (
+                best_checkpoint is None
+                or best_metric in batch_
+                and current_metric <= batch_[best_metric]
+            ):
                 # If it is the first checkpoint, or it is the best checkpoint
                 current_metric = batch_[best_metric]
                 best_checkpoint = copy.deepcopy(model.state_dict())
                 best_epoch = epoch_num
 
         epoch_num += 1
-    logger.debug('Training procedure has been finished!')
+    logger.debug("Training procedure has been finished!")
     return best_checkpoint
 
 
@@ -69,59 +88,54 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = \
-        utils.tensorboards.TensorboardWriter(config['experiment_name'])
+    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(
+        config["experiment_name"]
+    )
 
-    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
-    logger.debug('Current DEVICE: {}'.format(DEVICE))
+    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug("Current DEVICE: {}".format(DEVICE))
 
-    dataset = BaseDataset.create_from_config(config['dataset'])
+    dataset = BaseDataset.create_from_config(config["dataset"])
 
     train_sampler, validation_sampler, test_sampler = dataset.get_samplers()
 
     train_dataloader = BaseDataloader.create_from_config(
-        config['dataloader']['train'],
-        dataset=train_sampler,
-        **dataset.meta
+        config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
     )
 
     validation_dataloader = BaseDataloader.create_from_config(
-        config['dataloader']['validation'],
-        dataset=validation_sampler,
-        **dataset.meta
+        config["dataloader"]["validation"], dataset=validation_sampler, **dataset.meta
     )
 
     eval_dataloader = BaseDataloader.create_from_config(
-        config['dataloader']['validation'],
-        dataset=test_sampler,
-        **dataset.meta
+        config["dataloader"]["validation"], dataset=test_sampler, **dataset.meta
     )
 
-    model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
-    if 'checkpoint' in config:
-        checkpoint_path = os.path.join('../checkpoints', f'{config["checkpoint"]}.pth')
-        logger.debug('Loading checkpoint from {}'.format(checkpoint_path))
+    model = BaseModel.create_from_config(config["model"], **dataset.meta).to(DEVICE)
+    if "checkpoint" in config:
+        checkpoint_path = os.path.join("../checkpoints", f"{config['checkpoint']}.pth")
+        logger.debug("Loading checkpoint from {}".format(checkpoint_path))
         checkpoint = torch.load(checkpoint_path)
         print(checkpoint.keys())
         model.load_state_dict(checkpoint)
 
-    loss_function = BaseLoss.create_from_config(config['loss'])
+    loss_function = BaseLoss.create_from_config(config["loss"])
 
-    optimizer = BaseOptimizer.create_from_config(config['optimizer'], model=model)
+    optimizer = BaseOptimizer.create_from_config(config["optimizer"], model=model)
 
     callback = BaseCallback.create_from_config(
-        config['callback'],
+        config["callback"],
         model=model,
         train_dataloader=train_dataloader,
         validation_dataloader=validation_dataloader,
         eval_dataloader=eval_dataloader,
         optimizer=optimizer,
-        **dataset.meta
+        **dataset.meta,
     )
 
     # TODO add verbose option for all callbacks, multiple optimizer options (???)
     # TODO create pre/post callbacks
-    logger.debug('Everything is ready for training process!')
+    logger.debug("Everything is ready for training process!")
 
     # Train process
     _ = train(
@@ -130,16 +144,18 @@ def main():
         optimizer=optimizer,
         loss_function=loss_function,
         callback=callback,
-        epoch_cnt=config.get('train_epochs_num'),
-        step_cnt=config.get('train_steps_num'),
-        best_metric=config.get('best_metric')
+        epoch_cnt=config.get("train_epochs_num"),
+        step_cnt=config.get("train_steps_num"),
+        best_metric=config.get("best_metric"),
     )
 
-    logger.debug('Saving model...')
-    checkpoint_path = '../checkpoints/{}_final_state.pth'.format(config['experiment_name'])
+    logger.debug("Saving model...")
+    checkpoint_path = "../checkpoints/{}_final_state.pth".format(
+        config["experiment_name"]
+    )
     torch.save(model.state_dict(), checkpoint_path)
-    logger.debug('Saved model as {}'.format(checkpoint_path))
+    logger.debug("Saved model as {}".format(checkpoint_path))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/modeling/train_multiple.py b/modeling/train_multiple.py
index c42f77a6..8c118584 100644
--- a/modeling/train_multiple.py
+++ b/modeling/train_multiple.py
@@ -1,20 +1,26 @@
 import itertools
 import json
 import random
+
 import torch
 
 import utils
-from utils import parse_args, create_logger, DEVICE, Params, dict_to_str, fix_random_seed
-
-from train import train
-from infer import inference
-
 from callbacks import BaseCallback, EvalCallback, ValidationCallback
-from dataset import BaseDataset
 from dataloader import BaseDataloader
+from dataset import BaseDataset
+from infer import inference
 from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
+from train import train
+from utils import (
+    DEVICE,
+    Params,
+    create_logger,
+    dict_to_str,
+    fix_random_seed,
+    parse_args,
+)
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -24,24 +30,23 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
+    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
 
-    dataset_params = Params(config['dataset'], config['dataset_params'])
-    model_params = Params(config['model'], config['model_params'])
-    loss_function_params = Params(config['loss'], config['loss_params'])
-    optimizer_params = Params(config['optimizer'], config['optimizer_params'])
+    dataset_params = Params(config["dataset"], config["dataset_params"])
+    model_params = Params(config["model"], config["model_params"])
+    loss_function_params = Params(config["loss"], config["loss_params"])
+    optimizer_params = Params(config["optimizer"], config["optimizer_params"])
 
-    logger.debug('Everything is ready for training process!')
+    logger.debug("Everything is ready for training process!")
 
-    start_from = config.get('start_from', 0)
-    num = config.get('num_exps', None)
+    start_from = config.get("start_from", 0)
+    num = config.get("num_exps", None)
 
-    list_of_params = list(itertools.product(
-        dataset_params,
-        model_params,
-        loss_function_params,
-        optimizer_params
-    ))
+    list_of_params = list(
+        itertools.product(
+            dataset_params, model_params, loss_function_params, optimizer_params
+        )
+    )
 
     if num is None:
         num = len(list_of_params)
@@ -50,59 +55,61 @@ def main():
 
     cnt = 0
 
-    for dataset_param, model_param, loss_param, optimizer_param in list_of_params[start_from:num]:
+    for dataset_param, model_param, loss_param, optimizer_param in list_of_params[
+        start_from:num
+    ]:
         cnt += 1
         if cnt < start_from:
             continue
 
-        model_name = '_'.join([
-            config['experiment_name'],
-            dict_to_str(dataset_param, config['dataset_params']),
-            dict_to_str(model_param, config['model_params']),
-            dict_to_str(loss_param, config['loss_params']),
-            dict_to_str(optimizer_param, config['optimizer_params'])
-        ])
+        model_name = "_".join(
+            [
+                config["experiment_name"],
+                dict_to_str(dataset_param, config["dataset_params"]),
+                dict_to_str(model_param, config["model_params"]),
+                dict_to_str(loss_param, config["loss_params"]),
+                dict_to_str(optimizer_param, config["optimizer_params"]),
+            ]
+        )
 
-        logger.debug('Starting {}'.format(model_name))
+        logger.debug("Starting {}".format(model_name))
 
         dataset = BaseDataset.create_from_config(dataset_param)
 
         train_sampler, validation_sampler, eval_sampler = dataset.get_samplers()
 
         train_dataloader = BaseDataloader.create_from_config(
-            config['dataloader']['train'],
-            dataset=train_sampler,
-            **dataset.meta
+            config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
         )
 
         validation_dataloader = BaseDataloader.create_from_config(
-            config['dataloader']['validation'],
+            config["dataloader"]["validation"],
             dataset=validation_sampler,
-            **dataset.meta
+            **dataset.meta,
         )
 
         eval_dataloader = BaseDataloader.create_from_config(
-            config['dataloader']['validation'],
-            dataset=eval_sampler,
-            **dataset.meta
+            config["dataloader"]["validation"], dataset=eval_sampler, **dataset.meta
         )
 
         if utils.tensorboards.GLOBAL_TENSORBOARD_WRITER is not None:
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.close()
-        utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(model_name, use_time=False)
+        utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = (
+            utils.tensorboards.TensorboardWriter(model_name, use_time=False)
+        )
 
         model = BaseModel.create_from_config(model_param, **dataset.meta).to(DEVICE)
         loss_function = BaseLoss.create_from_config(loss_param)
         optimizer = BaseOptimizer.create_from_config(optimizer_param, model=model)
 
         callback = BaseCallback.create_from_config(
-            config['callback'],
+            config["callback"],
             model=model,
             train_dataloader=train_dataloader,
             validation_dataloader=validation_dataloader,
             eval_dataloader=eval_dataloader,
             optimizer=optimizer,
-            **dataset.meta
+            **dataset.meta,
         )
 
         best_model_checkpoint = train(
@@ -111,11 +118,13 @@ def main():
             optimizer=optimizer,
             loss_function=loss_function,
             callback=callback,
-            epoch_cnt=config.get('train_epochs_num'),
-            best_metric=config.get('best_metric')
+            epoch_cnt=config.get("train_epochs_num"),
+            best_metric=config.get("best_metric"),
         )
 
-        eval_model = BaseModel.create_from_config(model_param, **dataset.meta).to(DEVICE)
+        eval_model = BaseModel.create_from_config(model_param, **dataset.meta).to(
+            DEVICE
+        )
         eval_model.load_state_dict(best_model_checkpoint)
 
         for cl in callback._callbacks:
@@ -136,11 +145,11 @@ def main():
 
         inference(eval_dataloader, eval_model, metrics, pred_prefix, labels_prefix)
 
-        logger.debug('Saving best model checkpoint...')
-        checkpoint_path = '../checkpoints/{}_final_state.pth'.format(model_name)
+        logger.debug("Saving best model checkpoint...")
+        checkpoint_path = "../checkpoints/{}_final_state.pth".format(model_name)
         torch.save(best_model_checkpoint, checkpoint_path)
-        logger.debug('Saved model as {}'.format(checkpoint_path))
+        logger.debug("Saved model as {}".format(checkpoint_path))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 8b96ac52..1d62369e 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -1,39 +1,37 @@
-from utils.registry import MetaParent
-from utils.grid_search import Params
-import utils.tensorboards
-
+import argparse
 import json
-import random
 import logging
-import argparse
-import numpy as np
+import random
 
+import numpy as np
 import torch
 
+import utils.tensorboards
+from utils.grid_search import Params
+from utils.registry import MetaParent
+
 if torch.cuda.is_available():
-    DEVICE = torch.device('cuda:0')
-# elif torch.backends.mps.is_available():
-#     DEVICE = torch.device("mps:0")
+    DEVICE = torch.device("cuda")
 else:
-    DEVICE = torch.device('cpu')
+    DEVICE = torch.device("cpu")
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument('--params', required=True)
+    parser.add_argument("--params", required=True)
     args = parser.parse_args()
 
     with open(args.params) as f:
         params = json.load(f)
-    
+
     return params
 
 
 def create_logger(
-        name,
-        level=logging.DEBUG,
-        format='[%(asctime)s] [%(levelname)s]: %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S'
+    name,
+    level=logging.DEBUG,
+    format="[%(asctime)s] [%(levelname)s]: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
 ):
     logging.basicConfig(level=level, format=format, datefmt=datefmt)
     logger = logging.getLogger(name)
@@ -54,26 +52,31 @@ def maybe_to_list(values):
 
 
 def get_activation_function(name, **kwargs):
-    if name == 'relu':
+    if name == "relu":
         return torch.nn.ReLU()
-    elif name == 'gelu':
+    elif name == "gelu":
         return torch.nn.GELU()
-    elif name == 'elu':
-        return torch.nn.ELU(alpha=float(kwargs.get('alpha', 1.0)))
-    elif name == 'leaky':
-        return torch.nn.LeakyReLU(negative_slope=float(kwargs.get('negative_slope', 1e-2)))
-    elif name == 'sigmoid':
+    elif name == "elu":
+        return torch.nn.ELU(alpha=float(kwargs.get("alpha", 1.0)))
+    elif name == "leaky":
+        return torch.nn.LeakyReLU(
+            negative_slope=float(kwargs.get("negative_slope", 1e-2))
+        )
+    elif name == "sigmoid":
         return torch.nn.Sigmoid()
-    elif name == 'tanh':
+    elif name == "tanh":
         return torch.nn.Tanh()
-    elif name == 'softmax':
+    elif name == "softmax":
         return torch.nn.Softmax()
-    elif name == 'softplus':
-        return torch.nn.Softplus(beta=int(kwargs.get('beta', 1.0)), threshold=int(kwargs.get('threshold', 20)))
-    elif name == 'softmax_logit':
+    elif name == "softplus":
+        return torch.nn.Softplus(
+            beta=int(kwargs.get("beta", 1.0)),
+            threshold=int(kwargs.get("threshold", 20)),
+        )
+    elif name == "softmax_logit":
         return torch.nn.LogSoftmax()
     else:
-        raise ValueError('Unknown activation function name `{}`'.format(name))
+        raise ValueError("Unknown activation function name `{}`".format(name))
 
 
 def dict_to_str(x, params):
@@ -82,19 +85,21 @@ def dict_to_str(x, params):
         if k in params:
             if isinstance(v, dict):
                 # part = '_'.join([f'{k}-{sub_part}' for sub_part in dict_to_str(v, params[k]).split('_')])
-                part = '_'.join([f'{sub_part}' for sub_part in dict_to_str(v, params[k]).split('_')])
+                part = "_".join(
+                    [f"{sub_part}" for sub_part in dict_to_str(v, params[k]).split("_")]
+                )
             elif isinstance(v, tuple) or isinstance(v, list):
                 sub_strings = []
                 for i, sub_value in enumerate(v):
-                    sub_strings.append(f'({i})_{dict_to_str(v[i], params[k][i])}')
-                part = f'({"_".join(sub_strings)})'
+                    sub_strings.append(f"({i})_{dict_to_str(v[i], params[k][i])}")
+                part = f"({'_'.join(sub_strings)})"
             else:
                 # part = f'{k}-{v}'
-                part = f'{v}'
+                part = f"{v}"
             parts.append(part)
         else:
             continue
-    return '_'.join(parts).replace('.', '-')
+    return "_".join(parts).replace(".", "-")
 
 
 def create_masked_tensor(data, lengths):
@@ -103,20 +108,22 @@ def create_masked_tensor(data, lengths):
 
     if len(data.shape) == 1:  # only indices
         padded_tensor = torch.zeros(
-            batch_size, max_sequence_length,
-            dtype=data.dtype, device=DEVICE
+            batch_size, max_sequence_length, dtype=data.dtype, device=DEVICE
         )  # (batch_size, max_seq_len)
     else:
         assert len(data.shape) == 2  # embeddings
         padded_tensor = torch.zeros(
-            batch_size, max_sequence_length, data.shape[-1],
-            dtype=data.dtype, device=DEVICE
+            batch_size,
+            max_sequence_length,
+            data.shape[-1],
+            dtype=data.dtype,
+            device=DEVICE,
         )  # (batch_size, max_seq_len, emb_dim)
 
-    mask = torch.arange(
-        end=max_sequence_length,
-        device=DEVICE
-    )[None].tile([batch_size, 1]) < lengths[:, None]  # (batch_size, max_seq_len)
+    mask = (
+        torch.arange(end=max_sequence_length, device=DEVICE)[None].tile([batch_size, 1])
+        < lengths[:, None]
+    )  # (batch_size, max_seq_len)
 
     padded_tensor[mask] = data
 
diff --git a/modeling/utils/grid_search.py b/modeling/utils/grid_search.py
index 268d9458..d7330421 100644
--- a/modeling/utils/grid_search.py
+++ b/modeling/utils/grid_search.py
@@ -3,7 +3,6 @@
 
 
 class Params:
-
     def __init__(self, config, params):
         self._initial_config = copy.deepcopy(config)
         self._initial_params = copy.deepcopy(params)
@@ -12,7 +11,9 @@ def __iter__(self):
         keys = []
         values = []
 
-        all_keys = set(self._initial_config.keys()).union(set(self._initial_params.keys()))
+        all_keys = set(self._initial_config.keys()).union(
+            set(self._initial_params.keys())
+        )
 
         for field_name in all_keys:
             keys.append(field_name)
@@ -21,18 +22,28 @@ def __iter__(self):
             params_fields_value = self._initial_params.get(field_name)
 
             if initial_field_value:
-                if params_fields_value is None:  # We don't want to iterate through this field
+                if (
+                    params_fields_value is None
+                ):  # We don't want to iterate through this field
                     values.append([initial_field_value])
-                elif isinstance(initial_field_value, list) and isinstance(initial_field_value, list):
+                elif isinstance(initial_field_value, list) and isinstance(
+                    initial_field_value, list
+                ):
                     assert len(initial_field_value) == len(params_fields_value)
                     list_values = []
                     for i in range(len(initial_field_value)):
-                        field_variations = list(Params(initial_field_value[i], params_fields_value[i]))
+                        field_variations = list(
+                            Params(initial_field_value[i], params_fields_value[i])
+                        )
                         list_values.append(field_variations)
                     list_values = [p for p in product(*list_values)]
                     values.append(list_values)
-                elif isinstance(initial_field_value, dict):  # It is composite param, need to go inside
-                    field_variations = list(Params(initial_field_value, params_fields_value))
+                elif isinstance(
+                    initial_field_value, dict
+                ):  # It is composite param, need to go inside
+                    field_variations = list(
+                        Params(initial_field_value, params_fields_value)
+                    )
                     values.append(field_variations)
                 else:  # Simple param, can take as it is
                     values.append([initial_field_value] + params_fields_value)
diff --git a/modeling/utils/registry.py b/modeling/utils/registry.py
index c2225480..3942269b 100644
--- a/modeling/utils/registry.py
+++ b/modeling/utils/registry.py
@@ -2,7 +2,6 @@
 
 
 class MetaParent(type):
-
     def __init__(cls, name, base, params, **kwargs):
         super().__init__(name, base, params)
         is_base_class = cls.mro()[1] is object
@@ -12,10 +11,10 @@ def __init__(cls, name, base, params, **kwargs):
             base_class_found = False
             for key in cls.mro():
                 if isinstance(key, MetaParent) and key.mro()[1] is object:
-                    assert base_class_found is False, 'multiple base classes(bug)'
+                    assert base_class_found is False, "multiple base classes(bug)"
                     base_class = key
                     base_class_found = True
-            assert base_class_found is True, f'no base class for {name}'
+            assert base_class_found is True, f"no base class for {name}"
 
         if is_base_class:
             cls._subclasses = {}
@@ -25,7 +24,9 @@ def __init_subclass__(scls, config_name=None):
             super().__init_subclass__()
             if config_name is not None:
                 if config_name in base_class._subclasses:
-                    raise ValueError("Class with name `{}` is already registered".format(config_name))
+                    raise ValueError(
+                        "Class with name `{}` is already registered".format(config_name)
+                    )
                 scls.config_name = config_name
                 base_class._subclasses[config_name] = scls
 
@@ -33,15 +34,19 @@ def __init_subclass__(scls, config_name=None):
 
         @classmethod
         def parent_create_from_config(cls, config, **kwargs):
-            if 'type' in config:
-                return cls._subclasses[config['type']].create_from_config(config, **kwargs)
+            if "type" in config:
+                return cls._subclasses[config["type"]].create_from_config(
+                    config, **kwargs
+                )
             else:
-                raise ValueError('There is no `type` provided for the `{}` class'.format(name))
+                raise ValueError(
+                    "There is no `type` provided for the `{}` class".format(name)
+                )
 
         # Take kwargs for the last initialized baseclass
         init_kwargs = {}
         for bcls in cls.mro()[:-1]:  # Look into all base classes except object
-            if '__init__' not in bcls.__dict__:
+            if "__init__" not in bcls.__dict__:
                 continue
             init_kwargs = inspect.signature(bcls.__init__).parameters
             break
@@ -50,14 +55,16 @@ def parent_create_from_config(cls, config, **kwargs):
         def child_create_from_config(cls, config, **kwargs):
             kwargs = {}
             for key, argspec in init_kwargs.items():
-                if key == 'self':
+                if key == "self":
                     continue
                 value = config.get(key, argspec.default)
                 if value is inspect.Parameter.empty:
-                    msg = 'There is no value for `{}.__init__` required field `{}` in config `{}`'
+                    msg = "There is no value for `{}.__init__` required field `{}` in config `{}`"
                     raise ValueError(msg.format(cls, key, config))
                 kwargs[key] = value
             return cls(**kwargs)
 
-        if 'create_from_config' not in cls.__dict__:
-            cls.create_from_config = parent_create_from_config if is_base_class else child_create_from_config
+        if "create_from_config" not in cls.__dict__:
+            cls.create_from_config = (
+                parent_create_from_config if is_base_class else child_create_from_config
+            )
diff --git a/modeling/utils/tensorboards/__init__.py b/modeling/utils/tensorboards/__init__.py
index 6c470341..a2f8f279 100644
--- a/modeling/utils/tensorboards/__init__.py
+++ b/modeling/utils/tensorboards/__init__.py
@@ -1 +1 @@
-from .tensorboard_writers import TensorboardWriter, GLOBAL_TENSORBOARD_WRITER, LOGS_DIR
+from .tensorboard_writers import GLOBAL_TENSORBOARD_WRITER, LOGS_DIR, TensorboardWriter
diff --git a/modeling/utils/tensorboards/tensorboard_writers.py b/modeling/utils/tensorboards/tensorboard_writers.py
index 770fae63..e788a6f8 100644
--- a/modeling/utils/tensorboards/tensorboard_writers.py
+++ b/modeling/utils/tensorboards/tensorboard_writers.py
@@ -1,19 +1,21 @@
+import datetime
 import os
 import time
-import datetime
 
 from torch.utils.tensorboard import SummaryWriter
 
-LOGS_DIR = '../tensorboard_logs'
+LOGS_DIR = "../tensorboard_logs"
 GLOBAL_TENSORBOARD_WRITER = None
 
 
 class TensorboardWriter(SummaryWriter):
-
     def __init__(self, experiment_name, use_time=True):
         self._experiment_name = experiment_name
         super().__init__(
-            log_dir=os.path.join(LOGS_DIR, f'{experiment_name}_{datetime.datetime.now().strftime("%Y-%m-%dT%H:%M" if use_time else "")}')
+            log_dir=os.path.join(
+                LOGS_DIR,
+                f"{experiment_name}_{datetime.datetime.now().strftime('%Y-%m-%dT%H:%M' if use_time else '')}",
+            )
         )
 
     def add_scalar(self, *args, **kwargs):
@@ -21,7 +23,6 @@ def add_scalar(self, *args, **kwargs):
 
 
 class TensorboardTimer:
-
     def __init__(self, scope):
         super().__init__(LOGS_DIR)
         self._scope = scope

From e96ee1f53e8c4a100db2d9334556de02f3fa7653 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 12:36:59 +0300
Subject: [PATCH 128/175] refactor sasrec semantic

---
 modeling/models/sasrec_semantic.py | 28 ++++++++++++----------------
 1 file changed, 12 insertions(+), 16 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 68af3035..1976b5bf 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -1,7 +1,6 @@
 import torch
-from torch import nn
-
 from models import SequentialTorchModel
+from torch import nn
 from utils import DEVICE, create_masked_tensor
 
 from .tiger import TigerModel
@@ -43,9 +42,6 @@ def __init__(
 
         self._codebook_sizes = rqvae_model.codebook_sizes
 
-        self._item_id_to_semantic_id = item_id_to_semantic_id
-        self._item_id_to_residual = item_id_to_residual
-
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
         )  # + 2 for bos token & residual
@@ -55,21 +51,19 @@ def __init__(
         self._codebook_item_embeddings_stacked = torch.stack(
             [codebook for codebook in rqvae_model.codebooks]
         )
-        self._codebook_item_embeddings_stacked.requires_grad = (
-            False  # TODOPK maybe unfreeeze later
-        )
-
-        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
+        self._codebook_item_embeddings_stacked = nn.Parameter(
+            self._codebook_item_embeddings_stacked, requires_grad=True
+        ) # TODOPK fix to use single rqvae codebook pointer
 
         self._item_id_to_semantic_embedding, self._item_id_to_full_embedding = (
-            self.get_init_item_embeddings(item_ids)
+            self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual)
         )
 
         self._item_id_to_semantic_embedding = nn.Parameter(
-            self._item_id_to_semantic_embedding, requires_grad=False
+            self._item_id_to_semantic_embedding, requires_grad=True
         )
         self._item_id_to_full_embedding = nn.Parameter(
-            self._item_id_to_full_embedding, requires_grad=False
+            self._item_id_to_full_embedding, requires_grad=True
         )
 
     @classmethod
@@ -170,9 +164,11 @@ def get_item_embeddings(self, events):
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
         return embs.reshape(-1, self._embedding_dim)
 
-    def get_init_item_embeddings(self, events):
+    def get_init_item_embeddings(self, item_id_to_semantic_id, item_id_to_residual):
+        events = torch.arange(1, len(item_id_to_semantic_id) + 1)
+
         # convert to semantic ids
-        semantic_ids = self._item_id_to_semantic_id[
+        semantic_ids = item_id_to_semantic_id[
             events - 1
         ]  # len(events), len(codebook_sizes)
 
@@ -190,7 +186,7 @@ def get_init_item_embeddings(self, events):
         )  # len(events), len(codebook_sizes), embedding_dim
 
         # get residuals
-        residual = self._item_id_to_residual[events - 1]
+        residual = item_id_to_residual[events - 1]
         # text_embeddings = self._item_id_to_text_embedding[events - 1]
         # residual = text_embeddings - semantic_embeddings.sum(dim=1)
         residual = residual.unsqueeze(1)

From aa01179d9c9cb6dc8b2f4313947292ffbad61a2e Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 18:07:59 +0300
Subject: [PATCH 129/175] unify semantic sasrec

---
 .../train/sasrec_semantic_train_config.json   |  2 +-
 modeling/models/sasrec_semantic.py            | 46 ++++++-------------
 2 files changed, 15 insertions(+), 33 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 1cdf6762..ffe4fb8c 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty",
+  "experiment_name": "sasrec_semantic_beauty_learnable",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 1976b5bf..cd8028c2 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -48,22 +48,15 @@ def __init__(
 
         self._init_weights(initializer_range)
 
-        self._codebook_item_embeddings_stacked = torch.stack(
-            [codebook for codebook in rqvae_model.codebooks]
-        )
         self._codebook_item_embeddings_stacked = nn.Parameter(
-            self._codebook_item_embeddings_stacked, requires_grad=True
-        ) # TODOPK fix to use single rqvae codebook pointer
-
-        self._item_id_to_semantic_embedding, self._item_id_to_full_embedding = (
-            self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual)
-        )
+            torch.stack([codebook for codebook in rqvae_model.codebooks]),
+            requires_grad=True,
+        )  # TODOPK fix to use single rqvae codebook pointer
+        # (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
 
         self._item_id_to_semantic_embedding = nn.Parameter(
-            self._item_id_to_semantic_embedding, requires_grad=True
-        )
-        self._item_id_to_full_embedding = nn.Parameter(
-            self._item_id_to_full_embedding, requires_grad=True
+            self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),
+            requires_grad=True,
         )
 
     @classmethod
@@ -110,7 +103,7 @@ def forward(self, inputs):
             all_embeddings = torch.cat(
                 [
                     torch.zeros(1, self._embedding_dim, device=DEVICE),
-                    self._item_id_to_full_embedding,
+                    self._item_id_to_semantic_embedding.sum(dim=1),
                     torch.zeros(1, self._embedding_dim, device=DEVICE),
                 ],
                 dim=0,
@@ -149,14 +142,16 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_id_to_full_embedding
+                "bd,nd->bn",
+                last_embeddings,
+                self._item_id_to_semantic_embedding.sum(dim=1),
             )  # (batch_size, num_items)
 
             _, indices = torch.topk(
                 candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices + 1
+            return indices + 1  # tensors are 0 indexed
 
     def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
@@ -165,15 +160,8 @@ def get_item_embeddings(self, events):
         return embs.reshape(-1, self._embedding_dim)
 
     def get_init_item_embeddings(self, item_id_to_semantic_id, item_id_to_residual):
-        events = torch.arange(1, len(item_id_to_semantic_id) + 1)
-
-        # convert to semantic ids
-        semantic_ids = item_id_to_semantic_id[
-            events - 1
-        ]  # len(events), len(codebook_sizes)
-
         result = []
-        for semantic_id in semantic_ids:
+        for semantic_id in item_id_to_semantic_id:
             item_repr = []
             for codebook_idx, codebook_id in enumerate(semantic_id):
                 item_repr.append(
@@ -185,20 +173,14 @@ def get_init_item_embeddings(self, item_id_to_semantic_id, item_id_to_residual):
             result
         )  # len(events), len(codebook_sizes), embedding_dim
 
-        # get residuals
-        residual = item_id_to_residual[events - 1]
-        # text_embeddings = self._item_id_to_text_embedding[events - 1]
-        # residual = text_embeddings - semantic_embeddings.sum(dim=1)
-        residual = residual.unsqueeze(1)
+        residual = item_id_to_residual.unsqueeze(1)
 
         # get true item embeddings
         item_embeddings = torch.cat(
             [semantic_embeddings, residual], dim=1
         )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
 
-        full_embeddings = item_embeddings.sum(dim=1)
-
-        return item_embeddings, full_embeddings
+        return item_embeddings
 
     def _encoder_pos_embeddings(self, lengths, mask):
         def position_lambda(x):

From 930e2af127ae8a5e9aa46a7998812c67cd6ac597 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 18:35:47 +0300
Subject: [PATCH 130/175] fixes for fast exps

---
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 configs/train/sasrec_train_config.json          | 4 ++--
 modeling/models/sasrec_semantic.py              | 3 +--
 modeling/models/tiger.py                        | 2 +-
 4 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index ffe4fb8c..c0f4ed29 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -80,7 +80,7 @@
       },
       {
         "type": "validation",
-        "on_step": 64,
+        "on_step": 4096,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -124,7 +124,7 @@
       },
       {
         "type": "eval",
-        "on_step": 256,
+        "on_step": 8192,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_train_config.json
index 8c64df89..b0c617c4 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_train_config.json
@@ -77,7 +77,7 @@
       },
       {
         "type": "validation",
-        "on_step": 64,
+        "on_step": 4096,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -121,7 +121,7 @@
       },
       {
         "type": "eval",
-        "on_step": 256,
+        "on_step": 8192,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index cd8028c2..eca8b977 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -51,8 +51,7 @@ def __init__(
         self._codebook_item_embeddings_stacked = nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
             requires_grad=True,
-        )  # TODOPK fix to use single rqvae codebook pointer
-        # (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
+        )  # (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
 
         self._item_id_to_semantic_embedding = nn.Parameter(
             self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index f62203cf..3ca8d30e 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -113,7 +113,7 @@ def __init__(
         )
 
     @classmethod
-    def init_rqvae(cls, config) -> RqVaeModel:
+    def init_rqvae(cls, config):
         rqvae_config = json.load(open(config["rqvae_train_config_path"]))
         rqvae_config["model"]["should_init_codebooks"] = False
 

From 181817bd8b7759fa9f549a190a1079b4e690e665 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 19:12:42 +0300
Subject: [PATCH 131/175] refactor tiger init_embeddings

---
 modeling/models/tiger.py | 36 ++++++++++++------------------------
 1 file changed, 12 insertions(+), 24 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 3ca8d30e..0eaadc25 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -70,9 +70,6 @@ def __init__(
 
         self._solver: CollisionSolver = solver
 
-        self._item_id_to_semantic_id = item_id_to_semantic_id
-        self._item_id_to_residual = item_id_to_residual
-
         self._codebook_sizes = rqvae_model.codebook_sizes
         self._bos_token_id = self._codebook_sizes[0]
         self._bos_weight = nn.Parameter(
@@ -93,14 +90,12 @@ def __init__(
 
         self._codebook_item_embeddings_stacked = nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
-            requires_grad=False,  # TODOPK compare with unfrozen codebooks
+            requires_grad=True,
         )
 
-        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
-        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
         self._item_id_to_semantic_embedding = nn.Parameter(
-            self._item_id_to_semantic_embedding,
-            requires_grad=False,  # TODOPK compare with unfrozen codebooks
+            self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),
+            requires_grad=True,
         )
 
         self._trie = SimplifiedTree(self._codebook_item_embeddings_stacked)
@@ -108,7 +103,7 @@ def __init__(
         self._trie.build_tree_structure(
             item_id_to_semantic_id.to(DEVICE),
             item_id_to_residual.to(DEVICE),
-            item_ids.to(DEVICE),
+            torch.arange(1, len(item_id_to_semantic_id) + 1).to(DEVICE),
             sum_with_residuals=False,
         )
 
@@ -380,23 +375,16 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             )
 
         return semantic_ids, tgt_embeddings
-
+ 
     def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
             events - 1
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.view(
-            len(events) * (len(self._codebook_sizes) + 1), self._embedding_dim
-        )
-
-    def get_init_item_embeddings(self, events):
-        # convert to semantic ids
-        semantic_ids = self._item_id_to_semantic_id[
-            events - 1
-        ]  # len(events), len(codebook_sizes)
+        return embs.reshape(-1, self._embedding_dim)
 
+    def get_init_item_embeddings(self, item_id_to_semantic_id, item_id_to_residual):
         result = []
-        for semantic_id in semantic_ids:
+        for semantic_id in item_id_to_semantic_id:
             item_repr = []
             for codebook_idx, codebook_id in enumerate(semantic_id):
                 item_repr.append(
@@ -404,11 +392,11 @@ def get_init_item_embeddings(self, events):
                 )
             result.append(torch.stack(item_repr))
 
-        semantic_embeddings = torch.stack(result)
+        semantic_embeddings = torch.stack(
+            result
+        )  # len(events), len(codebook_sizes), embedding_dim
 
-        # get residuals
-        residual = self._item_id_to_residual[events - 1]
-        residual = residual.unsqueeze(1)
+        residual = item_id_to_residual.unsqueeze(1)
 
         # get true item embeddings
         item_embeddings = torch.cat(

From 083176cb006269364388a17c852c5f2d178623b7 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 19:14:11 +0300
Subject: [PATCH 132/175] move decoder pos embs

---
 modeling/models/tiger.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 0eaadc25..75dad31a 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -305,6 +305,18 @@ def _apply_decoder(
         )  # (batch_size, dec_seq_len, embedding_dim)
 
         return decoder_outputs
+    
+    def _decoder_pos_embeddings(self, lengths, mask):
+        def codebook_lambda(x):
+            non_bos = x < len(self._codebook_sizes)
+            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
+            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ... len(self._codebook_sizes) = 3 for bos
+
+        codebook_embeddings = self._get_position_embeddings(
+            lengths, mask, codebook_lambda, self._codebook_embeddings
+        )
+
+        return codebook_embeddings
 
     def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
         batch_size = encoder_embeddings.shape[0]
@@ -427,18 +439,6 @@ def codebook_lambda(x):
 
         return position_embeddings + codebook_embeddings
 
-    def _decoder_pos_embeddings(self, lengths, mask):
-        def codebook_lambda(x):
-            non_bos = x < len(self._codebook_sizes)
-            x[non_bos] = (len(self._codebook_sizes) - 1) - x[non_bos]
-            return x  # 3, 0, 1, 2, 3, 0, 1, 2 ... len(self._codebook_sizes) = 3 for bos
-
-        codebook_embeddings = self._get_position_embeddings(
-            lengths, mask, codebook_lambda, self._codebook_embeddings
-        )
-
-        return codebook_embeddings
-
     def _get_position_embeddings(self, lengths, mask, position_lambda, embedding_layer):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]

From 0e878ee5bf726f1ef3a5ed280584b64ba88200fe Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 19:17:05 +0300
Subject: [PATCH 133/175] small refactors

---
 modeling/models/sasrec_semantic.py | 2 +-
 modeling/models/tiger.py           | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index eca8b977..399554f1 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -51,7 +51,7 @@ def __init__(
         self._codebook_item_embeddings_stacked = nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
             requires_grad=True,
-        )  # (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
+        )  # TODOPK (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
 
         self._item_id_to_semantic_embedding = nn.Parameter(
             self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 75dad31a..03bdb2e1 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -71,7 +71,6 @@ def __init__(
         self._solver: CollisionSolver = solver
 
         self._codebook_sizes = rqvae_model.codebook_sizes
-        self._bos_token_id = self._codebook_sizes[0]
         self._bos_weight = nn.Parameter(
             torch.nn.init.trunc_normal_(
                 torch.zeros(embedding_dim),
@@ -91,7 +90,7 @@ def __init__(
         self._codebook_item_embeddings_stacked = nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
             requires_grad=True,
-        )
+        )  # TODOPK (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
 
         self._item_id_to_semantic_embedding = nn.Parameter(
             self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),

From af08f7d94a8b1e40dfcce7cfb5231de9f399233a Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sat, 1 Mar 2025 23:08:41 +0300
Subject: [PATCH 134/175] test sasrec-like eval in tiger

---
 configs/train/tiger_train_config.json |  4 +-
 modeling/models/tiger.py              | 75 ++++++++++++++++-----------
 2 files changed, 46 insertions(+), 33 deletions(-)

diff --git a/configs/train/tiger_train_config.json b/configs/train/tiger_train_config.json
index 119337d1..4b14df40 100644
--- a/configs/train/tiger_train_config.json
+++ b/configs/train/tiger_train_config.json
@@ -89,7 +89,7 @@
       },
       {
         "type": "validation",
-        "on_step": 64,
+        "on_step": 1024,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -133,7 +133,7 @@
       },
       {
         "type": "eval",
-        "on_step": 256,
+        "on_step": 2048,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 03bdb2e1..ccf26af8 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -92,6 +92,9 @@ def __init__(
             requires_grad=True,
         )  # TODOPK (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
 
+        self._item_id_to_semantic_id = item_id_to_semantic_id
+        self._item_id_to_residual = item_id_to_residual
+
         self._item_id_to_semantic_embedding = nn.Parameter(
             self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),
             requires_grad=True,
@@ -177,9 +180,8 @@ def forward(self, inputs):
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
-        all_sample_lengths = all_sample_lengths * (len(self._codebook_sizes) + 1)
         encoder_embeddings, encoder_mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
+            all_sample_events, all_sample_lengths * (len(self._codebook_sizes) + 1)
         )  # (batch_size, enc_seq_len, embedding_dim), (batch_size, enc_seq_len)
 
         if self.training:
@@ -223,39 +225,50 @@ def forward(self, inputs):
                 "dedup.logits": pred_info["pred_scores"],
                 "dedup.labels.ids": true_info["true_dedup_tokens"],
             }
-        else:
-            semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
-                encoder_embeddings, encoder_mask
-            )  # (batch_size, len(self._codebook_sizes) (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
+        # else:
+        #     semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
+        #         encoder_embeddings, encoder_mask
+        #     )  # (batch_size, len(self._codebook_sizes) (bos, residual)), (batch_size, len(self._codebook_sizes) + 2 (bos, residual), embedding_dim)
+        #     TODOPK
+        #     # 1 4 6 -> lookup -> sum = emb (last embedding) # bs, embedding_dim
+        #     # take all embedings (from stacked) # all_items, embedding_dim
+        #     # take from sasrec eval (indices + 1)
+        #     # guarantee that all items are in correct order
 
-            # 1 4 6 -> lookup -> sum = emb (last embedding) # bs, embedding_dim
-            # take all embedings (from stacked) # all_items, embedding_dim
-            # take from sasrec eval (indices + 1)
-            # guarantee that all items are in correct order
+        #     residuals = tgt_embeddings[:, -1, :]
+        #     semantic_ids = semantic_ids.to(torch.int64)
 
-            residuals = tgt_embeddings[:, -1, :]
-            semantic_ids = semantic_ids.to(torch.int64)
+        #     item_ids = self._trie.query(semantic_ids, items_to_query=20)
 
-            item_ids = self._trie.query(semantic_ids, items_to_query=20)
+        #     return item_ids
 
-            return item_ids
+        else:  # eval mode
+            semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
+                encoder_embeddings, encoder_mask
+            )  # (batch_size, len(self._codebook_sizes)), (batch_size, len(self._codebook_sizes) + 2, embedding_dim)
 
-        # TODOPK (decompose tree)
-        # else:  # eval mode
-        #     last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
-        #     # b - batch_size, n - num_candidates, d - embedding_dim
-        #     candidate_scores = torch.einsum(
-        #         'bd,nd->bn',
-        #         last_embeddings,
-        #         self._item_embeddings.weight
-        #     )  # (batch_size, num_items + 2)
+            embs = []
+            for semantic_id in semantic_ids:
+                cur_emb = []
+                for idx, codebook_id in enumerate(semantic_id):
+                    cur_emb.append(
+                        self._codebook_item_embeddings_stacked[idx][codebook_id.item()]
+                    )
+                embs.append(torch.stack(cur_emb))
 
-        #     _, indices = torch.topk(
-        #         candidate_scores,
-        #         k=20, dim=-1, largest=True
-        #     )  # (batch_size, 20)
+            last_embeddings = torch.stack(embs).sum(dim=1)  # batch_size, embedding_dim
 
-        #     return indices + 1
+            candidate_scores = torch.einsum(
+                "bd,nd->bn",
+                last_embeddings,
+                self._item_id_to_semantic_embedding.sum(dim=1),
+            )  # (batch_size, num_items)
+
+            _, indices = torch.topk(
+                candidate_scores, k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices + 1  # tensors are 0 indexed
 
     def _apply_decoder(
         self, tgt_embeddings, label_lengths, encoder_embeddings, encoder_mask
@@ -304,7 +317,7 @@ def _apply_decoder(
         )  # (batch_size, dec_seq_len, embedding_dim)
 
         return decoder_outputs
-    
+
     def _decoder_pos_embeddings(self, lengths, mask):
         def codebook_lambda(x):
             non_bos = x < len(self._codebook_sizes)
@@ -327,7 +340,7 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             .expand(batch_size, 1, embedding_dim)
         )
 
-        semantic_ids = torch.tensor([], device=DEVICE)
+        semantic_ids = torch.tensor([], device=DEVICE, dtype=torch.int64)
 
         for step in range(len(self._codebook_sizes) + 1):  # semantic_id_seq + residual
             index = len(self._codebook_sizes) if step == 0 else step - 1
@@ -386,7 +399,7 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             )
 
         return semantic_ids, tgt_embeddings
- 
+
     def get_item_embeddings(self, events):
         embs = self._item_id_to_semantic_embedding[
             events - 1

From 870f3b4fdc4aa87ba34d76940e8b531d18c16006 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 2 Mar 2025 20:59:40 +0300
Subject: [PATCH 135/175] remove unneeded codebook in semantic

---
 modeling/models/sasrec_semantic.py | 13 +++++--------
 1 file changed, 5 insertions(+), 8 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 399554f1..6e0448e7 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -48,13 +48,8 @@ def __init__(
 
         self._init_weights(initializer_range)
 
-        self._codebook_item_embeddings_stacked = nn.Parameter(
-            torch.stack([codebook for codebook in rqvae_model.codebooks]),
-            requires_grad=True,
-        )  # TODOPK (ask is it ok to have separate codebooks and _item_id_to_semantic_embedding)
-
         self._item_id_to_semantic_embedding = nn.Parameter(
-            self.get_init_item_embeddings(item_id_to_semantic_id, item_id_to_residual),
+            self.get_init_item_embeddings(rqvae_model, item_id_to_semantic_id, item_id_to_residual),
             requires_grad=True,
         )
 
@@ -158,13 +153,15 @@ def get_item_embeddings(self, events):
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
         return embs.reshape(-1, self._embedding_dim)
 
-    def get_init_item_embeddings(self, item_id_to_semantic_id, item_id_to_residual):
+    def get_init_item_embeddings(self, rqvae_model, item_id_to_semantic_id, item_id_to_residual):
+        codebooks = torch.stack([codebook for codebook in rqvae_model.codebooks])
+
         result = []
         for semantic_id in item_id_to_semantic_id:
             item_repr = []
             for codebook_idx, codebook_id in enumerate(semantic_id):
                 item_repr.append(
-                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
+                    codebooks[codebook_idx][codebook_id]
                 )
             result.append(torch.stack(item_repr))
 

From 532dde024dbfb25492c3465a27a18e8daf637b65 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 2 Mar 2025 22:01:03 +0300
Subject: [PATCH 136/175] add causal mask

---
 modeling/models/tiger.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index ccf26af8..49015e2f 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -241,6 +241,9 @@ def forward(self, inputs):
         #     item_ids = self._trie.query(semantic_ids, items_to_query=20)
 
         #     return item_ids
+        #     TODOPK
+        #     uid -> hash (murmurhash32) -> modulo (2000) -> get_embedding -> prepend
+        #     first iteration -> for each user get embedding
 
         else:  # eval mode
             semantic_ids, tgt_embeddings = self._apply_decoder_autoregressive(
@@ -366,9 +369,14 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings[:, -1, :])
             # curr_embeddings[:, -1, :] = self._decoder_dropout(curr_embeddings[:, -1, :])
 
+            causal_mask = (
+                torch.tril(torch.ones(step + 1, step + 1)).bool().to(DEVICE)
+            )  # (dec_seq_len, dec_seq_len)
+
             decoder_output = self._decoder(
                 tgt=tgt_embeddings,
                 memory=encoder_embeddings,
+                tgt_mask=~causal_mask,
                 memory_key_padding_mask=~encoder_mask,
             )
 

From cf6bdbe4b0be4c8da00db532f1fd7aeb8a4e7015 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 5 Mar 2025 17:10:59 +0300
Subject: [PATCH 137/175] sasrec real draft

---
 .../samplers/last_item_prediction_neg.py      |  54 +++++++++
 modeling/loss/base.py                         |  28 +++++
 modeling/models/sasrec_real.py                | 104 ++++++++++++++++++
 modeling/models/tiger.py                      |   4 +-
 4 files changed, 189 insertions(+), 1 deletion(-)
 create mode 100644 modeling/dataset/samplers/last_item_prediction_neg.py
 create mode 100644 modeling/models/sasrec_real.py

diff --git a/modeling/dataset/samplers/last_item_prediction_neg.py b/modeling/dataset/samplers/last_item_prediction_neg.py
new file mode 100644
index 00000000..91444fa7
--- /dev/null
+++ b/modeling/dataset/samplers/last_item_prediction_neg.py
@@ -0,0 +1,54 @@
+import copy
+
+from dataset.samplers.base import EvalSampler, TrainSampler
+
+
+class LastItemPredictionTrainSampler(
+    TrainSampler, config_name="last_item_prediction_neg"
+):
+    def __init__(self, dataset, num_users, num_items):
+        super().__init__()
+        self._dataset = dataset
+        self._num_users = num_users
+        self._num_items = num_items
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+        )
+
+    def __getitem__(self, index):
+        sample = copy.deepcopy(self._dataset[index])
+
+        item_sequence = sample["item.ids"][:-1]
+        last_item = sample["item.ids"][-1]
+
+        negative_sequence = self._negative_sampler.generate_negative_samples(
+            sample, num_negatives
+        )
+
+        return {
+            "user.ids": sample["user.ids"],
+            "user.length": sample["user.length"],
+            "item.ids": item_sequence,
+            "item.length": len(item_sequence),
+            "labels.ids": [last_item],
+            "labels.length": 1,
+            "negative.ids": negative_sequence,
+            "negative.length": len(negative_sequence),
+        }
+
+
+class LastItemPredictionEvalSampler(
+    EvalSampler, config_name="last_item_prediction_neg"
+):
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            dataset=kwargs["dataset"],
+            num_users=kwargs["num_users"],
+            num_items=kwargs["num_items"],
+        )
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index d39fff6b..a0f9b180 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -320,6 +320,34 @@ def forward(self, inputs):
         return loss
 
 
+class SASRecRealLoss(TorchLoss, config_name='sasrec_real'):
+
+    def __init__(
+            self,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
+    ):
+        super().__init__()
+        self._positive_prefix = positive_prefix
+        self._negative_prefix = negative_prefix
+        self._output_prefix = output_prefix
+
+    def forward(self, inputs):
+        positive_scores = inputs[self._positive_prefix]  # (x)
+        negative_scores = inputs[self._negative_prefix]  # (x)
+        assert positive_scores.shape[0] == negative_scores.shape[0]
+
+        positive_loss = torch.log(nn.functional.sigmoid(positive_scores))  # (x)
+        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores))  # (x)
+        loss = positive_loss + negative_loss  # (x)
+        loss = -loss.mean()  # (1)
+
+        if self._output_prefix is not None:
+            inputs[self._output_prefix] = loss.cpu().item()
+
+        return loss
+
 class SASRecLoss(TorchLoss, config_name="sasrec"):
     def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
         super().__init__()
diff --git a/modeling/models/sasrec_real.py b/modeling/models/sasrec_real.py
new file mode 100644
index 00000000..5aca2457
--- /dev/null
+++ b/modeling/models/sasrec_real.py
@@ -0,0 +1,104 @@
+class SasRecRealModel(SequentialTorchModel, config_name='sasrec_real'):
+
+    def __init__(
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True
+        )
+        self._sequence_prefix = sequence_prefix
+        self._positive_prefix = positive_prefix
+        self._negative_prefix = negative_prefix
+
+        self._init_weights(initializer_range)
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
+        )
+
+    def forward(self, inputs):
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+
+        embeddings, mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        if self.training:  # training mode
+            # queries
+            in_batch_queries_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+
+            # positives
+            in_batch_positive_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            in_batch_positive_embeddings = self._item_embeddings(
+                in_batch_positive_events
+            )  # (all_batch_events, embedding_dim)
+            positive_scores = torch.einsum(
+                'bd,bd->b', in_batch_queries_embeddings, in_batch_positive_embeddings
+            ) # (all_batch_events)
+
+            # negatives
+            in_batch_negative_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+            in_batch_negative_embeddings = self._item_embeddings(
+                in_batch_negative_events
+            ) # (all_batch_events, embedding_dim)
+            negative_scores = torch.einsum(
+                'bd,bd->b', in_batch_queries_embeddings, in_batch_negative_embeddings
+            )  # (all_batch_events)
+
+            return {
+                'positive_scores': positive_scores, 
+                'negative_scores': negative_scores
+            }
+        else:  # eval mode
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+
+            # b - batch_size, n - num_candidates, d - embedding_dim
+            candidate_scores = torch.einsum(
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
+            )  # (batch_size, num_items + 2)
+            candidate_scores[:, 0] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
+
+            _, indices = torch.topk(
+                candidate_scores,
+                k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices
\ No newline at end of file
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index 49015e2f..c44c13f3 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -78,7 +78,7 @@ def __init__(
                 a=-2 * initializer_range,
                 b=2 * initializer_range,
             ),
-            requires_grad=True,  # TODOPK added for bos
+            requires_grad=True,
         )
 
         self._codebook_embeddings = nn.Embedding(
@@ -380,6 +380,8 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
                 memory_key_padding_mask=~encoder_mask,
             )
 
+            # TODOPK add assert for all except last layer (check if only last layer changes)
+            # TODOPK check decoder output for several outputs
             # TODOPK ASK it is not true?
             # assert that prelast items don't change
             # assert decoder changes only last index in dim = 1

From 91e49ff2a6cf8e0c267a239f9f73474cbcf4ed5c Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Wed, 5 Mar 2025 20:47:42 +0300
Subject: [PATCH 138/175] add sasrec real / semantic

---
 .gitignore                                    |   2 +-
 configs/train/sasrec_real_train_config.json   | 169 ++++++++++++++++++
 .../train/sasrec_semantic_train_config.json   |   3 +-
 .../dataset/samplers/last_item_prediction.py  |  43 ++++-
 .../samplers/last_item_prediction_neg.py      |  54 ------
 modeling/models/__init__.py                   |   1 +
 modeling/models/sasrec_real.py                | 106 ++++++-----
 modeling/models/sasrec_semantic.py            |  82 ++++-----
 8 files changed, 302 insertions(+), 158 deletions(-)
 create mode 100644 configs/train/sasrec_real_train_config.json
 delete mode 100644 modeling/dataset/samplers/last_item_prediction_neg.py

diff --git a/.gitignore b/.gitignore
index 51358cc9..8e376638 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
 .idea
 __pycache__
 data/*
-tensorboard_logs/*
+*tensorboard_logs*/*
 saved_logs/*
 .venv
 papers
diff --git a/configs/train/sasrec_real_train_config.json b/configs/train/sasrec_real_train_config.json
new file mode 100644
index 00000000..4301c2b3
--- /dev/null
+++ b/configs/train/sasrec_real_train_config.json
@@ -0,0 +1,169 @@
+{
+  "experiment_name": "sasrec_real_beauty",
+  "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
+  "dataset": {
+    "type": "sequence_full",
+    "path_to_data_dir": "../data",
+    "name": "Beauty",
+    "max_sequence_length": 50,
+    "samplers": {
+      "type": "last_item_prediction",
+      "num_negatives_train": 1,
+      "negative_sampler_type": "random"
+    }
+  },
+  "dataloader": {
+    "train": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": true,
+      "shuffle": true
+    },
+    "validation": {
+      "type": "torch",
+      "batch_size": 256,
+      "batch_processor": {
+        "type": "basic"
+      },
+      "drop_last": false,
+      "shuffle": false
+    }
+  },
+  "model": {
+    "type": "sasrec_real",
+    "sequence_prefix": "item",
+    "positive_prefix": "labels",
+    "negative_prefix": "negative",
+    "candidate_prefix": "candidates",
+    "embedding_dim": 64,
+    "num_heads": 2,
+    "num_layers": 2,
+    "dim_feedforward": 256,
+    "dropout": 0.3,
+    "activation": "gelu",
+    "layer_norm_eps": 1e-9,
+    "initializer_range": 0.02
+  },
+  "optimizer": {
+    "type": "basic",
+    "optimizer": {
+      "type": "adam",
+      "lr": 0.001
+    },
+    "clip_grad_threshold": 5.0
+  },
+  "loss": {
+    "type": "composite",
+    "losses": [
+      {
+        "type": "sasrec_real",
+        "positive_prefix": "positive_scores",
+        "negative_prefix": "negative_scores",
+        "output_prefix": "downstream_loss"
+      }
+    ],
+    "output_prefix": "loss"
+  },
+  "callback": {
+    "type": "composite",
+    "callbacks": [
+      {
+        "type": "metric",
+        "on_step": 1,
+        "loss_prefix": "loss"
+      },
+      {
+        "type": "validation",
+        "on_step": 4096,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      },
+      {
+        "type": "eval",
+        "on_step": 8192,
+        "pred_prefix": "logits",
+        "labels_prefix": "labels",
+        "metrics": {
+          "ndcg@5": {
+            "type": "ndcg",
+            "k": 5
+          },
+          "ndcg@10": {
+            "type": "ndcg",
+            "k": 10
+          },
+          "ndcg@20": {
+            "type": "ndcg",
+            "k": 20
+          },
+          "recall@5": {
+            "type": "recall",
+            "k": 5
+          },
+          "recall@10": {
+            "type": "recall",
+            "k": 10
+          },
+          "recall@20": {
+            "type": "recall",
+            "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
+          }
+        }
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index c0f4ed29..64485feb 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -9,6 +9,7 @@
     "max_sequence_length": 50,
     "samplers": {
       "type": "last_item_prediction",
+      "num_negatives_train": 1,
       "negative_sampler_type": "random"
     }
   },
@@ -62,7 +63,7 @@
     "type": "composite",
     "losses": [
       {
-        "type": "sasrec",
+        "type": "sasrec_real",
         "positive_prefix": "positive_scores",
         "negative_prefix": "negative_scores",
         "output_prefix": "downstream_loss"
diff --git a/modeling/dataset/samplers/last_item_prediction.py b/modeling/dataset/samplers/last_item_prediction.py
index 22eeed51..c9da6570 100644
--- a/modeling/dataset/samplers/last_item_prediction.py
+++ b/modeling/dataset/samplers/last_item_prediction.py
@@ -1,21 +1,30 @@
 import copy
 
 from dataset.samplers.base import EvalSampler, TrainSampler
+from dataset.negative_samplers.base import BaseNegativeSampler
 
 
 class LastItemPredictionTrainSampler(TrainSampler, config_name="last_item_prediction"):
-    def __init__(self, dataset, num_users, num_items):
+    def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives):
         super().__init__()
         self._dataset = dataset
         self._num_users = num_users
         self._num_items = num_items
+        self._negative_sampler = negative_sampler
+        self._num_negatives = num_negatives
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
+        negative_sampler = BaseNegativeSampler.create_from_config(
+            {"type": config["negative_sampler_type"]}, **kwargs
+        )
+
         return cls(
             dataset=kwargs["dataset"],
             num_users=kwargs["num_users"],
             num_items=kwargs["num_items"],
+            negative_sampler=negative_sampler,
+            num_negatives=config.get("num_negatives_train", 0),
         )
 
     def __getitem__(self, index):
@@ -24,14 +33,30 @@ def __getitem__(self, index):
         item_sequence = sample["item.ids"][:-1]
         last_item = sample["item.ids"][-1]
 
-        return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "labels.ids": [last_item],
-            "labels.length": 1,
-        }
+        if self._num_negatives == 0:
+            return {
+                "user.ids": sample["user.ids"],
+                "user.length": sample["user.length"],
+                "item.ids": item_sequence,
+                "item.length": len(item_sequence),
+                "labels.ids": [last_item],
+                "labels.length": 1,
+            }
+        else:
+            negative_sequence = self._negative_sampler.generate_negative_samples(
+                sample, self._num_negatives
+            )
+
+            return {
+                "user.ids": sample["user.ids"],
+                "user.length": sample["user.length"],
+                "item.ids": item_sequence,
+                "item.length": len(item_sequence),
+                "labels.ids": [last_item],
+                "labels.length": 1,
+                "negative.ids": negative_sequence,
+                "negative.length": len(negative_sequence),
+            }
 
 
 class LastItemPredictionEvalSampler(EvalSampler, config_name="last_item_prediction"):
diff --git a/modeling/dataset/samplers/last_item_prediction_neg.py b/modeling/dataset/samplers/last_item_prediction_neg.py
deleted file mode 100644
index 91444fa7..00000000
--- a/modeling/dataset/samplers/last_item_prediction_neg.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import copy
-
-from dataset.samplers.base import EvalSampler, TrainSampler
-
-
-class LastItemPredictionTrainSampler(
-    TrainSampler, config_name="last_item_prediction_neg"
-):
-    def __init__(self, dataset, num_users, num_items):
-        super().__init__()
-        self._dataset = dataset
-        self._num_users = num_users
-        self._num_items = num_items
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-        )
-
-    def __getitem__(self, index):
-        sample = copy.deepcopy(self._dataset[index])
-
-        item_sequence = sample["item.ids"][:-1]
-        last_item = sample["item.ids"][-1]
-
-        negative_sequence = self._negative_sampler.generate_negative_samples(
-            sample, num_negatives
-        )
-
-        return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "labels.ids": [last_item],
-            "labels.length": 1,
-            "negative.ids": negative_sequence,
-            "negative.length": len(negative_sequence),
-        }
-
-
-class LastItemPredictionEvalSampler(
-    EvalSampler, config_name="last_item_prediction_neg"
-):
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-        )
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index 8f8e2039..92df1c34 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -17,5 +17,6 @@
 from .sasrec import SasRecInBatchModel, SasRecModel
 from .sasrec_ce import SasRecCeModel
 from .sasrec_freezed import SasRecFreezedModel
+from .sasrec_real import SasRecRealModel
 from .sasrec_semantic import SasRecSemanticModel
 from .tiger import TigerModel
diff --git a/modeling/models/sasrec_real.py b/modeling/models/sasrec_real.py
index 5aca2457..3776806c 100644
--- a/modeling/models/sasrec_real.py
+++ b/modeling/models/sasrec_real.py
@@ -1,20 +1,24 @@
-class SasRecRealModel(SequentialTorchModel, config_name='sasrec_real'):
+import torch
+from models.base import SequentialTorchModel
+
+
+class SasRecRealModel(SequentialTorchModel, config_name="sasrec_real"):
 
     def __init__(
-            self,
-            sequence_prefix,
-            positive_prefix,
-            negative_prefix,
-            num_items,
-            max_sequence_length,
-            embedding_dim,
-            num_heads,
-            num_layers,
-            dim_feedforward,
-            dropout=0.0,
-            activation='relu',
-            layer_norm_eps=1e-9,
-            initializer_range=0.02
+        self,
+        sequence_prefix,
+        positive_prefix,
+        negative_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
     ):
         super().__init__(
             num_items=num_items,
@@ -26,7 +30,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True
+            is_causal=True,
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -37,68 +41,76 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config['sequence_prefix'],
-            positive_prefix=config['positive_prefix'],
-            negative_prefix=config['negative_prefix'],
-            num_items=kwargs['num_items'],
-            max_sequence_length=kwargs['max_sequence_length'],
-            embedding_dim=config['embedding_dim'],
-            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
-            num_layers=config['num_layers'],
-            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
-            dropout=config.get('dropout', 0.0),
-            initializer_range=config.get('initializer_range', 0.02)
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
-        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         if self.training:  # training mode
-            # queries
-            in_batch_queries_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
             # positives
-            in_batch_positive_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            in_batch_positive_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
             in_batch_positive_embeddings = self._item_embeddings(
                 in_batch_positive_events
             )  # (all_batch_events, embedding_dim)
             positive_scores = torch.einsum(
-                'bd,bd->b', in_batch_queries_embeddings, in_batch_positive_embeddings
-            ) # (all_batch_events)
+                "bd,bd->b", last_embeddings, in_batch_positive_embeddings
+            )  # (all_batch_events)
 
             # negatives
-            in_batch_negative_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+            in_batch_negative_events = inputs[
+                "{}.ids".format(self._negative_prefix)
+            ]  # (all_batch_events)
             in_batch_negative_embeddings = self._item_embeddings(
                 in_batch_negative_events
-            ) # (all_batch_events, embedding_dim)
+            )  # (all_batch_events, embedding_dim)
             negative_scores = torch.einsum(
-                'bd,bd->b', in_batch_queries_embeddings, in_batch_negative_embeddings
+                "bd,bd->b", last_embeddings, in_batch_negative_embeddings
             )  # (all_batch_events)
 
             return {
-                'positive_scores': positive_scores, 
-                'negative_scores': negative_scores
+                "positive_scores": positive_scores,
+                "negative_scores": negative_scores,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
 
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                'bd,nd->bn',
-                last_embeddings,
-                self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1:] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores,
-                k=20, dim=-1, largest=True
+                candidate_scores, k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
\ No newline at end of file
+            return indices
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 6e0448e7..7ba174ac 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -14,6 +14,7 @@ def __init__(
         item_id_to_residual,
         sequence_prefix,
         positive_prefix,
+        negative_prefix,
         num_items,
         max_sequence_length,
         embedding_dim,
@@ -39,6 +40,7 @@ def __init__(
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
+        self._negative_prefix = negative_prefix
 
         self._codebook_sizes = rqvae_model.codebook_sizes
 
@@ -49,7 +51,9 @@ def __init__(
         self._init_weights(initializer_range)
 
         self._item_id_to_semantic_embedding = nn.Parameter(
-            self.get_init_item_embeddings(rqvae_model, item_id_to_semantic_id, item_id_to_residual),
+            self.get_init_item_embeddings(
+                rqvae_model, item_id_to_semantic_id, item_id_to_residual
+            ),
             requires_grad=True,
         )
 
@@ -63,6 +67,7 @@ def create_from_config(cls, config, **kwargs):
             item_id_to_residual=residuals,
             sequence_prefix=config["sequence_prefix"],
             positive_prefix=config["positive_prefix"],
+            negative_prefix=config["negative_prefix"],
             num_items=kwargs["num_items"],
             max_sequence_length=kwargs["max_sequence_length"],
             embedding_dim=config["embedding_dim"],
@@ -85,60 +90,45 @@ def forward(self, inputs):
             all_sample_events, all_sample_lengths * (len(self._codebook_sizes) + 1)
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
+
+        item_embeddings = self._item_id_to_semantic_embedding.sum(dim=1)
+
         if self.training:  # training mode
-            all_positive_sample_events = inputs[
+            # positives
+            in_batch_positive_events = inputs[
                 "{}.ids".format(self._positive_prefix)
             ]  # (all_batch_events)
-
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
-            all_embeddings = torch.cat(
-                [
-                    torch.zeros(1, self._embedding_dim, device=DEVICE),
-                    self._item_id_to_semantic_embedding.sum(dim=1),
-                    torch.zeros(1, self._embedding_dim, device=DEVICE),
-                ],
-                dim=0,
-            )  # (num_items + 2, embedding_dim)
-
-            # a -- all_batch_events, n -- num_items, d -- embedding_dim
-            all_scores = torch.einsum(
-                "ad,nd->an", last_embeddings, all_embeddings
-            )  # (batch_size, num_items + 2)
-
-            positive_scores = torch.gather(
-                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
-            )  # (batch_size, 1)
-
-            sample_ids, _ = create_masked_tensor(
-                data=all_sample_events, lengths=all_sample_lengths
-            )  # (batch_size, seq_len)
-
-            negative_scores = torch.scatter(
-                input=all_scores,
-                dim=1,
-                index=sample_ids,
-                src=torch.ones_like(sample_ids) * (-torch.inf),
-            )  # (all_batch_events, num_items + 2)
-            negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
+            in_batch_positive_embeddings = item_embeddings[
+                in_batch_positive_events - 1
+            ]  # (all_batch_events, embedding_dim)
+            positive_scores = torch.einsum(
+                "bd,bd->b", last_embeddings, in_batch_positive_embeddings
+            )  # (all_batch_events)
+
+            # negatives
+            in_batch_negative_events = inputs[
+                "{}.ids".format(self._negative_prefix)
+            ]  # (all_batch_events)
+            in_batch_negative_embeddings = item_embeddings[
+                in_batch_negative_events - 1
+            ]  # (all_batch_events, embedding_dim)
+            negative_scores = torch.einsum(
+                "bd,bd->b", last_embeddings, in_batch_negative_embeddings
+            )  # (all_batch_events)
 
             return {
                 "positive_scores": positive_scores,
                 "negative_scores": negative_scores,
-                "sample_ids": sample_ids,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
                 "bd,nd->bn",
                 last_embeddings,
-                self._item_id_to_semantic_embedding.sum(dim=1),
+                item_embeddings,
             )  # (batch_size, num_items)
 
             _, indices = torch.topk(
@@ -153,16 +143,16 @@ def get_item_embeddings(self, events):
         ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
         return embs.reshape(-1, self._embedding_dim)
 
-    def get_init_item_embeddings(self, rqvae_model, item_id_to_semantic_id, item_id_to_residual):
+    def get_init_item_embeddings(
+        self, rqvae_model, item_id_to_semantic_id, item_id_to_residual
+    ):
         codebooks = torch.stack([codebook for codebook in rqvae_model.codebooks])
 
         result = []
         for semantic_id in item_id_to_semantic_id:
             item_repr = []
             for codebook_idx, codebook_id in enumerate(semantic_id):
-                item_repr.append(
-                    codebooks[codebook_idx][codebook_id]
-                )
+                item_repr.append(codebooks[codebook_idx][codebook_id])
             result.append(torch.stack(item_repr))
 
         semantic_embeddings = torch.stack(

From b0b893cceacc3f8f7ff677c956c697a5a0efa9d6 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Wed, 5 Mar 2025 21:56:09 +0300
Subject: [PATCH 139/175] add uid embeds

---
 configs/train/sasrec_semantic_train_config.json |  2 +-
 modeling/models/base.py                         | 12 +++++++++++-
 modeling/models/sasrec_semantic.py              | 14 +++++++++++++-
 3 files changed, 25 insertions(+), 3 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 64485feb..95371221 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_learnable",
+  "experiment_name": "sasrec_semantic_beauty_learnable_uid",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/base.py b/modeling/models/base.py
index b4dd440a..786ef12c 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -102,7 +102,9 @@ def __init__(
     def get_item_embeddings(self, events):
         return self._item_embeddings(events)
 
-    def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
+    def _apply_sequential_encoder(
+        self, events, lengths, add_cls_token=False, user_embeddings=None
+    ):
         embeddings = self.get_item_embeddings(
             events
         )  # (all_batch_events, embedding_dim)
@@ -137,6 +139,14 @@ def _apply_sequential_encoder(self, events, lengths, add_cls_token=False):
                 dim=1,
             )
 
+        if user_embeddings is not None:
+            embeddings = torch.cat((user_embeddings.unsqueeze(1), embeddings), dim=1)
+            mask = torch.cat(
+                (torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask),
+                dim=1,
+            )
+            seq_len += 1  # TODOPK ask if this is correct
+
         if self._is_causal:
             causal_mask = (
                 torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 7ba174ac..b8c778a2 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -16,6 +16,7 @@ def __init__(
         positive_prefix,
         negative_prefix,
         num_items,
+        num_users,
         max_sequence_length,
         embedding_dim,
         num_heads,
@@ -42,12 +43,18 @@ def __init__(
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
 
+        self._num_users = num_users
+
         self._codebook_sizes = rqvae_model.codebook_sizes
 
         self._codebook_embeddings = nn.Embedding(
             num_embeddings=len(self._codebook_sizes) + 2, embedding_dim=embedding_dim
         )  # + 2 for bos token & residual
 
+        self._user_embeddings = nn.Embedding(
+            num_embeddings=self._num_users + 1, embedding_dim=embedding_dim
+        )
+
         self._init_weights(initializer_range)
 
         self._item_id_to_semantic_embedding = nn.Parameter(
@@ -69,6 +76,7 @@ def create_from_config(cls, config, **kwargs):
             positive_prefix=config["positive_prefix"],
             negative_prefix=config["negative_prefix"],
             num_items=kwargs["num_items"],
+            num_users=kwargs["num_users"],
             max_sequence_length=kwargs["max_sequence_length"],
             embedding_dim=config["embedding_dim"],
             num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
@@ -86,8 +94,12 @@ def forward(self, inputs):
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
+        user_embeddings = self._user_embeddings(inputs["user.ids"])
+
         embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths * (len(self._codebook_sizes) + 1)
+            all_sample_events,
+            all_sample_lengths * (len(self._codebook_sizes) + 1),
+            user_embeddings=user_embeddings,
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         last_embeddings = self._get_last_embedding(

From b25b001d0dc02dbea3ea62c6ebdecb6df5faf8e2 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 5 Mar 2025 23:26:36 +0300
Subject: [PATCH 140/175] fix random sampler

---
 modeling/dataset/negative_samplers/random.py | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/modeling/dataset/negative_samplers/random.py b/modeling/dataset/negative_samplers/random.py
index b4f44f8c..24055217 100644
--- a/modeling/dataset/negative_samplers/random.py
+++ b/modeling/dataset/negative_samplers/random.py
@@ -17,15 +17,11 @@ def create_from_config(cls, _, **kwargs):
 
     def generate_negative_samples(self, sample, num_negatives):
         user_id = sample["user.ids"][0]
-        all_items = list(range(1, self._num_items + 1))
-        np.random.shuffle(all_items)
+        negatives = set()
 
-        negatives = []
-        running_idx = 0
-        while len(negatives) < num_negatives and running_idx < len(all_items):
-            negative_idx = all_items[running_idx]
-            if negative_idx not in self._seen_items[user_id]:
-                negatives.append(negative_idx)
-            running_idx += 1
+        while len(negatives) < num_negatives:
+            candidate = np.random.randint(1, self._num_items + 1)
+            if candidate not in self._seen_items[user_id]:
+                negatives.add(candidate)
 
-        return negatives
+        return list(negatives)

From 7b6b3d0648c628aeec43d64378f1860bcdb69bf0 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 14:03:02 +0300
Subject: [PATCH 141/175] scientific full

---
 configs/train/sasrec_real_train_config.json |   4 +-
 modeling/dataset/base.py                    | 116 ++++++++++++++++++++
 2 files changed, 118 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_real_train_config.json b/configs/train/sasrec_real_train_config.json
index 4301c2b3..6c53dcc4 100644
--- a/configs/train/sasrec_real_train_config.json
+++ b/configs/train/sasrec_real_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_real_beauty",
+  "experiment_name": "sasrec_real_beauty_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence_full",
+    "type": "scientific_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 4e0aea5b..9997bc44 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -860,6 +860,122 @@ def meta(self):
         }
 
 
+class ScientificFullDataset(ScientificDataset, config_name="scientific_full"):
+    def __init__(
+        self,
+        train_sampler,
+        validation_sampler,
+        test_sampler,
+        num_users,
+        num_items,
+        max_sequence_length,
+    ):
+        self._train_sampler = train_sampler
+        self._validation_sampler = validation_sampler
+        self._test_sampler = test_sampler
+        self._num_users = num_users
+        self._num_items = num_items
+        self._max_sequence_length = max_sequence_length
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        max_sequence_length = config["max_sequence_length"]
+        max_user_id, max_item_id = 0, 0
+        train_dataset, validation_dataset, test_dataset = [], [], []
+
+        dataset_path = os.path.join(data_dir_path, "{}.txt".format("all_data"))
+        with open(dataset_path, "r") as f:
+            data = f.readlines()
+
+        for sample in data:
+            sample = sample.strip("\n").split(" ")
+            user_id = int(sample[0])
+            item_ids = [int(item_id) for item_id in sample[1:]]
+
+            max_user_id = max(max_user_id, user_id)
+            max_item_id = max(max_item_id, max(item_ids))
+
+            assert len(item_ids) >= 5
+
+            for prefix_length in range(5, len(item_ids) + 1):
+                prefix = item_ids[:prefix_length]
+
+                train_dataset.append(
+                    {
+                        "user.ids": [user_id],
+                        "user.length": 1,
+                        "item.ids": prefix[:-2][-max_sequence_length:],
+                        "item.length": len(prefix[:-2][-max_sequence_length:]),
+                    }
+                )
+                assert len(prefix[:-2][-max_sequence_length:]) == len(
+                    set(prefix[:-2][-max_sequence_length:])
+                )
+                validation_dataset.append(
+                    {
+                        "user.ids": [user_id],
+                        "user.length": 1,
+                        "item.ids": prefix[:-1][-max_sequence_length:],
+                        "item.length": len(prefix[:-1][-max_sequence_length:]),
+                    }
+                )
+                assert len(prefix[:-1][-max_sequence_length:]) == len(
+                    set(prefix[:-1][-max_sequence_length:])
+                )
+                test_dataset.append(
+                    {
+                        "user.ids": [user_id],
+                        "user.length": 1,
+                        "item.ids": prefix[-max_sequence_length:],
+                        "item.length": len(prefix[-max_sequence_length:]),
+                    }
+                )
+                assert len(prefix[-max_sequence_length:]) == len(
+                    set(prefix[-max_sequence_length:])
+                )
+
+        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Test dataset size: {}".format(len(test_dataset)))
+        logger.info("Max user id: {}".format(max_user_id))
+        logger.info("Max item id: {}".format(max_item_id))
+        logger.info("Max sequence length: {}".format(max_sequence_length))
+        logger.info(
+            "{} dataset sparsity: {}".format(
+                config["name"],
+                (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id,
+            )
+        )
+
+        train_sampler = TrainSampler.create_from_config(
+            config["samplers"],
+            dataset=train_dataset,
+            num_users=max_user_id,
+            num_items=max_item_id,
+        )
+        validation_sampler = EvalSampler.create_from_config(
+            config["samplers"],
+            dataset=validation_dataset,
+            num_users=max_user_id,
+            num_items=max_item_id,
+        )
+        test_sampler = EvalSampler.create_from_config(
+            config["samplers"],
+            dataset=test_dataset,
+            num_users=max_user_id,
+            num_items=max_item_id,
+        )
+
+        return cls(
+            train_sampler=train_sampler,
+            validation_sampler=validation_sampler,
+            test_sampler=test_sampler,
+            num_users=max_user_id,
+            num_items=max_item_id,
+            max_sequence_length=max_sequence_length,
+        )
+
+
 class RqVaeDataset(BaseDataset, config_name="rqvae"):
     def __init__(self, train_sampler, validation_sampler, test_sampler, num_items):
         self._train_sampler = train_sampler

From 2f58166c1a1834550988112f055dc0a066d6b777 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 14:06:46 +0300
Subject: [PATCH 142/175] semantic learnable uid loo

---
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 95371221..e1debce6 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_learnable_uid",
+  "experiment_name": "sasrec_semantic_beauty_learnable_uid_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence_full",
+    "type": "scientific_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,

From 49dcb9624ccd22a47c3dadd2feac97e1a8991187 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 14:11:25 +0300
Subject: [PATCH 143/175] freezed sasrec semantic

---
 configs/train/sasrec_semantic_train_config.json | 2 +-
 modeling/models/sasrec_semantic.py              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index e1debce6..0f1f465d 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_learnable_uid_loo",
+  "experiment_name": "sasrec_semantic_beauty_freezed_uid_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index b8c778a2..e6b43e85 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -61,7 +61,7 @@ def __init__(
             self.get_init_item_embeddings(
                 rqvae_model, item_id_to_semantic_id, item_id_to_residual
             ),
-            requires_grad=True,
+            requires_grad=False,
         )
 
     @classmethod

From 48530024017e525dd5fc5802a8069559c65b1789 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 14:13:36 +0300
Subject: [PATCH 144/175] freezed loo

---
 configs/train/sasrec_semantic_train_config.json | 2 +-
 modeling/models/sasrec_semantic.py              | 3 ---
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 0f1f465d..c3bb906f 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_freezed_uid_loo",
+  "experiment_name": "sasrec_semantic_beauty_freezed_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index e6b43e85..1fdd62b4 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -94,12 +94,9 @@ def forward(self, inputs):
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
-        user_embeddings = self._user_embeddings(inputs["user.ids"])
-
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events,
             all_sample_lengths * (len(self._codebook_sizes) + 1),
-            user_embeddings=user_embeddings,
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         last_embeddings = self._get_last_embedding(

From f3c3c485ffefa788618087046a46af453f8822f0 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 14:15:01 +0300
Subject: [PATCH 145/175] learnable loo

---
 configs/train/sasrec_semantic_train_config.json | 2 +-
 modeling/models/sasrec_semantic.py              | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index c3bb906f..2adff8dd 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_freezed_loo",
+  "experiment_name": "sasrec_semantic_beauty_learnable_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 1fdd62b4..8c759abb 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -61,7 +61,7 @@ def __init__(
             self.get_init_item_embeddings(
                 rqvae_model, item_id_to_semantic_id, item_id_to_residual
             ),
-            requires_grad=False,
+            requires_grad=True,
         )
 
     @classmethod

From 785b1e3b53b06feefb7a36e428c337c1e78fa60c Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 21:00:41 +0300
Subject: [PATCH 146/175] loo & sequence dataset fixes

---
 modeling/dataset/base.py | 65 +++++++++++++++++++++++-----------------
 1 file changed, 38 insertions(+), 27 deletions(-)

diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 9997bc44..3ab6b9d9 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -323,7 +323,7 @@ def create_from_config(cls, config, **kwargs):
 
     @classmethod
     def flatten_item_sequence(cls, item_ids):
-        min_history_length = 3  # TODOPK make this configurable
+        min_history_length = 3
         histories = []
         for i in range(min_history_length - 1, len(item_ids)):
             histories.append(item_ids[: i + 1])
@@ -373,14 +373,24 @@ def _create_dataset(
 
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
-                flattened_item_ids = cls.flatten_item_sequence(item_ids)
-                for seq in flattened_item_ids:
+                if part == "train":
+                    flattened_item_ids = cls.flatten_item_sequence(item_ids)
+                    for seq in flattened_item_ids:
+                        dataset.append(
+                            {
+                                "user.ids": [user_id],
+                                "user.length": 1,
+                                "item.ids": seq,
+                                "item.length": len(seq),
+                            }
+                        )
+                else:
                     dataset.append(
                         {
                             "user.ids": [user_id],
                             "user.length": 1,
-                            "item.ids": seq,
-                            "item.length": len(seq),
+                            "item.ids": item_ids,
+                            "item.length": len(item_ids),
                         }
                     )
 
@@ -912,28 +922,29 @@ def create_from_config(cls, config, **kwargs):
                 assert len(prefix[:-2][-max_sequence_length:]) == len(
                     set(prefix[:-2][-max_sequence_length:])
                 )
-                validation_dataset.append(
-                    {
-                        "user.ids": [user_id],
-                        "user.length": 1,
-                        "item.ids": prefix[:-1][-max_sequence_length:],
-                        "item.length": len(prefix[:-1][-max_sequence_length:]),
-                    }
-                )
-                assert len(prefix[:-1][-max_sequence_length:]) == len(
-                    set(prefix[:-1][-max_sequence_length:])
-                )
-                test_dataset.append(
-                    {
-                        "user.ids": [user_id],
-                        "user.length": 1,
-                        "item.ids": prefix[-max_sequence_length:],
-                        "item.length": len(prefix[-max_sequence_length:]),
-                    }
-                )
-                assert len(prefix[-max_sequence_length:]) == len(
-                    set(prefix[-max_sequence_length:])
-                )
+
+            validation_dataset.append(
+                {
+                    "user.ids": [user_id],
+                    "user.length": 1,
+                    "item.ids": item_ids[:-1][-max_sequence_length:],
+                    "item.length": len(item_ids[:-1][-max_sequence_length:]),
+                }
+            )
+            assert len(item_ids[:-1][-max_sequence_length:]) == len(
+                set(item_ids[:-1][-max_sequence_length:])
+            )
+            test_dataset.append(
+                {
+                    "user.ids": [user_id],
+                    "user.length": 1,
+                    "item.ids": item_ids[-max_sequence_length:],
+                    "item.length": len(item_ids[-max_sequence_length:]),
+                }
+            )
+            assert len(item_ids[-max_sequence_length:]) == len(
+                set(item_ids[-max_sequence_length:])
+            )
 
         logger.info("Train dataset size: {}".format(len(train_dataset)))
         logger.info("Test dataset size: {}".format(len(test_dataset)))

From 5fc4317e607389b2ec617f48f1a9407e00657b88 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 21:07:10 +0300
Subject: [PATCH 147/175] add validation size logging

---
 configs/train/sasrec_real_train_config.json     | 4 ++--
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 modeling/dataset/base.py                        | 6 +++++-
 3 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/configs/train/sasrec_real_train_config.json b/configs/train/sasrec_real_train_config.json
index 6c53dcc4..ae3118ca 100644
--- a/configs/train/sasrec_real_train_config.json
+++ b/configs/train/sasrec_real_train_config.json
@@ -78,7 +78,7 @@
       },
       {
         "type": "validation",
-        "on_step": 4096,
+        "on_step": 1024,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -122,7 +122,7 @@
       },
       {
         "type": "eval",
-        "on_step": 8192,
+        "on_step": 2048,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 2adff8dd..822a7f08 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -81,7 +81,7 @@
       },
       {
         "type": "validation",
-        "on_step": 4096,
+        "on_step": 1024,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -125,7 +125,7 @@
       },
       {
         "type": "eval",
-        "on_step": 8192,
+        "on_step": 2048,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 3ab6b9d9..b2e53b3e 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -274,6 +274,7 @@ def create_from_config(cls, config, **kwargs):
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
         logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Validation dataset size: {}".format(len(validation_dataset)))
         logger.info("Test dataset size: {}".format(len(test_dataset)))
         logger.info("Max user id: {}".format(max_user_id))
         logger.info("Max item id: {}".format(max_item_id))
@@ -909,7 +910,9 @@ def create_from_config(cls, config, **kwargs):
             assert len(item_ids) >= 5
 
             for prefix_length in range(5, len(item_ids) + 1):
-                prefix = item_ids[:prefix_length]
+                prefix = item_ids[
+                    :prefix_length
+                ]  # TODOPK no sliding window, only incrmenting sequence from last 50 items
 
                 train_dataset.append(
                     {
@@ -947,6 +950,7 @@ def create_from_config(cls, config, **kwargs):
             )
 
         logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info("Validation dataset size: {}".format(len(validation_dataset)))
         logger.info("Test dataset size: {}".format(len(test_dataset)))
         logger.info("Max user id: {}".format(max_user_id))
         logger.info("Max item id: {}".format(max_item_id))

From 2f484cbb33ebf80ccab4a10c6c82ebee02b2dcc3 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 21:12:45 +0300
Subject: [PATCH 148/175] uid

---
 configs/train/sasrec_semantic_train_config.json | 2 +-
 modeling/models/sasrec_semantic.py              | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 822a7f08..87b12bec 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_learnable_loo",
+  "experiment_name": "sasrec_semantic_beauty_learnable_uid_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 8c759abb..b8c778a2 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -94,9 +94,12 @@ def forward(self, inputs):
             "{}.length".format(self._sequence_prefix)
         ]  # (batch_size)
 
+        user_embeddings = self._user_embeddings(inputs["user.ids"])
+
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events,
             all_sample_lengths * (len(self._codebook_sizes) + 1),
+            user_embeddings=user_embeddings,
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         last_embeddings = self._get_last_embedding(

From d3d05d6cead26ffb5fa458778eb5bafbeaa6b668 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 21:27:42 +0300
Subject: [PATCH 149/175] sequence dfs

---
 configs/train/sasrec_real_train_config.json     | 4 ++--
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/configs/train/sasrec_real_train_config.json b/configs/train/sasrec_real_train_config.json
index ae3118ca..c43c3d42 100644
--- a/configs/train/sasrec_real_train_config.json
+++ b/configs/train/sasrec_real_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_real_beauty_loo",
+  "experiment_name": "sasrec_real_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "scientific_full",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 87b12bec..5c3630ee 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_learnable_uid_loo",
+  "experiment_name": "sasrec_semantic_beauty_learnable_uid",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "scientific_full",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,

From 83f07a676e83f5607e4acd7fd400b81d8779eb51 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 21:28:02 +0300
Subject: [PATCH 150/175] use second cuda

---
 modeling/utils/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 1d62369e..81fae0e4 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -11,7 +11,7 @@
 from utils.registry import MetaParent
 
 if torch.cuda.is_available():
-    DEVICE = torch.device("cuda")
+    DEVICE = torch.device("cuda:1")
 else:
     DEVICE = torch.device("cpu")
 

From 521fe868cd5e77625a352c9680d9fd05b4233601 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 8 Mar 2025 22:19:01 +0300
Subject: [PATCH 151/175] revert cuda & remove freezed

---
 .../train/sasrec_freezed_train_config.json    | 171 ------------------
 modeling/utils/__init__.py                    |   2 +-
 2 files changed, 1 insertion(+), 172 deletions(-)
 delete mode 100644 configs/train/sasrec_freezed_train_config.json

diff --git a/configs/train/sasrec_freezed_train_config.json b/configs/train/sasrec_freezed_train_config.json
deleted file mode 100644
index 209a5671..00000000
--- a/configs/train/sasrec_freezed_train_config.json
+++ /dev/null
@@ -1,171 +0,0 @@
-{
-  "experiment_name": "sasrec_freezed_beauty",
-  "best_metric": "validation/ndcg@20",
-  "train_epochs_num": 100,
-  "dataset": {
-    "type": "sequence_full",
-    "path_to_data_dir": "../data",
-    "name": "Beauty",
-    "max_sequence_length": 50,
-    "samplers": {
-      "type": "last_item_prediction",
-      "negative_sampler_type": "random"
-    }
-  },
-  "dataloader": {
-    "train": {
-      "type": "torch",
-      "batch_size": 256,
-      "batch_processor": {
-        "type": "basic"
-      },
-      "drop_last": true,
-      "shuffle": true
-    },
-    "validation": {
-      "type": "torch",
-      "batch_size": 256,
-      "batch_processor": {
-        "type": "basic"
-      },
-      "drop_last": false,
-      "shuffle": false
-    }
-  },
-  "model": {
-    "type": "sasrec_freezed",
-    "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
-    "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
-    "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
-    "sequence_prefix": "item",
-    "positive_prefix": "labels",
-    "negative_prefix": "negative",
-    "candidate_prefix": "candidates",
-    "embedding_dim": 64,
-    "num_heads": 2,
-    "num_layers": 2,
-    "dim_feedforward": 256,
-    "dropout": 0.3,
-    "activation": "gelu",
-    "layer_norm_eps": 1e-9,
-    "initializer_range": 0.02
-  },
-  "optimizer": {
-    "type": "basic",
-    "optimizer": {
-      "type": "adam",
-      "lr": 0.001
-    },
-    "clip_grad_threshold": 5.0
-  },
-  "loss": {
-    "type": "composite",
-    "losses": [
-      {
-        "type": "sasrec",
-        "positive_prefix": "positive_scores",
-        "negative_prefix": "negative_scores",
-        "output_prefix": "downstream_loss"
-      }
-    ],
-    "output_prefix": "loss"
-  },
-  "callback": {
-    "type": "composite",
-    "callbacks": [
-      {
-        "type": "metric",
-        "on_step": 1,
-        "loss_prefix": "loss"
-      },
-      {
-        "type": "validation",
-        "on_step": 64,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      },
-      {
-        "type": "eval",
-        "on_step": 256,
-        "pred_prefix": "logits",
-        "labels_prefix": "labels",
-        "metrics": {
-          "ndcg@5": {
-            "type": "ndcg",
-            "k": 5
-          },
-          "ndcg@10": {
-            "type": "ndcg",
-            "k": 10
-          },
-          "ndcg@20": {
-            "type": "ndcg",
-            "k": 20
-          },
-          "recall@5": {
-            "type": "recall",
-            "k": 5
-          },
-          "recall@10": {
-            "type": "recall",
-            "k": 10
-          },
-          "recall@20": {
-            "type": "recall",
-            "k": 20
-          },
-          "coverage@5": {
-            "type": "coverage",
-            "k": 5
-          },
-          "coverage@10": {
-            "type": "coverage",
-            "k": 10
-          },
-          "coverage@20": {
-            "type": "coverage",
-            "k": 20
-          }
-        }
-      }
-    ]
-  }
-}
\ No newline at end of file
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 81fae0e4..1d62369e 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -11,7 +11,7 @@
 from utils.registry import MetaParent
 
 if torch.cuda.is_available():
-    DEVICE = torch.device("cuda:1")
+    DEVICE = torch.device("cuda")
 else:
     DEVICE = torch.device("cpu")
 

From 3c8a9999e8a258418acabf58c0e994ff05af5967 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 12 Mar 2025 20:03:43 +0300
Subject: [PATCH 152/175] update all sasrec model (last item left TODO)

---
 modeling/loss/base.py              |  31 ++--
 modeling/models/__init__.py        |   4 +-
 modeling/models/sasrec.py          | 227 -----------------------------
 modeling/models/sasrec_freezed.py  | 183 -----------------------
 modeling/models/sasrec_full.py     |  97 ++++++++++++
 modeling/models/sasrec_in_batch.py | 122 ++++++++++++++++
 modeling/models/sasrec_real.py     |  13 +-
 7 files changed, 240 insertions(+), 437 deletions(-)
 delete mode 100644 modeling/models/sasrec.py
 delete mode 100644 modeling/models/sasrec_freezed.py
 create mode 100644 modeling/models/sasrec_full.py
 create mode 100644 modeling/models/sasrec_in_batch.py

diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index a0f9b180..930d0eac 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -320,14 +320,8 @@ def forward(self, inputs):
         return loss
 
 
-class SASRecRealLoss(TorchLoss, config_name='sasrec_real'):
-
-    def __init__(
-            self,
-            positive_prefix,
-            negative_prefix,
-            output_prefix=None
-    ):
+class SASRecRealLoss(TorchLoss, config_name="sasrec_real"):
+    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -348,6 +342,7 @@ def forward(self, inputs):
 
         return loss
 
+
 class SASRecLoss(TorchLoss, config_name="sasrec"):
     def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
         super().__init__()
@@ -414,10 +409,10 @@ def __init__(
 
     def forward(self, inputs):
         queries_embeddings = inputs[self._queries_prefix]  # (batch_size, embedding_dim)
-        positive_embeddings = inputs[
+        positive_ids, positive_embeddings = inputs[
             self._positive_prefix
         ]  # (batch_size, embedding_dim)
-        negative_embeddings = inputs[
+        negative_ids, negative_embeddings = inputs[
             self._negative_prefix
         ]  # (num_negatives, embedding_dim) or (batch_size, num_negatives, embedding_dim)
 
@@ -431,6 +426,15 @@ def forward(self, inputs):
             negative_scores = torch.einsum(
                 "bd,nd->bn", queries_embeddings, negative_embeddings
             )  # (batch_size, num_negatives)
+
+            all_scores = torch.cat(
+                [positive_scores, negative_scores], dim=1
+            )  # (batch_size, 1 + num_negatives)
+            logits = torch.log_softmax(
+                all_scores, dim=1
+            )  # (batch_size, 1 + num_negatives)
+            loss = (-logits)[:, 0]  # (batch_size)
+            loss = loss.mean()  # (1)
         else:
             assert (
                 negative_embeddings.dim() == 3
@@ -439,13 +443,8 @@ def forward(self, inputs):
             negative_scores = torch.einsum(
                 "bd,bnd->bn", queries_embeddings, negative_embeddings
             )  # (batch_size, num_negatives)
-        all_scores = torch.cat(
-            [positive_scores, negative_scores], dim=1
-        )  # (batch_size, 1 + num_negatives)
 
-        logits = torch.log_softmax(all_scores, dim=1)  # (batch_size, 1 + num_negatives)
-        loss = (-logits)[:, 0]  # (batch_size)
-        loss = loss.mean()  # (1)
+            assert False, "ask Vladimir wtf is it "
 
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index 92df1c34..377a703d 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -14,9 +14,9 @@
 from .random import RandomModel
 from .rqvae import RqVaeModel
 from .s3rec import S3RecModel
-from .sasrec import SasRecInBatchModel, SasRecModel
+from .sasrec_in_batch import SasRecInBatchModel
 from .sasrec_ce import SasRecCeModel
-from .sasrec_freezed import SasRecFreezedModel
+from .sasrec_full import SasRecFullModel
 from .sasrec_real import SasRecRealModel
 from .sasrec_semantic import SasRecSemanticModel
 from .tiger import TigerModel
diff --git a/modeling/models/sasrec.py b/modeling/models/sasrec.py
deleted file mode 100644
index b1d7044f..00000000
--- a/modeling/models/sasrec.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import torch
-
-from models import SequentialTorchModel
-from utils import create_masked_tensor
-
-
-class SasRecModel(SequentialTorchModel, config_name="sasrec"):
-    def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-    ):
-        super().__init__(
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-            embedding_dim=embedding_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=activation,
-            layer_norm_eps=layer_norm_eps,
-            is_causal=True,
-        )
-        self._sequence_prefix = sequence_prefix
-        self._positive_prefix = positive_prefix
-
-        self._init_weights(initializer_range)
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-        )
-
-    def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-
-        embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
-        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-
-        if self.training:  # training mode
-            all_positive_sample_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
-            all_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items + 2, embedding_dim)
-
-            # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
-            all_scores = torch.einsum(
-                "ad,nd->an", last_embeddings, all_embeddings
-            )  # (batch_size, num_items + 2)
-
-            positive_scores = torch.gather(
-                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
-            )  # (batch_size, 1)
-
-            sample_ids, _ = create_masked_tensor(
-                data=all_sample_events, lengths=all_sample_lengths
-            )  # (batch_size, seq_len)
-
-            negative_scores = torch.scatter(
-                input=all_scores,
-                dim=1,
-                index=sample_ids,
-                src=torch.ones_like(sample_ids) * (-torch.inf),
-            )  # (batch_size, num_items + 2)
-            negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
-
-            return {
-                "positive_scores": positive_scores,
-                "negative_scores": negative_scores,
-                "sample_ids": sample_ids,
-            }
-        else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-            # b - batch_size, n - num_candidates, d - embedding_dim
-            candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
-            )  # (batch_size, num_items + 2)
-            candidate_scores[:, 0] = -torch.inf  # Padding id
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
-
-            _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
-            )  # (batch_size, 20)
-
-            return indices
-
-
-class SasRecInBatchModel(SasRecModel, config_name="sasrec_in_batch"):
-    def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-    ):
-        super().__init__(
-            sequence_prefix=sequence_prefix,
-            positive_prefix=positive_prefix,
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-            embedding_dim=embedding_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=activation,
-            layer_norm_eps=layer_norm_eps,
-            initializer_range=initializer_range,
-        )
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-        )
-
-    def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-
-        embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
-        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-
-        if self.training:  # training mode
-            # queries
-            in_batch_queries_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
-
-            # positives
-            in_batch_positive_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            in_batch_positive_embeddings = self._item_embeddings(
-                in_batch_positive_events
-            )  # (all_batch_events, embedding_dim)
-
-            # negatives
-            batch_size = all_sample_lengths.shape[0]
-            random_ids = torch.randperm(in_batch_positive_events.shape[0])
-            in_batch_negative_ids = in_batch_positive_events[random_ids][:batch_size]
-
-            in_batch_negative_embeddings = self._item_embeddings(
-                in_batch_negative_ids
-            )  # (batch_size, embedding_dim)
-
-            return {
-                "query_embeddings": in_batch_queries_embeddings,
-                "positive_embeddings": in_batch_positive_embeddings,
-                "negative_embeddings": in_batch_negative_embeddings,
-            }
-        else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
-            # b - batch_size, n - num_candidates, d - embedding_dim
-            candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
-            )  # (batch_size, num_items + 2)
-            candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
-
-            _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
-            )  # (batch_size, 20)
-
-            return indices
diff --git a/modeling/models/sasrec_freezed.py b/modeling/models/sasrec_freezed.py
deleted file mode 100644
index 9f3b5b9e..00000000
--- a/modeling/models/sasrec_freezed.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import torch
-
-from models import SequentialTorchModel
-from models.tiger import TigerModel
-from utils import DEVICE, create_masked_tensor
-
-
-class SasRecFreezedModel(SequentialTorchModel, config_name="sasrec_freezed"):
-    def __init__(
-        self,
-        rqvae_model,
-        item_id_to_semantic_id,
-        item_id_to_residual,
-        sequence_prefix,
-        positive_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-    ):
-        super().__init__(
-            num_items=num_items,
-            max_sequence_length=max_sequence_length,
-            embedding_dim=embedding_dim,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            dim_feedforward=dim_feedforward,
-            dropout=dropout,
-            activation=activation,
-            layer_norm_eps=layer_norm_eps,
-            is_causal=True,
-        )
-        self._sequence_prefix = sequence_prefix
-        self._positive_prefix = positive_prefix
-
-        self._init_weights(initializer_range)
-
-        self._codebook_item_embeddings_stacked = torch.nn.Parameter(
-            torch.stack([codebook for codebook in rqvae_model.codebooks]),
-            requires_grad=False,  # TODOPK compare with unfrozen codebooks
-        )
-        self._item_id_to_semantic_id = item_id_to_semantic_id
-        self._item_id_to_residual = item_id_to_residual
-
-        item_ids = torch.arange(1, len(item_id_to_semantic_id) + 1)
-        self._item_id_to_semantic_embedding = self.get_init_item_embeddings(item_ids)
-        self._item_id_to_semantic_embedding = torch.nn.Parameter(
-            self._item_id_to_semantic_embedding.sum(dim=1), requires_grad=False
-        )  # len(events), embedding_dim
-
-    def get_init_item_embeddings(self, events):
-        # convert to semantic ids
-        semantic_ids = self._item_id_to_semantic_id[
-            events - 1
-        ]  # len(events), len(codebook_sizes)
-
-        result = []
-        for semantic_id in semantic_ids:
-            item_repr = []
-            for codebook_idx, codebook_id in enumerate(semantic_id):
-                item_repr.append(
-                    self._codebook_item_embeddings_stacked[codebook_idx][codebook_id]
-                )
-            result.append(torch.stack(item_repr))
-
-        semantic_embeddings = torch.stack(result)
-
-        # get residuals
-        residual = self._item_id_to_residual[events - 1]
-        residual = residual.unsqueeze(1)
-
-        # get true item embeddings
-        item_embeddings = torch.cat(
-            [semantic_embeddings, residual], dim=1
-        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-
-        return item_embeddings
-
-    @classmethod
-    def create_from_config(cls, config, **kwargs):
-        rqvae_model, semantic_ids, residuals, item_ids = TigerModel.init_rqvae(config)
-
-        return cls(
-            rqvae_model=rqvae_model,
-            item_id_to_semantic_id=semantic_ids,
-            item_id_to_residual=residuals,
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-        )
-
-    def get_item_embeddings(self, events=None):
-        if events is None:
-            return self._item_id_to_semantic_embedding
-        else:
-            return self._item_id_to_semantic_embedding[events - 1]
-
-    def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-
-        embeddings, mask = self._apply_sequential_encoder(
-            all_sample_events, all_sample_lengths
-        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-
-        if self.training:  # training mode
-            all_positive_sample_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
-            all_embeddings = torch.cat(
-                [
-                    torch.zeros(1, self._embedding_dim, device=DEVICE),
-                    self._item_id_to_semantic_embedding,
-                    torch.zeros(1, self._embedding_dim, device=DEVICE),
-                ],
-                dim=0,
-            )  # (num_items + 2, embedding_dim)
-
-            # a -- all_batch_events, n -- num_items + 2, d -- embedding_dim
-            all_scores = torch.einsum(
-                "ad,nd->an", last_embeddings, all_embeddings
-            )  # (batch_size, num_items + 2)
-
-            positive_scores = torch.gather(
-                input=all_scores, dim=1, index=all_positive_sample_events[..., None]
-            )  # (batch_size, 1)
-
-            sample_ids, _ = create_masked_tensor(
-                data=all_sample_events, lengths=all_sample_lengths
-            )  # (batch_size, seq_len)
-
-            negative_scores = torch.scatter(
-                input=all_scores,
-                dim=1,
-                index=sample_ids,
-                src=torch.ones_like(sample_ids) * (-torch.inf),
-            )  # (all_batch_events, num_items + 2)
-            negative_scores[:, 0] = -torch.inf  # Padding idx
-            negative_scores[:, self._num_items + 1 :] = -torch.inf  # Mask idx
-
-            return {
-                "positive_scores": positive_scores,
-                "negative_scores": negative_scores,
-                "sample_ids": sample_ids,
-            }
-        else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-            # b - batch_size, n - num_candidates, d - embedding_dim
-            candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self.get_item_embeddings()
-            )  # (batch_size, num_items + 2)
-            candidate_scores[:, 0] = -torch.inf  # Padding id
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf  # Mask id
-
-            _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
-            )  # (batch_size, 20)
-
-            return indices
diff --git a/modeling/models/sasrec_full.py b/modeling/models/sasrec_full.py
new file mode 100644
index 00000000..4f725e8f
--- /dev/null
+++ b/modeling/models/sasrec_full.py
@@ -0,0 +1,97 @@
+import torch
+from models.base import SequentialTorchModel
+
+
+class SasRecFullModel(SequentialTorchModel, config_name="sasrec_full"):
+    def __init__(
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True,
+        )
+        self._sequence_prefix = sequence_prefix
+        self._positive_prefix = positive_prefix
+
+        self._init_weights(initializer_range)
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
+        )
+
+    def forward(self, inputs):
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+
+        embeddings, mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        if self.training:  # training mode
+            # queries
+            in_batch_queries_embeddings = embeddings[
+                mask
+            ]  # (all_batch_events, embedding_dim)
+
+            all_scores = torch.einsum(
+                "bd,nd->bn", in_batch_queries_embeddings, self._item_embeddings.weight
+            )  # (all_batch_events, num_items + 2)
+
+            # positives
+            in_batch_positive_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+
+            return {"labels.ids": in_batch_positive_events, "logits": all_scores}
+        else:  # eval mode
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
+
+            # b - batch_size, n - num_candidates, d - embedding_dim
+            candidate_scores = torch.einsum(
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+            )  # (batch_size, num_items + 2)
+            candidate_scores[:, 0] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+
+            _, indices = torch.topk(
+                candidate_scores, k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices
diff --git a/modeling/models/sasrec_in_batch.py b/modeling/models/sasrec_in_batch.py
new file mode 100644
index 00000000..a8a40e1e
--- /dev/null
+++ b/modeling/models/sasrec_in_batch.py
@@ -0,0 +1,122 @@
+import torch
+from models.base import SequentialTorchModel
+
+
+class SasRecInBatchModel(SequentialTorchModel, config_name="sasrec_in_batch"):
+    def __init__(
+        self,
+        sequence_prefix,
+        positive_prefix,
+        num_items,
+        max_sequence_length,
+        embedding_dim,
+        num_heads,
+        num_layers,
+        dim_feedforward,
+        num_in_batch_negatives=-1,
+        dropout=0.0,
+        activation="relu",
+        layer_norm_eps=1e-9,
+        initializer_range=0.02,
+    ):
+        super().__init__(
+            num_items=num_items,
+            max_sequence_length=max_sequence_length,
+            embedding_dim=embedding_dim,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation=activation,
+            layer_norm_eps=layer_norm_eps,
+            is_causal=True,
+        )
+        self._sequence_prefix = sequence_prefix
+        self._positive_prefix = positive_prefix
+        self._num_in_batch_negatives = num_in_batch_negatives
+        self._init_weights(initializer_range)
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        return cls(
+            sequence_prefix=config["sequence_prefix"],
+            positive_prefix=config["positive_prefix"],
+            num_items=kwargs["num_items"],
+            max_sequence_length=kwargs["max_sequence_length"],
+            embedding_dim=config["embedding_dim"],
+            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
+            num_layers=config["num_layers"],
+            num_in_batch_negatives=config.get("num_in_batch_negatives", -1),
+            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
+            dropout=config.get("dropout", 0.0),
+            initializer_range=config.get("initializer_range", 0.02),
+        )
+
+    def forward(self, inputs):
+        all_sample_events = inputs[
+            "{}.ids".format(self._sequence_prefix)
+        ]  # (all_batch_events)
+        all_sample_lengths = inputs[
+            "{}.length".format(self._sequence_prefix)
+        ]  # (batch_size)
+
+        embeddings, mask = self._apply_sequential_encoder(
+            all_sample_events, all_sample_lengths
+        )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
+
+        if self.training:  # training mode
+            # queries
+            in_batch_queries_embeddings = embeddings[
+                mask
+            ]  # (all_batch_events, embedding_dim)
+
+            # positives
+            in_batch_positive_events = inputs[
+                "{}.ids".format(self._positive_prefix)
+            ]  # (all_batch_events)
+            in_batch_positive_embeddings = self._item_embeddings(
+                in_batch_positive_events
+            )  # (all_batch_events, embedding_dim)
+
+            # negatives
+            num_in_batch_negatives = self._num_in_batch_negatives
+            batch_size = all_sample_lengths.shape[0]
+            random_ids = torch.randperm(in_batch_positive_events.shape[0])
+            if num_in_batch_negatives == -1:
+                num_in_batch_negatives = batch_size
+            in_batch_negative_ids = in_batch_positive_events[random_ids][
+                :num_in_batch_negatives
+            ]
+
+            in_batch_negative_embeddings = self._item_embeddings(
+                in_batch_negative_ids
+            )  # (num_in_batch_negatives, embedding_dim)
+
+            return {
+                "query_embeddings": in_batch_queries_embeddings,
+                "positive_embeddings": (
+                    in_batch_positive_events,
+                    in_batch_positive_embeddings,
+                ),
+                "negative_embeddings": (
+                    in_batch_negative_ids,
+                    in_batch_negative_embeddings,
+                ),
+            }
+        else:  # eval mode
+            last_embeddings = self._get_last_embedding(
+                embeddings, mask
+            )  # (batch_size, embedding_dim)
+
+            # b - batch_size, n - num_candidates, d - embedding_dim
+            candidate_scores = torch.einsum(
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+            )  # (batch_size, num_items + 2)
+            candidate_scores[:, 0] = -torch.inf
+            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+
+            _, indices = torch.topk(
+                candidate_scores, k=20, dim=-1, largest=True
+            )  # (batch_size, 20)
+
+            return indices
diff --git a/modeling/models/sasrec_real.py b/modeling/models/sasrec_real.py
index 3776806c..d87743f4 100644
--- a/modeling/models/sasrec_real.py
+++ b/modeling/models/sasrec_real.py
@@ -3,7 +3,6 @@
 
 
 class SasRecRealModel(SequentialTorchModel, config_name="sasrec_real"):
-
     def __init__(
         self,
         sequence_prefix,
@@ -66,11 +65,11 @@ def forward(self, inputs):
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        if self.training:  # training mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
 
+        if self.training:  # training mode
             # positives
             in_batch_positive_events = inputs[
                 "{}.ids".format(self._positive_prefix)
@@ -98,10 +97,6 @@ def forward(self, inputs):
                 "negative_scores": negative_scores,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
                 "bd,nd->bn", last_embeddings, self._item_embeddings.weight

From d0216763cfedcdec42037e6e99cf503e66442027 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 12 Mar 2025 20:08:24 +0300
Subject: [PATCH 153/175] adapt full & in batch to last item

---
 modeling/models/sasrec_full.py     | 15 +++++----------
 modeling/models/sasrec_in_batch.py | 15 +++++----------
 2 files changed, 10 insertions(+), 20 deletions(-)

diff --git a/modeling/models/sasrec_full.py b/modeling/models/sasrec_full.py
index 4f725e8f..5d484319 100644
--- a/modeling/models/sasrec_full.py
+++ b/modeling/models/sasrec_full.py
@@ -62,14 +62,13 @@ def forward(self, inputs):
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        if self.training:  # training mode
-            # queries
-            in_batch_queries_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
 
+        if self.training:  # training mode
             all_scores = torch.einsum(
-                "bd,nd->bn", in_batch_queries_embeddings, self._item_embeddings.weight
+                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
             )  # (all_batch_events, num_items + 2)
 
             # positives
@@ -79,10 +78,6 @@ def forward(self, inputs):
 
             return {"labels.ids": in_batch_positive_events, "logits": all_scores}
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
                 "bd,nd->bn", last_embeddings, self._item_embeddings.weight
diff --git a/modeling/models/sasrec_in_batch.py b/modeling/models/sasrec_in_batch.py
index a8a40e1e..2d566fee 100644
--- a/modeling/models/sasrec_in_batch.py
+++ b/modeling/models/sasrec_in_batch.py
@@ -64,12 +64,11 @@ def forward(self, inputs):
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        if self.training:  # training mode
-            # queries
-            in_batch_queries_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
+        last_embeddings = self._get_last_embedding(
+            embeddings, mask
+        )  # (batch_size, embedding_dim)
 
+        if self.training:  # training mode
             # positives
             in_batch_positive_events = inputs[
                 "{}.ids".format(self._positive_prefix)
@@ -93,7 +92,7 @@ def forward(self, inputs):
             )  # (num_in_batch_negatives, embedding_dim)
 
             return {
-                "query_embeddings": in_batch_queries_embeddings,
+                "query_embeddings": last_embeddings,
                 "positive_embeddings": (
                     in_batch_positive_events,
                     in_batch_positive_embeddings,
@@ -104,10 +103,6 @@ def forward(self, inputs):
                 ),
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
-
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
                 "bd,nd->bn", last_embeddings, self._item_embeddings.weight

From 9182a554c68e70cf49a9c5b4f740d7b98a7aa74e Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 12 Mar 2025 20:44:01 +0300
Subject: [PATCH 154/175] fix configs for sasrec's last item

---
 ...fig.json => sasrec_full_train_config.json} | 14 +++---
 .../train/sasrec_in_batch_train_config.json   | 43 ++++++++++++++-----
 modeling/models/sasrec_in_batch.py            |  4 +-
 3 files changed, 43 insertions(+), 18 deletions(-)
 rename configs/train/{sasrec_train_config.json => sasrec_full_train_config.json} (93%)

diff --git a/configs/train/sasrec_train_config.json b/configs/train/sasrec_full_train_config.json
similarity index 93%
rename from configs/train/sasrec_train_config.json
rename to configs/train/sasrec_full_train_config.json
index b0c617c4..bbff4ee5 100644
--- a/configs/train/sasrec_train_config.json
+++ b/configs/train/sasrec_full_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_beauty",
+  "experiment_name": "sasrec_full_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
@@ -33,7 +33,7 @@
     }
   },
   "model": {
-    "type": "sasrec",
+    "type": "sasrec_full",
     "sequence_prefix": "item",
     "positive_prefix": "labels",
     "negative_prefix": "negative",
@@ -59,9 +59,9 @@
     "type": "composite",
     "losses": [
       {
-        "type": "sasrec",
-        "positive_prefix": "positive_scores",
-        "negative_prefix": "negative_scores",
+        "type": "ce",
+        "predictions_prefix": "logits",
+        "labels_prefix": "labels",
         "output_prefix": "downstream_loss"
       }
     ],
@@ -77,7 +77,7 @@
       },
       {
         "type": "validation",
-        "on_step": 4096,
+        "on_step": 1024,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -121,7 +121,7 @@
       },
       {
         "type": "eval",
-        "on_step": 8192,
+        "on_step": 2048,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
diff --git a/configs/train/sasrec_in_batch_train_config.json b/configs/train/sasrec_in_batch_train_config.json
index 8bde73f7..090a64d9 100644
--- a/configs/train/sasrec_in_batch_train_config.json
+++ b/configs/train/sasrec_in_batch_train_config.json
@@ -1,14 +1,14 @@
 {
-  "experiment_name": "sasrec_in_batch_test",
-  "best_metric": "eval/ndcg@20",
+  "experiment_name": "sasrec_in_batch_beauty",
+  "best_metric": "validation/ndcg@20",
+  "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
     "samplers": {
-      "num_negatives_val": 100,
-      "type": "next_item_prediction",
+      "type": "last_item_prediction",
       "negative_sampler_type": "random"
     }
   },
@@ -35,7 +35,7 @@
   "model": {
     "type": "sasrec_in_batch",
     "sequence_prefix": "item",
-    "positive_prefix": "positive",
+    "positive_prefix": "labels",
     "negative_prefix": "negative",
     "candidate_prefix": "candidates",
     "embedding_dim": 64,
@@ -44,7 +44,6 @@
     "dim_feedforward": 256,
     "dropout": 0.3,
     "activation": "gelu",
-    "use_ce": true,
     "layer_norm_eps": 1e-9,
     "initializer_range": 0.02
   },
@@ -79,7 +78,7 @@
       },
       {
         "type": "validation",
-        "on_step": 64,
+        "on_step": 1024,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -106,12 +105,24 @@
           "recall@20": {
             "type": "recall",
             "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
           }
         }
       },
       {
         "type": "eval",
-        "on_step": 256,
+        "on_step": 2048,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -138,9 +149,21 @@
           "recall@20": {
             "type": "recall",
             "k": 20
+          },
+          "coverage@5": {
+            "type": "coverage",
+            "k": 5
+          },
+          "coverage@10": {
+            "type": "coverage",
+            "k": 10
+          },
+          "coverage@20": {
+            "type": "coverage",
+            "k": 20
           }
         }
       }
     ]
   }
-}
+}
\ No newline at end of file
diff --git a/modeling/models/sasrec_in_batch.py b/modeling/models/sasrec_in_batch.py
index 2d566fee..b755d4e2 100644
--- a/modeling/models/sasrec_in_batch.py
+++ b/modeling/models/sasrec_in_batch.py
@@ -83,7 +83,9 @@ def forward(self, inputs):
             random_ids = torch.randperm(in_batch_positive_events.shape[0])
             if num_in_batch_negatives == -1:
                 num_in_batch_negatives = batch_size
-            in_batch_negative_ids = in_batch_positive_events[random_ids][
+            in_batch_negative_ids = in_batch_positive_events[
+                random_ids
+            ][  # TODOPK as khow many negatives here (currently whole batch)
                 :num_in_batch_negatives
             ]
 

From 98b45d5cc2b4b9b574e0223532fbf8a9108673e9 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 12 Mar 2025 20:47:13 +0300
Subject: [PATCH 155/175] rename sasrec semantic config

---
 configs/train/sasrec_semantic_train_config.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 5c3630ee..33586d92 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,5 +1,5 @@
 {
-  "experiment_name": "sasrec_semantic_beauty_learnable_uid",
+  "experiment_name": "sasrec_semantic_learnable_uid_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {

From 63f20ea255c3205c840a7936727476bd9149a998 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Wed, 12 Mar 2025 21:37:56 +0300
Subject: [PATCH 156/175] loo configs

---
 configs/train/sasrec_full_train_config.json     | 4 ++--
 configs/train/sasrec_in_batch_train_config.json | 4 ++--
 configs/train/sasrec_real_train_config.json     | 4 ++--
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/configs/train/sasrec_full_train_config.json b/configs/train/sasrec_full_train_config.json
index bbff4ee5..d870e9c7 100644
--- a/configs/train/sasrec_full_train_config.json
+++ b/configs/train/sasrec_full_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_full_beauty",
+  "experiment_name": "sasrec_full_beauty_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence_full",
+    "type": "scientific_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_in_batch_train_config.json b/configs/train/sasrec_in_batch_train_config.json
index 090a64d9..bd985991 100644
--- a/configs/train/sasrec_in_batch_train_config.json
+++ b/configs/train/sasrec_in_batch_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_in_batch_beauty",
+  "experiment_name": "sasrec_in_batch_beauty_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence_full",
+    "type": "scientific_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_real_train_config.json b/configs/train/sasrec_real_train_config.json
index c43c3d42..ae3118ca 100644
--- a/configs/train/sasrec_real_train_config.json
+++ b/configs/train/sasrec_real_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_real_beauty",
+  "experiment_name": "sasrec_real_beauty_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence_full",
+    "type": "scientific_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 33586d92..5a7d97e7 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_semantic_learnable_uid_beauty",
+  "experiment_name": "sasrec_semantic_learnable_uid_beauty_loo",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "sequence_full",
+    "type": "scientific_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,

From 37a8dbca75ca9bc2ffa1db17d120d1742bfe084c Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Mar 2025 13:09:06 +0300
Subject: [PATCH 157/175] add loss fix (1e-9)

---
 modeling/loss/base.py              | 4 ++--
 modeling/models/sasrec_in_batch.py | 2 +-
 modeling/models/sasrec_semantic.py | 2 ++
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index 930d0eac..e5a6ce5c 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -332,8 +332,8 @@ def forward(self, inputs):
         negative_scores = inputs[self._negative_prefix]  # (x)
         assert positive_scores.shape[0] == negative_scores.shape[0]
 
-        positive_loss = torch.log(nn.functional.sigmoid(positive_scores))  # (x)
-        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores))  # (x)
+        positive_loss = torch.log(nn.functional.sigmoid(positive_scores) + 1e-9)  # (x)
+        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores) + 1e-9)  # (x)
         loss = positive_loss + negative_loss  # (x)
         loss = -loss.mean()  # (1)
 
diff --git a/modeling/models/sasrec_in_batch.py b/modeling/models/sasrec_in_batch.py
index b755d4e2..e9cfb930 100644
--- a/modeling/models/sasrec_in_batch.py
+++ b/modeling/models/sasrec_in_batch.py
@@ -85,7 +85,7 @@ def forward(self, inputs):
                 num_in_batch_negatives = batch_size
             in_batch_negative_ids = in_batch_positive_events[
                 random_ids
-            ][  # TODOPK as khow many negatives here (currently whole batch)
+            ][
                 :num_in_batch_negatives
             ]
 
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index b8c778a2..3632b2b8 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -120,6 +120,8 @@ def forward(self, inputs):
                 "bd,bd->b", last_embeddings, in_batch_positive_embeddings
             )  # (all_batch_events)
 
+            # TODOPK normalize in all models embeddings for stability
+
             # negatives
             in_batch_negative_events = inputs[
                 "{}.ids".format(self._negative_prefix)

From ecb2113af838517f6d0879581d7da54143d2ec33 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sat, 15 Mar 2025 14:19:27 +0300
Subject: [PATCH 158/175] sequence on fixed sasrec real

---
 configs/train/sasrec_full_train_config.json     | 4 ++--
 configs/train/sasrec_in_batch_train_config.json | 4 ++--
 configs/train/sasrec_real_train_config.json     | 4 ++--
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/configs/train/sasrec_full_train_config.json b/configs/train/sasrec_full_train_config.json
index d870e9c7..bbff4ee5 100644
--- a/configs/train/sasrec_full_train_config.json
+++ b/configs/train/sasrec_full_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_full_beauty_loo",
+  "experiment_name": "sasrec_full_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "scientific_full",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_in_batch_train_config.json b/configs/train/sasrec_in_batch_train_config.json
index bd985991..090a64d9 100644
--- a/configs/train/sasrec_in_batch_train_config.json
+++ b/configs/train/sasrec_in_batch_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_in_batch_beauty_loo",
+  "experiment_name": "sasrec_in_batch_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "scientific_full",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_real_train_config.json b/configs/train/sasrec_real_train_config.json
index ae3118ca..c43c3d42 100644
--- a/configs/train/sasrec_real_train_config.json
+++ b/configs/train/sasrec_real_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_real_beauty_loo",
+  "experiment_name": "sasrec_real_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "scientific_full",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,
diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 5a7d97e7..33586d92 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -1,9 +1,9 @@
 {
-  "experiment_name": "sasrec_semantic_learnable_uid_beauty_loo",
+  "experiment_name": "sasrec_semantic_learnable_uid_beauty",
   "best_metric": "validation/ndcg@20",
   "train_epochs_num": 100,
   "dataset": {
-    "type": "scientific_full",
+    "type": "sequence_full",
     "path_to_data_dir": "../data",
     "name": "Beauty",
     "max_sequence_length": 50,

From c429951c0406af83e18cc0d82f07a16135676da1 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Mar 2025 17:51:17 +0300
Subject: [PATCH 159/175] correct sasrec (use codebook only)

---
 modeling/models/sasrec_semantic.py | 60 +++++++++++++++---------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 3632b2b8..74654eea 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -57,12 +57,13 @@ def __init__(
 
         self._init_weights(initializer_range)
 
-        self._item_id_to_semantic_embedding = nn.Parameter(
-            self.get_init_item_embeddings(
-                rqvae_model, item_id_to_semantic_id, item_id_to_residual
-            ),
+        self._item_id_to_semantic_id = item_id_to_semantic_id # len(num_items), len(self._codebook_sizes)
+        self._item_id_to_residual = item_id_to_residual # len(num_items), embedding_dim
+
+        self._codebooks = nn.Parameter(
+            torch.stack([codebook for codebook in rqvae_model.codebooks]),
             requires_grad=True,
-        )
+        )  # len(self._codebook_sizes), codebook_size, embedding_dim
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
@@ -106,16 +107,14 @@ def forward(self, inputs):
             embeddings, mask
         )  # (batch_size, embedding_dim)
 
-        item_embeddings = self._item_id_to_semantic_embedding.sum(dim=1)
-
         if self.training:  # training mode
             # positives
             in_batch_positive_events = inputs[
                 "{}.ids".format(self._positive_prefix)
             ]  # (all_batch_events)
-            in_batch_positive_embeddings = item_embeddings[
+            in_batch_positive_embeddings = self.get_embeddings(
                 in_batch_positive_events - 1
-            ]  # (all_batch_events, embedding_dim)
+            ).sum(dim=1)  # (all_batch_events, embedding_dim)
             positive_scores = torch.einsum(
                 "bd,bd->b", last_embeddings, in_batch_positive_embeddings
             )  # (all_batch_events)
@@ -126,9 +125,9 @@ def forward(self, inputs):
             in_batch_negative_events = inputs[
                 "{}.ids".format(self._negative_prefix)
             ]  # (all_batch_events)
-            in_batch_negative_embeddings = item_embeddings[
+            in_batch_negative_embeddings = self.get_embeddings(
                 in_batch_negative_events - 1
-            ]  # (all_batch_events, embedding_dim)
+            ).sum(dim=1)  # (all_batch_events, embedding_dim)
             negative_scores = torch.einsum(
                 "bd,bd->b", last_embeddings, in_batch_negative_embeddings
             )  # (all_batch_events)
@@ -138,6 +137,9 @@ def forward(self, inputs):
                 "negative_scores": negative_scores,
             }
         else:  # eval mode
+            item_embeddings = self.get_embeddings(torch.arange(self._num_items)).sum(
+                dim=1
+            )  # num_items, embedding_dim
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
                 "bd,nd->bn",
@@ -152,28 +154,26 @@ def forward(self, inputs):
             return indices + 1  # tensors are 0 indexed
 
     def get_item_embeddings(self, events):
-        embs = self._item_id_to_semantic_embedding[
-            events - 1
-        ]  # len(events), len(self._codebook_sizes) + 1, embedding_dim
-        return embs.reshape(-1, self._embedding_dim)
-
-    def get_init_item_embeddings(
-        self, rqvae_model, item_id_to_semantic_id, item_id_to_residual
-    ):
-        codebooks = torch.stack([codebook for codebook in rqvae_model.codebooks])
+        item_embeddings = self.get_embeddings(
+            events
+        )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
+        return item_embeddings.reshape(-1, self._embedding_dim)
 
-        result = []
-        for semantic_id in item_id_to_semantic_id:
-            item_repr = []
-            for codebook_idx, codebook_id in enumerate(semantic_id):
-                item_repr.append(codebooks[codebook_idx][codebook_id])
-            result.append(torch.stack(item_repr))
+    def get_embeddings(self, events):
+        semantic_ids = self._item_id_to_semantic_id[
+            events - 1
+        ]  # len(events), len(self._codebook_sizes)
+        residuals = self._item_id_to_residual[events - 1]  # len(events), embedding_dim
 
         semantic_embeddings = torch.stack(
-            result
-        )  # len(events), len(codebook_sizes), embedding_dim
-
-        residual = item_id_to_residual.unsqueeze(1)
+            [
+                codebook[semantic_ids[:, i]]
+                for i, codebook in enumerate(self._codebooks)
+            ],
+            dim=1,
+        )  # len(events), len(self._codebook_sizes), embedding_dim
+
+        residual = residuals.unsqueeze(1)
 
         # get true item embeddings
         item_embeddings = torch.cat(

From 0eb0b13709d35a38d78c9626d232dfda32b58fd0 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Mar 2025 17:51:42 +0300
Subject: [PATCH 160/175] more frequent checks

---
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 33586d92..735cd9ac 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -81,7 +81,7 @@
       },
       {
         "type": "validation",
-        "on_step": 1024,
+        "on_step": 128,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -125,7 +125,7 @@
       },
       {
         "type": "eval",
-        "on_step": 2048,
+        "on_step": 512,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {

From 1ebb5b9c4804e42745059ef562d010326f06a326 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Mar 2025 18:17:34 +0300
Subject: [PATCH 161/175] fix event id indexing

---
 modeling/models/sasrec_semantic.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index 74654eea..c1089e23 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -57,8 +57,10 @@ def __init__(
 
         self._init_weights(initializer_range)
 
-        self._item_id_to_semantic_id = item_id_to_semantic_id # len(num_items), len(self._codebook_sizes)
-        self._item_id_to_residual = item_id_to_residual # len(num_items), embedding_dim
+        self._item_id_to_semantic_id = (
+            item_id_to_semantic_id  # len(num_items), len(self._codebook_sizes)
+        )
+        self._item_id_to_residual = item_id_to_residual  # len(num_items), embedding_dim
 
         self._codebooks = nn.Parameter(
             torch.stack([codebook for codebook in rqvae_model.codebooks]),
@@ -155,15 +157,15 @@ def forward(self, inputs):
 
     def get_item_embeddings(self, events):
         item_embeddings = self.get_embeddings(
-            events
+            events - 1
         )  # len(events), len(self._codebook_sizes) + 1, embedding_dim
         return item_embeddings.reshape(-1, self._embedding_dim)
 
-    def get_embeddings(self, events):
+    def get_embeddings(self, events):  # events = 0 ... num_items - 1
         semantic_ids = self._item_id_to_semantic_id[
-            events - 1
+            events
         ]  # len(events), len(self._codebook_sizes)
-        residuals = self._item_id_to_residual[events - 1]  # len(events), embedding_dim
+        residuals = self._item_id_to_residual[events]  # len(events), embedding_dim
 
         semantic_embeddings = torch.stack(
             [

From 162745b61e0f11de9327efbbd47cd81201c007b7 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 23 Mar 2025 18:18:00 +0300
Subject: [PATCH 162/175] faster semantic train

---
 configs/train/sasrec_semantic_train_config.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/train/sasrec_semantic_train_config.json b/configs/train/sasrec_semantic_train_config.json
index 735cd9ac..f37c6fc5 100644
--- a/configs/train/sasrec_semantic_train_config.json
+++ b/configs/train/sasrec_semantic_train_config.json
@@ -81,7 +81,7 @@
       },
       {
         "type": "validation",
-        "on_step": 128,
+        "on_step": 512,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {
@@ -125,7 +125,7 @@
       },
       {
         "type": "eval",
-        "on_step": 512,
+        "on_step": 1024,
         "pred_prefix": "logits",
         "labels_prefix": "labels",
         "metrics": {

From 38c6fd394bb17d219477feb8dd82e9d76dee54a6 Mon Sep 17 00:00:00 2001
From: Peter Korolev <peterkkor@gmail.com>
Date: Sun, 6 Apr 2025 18:46:06 +0300
Subject: [PATCH 163/175] add uv deps

---
 .python-version |   1 +
 pyproject.toml  |  30 ++
 uv.lock         | 952 ++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 983 insertions(+)
 create mode 100644 .python-version
 create mode 100644 pyproject.toml
 create mode 100644 uv.lock

diff --git a/.python-version b/.python-version
new file mode 100644
index 00000000..24ee5b1b
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..77289265
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,30 @@
+[project]
+name = "irec"
+version = "0.1.0"
+description = "IRec framework"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "faiss-cpu>=1.10.0",
+    "pandas>=2.2.3",
+    "scipy>=1.15.2",
+    "seaborn>=0.13.2",
+    "tensorboard>=2.19.0",
+    "torch>=2.6.0",
+    "transformers>=4.51.0",
+]
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch-cu124" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cu124"
+url = "https://download.pytorch.org/whl/cu124"
+explicit = true
+
+[dependency-groups]
+dev = [
+    "ruff>=0.11.4",
+]
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 00000000..24c7adf0
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,952 @@
+version = 1
+revision = 1
+requires-python = ">=3.13"
+
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From 92f28b33a9fb9b8f0c2f35e03e78bac94dd642da Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 8 Jun 2025 22:17:55 +0300
Subject: [PATCH 164/175] Revert "ruff format"

This reverts commit 4077b0579fa1ee0a938e635ee8e7e0ad1603a7bc.
---
 modeling/callbacks/__init__.py                |   8 +-
 modeling/callbacks/base.py                    | 293 ++++----
 modeling/dataloader/base.py                   |  24 +-
 modeling/dataloader/batch_processors.py       |  34 +-
 modeling/dataset/base.py                      | 639 ++++++++----------
 .../dataset/negative_samplers/__init__.py     |   6 +-
 modeling/dataset/negative_samplers/base.py    |  12 +-
 modeling/dataset/negative_samplers/popular.py |  28 +-
 modeling/dataset/negative_samplers/random.py  |  12 +-
 modeling/dataset/samplers/__init__.py         |  29 +-
 modeling/dataset/samplers/base.py             |  24 +-
 modeling/dataset/samplers/cl4srec.py          | 110 ++-
 modeling/dataset/samplers/duorec.py           |  44 +-
 modeling/dataset/samplers/identity.py         |  19 +-
 .../dataset/samplers/last_item_prediction.py  |  16 +-
 .../samplers/masked_item_prediction.py        |  59 +-
 modeling/dataset/samplers/mclsr.py            |  45 +-
 .../dataset/samplers/next_item_prediction.py  |  69 +-
 modeling/dataset/samplers/pop.py              |  52 +-
 modeling/dataset/samplers/s3rec.py            |  92 ++-
 modeling/infer.py                             |  69 +-
 modeling/loss/base.py                         | 394 ++++++-----
 modeling/metric/base.py                       |  65 +-
 modeling/models/__init__.py                   |   2 +-
 modeling/models/base.py                       | 111 ++-
 modeling/models/bert4rec.py                   |  94 ++-
 modeling/models/bert4rec_cls.py               |  91 +--
 modeling/models/cl4srec.py                    | 127 ++--
 modeling/models/duorec.py                     | 123 ++--
 modeling/models/graph_seq_rec.py              | 225 +++---
 modeling/models/gru4rec.py                    | 172 +++--
 modeling/models/gtorec.py                     | 475 +++++--------
 modeling/models/lightgcn.py                   | 129 ++--
 modeling/models/mclsr.py                      | 274 ++++----
 modeling/models/mrgsrec.py                    | 115 ++--
 modeling/models/ngcf.py                       | 133 ++--
 modeling/models/pop.py                        |  36 +-
 modeling/models/pure_mf.py                    | 107 ++-
 modeling/models/pure_svd.py                   |   6 +-
 modeling/models/random.py                     |  29 +-
 modeling/models/rqvae.py                      |  81 ++-
 modeling/models/s3rec.py                      | 214 +++---
 modeling/models/sasrec_ce.py                  |  85 ++-
 modeling/models/sasrec_semantic.py            |   4 +-
 modeling/models/tiger.py                      |  11 +-
 modeling/optimizer/base.py                    |  41 +-
 modeling/pretrain.py                          |  65 +-
 modeling/rqvae_utils/__init__.py              |   4 +-
 modeling/rqvae_utils/collision_solver.py      | 156 ++---
 modeling/rqvae_utils/rqvae_data.py            |  13 +-
 modeling/rqvae_utils/rqvae_test.py            |  25 +-
 modeling/rqvae_utils/simplified_tree.py       |  70 +-
 modeling/rqvae_utils/tree.py                  | 191 ++----
 modeling/rqvae_utils/tree_comparing.py        |  18 +-
 modeling/rqvae_utils/trie.py                  |   1 -
 modeling/train.py                             | 106 ++-
 modeling/train_multiple.py                    | 103 ++-
 modeling/utils/__init__.py                    |  93 ++-
 modeling/utils/grid_search.py                 |  25 +-
 modeling/utils/registry.py                    |  31 +-
 modeling/utils/tensorboards/__init__.py       |   2 +-
 .../utils/tensorboards/tensorboard_writers.py |  11 +-
 62 files changed, 2579 insertions(+), 3163 deletions(-)

diff --git a/modeling/callbacks/__init__.py b/modeling/callbacks/__init__.py
index 81d5054e..a23d5ba3 100644
--- a/modeling/callbacks/__init__.py
+++ b/modeling/callbacks/__init__.py
@@ -1,7 +1 @@
-from .base import (
-    BaseCallback,
-    CompositeCallback,
-    EvalCallback,
-    InferenceCallback,
-    ValidationCallback,
-)
+from .base import BaseCallback, CompositeCallback, EvalCallback, InferenceCallback, ValidationCallback
diff --git a/modeling/callbacks/base.py b/modeling/callbacks/base.py
index 900b492e..1ae26267 100644
--- a/modeling/callbacks/base.py
+++ b/modeling/callbacks/base.py
@@ -1,19 +1,25 @@
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
+from metric import BaseMetric, StatefullMetric
 
 import utils
-from metric import BaseMetric, StatefullMetric
 from utils import MetaParent, create_logger
 
+import numpy as np
+import os
+import torch
+from pathlib import Path
+
 logger = create_logger(name=__name__)
 
 
 class BaseCallback(metaclass=MetaParent):
+
     def __init__(
-        self, model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
     ):
         self._model = model
         self._train_dataloader = train_dataloader
@@ -25,20 +31,25 @@ def __call__(self, inputs, step_num):
         raise NotImplementedError
 
 
-class MetricCallback(BaseCallback, config_name="metric"):
+class MetricCallback(BaseCallback, config_name='metric'):
+
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        on_step,
-        metrics,
-        loss_prefix,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            on_step,
+            metrics,
+            loss_prefix
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._on_step = on_step
         self._loss_prefix = loss_prefix
@@ -47,103 +58,113 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
-            metrics=config.get("metrics", None),
-            loss_prefix=config["loss_prefix"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
+            metrics=config.get('metrics', None),
+            loss_prefix=config['loss_prefix']
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:
             for metric_name, metric_function in self._metrics.items():
                 metric_value = metric_function(
-                    ground_truth=inputs[self._model.schema["ground_truth_prefix"]],
-                    predictions=inputs[self._model.schema["predictions_prefix"]],
+                    ground_truth=inputs[self._model.schema['ground_truth_prefix']],
+                    predictions=inputs[self._model.schema['predictions_prefix']]
                 )
 
                 utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                    "train/{}".format(metric_name), metric_value, step_num
+                    'train/{}'.format(metric_name),
+                    metric_value,
+                    step_num
                 )
 
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                "train/{}".format(self._loss_prefix),
+                'train/{}'.format(self._loss_prefix),
                 inputs[self._loss_prefix],
-                step_num,
+                step_num
             )
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.flush()
 
 
-class CheckpointCallback(BaseCallback, config_name="checkpoint"):
+class CheckpointCallback(BaseCallback, config_name='checkpoint'):
+
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        on_step,
-        save_path,
-        model_name,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            on_step,
+            save_path,
+            model_name
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._on_step = on_step
         self._save_path = Path(os.path.join(save_path, model_name))
         if self._save_path.exists():
-            logger.warning(
-                "Checkpoint path `{}` is already exists!".format(self._save_path)
-            )
+            logger.warning('Checkpoint path `{}` is already exists!'.format(self._save_path))
         else:
             self._save_path.mkdir(parents=True, exist_ok=True)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
-            save_path=config["save_path"],
-            model_name=config["model_name"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
+            save_path=config['save_path'],
+            model_name=config['model_name']
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:
-            logger.debug("Saving model state on step {}...".format(step_num))
+            logger.debug('Saving model state on step {}...'.format(step_num))
             torch.save(
                 {
-                    "step_num": step_num,
-                    "model_state_dict": self._model.state_dict(),
-                    "optimizer_state_dict": self._optimizer.state_dict(),
+                    'step_num': step_num,
+                    'model_state_dict': self._model.state_dict(),
+                    'optimizer_state_dict': self._optimizer.state_dict(),
                 },
-                os.path.join(self._save_path, "checkpoint_{}.pth".format(step_num)),
+                os.path.join(self._save_path, 'checkpoint_{}.pth'.format(step_num))
             )
-            logger.debug("Saving done!")
+            logger.debug('Saving done!')
 
 
 class InferenceCallback(BaseCallback):
+
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        on_step,
-        pred_prefix,
-        labels_prefix,
-        metrics=None,
-        loss_prefix=None,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            on_step,
+            pred_prefix,
+            labels_prefix,
+            metrics=None,
+            loss_prefix=None,
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._on_step = on_step
         self._metrics = metrics if metrics is not None else {}
@@ -155,24 +176,24 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config["metrics"].items()
+            for metric_name, metric_cfg in config['metrics'].items()
         }
 
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
             metrics=metrics,
-            pred_prefix=config["pred_prefix"],
-            labels_prefix=config["labels_prefix"],
+            pred_prefix=config['pred_prefix'],
+            labels_prefix=config['labels_prefix']
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:  # TODO Add time monitoring
-            logger.debug(f"Running {self._get_name()} on step {step_num}...")
+            logger.debug(f'Running {self._get_name()} on step {step_num}...')
             running_params = {}
             for metric_name, metric_function in self._metrics.items():
                 running_params[metric_name] = []
@@ -191,34 +212,30 @@ def __call__(self, inputs, step_num):
                         batch[key] = values.cpu()
 
                     for metric_name, metric_function in self._metrics.items():
-                        running_params[metric_name].extend(
-                            metric_function(
-                                inputs=batch,
-                                pred_prefix=self._pred_prefix,
-                                labels_prefix=self._labels_prefix,
-                            )
-                        )
+                        running_params[metric_name].extend(metric_function(
+                            inputs=batch,
+                            pred_prefix=self._pred_prefix,
+                            labels_prefix=self._labels_prefix,
+                        ))
 
                     if self._loss_prefix is not None:
-                        running_params[self._loss_prefix] += batch[
-                            self._loss_prefix
-                        ].item()
-
+                        running_params[self._loss_prefix] += batch[self._loss_prefix].item()
+            
             for metric_name, metric_function in self._metrics.items():
                 if isinstance(metric_function, StatefullMetric):
-                    running_params[metric_name] = metric_function.reduce(
-                        running_params[metric_name]
-                    )
+                    running_params[metric_name] = metric_function.reduce(running_params[metric_name])
 
             for label, value in running_params.items():
-                inputs[f"{self._get_name()}/{label}"] = np.mean(value)
+                inputs[f'{self._get_name()}/{label}'] = np.mean(value)
                 utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                    f"{self._get_name()}/{label}", np.mean(value), step_num
+                    f'{self._get_name()}/{label}',
+                    np.mean(value),
+                    step_num
                 )
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.flush()
 
-            logger.debug(f"Running {self._get_name()} on step {step_num} is done!")
-
+            logger.debug(f'Running {self._get_name()} on step {step_num} is done!')
+        
     def _get_name(self):
         return self.config_name
 
@@ -226,81 +243,87 @@ def _get_dataloader(self):
         raise NotImplementedError
 
 
-class ValidationCallback(InferenceCallback, config_name="validation"):
+class ValidationCallback(InferenceCallback, config_name='validation'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config["metrics"].items()
+            for metric_name, metric_cfg in config['metrics'].items()
         }
 
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
             metrics=metrics,
-            pred_prefix=config["pred_prefix"],
-            labels_prefix=config["labels_prefix"],
+            pred_prefix=config['pred_prefix'],
+            labels_prefix=config['labels_prefix']
         )
 
     def _get_dataloader(self):
         return self._validation_dataloader
 
 
-class EvalCallback(InferenceCallback, config_name="eval"):
+class EvalCallback(InferenceCallback, config_name='eval'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config["metrics"].items()
+            for metric_name, metric_cfg in config['metrics'].items()
         }
 
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
             metrics=metrics,
-            pred_prefix=config["pred_prefix"],
-            labels_prefix=config["labels_prefix"],
+            pred_prefix=config['pred_prefix'],
+            labels_prefix=config['labels_prefix']
         )
 
     def _get_dataloader(self):
         return self._eval_dataloader
 
 
-class CompositeCallback(BaseCallback, config_name="composite"):
+class CompositeCallback(BaseCallback, config_name='composite'):
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        callbacks,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            callbacks
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._callbacks = callbacks
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
             callbacks=[
                 BaseCallback.create_from_config(cfg, **kwargs)
-                for cfg in config["callbacks"]
-            ],
+                for cfg in config['callbacks']
+            ]
         )
 
     def __call__(self, inputs, step_num):
diff --git a/modeling/dataloader/base.py b/modeling/dataloader/base.py
index 71e7f6e7..2e23876d 100644
--- a/modeling/dataloader/base.py
+++ b/modeling/dataloader/base.py
@@ -1,13 +1,12 @@
 import copy
-import logging
-
-import numpy as np
-from torch.utils.data import DataLoader, random_split
 
 from utils import MetaParent
-
 from .batch_processors import BaseBatchProcessor
 
+import logging
+import numpy as np
+from torch.utils.data import DataLoader, random_split
+
 logger = logging.getLogger(__name__)
 
 
@@ -15,7 +14,8 @@ class BaseDataloader(metaclass=MetaParent):
     pass
 
 
-class TorchDataloader(BaseDataloader, config_name="torch"):
+class TorchDataloader(BaseDataloader, config_name='torch'):
+
     def __init__(self, dataloader):
         self._dataloader = dataloader
 
@@ -29,13 +29,7 @@ def __len__(self):
     def create_from_config(cls, config, **kwargs):
         create_config = copy.deepcopy(config)
         batch_processor = BaseBatchProcessor.create_from_config(
-            create_config.pop("batch_processor")
-            if "batch_processor" in create_config
-            else {"type": "identity"}
-        )
-        create_config.pop("type")  # For passing as **config in torch DataLoader
-        return cls(
-            dataloader=DataLoader(
-                kwargs["dataset"], collate_fn=batch_processor, **create_config
-            )
+            create_config.pop('batch_processor') if 'batch_processor' in create_config else {'type': 'identity'}
         )
+        create_config.pop('type')  # For passing as **config in torch DataLoader
+        return cls(dataloader=DataLoader(kwargs['dataset'], collate_fn=batch_processor, **create_config))
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index bd45c86c..9991a073 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,43 +1,43 @@
 import torch
-
 from utils import MetaParent
 
 
 class BaseBatchProcessor(metaclass=MetaParent):
+
     def __call__(self, batch):
         raise NotImplementedError
 
 
-class IdentityBatchProcessor(BaseBatchProcessor, config_name="identity"):
+class IdentityBatchProcessor(BaseBatchProcessor, config_name='identity'):
+
     def __call__(self, batch):
         return torch.tensor(batch)
+    
+class EmbedBatchProcessor(BaseBatchProcessor, config_name='embed'):
 
-
-class EmbedBatchProcessor(BaseBatchProcessor, config_name="embed"):
     def __call__(self, batch):
-        ids = torch.tensor([entry["item.id"] for entry in batch])
-        embeds = torch.stack([entry["item.embed"] for entry in batch])
+        ids = torch.tensor([entry['item.id'] for entry in batch])
+        embeds = torch.stack([entry['item.embed'] for entry in batch])
+        
+        return {'ids': ids, 'embeddings': embeds}
 
-        return {"ids": ids, "embeddings": embeds}
 
+class BasicBatchProcessor(BaseBatchProcessor, config_name='basic'):
 
-class BasicBatchProcessor(BaseBatchProcessor, config_name="basic"):
     def __call__(self, batch):
         processed_batch = {}
 
         for key in batch[0].keys():
-            if key.endswith(".ids"):
-                prefix = key.split(".")[0]
-                assert "{}.length".format(prefix) in batch[0]
+            if key.endswith('.ids'):
+                prefix = key.split('.')[0]
+                assert '{}.length'.format(prefix) in batch[0]
 
-                processed_batch[f"{prefix}.ids"] = []
-                processed_batch[f"{prefix}.length"] = []
+                processed_batch[f'{prefix}.ids'] = []
+                processed_batch[f'{prefix}.length'] = []
 
                 for sample in batch:
-                    processed_batch[f"{prefix}.ids"].extend(sample[f"{prefix}.ids"])
-                    processed_batch[f"{prefix}.length"].append(
-                        sample[f"{prefix}.length"]
-                    )
+                    processed_batch[f'{prefix}.ids'].extend(sample[f'{prefix}.ids'])
+                    processed_batch[f'{prefix}.length'].append(sample[f'{prefix}.length'])
 
         for part, values in processed_batch.items():
             processed_batch[part] = torch.tensor(values, dtype=torch.long)
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index b2e53b3e..ff040d6e 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -1,35 +1,40 @@
-import json
-import logging
-import os
-import pickle
 from collections import defaultdict
+import json
 
+from tqdm import tqdm
+
+from dataset.samplers import TrainSampler, EvalSampler
+
+from utils import MetaParent, DEVICE
+
+import pickle
+import torch
 import numpy as np
 import scipy.sparse as sp
-import torch
 from scipy.sparse import csr_matrix
-from tqdm import tqdm
 
-from dataset.samplers import EvalSampler, TrainSampler
-from utils import DEVICE, MetaParent
+import os
+import logging
 
 logger = logging.getLogger(__name__)
 
 
 class BaseDataset(metaclass=MetaParent):
+
     def get_samplers(self):
         raise NotImplementedError
 
 
-class SequenceDataset(BaseDataset, config_name="sequence"):
+class SequenceDataset(BaseDataset, config_name='sequence'):
+
     def __init__(
-        self,
-        train_sampler,
-        validation_sampler,
-        test_sampler,
-        num_users,
-        num_items,
-        max_sequence_length,
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_users,
+            num_items,
+            max_sequence_length
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -40,74 +45,61 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
 
-        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="train",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='train',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="valid",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='valid',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="test",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='test',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
 
         max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
         max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
-        logger.info("Max user id: {}".format(max_user_id))
-        logger.info("Max item id: {}".format(max_item_id))
-        logger.info("Max sequence length: {}".format(max_seq_len))
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_seq_len))
 
-        train_interactions = sum(
-            list(map(lambda x: len(x), train_dataset))
-        )  # whole user history as a sample
+        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
         valid_interactions = len(validation_dataset)  # each new interaction as a sample
-        test_interactions = len(test_dataset)  # each new interaction as a sample
-        logger.info(
-            "{} dataset sparsity: {}".format(
-                config["name"],
-                (train_interactions + valid_interactions + test_interactions)
-                / max_user_id
-                / max_item_id,
-            )
-        )
+        test_interactions = len(test_dataset) # each new interaction as a sample
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
+        ))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
 
         return cls(
@@ -116,72 +108,45 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_seq_len,
+            max_sequence_length=max_seq_len
         )
 
     @classmethod
-    def _create_dataset(
-        cls, dir_path, part, max_sequence_length=None, use_cached=False
-    ):
+    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
         max_user_id = 0
         max_item_id = 0
         max_sequence_len = 0
 
-        if use_cached and os.path.exists(os.path.join(dir_path, "{}.pkl".format(part))):
-            logger.info(
-                f"Take cached dataset from {os.path.join(dir_path, '{}.pkl'.format(part))}"
-            )
+        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
+            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "rb"
-            ) as dataset_file:
-                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(
-                    dataset_file
-                )
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
         else:
-            logger.info(
-                "Cache is forecefully ignored."
-                if not use_cached
-                else "No cached dataset has been found."
-            )
-            logger.info(
-                f"Creating a dataset {os.path.join(dir_path, '{}.txt'.format(part))}..."
-            )
+            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
+            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
 
-            dataset_path = os.path.join(dir_path, "{}.txt".format(part))
-            with open(dataset_path, "r") as f:
+            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
+            with open(dataset_path, 'r') as f:
                 data = f.readlines()
 
             sequence_info = cls._create_sequences(data, max_sequence_length)
-            (
-                user_sequences,
-                item_sequences,
-                max_user_id,
-                max_item_id,
-                max_sequence_len,
-            ) = sequence_info
+            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
 
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
-                dataset.append(
-                    {
-                        "user.ids": [user_id],
-                        "user.length": 1,
-                        "item.ids": item_ids,
-                        "item.length": len(item_ids),
-                    }
-                )
+                dataset.append({
+                    'user.ids': [user_id], 'user.length': 1,
+                    'item.ids': item_ids, 'item.length': len(item_ids)
+                })
 
-            logger.info("{} dataset size: {}".format(part, len(dataset)))
-            logger.info(
-                "{} dataset max sequence length: {}".format(part, max_sequence_len)
-            )
+            logger.info('{} dataset size: {}'.format(part, len(dataset)))
+            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "wb"
-            ) as dataset_file:
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
                 pickle.dump(
-                    (dataset, max_user_id, max_item_id, max_sequence_len), dataset_file
+                    (dataset, max_user_id, max_item_id, max_sequence_len),
+                    dataset_file
                 )
 
         return dataset, max_user_id, max_item_id, max_sequence_len
@@ -196,7 +161,7 @@ def _create_sequences(data, max_sample_len):
         max_sequence_length = 0
 
         for sample in data:
-            sample = sample.strip("\n").split(" ")
+            sample = sample.strip('\n').split(' ')
             item_ids = [int(item_id) for item_id in sample[1:]][-max_sample_len:]
             user_id = int(sample[0])
 
@@ -207,13 +172,7 @@ def _create_sequences(data, max_sample_len):
             user_sequences.append(user_id)
             item_sequences.append(item_ids)
 
-        return (
-            user_sequences,
-            item_sequences,
-            max_user_id,
-            max_item_id,
-            max_sequence_length,
-        )
+        return user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_length
 
     def get_samplers(self):
         return self._train_sampler, self._validation_sampler, self._test_sampler
@@ -233,84 +192,71 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            "num_users": self.num_users,
-            "num_items": self.num_items,
-            "max_sequence_length": self.max_sequence_length,
+            'num_users': self.num_users,
+            'num_items': self.num_items,
+            'max_sequence_length': self.max_sequence_length
         }
-
-
-class SequenceFullDataset(SequenceDataset, config_name="sequence_full"):
+        
+        
+class SequenceFullDataset(SequenceDataset, config_name='sequence_full'):
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
 
-        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="train",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='train',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="valid",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='valid',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="test",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='test',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
 
         max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
         max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
         logger.info("Validation dataset size: {}".format(len(validation_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
-        logger.info("Max user id: {}".format(max_user_id))
-        logger.info("Max item id: {}".format(max_item_id))
-        logger.info("Max sequence length: {}".format(max_seq_len))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_seq_len))
 
-        train_interactions = sum(
-            list(map(lambda x: len(x), train_dataset))
-        )  # whole user history as a sample
+        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
         valid_interactions = len(validation_dataset)  # each new interaction as a sample
-        test_interactions = len(test_dataset)  # each new interaction as a sample
-        logger.info(
-            "{} dataset sparsity: {}".format(
-                config["name"],
-                (train_interactions + valid_interactions + test_interactions)
-                / max_user_id
-                / max_item_id,
-            )
-        )
+        test_interactions = len(test_dataset) # each new interaction as a sample
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
+        ))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
 
         return cls(
@@ -319,59 +265,40 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_seq_len,
+            max_sequence_length=max_seq_len
         )
-
+        
     @classmethod
     def flatten_item_sequence(cls, item_ids):
-        min_history_length = 3
+        min_history_length = 3 # TODOPK make this configurable
         histories = []
-        for i in range(min_history_length - 1, len(item_ids)):
-            histories.append(item_ids[: i + 1])
+        for i in range(min_history_length-1, len(item_ids)):
+            histories.append(item_ids[:i+1])
         return histories
-
+    
     @classmethod
-    def _create_dataset(
-        cls, dir_path, part, max_sequence_length=None, use_cached=False
-    ):
+    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
         max_user_id = 0
         max_item_id = 0
         max_sequence_len = 0
 
-        if use_cached and os.path.exists(os.path.join(dir_path, "{}.pkl".format(part))):
-            logger.info(
-                f"Take cached dataset from {os.path.join(dir_path, '{}.pkl'.format(part))}"
-            )
+        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
+            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "rb"
-            ) as dataset_file:
-                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(
-                    dataset_file
-                )
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
         else:
-            logger.info(
-                "Cache is forecefully ignored."
-                if not use_cached
-                else "No cached dataset has been found."
-            )
-            logger.info(
-                f"Creating a dataset {os.path.join(dir_path, '{}.txt'.format(part))}..."
-            )
+            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
+            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
 
-            dataset_path = os.path.join(dir_path, "{}.txt".format(part))
-            with open(dataset_path, "r") as f:
+            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
+            with open(dataset_path, 'r') as f:
                 data = f.readlines()
 
             sequence_info = cls._create_sequences(data, max_sequence_length)
-            (
-                user_sequences,
-                item_sequences,
-                max_user_id,
-                max_item_id,
-                max_sequence_len,
-            ) = sequence_info
+            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
 
+            # TODOPK check
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
                 if part == "train":
@@ -395,29 +322,26 @@ def _create_dataset(
                         }
                     )
 
-            logger.info("{} dataset size: {}".format(part, len(dataset)))
-            logger.info(
-                "{} dataset max sequence length: {}".format(part, max_sequence_len)
-            )
+            logger.info('{} dataset size: {}'.format(part, len(dataset)))
+            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "wb"
-            ) as dataset_file:
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
                 pickle.dump(
-                    (dataset, max_user_id, max_item_id, max_sequence_len), dataset_file
+                    (dataset, max_user_id, max_item_id, max_sequence_len),
+                    dataset_file
                 )
 
         return dataset, max_user_id, max_item_id, max_sequence_len
 
+class GraphDataset(BaseDataset, config_name='graph'):
 
-class GraphDataset(BaseDataset, config_name="graph"):
     def __init__(
-        self,
-        dataset,
-        graph_dir_path,
-        use_train_data_only=True,
-        use_user_graph=False,
-        use_item_graph=False,
+            self,
+            dataset,
+            graph_dir_path,
+            use_train_data_only=True,
+            use_user_graph=False,
+            use_item_graph=False
     ):
         self._dataset = dataset
         self._graph_dir_path = graph_dir_path
@@ -430,19 +354,15 @@ def __init__(
 
         train_sampler, validation_sampler, test_sampler = dataset.get_samplers()
 
-        train_interactions, train_user_interactions, train_item_interactions = (
-            [],
-            [],
-            [],
-        )
+        train_interactions, train_user_interactions, train_item_interactions = [], [], []
 
         train_user_2_items = defaultdict(set)
         train_item_2_users = defaultdict(set)
         visited_user_item_pairs = set()
 
         for sample in train_sampler.dataset:
-            user_id = sample["user.ids"][0]
-            item_ids = sample["item.ids"]
+            user_id = sample['user.ids'][0]
+            item_ids = sample['item.ids']
 
             for item_id in item_ids:
                 if (user_id, item_id) not in visited_user_item_pairs:
@@ -458,8 +378,8 @@ def __init__(
         # TODO create separated function
         if not self._use_train_data_only:
             for sample in validation_sampler.dataset:
-                user_id = sample["user.ids"][0]
-                item_ids = sample["item.ids"]
+                user_id = sample['user.ids'][0]
+                item_ids = sample['item.ids']
 
                 for item_id in item_ids:
                     if (user_id, item_id) not in visited_user_item_pairs:
@@ -473,8 +393,8 @@ def __init__(
                         visited_user_item_pairs.add((user_id, item_id))
 
             for sample in test_sampler.dataset:
-                user_id = sample["user.ids"][0]
-                item_ids = sample["item.ids"]
+                user_id = sample['user.ids'][0]
+                item_ids = sample['item.ids']
 
                 for item_id in item_ids:
                     if (user_id, item_id) not in visited_user_item_pairs:
@@ -491,32 +411,27 @@ def __init__(
         self._train_user_interactions = np.array(train_user_interactions)
         self._train_item_interactions = np.array(train_item_interactions)
 
-        path_to_graph = os.path.join(graph_dir_path, "general_graph.npz")
+        path_to_graph = os.path.join(graph_dir_path, 'general_graph.npz')
         if os.path.exists(path_to_graph):
             self._graph = sp.load_npz(path_to_graph)
         else:
             # place ones only when co-occurrence happens
             user2item_connections = csr_matrix(
-                (
-                    np.ones(len(train_user_interactions)),
-                    (train_user_interactions, train_item_interactions),
-                ),
-                shape=(self._num_users + 2, self._num_items + 2),
+                (np.ones(len(train_user_interactions)), (train_user_interactions, train_item_interactions)),
+                shape=(self._num_users + 2, self._num_items + 2)
             )  # (num_users + 2, num_items + 2), bipartite graph
             self._graph = self.get_sparse_graph_layer(
                 user2item_connections,
                 self._num_users + 2,
                 self._num_items + 2,
-                biparite=True,
+                biparite=True
             )
             sp.save_npz(path_to_graph, self._graph)
 
-        self._graph = (
-            self._convert_sp_mat_to_sp_tensor(self._graph).coalesce().to(DEVICE)
-        )
+        self._graph = self._convert_sp_mat_to_sp_tensor(self._graph).coalesce().to(DEVICE)
 
         if self._use_user_graph:
-            path_to_user_graph = os.path.join(graph_dir_path, "user_graph.npz")
+            path_to_user_graph = os.path.join(graph_dir_path, 'user_graph.npz')
             if os.path.exists(path_to_user_graph):
                 self._user_graph = sp.load_npz(path_to_user_graph)
             else:
@@ -525,18 +440,13 @@ def __init__(
                 visited_user_item_pairs = set()
                 visited_user_user_pairs = set()
 
-                for user_id, item_id in tqdm(
-                    zip(self._train_user_interactions, self._train_item_interactions)
-                ):
+                for user_id, item_id in tqdm(zip(self._train_user_interactions, self._train_item_interactions)):
                     if (user_id, item_id) in visited_user_item_pairs:
                         continue  # process (user, item) pair only once
                     visited_user_item_pairs.add((user_id, item_id))
 
                     for connected_user_id in train_item_2_users[item_id]:
-                        if (
-                            user_id,
-                            connected_user_id,
-                        ) in visited_user_user_pairs or user_id == connected_user_id:
+                        if (user_id, connected_user_id) in visited_user_user_pairs or user_id == connected_user_id:
                             continue  # add (user, user) to graph connections pair only once
                         visited_user_user_pairs.add((user_id, connected_user_id))
 
@@ -546,30 +456,24 @@ def __init__(
                 # (user, user) graph
                 user2user_connections = csr_matrix(
                     (
-                        np.ones(len(user2user_interactions_fst)),
-                        (user2user_interactions_fst, user2user_interactions_snd),
-                    ),
-                    shape=(self._num_users + 2, self._num_users + 2),
+                    np.ones(len(user2user_interactions_fst)), (user2user_interactions_fst, user2user_interactions_snd)),
+                    shape=(self._num_users + 2, self._num_users + 2)
                 )
 
                 self._user_graph = self.get_sparse_graph_layer(
                     user2user_connections,
                     self._num_users + 2,
                     self._num_users + 2,
-                    biparite=False,
+                    biparite=False
                 )
                 sp.save_npz(path_to_user_graph, self._user_graph)
 
-            self._user_graph = (
-                self._convert_sp_mat_to_sp_tensor(self._user_graph)
-                .coalesce()
-                .to(DEVICE)
-            )
+            self._user_graph = self._convert_sp_mat_to_sp_tensor(self._user_graph).coalesce().to(DEVICE)
         else:
             self._user_graph = None
 
         if self._use_item_graph:
-            path_to_item_graph = os.path.join(graph_dir_path, "item_graph.npz")
+            path_to_item_graph = os.path.join(graph_dir_path, 'item_graph.npz')
             if os.path.exists(path_to_item_graph):
                 self._item_graph = sp.load_npz(path_to_item_graph)
             else:
@@ -578,18 +482,13 @@ def __init__(
                 visited_user_item_pairs = set()
                 visited_item_item_pairs = set()
 
-                for user_id, item_id in tqdm(
-                    zip(self._train_user_interactions, self._train_item_interactions)
-                ):
+                for user_id, item_id in tqdm(zip(self._train_user_interactions, self._train_item_interactions)):
                     if (user_id, item_id) in visited_user_item_pairs:
                         continue  # process (user, item) pair only once
                     visited_user_item_pairs.add((user_id, item_id))
 
                     for connected_item_id in train_user_2_items[user_id]:
-                        if (
-                            item_id,
-                            connected_item_id,
-                        ) in visited_item_item_pairs or item_id == connected_item_id:
+                        if (item_id, connected_item_id) in visited_item_item_pairs or item_id == connected_item_id:
                             continue  # add (item, item) to graph connections pair only once
                         visited_item_item_pairs.add((item_id, connected_item_id))
 
@@ -599,42 +498,39 @@ def __init__(
                 # (item, item) graph
                 item2item_connections = csr_matrix(
                     (
-                        np.ones(len(item2item_interactions_fst)),
-                        (item2item_interactions_fst, item2item_interactions_snd),
-                    ),
-                    shape=(self._num_items + 2, self._num_items + 2),
+                    np.ones(len(item2item_interactions_fst)), (item2item_interactions_fst, item2item_interactions_snd)),
+                    shape=(self._num_items + 2, self._num_items + 2)
                 )
                 self._item_graph = self.get_sparse_graph_layer(
                     item2item_connections,
                     self._num_items + 2,
                     self._num_items + 2,
-                    biparite=False,
+                    biparite=False
                 )
                 sp.save_npz(path_to_item_graph, self._item_graph)
 
-            self._item_graph = (
-                self._convert_sp_mat_to_sp_tensor(self._item_graph)
-                .coalesce()
-                .to(DEVICE)
-            )
+            self._item_graph = self._convert_sp_mat_to_sp_tensor(self._item_graph).coalesce().to(DEVICE)
         else:
             self._item_graph = None
 
     @classmethod
     def create_from_config(cls, config):
-        dataset = BaseDataset.create_from_config(config["dataset"])
+        dataset = BaseDataset.create_from_config(config['dataset'])
         return cls(
             dataset=dataset,
-            graph_dir_path=config["graph_dir_path"],
-            use_user_graph=config.get("use_user_graph", False),
-            use_item_graph=config.get("use_item_graph", False),
+            graph_dir_path=config['graph_dir_path'],
+            use_user_graph=config.get('use_user_graph', False),
+            use_item_graph=config.get('use_item_graph', False)
         )
 
     @staticmethod
     def get_sparse_graph_layer(sparse_matrix, fst_dim, snd_dim, biparite=False):
         mat_dim_size = fst_dim + snd_dim if biparite else fst_dim
 
-        adj_mat = sp.dok_matrix((mat_dim_size, mat_dim_size), dtype=np.float32)
+        adj_mat = sp.dok_matrix(
+            (mat_dim_size, mat_dim_size),
+            dtype=np.float32
+        )
         adj_mat = adj_mat.tolil()
 
         R = sparse_matrix.tolil()  # list of lists (fst_dim, snd_dim)
@@ -652,11 +548,11 @@ def get_sparse_graph_layer(sparse_matrix, fst_dim, snd_dim, biparite=False):
 
         rowsum = np.array(adj_mat.sum(1))
         d_inv = np.power(rowsum, -1).flatten()
-        d_inv[np.isinf(d_inv)] = 0.0
+        d_inv[np.isinf(d_inv)] = 0.
         d_mat_inv = sp.diags(d_inv)
 
         d_inv = np.power(edges_degree, -0.5).flatten()  # D^(-0.5)
-        d_inv[np.isinf(d_inv)] = 0.0  # fix NaNs in case if row with zero connections
+        d_inv[np.isinf(d_inv)] = 0.  # fix NaNs in case if row with zero connections
         d_mat = sp.diags(d_inv)  # make it square matrix
 
         # D^(-0.5) @ A @ D^(-0.5)
@@ -687,15 +583,16 @@ def get_samplers(self):
     @property
     def meta(self):
         meta = {
-            "user_graph": self._user_graph,
-            "item_graph": self._item_graph,
-            "graph": self._graph,
-            **self._dataset.meta,
+            'user_graph': self._user_graph,
+            'item_graph': self._item_graph,
+            'graph': self._graph,
+            **self._dataset.meta
         }
         return meta
 
 
-class DuorecDataset(BaseDataset, config_name="duorec"):
+class DuorecDataset(BaseDataset, config_name='duorec'):
+
     def __init__(self, dataset):
         self._dataset = dataset
         self._num_users = dataset.num_users
@@ -705,7 +602,7 @@ def __init__(self, dataset):
 
         target_2_sequences = defaultdict(list)
         for sample in train_sampler.dataset:
-            item_ids = sample["item.ids"]
+            item_ids = sample['item.ids']
 
             target_item = item_ids[-1]
             semantic_similar_item_ids = item_ids[:-1]
@@ -716,7 +613,7 @@ def __init__(self, dataset):
 
     @classmethod
     def create_from_config(cls, config):
-        dataset = BaseDataset.create_from_config(config["dataset"])
+        dataset = BaseDataset.create_from_config(config['dataset'])
         return cls(dataset)
 
     @property
@@ -735,15 +632,16 @@ def meta(self):
         return self._dataset.meta
 
 
-class ScientificDataset(BaseDataset, config_name="scientific"):
+class ScientificDataset(BaseDataset, config_name='scientific'):
+
     def __init__(
-        self,
-        train_sampler,
-        validation_sampler,
-        test_sampler,
-        num_users,
-        num_items,
-        max_sequence_length,
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_users,
+            num_items,
+            max_sequence_length
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -754,17 +652,17 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
-        max_sequence_length = config["max_sequence_length"]
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
+        max_sequence_length = config['max_sequence_length']
         max_user_id, max_item_id = 0, 0
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, "{}.txt".format("all_data"))
-        with open(dataset_path, "r") as f:
+        dataset_path = os.path.join(data_dir_path, '{}.txt'.format('all_data'))
+        with open(dataset_path, 'r') as f:
             data = f.readlines()
 
         for sample in data:
-            sample = sample.strip("\n").split(" ")
+            sample = sample.strip('\n').split(' ')
             user_id = int(sample[0])
             item_ids = [int(item_id) for item_id in sample[1:]]
 
@@ -773,69 +671,54 @@ def create_from_config(cls, config, **kwargs):
 
             assert len(item_ids) >= 5
 
-            train_dataset.append(
-                {
-                    "user.ids": [user_id],
-                    "user.length": 1,
-                    "item.ids": item_ids[:-2][-max_sequence_length:],
-                    "item.length": len(item_ids[:-2][-max_sequence_length:]),
-                }
-            )
-            assert len(item_ids[:-2][-max_sequence_length:]) == len(
-                set(item_ids[:-2][-max_sequence_length:])
-            )
-            validation_dataset.append(
-                {
-                    "user.ids": [user_id],
-                    "user.length": 1,
-                    "item.ids": item_ids[:-1][-max_sequence_length:],
-                    "item.length": len(item_ids[:-1][-max_sequence_length:]),
-                }
-            )
-            assert len(item_ids[:-1][-max_sequence_length:]) == len(
-                set(item_ids[:-1][-max_sequence_length:])
-            )
-            test_dataset.append(
-                {
-                    "user.ids": [user_id],
-                    "user.length": 1,
-                    "item.ids": item_ids[-max_sequence_length:],
-                    "item.length": len(item_ids[-max_sequence_length:]),
-                }
-            )
-            assert len(item_ids[-max_sequence_length:]) == len(
-                set(item_ids[-max_sequence_length:])
-            )
-
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
-        logger.info("Max user id: {}".format(max_user_id))
-        logger.info("Max item id: {}".format(max_item_id))
-        logger.info("Max sequence length: {}".format(max_sequence_length))
-        logger.info(
-            "{} dataset sparsity: {}".format(
-                config["name"],
-                (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id,
-            )
-        )
+            train_dataset.append({
+                'user.ids': [user_id],
+                'user.length': 1,
+                'item.ids': item_ids[:-2][-max_sequence_length:],
+                'item.length': len(item_ids[:-2][-max_sequence_length:])
+            })
+            assert len(item_ids[:-2][-max_sequence_length:]) == len(set(item_ids[:-2][-max_sequence_length:]))
+            validation_dataset.append({
+                'user.ids': [user_id],
+                'user.length': 1,
+                'item.ids': item_ids[:-1][-max_sequence_length:],
+                'item.length': len(item_ids[:-1][-max_sequence_length:])
+            })
+            assert len(item_ids[:-1][-max_sequence_length:]) == len(set(item_ids[:-1][-max_sequence_length:]))
+            test_dataset.append({
+                'user.ids': [user_id],
+                'user.length': 1,
+                'item.ids': item_ids[-max_sequence_length:],
+                'item.length': len(item_ids[-max_sequence_length:])
+            })
+            assert len(item_ids[-max_sequence_length:]) == len(set(item_ids[-max_sequence_length:]))
+
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_sequence_length))
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id
+        ))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
 
         return cls(
@@ -844,7 +727,7 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_sequence_length,
+            max_sequence_length=max_sequence_length
         )
 
     def get_samplers(self):
@@ -865,9 +748,9 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            "num_users": self.num_users,
-            "num_items": self.num_items,
-            "max_sequence_length": self.max_sequence_length,
+            'num_users': self.num_users,
+            'num_items': self.num_items,
+            'max_sequence_length': self.max_sequence_length
         }
 
 
@@ -991,8 +874,15 @@ def create_from_config(cls, config, **kwargs):
         )
 
 
-class RqVaeDataset(BaseDataset, config_name="rqvae"):
-    def __init__(self, train_sampler, validation_sampler, test_sampler, num_items):
+class RqVaeDataset(BaseDataset, config_name='rqvae'):
+
+    def __init__(
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_items
+    ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
         self._test_sampler = test_sampler
@@ -1000,33 +890,39 @@ def __init__(self, train_sampler, validation_sampler, test_sampler, num_items):
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, "{}.pt".format("data_full"))
+        dataset_path = os.path.join(data_dir_path, '{}.pt'.format('data_full'))
         df = torch.load(dataset_path, weights_only=False)
 
         for idx, sample in df.iterrows():
-            train_dataset.append({"item.id": idx, "item.embed": sample["embeddings"]})
-
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
+            train_dataset.append({
+                'item.id': idx,
+                'item.embed': sample["embeddings"]
+            })
+            
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"], dataset=train_dataset
+            config['samplers'],
+            dataset=train_dataset
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"], dataset=validation_dataset
+            config['samplers'],
+            dataset=validation_dataset
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"], dataset=test_dataset
+            config['samplers'],
+            dataset=test_dataset
         )
 
         return cls(
             train_sampler=train_sampler,
             validation_sampler=validation_sampler,
             test_sampler=test_sampler,
-            num_items=len(df),
+            num_items=len(df)
         )
 
     def get_samplers(self):
@@ -1042,4 +938,7 @@ def max_sequence_length(self):
 
     @property
     def meta(self):
-        return {"num_items": self.num_items, "train_sampler": self._train_sampler}
+        return {
+            'num_items': self.num_items,
+            'train_sampler': self._train_sampler
+        }
diff --git a/modeling/dataset/negative_samplers/__init__.py b/modeling/dataset/negative_samplers/__init__.py
index 6cac99af..498de21f 100644
--- a/modeling/dataset/negative_samplers/__init__.py
+++ b/modeling/dataset/negative_samplers/__init__.py
@@ -2,4 +2,8 @@
 from .popular import PopularNegativeSampler
 from .random import RandomNegativeSampler
 
-__all__ = ["BaseNegativeSampler", "PopularNegativeSampler", "RandomNegativeSampler"]
+__all__ = [
+    'BaseNegativeSampler',
+    'PopularNegativeSampler',
+    'RandomNegativeSampler'
+]
diff --git a/modeling/dataset/negative_samplers/base.py b/modeling/dataset/negative_samplers/base.py
index ebaef60c..5483bd29 100644
--- a/modeling/dataset/negative_samplers/base.py
+++ b/modeling/dataset/negative_samplers/base.py
@@ -4,15 +4,21 @@
 
 
 class BaseNegativeSampler(metaclass=MetaParent):
-    def __init__(self, dataset, num_users, num_items):
+
+    def __init__(
+            self,
+            dataset,
+            num_users,
+            num_items
+    ):
         self._dataset = dataset
         self._num_users = num_users
         self._num_items = num_items
 
         self._seen_items = defaultdict(set)
         for sample in self._dataset:
-            user_id = sample["user.ids"][0]
-            items = list(sample["item.ids"])
+            user_id = sample['user.ids'][0]
+            items = list(sample['item.ids'])
             self._seen_items[user_id].update(items)
 
     def generate_negative_samples(self, sample, num_negatives):
diff --git a/modeling/dataset/negative_samplers/popular.py b/modeling/dataset/negative_samplers/popular.py
index 6e478e0c..68476651 100644
--- a/modeling/dataset/negative_samplers/popular.py
+++ b/modeling/dataset/negative_samplers/popular.py
@@ -1,34 +1,44 @@
+from dataset.negative_samplers.base import BaseNegativeSampler
+
 from collections import Counter
 
-from dataset.negative_samplers.base import BaseNegativeSampler
 
+class PopularNegativeSampler(BaseNegativeSampler, config_name='popular'):
 
-class PopularNegativeSampler(BaseNegativeSampler, config_name="popular"):
-    def __init__(self, dataset, num_users, num_items):
-        super().__init__(dataset=dataset, num_users=num_users, num_items=num_items)
+    def __init__(
+            self,
+            dataset,
+            num_users,
+            num_items
+    ):
+        super().__init__(
+            dataset=dataset,
+            num_users=num_users,
+            num_items=num_items
+        )
 
         self._popular_items = self._items_by_popularity()
 
     @classmethod
     def create_from_config(cls, _, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def _items_by_popularity(self):
         popularity = Counter()
 
         for sample in self._dataset:
-            for item_id in sample["item.ids"]:
+            for item_id in sample['item.ids']:
                 popularity[item_id] += 1
 
         popular_items = sorted(popularity, key=popularity.get, reverse=True)
         return popular_items
 
     def generate_negative_samples(self, sample, num_negatives):
-        user_id = sample["user.ids"][0]
+        user_id = sample['user.ids'][0]
         popularity_idx = 0
         negatives = []
         while len(negatives) < num_negatives:
diff --git a/modeling/dataset/negative_samplers/random.py b/modeling/dataset/negative_samplers/random.py
index 24055217..cf8e24e9 100644
--- a/modeling/dataset/negative_samplers/random.py
+++ b/modeling/dataset/negative_samplers/random.py
@@ -1,18 +1,20 @@
 from collections import defaultdict
 
-import numpy as np
 from tqdm import tqdm
 
 from dataset.negative_samplers.base import BaseNegativeSampler
 
+import numpy as np
+
+
+class RandomNegativeSampler(BaseNegativeSampler, config_name='random'):
 
-class RandomNegativeSampler(BaseNegativeSampler, config_name="random"):
     @classmethod
     def create_from_config(cls, _, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def generate_negative_samples(self, sample, num_negatives):
diff --git a/modeling/dataset/samplers/__init__.py b/modeling/dataset/samplers/__init__.py
index 40ffd454..6ed31eed 100644
--- a/modeling/dataset/samplers/__init__.py
+++ b/modeling/dataset/samplers/__init__.py
@@ -1,19 +1,10 @@
-from .base import EvalSampler, TrainSampler
-from .cl4srec import Cl4SRecEvalSampler, Cl4SRecTrainSampler
-from .duorec import DuoRecEvalSampler, DuorecTrainSampler
-from .identity import IdentityEvalSampler, IdentityTrainSampler
-from .last_item_prediction import (
-    LastItemPredictionEvalSampler,
-    LastItemPredictionTrainSampler,
-)
-from .masked_item_prediction import (
-    MaskedItemPredictionEvalSampler,
-    MaskedItemPredictionTrainSampler,
-)
-from .mclsr import MCLSRPredictionEvalSampler, MCLSRTrainSampler
-from .next_item_prediction import (
-    NextItemPredictionEvalSampler,
-    NextItemPredictionTrainSampler,
-)
-from .pop import PopEvalSampler, PopTrainSampler
-from .s3rec import S3RecPretrainEvalSampler, S3RecPretrainTrainSampler
+from .base import TrainSampler, EvalSampler
+from .cl4srec import Cl4SRecTrainSampler, Cl4SRecEvalSampler
+from .duorec import DuorecTrainSampler, DuoRecEvalSampler
+from .next_item_prediction import NextItemPredictionTrainSampler, NextItemPredictionEvalSampler
+from .last_item_prediction import LastItemPredictionTrainSampler, LastItemPredictionEvalSampler
+from .masked_item_prediction import MaskedItemPredictionTrainSampler, MaskedItemPredictionEvalSampler
+from .mclsr import MCLSRTrainSampler, MCLSRPredictionEvalSampler
+from .pop import PopTrainSampler, PopEvalSampler
+from .s3rec import S3RecPretrainTrainSampler, S3RecPretrainEvalSampler
+from .identity import IdentityTrainSampler, IdentityEvalSampler
diff --git a/modeling/dataset/samplers/base.py b/modeling/dataset/samplers/base.py
index 723abb0a..2dbfa705 100644
--- a/modeling/dataset/samplers/base.py
+++ b/modeling/dataset/samplers/base.py
@@ -1,9 +1,10 @@
-import copy
-
 from utils import MetaParent
 
+import copy
+
 
 class TrainSampler(metaclass=MetaParent):
+
     def __init__(self):
         self._dataset = None
 
@@ -19,6 +20,7 @@ def __getitem__(self, index):
 
 
 class EvalSampler(metaclass=MetaParent):
+
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -35,14 +37,16 @@ def __len__(self):
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item = sample["item.ids"][-1]
+        item_sequence = sample['item.ids'][:-1]
+        next_item = sample['item.ids'][-1]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "labels.ids": [next_item],
-            "labels.length": 1,
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'labels.ids': [next_item],
+            'labels.length': 1
         }
diff --git a/modeling/dataset/samplers/cl4srec.py b/modeling/dataset/samplers/cl4srec.py
index 6fbcd143..8aba1de1 100644
--- a/modeling/dataset/samplers/cl4srec.py
+++ b/modeling/dataset/samplers/cl4srec.py
@@ -1,19 +1,20 @@
-import copy
-
 import numpy as np
 
-from dataset.samplers.base import EvalSampler, TrainSampler
+from dataset.samplers.base import TrainSampler, EvalSampler
+
+import copy
+
 
+class Cl4SRecTrainSampler(TrainSampler, config_name='cl4srec'):
 
-class Cl4SRecTrainSampler(TrainSampler, config_name="cl4srec"):
     def __init__(
-        self,
-        dataset,
-        num_users,
-        num_items,
-        item_crop_portion,
-        item_mask_portion,
-        item_reorder_portion,
+            self,
+            dataset,
+            num_users,
+            num_items,
+            item_crop_portion,
+            item_mask_portion,
+            item_reorder_portion
     ):
         super().__init__()
         self._dataset = dataset
@@ -27,23 +28,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            item_crop_portion=config["item_crop_portion"],
-            item_mask_portion=config["item_mask_portion"],
-            item_reorder_portion=config["item_reorder_portion"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            item_crop_portion=config['item_crop_portion'],
+            item_mask_portion=config['item_mask_portion'],
+            item_reorder_portion=config['item_reorder_portion']
         )
 
     def _apply_crop_augmentation(self, item_sequence):
         num_elements_to_crop = max(1, int(self._item_crop_portion * len(item_sequence)))
-        crop_start_index = np.random.randint(
-            low=0, high=len(item_sequence) - num_elements_to_crop + 1
-        )
+        crop_start_index = np.random.randint(low=0, high=len(item_sequence) - num_elements_to_crop + 1)
         assert 0 <= crop_start_index <= len(item_sequence) - num_elements_to_crop
-        item_sequence = item_sequence[
-            crop_start_index : crop_start_index + num_elements_to_crop
-        ]
+        item_sequence = item_sequence[crop_start_index: crop_start_index + num_elements_to_crop]
         return item_sequence
 
     def _apply_mask_augmentation(self, item_sequence):
@@ -68,28 +65,22 @@ def _apply_mask_augmentation(self, item_sequence):
 
     def _apply_reorder_augmentation(self, item_sequence):
         num_elements_to_reorder = int(self._item_reorder_portion * len(item_sequence))
-        reorder_start_index = np.random.randint(
-            low=0, high=len(item_sequence) - num_elements_to_reorder + 1
-        )
+        reorder_start_index = np.random.randint(low=0, high=len(item_sequence) - num_elements_to_reorder + 1)
         assert 0 <= reorder_start_index <= len(item_sequence) - num_elements_to_reorder
-        reordered_subsequence = item_sequence[
-            reorder_start_index : reorder_start_index + num_elements_to_reorder
-        ]
+        reordered_subsequence = item_sequence[reorder_start_index: reorder_start_index + num_elements_to_reorder]
         np.random.shuffle(reordered_subsequence)  # This works in-place
-        item_sequence = (
-            item_sequence[:reorder_start_index]
-            + reordered_subsequence
-            + item_sequence[reorder_start_index + num_elements_to_reorder :]
-        )
+        item_sequence = item_sequence[:reorder_start_index] \
+                        + reordered_subsequence \
+                        + item_sequence[reorder_start_index + num_elements_to_reorder:]
         return item_sequence
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item = sample["item.ids"][-1]
+        item_sequence = sample['item.ids'][:-1]
+        next_item = sample['item.ids'][-1]
 
-        sample_items = set(sample["item.ids"])
+        sample_items = set(sample['item.ids'])
         negative = np.random.randint(low=1, high=self._num_items + 1)
         while negative in sample_items:
             negative = np.random.randint(low=1, high=self._num_items + 1)
@@ -97,7 +88,7 @@ def __getitem__(self, index):
         augmentation_list = [
             self._apply_crop_augmentation,
             self._apply_mask_augmentation,
-            self._apply_reorder_augmentation,
+            self._apply_reorder_augmentation
         ]
 
         fst_augmentation = np.random.choice(augmentation_list)
@@ -107,28 +98,35 @@ def __getitem__(self, index):
         snd_augmented_sequence = snd_augmentation(item_sequence)
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "fst_augmented_item.ids": fst_augmented_sequence,
-            "fst_augmented_item.length": len(fst_augmented_sequence),
-            "snd_augmented_item.ids": snd_augmented_sequence,
-            "snd_augmented_item.length": len(snd_augmented_sequence),
-            "labels.ids": [next_item],
-            "labels.length": 1,
-            "positive.ids": [next_item],
-            "positive.length": 1,
-            "negative.ids": [negative],
-            "negative.length": 1,
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'fst_augmented_item.ids': fst_augmented_sequence,
+            'fst_augmented_item.length': len(fst_augmented_sequence),
+
+            'snd_augmented_item.ids': snd_augmented_sequence,
+            'snd_augmented_item.length': len(snd_augmented_sequence),
+
+            'labels.ids': [next_item],
+            'labels.length': 1,
+
+            'positive.ids': [next_item],
+            'positive.length': 1,
+
+            'negative.ids': [negative],
+            'negative.length': 1
         }
 
 
-class Cl4SRecEvalSampler(EvalSampler, config_name="cl4srec"):
+class Cl4SRecEvalSampler(EvalSampler, config_name='cl4srec'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/duorec.py b/modeling/dataset/samplers/duorec.py
index f45ae100..a3067fdd 100644
--- a/modeling/dataset/samplers/duorec.py
+++ b/modeling/dataset/samplers/duorec.py
@@ -1,10 +1,12 @@
-import copy
 import random
 
-from dataset.samplers.base import EvalSampler, TrainSampler
+from dataset.samplers.base import TrainSampler, EvalSampler
+
+import copy
+
 
+class DuorecTrainSampler(TrainSampler, config_name='duorec'):
 
-class DuorecTrainSampler(TrainSampler, config_name="duorec"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -14,15 +16,15 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
 
         target_item = item_sequence[-1]
         item_sequence = item_sequence[:-1]
@@ -31,22 +33,26 @@ def __getitem__(self, index):
         semantic_similar_sequence = random.choice(self._target_2_sequences[target_item])
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "labels.ids": [target_item],
-            "labels.length": 1,
-            "semantic_similar_item.ids": semantic_similar_sequence,
-            "semantic_similar_item.length": len(semantic_similar_sequence),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'labels.ids': [target_item],
+            'labels.length': 1,
+
+            'semantic_similar_item.ids': semantic_similar_sequence,
+            'semantic_similar_item.length': len(semantic_similar_sequence)
         }
 
 
-class DuoRecEvalSampler(EvalSampler, config_name="duorec"):
+class DuoRecEvalSampler(EvalSampler, config_name='duorec'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/identity.py b/modeling/dataset/samplers/identity.py
index 4b34b03e..ffe01e23 100644
--- a/modeling/dataset/samplers/identity.py
+++ b/modeling/dataset/samplers/identity.py
@@ -1,30 +1,35 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class IdentityTrainSampler(TrainSampler, config_name='identity'):
 
-class IdentityTrainSampler(TrainSampler, config_name="identity"):
     def __init__(self, dataset):
         super().__init__()
         self._dataset = dataset
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(dataset=kwargs["dataset"])
+        return cls(
+            dataset=kwargs['dataset']
+        )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
         return sample
 
 
-class IdentityEvalSampler(EvalSampler, config_name="identity"):
+class IdentityEvalSampler(EvalSampler, config_name='identity'):
     def __init__(self, dataset):
         self._dataset = dataset
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(dataset=kwargs["dataset"])
-
+        return cls(
+            dataset=kwargs['dataset']
+        )
+    
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        return sample
+        return sample
\ No newline at end of file
diff --git a/modeling/dataset/samplers/last_item_prediction.py b/modeling/dataset/samplers/last_item_prediction.py
index c9da6570..e6f9ba73 100644
--- a/modeling/dataset/samplers/last_item_prediction.py
+++ b/modeling/dataset/samplers/last_item_prediction.py
@@ -1,8 +1,11 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
 
 from dataset.samplers.base import EvalSampler, TrainSampler
 from dataset.negative_samplers.base import BaseNegativeSampler
 
+class LastItemPredictionTrainSampler(TrainSampler, config_name='last_item_prediction'):
 
 class LastItemPredictionTrainSampler(TrainSampler, config_name="last_item_prediction"):
     def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives):
@@ -30,8 +33,8 @@ def create_from_config(cls, config, **kwargs):
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        last_item = sample["item.ids"][-1]
+        item_sequence = sample['item.ids'][:-1]
+        last_item = sample['item.ids'][-1]
 
         if self._num_negatives == 0:
             return {
@@ -59,11 +62,12 @@ def __getitem__(self, index):
             }
 
 
-class LastItemPredictionEvalSampler(EvalSampler, config_name="last_item_prediction"):
+class LastItemPredictionEvalSampler(EvalSampler, config_name='last_item_prediction'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/masked_item_prediction.py b/modeling/dataset/samplers/masked_item_prediction.py
index 2243e7ca..0100f731 100644
--- a/modeling/dataset/samplers/masked_item_prediction.py
+++ b/modeling/dataset/samplers/masked_item_prediction.py
@@ -1,13 +1,11 @@
-import copy
+from dataset.samplers.base import TrainSampler, EvalSampler
 
+import copy
 import numpy as np
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class MaskedItemPredictionTrainSampler(TrainSampler, config_name='masked_item_prediction'):
 
-class MaskedItemPredictionTrainSampler(
-    TrainSampler, config_name="masked_item_prediction"
-):
     def __init__(self, dataset, num_users, num_items, mask_prob=0.0):
         super().__init__()
         self._dataset = dataset
@@ -19,16 +17,16 @@ def __init__(self, dataset, num_users, num_items, mask_prob=0.0):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            mask_prob=config.get("mask_prob", 0.0),
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            mask_prob=config.get('mask_prob', 0.0)
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
 
         masked_sequence = []
         labels = []
@@ -56,18 +54,19 @@ def __getitem__(self, index):
         labels[-1] = item_sequence[-1]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": masked_sequence,
-            "item.length": len(masked_sequence),
-            "labels.ids": labels,
-            "labels.length": len(labels),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': masked_sequence,
+            'item.length': len(masked_sequence),
+
+            'labels.ids': labels,
+            'labels.length': len(labels)
         }
 
 
-class MaskedItemPredictionEvalSampler(
-    EvalSampler, config_name="masked_item_prediction"
-):
+class MaskedItemPredictionEvalSampler(EvalSampler, config_name='masked_item_prediction'):
+
     def __init__(self, dataset, num_users, num_items):
         super().__init__(dataset, num_users, num_items)
         self._mask_item_idx = self._num_items + 1
@@ -75,22 +74,24 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
         labels = [item_sequence[-1]]
         sequence = item_sequence[:-1] + [self._mask_item_idx]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": sequence,
-            "item.length": len(sequence),
-            "labels.ids": labels,
-            "labels.length": len(labels),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': sequence,
+            'item.length': len(sequence),
+
+            'labels.ids': labels,
+            'labels.length': len(labels)
         }
diff --git a/modeling/dataset/samplers/mclsr.py b/modeling/dataset/samplers/mclsr.py
index e3610547..2da0986c 100644
--- a/modeling/dataset/samplers/mclsr.py
+++ b/modeling/dataset/samplers/mclsr.py
@@ -1,9 +1,10 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class MCLSRTrainSampler(TrainSampler, config_name='mclsr'):
 
-class MCLSRTrainSampler(TrainSampler, config_name="mclsr"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -13,35 +14,39 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item = sample["item.ids"][-1]
-        next_item_sequence = sample["item.ids"][1:]
+        item_sequence = sample['item.ids'][:-1]
+        next_item = sample['item.ids'][-1]
+        next_item_sequence = sample['item.ids'][1:]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "positive.ids": next_item_sequence,
-            "positive.length": len(next_item_sequence),
-            "labels.ids": [next_item],
-            "labels.length": 1,
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'positive.ids': next_item_sequence,
+            'positive.length': len(next_item_sequence),
+
+            'labels.ids': [next_item],
+            'labels.length': 1
         }
 
 
-class MCLSRPredictionEvalSampler(EvalSampler, config_name="mclsr"):
+class MCLSRPredictionEvalSampler(EvalSampler, config_name='mclsr'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/next_item_prediction.py b/modeling/dataset/samplers/next_item_prediction.py
index cf9321ad..c141b065 100644
--- a/modeling/dataset/samplers/next_item_prediction.py
+++ b/modeling/dataset/samplers/next_item_prediction.py
@@ -1,13 +1,12 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+from dataset.negative_samplers.base import BaseNegativeSampler
+
 import copy
 
-from dataset.negative_samplers.base import BaseNegativeSampler
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class NextItemPredictionTrainSampler(TrainSampler, config_name='next_item_prediction'):
 
-class NextItemPredictionTrainSampler(TrainSampler, config_name="next_item_prediction"):
-    def __init__(
-        self, dataset, num_users, num_items, negative_sampler, num_negatives=0
-    ):
+    def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives=0):
         super().__init__()
         self._dataset = dataset
         self._num_users = num_users
@@ -17,32 +16,32 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        negative_sampler = BaseNegativeSampler.create_from_config(
-            {"type": config["negative_sampler_type"]}, **kwargs
-        )
+        negative_sampler = BaseNegativeSampler.create_from_config({'type': config['negative_sampler_type']}, **kwargs)
 
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
             negative_sampler=negative_sampler,
-            num_negatives=config.get("num_negatives_train", 0),
+            num_negatives=config.get('num_negatives_train', 0)
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item_sequence = sample["item.ids"][1:]
+        item_sequence = sample['item.ids'][:-1]
+        next_item_sequence = sample['item.ids'][1:]
 
         if self._num_negatives == 0:
             return {
-                "user.ids": sample["user.ids"],
-                "user.length": sample["user.length"],
-                "item.ids": item_sequence,
-                "item.length": len(item_sequence),
-                "positive.ids": next_item_sequence,
-                "positive.length": len(next_item_sequence),
+                'user.ids': sample['user.ids'],
+                'user.length': sample['user.length'],
+
+                'item.ids': item_sequence,
+                'item.length': len(item_sequence),
+
+                'positive.ids': next_item_sequence,
+                'positive.length': len(next_item_sequence)
             }
         else:
             negative_sequence = self._negative_sampler.generate_negative_samples(
@@ -50,22 +49,26 @@ def __getitem__(self, index):
             )
 
             return {
-                "user.ids": sample["user.ids"],
-                "user.length": sample["user.length"],
-                "item.ids": item_sequence,
-                "item.length": len(item_sequence),
-                "positive.ids": next_item_sequence,
-                "positive.length": len(next_item_sequence),
-                "negative.ids": negative_sequence,
-                "negative.length": len(negative_sequence),
+                'user.ids': sample['user.ids'],
+                'user.length': sample['user.length'],
+
+                'item.ids': item_sequence,
+                'item.length': len(item_sequence),
+
+                'positive.ids': next_item_sequence,
+                'positive.length': len(next_item_sequence),
+
+                'negative.ids': negative_sequence,
+                'negative.length': len(negative_sequence)
             }
 
 
-class NextItemPredictionEvalSampler(EvalSampler, config_name="next_item_prediction"):
+class NextItemPredictionEvalSampler(EvalSampler, config_name='next_item_prediction'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/pop.py b/modeling/dataset/samplers/pop.py
index 1d3ace34..afd8b208 100644
--- a/modeling/dataset/samplers/pop.py
+++ b/modeling/dataset/samplers/pop.py
@@ -1,12 +1,15 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
+
 from collections import Counter
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
 CANDIDATE_COUNTS = None
 
 
-class PopTrainSampler(TrainSampler, config_name="pop"):
+class PopTrainSampler(TrainSampler, config_name='pop'):
+
     def __init__(self, dataset, num_items):
         super().__init__()
 
@@ -16,48 +19,49 @@ def __init__(self, dataset, num_items):
             item_2_count = Counter()
 
             for sample in dataset:
-                items = sample["item.ids"]
+                items = sample['item.ids']
                 for item in items:
                     item_2_count[item] += 1
 
-            CANDIDATE_COUNTS = (
-                [0]
-                + [
-                    self._item_2_count[item_id]
-                    for item_id in range(1, self._num_items + 1)
-                ]
-                + [0]
-            )  # Mask + items + padding
-
+            CANDIDATE_COUNTS = [0] + [
+                self._item_2_count[item_id] for item_id in range(1, self._num_items + 1)
+            ] + [0]  # Mask + items + padding
+        
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(dataset=kwargs["dataset"], num_items=kwargs["num_items"])
+        return cls(
+            dataset=kwargs['dataset'],
+            num_items=kwargs['num_items']
+        )
+
 
+class PopEvalSampler(EvalSampler, config_name='pop'):
 
-class PopEvalSampler(EvalSampler, config_name="pop"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__(dataset, num_users, num_items)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        labels = [sample["item.ids"][-1]]
+        labels = [sample['item.ids'][-1]]
 
         global CANDIDATE_COUNTS
         assert CANDIDATE_COUNTS is not None
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "labels.ids": labels,
-            "labels.length": len(labels),
-            "candidates_counts.ids": CANDIDATE_COUNTS,
-            "candidates_counts.length": len(CANDIDATE_COUNTS),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'labels.ids': labels,
+            'labels.length': len(labels),
+
+            'candidates_counts.ids': CANDIDATE_COUNTS,
+            'candidates_counts.length': len(CANDIDATE_COUNTS)
         }
diff --git a/modeling/dataset/samplers/s3rec.py b/modeling/dataset/samplers/s3rec.py
index 73bf9ff4..d1b424cf 100644
--- a/modeling/dataset/samplers/s3rec.py
+++ b/modeling/dataset/samplers/s3rec.py
@@ -1,12 +1,12 @@
-import copy
+from dataset.samplers.base import TrainSampler, EvalSampler
+from dataset.negative_samplers.base import BaseNegativeSampler
 
+import copy
 import numpy as np
 
-from dataset.negative_samplers.base import BaseNegativeSampler
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class S3RecPretrainTrainSampler(TrainSampler, config_name='s3rec_pretrain'):
 
-class S3RecPretrainTrainSampler(TrainSampler, config_name="s3rec_pretrain"):
     def __init__(self, dataset, num_users, num_items, negative_sampler, mask_prob=0.0):
         super().__init__()
         self._dataset = dataset
@@ -18,29 +18,27 @@ def __init__(self, dataset, num_users, num_items, negative_sampler, mask_prob=0.
 
         self._long_sequence = []
         for sample in self._dataset:
-            self._long_sequence.extend(copy.deepcopy(sample["item.ids"]))
+            self._long_sequence.extend(copy.deepcopy(sample['item.ids']))
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        negative_sampler = BaseNegativeSampler.create_from_config(
-            {"type": config["negative_sampler_type"]}, **kwargs
-        )
+        negative_sampler = BaseNegativeSampler.create_from_config({'type': config['negative_sampler_type']}, **kwargs)
 
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
             negative_sampler=negative_sampler,
-            mask_prob=config.get("mask_prob", 0.0),
+            mask_prob=config.get('mask_prob', 0.0)
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
 
         if len(item_sequence) < 3:
-            assert False, "Something strange is happening"
+            assert False, 'Something strange is happening'
 
         # Masked Item Prediction
         masked_sequence = []
@@ -65,56 +63,48 @@ def __getitem__(self, index):
         # Mask last item
         masked_sequence[-1] = self._mask_item_idx
         positive_sequence[-1] = item_sequence[-1]
-        negative_sequence[-1] = self._negative_sampler.generate_negative_samples(
-            sample, 1
-        )[0]
-        assert (
-            len(positive_sequence)
-            == len(negative_sequence)
-            == len(masked_sequence)
-            == len(item_sequence)
-        )
+        negative_sequence[-1] = self._negative_sampler.generate_negative_samples(sample, 1)[0]
+        assert len(positive_sequence) == len(negative_sequence) == len(masked_sequence) == len(item_sequence)
 
         # Segment Prediction
         sample_length = np.random.randint(1, (len(item_sequence) + 1) // 2)
         start_id = np.random.randint(0, len(item_sequence) - sample_length)
-        negative_start_id = np.random.randint(
-            0, len(self._long_sequence) - sample_length
-        )
-        masked_segment_sequence = (
-            item_sequence[:start_id]
-            + [self._mask_item_idx] * sample_length
-            + item_sequence[start_id + sample_length :]
-        )
-        positive_segment = item_sequence[start_id : start_id + sample_length]
-        negative_segment = self._long_sequence[
-            negative_start_id : negative_start_id + sample_length
-        ]
+        negative_start_id = np.random.randint(0, len(self._long_sequence) - sample_length)
+        masked_segment_sequence = item_sequence[:start_id] + [self._mask_item_idx] * sample_length + item_sequence[start_id + sample_length:]
+        positive_segment = item_sequence[start_id: start_id + sample_length]
+        negative_segment = self._long_sequence[negative_start_id:negative_start_id + sample_length]
         assert len(positive_segment) == len(negative_segment) == sample_length
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": masked_sequence,
-            "item.length": len(masked_sequence),
-            "positive.ids": item_sequence,
-            "positive.length": len(item_sequence),
-            "negative.ids": negative_sequence,
-            "negative.length": len(negative_sequence),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': masked_sequence,
+            'item.length': len(masked_sequence),
+
+            'positive.ids': item_sequence,
+            'positive.length': len(item_sequence),
+
+            'negative.ids': negative_sequence,
+            'negative.length': len(negative_sequence),
+
             "item_segment.ids": masked_segment_sequence,
             "item_segment.length": len(masked_segment_sequence),
-            "positive_segment.ids": positive_segment,
-            "positive_segment.length": len(positive_segment),
-            "negative_segment.ids": negative_segment,
-            "negative_segment.length": len(negative_segment),
+
+            'positive_segment.ids': positive_segment,
+            'positive_segment.length': len(positive_segment),
+
+            'negative_segment.ids': negative_segment,
+            'negative_segment.length': len(negative_segment)
         }
 
 
-class S3RecPretrainEvalSampler(EvalSampler, config_name="s3rec_pretrain"):
+class S3RecPretrainEvalSampler(EvalSampler, config_name='s3rec_pretrain'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/infer.py b/modeling/infer.py
index 5bd4664e..c3abe16c 100644
--- a/modeling/infer.py
+++ b/modeling/infer.py
@@ -1,14 +1,15 @@
-import datetime
-import json
+from utils import parse_args, create_logger, fix_random_seed, DEVICE
+
+from dataset import BaseDataset
+from dataloader import BaseDataloader
+from models import BaseModel, TorchModel
+from metric import BaseMetric, StatefullMetric
 
+import json
 import numpy as np
 import torch
+import datetime
 
-from dataloader import BaseDataloader
-from dataset import BaseDataset
-from metric import BaseMetric, StatefullMetric
-from models import BaseModel, TorchModel
-from utils import DEVICE, create_logger, fix_random_seed, parse_args
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -24,6 +25,7 @@ def inference(dataloader, model, metrics, pred_prefix, labels_prefix):
 
     with torch.no_grad():
         for idx, batch in enumerate(dataloader):
+
             for key, value in batch.items():
                 batch[key] = value.to(DEVICE)
             batch[pred_prefix] = model(batch)
@@ -32,62 +34,57 @@ def inference(dataloader, model, metrics, pred_prefix, labels_prefix):
                 batch[key] = values.cpu()
 
             for metric_name, metric_function in metrics.items():
-                running_metrics[metric_name].extend(
-                    metric_function(
-                        inputs=batch,
-                        pred_prefix=pred_prefix,
-                        labels_prefix=labels_prefix,
-                    )
-                )
-
+                running_metrics[metric_name].extend(metric_function(
+                    inputs=batch,
+                    pred_prefix=pred_prefix,
+                    labels_prefix=labels_prefix,
+                ))
+            
         for metric_name, metric_function in metrics.items():
             if isinstance(metric_function, StatefullMetric):
-                running_metrics[metric_name] = metric_function.reduce(
-                    running_metrics[metric_name]
-                )
+                running_metrics[metric_name] = metric_function.reduce(running_metrics[metric_name])
 
-    logger.debug("Inference procedure has been finished!")
-    logger.debug("Metrics are the following:")
+    logger.debug('Inference procedure has been finished!')
+    logger.debug('Metrics are the following:')
     for metric_name, metric_value in running_metrics.items():
-        logger.info("{}: {}".format(metric_name, np.mean(metric_value)))
+        logger.info('{}: {}'.format(metric_name, np.mean(metric_value)))
 
 
 def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    logger.debug("Inference config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug('Inference config: \n{}'.format(json.dumps(config, indent=2)))
 
-    dataset = BaseDataset.create_from_config(config["dataset"])
+    dataset = BaseDataset.create_from_config(config['dataset'])
 
     _, _, eval_dataset = dataset.get_samplers()
 
     eval_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=eval_dataset
+        config['dataloader']['validation'],
+        dataset=eval_dataset
     )
 
-    model = BaseModel.create_from_config(config["model"], **dataset.meta)
+    model = BaseModel.create_from_config(config['model'], **dataset.meta)
 
     if isinstance(model, TorchModel):
         model = model.to(DEVICE)
-        checkpoint_path = "../checkpoints/{}_final_state.pth".format(
-            config["experiment_name"]
-        )
+        checkpoint_path = '../checkpoints/{}_final_state.pth'.format(config['experiment_name'])
         model.load_state_dict(torch.load(checkpoint_path))
 
     metrics = {
-        metric_name: BaseMetric.create_from_config(metric_cfg, **dataset.meta)
-        for metric_name, metric_cfg in config["metrics"].items()
+        metric_name: BaseMetric.create_from_config(metric_cfg , **dataset.meta)
+        for metric_name, metric_cfg in config['metrics'].items()
     }
 
     _ = inference(
-        dataloader=eval_dataloader,
-        model=model,
-        metrics=metrics,
-        pred_prefix=config["pred_prefix"],
-        labels_prefix=config["label_prefix"],
+        dataloader=eval_dataloader, 
+        model=model, 
+        metrics=metrics, 
+        pred_prefix=config['pred_prefix'], 
+        labels_prefix=config['label_prefix']
     )
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index e5a6ce5c..8ec91326 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -1,11 +1,11 @@
 import copy
 
+from utils import MetaParent, get_activation_function, maybe_to_list, DEVICE
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from utils import DEVICE, MetaParent, get_activation_function, maybe_to_list
-
 
 class BaseLoss(metaclass=MetaParent):
     pass
@@ -15,12 +15,14 @@ class TorchLoss(BaseLoss, nn.Module):
     pass
 
 
-class IdentityLoss(BaseLoss, config_name="identity"):
+class IdentityLoss(BaseLoss, config_name='identity'):
+
     def __call__(self, inputs):
         return inputs
 
 
-class CompositeLoss(TorchLoss, config_name="composite"):
+class CompositeLoss(TorchLoss, config_name='composite'):
+
     def __init__(self, losses, weights=None, output_prefix=None):
         super().__init__()
         self._losses = losses
@@ -32,16 +34,14 @@ def create_from_config(cls, config, **kwargs):
         losses = []
         weights = []
 
-        for loss_cfg in copy.deepcopy(config)["losses"]:
-            weight = loss_cfg.pop("weight") if "weight" in loss_cfg else 1.0
+        for loss_cfg in copy.deepcopy(config)['losses']:
+            weight = loss_cfg.pop('weight') if 'weight' in loss_cfg else 1.0
             loss_function = BaseLoss.create_from_config(loss_cfg)
 
             weights.append(weight)
             losses.append(loss_function)
 
-        return cls(
-            losses=losses, weights=weights, output_prefix=config.get("output_prefix")
-        )
+        return cls(losses=losses, weights=weights, output_prefix=config.get('output_prefix'))
 
     def forward(self, inputs):
         total_loss = 0.0
@@ -53,8 +53,7 @@ def forward(self, inputs):
 
         return total_loss
 
-
-class SampleLogSoftmaxLoss(TorchLoss, config_name="sample_logsoftmax"):
+class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
     def __init__(self, predictions_prefix, labels):
         super().__init__()
         self._predictions_prefix = predictions_prefix
@@ -63,38 +62,37 @@ def __init__(self, predictions_prefix, labels):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            predictions_prefix=config.get("predictions_prefix"),
-            labels=config.get("labels"),
+            predictions_prefix=config.get('predictions_prefix'),
+            labels=config.get('labels')
         )
 
     def forward(self, inputs):  # use log soft max
         logits = inputs[self._predictions_prefix]
         candidates = inputs[self._labels]
-
+        
         assert len(logits.shape) in [2, 3]
-
+        
         batch_size = logits.shape[0]
         seq_len = logits.shape[1]
-
+        
         if len(logits.shape) == 3:
             loss = -torch.gather(
-                torch.log_softmax(logits, dim=-1).reshape(
-                    batch_size * seq_len, logits.shape[-1]
-                ),
-                dim=-1,
-                index=candidates.reshape(batch_size * seq_len, 1),
+                torch.log_softmax(logits, dim=-1).reshape(batch_size * seq_len, logits.shape[-1]),
+                dim=-1, 
+                index=candidates.reshape(batch_size * seq_len, 1)
             ).mean()
         else:
             loss = -torch.gather(
                 torch.log_softmax(logits, dim=-1),
-                dim=-1,
-                index=candidates.reshape(batch_size, 1),
+                dim=-1, 
+                index=candidates.reshape(batch_size, 1)
             ).mean()
-
+            
         return loss
 
 
-class BatchLogSoftmaxLoss(TorchLoss, config_name="batch_logsoftmax"):
+class BatchLogSoftmaxLoss(TorchLoss, config_name='batch_logsoftmax'):
+
     def __init__(self, predictions_prefix, candidates_prefix):
         super().__init__()
         self._predictions_prefix = predictions_prefix
@@ -103,8 +101,8 @@ def __init__(self, predictions_prefix, candidates_prefix):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            predictions_prefix=config.get("predictions_prefix"),
-            candidates_prefix=config.get("candidates_prefix"),
+            predictions_prefix=config.get('predictions_prefix'),
+            candidates_prefix=config.get('candidates_prefix')
         )
 
     def forward(self, inputs):  # use log soft max
@@ -123,7 +121,8 @@ def forward(self, inputs):  # use log soft max
         return loss
 
 
-class CrossEntropyLoss(TorchLoss, config_name="ce"):
+class CrossEntropyLoss(TorchLoss, config_name='ce'):
+
     def __init__(self, predictions_prefix, labels_prefix, output_prefix=None):
         super().__init__()
         self._pred_prefix = predictions_prefix
@@ -134,7 +133,7 @@ def __init__(self, predictions_prefix, labels_prefix, output_prefix=None):
 
     def forward(self, inputs):
         all_logits = inputs[self._pred_prefix]  # (all_items, num_classes)
-        all_labels = inputs["{}.ids".format(self._labels_prefix)]  # (all_items)
+        all_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_items)
         assert all_logits.shape[0] == all_labels.shape[0]
 
         loss = self._loss(all_logits, all_labels)  # (1)
@@ -142,48 +141,55 @@ def forward(self, inputs):
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
+    
+class RqVaeLoss(TorchLoss, config_name='rqvae_loss'):
 
-
-class RqVaeLoss(TorchLoss, config_name="rqvae_loss"):
     def __init__(self, beta, output_prefix=None):
         super().__init__()
         self.beta = beta
         self._output_prefix = output_prefix
-
+        
         self._loss = nn.MSELoss()
-
+        
     @classmethod
     def create_from_config(cls, config, **kwargs):
         # 0.25 is default Beta in paper
         return cls(
-            beta=config.get("beta", 0.25),
-            output_prefix=config["output_prefix"],
+            beta = config.get('beta', 0.25),
+            output_prefix = config['output_prefix'],
         )
-
+                    
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
         embeddings_restored = inputs["embeddings_restored"]
         remainders = inputs["remainders"]
         codebooks_vectors = inputs["codebooks_vectors"]
-
+        
         rqvae_loss = 0
-
+        
         for remainder, codebook_vectors in zip(remainders, codebooks_vectors):
-            rqvae_loss += self.beta * self._loss(remainder, codebook_vectors.detach())
+            rqvae_loss += self.beta * self._loss(
+                remainder, codebook_vectors.detach()
+            )
             rqvae_loss += self._loss(codebook_vectors, remainder.detach())
-
+            
         recon_loss = self._loss(embeddings_restored, embeddings)
         loss = (recon_loss + rqvae_loss).mean(dim=0)
-
+        
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
-
+        
         return loss
 
 
-class BinaryCrossEntropyLoss(TorchLoss, config_name="bce"):
+class BinaryCrossEntropyLoss(TorchLoss, config_name='bce'):
+
     def __init__(
-        self, predictions_prefix, labels_prefix, with_logits=True, output_prefix=None
+            self,
+            predictions_prefix,
+            labels_prefix,
+            with_logits=True,
+            output_prefix=None
     ):
         super().__init__()
         self._pred_prefix = predictions_prefix
@@ -207,8 +213,14 @@ def forward(self, inputs):
         return loss
 
 
-class BPRLoss(TorchLoss, config_name="bpr"):
-    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
+class BPRLoss(TorchLoss, config_name='bpr'):
+
+    def __init__(
+            self,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
+    ):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -225,7 +237,8 @@ def forward(self, inputs):
         return loss
 
 
-class RegularizationLoss(TorchLoss, config_name="regularization"):
+class RegularizationLoss(TorchLoss, config_name='regularization'):
+
     def __init__(self, prefix, output_prefix=None):
         super().__init__()
         self._prefix = maybe_to_list(prefix)
@@ -234,7 +247,7 @@ def __init__(self, prefix, output_prefix=None):
     def forward(self, inputs):
         loss = 0.0
         for prefix in self._prefix:
-            loss += (1 / 2) * inputs[prefix].pow(2).mean()
+            loss += (1/2) * inputs[prefix].pow(2).mean()
 
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
@@ -242,23 +255,22 @@ def forward(self, inputs):
         return loss
 
 
-class FpsLoss(TorchLoss, config_name="fps"):
+class FpsLoss(TorchLoss, config_name='fps'):
+
     def __init__(
-        self,
-        fst_embeddings_prefix,
-        snd_embeddings_prefix,
-        tau=1.0,
-        normalize_embeddings=False,
-        use_mean=True,
-        output_prefix=None,
+            self,
+            fst_embeddings_prefix,
+            snd_embeddings_prefix,
+            tau=1.0,
+            normalize_embeddings=False,
+            use_mean=True,
+            output_prefix=None
     ):
         super().__init__()
         self._fst_embeddings_prefix = fst_embeddings_prefix
         self._snd_embeddings_prefix = snd_embeddings_prefix
         self._tau = tau
-        self._loss_function = nn.CrossEntropyLoss(
-            reduction="mean" if use_mean else "sum"
-        )
+        self._loss_function = nn.CrossEntropyLoss(reduction='mean' if use_mean else 'sum')
         self._normalize_embeddings = normalize_embeddings
         self._output_prefix = output_prefix
 
@@ -268,49 +280,34 @@ def forward(self, inputs):
 
         batch_size = fst_embeddings.shape[0]
 
-        combined_embeddings = torch.cat(
-            (fst_embeddings, snd_embeddings), dim=0
-        )  # (2 * x, embedding_dim)
+        combined_embeddings = torch.cat((fst_embeddings, snd_embeddings), dim=0)  # (2 * x, embedding_dim)
 
         if self._normalize_embeddings:
             combined_embeddings = torch.nn.functional.normalize(
                 combined_embeddings, p=2, dim=-1, eps=1e-6
             )  # (2 * x, embedding_dim)
 
-        similarity_scores = (
-            torch.mm(combined_embeddings, combined_embeddings.T) / self._tau
-        )  # (2 * x, 2 * x)
+        similarity_scores = torch.mm(
+            combined_embeddings,
+            combined_embeddings.T
+        ) / self._tau  # (2 * x, 2 * x)
 
         positive_samples = torch.cat(
-            (
-                torch.diag(similarity_scores, batch_size),
-                torch.diag(similarity_scores, -batch_size),
-            ),
-            dim=0,
+            (torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size)),
+            dim=0
         ).reshape(2 * batch_size, 1)  # (2 * x, 1)
-        assert torch.allclose(
-            torch.diag(similarity_scores, batch_size),
-            torch.diag(similarity_scores, -batch_size),
-        )
+        assert torch.allclose(torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size))
 
-        mask = torch.ones(
-            2 * batch_size, 2 * batch_size, dtype=torch.bool
-        )  # (2 * x, 2 * x)
+        mask = torch.ones(2 * batch_size, 2 * batch_size, dtype=torch.bool)  # (2 * x, 2 * x)
         mask = mask.fill_diagonal_(0)  # Remove equal embeddings scores
         for i in range(batch_size):  # Remove positives
             mask[i, batch_size + i] = 0
             mask[batch_size + i, i] = 0
 
-        negative_samples = similarity_scores[mask].reshape(
-            2 * batch_size, -1
-        )  # (2 * x, 2 * x - 2)
+        negative_samples = similarity_scores[mask].reshape(2 * batch_size, -1)  # (2 * x, 2 * x - 2)
 
-        labels = (
-            torch.zeros(2 * batch_size).to(positive_samples.device).long()
-        )  # (2 * x)
-        logits = torch.cat(
-            (positive_samples, negative_samples), dim=1
-        )  # (2 * x, 2 * x - 1)
+        labels = torch.zeros(2 * batch_size).to(positive_samples.device).long()  # (2 * x)
+        logits = torch.cat((positive_samples, negative_samples), dim=1)  # (2 * x, 2 * x - 1)
 
         loss = self._loss_function(logits, labels) / 2  # (1)
 
@@ -343,8 +340,14 @@ def forward(self, inputs):
         return loss
 
 
-class SASRecLoss(TorchLoss, config_name="sasrec"):
-    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
+class SASRecLoss(TorchLoss, config_name='sasrec'):
+
+    def __init__(
+            self,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
+    ):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -354,40 +357,27 @@ def forward(self, inputs):
         positive_scores = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_scores = inputs[self._negative_prefix]  # (x, embedding_dim)
         sample_ids = inputs["sample_ids"]
-
+        
         num_items = negative_scores.shape[1] - 2
-
-        possible_indices = torch.arange(
-            1, num_items + 1, device=negative_scores.device
-        )  # 1, 2, ... num_items
-        mask = torch.ones_like(
-            possible_indices, dtype=torch.bool
-        )  # True, True, ... True
-        mask[sample_ids - 1] = False  # True, False, ... False, True, ... True
-        valid_indices = possible_indices[mask]  # 1, 2, ... num_items, except sample_ids
-
-        rand_idx = torch.randint(
-            0,
-            len(valid_indices),
-            size=(negative_scores.shape[0], 1),
-            device=negative_scores.device,
-        )
+        
+        possible_indices = torch.arange(1, num_items + 1, device=negative_scores.device) # 1, 2, ... num_items
+        mask = torch.ones_like(possible_indices, dtype=torch.bool) # True, True, ... True
+        mask[sample_ids - 1] = False # True, False, ... False, True, ... True
+        valid_indices = possible_indices[mask] # 1, 2, ... num_items, except sample_ids
+        
+        rand_idx = torch.randint(0, len(valid_indices), size=(negative_scores.shape[0], 1), device=negative_scores.device)
         index = valid_indices[rand_idx]
-
+        
         negative_scores = torch.gather(
             input=negative_scores,
             dim=1,
             index=index,
         )
-
+        
         assert positive_scores.shape[0] == negative_scores.shape[0]
 
-        positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(
-            dim=-1
-        )  # (x)
-        negative_loss = torch.log(
-            1.0 - nn.functional.sigmoid(negative_scores) + 1e-9
-        ).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
+        positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(dim=-1)  # (x)
+        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores) + 1e-9).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
         loss = positive_loss + negative_loss  # (x)
         loss = -loss.sum()  # (1)
 
@@ -397,9 +387,14 @@ def forward(self, inputs):
         return loss
 
 
-class SamplesSoftmaxLoss(TorchLoss, config_name="sampled_softmax"):
+class SamplesSoftmaxLoss(TorchLoss, config_name='sampled_softmax'):
+
     def __init__(
-        self, queries_prefix, positive_prefix, negative_prefix, output_prefix=None
+            self,
+            queries_prefix,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
     ):
         super().__init__()
         self._queries_prefix = queries_prefix
@@ -409,6 +404,7 @@ def __init__(
 
     def forward(self, inputs):
         queries_embeddings = inputs[self._queries_prefix]  # (batch_size, embedding_dim)
+        # TODOPK check
         positive_ids, positive_embeddings = inputs[
             self._positive_prefix
         ]  # (batch_size, embedding_dim)
@@ -418,13 +414,17 @@ def forward(self, inputs):
 
         # b -- batch_size, d -- embedding_dim
         positive_scores = torch.einsum(
-            "bd,bd->b", queries_embeddings, positive_embeddings
+            'bd,bd->b',
+            queries_embeddings,
+            positive_embeddings
         ).unsqueeze(-1)  # (batch_size, 1)
 
         if negative_embeddings.dim() == 2:  # (num_negatives, embedding_dim)
             # b -- batch_size, n -- num_negatives, d -- embedding_dim
             negative_scores = torch.einsum(
-                "bd,nd->bn", queries_embeddings, negative_embeddings
+                'bd,nd->bn',
+                queries_embeddings,
+                negative_embeddings
             )  # (batch_size, num_negatives)
 
             all_scores = torch.cat(
@@ -436,12 +436,12 @@ def forward(self, inputs):
             loss = (-logits)[:, 0]  # (batch_size)
             loss = loss.mean()  # (1)
         else:
-            assert (
-                negative_embeddings.dim() == 3
-            )  # (batch_size, num_negatives, embedding_dim)
+            assert negative_embeddings.dim() == 3  # (batch_size, num_negatives, embedding_dim)
             # b -- batch_size, n -- num_negatives, d -- embedding_dim
             negative_scores = torch.einsum(
-                "bd,bnd->bn", queries_embeddings, negative_embeddings
+                'bd,bnd->bn',
+                queries_embeddings,
+                negative_embeddings
             )  # (batch_size, num_negatives)
 
             assert False, "ask Vladimir wtf is it "
@@ -452,13 +452,14 @@ def forward(self, inputs):
         return loss
 
 
-class S3RecPretrainLoss(TorchLoss, config_name="s3rec_pretrain"):
+class S3RecPretrainLoss(TorchLoss, config_name='s3rec_pretrain'):
+
     def __init__(
-        self,
-        positive_prefix,
-        negative_prefix,
-        representation_prefix,
-        output_prefix=None,
+            self,
+            positive_prefix,
+            negative_prefix,
+            representation_prefix,
+            output_prefix=None
     ):
         super().__init__()
         self._positive_prefix = positive_prefix
@@ -471,39 +472,36 @@ def forward(self, inputs):
         positive_embeddings = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_embeddings = inputs[self._negative_prefix]  # (x, embedding_dim)
         current_embeddings = inputs[self._representation_prefix]  # (x, embedding_dim)
-        assert (
-            positive_embeddings.shape[0]
-            == negative_embeddings.shape[0]
-            == current_embeddings.shape[0]
-        )
+        assert positive_embeddings.shape[0] == negative_embeddings.shape[0] == current_embeddings.shape[0]
 
         positive_scores = torch.einsum(
-            "bd,bd->b", positive_embeddings, current_embeddings
+            'bd,bd->b',
+            positive_embeddings,
+            current_embeddings
         )  # (x)
 
         negative_scores = torch.einsum(
-            "bd,bd->b", negative_embeddings, current_embeddings
+            'bd,bd->b',
+            negative_embeddings,
+            current_embeddings
         )  # (x)
 
-        distance = torch.sigmoid(positive_scores) - torch.sigmoid(
-            negative_scores
-        )  # (x)
-        loss = torch.sum(
-            self._criterion(distance, torch.ones_like(distance, dtype=torch.float32))
-        )  # (1)
+        distance = torch.sigmoid(positive_scores) - torch.sigmoid(negative_scores)  # (x)
+        loss = torch.sum(self._criterion(distance, torch.ones_like(distance, dtype=torch.float32)))  # (1)
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
 
 
-class Cl4sRecLoss(TorchLoss, config_name="cl4srec"):
+class Cl4sRecLoss(TorchLoss, config_name='cl4srec'):
+
     def __init__(
-        self,
-        current_representation,
-        all_items_representation,
-        tau=1.0,
-        output_prefix=None,
+            self,
+            current_representation,
+            all_items_representation,
+            tau=1.0,
+            output_prefix=None
     ):
         super().__init__()
         self._current_representation = current_representation
@@ -513,9 +511,7 @@ def __init__(
         self._output_prefix = output_prefix
 
     def forward(self, inputs):
-        current_representation = inputs[
-            self._current_representation
-        ]  # (batch_size, embedding_dim)
+        current_representation = inputs[self._current_representation]  # (batch_size, embedding_dim)
         all_items_representation = inputs[
             self._all_items_representation
         ]  # (batch_size, num_negatives + 1, embedding_dim)
@@ -523,7 +519,9 @@ def forward(self, inputs):
         batch_size = current_representation.shape[0]
 
         logits = torch.einsum(
-            "bnd,bd->bn", all_items_representation, current_representation
+            'bnd,bd->bn',
+            all_items_representation,
+            current_representation
         )  # (batch_size, num_negatives + 1)
         labels = logits.new_zeros(batch_size)  # (batch_size)
 
@@ -535,15 +533,16 @@ def forward(self, inputs):
         return loss
 
 
-class DuorecSSLLoss(TorchLoss, config_name="duorec_ssl"):
+class DuorecSSLLoss(TorchLoss, config_name='duorec_ssl'):
+
     def __init__(
-        self,
-        original_embedding_prefix,
-        dropout_embedding_prefix,
-        similar_embedding_prefix,
-        normalize_embeddings=False,
-        tau=1.0,
-        output_prefix=None,
+            self,
+            original_embedding_prefix,
+            dropout_embedding_prefix,
+            similar_embedding_prefix,
+            normalize_embeddings=False,
+            tau=1.0,
+            output_prefix=None
     ):
         super().__init__()
         self._original_embedding_prefix = original_embedding_prefix
@@ -552,13 +551,14 @@ def __init__(
         self._normalize_embeddings = normalize_embeddings
         self._output_prefix = output_prefix
         self._tau = tau
-        self._loss_function = nn.CrossEntropyLoss(reduction="mean")
+        self._loss_function = nn.CrossEntropyLoss(reduction='mean')
 
     def _compute_partial_loss(self, fst_embeddings, snd_embeddings):
         batch_size = fst_embeddings.shape[0]
 
         combined_embeddings = torch.cat(
-            (fst_embeddings, snd_embeddings), dim=0
+            (fst_embeddings, snd_embeddings),
+            dim=0
         )  # (2 * x, embedding_dim)
 
         if self._normalize_embeddings:
@@ -566,76 +566,59 @@ def _compute_partial_loss(self, fst_embeddings, snd_embeddings):
                 combined_embeddings, p=2, dim=-1, eps=1e-6
             )
 
-        similarity_scores = (
-            torch.mm(combined_embeddings, combined_embeddings.T) / self._tau
-        )  # (2 * x, 2 * x)
+        similarity_scores = torch.mm(
+            combined_embeddings,
+            combined_embeddings.T
+        ) / self._tau  # (2 * x, 2 * x)
 
         positive_samples = torch.cat(
-            (
-                torch.diag(similarity_scores, batch_size),
-                torch.diag(similarity_scores, -batch_size),
-            ),
-            dim=0,
+            (torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size)),
+            dim=0
         ).reshape(2 * batch_size, 1)  # (2 * x, 1)
 
         # TODO optimize
-        mask = torch.ones(
-            2 * batch_size, 2 * batch_size, dtype=torch.bool
-        )  # (2 * x, 2 * x)
+        mask = torch.ones(2 * batch_size, 2 * batch_size, dtype=torch.bool)  # (2 * x, 2 * x)
         mask = mask.fill_diagonal_(0)  # Remove equal embeddings scores
         for i in range(batch_size):  # Remove positives
             mask[i, batch_size + i] = 0
             mask[batch_size + i, i] = 0
 
-        negative_samples = similarity_scores[mask].reshape(
-            2 * batch_size, -1
-        )  # (2 * x, 2 * x - 2)
+        negative_samples = similarity_scores[mask].reshape(2 * batch_size, -1)  # (2 * x, 2 * x - 2)
 
-        labels = (
-            torch.zeros(2 * batch_size).to(positive_samples.device).long()
-        )  # (2 * x)
-        logits = torch.cat(
-            (positive_samples, negative_samples), dim=1
-        )  # (2 * x, 2 * x - 1)
+        labels = torch.zeros(2 * batch_size).to(positive_samples.device).long()  # (2 * x)
+        logits = torch.cat((positive_samples, negative_samples), dim=1)  # (2 * x, 2 * x - 1)
 
         loss = self._loss_function(logits, labels) / 2  # (1)
 
         return loss
 
     def forward(self, inputs):
-        original_embeddings = inputs[
-            self._original_embedding_prefix
-        ]  # (x, embedding_dim)
-        dropout_embeddings = inputs[
-            self._dropout_embedding_prefix
-        ]  # (x, embedding_dim)
-        similar_embeddings = inputs[
-            self._similar_embedding_prefix
-        ]  # (x, embedding_dim)
-
-        dropout_loss = self._compute_partial_loss(
-            original_embeddings, dropout_embeddings
-        )
+        original_embeddings = inputs[self._original_embedding_prefix]  # (x, embedding_dim)
+        dropout_embeddings = inputs[self._dropout_embedding_prefix]  # (x, embedding_dim)
+        similar_embeddings = inputs[self._similar_embedding_prefix]  # (x, embedding_dim)
+
+        dropout_loss = self._compute_partial_loss(original_embeddings, dropout_embeddings)
         ssl_loss = self._compute_partial_loss(original_embeddings, similar_embeddings)
 
         loss = dropout_loss + ssl_loss
 
         if self._output_prefix is not None:
-            inputs[f"{self._output_prefix}_dropout"] = dropout_loss.cpu().item()
-            inputs[f"{self._output_prefix}_ssl"] = ssl_loss.cpu().item()
+            inputs[f'{self._output_prefix}_dropout'] = dropout_loss.cpu().item()
+            inputs[f'{self._output_prefix}_ssl'] = ssl_loss.cpu().item()
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
 
 
-class MCLSRLoss(TorchLoss, config_name="mclsr"):
+class MCLSRLoss(TorchLoss, config_name='mclsr'):
+
     def __init__(
-        self,
-        all_scores_prefix,
-        mask_prefix,
-        normalize_embeddings=False,
-        tau=1.0,
-        output_prefix=None,
+            self,
+            all_scores_prefix,
+            mask_prefix,
+            normalize_embeddings=False,
+            tau=1.0,
+            output_prefix=None
     ):
         super().__init__()
         self._all_scores_prefix = all_scores_prefix
@@ -645,9 +628,7 @@ def __init__(
         self._tau = tau
 
     def forward(self, inputs):
-        all_scores = inputs[
-            self._all_scores_prefix
-        ]  # (batch_size, batch_size, seq_len)
+        all_scores = inputs[self._all_scores_prefix]  # (batch_size, batch_size, seq_len)
         mask = inputs[self._mask_prefix]  # (batch_size)
 
         batch_size = mask.shape[0]
@@ -657,7 +638,8 @@ def forward(self, inputs):
 
         positive_scores = all_scores[positive_mask]  # (batch_size, seq_len)
         negative_scores = torch.reshape(
-            all_scores[~positive_mask], shape=(batch_size, batch_size - 1, seq_len)
+            all_scores[~positive_mask],
+            shape=(batch_size, batch_size - 1, seq_len)
         )  # (batch_size, batch_size - 1, seq_len)
         assert torch.allclose(all_scores[0, 1], negative_scores[0, 0])
         assert torch.allclose(all_scores[-1, -2], negative_scores[-1, -1])
diff --git a/modeling/metric/base.py b/modeling/metric/base.py
index e22c1d20..677f9086 100644
--- a/modeling/metric/base.py
+++ b/modeling/metric/base.py
@@ -1,18 +1,19 @@
-import torch
-
 from utils import MetaParent
 
+import torch
+
 
 class BaseMetric(metaclass=MetaParent):
     pass
 
 
 class StatefullMetric(BaseMetric):
+
     def reduce(self):
         raise NotImplementedError
 
 
-class StaticMetric(BaseMetric, config_name="dummy"):
+class StaticMetric(BaseMetric, config_name='dummy'):
     def __init__(self, name, value):
         self._name = name
         self._value = value
@@ -23,15 +24,17 @@ def __call__(self, inputs):
         return inputs
 
 
-class CompositeMetric(BaseMetric, config_name="composite"):
+class CompositeMetric(BaseMetric, config_name='composite'):
+
     def __init__(self, metrics):
         self._metrics = metrics
 
     @classmethod
     def create_from_config(cls, config):
-        return cls(
-            metrics=[BaseMetric.create_from_config(cfg) for cfg in config["metrics"]]
-        )
+        return cls(metrics=[
+            BaseMetric.create_from_config(cfg)
+            for cfg in config['metrics']
+        ])
 
     def __call__(self, inputs):
         for metric in self._metrics:
@@ -39,63 +42,57 @@ def __call__(self, inputs):
         return inputs
 
 
-class NDCGMetric(BaseMetric, config_name="ndcg"):
+class NDCGMetric(BaseMetric, config_name='ndcg'):
+
     def __init__(self, k):
         self._k = k
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][
-            :, : self._k
-        ].float()  # (batch_size, top_k_indices)
-        labels = inputs["{}.ids".format(labels_prefix)].float()  # (batch_size)
+        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
+        labels = inputs['{}.ids'.format(labels_prefix)].float()  # (batch_size)
 
         assert labels.shape[0] == predictions.shape[0]
 
-        hits = torch.eq(
-            predictions, labels[..., None]
-        ).float()  # (batch_size, top_k_indices)
-        discount_factor = 1 / torch.log2(
-            torch.arange(1, self._k + 1, 1).float() + 1.0
-        ).to(hits.device)  # (k)
-        dcg = torch.einsum("bk,k->b", hits, discount_factor)  # (batch_size)
+        hits = torch.eq(predictions, labels[..., None]).float()  # (batch_size, top_k_indices)
+        discount_factor = 1 / torch.log2(torch.arange(1, self._k + 1, 1).float() + 1.).to(hits.device)  # (k)
+        dcg = torch.einsum('bk,k->b', hits, discount_factor)  # (batch_size)
 
         return dcg.cpu().tolist()
 
 
-class RecallMetric(BaseMetric, config_name="recall"):
+class RecallMetric(BaseMetric, config_name='recall'):
+
     def __init__(self, k):
         self._k = k
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][
-            :, : self._k
-        ].float()  # (batch_size, top_k_indices)
-        labels = inputs["{}.ids".format(labels_prefix)].float()  # (batch_size)
+        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
+        labels = inputs['{}.ids'.format(labels_prefix)].float()  # (batch_size)
 
         assert labels.shape[0] == predictions.shape[0]
 
-        hits = torch.eq(
-            predictions, labels[..., None]
-        ).float()  # (batch_size, top_k_indices)
+        hits = torch.eq(predictions, labels[..., None]).float()  # (batch_size, top_k_indices)
         recall = hits.sum(dim=-1)  # (batch_size)
 
         return recall.cpu().tolist()
 
 
-class CoverageMetric(StatefullMetric, config_name="coverage"):
+class CoverageMetric(StatefullMetric, config_name='coverage'):
+
     def __init__(self, k, num_items):
         self._k = k
         self._num_items = num_items
-
+    
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(k=config["k"], num_items=kwargs["num_items"])
+        return cls(
+            k=config['k'],
+            num_items=kwargs['num_items']
+        )
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][
-            :, : self._k
-        ].float()  # (batch_size, top_k_indices)
+        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
         return predictions.view(-1).long().cpu().detach().tolist()  # (batch_size * k)
-
+    
     def reduce(self, values):
         return len(set(values)) / self._num_items
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index 377a703d..71fc9643 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -14,9 +14,9 @@
 from .random import RandomModel
 from .rqvae import RqVaeModel
 from .s3rec import S3RecModel
-from .sasrec_in_batch import SasRecInBatchModel
 from .sasrec_ce import SasRecCeModel
 from .sasrec_full import SasRecFullModel
+from .sasrec_in_batch import SasRecInBatchModel
 from .sasrec_real import SasRecRealModel
 from .sasrec_semantic import SasRecSemanticModel
 from .tiger import TigerModel
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 786ef12c..a1384384 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-
 from utils import DEVICE, MetaParent, create_masked_tensor, get_activation_function
 
 
@@ -9,42 +8,39 @@ class BaseModel(metaclass=MetaParent):
 
 
 class TorchModel(nn.Module, BaseModel):
+
     @torch.no_grad()
     def _init_weights(self, initializer_range):
         for key, value in self.named_parameters():
-            if "weight" in key:
-                if "norm" in key:
+            if 'weight' in key:
+                if 'norm' in key:
                     nn.init.ones_(value.data)
                 else:
                     nn.init.trunc_normal_(
                         value.data,
                         std=initializer_range,
                         a=-2 * initializer_range,
-                        b=2 * initializer_range,
+                        b=2 * initializer_range
                     )
-            elif "bias" in key:
+            elif 'bias' in key:
                 nn.init.zeros_(value.data)
-            elif "codebook" in key:
+            elif 'codebook' in key:
                 nn.init.trunc_normal_(
                     value.data,
                     std=initializer_range,
                     a=-2 * initializer_range,
-                    b=2 * initializer_range,
+                    b=2 * initializer_range
                 )
             else:
-                raise ValueError(f"Unknown transformer weight: {key}")
+                raise ValueError(f'Unknown transformer weight: {key}')
 
     @staticmethod
     def _get_last_embedding(embeddings, mask):
         lengths = torch.sum(mask, dim=-1)  # (batch_size)
-        lengths = lengths - 1  # (batch_size)
+        lengths = (lengths - 1)  # (batch_size)
         last_masks = mask.gather(dim=1, index=lengths[:, None])  # (batch_size, 1)
-        lengths = torch.tile(
-            lengths[:, None, None], (1, 1, embeddings.shape[-1])
-        )  # (batch_size, 1, emb_dim)
-        last_embeddings = embeddings.gather(
-            dim=1, index=lengths
-        )  # (batch_size, 1, emb_dim)
+        lengths = torch.tile(lengths[:, None, None], (1, 1, embeddings.shape[-1]))  # (batch_size, 1, emb_dim)
+        last_embeddings = embeddings.gather(dim=1, index=lengths)  # (batch_size, 1, emb_dim)
         last_embeddings = last_embeddings[last_masks]  # (batch_size, emb_dim)
         if not torch.allclose(embeddings[mask][-1], last_embeddings[-1]):
             print(embeddings)
@@ -56,18 +52,19 @@ def _get_last_embedding(embeddings, mask):
 
 
 class SequentialTorchModel(TorchModel):
+
     def __init__(
-        self,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        is_causal=True,
+            self,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            is_causal=True
     ):
         super().__init__()
         self._is_causal = is_causal
@@ -77,12 +74,11 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -95,10 +91,10 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True,
+            batch_first=True
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
-
+    
     def get_item_embeddings(self, events):
         return self._item_embeddings(events)
 
@@ -112,7 +108,8 @@ def _apply_sequential_encoder(
         assert embeddings.shape[0] == sum(lengths)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=lengths
+            data=embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
@@ -121,9 +118,7 @@ def _apply_sequential_encoder(
         position_embeddings = self._encoder_pos_embeddings(lengths, mask)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -134,10 +129,7 @@ def _apply_sequential_encoder(
             cls_token_tensor = self._cls_token.unsqueeze(0).unsqueeze(0)
             cls_token_expanded = torch.tile(cls_token_tensor, (batch_size, 1, 1))
             embeddings = torch.cat((cls_token_expanded, embeddings), dim=1)
-            mask = torch.cat(
-                (torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask),
-                dim=1,
-            )
+            mask = torch.cat((torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask), dim=1)
 
         if user_embeddings is not None:
             embeddings = torch.cat((user_embeddings.unsqueeze(1), embeddings), dim=1)
@@ -148,15 +140,16 @@ def _apply_sequential_encoder(
             seq_len += 1  # TODOPK ask if this is correct
 
         if self._is_causal:
-            causal_mask = (
-                torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)
-            )  # (seq_len, seq_len)
+            causal_mask = torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)  # (seq_len, seq_len)
             embeddings = self._encoder(
-                src=embeddings, mask=~causal_mask, src_key_padding_mask=~mask
+                src=embeddings,
+                mask=~causal_mask,
+                src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
         else:
             embeddings = self._encoder(
-                src=embeddings, src_key_padding_mask=~mask
+                src=embeddings,
+                src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
 
         return embeddings, mask
@@ -165,19 +158,16 @@ def _encoder_pos_embeddings(self, lengths, mask):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
+            data=position_embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         return position_embeddings
 
@@ -187,18 +177,17 @@ def _add_cls_token(items, lengths, cls_token_id=0):
         batch_size = lengths.shape[0]
         num_new_items = num_items + batch_size
 
-        new_items = (
-            torch.ones(num_new_items, dtype=items.dtype, device=items.device)
-            * cls_token_id
-        )  # (num_new_items)
+        new_items = torch.ones(
+            num_new_items,
+            dtype=items.dtype,
+            device=items.device
+        ) * cls_token_id  # (num_new_items)
 
         old_items_mask = torch.zeros_like(new_items).bool()  # (num_new_items)
         old_items_mask = ~old_items_mask.scatter(
             src=torch.ones_like(lengths).bool(),
             dim=0,
-            index=torch.cat([torch.LongTensor([0]).to(DEVICE), lengths + 1]).cumsum(
-                dim=0
-            )[:-1],
+            index=torch.cat([torch.LongTensor([0]).to(DEVICE), lengths + 1]).cumsum(dim=0)[:-1]
         )  # (num_new_items)
         new_items[old_items_mask] = items
         new_length = lengths + 1
diff --git a/modeling/models/bert4rec.py b/modeling/models/bert4rec.py
index 69794fb6..40f1d331 100644
--- a/modeling/models/bert4rec.py
+++ b/modeling/models/bert4rec.py
@@ -1,24 +1,25 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 
+class Bert4RecModel(SequentialTorchModel, config_name='bert4rec'):
 
-class Bert4RecModel(SequentialTorchModel, config_name="bert4rec"):
     def __init__(
-        self,
-        sequence_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="gelu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='gelu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -30,77 +31,70 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=False,
+            is_causal=False
         )
         self._sequence_prefix = sequence_prefix
         self._labels_prefix = labels_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
+        )
 
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        embeddings = torch.nn.functional.gelu(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = torch.einsum(
-            "bsd,nd->bsn", embeddings, self._item_embeddings.weight
+            'bsd,nd->bsn', embeddings, self._item_embeddings.weight
         )  # (batch_size, seq_len, num_items)
         embeddings += self._bias[None, None, :]  # (batch_size, seq_len, num_items)
 
         if self.training:  # training mode
-            all_sample_labels = inputs[
-                "{}.ids".format(self._labels_prefix)
-            ]  # (all_batch_events)
+            all_sample_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_batch_events)
             embeddings = embeddings[mask]  # (all_batch_events, num_items)
             labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events)
 
             needed_logits = embeddings[labels_mask]  # (non_zero_events, num_items)
             needed_labels = all_sample_labels[labels_mask]  # (non_zero_events)
 
-            return {"logits": needed_logits, "labels.ids": needed_labels}
+            return {'logits': needed_logits, 'labels.ids': needed_labels}
         else:  # eval mode
-            candidate_scores = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, num_items)
+            candidate_scores = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/bert4rec_cls.py b/modeling/models/bert4rec_cls.py
index c0bbc61a..d6e59b0a 100644
--- a/modeling/models/bert4rec_cls.py
+++ b/modeling/models/bert4rec_cls.py
@@ -1,24 +1,25 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 
+class Bert4RecModelCLS(SequentialTorchModel, config_name='bert4rec_cls'):
 
-class Bert4RecModelCLS(SequentialTorchModel, config_name="bert4rec_cls"):
     def __init__(
-        self,
-        sequence_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="gelu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='gelu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -30,16 +31,20 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=False,
+            is_causal=False
         )
         self._sequence_prefix = sequence_prefix
         self._labels_prefix = labels_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
+        )
 
         self._init_weights(initializer_range)
 
@@ -48,50 +53,48 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
-            events=all_sample_events, lengths=all_sample_lengths, add_cls_token=True
+            events=all_sample_events,
+            lengths=all_sample_lengths,
+            add_cls_token=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
         predictions = embeddings[:, 0, :]  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             candidates = self._item_embeddings(
-                inputs["{}.ids".format(self._labels_prefix)]
-            )  # (batch_size, embedding_dim)
+                inputs['{}.ids'.format(self._labels_prefix)])  # (batch_size, embedding_dim)
 
-            return {"predictions": predictions, "candidates": candidates}
+            return {'predictions': predictions, 'candidates': candidates}
         else:  # eval mode
             candidate_scores = torch.einsum(
-                "bd,nd->bn", predictions, self._item_embeddings.weight
+                'bd,nd->bn',
+                predictions,
+                self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/cl4srec.py b/modeling/models/cl4srec.py
index e1bf7152..945c2a92 100644
--- a/modeling/models/cl4srec.py
+++ b/modeling/models/cl4srec.py
@@ -1,27 +1,28 @@
+from models.base import SequentialTorchModel
+
 import torch
 
-from models.base import SequentialTorchModel
 
+class Cl4SRecModel(SequentialTorchModel, config_name='cl4srec'):
 
-class Cl4SRecModel(SequentialTorchModel, config_name="cl4srec"):
     def __init__(
-        self,
-        sequence_prefix,
-        fst_augmented_sequence_prefix,
-        snd_augmented_sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            fst_augmented_sequence_prefix,
+            snd_augmented_sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -33,7 +34,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True
         )
         self._sequence_prefix = sequence_prefix
         self._fst_augmented_sequence_prefix = fst_augmented_sequence_prefix
@@ -46,56 +47,51 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            fst_augmented_sequence_prefix=config["fst_augmented_sequence_prefix"],
-            snd_augmented_sequence_prefix=config["snd_augmented_sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            num_layers=config["num_layers"],
-            num_heads=config["num_heads"],
-            embedding_dim=config["embedding_dim"],
-            dim_feedforward=config["dim_feedforward"],
-            dropout=config["dropout"],
-            activation=config["activation"],
-            layer_norm_eps=config["layer_norm_eps"],
-            initializer_range=config["initializer_range"],
+            sequence_prefix=config['sequence_prefix'],
+            fst_augmented_sequence_prefix=config['fst_augmented_sequence_prefix'],
+            snd_augmented_sequence_prefix=config['snd_augmented_sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            num_layers=config['num_layers'],
+            num_heads=config['num_heads'],
+            embedding_dim=config['embedding_dim'],
+            dim_feedforward=config['dim_feedforward'],
+            dropout=config['dropout'],
+            activation=config['activation'],
+            layer_norm_eps=config['layer_norm_eps'],
+            initializer_range=config['initializer_range']
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(
-            embeddings, mask
-        )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             items_logits = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items)
 
             # TODO remove this check
-            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
+            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
             assert torch.allclose(
-                self._item_embeddings(labels), self._item_embeddings.weight[labels]
+                self._item_embeddings(labels),
+                self._item_embeddings.weight[labels]
             )
 
             all_fst_aug_sample_events = inputs[
-                "{}.ids".format(self._fst_augmented_sequence_prefix)
+                '{}.ids'.format(self._fst_augmented_sequence_prefix)
             ]  # (all_batch_events)
-            all_fst_aug_sample_lengths = inputs[
-                "{}.length".format(self._fst_augmented_sequence_prefix)
-            ]  # (batch_size)
+            all_fst_aug_sample_lengths = inputs['{}.length'.format(self._fst_augmented_sequence_prefix)]  # (batch_size)
             fst_aug_embeddings, fst_aug_mask = self._apply_sequential_encoder(
                 all_fst_aug_sample_events, all_fst_aug_sample_lengths
             )  # (batch_size, fst_aug_seq_len, embedding_dim), (batch_size, fst_aug_seq_len)
@@ -104,11 +100,9 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             all_snd_aug_sample_events = inputs[
-                "{}.ids".format(self._snd_augmented_sequence_prefix)
+                '{}.ids'.format(self._snd_augmented_sequence_prefix)
             ]  # (all_batch_events)
-            all_snd_aug_sample_lengths = inputs[
-                "{}.length".format(self._snd_augmented_sequence_prefix)
-            ]  # (batch_size)
+            all_snd_aug_sample_lengths = inputs['{}.length'.format(self._snd_augmented_sequence_prefix)]  # (batch_size)
             snd_aug_embeddings, snd_aug_mask = self._apply_sequential_encoder(
                 all_snd_aug_sample_events, all_snd_aug_sample_lengths
             )  # (batch_size, snd_aug_seq_len, embedding_dim), (batch_size, snd_aug_seq_len)
@@ -117,23 +111,24 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             return {
-                "logits": items_logits,
-                "sequence_representation": last_embeddings,
-                "fst_aug_sequence_representation": last_fst_aug_embeddings,
-                "snd_aug_sequence_representation": last_snd_aug_embeddings,
+                'logits': items_logits,
+                'sequence_representation': last_embeddings,
+                'fst_aug_sequence_representation': last_fst_aug_embeddings,
+                'snd_aug_sequence_representation': last_snd_aug_embeddings
             }
         else:  # eval mode
-            candidate_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items, embedding_dim)
+            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, candidate_embeddings
+                'bd,nd->bn',
+                last_embeddings,
+                candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/duorec.py b/modeling/models/duorec.py
index 68f9df33..380a6675 100644
--- a/modeling/models/duorec.py
+++ b/modeling/models/duorec.py
@@ -1,27 +1,29 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 from utils import create_masked_tensor
 
 
-class DuoRecModel(SequentialTorchModel, config_name="duorec"):
+class DuoRecModel(SequentialTorchModel, config_name='duorec'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        augmented_sequence_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
-        is_causal=True,
+            self,
+            sequence_prefix,
+            augmented_sequence_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02,
+            is_causal=True
     ):
         super().__init__(
             num_items=num_items,
@@ -33,7 +35,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=is_causal,
+            is_causal=is_causal
         )
         self._sequence_prefix = sequence_prefix
         self._augmented_sequence_prefix = augmented_sequence_prefix
@@ -47,19 +49,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            augmented_sequence_prefix=config["augmented_sequence_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            num_layers=config["num_layers"],
-            num_heads=config["num_heads"],
-            embedding_dim=config["embedding_dim"],
-            dim_feedforward=config["dim_feedforward"],
-            dropout=config["dropout"],
-            activation=config["activation"],
-            layer_norm_eps=config["layer_norm_eps"],
-            initializer_range=config["initializer_range"],
+            sequence_prefix=config['sequence_prefix'],
+            augmented_sequence_prefix=config['augmented_sequence_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            num_layers=config['num_layers'],
+            num_heads=config['num_heads'],
+            embedding_dim=config['embedding_dim'],
+            dim_feedforward=config['dim_feedforward'],
+            dropout=config['dropout'],
+            activation=config['activation'],
+            layer_norm_eps=config['layer_norm_eps'],
+            initializer_range=config['initializer_range']
         )
 
     # TODO taken from duorec github
@@ -75,53 +77,47 @@ def _init_weights(self, module):
             module.bias.data.zero_()
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(
-            embeddings, mask
-        )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             items_logits = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items)
             training_output = {
-                "logits": items_logits,
-                "sequence_representation": last_embeddings,
+                'logits': items_logits,
+                'sequence_representation': last_embeddings
             }
 
             # TODO remove this check
-            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
+            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
             assert torch.allclose(
-                self._item_embeddings(labels), self._item_embeddings.weight[labels]
+                self._item_embeddings(labels),
+                self._item_embeddings.weight[labels]
             )
 
             # Unsupervised Augmentation
             embeddings_, mask_ = self._apply_sequential_encoder(
                 all_sample_events, all_sample_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-            last_embeddings_ = self._get_last_embedding(
-                embeddings_, mask_
-            )  # (batch_size, embedding_dim)
-            training_output["similar_sequence_representation"] = last_embeddings_
-            assert not torch.allclose(last_embeddings, last_embeddings_), (
-                "Embedding must be different because of dropout"
-            )
+            last_embeddings_ = self._get_last_embedding(embeddings_, mask_)  # (batch_size, embedding_dim)
+            training_output['similar_sequence_representation'] = last_embeddings_
+            assert not torch.allclose(last_embeddings, last_embeddings_), \
+                'Embedding must be different because of dropout'
 
             # Semantic Similarity
             all_sample_augmented_events = inputs[
-                "{}.ids".format(self._augmented_sequence_prefix)
+                '{}.ids'.format(self._augmented_sequence_prefix)
             ]  # (all_batch_events)
             all_sample_augmented_lengths = inputs[
-                "{}.length".format(self._augmented_sequence_prefix)
+                '{}.length'.format(self._augmented_sequence_prefix)
             ]  # (batch_size)
 
             augmented_embeddings, augmented_mask = self._apply_sequential_encoder(
@@ -130,23 +126,22 @@ def forward(self, inputs):
             last_augmented_embeddings = self._get_last_embedding(
                 augmented_embeddings, augmented_mask
             )  # (batch_size, embedding_dim)
-            training_output["augmented_sequence_representation"] = (
-                last_augmented_embeddings
-            )
+            training_output['augmented_sequence_representation'] = last_augmented_embeddings
 
             return training_output
         else:  # eval mode
-            candidate_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items, embedding_dim)
+            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, candidate_embeddings
+                'bd,nd->bn',
+                last_embeddings,
+                candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
+            return indices
\ No newline at end of file
diff --git a/modeling/models/graph_seq_rec.py b/modeling/models/graph_seq_rec.py
index caa7e2e0..b227f184 100644
--- a/modeling/models/graph_seq_rec.py
+++ b/modeling/models/graph_seq_rec.py
@@ -1,33 +1,35 @@
+from models.base import SequentialTorchModel
+
+from utils import create_masked_tensor, DEVICE
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
-from utils import DEVICE, create_masked_tensor
 
+class GraphSeqRecModel(SequentialTorchModel, config_name='graph_seq_rec'):
 
-class GraphSeqRecModel(SequentialTorchModel, config_name="graph_seq_rec"):
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        candidate_prefix,
-        common_graph,
-        user_graph,
-        item_graph,
-        num_hops,
-        graph_dropout,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        use_ce=False,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            candidate_prefix,
+            common_graph,
+            user_graph,
+            item_graph,
+            num_hops,
+            graph_dropout,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            use_ce=False,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -39,7 +41,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -55,34 +57,38 @@ def __init__(
         self._graph_dropout = graph_dropout
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
+        )
 
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            common_graph=kwargs["graph"],
-            user_graph=kwargs["user_graph"],
-            item_graph=kwargs["item_graph"],
-            num_hops=config["num_hops"],
-            graph_dropout=config["graph_dropout"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            use_ce=config.get("use_ce", False),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            common_graph=kwargs['graph'],
+            user_graph=kwargs['user_graph'],
+            item_graph=kwargs['item_graph'],
+            num_hops=config['num_hops'],
+            graph_dropout=config['graph_dropout'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            use_ce=config.get('use_ce', False),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _apply_graph_encoder(self, embeddings, graph):
@@ -104,45 +110,38 @@ def _apply_graph_encoder(self, embeddings, graph):
         return embeddings
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        lengths = inputs["{}.length".format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         common_graph_embeddings = self._apply_graph_encoder(
-            embeddings=self._item_embeddings.weight, graph=self._item_graph
+            embeddings=self._item_embeddings.weight,
+            graph=self._item_graph
         )  # (num_items + 2, embedding_dim)
 
-        embeddings = common_graph_embeddings[
-            all_sample_events
-        ]  # (all_batch_events, embedding_dim)
+        embeddings = common_graph_embeddings[all_sample_events]  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=lengths
+            data=embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
+            data=position_embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -150,52 +149,35 @@ def forward(self, inputs):
         embeddings[~mask] = 0
 
         if self._is_causal:
-            causal_mask = (
-                torch.tril(
-                    torch.tile(mask.unsqueeze(1), dims=[self._num_heads, seq_len, 1])
-                )
-                .bool()
-                .to(DEVICE)
-            )  # (seq_len, seq_len)
+            causal_mask = torch.tril(torch.tile(mask.unsqueeze(1), dims=[self._num_heads, seq_len, 1])).bool().to(DEVICE)  # (seq_len, seq_len)
             embeddings = self._encoder(
                 src=embeddings,
                 mask=~causal_mask,
             )  # (batch_size, seq_len, embedding_dim)
         else:
             embeddings = self._encoder(
-                src=embeddings, src_key_padding_mask=~mask
+                src=embeddings,
+                src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
 
         if self._use_ce:
-            embeddings = self._output_projection(
-                embeddings
-            )  # (batch_size, seq_len, embedding_dim)
-            embeddings = torch.nn.functional.gelu(
-                embeddings
-            )  # (batch_size, seq_len, embedding_dim)
+            embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+            embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
             embeddings = torch.einsum(
-                "bsd,nd->bsn", embeddings, self._item_embeddings.weight
+                'bsd,nd->bsn', embeddings, self._item_embeddings.weight
             )  # (batch_size, seq_len, num_items)
             embeddings += self._bias[None, None, :]  # (batch_size, seq_len, num_items)
         else:
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             if self._use_ce:
-                return {"logits": embeddings[mask]}
+                return {'logits': embeddings[mask]}
             else:
-                all_positive_sample_events = inputs[
-                    "{}.ids".format(self._positive_prefix)
-                ]  # (all_batch_events)
-                all_negative_sample_events = inputs[
-                    "{}.ids".format(self._negative_prefix)
-                ]  # (all_batch_events)
-
-                all_sample_embeddings = embeddings[
-                    mask
-                ]  # (all_batch_events, embedding_dim)
+                all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+                all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+
+                all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
                 all_positive_sample_embeddings = self._item_embeddings(
                     all_positive_sample_events
                 )  # (all_batch_events, embedding_dim)
@@ -204,27 +186,21 @@ def forward(self, inputs):
                 )  # (all_batch_events, embedding_dim)
 
                 return {
-                    "current_embeddings": all_sample_embeddings,
-                    "positive_embeddings": all_positive_sample_embeddings,
-                    "negative_embeddings": all_negative_sample_embeddings,
+                    'current_embeddings': all_sample_embeddings,
+                    'positive_embeddings': all_positive_sample_embeddings,
+                    'negative_embeddings': all_negative_sample_embeddings
                 }
         else:  # eval mode
             if self._use_ce:
-                last_embeddings = self._get_last_embedding(
-                    embeddings, mask
-                )  # (batch_size, num_items)
-
-                if "{}.ids".format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs[
-                        "{}.ids".format(self._candidate_prefix)
-                    ]  # (all_batch_candidates)
-                    candidate_lengths = inputs[
-                        "{}.length".format(self._candidate_prefix)
-                    ]  # (batch_size)
+                last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items)
+
+                if '{}.ids'.format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                     candidate_ids = torch.reshape(
                         candidate_events,
-                        (candidate_lengths.shape[0], candidate_lengths[0]),
+                        (candidate_lengths.shape[0], candidate_lengths[0])
                     )  # (batch_size, num_candidates)
                     candidate_scores = last_embeddings.gather(
                         dim=1, index=candidate_ids
@@ -232,35 +208,34 @@ def forward(self, inputs):
                 else:
                     candidate_scores = last_embeddings  # (batch_size, num_items + 2)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                    candidate_scores[:, self._num_items + 1:] = -torch.inf
             else:
-                if "{}.ids".format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs[
-                        "{}.ids".format(self._candidate_prefix)
-                    ]  # (all_batch_candidates)
-                    candidate_lengths = inputs[
-                        "{}.length".format(self._candidate_prefix)
-                    ]  # (batch_size)
+                if '{}.ids'.format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                     candidate_embeddings = self._item_embeddings(
                         candidate_events
                     )  # (all_batch_candidates, embedding_dim)
 
                     candidate_embeddings, _ = create_masked_tensor(
-                        data=candidate_embeddings, lengths=candidate_lengths
+                        data=candidate_embeddings,
+                        lengths=candidate_lengths
                     )  # (batch_size, num_candidates, embedding_dim)
 
                     candidate_scores = torch.einsum(
-                        "bd,bnd->bn", last_embeddings, candidate_embeddings
+                        'bd,bnd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_candidates)
                 else:
-                    candidate_embeddings = (
-                        self._item_embeddings.weight
-                    )  # (num_items, embedding_dim)
+                    candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
                     candidate_scores = torch.einsum(
-                        "bd,nd->bn", last_embeddings, candidate_embeddings
+                        'bd,nd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_items)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                    candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             return candidate_scores
diff --git a/modeling/models/gru4rec.py b/modeling/models/gru4rec.py
index 474743df..927445ac 100644
--- a/modeling/models/gru4rec.py
+++ b/modeling/models/gru4rec.py
@@ -1,20 +1,22 @@
-import torch
-from torch import nn
-
 from models.base import TorchModel
+
 from utils import create_masked_tensor, get_activation_function
 
+import torch
+from torch import nn
+
 
 class GRUModel(TorchModel):
+
     def __init__(
-        self,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
+            self,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5
     ):
         super().__init__()
         self._num_items = num_items
@@ -23,12 +25,11 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -40,7 +41,7 @@ def __init__(
             num_layers=num_layers,
             batch_first=True,
             dropout=dropout,
-            bidirectional=False,
+            bidirectional=False
         )
 
         self._hidden_to_output_projection = nn.Linear(embedding_dim, num_items)
@@ -50,31 +51,27 @@ def _apply_sequential_encoder(self, events, lengths):
         embeddings = self._item_embeddings(events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=lengths
+            data=embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
+            data=position_embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -85,16 +82,14 @@ def _apply_sequential_encoder(self, events, lengths):
             input=embeddings,
             lengths=lengths.cpu(),
             batch_first=True,
-            enforce_sorted=False,
+            enforce_sorted=False
         )
 
         hidden = torch.zeros(
-            self._num_layers,
-            batch_size,
-            self._embedding_dim,
+            self._num_layers, batch_size, self._embedding_dim,
             dtype=embeddings.dtype,
             device=embeddings.device,
-            requires_grad=True,
+            requires_grad=True
         )  # (num_layers, batch_size, embedding_dim)
         out, hidden = self._encoder(packed_embeddings, hidden)
         embeddings, embedding_lengths = torch.nn.utils.rnn.pad_packed_sequence(
@@ -107,20 +102,21 @@ def _apply_sequential_encoder(self, events, lengths):
         return embeddings, mask
 
 
-class GRU4RecModel(GRUModel, config_name="gru4rec"):
+class GRU4RecModel(GRUModel, config_name='gru4rec'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -129,7 +125,7 @@ def __init__(
             num_layers=num_layers,
             dropout=dropout,
             activation=activation,
-            layer_norm_eps=layer_norm_eps,
+            layer_norm_eps=layer_norm_eps
         )
 
         self._sequence_prefix = sequence_prefix
@@ -140,42 +136,35 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_layers=config["num_layers"],
-            dropout=config.get("dropout", 0.0),
-            activation=config.get("activation", "tanh"),
-            layer_norm_eps=config.get("layer_norm_eps", 1e-5),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_layers=config['num_layers'],
+            dropout=config.get('dropout', 0.0),
+            activation=config.get('activation', 'tanh'),
+            layer_norm_eps=config.get('layer_norm_eps', 1e-5),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
-            events=all_sample_events, lengths=all_sample_lengths
+            events=all_sample_events,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
 
         if self.training:  # training mode
-            all_positive_sample_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
+            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
             all_positive_sample_embeddings = self._item_embeddings(
                 all_positive_sample_events
             )  # (all_batch_events, embedding_dim)
 
-            all_sample_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
+            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
 
             sample_end_idx = torch.cumsum(all_sample_lengths, dim=0)  # (batch_size)
             sample_begin_idx = sample_end_idx - all_sample_lengths  # (batch_size)
@@ -184,47 +173,44 @@ def forward(self, inputs):
             sample_begin_idx = sample_begin_idx[:, None]  # (batch_size, 1)
 
             negative_indices = torch.tile(
-                torch.arange(
-                    start=0,
-                    end=all_positive_sample_events.shape[0],
-                    device=all_sample_lengths.device,
-                ).long()[None],
-                dims=[all_sample_lengths.shape[0], 1],
+                torch.arange(start=0, end=all_positive_sample_events.shape[0], device=all_sample_lengths.device).long()[None],
+                dims=[all_sample_lengths.shape[0], 1]
             )  # (batch_size, all_batch_events)
 
-            negative_mask = (negative_indices >= sample_begin_idx) & (
-                negative_indices < sample_end_idx
-            )
+            negative_mask = (negative_indices >= sample_begin_idx) & (negative_indices < sample_end_idx)
             negative_mask = torch.repeat_interleave(
                 negative_mask, all_sample_lengths, dim=0
             )
 
             negative_scores = torch.einsum(
-                "ad,bd->ab",
+                'ad,bd->ab',
                 all_sample_embeddings,
-                self._item_embeddings(all_sample_events),
+                self._item_embeddings(all_sample_events)
             )  # (all_batch_events, all_batch_events)
-
+            
             positive_scores = torch.einsum(
-                "ad,ad->a", all_sample_embeddings, all_positive_sample_embeddings
+                'ad,ad->a',
+                all_sample_embeddings,
+                all_positive_sample_embeddings
             )  # (all_batch_events)
 
             return {
-                "positive_scores": positive_scores[..., None],
-                "negative_scores": negative_scores,
+                'positive_scores': positive_scores[..., None],
+                'negative_scores': negative_scores,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/gtorec.py b/modeling/models/gtorec.py
index d30e4616..dc29435f 100644
--- a/modeling/models/gtorec.py
+++ b/modeling/models/gtorec.py
@@ -1,39 +1,40 @@
+from models.base import SequentialTorchModel, TorchModel
+
+from utils import create_masked_tensor, get_activation_function
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.base import SequentialTorchModel, TorchModel
-from utils import create_masked_tensor, get_activation_function
-
 
-class GTOModel(SequentialTorchModel, config_name="gtorec"):
+class GTOModel(SequentialTorchModel, config_name='gtorec'):
     def __init__(
-        self,
-        # sequential params
-        sequence_prefix,  # =item_prefix
-        positive_prefix,
-        negative_prefix,
-        candidate_prefix,
-        source_domain,
-        num_users,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        # graph params
-        user_prefix,
-        graph,
-        graph_embedding_dim,
-        graph_num_layers,
-        # params with default values
-        dropout=0.0,
-        graph_dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-        norm_first=True,
+            self,
+            # sequential params 
+            sequence_prefix, # =item_prefix
+            positive_prefix, 
+            negative_prefix, 
+            candidate_prefix,
+            source_domain, 
+            num_users,  
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            # graph params
+            user_prefix,
+            graph,
+            graph_embedding_dim,
+            graph_num_layers,
+            # params with default values
+            dropout=0.0,
+            graph_dropout=0.0,
+            activation='relu', 
+            layer_norm_eps=1e-9, 
+            initializer_range=0.02,
+            norm_first=True
     ):
         super().__init__(
             num_items=num_items,
@@ -45,9 +46,9 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True 
         )
-        # sequential part
+        # sequential part 
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -55,9 +56,13 @@ def __init__(
         self._source_domain = source_domain
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
+        )
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
         )
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
 
         # graph part
         self._user_prefix = user_prefix
@@ -68,13 +73,15 @@ def __init__(
         self._graph_dropout = graph_dropout
 
         self._graph_user_embeddings = nn.Embedding(
-            num_embeddings=num_users + 2, embedding_dim=self._graph_embedding_dim
+            num_embeddings=num_users + 2,
+            embedding_dim=self._graph_embedding_dim
         )
         self._graph_item_embeddings = nn.Embedding(
-            num_embeddings=num_items + 2, embedding_dim=self._graph_embedding_dim
+            num_embeddings=num_items + 2,
+            embedding_dim=self._graph_embedding_dim
         )
 
-        # cross_attention part
+        # cross_attention part 
         self._mha = nn.MultiheadAttention(
             embed_dim=embedding_dim,
             num_heads=num_heads,
@@ -100,39 +107,36 @@ def __init__(
             in_features=2 * embedding_dim,
             out_features=embedding_dim,
         )
-
+        
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
             # sequential part
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-            norm_first=config.get("norm_first", True),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02),
+            norm_first=config.get('norm_first', True),
             # graph part
-            user_prefix=config["user_prefix"],
-            num_users=kwargs["num_users"],
+            user_prefix=config['user_prefix'],
+            num_users=kwargs['num_users'],
             graph_embedding_dim=config["graph_embedding_dim"],
             graph_num_layers=config["graph_num_layers"],
-            graph_dropout=config.get("graph_dropout", 0.0),
+            graph_dropout=config.get("graph_dropout", 0.0)
         )
-
+    
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat(
-            (self._graph_user_embeddings.weight, self._graph_item_embeddings.weight),
-            dim=0,
-        )
+        ego_embeddings = torch.cat((self._graph_user_embeddings.weight, self._graph_item_embeddings.weight), dim=0)
         all_embeddings = [ego_embeddings]
 
         if self._graph_dropout > 0:  # drop some edges
@@ -163,8 +167,8 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def _get_graph_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs["{}.ids".format(prefix)]  # (batch_size)
-        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
+        ids = inputs['{}.ids'.format(prefix)]  # (batch_size)
+        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (batch_size, emb_dim)
         ego_embeddings = ego_embeddings(ids)  # (batch_size, emb_dim)
@@ -175,15 +179,13 @@ def _get_graph_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings
         assert torch.all(mask == ego_mask)
 
         return padded_embeddings, padded_ego_embeddings, mask
-
+    
     def _ca_block(self, q, k, v, attn_mask=None, key_padding_mask=None):
         x = self._mha(
-            q,
-            k,
-            v,
+            q, k, v,
             attn_mask=attn_mask,
             key_padding_mask=key_padding_mask,
-            need_weights=False,
+            need_weights=False
         )[0]  # (batch_size, seq_len, embedding_dim)
         return self.dropout1(x)  # (batch_size, seq_len, embedding_dim)
 
@@ -193,19 +195,11 @@ def _ff_block(self, x):
 
     def forward(self, inputs):
         # target domain item sequence
-        all_sample_events_target = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths_target = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events_target = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths_target = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
         # source domain item sequence
-        all_sample_events_source = inputs[
-            "{}.{}.ids".format(self._sequence_prefix, self._source_domain)
-        ]  # (all_batch_events)
-        all_sample_lengths_source = inputs[
-            "{}.{}.length".format(self._sequence_prefix, self._source_domain)
-        ]  # (batch_size)
+        all_sample_events_source = inputs['{}.{}.ids'.format(self._sequence_prefix, self._source_domain)]  # (all_batch_events)
+        all_sample_lengths_source = inputs['{}.{}.length'.format(self._sequence_prefix, self._source_domain)]  # (batch_size)
 
         # sequential model encoder and target domain items embeddings from sequential model
         seq_embeddings_target, seq_mask_target = self._apply_sequential_encoder(
@@ -213,137 +207,81 @@ def forward(self, inputs):
         )  # (batch_size, target_seq_len, embedding_dim), (batch_size, target_seq_len)
 
         # target domain items encoder for graph model
-        all_final_user_embeddings_target, all_final_item_embeddings_target = (
-            self._apply_graph_encoder(
-                all_sample_events_target, all_sample_lengths_target
-            )
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings_target, all_final_item_embeddings_target = \
+            self._apply_graph_encoder(all_sample_events_target, all_sample_lengths_target)  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
         # source domain items encoder for graph model
-        all_final_user_embeddings_source, all_final_item_embeddings_source = (
-            self._apply_graph_encoder(
-                all_sample_events_source, all_sample_lengths_source
-            )
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
-
+        all_final_user_embeddings_source, all_final_item_embeddings_source = \
+            self._apply_graph_encoder(all_sample_events_source, all_sample_lengths_source)  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        
         # target domain items embeddings from graph model
-        (
-            graph_embeddings_target,
-            graph_item_ego_embeddings_target,
-            graph_item_mask_target,
-        ) = self._get_graph_embeddings(
-            inputs,
-            self._sequence_prefix,
-            self._graph_item_embeddings,
-            all_final_item_embeddings_target,
+        graph_embeddings_target, graph_item_ego_embeddings_target, graph_item_mask_target = self._get_graph_embeddings(
+            inputs, self._sequence_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
         )
-        graph_item_embeddings_target = graph_embeddings_target[
-            graph_item_mask_target
-        ]  # (batch_size, target_seq_len, embedding_dim)
+        graph_item_embeddings_target = graph_embeddings_target[graph_item_mask_target]  # (batch_size, target_seq_len, embedding_dim) 
         # source domain items embeddings from graph model
-        (
-            graph_embeddings_source,
-            graph_item_ego_embeddings_source,
-            graph_item_mask_source,
-        ) = self._get_graph_embeddings(
-            inputs,
-            self._sequence_prefix,
-            self._graph_item_embeddings,
-            all_final_item_embeddings_source,
+        graph_embeddings_source, graph_item_ego_embeddings_source, graph_item_mask_source = self._get_graph_embeddings(
+            inputs, self._sequence_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
         )
-        graph_item_embeddings_source = graph_embeddings_source[
-            graph_item_mask_source
-        ]  # (batch_size, source_seq_len, embedding_dim)
-
+        graph_item_embeddings_source = graph_embeddings_source[graph_item_mask_source]  # (batch_size, source_seq_len, embedding_dim) 
+        
         # embeddings + graph_embeddings_target -> cross-attention
         # query   = embeddings
         # keys    = graph_embeddings_target
         # values  = graph_embeddings_target
-        if self.norm_first:
-            graph_embeddings_target = graph_embeddings_target + self.norm1(
-                self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_target,
-                    v=graph_embeddings_target,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_target,
-                )
-            )  # (batch_size, target_seq_len, embedding_dim)
-            graph_embeddings_target = graph_embeddings_target + self.norm2(
-                self._ff_block(graph_embeddings_target)
-            )
+        if self.norm_first: 
+            graph_embeddings_target = graph_embeddings_target + self.norm1(self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_target,
+                v=graph_embeddings_target,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_target
+            ))  # (batch_size, target_seq_len, embedding_dim)
+            graph_embeddings_target = graph_embeddings_target + self.norm2(self._ff_block(graph_embeddings_target))
         else:
-            graph_embeddings_target = self.norm1(
-                graph_embeddings_target
-                + self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_target,
-                    v=graph_embeddings_target,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_target,
-                )
-            )  # (batch_size, target_seq_len, embedding_dim)
-            graph_embeddings_target = self.norm2(
-                graph_embeddings_target + self._ff_block(graph_embeddings_target)
-            )
+            graph_embeddings_target = self.norm1(graph_embeddings_target + self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_target,
+                v=graph_embeddings_target,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_target
+            ))  # (batch_size, target_seq_len, embedding_dim)
+            graph_embeddings_target = self.norm2(graph_embeddings_target + self._ff_block(graph_embeddings_target))
         # target-target cross-attention result
-        mha_embeddings_target = torch.cat(
-            [seq_embeddings_target, graph_embeddings_target], dim=-1
-        )
-        mha_embeddings_target = self._mha_output_projection(
-            mha_embeddings_target
-        )  # (batch_size, target_seq_len, embedding_dim)
+        mha_embeddings_target = torch.cat([seq_embeddings_target, graph_embeddings_target], dim=-1)
+        mha_embeddings_target = self._mha_output_projection(mha_embeddings_target)  # (batch_size, target_seq_len, embedding_dim)
 
         # embeddings + graph_embeddings_source -> cross-attention
         # query   = embeddings
         # keys    = graph_embeddings_source
         # values  = graph_embeddings_source
-        if self.norm_first:
-            graph_embeddings_source = graph_embeddings_source + self.norm1(
-                self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_source,
-                    v=graph_embeddings_source,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_source,
-                )
-            )  # (batch_size, seq_len, embedding_dim)
-            graph_embeddings_source = graph_embeddings_source + self.norm2(
-                self._ff_block(graph_embeddings_source)
-            )
+        if self.norm_first: 
+            graph_embeddings_source = graph_embeddings_source + self.norm1(self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_source,
+                v=graph_embeddings_source,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_source
+            ))  # (batch_size, seq_len, embedding_dim)
+            graph_embeddings_source = graph_embeddings_source + self.norm2(self._ff_block(graph_embeddings_source))
         else:
-            graph_embeddings_source = self.norm1(
-                graph_embeddings_source
-                + self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_source,
-                    v=graph_embeddings_source,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_source,
-                )
-            )  # (batch_size, seq_len, embedding_dim)
-            graph_embeddings_source = self.norm2(
-                graph_embeddings_source + self._ff_block(graph_embeddings_source)
-            )
+            graph_embeddings_source = self.norm1(graph_embeddings_source + self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_source,
+                v=graph_embeddings_source,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_source
+            ))  # (batch_size, seq_len, embedding_dim)
+            graph_embeddings_source = self.norm2(graph_embeddings_source + self._ff_block(graph_embeddings_source))
         # source-target cross-attention result
-        mha_embeddings_source = torch.cat(
-            [seq_embeddings_target, graph_embeddings_source], dim=-1
-        )
-        mha_embeddings_source = self._mha_output_projection(
-            mha_embeddings_source
-        )  # (batch_size, seq_len, embedding_dim)
+        mha_embeddings_source = torch.cat([seq_embeddings_target, graph_embeddings_source], dim=-1)
+        mha_embeddings_source = self._mha_output_projection(mha_embeddings_source)  # (batch_size, seq_len, embedding_dim)
 
         if self.training:  # training mode
             # sequential part
-            all_positive_sample_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            all_negative_sample_events = inputs[
-                "{}.ids".format(self._negative_prefix)
-            ]  # (all_batch_events)
-
-            all_sample_embeddings = seq_embeddings_target[
-                seq_mask_target
-            ]  # (all_batch_events, embedding_dim)
+            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+
+            all_sample_embeddings = seq_embeddings_target[seq_mask_target]  # (all_batch_events, embedding_dim)
             all_positive_sample_embeddings = self._item_embeddings(
                 all_positive_sample_events
             )  # (all_batch_events, embedding_dim)
@@ -352,150 +290,96 @@ def forward(self, inputs):
             )  # (all_batch_events, embedding_dim)
 
             # graph part, target domain item embeddings
-            graph_positive_embeddings_target, _, graph_positive_mask_target = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._positive_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_target,
-                )
+            graph_positive_embeddings_target, _, graph_positive_mask_target = self._get_graph_embeddings(
+                inputs, self._positive_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
             )
-            graph_negative_embeddings_target, _, graph_negative_mask_target = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._negative_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_target,
-                )
+            graph_negative_embeddings_target, _, graph_negative_mask_target = self._get_graph_embeddings(
+                inputs, self._negative_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
             )
             # b - batch_size, s - seq_len, d - embedding_dim
             graph_positive_scores_target = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_target,
-                graph_positive_embeddings_target,
+                'bd,bsd->bs', graph_item_embeddings_target, graph_positive_embeddings_target
             )  # (batch_size, target_seq_len)
             graph_negative_scores_target = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_target,
-                graph_negative_embeddings_target,
+                'bd,bsd->bs', graph_item_embeddings_target, graph_negative_embeddings_target
             )  # (batch_size, target_seq_len)
-            graph_positive_scores_target = graph_positive_scores_target[
-                graph_positive_mask_target
-            ]  # (all_batch_events)
-            graph_negative_scores_target = graph_negative_scores_target[
-                graph_negative_mask_target
-            ]  # (all_batch_events)
+            graph_positive_scores_target = graph_positive_scores_target[graph_positive_mask_target]  # (all_batch_events)
+            graph_negative_scores_target = graph_negative_scores_target[graph_negative_mask_target]  # (all_batch_events)
 
             # graph part, source domain item embeddings
-            graph_positive_embeddings_source, _, graph_positive_mask_source = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._positive_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_source,
-                )
+            graph_positive_embeddings_source, _, graph_positive_mask_source = self._get_graph_embeddings(
+                inputs, self._positive_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
             )
-            graph_negative_embeddings_source, _, graph_negative_mask_source = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._negative_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_source,
-                )
+            graph_negative_embeddings_source, _, graph_negative_mask_source = self._get_graph_embeddings(
+                inputs, self._negative_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
             )
             # b - batch_size, s - seq_len, d - embedding_dim
             graph_positive_scores_source = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_source,
-                graph_positive_embeddings_source,
+                'bd,bsd->bs', graph_item_embeddings_source, graph_positive_embeddings_source
             )  # (batch_size, source_seq_len)
             graph_negative_scores_source = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_source,
-                graph_negative_embeddings_source,
+                'bd,bsd->bs', graph_item_embeddings_source, graph_negative_embeddings_source
             )  # (batch_size, source_seq_len)
-            graph_positive_scores_source = graph_positive_scores_source[
-                graph_positive_mask_source
-            ]  # (all_batch_events)
-            graph_negative_scores_source = graph_negative_scores_source[
-                graph_negative_mask_source
-            ]  # (all_batch_events)
+            graph_positive_scores_source = graph_positive_scores_source[graph_positive_mask_source]  # (all_batch_events)
+            graph_negative_scores_source = graph_negative_scores_source[graph_negative_mask_source]  # (all_batch_events)
 
             # mha part
-            mha_all_sample_embeddings_target = mha_embeddings_target[
-                seq_mask_target
-            ]  # (all_batch_events, embedding_dim)
-            mha_all_sample_embeddings_source = mha_embeddings_source[
-                seq_mask_target
-            ]  # (all_batch_events, embedding_dim)
+            mha_all_sample_embeddings_target = mha_embeddings_target[seq_mask_target]  # (all_batch_events, embedding_dim)
+            mha_all_sample_embeddings_source = mha_embeddings_source[seq_mask_target]  # (all_batch_events, embedding_dim)
 
             return {
                 # sequential part
                 # target domain item embeddings
-                "current_embeddings": all_sample_embeddings,
-                "positive_embeddings": all_positive_sample_embeddings,
-                "negative_embeddings": all_negative_sample_embeddings,
+                'current_embeddings': all_sample_embeddings, 
+                'positive_embeddings': all_positive_sample_embeddings,
+                'negative_embeddings': all_negative_sample_embeddings,
+                    
                 # graph part
                 # target domain item embeddings
-                "graph_positive_embeddings_target": graph_positive_embeddings_target[
-                    graph_positive_mask_target
-                ],
-                "graph_negative_embeddings_target": graph_negative_embeddings_target[
-                    graph_negative_mask_target
-                ],
-                "graph_positive_scores_target": graph_positive_scores_target,
-                "graph_negative_scores_target": graph_negative_scores_target,
-                "graph_item_embeddings_target": graph_item_embeddings_target,
+                'graph_positive_embeddings_target': graph_positive_embeddings_target[graph_positive_mask_target],
+                'graph_negative_embeddings_target': graph_negative_embeddings_target[graph_negative_mask_target],
+                'graph_positive_scores_target': graph_positive_scores_target,
+                'graph_negative_scores_target': graph_negative_scores_target,
+                'graph_item_embeddings_target': graph_item_embeddings_target,
                 # source domain item embeddings
-                "graph_positive_embeddings_source": graph_positive_embeddings_source[
-                    graph_positive_mask_source
-                ],
-                "graph_negative_embeddings_source": graph_negative_embeddings_source[
-                    graph_negative_mask_source
-                ],
-                "graph_positive_scores_source": graph_positive_scores_source,
-                "graph_negative_scores_source": graph_negative_scores_source,
-                "graph_item_embeddings_source": graph_item_embeddings_source,
+                'graph_positive_embeddings_source': graph_positive_embeddings_source[graph_positive_mask_source],
+                'graph_negative_embeddings_source': graph_negative_embeddings_source[graph_negative_mask_source],
+                'graph_positive_scores_source': graph_positive_scores_source,
+                'graph_negative_scores_source': graph_negative_scores_source,
+                'graph_item_embeddings_source': graph_item_embeddings_source,
+
                 # mha part
                 # target domain item embeddings
-                "mha_embeddings_target": mha_all_sample_embeddings_target,
-                "mha_positive_embeddings_target": all_positive_sample_embeddings,
-                "mha_negative_embeddings_target": all_negative_sample_embeddings,
+                'mha_embeddings_target': mha_all_sample_embeddings_target, 
+                'mha_positive_embeddings_target': all_positive_sample_embeddings,
+                'mha_negative_embeddings_target': all_negative_sample_embeddings,
                 # source domain item embeddings
-                "mha_embeddings_source": mha_all_sample_embeddings_source,
-                "mha_positive_embeddings_source": all_positive_sample_embeddings,
-                "mha_negative_embeddings_source": all_negative_sample_embeddings,
+                'mha_embeddings_source': mha_all_sample_embeddings_source, 
+                'mha_positive_embeddings_source': all_positive_sample_embeddings,
+                'mha_negative_embeddings_source': all_negative_sample_embeddings
             }
         else:  # eval mode
-            seq_last_embeddings_target = self._get_last_embedding(
-                seq_embeddings_target, seq_mask_target
-            )  # (batch_size, embedding_dim)
-            mha_last_embeddings_target = self._get_last_embedding(
-                mha_embeddings_target, seq_mask_target
-            )  # (batch_size, embedding_dim)
-            mha_last_embeddings_source = self._get_last_embedding(
-                mha_embeddings_source, seq_mask_target
-            )  # (batch_size, embedding_dim)
+            seq_last_embeddings_target = self._get_last_embedding(seq_embeddings_target, seq_mask_target)  # (batch_size, embedding_dim)
+            mha_last_embeddings_target = self._get_last_embedding(mha_embeddings_target, seq_mask_target)  # (batch_size, embedding_dim)
+            mha_last_embeddings_source = self._get_last_embedding(mha_embeddings_source, seq_mask_target)  # (batch_size, embedding_dim)
 
             aggregated_last_embeddings = torch.maximum(
-                seq_last_embeddings_target,
-                torch.maximum(mha_last_embeddings_target, mha_last_embeddings_source),
+                seq_last_embeddings_target, 
+                torch.maximum(mha_last_embeddings_target, mha_last_embeddings_source)
             )  # (batch_size, embedding_dim)
 
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", aggregated_last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                aggregated_last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
-            if "{}.ids".format(self._candidate_prefix) in inputs:
-                candidate_events = inputs[
-                    "{}.ids".format(self._candidate_prefix)
-                ]  # (all_batch_candidates)
-                candidate_lengths = inputs[
-                    "{}.length".format(self._candidate_prefix)
-                ]  # (batch_size)
+            if '{}.ids'.format(self._candidate_prefix) in inputs:
+                candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                 batch_size = candidate_lengths.shape[0]
                 num_candidates = candidate_lengths[0]
@@ -503,11 +387,12 @@ def forward(self, inputs):
                 candidate_scores = torch.gather(
                     input=candidate_scores,
                     dim=1,
-                    index=torch.reshape(candidate_events, [batch_size, num_candidates]),
+                    index=torch.reshape(candidate_events, [batch_size, num_candidates])
                 )  # (batch_size, num_candidates)
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20), (batch_size, 20)
 
             return indices
diff --git a/modeling/models/lightgcn.py b/modeling/models/lightgcn.py
index 19d79b80..79e5f788 100644
--- a/modeling/models/lightgcn.py
+++ b/modeling/models/lightgcn.py
@@ -1,23 +1,25 @@
+from models.base import TorchModel
+
+from utils import create_masked_tensor, DEVICE
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.base import TorchModel
-from utils import DEVICE, create_masked_tensor
 
+class LightGCNModel(TorchModel, config_name='light_gcn'):
 
-class LightGCNModel(TorchModel, config_name="light_gcn"):
     def __init__(
-        self,
-        user_prefix,
-        positive_prefix,
-        graph,
-        num_users,
-        num_items,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        initializer_range=0.02,
+            self,
+            user_prefix,
+            positive_prefix,
+            graph,
+            num_users,
+            num_items,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            initializer_range=0.02
     ):
         super().__init__()
         self._user_prefix = user_prefix
@@ -30,11 +32,13 @@ def __init__(
         self._dropout_rate = dropout
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -42,21 +46,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config["user_prefix"],
-            positive_prefix=config["positive_prefix"],
-            graph=kwargs["graph"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            embedding_dim=config["embedding_dim"],
-            num_layers=config["num_layers"],
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            user_prefix=config['user_prefix'],
+            positive_prefix=config['positive_prefix'],
+            graph=kwargs['graph'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            embedding_dim=config['embedding_dim'],
+            num_layers=config['num_layers'],
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat(
-            (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-        )
+        ego_embeddings = torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
         all_embeddings = [ego_embeddings]
 
         if self._dropout_rate > 0:  # drop some edges
@@ -87,8 +89,8 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs["{}.ids".format(prefix)]  # (all_batch_events)
-        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
+        ids = inputs['{}.ids'.format(prefix)]  # (all_batch_events)
+        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (all_batch_events, embedding_dim)
         ego_embeddings = ego_embeddings(ids)  # (all_batch_events, embedding_dim)
@@ -106,9 +108,8 @@ def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
         return padded_embeddings, padded_ego_embeddings, mask
 
     def forward(self, inputs):
-        all_final_user_embeddings, all_final_item_embeddings = (
-            self._apply_graph_encoder()
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings, all_final_item_embeddings = \
+            self._apply_graph_encoder()  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
         user_embeddings, user_ego_embeddings, user_mask = self._get_embeddings(
             inputs, self._user_prefix, self._user_embeddings, all_final_user_embeddings
@@ -116,69 +117,63 @@ def forward(self, inputs):
         user_embeddings = user_embeddings[user_mask]  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
-            positive_item_ids = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            positive_item_lengths = inputs[
-                "{}.length".format(self._positive_prefix)
-            ]  # (batch_size)
+            positive_item_ids = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            positive_item_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
 
             batch_size = positive_item_lengths.shape[0]
             max_sequence_length = positive_item_lengths.max().item()
 
-            mask = (
-                torch.arange(end=max_sequence_length, device=DEVICE)[None].tile(
-                    [batch_size, 1]
-                )
-                < positive_item_lengths[:, None]
-            )  # (batch_size, max_seq_len)
-
-            positive_user_ids = (
-                torch.arange(batch_size, device=DEVICE)[None]
-                .tile([max_sequence_length, 1])
-                .T
-            )  # (batch_size, max_seq_len)
+            mask = torch.arange(
+                end=max_sequence_length,
+                device=DEVICE
+            )[None].tile([batch_size, 1]) < positive_item_lengths[:, None]  # (batch_size, max_seq_len)
+
+            positive_user_ids = torch.arange(
+                batch_size,
+                device=DEVICE
+            )[None].tile([max_sequence_length, 1]).T  # (batch_size, max_seq_len)
             positive_user_ids = positive_user_ids[mask]  # (all_batch_items)
-            user_embeddings = user_embeddings[
-                positive_user_ids
-            ]  # (all_batch_items, embedding_dim)
+            user_embeddings = user_embeddings[positive_user_ids]  # (all_batch_items, embedding_dim)
 
             all_scores = torch.einsum(
-                "ad,nd->an", user_embeddings, all_final_item_embeddings
+                'ad,nd->an',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (all_batch_items, num_items + 2)
 
-            negative_mask = torch.zeros(
-                self._num_items + 2, dtype=torch.bool, device=DEVICE
-            )  # (num_items + 2)
+            negative_mask = torch.zeros(self._num_items + 2, dtype=torch.bool, device=DEVICE)  # (num_items + 2)
             negative_mask[positive_item_ids] = 1
 
             positive_scores = torch.gather(
-                input=all_scores, dim=1, index=positive_item_ids[..., None]
+                input=all_scores,
+                dim=1,
+                index=positive_item_ids[..., None]
             )  # (all_batch_items, 1)
 
             all_scores = torch.scatter_add(
                 input=all_scores,
                 dim=1,
                 index=positive_item_ids[..., None],
-                src=torch.ones_like(positive_item_ids[..., None]).float(),
+                src=torch.ones_like(positive_item_ids[..., None]).float()
             )  # (all_batch_items, num_items + 2)
 
             return {
-                "positive_scores": positive_scores,
-                "negative_scores": all_scores,
-                "item_embeddings": torch.cat(
-                    (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-                ),
+                'positive_scores': positive_scores,
+                'negative_scores': all_scores,
+                'item_embeddings': torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
             }
         else:  # eval mode
             candidate_scores = torch.einsum(
-                "bd,nd->bn", user_embeddings, all_final_item_embeddings
+                'bd,nd->bn',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/mclsr.py b/modeling/models/mclsr.py
index c7fefd3c..8f78cda4 100644
--- a/modeling/models/mclsr.py
+++ b/modeling/models/mclsr.py
@@ -1,30 +1,32 @@
+from models.base import TorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import TorchModel
 from utils import create_masked_tensor
 
 
-class MCLSRModel(TorchModel, config_name="mclsr"):
+class MCLSRModel(TorchModel, config_name='mclsr'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        user_prefix,
-        labels_prefix,
-        candidate_prefix,
-        num_users,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_graph_layers,
-        common_graph,
-        user_graph,
-        item_graph,
-        dropout=0.0,
-        layer_norm_eps=1e-5,
-        graph_dropout=0.0,
-        alpha=0.5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            user_prefix,
+            labels_prefix,
+            candidate_prefix,
+            num_users,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_graph_layers,
+            common_graph,
+            user_graph,
+            item_graph,
+            dropout=0.0,
+            layer_norm_eps=1e-5,
+            graph_dropout=0.0,
+            alpha=0.5,
+            initializer_range=0.02
     ):
         super().__init__()
         self._sequence_prefix = sequence_prefix
@@ -48,17 +50,16 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._user_embeddings = nn.Embedding(
             num_embeddings=num_users + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -66,43 +67,39 @@ def __init__(
 
         # Current interest learning
         self._current_interest_learning_encoder = nn.Sequential(
-            nn.Linear(
-                in_features=embedding_dim, out_features=4 * embedding_dim, bias=False
-            ),
+            nn.Linear(in_features=embedding_dim, out_features=4 * embedding_dim, bias=False),
             nn.Tanh(),
-            nn.Linear(in_features=4 * embedding_dim, out_features=1, bias=False),
+            nn.Linear(in_features=4 * embedding_dim, out_features=1, bias=False)
         )
 
         # General interest learning
         self._general_interest_learning_encoder = nn.Sequential(
-            nn.Linear(
-                in_features=embedding_dim, out_features=embedding_dim, bias=False
-            ),
-            nn.Tanh(),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=False),
+            nn.Tanh()
         )
 
         # Cross-view contrastive learning
         self._sequential_projector = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
         self._graph_projector = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
 
         self._user_projection = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
 
         self._item_projection = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
 
         self._init_weights(initializer_range)
@@ -110,22 +107,22 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            user_prefix=config["user_prefix"],
-            labels_prefix=config["labels_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_graph_layers=config["num_graph_layers"],
-            common_graph=kwargs["graph"],
-            user_graph=kwargs["user_graph"],
-            item_graph=kwargs["item_graph"],
-            dropout=config.get("dropout", 0.0),
-            layer_norm_eps=config.get("layer_norm_eps", 1e-5),
-            graph_dropout=config.get("graph_dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            user_prefix=config['user_prefix'],
+            labels_prefix=config['labels_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_graph_layers=config['num_graph_layers'],
+            common_graph=kwargs['graph'],
+            user_graph=kwargs['user_graph'],
+            item_graph=kwargs['item_graph'],
+            dropout=config.get('dropout', 0.0),
+            layer_norm_eps=config.get('layer_norm_eps', 1e-5),
+            graph_dropout=config.get('graph_dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _apply_graph_encoder(self, embeddings, graph, use_mean=False):
@@ -152,19 +149,14 @@ def _apply_graph_encoder(self, embeddings, graph, use_mean=False):
             return all_embeddings[-1]
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-        user_ids = inputs["{}.ids".format(self._user_prefix)]  # (batch_size)
-
-        embeddings = self._item_embeddings(
-            all_sample_events
-        )  # (all_batch_events, embedding_dim)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        user_ids = inputs['{}.ids'.format(self._user_prefix)]  # (batch_size)
+
+        embeddings = self._item_embeddings(all_sample_events)  # (all_batch_events, embedding_dim)
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=all_sample_lengths
+            data=embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
 
         batch_size = mask.shape[0]
@@ -172,108 +164,88 @@ def forward(self, inputs):
 
         # Current interest learning
         # 1) get embeddings with positions
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
-        positions_mask = (
-            positions < all_sample_lengths[:, None]
-        )  # (batch_size, max_seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions_mask = positions < all_sample_lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=all_sample_lengths
+            data=position_embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        positioned_embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        positioned_embeddings = self._layernorm(
-            positioned_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        positioned_embeddings = self._dropout(
-            positioned_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = self._layernorm(positioned_embeddings)  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = self._dropout(positioned_embeddings)  # (batch_size, seq_len, embedding_dim)
         positioned_embeddings[~mask] = 0
 
         sequential_attention_matrix = self._current_interest_learning_encoder(
             positioned_embeddings
         ).squeeze()  # (batch_size, seq_len)
         sequential_attention_matrix[~mask] = -torch.inf
-        sequential_attention_matrix = torch.softmax(
-            sequential_attention_matrix, dim=1
-        )  # (batch_size, seq_len)
+        sequential_attention_matrix = torch.softmax(sequential_attention_matrix, dim=1)  # (batch_size, seq_len)
         sequential_representation = torch.einsum(
-            "bs,bsd->bd", sequential_attention_matrix, embeddings
+            'bs,bsd->bd', sequential_attention_matrix, embeddings
         )  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             # General interest learning
             all_embeddings = torch.cat(
-                [self._user_embeddings.weight, self._item_embeddings.weight], dim=0
+                [self._user_embeddings.weight, self._item_embeddings.weight],
+                dim=0
             )  # (num_users + 2 + num_items + 2, embedding_dim)
             common_graph_embeddings = self._apply_graph_encoder(
-                embeddings=all_embeddings, graph=self._graph
+                embeddings=all_embeddings,
+                graph=self._graph
             )  # (num_users + 2 + num_items + 2, embedding_dim)
             common_graph_user_embeddings, common_graph_item_embeddings = torch.split(
-                common_graph_embeddings, [self._num_users + 2, self._num_items + 2]
+                common_graph_embeddings,
+                [self._num_users + 2, self._num_items + 2]
             )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
-            common_graph_user_embeddings = common_graph_user_embeddings[
-                user_ids
-            ]  # (batch_size, embedding_dim)
-            common_graph_item_embeddings = common_graph_item_embeddings[
-                all_sample_events
-            ]  # (all_batch_events, embedding_dim)
+            common_graph_user_embeddings = common_graph_user_embeddings[user_ids]  # (batch_size, embedding_dim)
+            common_graph_item_embeddings = common_graph_item_embeddings[all_sample_events]  # (all_batch_events, embedding_dim)
             common_graph_item_embeddings, _ = create_masked_tensor(
-                data=common_graph_item_embeddings, lengths=all_sample_lengths
+                data=common_graph_item_embeddings,
+                lengths=all_sample_lengths
             )  # (batch_size, seq_len, embedding_dim)
 
             graph_attention_matrix = torch.einsum(
-                "bd,bsd->bs",
+                'bd,bsd->bs',
                 self._general_interest_learning_encoder(common_graph_user_embeddings),
-                common_graph_item_embeddings,
+                common_graph_item_embeddings
             )  # (batch_size, seq_len)
             graph_attention_matrix[~mask] = -torch.inf
-            graph_attention_matrix = torch.softmax(
-                graph_attention_matrix, dim=1
-            )  # (batch_size, seq_len)
+            graph_attention_matrix = torch.softmax(graph_attention_matrix, dim=1)  # (batch_size, seq_len)
             graph_representation = torch.einsum(
-                "bs,bsd->bd", graph_attention_matrix, common_graph_item_embeddings
+                'bs,bsd->bd', graph_attention_matrix, common_graph_item_embeddings
             )  # (batch_size, embedding_dim)
 
             # Get final representation
-            combined_representation = (
-                self._alpha * sequential_representation
-                + (1 - self._alpha) * graph_representation
-            )  # (batch_size, embedding_dim)
+            combined_representation = \
+                self._alpha * sequential_representation + \
+                (1 - self._alpha) * graph_representation  # (batch_size, embedding_dim)
 
-            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
-            labels_embeddings = self._item_embeddings(
-                labels
-            )  # (batch_size, embedding_dim)
+            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
+            labels_embeddings = self._item_embeddings(labels)  # (batch_size, embedding_dim)
 
             # Cross-view contrastive learning
             sequential_representation = self._sequential_projector(
-                sequential_representation
-            )  # (batch_size, embedding_dim)
-            graph_representation = self._graph_projector(
-                graph_representation
-            )  # (batch_size, embedding_dim)
+                sequential_representation)  # (batch_size, embedding_dim)
+            graph_representation = self._graph_projector(graph_representation)  # (batch_size, embedding_dim)
 
             # Feature-level Contrastive Learning
             user_graph_user_embeddings = self._apply_graph_encoder(
-                embeddings=self._user_embeddings.weight, graph=self._user_graph
+                embeddings=self._user_embeddings.weight,
+                graph=self._user_graph
             )  # (num_users + 2, embedding_dim)
             user_graph_user_embeddings = torch.gather(
                 user_graph_user_embeddings,
                 dim=0,
-                index=user_ids[..., None].tile(1, self._embedding_dim),
+                index=user_ids[..., None].tile(1, self._embedding_dim)
             )  # (batch_size, embedding_dim)
 
             user_graph_user_embeddings = self._user_projection(
@@ -284,12 +256,13 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             item_graph_item_embeddings = self._apply_graph_encoder(
-                embeddings=self._item_embeddings.weight, graph=self._item_graph
+                embeddings=self._item_embeddings.weight,
+                graph=self._item_graph
             )  # (num_items + 2, embedding_dim)
             item_graph_item_embeddings = torch.gather(
                 item_graph_item_embeddings,
                 dim=0,
-                index=all_sample_events[..., None].tile(1, self._embedding_dim),
+                index=all_sample_events[..., None].tile(1, self._embedding_dim)
             )  # (all_sample_events, embedding_dim)
 
             item_graph_item_embeddings = self._item_projection(
@@ -301,50 +274,51 @@ def forward(self, inputs):
 
             return {
                 # Downstream task
-                "combined_representation": combined_representation,
-                "label_representation": labels_embeddings,
+                'combined_representation': combined_representation,
+                'label_representation': labels_embeddings,
+
                 # Interest-level Contrastive Learning
-                "sequential_representation": sequential_representation,
-                "graph_representation": graph_representation,
+                'sequential_representation': sequential_representation,
+                'graph_representation': graph_representation,
+
                 # Feature-level Contrastive Learning (users)
-                "user_graph_user_embeddings": user_graph_user_embeddings,
-                "common_graph_user_embeddings": common_graph_user_embeddings,
+                'user_graph_user_embeddings': user_graph_user_embeddings,
+                'common_graph_user_embeddings': common_graph_user_embeddings,
+
                 # Feature-level Contrastive Learning (items)
-                "item_graph_item_embeddings": item_graph_item_embeddings,
-                "common_graph_item_embeddings": common_graph_item_embeddings,
+                'item_graph_item_embeddings': item_graph_item_embeddings,
+                'common_graph_item_embeddings': common_graph_item_embeddings
             }
         else:  # eval mode
-            if "{}.ids".format(self._candidate_prefix) in inputs:
-                candidate_events = inputs[
-                    "{}.ids".format(self._candidate_prefix)
-                ]  # (all_batch_candidates)
-                candidate_lengths = inputs[
-                    "{}.length".format(self._candidate_prefix)
-                ]  # (batch_size)
-
-                candidate_embeddings = self._item_embeddings(
-                    candidate_events
-                )  # (all_batch_candidates, embedding_dim)
+            if '{}.ids'.format(self._candidate_prefix) in inputs:
+                candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
+
+                candidate_embeddings = self._item_embeddings(candidate_events)  # (all_batch_candidates, embedding_dim)
 
                 candidate_embeddings, _ = create_masked_tensor(
-                    data=candidate_embeddings, lengths=candidate_lengths
+                    data=candidate_embeddings,
+                    lengths=candidate_lengths
                 )  # (batch_size, num_candidates, embedding_dim)
 
                 candidate_scores = torch.einsum(
-                    "bd,bnd->bn", sequential_representation, candidate_embeddings
+                    'bd,bnd->bn',
+                    sequential_representation,
+                    candidate_embeddings
                 )  # (batch_size, num_candidates)
             else:
-                candidate_embeddings = (
-                    self._item_embeddings.weight
-                )  # (num_items, embedding_dim)
+                candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
                 candidate_scores = torch.einsum(
-                    "bd,nd->bn", sequential_representation, candidate_embeddings
+                    'bd,nd->bn',
+                    sequential_representation,
+                    candidate_embeddings
                 )  # (batch_size, num_items)
                 candidate_scores[:, 0] = -torch.inf
-                candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             values, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 100), (batch_size, 100)
 
             return indices
diff --git a/modeling/models/mrgsrec.py b/modeling/models/mrgsrec.py
index f53861a0..488c580a 100644
--- a/modeling/models/mrgsrec.py
+++ b/modeling/models/mrgsrec.py
@@ -1,29 +1,32 @@
-import torch
-import torch.nn as nn
 from torch.nn import MultiheadAttention
 
 from models.base import TorchModel
+
 from utils import create_masked_tensor, get_activation_function
 
+import torch
+import torch.nn as nn
+
+
+class MRGSRecModel(TorchModel, config_name='mrgsrec'):
 
-class MRGSRecModel(TorchModel, config_name="mrgsrec"):
     def __init__(
-        self,
-        sequence_prefix,
-        user_prefix,
-        positive_prefix,
-        negative_prefix,
-        candidate_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            user_prefix,
+            positive_prefix,
+            negative_prefix,
+            candidate_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
         super().__init__()
         self._sequence_prefix = sequence_prefix
@@ -37,12 +40,11 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -55,69 +57,60 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True,
+            batch_first=True
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
-        embeddings = self._item_embeddings(
-            all_sample_events
-        )  # (all_batch_events, embedding_dim)
+        embeddings = self._item_embeddings(all_sample_events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=all_sample_lengths
+            data=embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
-        positions_mask = (
-            positions < all_sample_lengths[:, None]
-        )  # (batch_size, max_seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions_mask = positions < all_sample_lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=all_sample_lengths
+            data=position_embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
 
         embeddings[~mask] = 0
+
+
+
diff --git a/modeling/models/ngcf.py b/modeling/models/ngcf.py
index 8df917eb..2fcc9081 100644
--- a/modeling/models/ngcf.py
+++ b/modeling/models/ngcf.py
@@ -1,23 +1,25 @@
+from models.base import TorchModel
+
+from utils import create_masked_tensor, DEVICE
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.base import TorchModel
-from utils import DEVICE, create_masked_tensor
 
+class NgcfModel(TorchModel, config_name='ngcf'):
 
-class NgcfModel(TorchModel, config_name="ngcf"):
     def __init__(
-        self,
-        user_prefix,
-        positive_prefix,
-        graph,
-        num_users,
-        num_items,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        initializer_range=0.02,
+            self,
+            user_prefix,
+            positive_prefix,
+            graph,
+            num_users,
+            num_items,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            initializer_range=0.02
     ):
         super().__init__()
         self._user_prefix = user_prefix
@@ -38,11 +40,13 @@ def __init__(
             self.Bi_Linear_list.append(nn.Linear(embedding_dim, embedding_dim))
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -50,20 +54,20 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config["user_prefix"],
-            positive_prefix=config["positive_prefix"],
-            graph=kwargs["graph"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            embedding_dim=config["embedding_dim"],
-            num_layers=config["num_layers"],
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            user_prefix=config['user_prefix'],
+            positive_prefix=config['positive_prefix'],
+            graph=kwargs['graph'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            embedding_dim=config['embedding_dim'],
+            num_layers=config['num_layers'],
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs["{}.ids".format(prefix)]  # (all_batch_events)
-        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
+        ids = inputs['{}.ids'.format(prefix)]  # (all_batch_events)
+        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (all_batch_events, embedding_dim)
         ego_embeddings = ego_embeddings(ids)  # (all_batch_events, embedding_dim)
@@ -81,9 +85,7 @@ def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
         return padded_embeddings, padded_ego_embeddings, mask
 
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat(
-            (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-        )
+        ego_embeddings = torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
         all_embeddings = [ego_embeddings]
 
         if self._dropout_rate > 0:  # drop some edges
@@ -120,82 +122,73 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def forward(self, inputs):
-        all_final_user_embeddings, all_final_item_embeddings = (
-            self._apply_graph_encoder()
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings, all_final_item_embeddings = \
+            self._apply_graph_encoder()  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
         user_embeddings, user_ego_embeddings, user_mask = self._get_embeddings(
             inputs, self._user_prefix, self._user_embeddings, all_final_user_embeddings
         )
-        user_embeddings = user_embeddings[
-            user_mask
-        ]  # (all_batch_events, embedding_dim)
+        user_embeddings = user_embeddings[user_mask]  # (all_batch_events, embedding_dim)
 
         if self.training:  # training mode
-            positive_item_ids = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            positive_item_lengths = inputs[
-                "{}.length".format(self._positive_prefix)
-            ]  # (batch_size)
+            positive_item_ids = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            positive_item_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
 
             batch_size = positive_item_lengths.shape[0]
             max_sequence_length = positive_item_lengths.max().item()
 
-            mask = (
-                torch.arange(end=max_sequence_length, device=DEVICE)[None].tile(
-                    [batch_size, 1]
-                )
-                < positive_item_lengths[:, None]
-            )  # (batch_size, max_seq_len)
-
-            positive_user_ids = (
-                torch.arange(batch_size, device=DEVICE)[None]
-                .tile([max_sequence_length, 1])
-                .T
-            )  # (batch_size, max_seq_len)
+            mask = torch.arange(
+                end=max_sequence_length,
+                device=DEVICE
+            )[None].tile([batch_size, 1]) < positive_item_lengths[:, None]  # (batch_size, max_seq_len)
+
+            positive_user_ids = torch.arange(
+                batch_size,
+                device=DEVICE
+            )[None].tile([max_sequence_length, 1]).T  # (batch_size, max_seq_len)
             positive_user_ids = positive_user_ids[mask]  # (all_batch_items)
-            user_embeddings = user_embeddings[
-                positive_user_ids
-            ]  # (all_batch_items, embedding_dim)
+            user_embeddings = user_embeddings[positive_user_ids]  # (all_batch_items, embedding_dim)
 
             all_scores = torch.einsum(
-                "ad,nd->an", user_embeddings, all_final_item_embeddings
+                'ad,nd->an',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (all_batch_items, num_items + 2)
 
-            negative_mask = torch.zeros(
-                self._num_items + 2, dtype=torch.bool, device=DEVICE
-            )  # (num_items + 2)
+            negative_mask = torch.zeros(self._num_items + 2, dtype=torch.bool, device=DEVICE)  # (num_items + 2)
             negative_mask[positive_item_ids] = 1
 
             positive_scores = torch.gather(
-                input=all_scores, dim=1, index=positive_item_ids[..., None]
+                input=all_scores,
+                dim=1,
+                index=positive_item_ids[..., None]
             )  # (all_batch_items, 1)
 
             all_scores = torch.scatter_add(
                 input=all_scores,
                 dim=1,
                 index=positive_item_ids[..., None],
-                src=torch.ones_like(positive_item_ids[..., None]).float(),
+                src=torch.ones_like(positive_item_ids[..., None]).float()
             )  # (all_batch_items, num_items + 2)
 
             return {
-                "positive_scores": positive_scores,
-                "negative_scores": all_scores,
-                "item_embeddings": torch.cat(
-                    (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-                ),
+                'positive_scores': positive_scores,
+                'negative_scores': all_scores,
+                'item_embeddings': torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
             }
         else:  # eval mode
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", user_embeddings, all_final_item_embeddings
+                'bd,nd->bn',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/pop.py b/modeling/models/pop.py
index ddf26881..bb0b6774 100644
--- a/modeling/models/pop.py
+++ b/modeling/models/pop.py
@@ -1,10 +1,16 @@
+from models.base import BaseModel
+
 import torch
 
-from models.base import BaseModel
 
+class PopModel(BaseModel, config_name='pop'):
 
-class PopModel(BaseModel, config_name="pop"):
-    def __init__(self, label_prefix, counts_prefix, num_items):
+    def __init__(
+            self,
+            label_prefix,
+            counts_prefix,
+            num_items
+    ):
         self._label_prefix = label_prefix
         self._counts_prefix = counts_prefix
         self._num_items = num_items
@@ -12,30 +18,26 @@ def __init__(self, label_prefix, counts_prefix, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            label_prefix=config["label_prefix"],
-            counts_prefix=config["counts_prefix"],
-            num_items=kwargs["num_items"],
+            label_prefix=config['label_prefix'],
+            counts_prefix=config['counts_prefix'],
+            num_items=kwargs['num_items']
         )
 
     def __call__(self, inputs):
-        candidate_counts = inputs[
-            "{}.ids".format(self._counts_prefix)
-        ]  # (all_batch_candidates)
-        candidate_counts_lengths = inputs[
-            "{}.length".format(self._counts_prefix)
-        ]  # (batch_size)
+        candidate_counts = inputs['{}.ids'.format(self._counts_prefix)]  # (all_batch_candidates)
+        candidate_counts_lengths = inputs['{}.length'.format(self._counts_prefix)]  # (batch_size)
         batch_size = candidate_counts_lengths.shape[0]
 
         candidate_scores = torch.reshape(
-            candidate_counts, shape=(batch_size, self._num_items + 2)
+            candidate_counts,
+            shape=(batch_size, self._num_items + 2)
         ).float()  # (batch_size, num_items)
         candidate_scores[:, 0] = -torch.inf  # zero (padding) token
-        candidate_scores[
-            :, self._num_items + 1 :
-        ] = -torch.inf  # all not real items-related things
+        candidate_scores[:, self._num_items + 1:] = -torch.inf  # all not real items-related things
 
         _, indices = torch.topk(
-            candidate_scores, k=20, dim=-1, largest=True
+            candidate_scores,
+            k=20, dim=-1, largest=True
         )  # (batch_size, 20)
 
         return indices
diff --git a/modeling/models/pure_mf.py b/modeling/models/pure_mf.py
index dc0b691c..6cbe8dc5 100644
--- a/modeling/models/pure_mf.py
+++ b/modeling/models/pure_mf.py
@@ -1,20 +1,22 @@
+from models.base import TorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import TorchModel
 from utils import create_masked_tensor
 
 
-class PureMF(TorchModel, config_name="pure_mf"):
+class PureMF(TorchModel, config_name='pure_mf'):
+
     def __init__(
-        self,
-        user_prefix,
-        positive_prefix,
-        negative_prefix,
-        num_users,
-        num_items,
-        embedding_dim,
-        initializer_range,
+            self,
+            user_prefix,
+            positive_prefix,
+            negative_prefix,
+            num_users,
+            num_items,
+            embedding_dim,
+            initializer_range
     ):
         super().__init__()
 
@@ -27,11 +29,13 @@ def __init__(
         self._embedding_dim = embedding_dim
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -39,73 +43,54 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config["user_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            embedding_dim=config["embedding_dim"],
-            initializer_range=config.get("initializer_range", 0.02),
+            user_prefix=config['user_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            embedding_dim=config['embedding_dim'],
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        user_ids = inputs["{}.ids".format(self._user_prefix)]  # (batch_size)
+        user_ids = inputs['{}.ids'.format(self._user_prefix)]  # (batch_size)
         user_embeddings = self._user_embeddings(user_ids)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
-            all_positive = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            all_positive_embeddings = self._item_embeddings(
-                all_positive
-            )  # (all_batch_events, embedding_dim)
-            positive_lengths = inputs[
-                "{}.length".format(self._positive_prefix)
-            ]  # (batch_size)
-
-            all_negative = inputs[
-                "{}.ids".format(self._negative_prefix)
-            ]  # (all_batch_events)
-            all_negative_embeddings = self._item_embeddings(
-                all_negative
-            )  # (all_batch_events, embedding_dim)
-            negative_lengths = inputs[
-                "{}.length".format(self._negative_prefix)
-            ]  # (batch_size)
-
-            positive_embeddings, positive_mask = create_masked_tensor(
-                all_positive_embeddings, positive_lengths
-            )
-            negative_embeddings, negative_mask = create_masked_tensor(
-                all_negative_embeddings, negative_lengths
-            )
-
-            positive_scores = torch.einsum(
-                "bd,bsd->bs", user_embeddings, positive_embeddings
-            )  # (batch_size, seq_len)
-            negative_scores = torch.einsum(
-                "bd,bsd->bs", user_embeddings, negative_embeddings
-            )  # (batch_size, seq_len)
+            all_positive = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_positive_embeddings = self._item_embeddings(all_positive)  # (all_batch_events, embedding_dim)
+            positive_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
+
+            all_negative = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+            all_negative_embeddings = self._item_embeddings(all_negative)  # (all_batch_events, embedding_dim)
+            negative_lengths = inputs['{}.length'.format(self._negative_prefix)]  # (batch_size)
+
+            positive_embeddings, positive_mask = create_masked_tensor(all_positive_embeddings, positive_lengths)
+            negative_embeddings, negative_mask = create_masked_tensor(all_negative_embeddings, negative_lengths)
+
+            positive_scores = torch.einsum('bd,bsd->bs', user_embeddings, positive_embeddings)  # (batch_size, seq_len)
+            negative_scores = torch.einsum('bd,bsd->bs', user_embeddings, negative_embeddings)  # (batch_size, seq_len)
 
             positive_scores = positive_scores[positive_mask]  # (all_batch_events)
             negative_scores = negative_scores[negative_mask]  # (all_batch_events)
 
             return {
-                "positive_scores": positive_scores,
-                "negative_scores": negative_scores,
+                'positive_scores': positive_scores,
+                'negative_scores': negative_scores
             }
         else:
-            candidate_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items, embedding_dim)
+            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", user_embeddings, candidate_embeddings
+                'bd,nd->bn',
+                user_embeddings,
+                candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/pure_svd.py b/modeling/models/pure_svd.py
index fcb55a49..f705543b 100644
--- a/modeling/models/pure_svd.py
+++ b/modeling/models/pure_svd.py
@@ -1,14 +1,16 @@
 from models.base import BaseModel
 
 
-class SVDModel(BaseModel, config_name="pure_svd"):
+class SVDModel(BaseModel, config_name='pure_svd'):
+
     def __init__(self, rank):
         super().__init__()
 
         self._rank = rank
-        self._method = "PureSVD"
+        self._method = 'PureSVD'
         self._factors = {}
 
     @property
     def rank(self):
         return self._rank
+
diff --git a/modeling/models/random.py b/modeling/models/random.py
index cfa8d5fa..d5fae7c5 100644
--- a/modeling/models/random.py
+++ b/modeling/models/random.py
@@ -1,31 +1,36 @@
+from models.base import BaseModel
+
 import torch
 
-from models.base import BaseModel
 
+class RandomModel(BaseModel, config_name='random'):
 
-class RandomModel(BaseModel, config_name="random"):
-    def __init__(self, label_prefix, num_items):
+    def __init__(
+            self,
+            label_prefix,
+            num_items
+    ):
         self._label_prefix = label_prefix
         self._num_items = num_items
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(label_prefix=config["label_prefix"], num_items=kwargs["num_items"])
+        return cls(
+            label_prefix=config['label_prefix'],
+            num_items=kwargs['num_items']
+        )
 
     def __call__(self, inputs):
-        labels_lengths = inputs["{}.length".format(self._label_prefix)]  # (batch_size)
+        labels_lengths = inputs['{}.length'.format(self._label_prefix)]  # (batch_size)
         batch_size = labels_lengths.shape[0]
 
-        candidate_scores = torch.rand(
-            batch_size, self._num_items + 1
-        )  # (batch_size, num_items)
+        candidate_scores = torch.rand(batch_size, self._num_items + 1)  # (batch_size, num_items)
         candidate_scores[:, 0] = -torch.inf  # zero (padding) token
-        candidate_scores[
-            :, self._num_items + 1 :
-        ] = -torch.inf  # all not real items-related things
+        candidate_scores[:, self._num_items + 1:] = -torch.inf  # all not real items-related things
 
         _, indices = torch.topk(
-            candidate_scores, k=20, dim=-1, largest=True
+            candidate_scores,
+            k=20, dim=-1, largest=True
         )  # (batch_size, 20)
 
         return indices
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index d7d1606e..7e150421 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,23 +1,22 @@
 import functools
+from utils import DEVICE
+from models.base import TorchModel
 
-import faiss
 import torch
+import faiss
 
-from models.base import TorchModel
-from utils import DEVICE
-
+class RqVaeModel(TorchModel, config_name='rqvae'):
 
-class RqVaeModel(TorchModel, config_name="rqvae"):
     def __init__(
-        self,
-        train_sampler,
-        input_dim: int,
-        hidden_dim: int,
-        n_iter: int,
-        codebook_sizes: list[int],
-        should_init_codebooks,
-        should_reinit_unused_clusters,
-        initializer_range,
+            self,
+            train_sampler,
+            input_dim: int,
+            hidden_dim: int,
+            n_iter: int,
+            codebook_sizes: list[int],
+            should_init_codebooks,
+            should_reinit_unused_clusters,
+            initializer_range
     ):
         super().__init__()
 
@@ -35,39 +34,35 @@ def __init__(
 
         # Default initialization of codebook
         self.codebooks = torch.nn.ParameterList()
-
+        
         self.codebook_sizes = codebook_sizes
-
+        
         for codebook_size in codebook_sizes:
             cb = torch.FloatTensor(codebook_size, hidden_dim)
             self.codebooks.append(cb)
-
+         
         self._init_weights(initializer_range)
-
+        
         if self.should_init_codebooks:
             if train_sampler is None:
                 raise AttributeError("Train sampler is None")
-
-            embeddings = torch.stack(
-                [entry["item.embed"] for entry in train_sampler._dataset]
-            )
+            
+            embeddings = torch.stack([entry['item.embed'] for entry in train_sampler._dataset])
             self.init_codebooks(embeddings)
-            print("Codebooks initialized with Faiss Kmeans")
+            print('Codebooks initialized with Faiss Kmeans')
             self.should_init_codebooks = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            train_sampler=kwargs.get("train_sampler"),
-            input_dim=config["embedding_dim"],
-            hidden_dim=config["hidden_dim"],
-            n_iter=config["n_iter"],
-            codebook_sizes=config["codebook_sizes"],
-            should_init_codebooks=config.get("should_init_codebooks", False),
-            should_reinit_unused_clusters=config.get(
-                "should_reinit_unused_clusters", False
-            ),
-            initializer_range=config.get("initializer_range", 0.02),
+            train_sampler=kwargs.get('train_sampler'),
+            input_dim=config['embedding_dim'],
+            hidden_dim=config['hidden_dim'],
+            n_iter=config['n_iter'],
+            codebook_sizes=config['codebook_sizes'],
+            should_init_codebooks=config.get('should_init_codebooks', False),
+            should_reinit_unused_clusters=config.get('should_reinit_unused_clusters', False),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def make_encoding_tower(self, d1: int, d2: int):
@@ -107,21 +102,21 @@ def reinit_unused_clusters(remainder, codebook, codebook_indices):
             is_used[unique_indices] = True
             rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(),))
             codebook[~is_used] = remainder[rand_input]
-
+    
     def train_pass(self, embeddings):
         latent_vector = self.encoder(embeddings)
 
         latent_restored = 0
-
+        
         num_unique_clusters = []
         remainder = latent_vector
-
+        
         remainders = []
         codebooks_vectors = []
-
+        
         for codebook in self.codebooks:
             remainders.append(remainder)
-
+            
             codebook_indices = self.get_codebook_indices(remainder, codebook)
             codebook_vectors = codebook[codebook_indices]
 
@@ -129,7 +124,7 @@ def train_pass(self, embeddings):
                 self.reinit_unused_clusters(remainder, codebook, codebook_indices)
 
             num_unique_clusters.append(codebook_indices.unique().shape[0])
-
+            
             codebooks_vectors.append(codebook_vectors)
 
             latent_restored = latent_restored + codebook_vectors
@@ -143,9 +138,9 @@ def train_pass(self, embeddings):
             "embeddings": embeddings,
             "embeddings_restored": embeddings_restored,
             "remainders": remainders,
-            "codebooks_vectors": codebooks_vectors,
+            "codebooks_vectors": codebooks_vectors
         }
-
+        
     def eval_pass(self, embeddings):
         ind_lists = []
         remainder = self.encoder(embeddings)
@@ -158,12 +153,12 @@ def eval_pass(self, embeddings):
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
-
+        
         if self.training:  # training mode
             return self.train_pass(embeddings)
         else:  # eval mode
             return self.eval_pass(embeddings)
-
+        
     @functools.cache
     def get_single_embedding(self, codebook_idx: int, codebook_id: int):
         return self.codebooks[codebook_idx][codebook_id]
diff --git a/modeling/models/s3rec.py b/modeling/models/s3rec.py
index f9f361d4..e1fcffb4 100644
--- a/modeling/models/s3rec.py
+++ b/modeling/models/s3rec.py
@@ -1,31 +1,33 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 from utils import create_masked_tensor
 
 
-class S3RecModel(SequentialTorchModel, config_name="s3rec"):
+class S3RecModel(SequentialTorchModel, config_name='s3rec'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        sequence_segment_prefix,
-        positive_segment_prefix,
-        negative_segment_prefix,
-        candidate_prefix,
-        num_items,
-        max_sequence_length,
-        is_training,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            sequence_segment_prefix,
+            positive_segment_prefix,
+            negative_segment_prefix,
+            candidate_prefix,
+            num_items,
+            max_sequence_length,
+            is_training,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -37,7 +39,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=is_training,
+            is_causal=is_training
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -59,60 +61,50 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            sequence_segment_prefix=config["sequence_segment_prefix"],
-            positive_segment_prefix=config["positive_segment_prefix"],
-            negative_segment_prefix=config["negative_segment_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            is_training=config["is_training"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            sequence_segment_prefix=config['sequence_segment_prefix'],
+            positive_segment_prefix=config['positive_segment_prefix'],
+            negative_segment_prefix=config['negative_segment_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            is_training=config['is_training'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def masked_item_prediction(self, sequence_embeddings, sequence_mask, target_item):
         all_items = sequence_embeddings[sequence_mask]  # (all_batch_items, emb_dim)
-        score = torch.einsum("ad,ad->a", all_items, target_item)  # (all_batch_items)
+        score = torch.einsum(
+            'ad,ad->a', all_items, target_item
+        )  # (all_batch_items)
         return torch.sigmoid(score)  # (all_batch_items)
 
     def segment_prediction(self, context, segment):
-        score = torch.einsum("bd,bd->b", self.sp_norm(context), segment)  # (batch_size)
+        score = torch.einsum('bd,bd->b', self.sp_norm(context), segment)  # (batch_size)
         return torch.sigmoid(score)  # (batch_size)
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(
-            embeddings, mask
-        )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self._is_training:
             if self.training:  # training mode
-                all_positive_sample_events = inputs[
-                    "{}.ids".format(self._positive_prefix)
-                ]  # (all_batch_events)
-                all_negative_sample_events = inputs[
-                    "{}.ids".format(self._negative_prefix)
-                ]  # (all_batch_events)
-
-                all_sample_embeddings = embeddings[
-                    mask
-                ]  # (all_batch_events, embedding_dim)
+                all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+                all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+
+                all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
                 all_positive_sample_embeddings = self._item_embeddings(
                     all_positive_sample_events
                 )  # (all_batch_events, embedding_dim)
@@ -121,68 +113,53 @@ def forward(self, inputs):
                 )  # (all_batch_events, embedding_dim)
 
                 return {
-                    "current_embeddings": all_sample_embeddings,
-                    "positive_embeddings": all_positive_sample_embeddings,
-                    "negative_embeddings": all_negative_sample_embeddings,
+                    'current_embeddings': all_sample_embeddings,
+                    'positive_embeddings': all_positive_sample_embeddings,
+                    'negative_embeddings': all_negative_sample_embeddings
                 }
             else:  # eval mode
-                if "{}.ids".format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs[
-                        "{}.ids".format(self._candidate_prefix)
-                    ]  # (all_batch_candidates)
-                    candidate_lengths = inputs[
-                        "{}.length".format(self._candidate_prefix)
-                    ]  # (batch_size)
+                if '{}.ids'.format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                     candidate_embeddings = self._item_embeddings(
                         candidate_events
                     )  # (all_batch_candidates, embedding_dim)
 
                     candidate_embeddings, _ = create_masked_tensor(
-                        data=candidate_embeddings, lengths=candidate_lengths
+                        data=candidate_embeddings,
+                        lengths=candidate_lengths
                     )  # (batch_size, num_candidates, embedding_dim)
 
                     candidate_scores = torch.einsum(
-                        "bd,bnd->bn", last_embeddings, candidate_embeddings
+                        'bd,bnd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_candidates)
                 else:
-                    candidate_embeddings = (
-                        self._item_embeddings.weight
-                    )  # (num_items, embedding_dim)
+                    candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
                     candidate_scores = torch.einsum(
-                        "bd,nd->bn", last_embeddings, candidate_embeddings
+                        'bd,nd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_items)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                    candidate_scores[:, self._num_items + 1:] = -torch.inf
 
                 return candidate_scores
         else:
             # Masked Item Prediction
-            mip_mask = (
-                all_sample_events == self._mask_item_idx
-            ).bool()  # (all_batch_events)
+            mip_mask = (all_sample_events == self._mask_item_idx).bool()  # (all_batch_events)
             embeddings = embeddings[mask][mip_mask]  # (all_batch_events, embedding_dim)
-            positive_item_events = inputs["{}.ids".format(self._positive_prefix)][
-                mip_mask
-            ]  # (all_batch_events)
-            negative_item_events = inputs["{}.ids".format(self._negative_prefix)][
-                mip_mask
-            ]  # (all_batch_events)
-
-            positive_item_embeddings = self._item_embeddings(
-                positive_item_events
-            )  # (all_batch_events, embedding_dim)
-            negative_item_embeddings = self._item_embeddings(
-                negative_item_events
-            )  # (all_batch_events, embedding_dim)
+            positive_item_events = inputs['{}.ids'.format(self._positive_prefix)][mip_mask]  # (all_batch_events)
+            negative_item_events = inputs['{}.ids'.format(self._negative_prefix)][mip_mask]  # (all_batch_events)
+
+            positive_item_embeddings = self._item_embeddings(positive_item_events)  # (all_batch_events, embedding_dim)
+            negative_item_embeddings = self._item_embeddings(negative_item_events)  # (all_batch_events, embedding_dim)
 
             # Sequence Prediction
-            all_segment_events = inputs[
-                "{}.ids".format(self._sequence_segment_prefix)
-            ]  # (all_batch_events)
-            all_segment_lengths = inputs[
-                "{}.length".format(self._sequence_segment_prefix)
-            ]  # (batch_size)
+            all_segment_events = inputs['{}.ids'.format(self._sequence_segment_prefix)]  # (all_batch_events)
+            all_segment_lengths = inputs['{}.length'.format(self._sequence_segment_prefix)]  # (batch_size)
             segment_embeddings, segment_mask = self._apply_sequential_encoder(
                 all_segment_events, all_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
@@ -190,41 +167,30 @@ def forward(self, inputs):
                 segment_embeddings, segment_mask
             )  # (batch_size, embedding_dim)
 
-            positive_segment_events = inputs[
-                "{}.ids".format(self._positive_segment_prefix)
-            ]  # (all_batch_events)
-            positive_segment_lengths = inputs[
-                "{}.length".format(self._positive_segment_prefix)
-            ]  # (batch_size)
-            positive_segment_embeddings, positive_segment_mask = (
-                self._apply_sequential_encoder(
-                    positive_segment_events, positive_segment_lengths
-                )
+            positive_segment_events = inputs['{}.ids'.format(self._positive_segment_prefix)]  # (all_batch_events)
+            positive_segment_lengths = inputs['{}.length'.format(self._positive_segment_prefix)]  # (batch_size)
+            positive_segment_embeddings, positive_segment_mask = self._apply_sequential_encoder(
+                positive_segment_events, positive_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
             last_positive_segment_embeddings = self._get_last_embedding(
                 positive_segment_embeddings, positive_segment_mask
             )  # (batch_size, embedding_dim)
 
-            negative_segment_events = inputs[
-                "{}.ids".format(self._negative_segment_prefix)
-            ]  # (all_batch_events)
-            negative_segment_lengths = inputs[
-                "{}.length".format(self._negative_segment_prefix)
-            ]  # (batch_size)
-            negative_segment_embeddings, negative_segment_mask = (
-                self._apply_sequential_encoder(
-                    negative_segment_events, negative_segment_lengths
-                )
+            negative_segment_events = inputs['{}.ids'.format(self._negative_segment_prefix)]  # (all_batch_events)
+            negative_segment_lengths = inputs['{}.length'.format(self._negative_segment_prefix)]  # (batch_size)
+            negative_segment_embeddings, negative_segment_mask = self._apply_sequential_encoder(
+                negative_segment_events, negative_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
             last_negative_segment_embeddings = self._get_last_embedding(
                 negative_segment_embeddings, negative_segment_mask
             )  # (batch_size, embedding_dim)
 
             return {
-                "positive_representation": positive_item_embeddings,
-                "negative_representation": negative_item_embeddings,
-                "current_representation": embeddings,
-                "positive_segment_representation": last_positive_segment_embeddings,
-                "negative_segment_representation": last_negative_segment_embeddings,
-                "current_segment_representation": last_segment_embeddings,
+                'positive_representation': positive_item_embeddings,
+                'negative_representation': negative_item_embeddings,
+                'current_representation': embeddings,
+
+                'positive_segment_representation': last_positive_segment_embeddings,
+                'negative_segment_representation': last_negative_segment_embeddings,
+                'current_segment_representation': last_segment_embeddings
             }
diff --git a/modeling/models/sasrec_ce.py b/modeling/models/sasrec_ce.py
index d9eb882b..a0316853 100644
--- a/modeling/models/sasrec_ce.py
+++ b/modeling/models/sasrec_ce.py
@@ -1,24 +1,25 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 
+class SasRecCeModel(SequentialTorchModel, config_name='sasrec_ce'):
 
-class SasRecCeModel(SequentialTorchModel, config_name="sasrec_ce"):
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -30,13 +31,14 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -44,51 +46,42 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        embeddings = torch.nn.functional.gelu(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = torch.einsum(
-            "bsd,nd->bsn", embeddings, self._item_embeddings.weight
+            'bsd,nd->bsn', embeddings, self._item_embeddings.weight
         )  # (batch_size, seq_len, num_items + 2)
 
         if self.training:  # training mode
-            return {"logits": embeddings[mask]}
+            return {'logits': embeddings[mask]}
         else:  # eval mode
-            candidate_scores = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, num_items + 2)
+            candidate_scores = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index c1089e23..0230f41e 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -1,9 +1,9 @@
 import torch
+from .tiger import TigerModel
 from models import SequentialTorchModel
 from torch import nn
 from utils import DEVICE, create_masked_tensor
-
-from .tiger import TigerModel
+from torch import nn
 
 
 class SasRecSemanticModel(SequentialTorchModel, config_name="sasrec_semantic"):
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index c44c13f3..2e527bf8 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,10 +1,9 @@
 import json
 
 import torch
-from torch import nn
-
 from models.base import SequentialTorchModel
 from rqvae_utils import CollisionSolver, SimplifiedTree
+from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
 from .rqvae import RqVaeModel
@@ -353,17 +352,13 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             )
 
             assert last_position_embedding.shape == tgt_embeddings[:, -1, :].shape
-            assert tgt_embeddings.shape == torch.Size(
-                [batch_size, step + 1, embedding_dim]
-            )
+            assert tgt_embeddings.shape == torch.Size([batch_size, step + 1, embedding_dim])
 
             curr_step_embeddings = tgt_embeddings.clone()
             curr_step_embeddings[:, -1, :] = (
                 tgt_embeddings[:, -1, :] + last_position_embedding
             )
-            assert torch.allclose(
-                tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :]
-            )
+            assert torch.allclose(tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :])
             tgt_embeddings = curr_step_embeddings
 
             # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings[:, -1, :])
diff --git a/modeling/optimizer/base.py b/modeling/optimizer/base.py
index 0e85cab8..86c63537 100644
--- a/modeling/optimizer/base.py
+++ b/modeling/optimizer/base.py
@@ -1,18 +1,17 @@
 import copy
 
 import torch
-
 from utils import MetaParent
 
 OPTIMIZERS = {
-    "sgd": torch.optim.SGD,
-    "adam": torch.optim.Adam,
-    "adamw": torch.optim.AdamW,
+    'sgd': torch.optim.SGD,
+    'adam': torch.optim.Adam,
+    'adamw': torch.optim.AdamW
 }
 
 SCHEDULERS = {
-    "step": torch.optim.lr_scheduler.StepLR,
-    "cyclic": torch.optim.lr_scheduler.CyclicLR,
+    'step': torch.optim.lr_scheduler.StepLR,
+    'cyclic': torch.optim.lr_scheduler.CyclicLR
 }
 
 
@@ -20,7 +19,7 @@ class BaseOptimizer(metaclass=MetaParent):
     pass
 
 
-class BasicOptimizer(BaseOptimizer, config_name="basic"):
+class BasicOptimizer(BaseOptimizer, config_name='basic'):
     def __init__(self, model, optimizer, scheduler=None, clip_grad_threshold=None):
         self._model = model
         self._optimizer = optimizer
@@ -29,24 +28,26 @@ def __init__(self, model, optimizer, scheduler=None, clip_grad_threshold=None):
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        optimizer_cfg = copy.deepcopy(config["optimizer"])
-        optimizer = OPTIMIZERS[optimizer_cfg.pop("type")](
-            kwargs["model"].parameters(), **optimizer_cfg
+        optimizer_cfg = copy.deepcopy(config['optimizer'])
+        optimizer = OPTIMIZERS[optimizer_cfg.pop('type')](
+            kwargs['model'].parameters(),
+            **optimizer_cfg
         )
 
-        if "scheduler" in config:
-            scheduler_cfg = copy.deepcopy(config["scheduler"])
-            scheduler = SCHEDULERS[scheduler_cfg.pop("type")](
-                optimizer, **scheduler_cfg
+        if 'scheduler' in config:
+            scheduler_cfg = copy.deepcopy(config['scheduler'])
+            scheduler = SCHEDULERS[scheduler_cfg.pop('type')](
+                optimizer,
+                **scheduler_cfg
             )
         else:
             scheduler = None
 
         return cls(
-            model=kwargs["model"],
+            model=kwargs['model'],
             optimizer=optimizer,
             scheduler=scheduler,
-            clip_grad_threshold=config.get("clip_grad_threshold", None),
+            clip_grad_threshold=config.get('clip_grad_threshold', None)
         )
 
     def step(self, loss):
@@ -54,16 +55,14 @@ def step(self, loss):
         loss.backward()
 
         if self._clip_grad_threshold is not None:
-            torch.nn.utils.clip_grad_norm_(
-                self._model.parameters(), self._clip_grad_threshold
-            )
+            torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._clip_grad_threshold)
 
         self._optimizer.step()
         if self._scheduler is not None:
             self._scheduler.step()
 
     def state_dict(self):
-        state_dict = {"optimizer": self._optimizer.state_dict()}
+        state_dict = {'optimizer': self._optimizer.state_dict()}
         if self._scheduler is not None:
-            state_dict.update({"scheduler": self._scheduler.state_dict()})
+            state_dict.update({'scheduler': self._scheduler.state_dict()})
         return state_dict
diff --git a/modeling/pretrain.py b/modeling/pretrain.py
index 4221e62c..4597e3e8 100644
--- a/modeling/pretrain.py
+++ b/modeling/pretrain.py
@@ -1,16 +1,16 @@
-import copy
-import json
-
-import torch
-
 import utils
-from callbacks import BaseCallback
-from dataloader import BaseDataloader
+from utils import parse_args, create_logger, fix_random_seed, DEVICE
+
 from dataset import BaseDataset
-from loss import BaseLoss
+from dataloader import BaseDataloader
 from models import BaseModel
 from optimizer import BaseOptimizer
-from utils import DEVICE, create_logger, fix_random_seed, parse_args
+from loss import BaseLoss
+from callbacks import BaseCallback
+
+import copy
+import json
+import torch
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -20,10 +20,10 @@ def pretrain(dataloader, model, optimizer, loss_function, callback, epoch_cnt):
     step_num = 0
     best_checkpoint = None
 
-    logger.debug("Start pretraining...")
+    logger.debug('Start pretraining...')
 
     for epoch in range(epoch_cnt):
-        logger.debug(f"Start epoch {epoch}")
+        logger.debug(f'Start epoch {epoch}')
         for step, batch in enumerate(dataloader):
             model.train()
 
@@ -39,7 +39,7 @@ def pretrain(dataloader, model, optimizer, loss_function, callback, epoch_cnt):
 
             best_checkpoint = copy.deepcopy(model.state_dict())
 
-    logger.debug("Pretraining procedure has been finished!")
+    logger.debug('Pretraining procedure has been finished!')
     return best_checkpoint
 
 
@@ -47,38 +47,41 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(
-        config["experiment_name"]
-    )
+    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = \
+        utils.tensorboards.TensorboardWriter(config['experiment_name'])
 
-    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
 
-    dataset = BaseDataset.create_from_config(config["dataset"])
+    dataset = BaseDataset.create_from_config(config['dataset'])
 
     train_sampler, test_sampler = dataset.get_samplers()
 
     train_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
+        config['dataloader']['train'],
+        dataset=train_sampler,
+        **dataset.meta
     )
 
     validation_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=test_sampler, **dataset.meta
+        config['dataloader']['validation'],
+        dataset=test_sampler,
+        **dataset.meta
     )
 
-    model = BaseModel.create_from_config(config["model"], **dataset.meta).to(DEVICE)
+    model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
 
-    loss_function = BaseLoss.create_from_config(config["loss"])
+    loss_function = BaseLoss.create_from_config(config['loss'])
 
-    optimizer = BaseOptimizer.create_from_config(config["optimizer"], model=model)
+    optimizer = BaseOptimizer.create_from_config(config['optimizer'], model=model)
 
     callback = BaseCallback.create_from_config(
-        config["callback"],
+        config['callback'],
         model=model,
         dataloader=validation_dataloader,
-        optimizer=optimizer,
+        optimizer=optimizer
     )
 
-    logger.debug("Everything is ready for pretraining process!")
+    logger.debug('Everything is ready for pretraining process!')
 
     # Pretrain process
     pretrain(
@@ -87,16 +90,14 @@ def main():
         optimizer=optimizer,
         loss_function=loss_function,
         callback=callback,
-        epoch_cnt=config["pretrain_epochs_num"],
+        epoch_cnt=config['pretrain_epochs_num']
     )
 
-    logger.debug("Saving model...")
-    checkpoint_path = "../checkpoints/pretrain_{}_final_state.pth".format(
-        config["experiment_name"]
-    )
+    logger.debug('Saving model...')
+    checkpoint_path = '../checkpoints/pretrain_{}_final_state.pth'.format(config['experiment_name'])
     torch.save(model.state_dict(), checkpoint_path)
-    logger.debug("Saved model as {}".format(checkpoint_path))
+    logger.debug('Saved model as {}'.format(checkpoint_path))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
index 33c649b9..2722515a 100644
--- a/modeling/rqvae_utils/__init__.py
+++ b/modeling/rqvae_utils/__init__.py
@@ -1,4 +1,4 @@
 from .collision_solver import CollisionSolver
-from .simplified_tree import SimplifiedTree
-from .tree import Tree
 from .trie import Trie
+from .tree import Tree
+from .simplified_tree import SimplifiedTree
\ No newline at end of file
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index 426cf1bf..f673c6dd 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -6,45 +6,32 @@
 
 
 class CollisionSolver:
-    def __init__(
-        self,
-        emb_dim: int,
-        sem_id_len: int,
-        codebook_size: int,
-        device: torch.device = DEVICE,
-    ):
+    def __init__(self,
+                 emb_dim: int,
+                 sem_id_len: int,
+                 codebook_size: int,
+                 device: torch.device=DEVICE):
         """
         :param emb_dim: Длина остатка
         :param codebook_size: Количество элементов в одном кодбуке
         :param sem_id_len: Длина semantic_id (без токена решающего коллизии)
         :param device: Устройство
         """
-        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty(
-            (0, 0)
-        )  # тензор группирирующий остатки по semantic_id
+        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0))  # тензор группирирующий остатки по semantic_id
         self.item_ids_sparse_tensor: torch.Tensor = torch.empty(
-            (0, 0)
-        )  # тензор группирирующий реальные айди айтемов по semantic_id
-        self.counts_dict: dict[int, int] = defaultdict(
-            int
-        )  # тензор храняющий количество коллизий по semantic_id
+            (0, 0))  # тензор группирирующий реальные айди айтемов по semantic_id
+        self.counts_dict: dict[int, int] = defaultdict(int)  # тензор храняющий количество коллизий по semantic_id
         self.emb_dim: int = emb_dim  # длина остатка
         self.sem_id_len: int = sem_id_len  # длина semantic_id
         self.codebook_size: int = codebook_size  # количество элементов в одном кодбуке
         self.device: torch.device = device  # девайс
 
-        self.key: torch.Tensor = torch.tensor(
-            [self.codebook_size**i for i in range(self.sem_id_len)],
-            dtype=torch.long,
-            device=self.device,
-        )  # ключ для сопоставления числа каждому semantic_id
-
-    def create_query_candidates_dict(
-        self,
-        item_ids: torch.Tensor,
-        semantic_ids: torch.Tensor,
-        residuals: torch.Tensor,
-    ) -> None:
+        self.key: torch.Tensor = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len)],
+                                              dtype=torch.long,
+                                              device=self.device)  # ключ для сопоставления числа каждому semantic_id
+
+    def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor,
+                                     residuals: torch.Tensor) -> None:
         """
         Создает разреженный тензор, который содержит сгруппированные по semantic id элементы
 
@@ -65,53 +52,35 @@ def create_query_candidates_dict(
         semantic_ids = semantic_ids.to(self.device)
 
         unique_id = (semantic_ids * self.key).sum(dim=1)  # хэши
-        unique_ids, inverse_indices, counts = torch.unique(
-            unique_id, return_inverse=True, return_counts=True
-        )
-        sorted_indices = torch.argsort(
-            inverse_indices
-        )  # сортированные индексы чтобы совпадающие хэши шли подряд
+        unique_ids, inverse_indices, counts = torch.unique(unique_id, return_inverse=True, return_counts=True)
+        sorted_indices = torch.argsort(inverse_indices)  # сортированные индексы чтобы совпадающие хэши шли подряд
 
         row_indices = inverse_indices[sorted_indices]  # отсортированные хэши
 
         offsets = torch.cumsum(counts, dim=0) - counts
-        col_indices = (
-            torch.arange(semantic_ids_count, device=self.device) - offsets[row_indices]
-        )  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
-
-        indices = torch.stack(
-            [unique_ids[row_indices], col_indices], dim=0
-        )  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
-
-        max_residuals_count = int(
-            counts.max().item()
-        )  # максимальное количество коллизий для одного sem_id
-        max_sid = int(
-            self.codebook_size**self.sem_id_len
-        )  # максимальный хэш sem_id который может быть
-
-        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(
-            indices,
-            residuals[sorted_indices],
-            size=(max_sid, max_residuals_count, self.emb_dim),
-            device=self.device,
-        )  # (max_sid, max_residuals_count, emb_dim)
-
-        self.counts_dict = defaultdict(
-            int, zip(unique_ids.tolist(), counts.tolist())
-        )  # sid -> collision count
-
-        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(
-            indices,
-            item_ids[sorted_indices],
-            size=(max_sid, max_residuals_count),
-            device=self.device,
-            dtype=torch.int32,
-        )  # (max_sid, max_residuals_count)
-
-    def get_residuals_by_semantic_id_batch(
-        self, semantic_ids: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+        col_indices = torch.arange(semantic_ids_count, device=self.device) - offsets[
+            row_indices]  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
+
+        indices = torch.stack([
+            unique_ids[row_indices],
+            col_indices
+        ],
+            dim=0)  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
+
+        max_residuals_count = int(counts.max().item())  # максимальное количество коллизий для одного sem_id
+        max_sid = int(self.codebook_size ** self.sem_id_len)  # максимальный хэш sem_id который может быть
+
+        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices],
+                                                              size=(max_sid, max_residuals_count, self.emb_dim),
+                                                              device=self.device)  # (max_sid, max_residuals_count, emb_dim)
+
+        self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))  # sid -> collision count
+
+        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(indices, item_ids[sorted_indices],
+                                                              size=(max_sid, max_residuals_count), device=self.device,
+                                                              dtype=torch.int32)  # (max_sid, max_residuals_count)
+
+    def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         :param semantic_ids батч из semantic ids (batch_size, sem_id_len)
 
@@ -124,21 +93,13 @@ def get_residuals_by_semantic_id_batch(
         semantic_ids = semantic_ids.to(self.device)
         unique_ids = (semantic_ids * self.key).sum(dim=1)
 
-        candidates = torch.stack(
-            [self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids]
-        )
-        counts = torch.tensor(
-            [self.counts_dict[key.item()] for key in unique_ids], device=self.device
-        )
-        mask = torch.arange(candidates.shape[1], device=self.device).expand(
-            len(unique_ids), -1
-        ) < counts.view(-1, 1)
+        candidates = torch.stack([self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids])
+        counts = torch.tensor([self.counts_dict[key.item()] for key in unique_ids], device=self.device)
+        mask = torch.arange(candidates.shape[1], device=self.device).expand(len(unique_ids), -1) < counts.view(-1, 1)
 
         return candidates, mask
 
-    def get_pred_scores(
-        self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor
-    ) -> dict[str, torch.Tensor]:
+    def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param pred_residuals: [batch_size, emb_dim] предсказанные остатки
@@ -159,26 +120,19 @@ def get_pred_scores(
 
         candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_ids)
 
-        pred_scores = torch.einsum(
-            "njk,nk->nj", candidates, pred_residuals
-        ).masked_fill(~mask, -torch.inf)
+        pred_scores = torch.einsum('njk,nk->nj', candidates, pred_residuals).masked_fill(~mask, -torch.inf)
         pred_indices = torch.argmax(pred_scores, dim=1)
         pred_item_ids = torch.stack(
-            [
-                self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]]
-                for i in range(semantic_ids.shape[0])
-            ]
-        )
+            [self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]] for i in range(semantic_ids.shape[0])])
 
         return {
             "pred_scores_mask": mask,
             "pred_scores": pred_scores,
-            "pred_item_ids": pred_item_ids,
+            "pred_item_ids": pred_item_ids
         }
 
-    def get_true_dedup_tokens(
-        self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor
-    ) -> dict[str, torch.Tensor]:
+    def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[
+        str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param true_residuals: [batch_size, emb_dim] реальные остатки
@@ -200,11 +154,11 @@ def get_true_dedup_tokens(
 
         assert matches.any(dim=1).all(), "Не у всех батчей есть совпадение"
 
-        return {"true_dedup_tokens": true_dedup_tokens}
+        return {
+            "true_dedup_tokens": true_dedup_tokens
+        }
 
-    def get_item_ids_batch(
-        self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor
-    ) -> torch.Tensor:
+    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> torch.Tensor:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токенов решающего коллизии)
         :param dedup_tokens: [batch_size] токены решающие коллизии
@@ -220,10 +174,6 @@ def get_item_ids_batch(
         unique_ids = (semantic_ids * self.key).sum(dim=1)
 
         item_ids = torch.stack(
-            [
-                self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]]
-                for i in range(semantic_ids.shape[0])
-            ]
-        )
+            [self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])
 
         return item_ids
diff --git a/modeling/rqvae_utils/rqvae_data.py b/modeling/rqvae_utils/rqvae_data.py
index 5ffec08e..47692cae 100644
--- a/modeling/rqvae_utils/rqvae_data.py
+++ b/modeling/rqvae_utils/rqvae_data.py
@@ -1,11 +1,11 @@
-import gzip
+import pandas as pd
 import json
+import gzip
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 import random
 
-import pandas as pd
-import torch
 from tqdm import tqdm
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 tqdm.pandas()
 
@@ -76,7 +76,7 @@ def get_data(cached=True):
             df["embeddings"] = df["combined_text"].progress_apply(encode_text)
     else:
         df = torch.load("../data/Beauty/data_full.pt", weights_only=False)
-
+        
     return df
 
 
@@ -91,3 +91,6 @@ def search_similar_items(items_with_tuples, clust2search, max_cnt=5):
         if cnt >= max_cnt:
             return similars
     return similars
+
+
+
diff --git a/modeling/rqvae_utils/rqvae_test.py b/modeling/rqvae_utils/rqvae_test.py
index 03f8e3e8..611e7739 100644
--- a/modeling/rqvae_utils/rqvae_test.py
+++ b/modeling/rqvae_utils/rqvae_test.py
@@ -6,7 +6,6 @@
 from models import RqVaeModel
 from utils import DEVICE
 
-
 def test(a, b):
     cos_sim = torch.nn.functional.cosine_similarity(a, b, dim=0)
     norm_a = torch.norm(a, p=2)
@@ -14,7 +13,6 @@ def test(a, b):
     l2_dist = torch.norm(a - b, p=2) / (norm_a + norm_b + 1e-8)
     return cos_sim, l2_dist
 
-
 if __name__ == "__main__":
     config = json.load(open("../configs/train/tiger_train_config.json"))
     config = config["model"]
@@ -26,16 +24,14 @@ def test(a, b):
     )
     df = torch.load(config["embs_extractor_path"], weights_only=False)
     embeddings_array = np.stack(df["embeddings"].values)
-    tensor_embeddings = torch.tensor(
-        embeddings_array, dtype=torch.float32, device=DEVICE
-    )
-    inputs = {"embeddings": tensor_embeddings}
+    tensor_embeddings = torch.tensor(embeddings_array, dtype=torch.float32, device=DEVICE)
+    inputs = {'embeddings': tensor_embeddings}
 
     rqvae_model.eval()
     sem_ids, residuals = rqvae_model.forward(inputs)
     scores = residuals.detach()
     print(torch.norm(residuals, p=2, dim=1).median())
-    for i, codebook in enumerate(rqvae_model.codebooks):
+    for (i, codebook) in enumerate(rqvae_model.codebooks):
         scores += codebook[sem_ids[:, i]].detach()
     decoder_output = rqvae_model.decoder(scores.detach()).detach()
 
@@ -45,14 +41,11 @@ def test(a, b):
     print("косинусное расстояние", cos_sim)
     print("евклидово расстояние", l2_dist)
 
-    cos_sim = torch.nn.functional.cosine_similarity(
-        tensor_embeddings, decoder_output, dim=1
-    )
+    cos_sim = torch.nn.functional.cosine_similarity(tensor_embeddings, decoder_output, dim=1)
     print("косинусное расстояние", cos_sim.mean(), cos_sim.min(), cos_sim.max())
 
-    norm_a = torch.norm(tensor_embeddings, p=2, dim=1)
-    norm_b = torch.norm(decoder_output, p=2, dim=1)
-    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim=1) / (
-        norm_a + norm_b + 1e-8
-    )
-    print("евклидово расстояние", l2_dist.median(), l2_dist.min(), l2_dist.max())
+    norm_a = torch.norm(tensor_embeddings, p=2, dim = 1)
+    norm_b = torch.norm(decoder_output, p=2, dim = 1)
+    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim = 1) / (norm_a + norm_b + 1e-8)
+    print("евклидово расстояние",l2_dist.median(), l2_dist.min(), l2_dist.max())
+
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 120f9a80..5a09dcb4 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -10,21 +10,14 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = (
-            embedding_table  # (semantic_id_len, codebook_size, emb_dim)
-        )
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(
-        self,
-        semantic_ids: torch.Tensor,
-        residuals: torch.Tensor,
-        item_ids: torch.Tensor,
-        sum_with_residuals: bool = True,
-    ) -> None:
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor,
+                             sum_with_residuals: bool = True) -> None:
         """
         :param sum_with_residuals: флаг, отвечающий за то учитывать ли остатки при выборе кандидатов
         :param semantic_ids: (sem_ids_count, sem_id_len)
@@ -37,11 +30,9 @@ def build_tree_structure(
         assert item_ids.shape == (self.sem_ids_count,)
 
         semantic_ids = semantic_ids.to(self.device)
-        residuals = (
-            residuals.to(self.device).float()
-            if sum_with_residuals
-            else torch.zeros_like(residuals, device=self.device, dtype=torch.float)
-        )
+        residuals = residuals.to(self.device).float() if sum_with_residuals else torch.zeros_like(residuals,
+                                                                                                  device=self.device,
+                                                                                                  dtype=torch.float)
         self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
         self.item_ids = item_ids
 
@@ -53,17 +44,14 @@ def calculate_full(self, sem_ids: torch.Tensor) -> torch.Tensor:
         assert sem_ids.shape[1] == self.sem_id_len
         sem_ids = sem_ids.to(self.device)
 
-        expanded_emb_table = self.embedding_table.unsqueeze(0).expand(
-            sem_ids.shape[0], -1, -1, -1
-        )  # (count, sem_id_len, codebook_size, emb_dim)
+        expanded_emb_table = (self.embedding_table.unsqueeze(0)
+                              .expand(sem_ids.shape[0], -1, -1, -1))  # (count, sem_id_len, codebook_size, emb_dim)
 
-        index = (
-            sem_ids.unsqueeze(-1).expand(-1, -1, self.emb_dim).unsqueeze(2)
-        )  # (count, sem_id_len, 1, emb_dim)
+        index = (sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (count, sem_id_len, 1, emb_dim)
 
-        return (
-            torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)
-        )  # (count, emb_dim)
+        return torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)  # (count, emb_dim)
 
     def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Tensor:
         """
@@ -75,21 +63,14 @@ def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Ten
         assert 0 < items_to_query <= self.sem_ids_count
 
         request_sem_ids = request_sem_ids.to(self.device)
-        request_embeddings = self.calculate_full(
-            request_sem_ids
-        )  # (batch_size, emb_dim)
+        request_embeddings = self.calculate_full(request_sem_ids)  # (batch_size, emb_dim)
 
-        request_embeddings = request_embeddings.unsqueeze(1).expand(
-            -1, self.sem_ids_count, -1
-        )  # (batch_size, sem_ids_count, emb_dim)
+        request_embeddings = (request_embeddings.unsqueeze(1)
+                              .expand(-1, self.sem_ids_count, -1))  # (batch_size, sem_ids_count, emb_dim)
 
-        diff_norm = torch.norm(
-            self.full_embeddings - request_embeddings, p=2, dim=2
-        )  # (batch_size, sem_ids_count)
+        diff_norm = torch.norm(self.full_embeddings - request_embeddings, p=2, dim=2)  # (batch_size, sem_ids_count)
 
-        indices = torch.argsort(diff_norm, descending=False, dim=1)[
-            :, :items_to_query
-        ]  # (batch_size, k)
+        indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :items_to_query]  # (batch_size, k)
         return self.item_ids[indices]
 
     def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
@@ -103,21 +84,18 @@ def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         assert 0 < k <= self.sem_ids_count
         request_sem_ids = request_sem_ids.to(self.device)
 
-        index = (
-            request_sem_ids.unsqueeze(-1).expand(-1, -1, self.emb_dim).unsqueeze(2)
-        )  # (batch_size, sem_id_len, 1, emb_dim)
+        index = (request_sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (batch_size, sem_id_len, 1, emb_dim)
 
         request_embeddings = torch.gather(
-            input=self.embedding_table.unsqueeze(0).expand(
-                request_sem_ids.shape[0], -1, -1, -1
-            ),
+            input=self.embedding_table.unsqueeze(0).expand(request_sem_ids.shape[0], -1, -1, -1),
             dim=2,
-            index=index,
+            index=index
         ).sum(1)  # (batch_size, emb_dim)
 
-        diff_norm = torch.cdist(
-            self.full_embeddings, request_embeddings.unsqueeze(1), p=2
-        ).squeeze(1)  # (batch_size, sem_ids_count)
+        diff_norm = torch.cdist(self.full_embeddings, request_embeddings.unsqueeze(1), p=2).squeeze(
+            1)  # (batch_size, sem_ids_count)
 
         _, indices = torch.topk(diff_norm, k=k, dim=1, largest=False)  # (batch_size, k)
         return self.item_ids[indices.squeeze(-1)]
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index 336f6dbe..b09cd049 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -11,9 +11,7 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = (
-            embedding_table  # (semantic_id_len, codebook_size, emb_dim)
-        )
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.key: torch.Tensor = torch.empty((0, 0))
         self.A: torch.Tensor = torch.empty((0, 0))  # будет (max_sem_id, )
@@ -22,12 +20,7 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         self.sids: torch.Tensor = torch.empty((0, 0))  # будет (sem_id_len, )
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(
-        self,
-        semantic_ids: torch.Tensor,
-        residuals: torch.Tensor,
-        item_ids: torch.Tensor,
-    ):
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor):
         """
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
@@ -41,41 +34,22 @@ def build_tree_structure(
         assert item_ids.shape == (self.sem_ids_count,)
 
         self.item_ids = item_ids
-        self.key = torch.tensor(
-            [self.codebook_size**i for i in range(self.sem_id_len - 1, -1, -1)],
-            dtype=torch.long,
-            device=self.device,
-        )
+        self.key = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len - 1, -1, -1)],
+                                dtype=torch.long, device=self.device)
         self.sids = self.get_sids(semantic_ids.float())  # (sem_id_len, )
         self.sem_ids_embs = self.calculate_full(semantic_ids, residuals)
 
-        result = torch.full(
-            size=[self.codebook_size**self.sem_id_len],
-            fill_value=0,
-            dtype=torch.int64,
-            device=self.device,
-        )
+        result = torch.full(size=[self.codebook_size ** self.sem_id_len], fill_value=0, dtype=torch.int64,
+                            device=self.device)
         temp_unique_id = self.sids * self.codebook_size
-        temp_sem_ids = torch.concat(
-            [
-                semantic_ids,
-                torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1),
-            ],
-            dim=-1,
-        )
+        temp_sem_ids = torch.concat([semantic_ids, torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1)],
+                                    dim=-1)
 
         for i in range(0, self.sem_id_len + 1):
-            temp_unique_id = (
-                temp_unique_id
-                - (self.codebook_size**i) * temp_sem_ids[:, self.sem_id_len - i]
-            )
-            temp_unique_ids, temp_inverse_indices = torch.unique(
-                temp_unique_id, return_inverse=True
-            )
+            temp_unique_id = temp_unique_id - (self.codebook_size ** i) * temp_sem_ids[:, self.sem_id_len - i]
+            temp_unique_ids, temp_inverse_indices = torch.unique(temp_unique_id, return_inverse=True)
             temp_counts = torch.bincount(temp_inverse_indices)
-            truncated_ids = torch.floor_divide(
-                input=temp_unique_id, other=(self.codebook_size ** (i + 1))
-            ).long()
+            truncated_ids = torch.floor_divide(input=temp_unique_id, other=(self.codebook_size ** (i + 1))).long()
             result[truncated_ids] = temp_counts[temp_inverse_indices]
 
         self.A = result
@@ -90,21 +64,13 @@ def get_counts(self, sem_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]
         offsets = torch.arange(self.sem_id_len + 1, device=self.device)
         i = torch.arange(self.sem_id_len, device=self.device)
 
-        mask_sem = (
-            i < (self.sem_id_len - offsets.unsqueeze(1))
-        ).long()  # (sem_id_len + 1, sem_id_len)
+        mask_sem = (i < (self.sem_id_len - offsets.unsqueeze(1))).long()  # (sem_id_len + 1, sem_id_len)
         divs = torch.pow(self.codebook_size, offsets)  # (sem_id_len + 1,)
 
-        C = (
-            sem_ids.unsqueeze(1)
-            * mask_sem.unsqueeze(0)
-            * self.key.unsqueeze(0).unsqueeze(1)
-        ).sum(dim=-1)
+        C = (sem_ids.unsqueeze(1) * mask_sem.unsqueeze(0) * self.key.unsqueeze(0).unsqueeze(1)).sum(dim=-1)
         B = C // divs.unsqueeze(0)
 
-        return C, self.A[
-            B
-        ]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
+        return C, self.A[B]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
 
     def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
         """
@@ -112,11 +78,9 @@ def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
         :return: хэши sem_ids (sem_id_count,)
         """
         assert sem_ids.shape[1] == self.sem_id_len
-        return torch.einsum("nc,c->n", sem_ids, self.key.float())  # (sem_ids_count,)
+        return torch.einsum('nc,c->n', sem_ids, self.key.float())  # (sem_ids_count,)
 
-    def calc_ol(
-        self, batch_ids: torch.Tensor, k: int
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    def calc_ol(self, batch_ids: torch.Tensor, k: int) -> tuple[torch.Tensor, torch.Tensor]:
         """
         :param batch_ids: (batch_size, sem_id_len)
         :param k: int
@@ -130,8 +94,7 @@ def calc_ol(
         gather_ol = torch.gather(c, dim=1, index=ol.unsqueeze(1)).squeeze()  # (bs,)
 
         mask_ol = (gather_ol.unsqueeze(-1) <= self.sids) & (
-            self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1)
-        )
+                self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1))
         return ol, mask_ol  # (bs,) (bs, sem_ids_count)
 
     def calc_il(self, batch_ids, k):
@@ -145,27 +108,19 @@ def calc_il(self, batch_ids, k):
 
         batch_dim = batch_ids.shape[0]
         c, a = self.get_counts(batch_ids)
-        extended_c = torch.concat(
-            [torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c],
-            dim=1,
-        )
+        extended_c = torch.concat([torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c], dim=1)
 
         il = torch.argmax((a > k).long(), dim=-1) - 1  # (bs,)
-        gather_il = torch.gather(
-            extended_c, dim=1, index=(il + 1).unsqueeze(1)
-        ).squeeze()  # (bs,)
+        gather_il = torch.gather(extended_c, dim=1, index=(il + 1).unsqueeze(1)).squeeze()  # (bs,)
 
         mask_il = (gather_il.unsqueeze(-1) <= self.sids) & (
-            self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1)
-        )
+                self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1))
         return il, mask_il  # (bs,) (bs, sem_ids_count)
 
     def get_repeated_sids(self, k: int) -> torch.Tensor:
         return self.sids.repeat(k, 1)  # (k, sem_ids_count)
 
-    def get_request_embeddings(
-        self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor
-    ) -> torch.Tensor:
+    def get_request_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
         """
         :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
         :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
@@ -174,16 +129,11 @@ def get_request_embeddings(
         assert decomposed_embeddings.shape[1:] == (self.sem_id_len + 1, self.emb_dim)
         assert levels.shape == (decomposed_embeddings.shape[0],)
 
-        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(
-            self.sem_id_len + 2, 0, -1, device=self.device
-        ).unsqueeze(1)
-        return torch.sum(
-            decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1
-        )  # (bs, emb_dim)
+        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(self.sem_id_len + 2, 0, -1,
+                                                                                        device=self.device).unsqueeze(1)
+        return torch.sum(decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1)  # (bs, emb_dim)
 
-    def calculate_full(
-        self, sem_ids: torch.Tensor, residuals: torch.Tensor
-    ) -> torch.Tensor:
+    def calculate_full(self, sem_ids: torch.Tensor, residuals: torch.Tensor) -> torch.Tensor:
         """
         :param sem_ids: sem_ids (count, sem_id_len)
         :param residuals: остатки для каждого sem_id (count, emb_dim)
@@ -195,56 +145,31 @@ def calculate_full(
 
         count = residuals.shape[0]
         index = sem_ids.view(count, -1, 1, 1).expand(-1, -1, -1, self.emb_dim)
-        embs = torch.gather(
-            input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1),
-            dim=2,
-            index=index,
-        )  # expand бесплатный по памяти
-        decomposed_embs = torch.concat(
-            [embs.squeeze(2), residuals.unsqueeze(1)], dim=1
-        )  # (sem_ids_count, emb_dim)
-
-        assert decomposed_embs.shape == (
-            sem_ids.shape[0],
-            self.sem_id_len + 1,
-            self.emb_dim,
-        )
+        embs = torch.gather(input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1), dim=2,
+                            index=index)  # expand бесплатный по памяти
+        decomposed_embs = torch.concat([embs.squeeze(2), residuals.unsqueeze(1)], dim=1)  # (sem_ids_count, emb_dim)
+
+        assert decomposed_embs.shape == (sem_ids.shape[0], self.sem_id_len + 1, self.emb_dim)
         return decomposed_embs
 
-    def calculate_level_embeddings(
-        self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor
-    ) -> torch.Tensor:
+    def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
         """
         :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
         :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
         :return: эмбеддинги для всех sem_ids для нужных глубин (batch_size, sem_ids_count, emb_dim)
         """
-        assert decomposed_embeddings.shape == (
-            self.sem_ids_count,
-            self.sem_id_len + 1,
-            self.emb_dim,
-        )
-
-        mask = (
-            torch.arange(1, self.sem_id_len + 2, device=self.device)
-            >= torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)
-        ).float()
+        assert decomposed_embeddings.shape == (self.sem_ids_count, self.sem_id_len + 1, self.emb_dim)
+
+        mask = (torch.arange(1, self.sem_id_len + 2, device=self.device) >=
+                torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)).float()
         sids_mask = mask[levels + 1].unsqueeze(-1)  # (batch_size, sem_id_len + 1, 1)
-        return torch.einsum(
-            "nld,bld->bnd", decomposed_embeddings, sids_mask
-        )  # (batch_size, sem_ids_count, emb_dim)
+        return torch.einsum('nld,bld->bnd', decomposed_embeddings, sids_mask)  # (batch_size, sem_ids_count, emb_dim)
 
     def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        return torch.where(
-            mask, result, torch.tensor(float("-inf"), device=self.device)
-        )
-
-    def query(
-        self,
-        request_sem_ids: torch.Tensor,
-        request_residuals: torch.Tensor,
-        items_to_query: int,
-    ) -> torch.Tensor:
+        return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
+
+    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor,
+              items_to_query: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
         :param request_residuals: батч из остатков (batch_size, emb_dim)
@@ -272,31 +197,13 @@ def query(
         ol_request_embeddings = self.get_request_embeddings(request_embs, ol)
         il_request_embeddings = self.get_request_embeddings(request_embs, il)
 
-        ol_scores = (
-            torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1))
-            .squeeze(-1)
-            .detach()
-            .cpu()
-        )
-
-        il_scores = (
-            torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1))
-            .squeeze(-1)
-            .detach()
-            .cpu()
-        )
-
-        ids = np.lexsort(
-            keys=(
-                -torch.cat([il_scores, ol_scores], dim=1),
-                ~torch.cat(
-                    [torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1
-                ),
-                ~torch.cat([il_mask, ol_mask], dim=1),
-            )
-        )
-
-        ids = (ids % self.sem_ids_count)[:, : self.sem_ids_count][
-            :, :items_to_query
-        ]  # (batch_size, k)
+        ol_scores = torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
+
+        il_scores = torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
+
+        ids = np.lexsort(keys=(-torch.cat([il_scores, ol_scores], dim=1),
+                               ~torch.cat([torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1),
+                               ~torch.cat([il_mask, ol_mask], dim=1)))
+
+        ids = (ids % self.sem_ids_count)[:, :self.sem_ids_count][:, :items_to_query]  # (batch_size, k)
         return self.item_ids[ids]
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index f73db7c6..3f1d3f38 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -6,20 +6,20 @@
 import torch
 
 from models.rqvae import RqVaeModel
-from rqvae_utils import SimplifiedTree, Tree, Trie
+from rqvae_utils import Trie, SimplifiedTree, Tree
 from utils import DEVICE
 
 
 def memory_stats(k):
     process = psutil.Process(os.getpid())
-    memory_usage = process.memory_info().rss / 1024**2
+    memory_usage = process.memory_info().rss / 1024 ** 2
     print(f"{k}. Использование памяти: {memory_usage:.2f} MB")
 
 
 def calc_sid(sid, codebook_size):
     res = sid[-1]
     for i in range(1, sid.shape[0]):
-        res += sid[-i - 1] * (codebook_size**i)
+        res += sid[-i - 1] * (codebook_size ** i)
     return res
 
 
@@ -41,7 +41,9 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     )
     rqvae_model.eval()
 
-    emb_table = torch.stack([cb for cb in rqvae_model.codebooks]).to(DEVICE)
+    emb_table = torch.stack(
+        [cb for cb in rqvae_model.codebooks]
+    ).to(DEVICE)
 
     trie = Trie(rqvae_model)
     tree = Tree(rqvae_model, DEVICE)
@@ -71,18 +73,14 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     now = time.time()
     simplified_tree_wr.build_tree_structure(semantic_ids, residuals, item_ids, False)
-    print(
-        f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms"
-    )
+    print(f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms")
 
     full_embeddings = tree.calculate_full(semantic_ids, residuals).sum(1)
     print(torch.all((full_embeddings == simplified_tree.full_embeddings) == True))
 
     items_to_query = 20
     batch_size = 256
-    q_semantic_ids = torch.randint(
-        0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE
-    )
+    q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE)
     q_residuals = torch.randn(batch_size, embedding_dim).to(DEVICE)
 
     total_time = 0
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 4a4ca5b7..b7971e86 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -2,7 +2,6 @@
 import time
 
 import torch
-
 from models.rqvae import RqVaeModel
 from utils import DEVICE
 
diff --git a/modeling/train.py b/modeling/train.py
index 4919be6f..d865620d 100644
--- a/modeling/train.py
+++ b/modeling/train.py
@@ -1,32 +1,23 @@
-import copy
-import json
-import os
-
-import torch
-
 import utils
+from utils import parse_args, create_logger, DEVICE, fix_random_seed
+
 from callbacks import BaseCallback
-from dataloader import BaseDataloader
 from dataset import BaseDataset
+from dataloader import BaseDataloader
 from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
-from utils import DEVICE, create_logger, fix_random_seed, parse_args
+
+import copy
+import json
+import os
+import torch
 
 logger = create_logger(name=__name__)
 seed_val = 42
 
 
-def train(
-    dataloader,
-    model,
-    optimizer,
-    loss_function,
-    callback,
-    epoch_cnt=None,
-    step_cnt=None,
-    best_metric=None,
-):
+def train(dataloader, model, optimizer, loss_function, callback, epoch_cnt=None, step_cnt=None, best_metric=None):
     step_num = 0
     epoch_num = 0
     current_metric = 0
@@ -36,20 +27,14 @@ def train(
     best_epoch = 0
     best_checkpoint = None
 
-    logger.debug("Start training...")
+    logger.debug('Start training...')
 
-    while (epoch_cnt is None or epoch_num < epoch_cnt) and (
-        step_cnt is None or step_num < step_cnt
-    ):
+    while (epoch_cnt is None or epoch_num < epoch_cnt) and (step_cnt is None or step_num < step_cnt):
         if best_epoch + epochs_threshold < epoch_num:
-            logger.debug(
-                "There is no progress during {} epochs. Finish training".format(
-                    epochs_threshold
-                )
-            )
+            logger.debug('There is no progress during {} epochs. Finish training'.format(epochs_threshold))
             break
 
-        logger.debug(f"Start epoch {epoch_num}")
+        logger.debug(f'Start epoch {epoch_num}')
         for step, batch in enumerate(dataloader):
             batch_ = copy.deepcopy(batch)
 
@@ -69,18 +54,14 @@ def train(
                 # Take the last model
                 best_checkpoint = copy.deepcopy(model.state_dict())
                 best_epoch = epoch_num
-            elif (
-                best_checkpoint is None
-                or best_metric in batch_
-                and current_metric <= batch_[best_metric]
-            ):
+            elif best_checkpoint is None or best_metric in batch_ and current_metric <= batch_[best_metric]:
                 # If it is the first checkpoint, or it is the best checkpoint
                 current_metric = batch_[best_metric]
                 best_checkpoint = copy.deepcopy(model.state_dict())
                 best_epoch = epoch_num
 
         epoch_num += 1
-    logger.debug("Training procedure has been finished!")
+    logger.debug('Training procedure has been finished!')
     return best_checkpoint
 
 
@@ -88,54 +69,59 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(
-        config["experiment_name"]
-    )
+    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = \
+        utils.tensorboards.TensorboardWriter(config['experiment_name'])
 
-    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
-    logger.debug("Current DEVICE: {}".format(DEVICE))
+    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
+    logger.debug('Current DEVICE: {}'.format(DEVICE))
 
-    dataset = BaseDataset.create_from_config(config["dataset"])
+    dataset = BaseDataset.create_from_config(config['dataset'])
 
     train_sampler, validation_sampler, test_sampler = dataset.get_samplers()
 
     train_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
+        config['dataloader']['train'],
+        dataset=train_sampler,
+        **dataset.meta
     )
 
     validation_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=validation_sampler, **dataset.meta
+        config['dataloader']['validation'],
+        dataset=validation_sampler,
+        **dataset.meta
     )
 
     eval_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=test_sampler, **dataset.meta
+        config['dataloader']['validation'],
+        dataset=test_sampler,
+        **dataset.meta
     )
 
-    model = BaseModel.create_from_config(config["model"], **dataset.meta).to(DEVICE)
-    if "checkpoint" in config:
-        checkpoint_path = os.path.join("../checkpoints", f"{config['checkpoint']}.pth")
-        logger.debug("Loading checkpoint from {}".format(checkpoint_path))
+    model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
+    if 'checkpoint' in config:
+        checkpoint_path = os.path.join('../checkpoints', f'{config["checkpoint"]}.pth')
+        logger.debug('Loading checkpoint from {}'.format(checkpoint_path))
         checkpoint = torch.load(checkpoint_path)
         print(checkpoint.keys())
         model.load_state_dict(checkpoint)
 
-    loss_function = BaseLoss.create_from_config(config["loss"])
+    loss_function = BaseLoss.create_from_config(config['loss'])
 
-    optimizer = BaseOptimizer.create_from_config(config["optimizer"], model=model)
+    optimizer = BaseOptimizer.create_from_config(config['optimizer'], model=model)
 
     callback = BaseCallback.create_from_config(
-        config["callback"],
+        config['callback'],
         model=model,
         train_dataloader=train_dataloader,
         validation_dataloader=validation_dataloader,
         eval_dataloader=eval_dataloader,
         optimizer=optimizer,
-        **dataset.meta,
+        **dataset.meta
     )
 
     # TODO add verbose option for all callbacks, multiple optimizer options (???)
     # TODO create pre/post callbacks
-    logger.debug("Everything is ready for training process!")
+    logger.debug('Everything is ready for training process!')
 
     # Train process
     _ = train(
@@ -144,18 +130,16 @@ def main():
         optimizer=optimizer,
         loss_function=loss_function,
         callback=callback,
-        epoch_cnt=config.get("train_epochs_num"),
-        step_cnt=config.get("train_steps_num"),
-        best_metric=config.get("best_metric"),
+        epoch_cnt=config.get('train_epochs_num'),
+        step_cnt=config.get('train_steps_num'),
+        best_metric=config.get('best_metric')
     )
 
-    logger.debug("Saving model...")
-    checkpoint_path = "../checkpoints/{}_final_state.pth".format(
-        config["experiment_name"]
-    )
+    logger.debug('Saving model...')
+    checkpoint_path = '../checkpoints/{}_final_state.pth'.format(config['experiment_name'])
     torch.save(model.state_dict(), checkpoint_path)
-    logger.debug("Saved model as {}".format(checkpoint_path))
+    logger.debug('Saved model as {}'.format(checkpoint_path))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/train_multiple.py b/modeling/train_multiple.py
index 8c118584..c42f77a6 100644
--- a/modeling/train_multiple.py
+++ b/modeling/train_multiple.py
@@ -1,26 +1,20 @@
 import itertools
 import json
 import random
-
 import torch
 
 import utils
+from utils import parse_args, create_logger, DEVICE, Params, dict_to_str, fix_random_seed
+
+from train import train
+from infer import inference
+
 from callbacks import BaseCallback, EvalCallback, ValidationCallback
-from dataloader import BaseDataloader
 from dataset import BaseDataset
-from infer import inference
+from dataloader import BaseDataloader
 from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
-from train import train
-from utils import (
-    DEVICE,
-    Params,
-    create_logger,
-    dict_to_str,
-    fix_random_seed,
-    parse_args,
-)
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -30,23 +24,24 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
 
-    dataset_params = Params(config["dataset"], config["dataset_params"])
-    model_params = Params(config["model"], config["model_params"])
-    loss_function_params = Params(config["loss"], config["loss_params"])
-    optimizer_params = Params(config["optimizer"], config["optimizer_params"])
+    dataset_params = Params(config['dataset'], config['dataset_params'])
+    model_params = Params(config['model'], config['model_params'])
+    loss_function_params = Params(config['loss'], config['loss_params'])
+    optimizer_params = Params(config['optimizer'], config['optimizer_params'])
 
-    logger.debug("Everything is ready for training process!")
+    logger.debug('Everything is ready for training process!')
 
-    start_from = config.get("start_from", 0)
-    num = config.get("num_exps", None)
+    start_from = config.get('start_from', 0)
+    num = config.get('num_exps', None)
 
-    list_of_params = list(
-        itertools.product(
-            dataset_params, model_params, loss_function_params, optimizer_params
-        )
-    )
+    list_of_params = list(itertools.product(
+        dataset_params,
+        model_params,
+        loss_function_params,
+        optimizer_params
+    ))
 
     if num is None:
         num = len(list_of_params)
@@ -55,61 +50,59 @@ def main():
 
     cnt = 0
 
-    for dataset_param, model_param, loss_param, optimizer_param in list_of_params[
-        start_from:num
-    ]:
+    for dataset_param, model_param, loss_param, optimizer_param in list_of_params[start_from:num]:
         cnt += 1
         if cnt < start_from:
             continue
 
-        model_name = "_".join(
-            [
-                config["experiment_name"],
-                dict_to_str(dataset_param, config["dataset_params"]),
-                dict_to_str(model_param, config["model_params"]),
-                dict_to_str(loss_param, config["loss_params"]),
-                dict_to_str(optimizer_param, config["optimizer_params"]),
-            ]
-        )
+        model_name = '_'.join([
+            config['experiment_name'],
+            dict_to_str(dataset_param, config['dataset_params']),
+            dict_to_str(model_param, config['model_params']),
+            dict_to_str(loss_param, config['loss_params']),
+            dict_to_str(optimizer_param, config['optimizer_params'])
+        ])
 
-        logger.debug("Starting {}".format(model_name))
+        logger.debug('Starting {}'.format(model_name))
 
         dataset = BaseDataset.create_from_config(dataset_param)
 
         train_sampler, validation_sampler, eval_sampler = dataset.get_samplers()
 
         train_dataloader = BaseDataloader.create_from_config(
-            config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
+            config['dataloader']['train'],
+            dataset=train_sampler,
+            **dataset.meta
         )
 
         validation_dataloader = BaseDataloader.create_from_config(
-            config["dataloader"]["validation"],
+            config['dataloader']['validation'],
             dataset=validation_sampler,
-            **dataset.meta,
+            **dataset.meta
         )
 
         eval_dataloader = BaseDataloader.create_from_config(
-            config["dataloader"]["validation"], dataset=eval_sampler, **dataset.meta
+            config['dataloader']['validation'],
+            dataset=eval_sampler,
+            **dataset.meta
         )
 
         if utils.tensorboards.GLOBAL_TENSORBOARD_WRITER is not None:
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.close()
-        utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = (
-            utils.tensorboards.TensorboardWriter(model_name, use_time=False)
-        )
+        utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(model_name, use_time=False)
 
         model = BaseModel.create_from_config(model_param, **dataset.meta).to(DEVICE)
         loss_function = BaseLoss.create_from_config(loss_param)
         optimizer = BaseOptimizer.create_from_config(optimizer_param, model=model)
 
         callback = BaseCallback.create_from_config(
-            config["callback"],
+            config['callback'],
             model=model,
             train_dataloader=train_dataloader,
             validation_dataloader=validation_dataloader,
             eval_dataloader=eval_dataloader,
             optimizer=optimizer,
-            **dataset.meta,
+            **dataset.meta
         )
 
         best_model_checkpoint = train(
@@ -118,13 +111,11 @@ def main():
             optimizer=optimizer,
             loss_function=loss_function,
             callback=callback,
-            epoch_cnt=config.get("train_epochs_num"),
-            best_metric=config.get("best_metric"),
+            epoch_cnt=config.get('train_epochs_num'),
+            best_metric=config.get('best_metric')
         )
 
-        eval_model = BaseModel.create_from_config(model_param, **dataset.meta).to(
-            DEVICE
-        )
+        eval_model = BaseModel.create_from_config(model_param, **dataset.meta).to(DEVICE)
         eval_model.load_state_dict(best_model_checkpoint)
 
         for cl in callback._callbacks:
@@ -145,11 +136,11 @@ def main():
 
         inference(eval_dataloader, eval_model, metrics, pred_prefix, labels_prefix)
 
-        logger.debug("Saving best model checkpoint...")
-        checkpoint_path = "../checkpoints/{}_final_state.pth".format(model_name)
+        logger.debug('Saving best model checkpoint...')
+        checkpoint_path = '../checkpoints/{}_final_state.pth'.format(model_name)
         torch.save(best_model_checkpoint, checkpoint_path)
-        logger.debug("Saved model as {}".format(checkpoint_path))
+        logger.debug('Saved model as {}'.format(checkpoint_path))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 1d62369e..8b96ac52 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -1,37 +1,39 @@
-import argparse
+from utils.registry import MetaParent
+from utils.grid_search import Params
+import utils.tensorboards
+
 import json
-import logging
 import random
-
+import logging
+import argparse
 import numpy as np
-import torch
 
-import utils.tensorboards
-from utils.grid_search import Params
-from utils.registry import MetaParent
+import torch
 
 if torch.cuda.is_available():
-    DEVICE = torch.device("cuda")
+    DEVICE = torch.device('cuda:0')
+# elif torch.backends.mps.is_available():
+#     DEVICE = torch.device("mps:0")
 else:
-    DEVICE = torch.device("cpu")
+    DEVICE = torch.device('cpu')
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--params", required=True)
+    parser.add_argument('--params', required=True)
     args = parser.parse_args()
 
     with open(args.params) as f:
         params = json.load(f)
-
+    
     return params
 
 
 def create_logger(
-    name,
-    level=logging.DEBUG,
-    format="[%(asctime)s] [%(levelname)s]: %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
+        name,
+        level=logging.DEBUG,
+        format='[%(asctime)s] [%(levelname)s]: %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S'
 ):
     logging.basicConfig(level=level, format=format, datefmt=datefmt)
     logger = logging.getLogger(name)
@@ -52,31 +54,26 @@ def maybe_to_list(values):
 
 
 def get_activation_function(name, **kwargs):
-    if name == "relu":
+    if name == 'relu':
         return torch.nn.ReLU()
-    elif name == "gelu":
+    elif name == 'gelu':
         return torch.nn.GELU()
-    elif name == "elu":
-        return torch.nn.ELU(alpha=float(kwargs.get("alpha", 1.0)))
-    elif name == "leaky":
-        return torch.nn.LeakyReLU(
-            negative_slope=float(kwargs.get("negative_slope", 1e-2))
-        )
-    elif name == "sigmoid":
+    elif name == 'elu':
+        return torch.nn.ELU(alpha=float(kwargs.get('alpha', 1.0)))
+    elif name == 'leaky':
+        return torch.nn.LeakyReLU(negative_slope=float(kwargs.get('negative_slope', 1e-2)))
+    elif name == 'sigmoid':
         return torch.nn.Sigmoid()
-    elif name == "tanh":
+    elif name == 'tanh':
         return torch.nn.Tanh()
-    elif name == "softmax":
+    elif name == 'softmax':
         return torch.nn.Softmax()
-    elif name == "softplus":
-        return torch.nn.Softplus(
-            beta=int(kwargs.get("beta", 1.0)),
-            threshold=int(kwargs.get("threshold", 20)),
-        )
-    elif name == "softmax_logit":
+    elif name == 'softplus':
+        return torch.nn.Softplus(beta=int(kwargs.get('beta', 1.0)), threshold=int(kwargs.get('threshold', 20)))
+    elif name == 'softmax_logit':
         return torch.nn.LogSoftmax()
     else:
-        raise ValueError("Unknown activation function name `{}`".format(name))
+        raise ValueError('Unknown activation function name `{}`'.format(name))
 
 
 def dict_to_str(x, params):
@@ -85,21 +82,19 @@ def dict_to_str(x, params):
         if k in params:
             if isinstance(v, dict):
                 # part = '_'.join([f'{k}-{sub_part}' for sub_part in dict_to_str(v, params[k]).split('_')])
-                part = "_".join(
-                    [f"{sub_part}" for sub_part in dict_to_str(v, params[k]).split("_")]
-                )
+                part = '_'.join([f'{sub_part}' for sub_part in dict_to_str(v, params[k]).split('_')])
             elif isinstance(v, tuple) or isinstance(v, list):
                 sub_strings = []
                 for i, sub_value in enumerate(v):
-                    sub_strings.append(f"({i})_{dict_to_str(v[i], params[k][i])}")
-                part = f"({'_'.join(sub_strings)})"
+                    sub_strings.append(f'({i})_{dict_to_str(v[i], params[k][i])}')
+                part = f'({"_".join(sub_strings)})'
             else:
                 # part = f'{k}-{v}'
-                part = f"{v}"
+                part = f'{v}'
             parts.append(part)
         else:
             continue
-    return "_".join(parts).replace(".", "-")
+    return '_'.join(parts).replace('.', '-')
 
 
 def create_masked_tensor(data, lengths):
@@ -108,22 +103,20 @@ def create_masked_tensor(data, lengths):
 
     if len(data.shape) == 1:  # only indices
         padded_tensor = torch.zeros(
-            batch_size, max_sequence_length, dtype=data.dtype, device=DEVICE
+            batch_size, max_sequence_length,
+            dtype=data.dtype, device=DEVICE
         )  # (batch_size, max_seq_len)
     else:
         assert len(data.shape) == 2  # embeddings
         padded_tensor = torch.zeros(
-            batch_size,
-            max_sequence_length,
-            data.shape[-1],
-            dtype=data.dtype,
-            device=DEVICE,
+            batch_size, max_sequence_length, data.shape[-1],
+            dtype=data.dtype, device=DEVICE
         )  # (batch_size, max_seq_len, emb_dim)
 
-    mask = (
-        torch.arange(end=max_sequence_length, device=DEVICE)[None].tile([batch_size, 1])
-        < lengths[:, None]
-    )  # (batch_size, max_seq_len)
+    mask = torch.arange(
+        end=max_sequence_length,
+        device=DEVICE
+    )[None].tile([batch_size, 1]) < lengths[:, None]  # (batch_size, max_seq_len)
 
     padded_tensor[mask] = data
 
diff --git a/modeling/utils/grid_search.py b/modeling/utils/grid_search.py
index d7330421..268d9458 100644
--- a/modeling/utils/grid_search.py
+++ b/modeling/utils/grid_search.py
@@ -3,6 +3,7 @@
 
 
 class Params:
+
     def __init__(self, config, params):
         self._initial_config = copy.deepcopy(config)
         self._initial_params = copy.deepcopy(params)
@@ -11,9 +12,7 @@ def __iter__(self):
         keys = []
         values = []
 
-        all_keys = set(self._initial_config.keys()).union(
-            set(self._initial_params.keys())
-        )
+        all_keys = set(self._initial_config.keys()).union(set(self._initial_params.keys()))
 
         for field_name in all_keys:
             keys.append(field_name)
@@ -22,28 +21,18 @@ def __iter__(self):
             params_fields_value = self._initial_params.get(field_name)
 
             if initial_field_value:
-                if (
-                    params_fields_value is None
-                ):  # We don't want to iterate through this field
+                if params_fields_value is None:  # We don't want to iterate through this field
                     values.append([initial_field_value])
-                elif isinstance(initial_field_value, list) and isinstance(
-                    initial_field_value, list
-                ):
+                elif isinstance(initial_field_value, list) and isinstance(initial_field_value, list):
                     assert len(initial_field_value) == len(params_fields_value)
                     list_values = []
                     for i in range(len(initial_field_value)):
-                        field_variations = list(
-                            Params(initial_field_value[i], params_fields_value[i])
-                        )
+                        field_variations = list(Params(initial_field_value[i], params_fields_value[i]))
                         list_values.append(field_variations)
                     list_values = [p for p in product(*list_values)]
                     values.append(list_values)
-                elif isinstance(
-                    initial_field_value, dict
-                ):  # It is composite param, need to go inside
-                    field_variations = list(
-                        Params(initial_field_value, params_fields_value)
-                    )
+                elif isinstance(initial_field_value, dict):  # It is composite param, need to go inside
+                    field_variations = list(Params(initial_field_value, params_fields_value))
                     values.append(field_variations)
                 else:  # Simple param, can take as it is
                     values.append([initial_field_value] + params_fields_value)
diff --git a/modeling/utils/registry.py b/modeling/utils/registry.py
index 3942269b..c2225480 100644
--- a/modeling/utils/registry.py
+++ b/modeling/utils/registry.py
@@ -2,6 +2,7 @@
 
 
 class MetaParent(type):
+
     def __init__(cls, name, base, params, **kwargs):
         super().__init__(name, base, params)
         is_base_class = cls.mro()[1] is object
@@ -11,10 +12,10 @@ def __init__(cls, name, base, params, **kwargs):
             base_class_found = False
             for key in cls.mro():
                 if isinstance(key, MetaParent) and key.mro()[1] is object:
-                    assert base_class_found is False, "multiple base classes(bug)"
+                    assert base_class_found is False, 'multiple base classes(bug)'
                     base_class = key
                     base_class_found = True
-            assert base_class_found is True, f"no base class for {name}"
+            assert base_class_found is True, f'no base class for {name}'
 
         if is_base_class:
             cls._subclasses = {}
@@ -24,9 +25,7 @@ def __init_subclass__(scls, config_name=None):
             super().__init_subclass__()
             if config_name is not None:
                 if config_name in base_class._subclasses:
-                    raise ValueError(
-                        "Class with name `{}` is already registered".format(config_name)
-                    )
+                    raise ValueError("Class with name `{}` is already registered".format(config_name))
                 scls.config_name = config_name
                 base_class._subclasses[config_name] = scls
 
@@ -34,19 +33,15 @@ def __init_subclass__(scls, config_name=None):
 
         @classmethod
         def parent_create_from_config(cls, config, **kwargs):
-            if "type" in config:
-                return cls._subclasses[config["type"]].create_from_config(
-                    config, **kwargs
-                )
+            if 'type' in config:
+                return cls._subclasses[config['type']].create_from_config(config, **kwargs)
             else:
-                raise ValueError(
-                    "There is no `type` provided for the `{}` class".format(name)
-                )
+                raise ValueError('There is no `type` provided for the `{}` class'.format(name))
 
         # Take kwargs for the last initialized baseclass
         init_kwargs = {}
         for bcls in cls.mro()[:-1]:  # Look into all base classes except object
-            if "__init__" not in bcls.__dict__:
+            if '__init__' not in bcls.__dict__:
                 continue
             init_kwargs = inspect.signature(bcls.__init__).parameters
             break
@@ -55,16 +50,14 @@ def parent_create_from_config(cls, config, **kwargs):
         def child_create_from_config(cls, config, **kwargs):
             kwargs = {}
             for key, argspec in init_kwargs.items():
-                if key == "self":
+                if key == 'self':
                     continue
                 value = config.get(key, argspec.default)
                 if value is inspect.Parameter.empty:
-                    msg = "There is no value for `{}.__init__` required field `{}` in config `{}`"
+                    msg = 'There is no value for `{}.__init__` required field `{}` in config `{}`'
                     raise ValueError(msg.format(cls, key, config))
                 kwargs[key] = value
             return cls(**kwargs)
 
-        if "create_from_config" not in cls.__dict__:
-            cls.create_from_config = (
-                parent_create_from_config if is_base_class else child_create_from_config
-            )
+        if 'create_from_config' not in cls.__dict__:
+            cls.create_from_config = parent_create_from_config if is_base_class else child_create_from_config
diff --git a/modeling/utils/tensorboards/__init__.py b/modeling/utils/tensorboards/__init__.py
index a2f8f279..6c470341 100644
--- a/modeling/utils/tensorboards/__init__.py
+++ b/modeling/utils/tensorboards/__init__.py
@@ -1 +1 @@
-from .tensorboard_writers import GLOBAL_TENSORBOARD_WRITER, LOGS_DIR, TensorboardWriter
+from .tensorboard_writers import TensorboardWriter, GLOBAL_TENSORBOARD_WRITER, LOGS_DIR
diff --git a/modeling/utils/tensorboards/tensorboard_writers.py b/modeling/utils/tensorboards/tensorboard_writers.py
index e788a6f8..770fae63 100644
--- a/modeling/utils/tensorboards/tensorboard_writers.py
+++ b/modeling/utils/tensorboards/tensorboard_writers.py
@@ -1,21 +1,19 @@
-import datetime
 import os
 import time
+import datetime
 
 from torch.utils.tensorboard import SummaryWriter
 
-LOGS_DIR = "../tensorboard_logs"
+LOGS_DIR = '../tensorboard_logs'
 GLOBAL_TENSORBOARD_WRITER = None
 
 
 class TensorboardWriter(SummaryWriter):
+
     def __init__(self, experiment_name, use_time=True):
         self._experiment_name = experiment_name
         super().__init__(
-            log_dir=os.path.join(
-                LOGS_DIR,
-                f"{experiment_name}_{datetime.datetime.now().strftime('%Y-%m-%dT%H:%M' if use_time else '')}",
-            )
+            log_dir=os.path.join(LOGS_DIR, f'{experiment_name}_{datetime.datetime.now().strftime("%Y-%m-%dT%H:%M" if use_time else "")}')
         )
 
     def add_scalar(self, *args, **kwargs):
@@ -23,6 +21,7 @@ def add_scalar(self, *args, **kwargs):
 
 
 class TensorboardTimer:
+
     def __init__(self, scope):
         super().__init__(LOGS_DIR)
         self._scope = scope

From a1e6f103503cabdfda3c366804efd760e21c6c59 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 8 Jun 2025 22:24:52 +0300
Subject: [PATCH 165/175] Revert "ruff format"

This reverts commit 4077b0579fa1ee0a938e635ee8e7e0ad1603a7bc.
---
 modeling/callbacks/__init__.py                |   8 +-
 modeling/callbacks/base.py                    | 293 ++++----
 modeling/dataloader/base.py                   |  24 +-
 modeling/dataloader/batch_processors.py       |  34 +-
 modeling/dataset/base.py                      | 639 ++++++++----------
 .../dataset/negative_samplers/__init__.py     |   6 +-
 modeling/dataset/negative_samplers/base.py    |  12 +-
 modeling/dataset/negative_samplers/popular.py |  28 +-
 modeling/dataset/negative_samplers/random.py  |   3 +-
 modeling/dataset/samplers/__init__.py         |  29 +-
 modeling/dataset/samplers/base.py             |  24 +-
 modeling/dataset/samplers/cl4srec.py          | 110 ++-
 modeling/dataset/samplers/duorec.py           |  44 +-
 modeling/dataset/samplers/identity.py         |  19 +-
 .../dataset/samplers/last_item_prediction.py  |  16 +-
 .../samplers/masked_item_prediction.py        |  59 +-
 modeling/dataset/samplers/mclsr.py            |  45 +-
 .../dataset/samplers/next_item_prediction.py  |  69 +-
 modeling/dataset/samplers/pop.py              |  52 +-
 modeling/dataset/samplers/s3rec.py            |  92 ++-
 modeling/infer.py                             |  69 +-
 modeling/loss/base.py                         | 394 ++++++-----
 modeling/metric/base.py                       |  65 +-
 modeling/models/__init__.py                   |   2 +-
 modeling/models/base.py                       | 111 ++-
 modeling/models/bert4rec.py                   |  94 ++-
 modeling/models/bert4rec_cls.py               |  91 +--
 modeling/models/cl4srec.py                    | 127 ++--
 modeling/models/duorec.py                     | 123 ++--
 modeling/models/graph_seq_rec.py              | 225 +++---
 modeling/models/gru4rec.py                    | 172 +++--
 modeling/models/gtorec.py                     | 475 +++++--------
 modeling/models/lightgcn.py                   | 129 ++--
 modeling/models/mclsr.py                      | 274 ++++----
 modeling/models/mrgsrec.py                    | 115 ++--
 modeling/models/ngcf.py                       | 133 ++--
 modeling/models/pop.py                        |  36 +-
 modeling/models/pure_mf.py                    | 107 ++-
 modeling/models/pure_svd.py                   |   6 +-
 modeling/models/random.py                     |  29 +-
 modeling/models/rqvae.py                      |  81 ++-
 modeling/models/s3rec.py                      | 214 +++---
 modeling/models/sasrec_ce.py                  |  85 ++-
 modeling/models/sasrec_semantic.py            |   4 +-
 modeling/models/tiger.py                      |  13 +-
 modeling/optimizer/base.py                    |  41 +-
 modeling/pretrain.py                          |  65 +-
 modeling/rqvae_utils/__init__.py              |   4 +-
 modeling/rqvae_utils/collision_solver.py      | 156 ++---
 modeling/rqvae_utils/rqvae_data.py            |  13 +-
 modeling/rqvae_utils/rqvae_test.py            |  25 +-
 modeling/rqvae_utils/simplified_tree.py       |  70 +-
 modeling/rqvae_utils/tree.py                  | 191 ++----
 modeling/rqvae_utils/tree_comparing.py        |  18 +-
 modeling/rqvae_utils/trie.py                  |   1 -
 modeling/train.py                             | 106 ++-
 modeling/train_multiple.py                    | 103 ++-
 modeling/utils/__init__.py                    |  93 ++-
 modeling/utils/grid_search.py                 |  25 +-
 modeling/utils/registry.py                    |  31 +-
 modeling/utils/tensorboards/__init__.py       |   2 +-
 .../utils/tensorboards/tensorboard_writers.py |  11 +-
 62 files changed, 2574 insertions(+), 3161 deletions(-)

diff --git a/modeling/callbacks/__init__.py b/modeling/callbacks/__init__.py
index 81d5054e..a23d5ba3 100644
--- a/modeling/callbacks/__init__.py
+++ b/modeling/callbacks/__init__.py
@@ -1,7 +1 @@
-from .base import (
-    BaseCallback,
-    CompositeCallback,
-    EvalCallback,
-    InferenceCallback,
-    ValidationCallback,
-)
+from .base import BaseCallback, CompositeCallback, EvalCallback, InferenceCallback, ValidationCallback
diff --git a/modeling/callbacks/base.py b/modeling/callbacks/base.py
index 900b492e..1ae26267 100644
--- a/modeling/callbacks/base.py
+++ b/modeling/callbacks/base.py
@@ -1,19 +1,25 @@
-import os
-from pathlib import Path
-
-import numpy as np
-import torch
+from metric import BaseMetric, StatefullMetric
 
 import utils
-from metric import BaseMetric, StatefullMetric
 from utils import MetaParent, create_logger
 
+import numpy as np
+import os
+import torch
+from pathlib import Path
+
 logger = create_logger(name=__name__)
 
 
 class BaseCallback(metaclass=MetaParent):
+
     def __init__(
-        self, model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
     ):
         self._model = model
         self._train_dataloader = train_dataloader
@@ -25,20 +31,25 @@ def __call__(self, inputs, step_num):
         raise NotImplementedError
 
 
-class MetricCallback(BaseCallback, config_name="metric"):
+class MetricCallback(BaseCallback, config_name='metric'):
+
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        on_step,
-        metrics,
-        loss_prefix,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            on_step,
+            metrics,
+            loss_prefix
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._on_step = on_step
         self._loss_prefix = loss_prefix
@@ -47,103 +58,113 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
-            metrics=config.get("metrics", None),
-            loss_prefix=config["loss_prefix"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
+            metrics=config.get('metrics', None),
+            loss_prefix=config['loss_prefix']
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:
             for metric_name, metric_function in self._metrics.items():
                 metric_value = metric_function(
-                    ground_truth=inputs[self._model.schema["ground_truth_prefix"]],
-                    predictions=inputs[self._model.schema["predictions_prefix"]],
+                    ground_truth=inputs[self._model.schema['ground_truth_prefix']],
+                    predictions=inputs[self._model.schema['predictions_prefix']]
                 )
 
                 utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                    "train/{}".format(metric_name), metric_value, step_num
+                    'train/{}'.format(metric_name),
+                    metric_value,
+                    step_num
                 )
 
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                "train/{}".format(self._loss_prefix),
+                'train/{}'.format(self._loss_prefix),
                 inputs[self._loss_prefix],
-                step_num,
+                step_num
             )
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.flush()
 
 
-class CheckpointCallback(BaseCallback, config_name="checkpoint"):
+class CheckpointCallback(BaseCallback, config_name='checkpoint'):
+
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        on_step,
-        save_path,
-        model_name,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            on_step,
+            save_path,
+            model_name
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._on_step = on_step
         self._save_path = Path(os.path.join(save_path, model_name))
         if self._save_path.exists():
-            logger.warning(
-                "Checkpoint path `{}` is already exists!".format(self._save_path)
-            )
+            logger.warning('Checkpoint path `{}` is already exists!'.format(self._save_path))
         else:
             self._save_path.mkdir(parents=True, exist_ok=True)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
-            save_path=config["save_path"],
-            model_name=config["model_name"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
+            save_path=config['save_path'],
+            model_name=config['model_name']
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:
-            logger.debug("Saving model state on step {}...".format(step_num))
+            logger.debug('Saving model state on step {}...'.format(step_num))
             torch.save(
                 {
-                    "step_num": step_num,
-                    "model_state_dict": self._model.state_dict(),
-                    "optimizer_state_dict": self._optimizer.state_dict(),
+                    'step_num': step_num,
+                    'model_state_dict': self._model.state_dict(),
+                    'optimizer_state_dict': self._optimizer.state_dict(),
                 },
-                os.path.join(self._save_path, "checkpoint_{}.pth".format(step_num)),
+                os.path.join(self._save_path, 'checkpoint_{}.pth'.format(step_num))
             )
-            logger.debug("Saving done!")
+            logger.debug('Saving done!')
 
 
 class InferenceCallback(BaseCallback):
+
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        on_step,
-        pred_prefix,
-        labels_prefix,
-        metrics=None,
-        loss_prefix=None,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            on_step,
+            pred_prefix,
+            labels_prefix,
+            metrics=None,
+            loss_prefix=None,
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._on_step = on_step
         self._metrics = metrics if metrics is not None else {}
@@ -155,24 +176,24 @@ def __init__(
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config["metrics"].items()
+            for metric_name, metric_cfg in config['metrics'].items()
         }
 
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
             metrics=metrics,
-            pred_prefix=config["pred_prefix"],
-            labels_prefix=config["labels_prefix"],
+            pred_prefix=config['pred_prefix'],
+            labels_prefix=config['labels_prefix']
         )
 
     def __call__(self, inputs, step_num):
         if step_num % self._on_step == 0:  # TODO Add time monitoring
-            logger.debug(f"Running {self._get_name()} on step {step_num}...")
+            logger.debug(f'Running {self._get_name()} on step {step_num}...')
             running_params = {}
             for metric_name, metric_function in self._metrics.items():
                 running_params[metric_name] = []
@@ -191,34 +212,30 @@ def __call__(self, inputs, step_num):
                         batch[key] = values.cpu()
 
                     for metric_name, metric_function in self._metrics.items():
-                        running_params[metric_name].extend(
-                            metric_function(
-                                inputs=batch,
-                                pred_prefix=self._pred_prefix,
-                                labels_prefix=self._labels_prefix,
-                            )
-                        )
+                        running_params[metric_name].extend(metric_function(
+                            inputs=batch,
+                            pred_prefix=self._pred_prefix,
+                            labels_prefix=self._labels_prefix,
+                        ))
 
                     if self._loss_prefix is not None:
-                        running_params[self._loss_prefix] += batch[
-                            self._loss_prefix
-                        ].item()
-
+                        running_params[self._loss_prefix] += batch[self._loss_prefix].item()
+            
             for metric_name, metric_function in self._metrics.items():
                 if isinstance(metric_function, StatefullMetric):
-                    running_params[metric_name] = metric_function.reduce(
-                        running_params[metric_name]
-                    )
+                    running_params[metric_name] = metric_function.reduce(running_params[metric_name])
 
             for label, value in running_params.items():
-                inputs[f"{self._get_name()}/{label}"] = np.mean(value)
+                inputs[f'{self._get_name()}/{label}'] = np.mean(value)
                 utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.add_scalar(
-                    f"{self._get_name()}/{label}", np.mean(value), step_num
+                    f'{self._get_name()}/{label}',
+                    np.mean(value),
+                    step_num
                 )
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.flush()
 
-            logger.debug(f"Running {self._get_name()} on step {step_num} is done!")
-
+            logger.debug(f'Running {self._get_name()} on step {step_num} is done!')
+        
     def _get_name(self):
         return self.config_name
 
@@ -226,81 +243,87 @@ def _get_dataloader(self):
         raise NotImplementedError
 
 
-class ValidationCallback(InferenceCallback, config_name="validation"):
+class ValidationCallback(InferenceCallback, config_name='validation'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config["metrics"].items()
+            for metric_name, metric_cfg in config['metrics'].items()
         }
 
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
             metrics=metrics,
-            pred_prefix=config["pred_prefix"],
-            labels_prefix=config["labels_prefix"],
+            pred_prefix=config['pred_prefix'],
+            labels_prefix=config['labels_prefix']
         )
 
     def _get_dataloader(self):
         return self._validation_dataloader
 
 
-class EvalCallback(InferenceCallback, config_name="eval"):
+class EvalCallback(InferenceCallback, config_name='eval'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         metrics = {
             metric_name: BaseMetric.create_from_config(metric_cfg, **kwargs)
-            for metric_name, metric_cfg in config["metrics"].items()
+            for metric_name, metric_cfg in config['metrics'].items()
         }
 
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
-            on_step=config["on_step"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
+            on_step=config['on_step'],
             metrics=metrics,
-            pred_prefix=config["pred_prefix"],
-            labels_prefix=config["labels_prefix"],
+            pred_prefix=config['pred_prefix'],
+            labels_prefix=config['labels_prefix']
         )
 
     def _get_dataloader(self):
         return self._eval_dataloader
 
 
-class CompositeCallback(BaseCallback, config_name="composite"):
+class CompositeCallback(BaseCallback, config_name='composite'):
     def __init__(
-        self,
-        model,
-        train_dataloader,
-        validation_dataloader,
-        eval_dataloader,
-        optimizer,
-        callbacks,
+            self,
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer,
+            callbacks
     ):
         super().__init__(
-            model, train_dataloader, validation_dataloader, eval_dataloader, optimizer
+            model,
+            train_dataloader,
+            validation_dataloader,
+            eval_dataloader,
+            optimizer
         )
         self._callbacks = callbacks
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            model=kwargs["model"],
-            train_dataloader=kwargs["train_dataloader"],
-            validation_dataloader=kwargs["validation_dataloader"],
-            eval_dataloader=kwargs["eval_dataloader"],
-            optimizer=kwargs["optimizer"],
+            model=kwargs['model'],
+            train_dataloader=kwargs['train_dataloader'],
+            validation_dataloader=kwargs['validation_dataloader'],
+            eval_dataloader=kwargs['eval_dataloader'],
+            optimizer=kwargs['optimizer'],
             callbacks=[
                 BaseCallback.create_from_config(cfg, **kwargs)
-                for cfg in config["callbacks"]
-            ],
+                for cfg in config['callbacks']
+            ]
         )
 
     def __call__(self, inputs, step_num):
diff --git a/modeling/dataloader/base.py b/modeling/dataloader/base.py
index 71e7f6e7..2e23876d 100644
--- a/modeling/dataloader/base.py
+++ b/modeling/dataloader/base.py
@@ -1,13 +1,12 @@
 import copy
-import logging
-
-import numpy as np
-from torch.utils.data import DataLoader, random_split
 
 from utils import MetaParent
-
 from .batch_processors import BaseBatchProcessor
 
+import logging
+import numpy as np
+from torch.utils.data import DataLoader, random_split
+
 logger = logging.getLogger(__name__)
 
 
@@ -15,7 +14,8 @@ class BaseDataloader(metaclass=MetaParent):
     pass
 
 
-class TorchDataloader(BaseDataloader, config_name="torch"):
+class TorchDataloader(BaseDataloader, config_name='torch'):
+
     def __init__(self, dataloader):
         self._dataloader = dataloader
 
@@ -29,13 +29,7 @@ def __len__(self):
     def create_from_config(cls, config, **kwargs):
         create_config = copy.deepcopy(config)
         batch_processor = BaseBatchProcessor.create_from_config(
-            create_config.pop("batch_processor")
-            if "batch_processor" in create_config
-            else {"type": "identity"}
-        )
-        create_config.pop("type")  # For passing as **config in torch DataLoader
-        return cls(
-            dataloader=DataLoader(
-                kwargs["dataset"], collate_fn=batch_processor, **create_config
-            )
+            create_config.pop('batch_processor') if 'batch_processor' in create_config else {'type': 'identity'}
         )
+        create_config.pop('type')  # For passing as **config in torch DataLoader
+        return cls(dataloader=DataLoader(kwargs['dataset'], collate_fn=batch_processor, **create_config))
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index bd45c86c..9991a073 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,43 +1,43 @@
 import torch
-
 from utils import MetaParent
 
 
 class BaseBatchProcessor(metaclass=MetaParent):
+
     def __call__(self, batch):
         raise NotImplementedError
 
 
-class IdentityBatchProcessor(BaseBatchProcessor, config_name="identity"):
+class IdentityBatchProcessor(BaseBatchProcessor, config_name='identity'):
+
     def __call__(self, batch):
         return torch.tensor(batch)
+    
+class EmbedBatchProcessor(BaseBatchProcessor, config_name='embed'):
 
-
-class EmbedBatchProcessor(BaseBatchProcessor, config_name="embed"):
     def __call__(self, batch):
-        ids = torch.tensor([entry["item.id"] for entry in batch])
-        embeds = torch.stack([entry["item.embed"] for entry in batch])
+        ids = torch.tensor([entry['item.id'] for entry in batch])
+        embeds = torch.stack([entry['item.embed'] for entry in batch])
+        
+        return {'ids': ids, 'embeddings': embeds}
 
-        return {"ids": ids, "embeddings": embeds}
 
+class BasicBatchProcessor(BaseBatchProcessor, config_name='basic'):
 
-class BasicBatchProcessor(BaseBatchProcessor, config_name="basic"):
     def __call__(self, batch):
         processed_batch = {}
 
         for key in batch[0].keys():
-            if key.endswith(".ids"):
-                prefix = key.split(".")[0]
-                assert "{}.length".format(prefix) in batch[0]
+            if key.endswith('.ids'):
+                prefix = key.split('.')[0]
+                assert '{}.length'.format(prefix) in batch[0]
 
-                processed_batch[f"{prefix}.ids"] = []
-                processed_batch[f"{prefix}.length"] = []
+                processed_batch[f'{prefix}.ids'] = []
+                processed_batch[f'{prefix}.length'] = []
 
                 for sample in batch:
-                    processed_batch[f"{prefix}.ids"].extend(sample[f"{prefix}.ids"])
-                    processed_batch[f"{prefix}.length"].append(
-                        sample[f"{prefix}.length"]
-                    )
+                    processed_batch[f'{prefix}.ids'].extend(sample[f'{prefix}.ids'])
+                    processed_batch[f'{prefix}.length'].append(sample[f'{prefix}.length'])
 
         for part, values in processed_batch.items():
             processed_batch[part] = torch.tensor(values, dtype=torch.long)
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index b2e53b3e..ff040d6e 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -1,35 +1,40 @@
-import json
-import logging
-import os
-import pickle
 from collections import defaultdict
+import json
 
+from tqdm import tqdm
+
+from dataset.samplers import TrainSampler, EvalSampler
+
+from utils import MetaParent, DEVICE
+
+import pickle
+import torch
 import numpy as np
 import scipy.sparse as sp
-import torch
 from scipy.sparse import csr_matrix
-from tqdm import tqdm
 
-from dataset.samplers import EvalSampler, TrainSampler
-from utils import DEVICE, MetaParent
+import os
+import logging
 
 logger = logging.getLogger(__name__)
 
 
 class BaseDataset(metaclass=MetaParent):
+
     def get_samplers(self):
         raise NotImplementedError
 
 
-class SequenceDataset(BaseDataset, config_name="sequence"):
+class SequenceDataset(BaseDataset, config_name='sequence'):
+
     def __init__(
-        self,
-        train_sampler,
-        validation_sampler,
-        test_sampler,
-        num_users,
-        num_items,
-        max_sequence_length,
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_users,
+            num_items,
+            max_sequence_length
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -40,74 +45,61 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
 
-        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="train",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='train',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="valid",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='valid',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="test",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='test',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
 
         max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
         max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
-        logger.info("Max user id: {}".format(max_user_id))
-        logger.info("Max item id: {}".format(max_item_id))
-        logger.info("Max sequence length: {}".format(max_seq_len))
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_seq_len))
 
-        train_interactions = sum(
-            list(map(lambda x: len(x), train_dataset))
-        )  # whole user history as a sample
+        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
         valid_interactions = len(validation_dataset)  # each new interaction as a sample
-        test_interactions = len(test_dataset)  # each new interaction as a sample
-        logger.info(
-            "{} dataset sparsity: {}".format(
-                config["name"],
-                (train_interactions + valid_interactions + test_interactions)
-                / max_user_id
-                / max_item_id,
-            )
-        )
+        test_interactions = len(test_dataset) # each new interaction as a sample
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
+        ))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
 
         return cls(
@@ -116,72 +108,45 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_seq_len,
+            max_sequence_length=max_seq_len
         )
 
     @classmethod
-    def _create_dataset(
-        cls, dir_path, part, max_sequence_length=None, use_cached=False
-    ):
+    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
         max_user_id = 0
         max_item_id = 0
         max_sequence_len = 0
 
-        if use_cached and os.path.exists(os.path.join(dir_path, "{}.pkl".format(part))):
-            logger.info(
-                f"Take cached dataset from {os.path.join(dir_path, '{}.pkl'.format(part))}"
-            )
+        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
+            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "rb"
-            ) as dataset_file:
-                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(
-                    dataset_file
-                )
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
         else:
-            logger.info(
-                "Cache is forecefully ignored."
-                if not use_cached
-                else "No cached dataset has been found."
-            )
-            logger.info(
-                f"Creating a dataset {os.path.join(dir_path, '{}.txt'.format(part))}..."
-            )
+            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
+            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
 
-            dataset_path = os.path.join(dir_path, "{}.txt".format(part))
-            with open(dataset_path, "r") as f:
+            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
+            with open(dataset_path, 'r') as f:
                 data = f.readlines()
 
             sequence_info = cls._create_sequences(data, max_sequence_length)
-            (
-                user_sequences,
-                item_sequences,
-                max_user_id,
-                max_item_id,
-                max_sequence_len,
-            ) = sequence_info
+            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
 
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
-                dataset.append(
-                    {
-                        "user.ids": [user_id],
-                        "user.length": 1,
-                        "item.ids": item_ids,
-                        "item.length": len(item_ids),
-                    }
-                )
+                dataset.append({
+                    'user.ids': [user_id], 'user.length': 1,
+                    'item.ids': item_ids, 'item.length': len(item_ids)
+                })
 
-            logger.info("{} dataset size: {}".format(part, len(dataset)))
-            logger.info(
-                "{} dataset max sequence length: {}".format(part, max_sequence_len)
-            )
+            logger.info('{} dataset size: {}'.format(part, len(dataset)))
+            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "wb"
-            ) as dataset_file:
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
                 pickle.dump(
-                    (dataset, max_user_id, max_item_id, max_sequence_len), dataset_file
+                    (dataset, max_user_id, max_item_id, max_sequence_len),
+                    dataset_file
                 )
 
         return dataset, max_user_id, max_item_id, max_sequence_len
@@ -196,7 +161,7 @@ def _create_sequences(data, max_sample_len):
         max_sequence_length = 0
 
         for sample in data:
-            sample = sample.strip("\n").split(" ")
+            sample = sample.strip('\n').split(' ')
             item_ids = [int(item_id) for item_id in sample[1:]][-max_sample_len:]
             user_id = int(sample[0])
 
@@ -207,13 +172,7 @@ def _create_sequences(data, max_sample_len):
             user_sequences.append(user_id)
             item_sequences.append(item_ids)
 
-        return (
-            user_sequences,
-            item_sequences,
-            max_user_id,
-            max_item_id,
-            max_sequence_length,
-        )
+        return user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_length
 
     def get_samplers(self):
         return self._train_sampler, self._validation_sampler, self._test_sampler
@@ -233,84 +192,71 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            "num_users": self.num_users,
-            "num_items": self.num_items,
-            "max_sequence_length": self.max_sequence_length,
+            'num_users': self.num_users,
+            'num_items': self.num_items,
+            'max_sequence_length': self.max_sequence_length
         }
-
-
-class SequenceFullDataset(SequenceDataset, config_name="sequence_full"):
+        
+        
+class SequenceFullDataset(SequenceDataset, config_name='sequence_full'):
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
 
-        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="train",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        train_dataset, train_max_user_id, train_max_item_id, train_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='train',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="valid",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        validation_dataset, valid_max_user_id, valid_max_item_id, valid_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='valid',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
-        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = (
-            cls._create_dataset(
-                dir_path=data_dir_path,
-                part="test",
-                max_sequence_length=config["max_sequence_length"],
-                use_cached=config.get("use_cached", False),
-            )
+        test_dataset, test_max_user_id, test_max_item_id, test_seq_len = cls._create_dataset(
+            dir_path=data_dir_path,
+            part='test',
+            max_sequence_length=config['max_sequence_length'],
+            use_cached=config.get('use_cached', False)
         )
 
         max_user_id = max([train_max_user_id, valid_max_user_id, test_max_user_id])
         max_item_id = max([train_max_item_id, valid_max_item_id, test_max_item_id])
         max_seq_len = max([train_seq_len, valid_seq_len, test_seq_len])
 
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
         logger.info("Validation dataset size: {}".format(len(validation_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
-        logger.info("Max user id: {}".format(max_user_id))
-        logger.info("Max item id: {}".format(max_item_id))
-        logger.info("Max sequence length: {}".format(max_seq_len))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_seq_len))
 
-        train_interactions = sum(
-            list(map(lambda x: len(x), train_dataset))
-        )  # whole user history as a sample
+        train_interactions = sum(list(map(lambda x: len(x), train_dataset)))  # whole user history as a sample
         valid_interactions = len(validation_dataset)  # each new interaction as a sample
-        test_interactions = len(test_dataset)  # each new interaction as a sample
-        logger.info(
-            "{} dataset sparsity: {}".format(
-                config["name"],
-                (train_interactions + valid_interactions + test_interactions)
-                / max_user_id
-                / max_item_id,
-            )
-        )
+        test_interactions = len(test_dataset) # each new interaction as a sample
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (train_interactions + valid_interactions + test_interactions) / max_user_id / max_item_id
+        ))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
 
         return cls(
@@ -319,59 +265,40 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_seq_len,
+            max_sequence_length=max_seq_len
         )
-
+        
     @classmethod
     def flatten_item_sequence(cls, item_ids):
-        min_history_length = 3
+        min_history_length = 3 # TODOPK make this configurable
         histories = []
-        for i in range(min_history_length - 1, len(item_ids)):
-            histories.append(item_ids[: i + 1])
+        for i in range(min_history_length-1, len(item_ids)):
+            histories.append(item_ids[:i+1])
         return histories
-
+    
     @classmethod
-    def _create_dataset(
-        cls, dir_path, part, max_sequence_length=None, use_cached=False
-    ):
+    def _create_dataset(cls, dir_path, part, max_sequence_length=None, use_cached=False):
         max_user_id = 0
         max_item_id = 0
         max_sequence_len = 0
 
-        if use_cached and os.path.exists(os.path.join(dir_path, "{}.pkl".format(part))):
-            logger.info(
-                f"Take cached dataset from {os.path.join(dir_path, '{}.pkl'.format(part))}"
-            )
+        if use_cached and os.path.exists(os.path.join(dir_path, '{}.pkl'.format(part))):
+            logger.info(f'Take cached dataset from {os.path.join(dir_path, "{}.pkl".format(part))}')
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "rb"
-            ) as dataset_file:
-                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(
-                    dataset_file
-                )
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'rb') as dataset_file:
+                dataset, max_user_id, max_item_id, max_sequence_len = pickle.load(dataset_file)
         else:
-            logger.info(
-                "Cache is forecefully ignored."
-                if not use_cached
-                else "No cached dataset has been found."
-            )
-            logger.info(
-                f"Creating a dataset {os.path.join(dir_path, '{}.txt'.format(part))}..."
-            )
+            logger.info('Cache is forecefully ignored.' if not use_cached else 'No cached dataset has been found.')
+            logger.info(f'Creating a dataset {os.path.join(dir_path, "{}.txt".format(part))}...')
 
-            dataset_path = os.path.join(dir_path, "{}.txt".format(part))
-            with open(dataset_path, "r") as f:
+            dataset_path = os.path.join(dir_path, '{}.txt'.format(part))
+            with open(dataset_path, 'r') as f:
                 data = f.readlines()
 
             sequence_info = cls._create_sequences(data, max_sequence_length)
-            (
-                user_sequences,
-                item_sequences,
-                max_user_id,
-                max_item_id,
-                max_sequence_len,
-            ) = sequence_info
+            user_sequences, item_sequences, max_user_id, max_item_id, max_sequence_len = sequence_info
 
+            # TODOPK check
             dataset = []
             for user_id, item_ids in zip(user_sequences, item_sequences):
                 if part == "train":
@@ -395,29 +322,26 @@ def _create_dataset(
                         }
                     )
 
-            logger.info("{} dataset size: {}".format(part, len(dataset)))
-            logger.info(
-                "{} dataset max sequence length: {}".format(part, max_sequence_len)
-            )
+            logger.info('{} dataset size: {}'.format(part, len(dataset)))
+            logger.info('{} dataset max sequence length: {}'.format(part, max_sequence_len))
 
-            with open(
-                os.path.join(dir_path, "{}.pkl".format(part)), "wb"
-            ) as dataset_file:
+            with open(os.path.join(dir_path, '{}.pkl'.format(part)), 'wb') as dataset_file:
                 pickle.dump(
-                    (dataset, max_user_id, max_item_id, max_sequence_len), dataset_file
+                    (dataset, max_user_id, max_item_id, max_sequence_len),
+                    dataset_file
                 )
 
         return dataset, max_user_id, max_item_id, max_sequence_len
 
+class GraphDataset(BaseDataset, config_name='graph'):
 
-class GraphDataset(BaseDataset, config_name="graph"):
     def __init__(
-        self,
-        dataset,
-        graph_dir_path,
-        use_train_data_only=True,
-        use_user_graph=False,
-        use_item_graph=False,
+            self,
+            dataset,
+            graph_dir_path,
+            use_train_data_only=True,
+            use_user_graph=False,
+            use_item_graph=False
     ):
         self._dataset = dataset
         self._graph_dir_path = graph_dir_path
@@ -430,19 +354,15 @@ def __init__(
 
         train_sampler, validation_sampler, test_sampler = dataset.get_samplers()
 
-        train_interactions, train_user_interactions, train_item_interactions = (
-            [],
-            [],
-            [],
-        )
+        train_interactions, train_user_interactions, train_item_interactions = [], [], []
 
         train_user_2_items = defaultdict(set)
         train_item_2_users = defaultdict(set)
         visited_user_item_pairs = set()
 
         for sample in train_sampler.dataset:
-            user_id = sample["user.ids"][0]
-            item_ids = sample["item.ids"]
+            user_id = sample['user.ids'][0]
+            item_ids = sample['item.ids']
 
             for item_id in item_ids:
                 if (user_id, item_id) not in visited_user_item_pairs:
@@ -458,8 +378,8 @@ def __init__(
         # TODO create separated function
         if not self._use_train_data_only:
             for sample in validation_sampler.dataset:
-                user_id = sample["user.ids"][0]
-                item_ids = sample["item.ids"]
+                user_id = sample['user.ids'][0]
+                item_ids = sample['item.ids']
 
                 for item_id in item_ids:
                     if (user_id, item_id) not in visited_user_item_pairs:
@@ -473,8 +393,8 @@ def __init__(
                         visited_user_item_pairs.add((user_id, item_id))
 
             for sample in test_sampler.dataset:
-                user_id = sample["user.ids"][0]
-                item_ids = sample["item.ids"]
+                user_id = sample['user.ids'][0]
+                item_ids = sample['item.ids']
 
                 for item_id in item_ids:
                     if (user_id, item_id) not in visited_user_item_pairs:
@@ -491,32 +411,27 @@ def __init__(
         self._train_user_interactions = np.array(train_user_interactions)
         self._train_item_interactions = np.array(train_item_interactions)
 
-        path_to_graph = os.path.join(graph_dir_path, "general_graph.npz")
+        path_to_graph = os.path.join(graph_dir_path, 'general_graph.npz')
         if os.path.exists(path_to_graph):
             self._graph = sp.load_npz(path_to_graph)
         else:
             # place ones only when co-occurrence happens
             user2item_connections = csr_matrix(
-                (
-                    np.ones(len(train_user_interactions)),
-                    (train_user_interactions, train_item_interactions),
-                ),
-                shape=(self._num_users + 2, self._num_items + 2),
+                (np.ones(len(train_user_interactions)), (train_user_interactions, train_item_interactions)),
+                shape=(self._num_users + 2, self._num_items + 2)
             )  # (num_users + 2, num_items + 2), bipartite graph
             self._graph = self.get_sparse_graph_layer(
                 user2item_connections,
                 self._num_users + 2,
                 self._num_items + 2,
-                biparite=True,
+                biparite=True
             )
             sp.save_npz(path_to_graph, self._graph)
 
-        self._graph = (
-            self._convert_sp_mat_to_sp_tensor(self._graph).coalesce().to(DEVICE)
-        )
+        self._graph = self._convert_sp_mat_to_sp_tensor(self._graph).coalesce().to(DEVICE)
 
         if self._use_user_graph:
-            path_to_user_graph = os.path.join(graph_dir_path, "user_graph.npz")
+            path_to_user_graph = os.path.join(graph_dir_path, 'user_graph.npz')
             if os.path.exists(path_to_user_graph):
                 self._user_graph = sp.load_npz(path_to_user_graph)
             else:
@@ -525,18 +440,13 @@ def __init__(
                 visited_user_item_pairs = set()
                 visited_user_user_pairs = set()
 
-                for user_id, item_id in tqdm(
-                    zip(self._train_user_interactions, self._train_item_interactions)
-                ):
+                for user_id, item_id in tqdm(zip(self._train_user_interactions, self._train_item_interactions)):
                     if (user_id, item_id) in visited_user_item_pairs:
                         continue  # process (user, item) pair only once
                     visited_user_item_pairs.add((user_id, item_id))
 
                     for connected_user_id in train_item_2_users[item_id]:
-                        if (
-                            user_id,
-                            connected_user_id,
-                        ) in visited_user_user_pairs or user_id == connected_user_id:
+                        if (user_id, connected_user_id) in visited_user_user_pairs or user_id == connected_user_id:
                             continue  # add (user, user) to graph connections pair only once
                         visited_user_user_pairs.add((user_id, connected_user_id))
 
@@ -546,30 +456,24 @@ def __init__(
                 # (user, user) graph
                 user2user_connections = csr_matrix(
                     (
-                        np.ones(len(user2user_interactions_fst)),
-                        (user2user_interactions_fst, user2user_interactions_snd),
-                    ),
-                    shape=(self._num_users + 2, self._num_users + 2),
+                    np.ones(len(user2user_interactions_fst)), (user2user_interactions_fst, user2user_interactions_snd)),
+                    shape=(self._num_users + 2, self._num_users + 2)
                 )
 
                 self._user_graph = self.get_sparse_graph_layer(
                     user2user_connections,
                     self._num_users + 2,
                     self._num_users + 2,
-                    biparite=False,
+                    biparite=False
                 )
                 sp.save_npz(path_to_user_graph, self._user_graph)
 
-            self._user_graph = (
-                self._convert_sp_mat_to_sp_tensor(self._user_graph)
-                .coalesce()
-                .to(DEVICE)
-            )
+            self._user_graph = self._convert_sp_mat_to_sp_tensor(self._user_graph).coalesce().to(DEVICE)
         else:
             self._user_graph = None
 
         if self._use_item_graph:
-            path_to_item_graph = os.path.join(graph_dir_path, "item_graph.npz")
+            path_to_item_graph = os.path.join(graph_dir_path, 'item_graph.npz')
             if os.path.exists(path_to_item_graph):
                 self._item_graph = sp.load_npz(path_to_item_graph)
             else:
@@ -578,18 +482,13 @@ def __init__(
                 visited_user_item_pairs = set()
                 visited_item_item_pairs = set()
 
-                for user_id, item_id in tqdm(
-                    zip(self._train_user_interactions, self._train_item_interactions)
-                ):
+                for user_id, item_id in tqdm(zip(self._train_user_interactions, self._train_item_interactions)):
                     if (user_id, item_id) in visited_user_item_pairs:
                         continue  # process (user, item) pair only once
                     visited_user_item_pairs.add((user_id, item_id))
 
                     for connected_item_id in train_user_2_items[user_id]:
-                        if (
-                            item_id,
-                            connected_item_id,
-                        ) in visited_item_item_pairs or item_id == connected_item_id:
+                        if (item_id, connected_item_id) in visited_item_item_pairs or item_id == connected_item_id:
                             continue  # add (item, item) to graph connections pair only once
                         visited_item_item_pairs.add((item_id, connected_item_id))
 
@@ -599,42 +498,39 @@ def __init__(
                 # (item, item) graph
                 item2item_connections = csr_matrix(
                     (
-                        np.ones(len(item2item_interactions_fst)),
-                        (item2item_interactions_fst, item2item_interactions_snd),
-                    ),
-                    shape=(self._num_items + 2, self._num_items + 2),
+                    np.ones(len(item2item_interactions_fst)), (item2item_interactions_fst, item2item_interactions_snd)),
+                    shape=(self._num_items + 2, self._num_items + 2)
                 )
                 self._item_graph = self.get_sparse_graph_layer(
                     item2item_connections,
                     self._num_items + 2,
                     self._num_items + 2,
-                    biparite=False,
+                    biparite=False
                 )
                 sp.save_npz(path_to_item_graph, self._item_graph)
 
-            self._item_graph = (
-                self._convert_sp_mat_to_sp_tensor(self._item_graph)
-                .coalesce()
-                .to(DEVICE)
-            )
+            self._item_graph = self._convert_sp_mat_to_sp_tensor(self._item_graph).coalesce().to(DEVICE)
         else:
             self._item_graph = None
 
     @classmethod
     def create_from_config(cls, config):
-        dataset = BaseDataset.create_from_config(config["dataset"])
+        dataset = BaseDataset.create_from_config(config['dataset'])
         return cls(
             dataset=dataset,
-            graph_dir_path=config["graph_dir_path"],
-            use_user_graph=config.get("use_user_graph", False),
-            use_item_graph=config.get("use_item_graph", False),
+            graph_dir_path=config['graph_dir_path'],
+            use_user_graph=config.get('use_user_graph', False),
+            use_item_graph=config.get('use_item_graph', False)
         )
 
     @staticmethod
     def get_sparse_graph_layer(sparse_matrix, fst_dim, snd_dim, biparite=False):
         mat_dim_size = fst_dim + snd_dim if biparite else fst_dim
 
-        adj_mat = sp.dok_matrix((mat_dim_size, mat_dim_size), dtype=np.float32)
+        adj_mat = sp.dok_matrix(
+            (mat_dim_size, mat_dim_size),
+            dtype=np.float32
+        )
         adj_mat = adj_mat.tolil()
 
         R = sparse_matrix.tolil()  # list of lists (fst_dim, snd_dim)
@@ -652,11 +548,11 @@ def get_sparse_graph_layer(sparse_matrix, fst_dim, snd_dim, biparite=False):
 
         rowsum = np.array(adj_mat.sum(1))
         d_inv = np.power(rowsum, -1).flatten()
-        d_inv[np.isinf(d_inv)] = 0.0
+        d_inv[np.isinf(d_inv)] = 0.
         d_mat_inv = sp.diags(d_inv)
 
         d_inv = np.power(edges_degree, -0.5).flatten()  # D^(-0.5)
-        d_inv[np.isinf(d_inv)] = 0.0  # fix NaNs in case if row with zero connections
+        d_inv[np.isinf(d_inv)] = 0.  # fix NaNs in case if row with zero connections
         d_mat = sp.diags(d_inv)  # make it square matrix
 
         # D^(-0.5) @ A @ D^(-0.5)
@@ -687,15 +583,16 @@ def get_samplers(self):
     @property
     def meta(self):
         meta = {
-            "user_graph": self._user_graph,
-            "item_graph": self._item_graph,
-            "graph": self._graph,
-            **self._dataset.meta,
+            'user_graph': self._user_graph,
+            'item_graph': self._item_graph,
+            'graph': self._graph,
+            **self._dataset.meta
         }
         return meta
 
 
-class DuorecDataset(BaseDataset, config_name="duorec"):
+class DuorecDataset(BaseDataset, config_name='duorec'):
+
     def __init__(self, dataset):
         self._dataset = dataset
         self._num_users = dataset.num_users
@@ -705,7 +602,7 @@ def __init__(self, dataset):
 
         target_2_sequences = defaultdict(list)
         for sample in train_sampler.dataset:
-            item_ids = sample["item.ids"]
+            item_ids = sample['item.ids']
 
             target_item = item_ids[-1]
             semantic_similar_item_ids = item_ids[:-1]
@@ -716,7 +613,7 @@ def __init__(self, dataset):
 
     @classmethod
     def create_from_config(cls, config):
-        dataset = BaseDataset.create_from_config(config["dataset"])
+        dataset = BaseDataset.create_from_config(config['dataset'])
         return cls(dataset)
 
     @property
@@ -735,15 +632,16 @@ def meta(self):
         return self._dataset.meta
 
 
-class ScientificDataset(BaseDataset, config_name="scientific"):
+class ScientificDataset(BaseDataset, config_name='scientific'):
+
     def __init__(
-        self,
-        train_sampler,
-        validation_sampler,
-        test_sampler,
-        num_users,
-        num_items,
-        max_sequence_length,
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_users,
+            num_items,
+            max_sequence_length
     ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
@@ -754,17 +652,17 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
-        max_sequence_length = config["max_sequence_length"]
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
+        max_sequence_length = config['max_sequence_length']
         max_user_id, max_item_id = 0, 0
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, "{}.txt".format("all_data"))
-        with open(dataset_path, "r") as f:
+        dataset_path = os.path.join(data_dir_path, '{}.txt'.format('all_data'))
+        with open(dataset_path, 'r') as f:
             data = f.readlines()
 
         for sample in data:
-            sample = sample.strip("\n").split(" ")
+            sample = sample.strip('\n').split(' ')
             user_id = int(sample[0])
             item_ids = [int(item_id) for item_id in sample[1:]]
 
@@ -773,69 +671,54 @@ def create_from_config(cls, config, **kwargs):
 
             assert len(item_ids) >= 5
 
-            train_dataset.append(
-                {
-                    "user.ids": [user_id],
-                    "user.length": 1,
-                    "item.ids": item_ids[:-2][-max_sequence_length:],
-                    "item.length": len(item_ids[:-2][-max_sequence_length:]),
-                }
-            )
-            assert len(item_ids[:-2][-max_sequence_length:]) == len(
-                set(item_ids[:-2][-max_sequence_length:])
-            )
-            validation_dataset.append(
-                {
-                    "user.ids": [user_id],
-                    "user.length": 1,
-                    "item.ids": item_ids[:-1][-max_sequence_length:],
-                    "item.length": len(item_ids[:-1][-max_sequence_length:]),
-                }
-            )
-            assert len(item_ids[:-1][-max_sequence_length:]) == len(
-                set(item_ids[:-1][-max_sequence_length:])
-            )
-            test_dataset.append(
-                {
-                    "user.ids": [user_id],
-                    "user.length": 1,
-                    "item.ids": item_ids[-max_sequence_length:],
-                    "item.length": len(item_ids[-max_sequence_length:]),
-                }
-            )
-            assert len(item_ids[-max_sequence_length:]) == len(
-                set(item_ids[-max_sequence_length:])
-            )
-
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
-        logger.info("Max user id: {}".format(max_user_id))
-        logger.info("Max item id: {}".format(max_item_id))
-        logger.info("Max sequence length: {}".format(max_sequence_length))
-        logger.info(
-            "{} dataset sparsity: {}".format(
-                config["name"],
-                (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id,
-            )
-        )
+            train_dataset.append({
+                'user.ids': [user_id],
+                'user.length': 1,
+                'item.ids': item_ids[:-2][-max_sequence_length:],
+                'item.length': len(item_ids[:-2][-max_sequence_length:])
+            })
+            assert len(item_ids[:-2][-max_sequence_length:]) == len(set(item_ids[:-2][-max_sequence_length:]))
+            validation_dataset.append({
+                'user.ids': [user_id],
+                'user.length': 1,
+                'item.ids': item_ids[:-1][-max_sequence_length:],
+                'item.length': len(item_ids[:-1][-max_sequence_length:])
+            })
+            assert len(item_ids[:-1][-max_sequence_length:]) == len(set(item_ids[:-1][-max_sequence_length:]))
+            test_dataset.append({
+                'user.ids': [user_id],
+                'user.length': 1,
+                'item.ids': item_ids[-max_sequence_length:],
+                'item.length': len(item_ids[-max_sequence_length:])
+            })
+            assert len(item_ids[-max_sequence_length:]) == len(set(item_ids[-max_sequence_length:]))
+
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
+        logger.info('Max user id: {}'.format(max_user_id))
+        logger.info('Max item id: {}'.format(max_item_id))
+        logger.info('Max sequence length: {}'.format(max_sequence_length))
+        logger.info('{} dataset sparsity: {}'.format(
+            config['name'], (len(train_dataset) + len(test_dataset)) / max_user_id / max_item_id
+        ))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=train_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=validation_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"],
+            config['samplers'],
             dataset=test_dataset,
             num_users=max_user_id,
-            num_items=max_item_id,
+            num_items=max_item_id
         )
 
         return cls(
@@ -844,7 +727,7 @@ def create_from_config(cls, config, **kwargs):
             test_sampler=test_sampler,
             num_users=max_user_id,
             num_items=max_item_id,
-            max_sequence_length=max_sequence_length,
+            max_sequence_length=max_sequence_length
         )
 
     def get_samplers(self):
@@ -865,9 +748,9 @@ def max_sequence_length(self):
     @property
     def meta(self):
         return {
-            "num_users": self.num_users,
-            "num_items": self.num_items,
-            "max_sequence_length": self.max_sequence_length,
+            'num_users': self.num_users,
+            'num_items': self.num_items,
+            'max_sequence_length': self.max_sequence_length
         }
 
 
@@ -991,8 +874,15 @@ def create_from_config(cls, config, **kwargs):
         )
 
 
-class RqVaeDataset(BaseDataset, config_name="rqvae"):
-    def __init__(self, train_sampler, validation_sampler, test_sampler, num_items):
+class RqVaeDataset(BaseDataset, config_name='rqvae'):
+
+    def __init__(
+            self,
+            train_sampler,
+            validation_sampler,
+            test_sampler,
+            num_items
+    ):
         self._train_sampler = train_sampler
         self._validation_sampler = validation_sampler
         self._test_sampler = test_sampler
@@ -1000,33 +890,39 @@ def __init__(self, train_sampler, validation_sampler, test_sampler, num_items):
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        data_dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+        data_dir_path = os.path.join(config['path_to_data_dir'], config['name'])
         train_dataset, validation_dataset, test_dataset = [], [], []
 
-        dataset_path = os.path.join(data_dir_path, "{}.pt".format("data_full"))
+        dataset_path = os.path.join(data_dir_path, '{}.pt'.format('data_full'))
         df = torch.load(dataset_path, weights_only=False)
 
         for idx, sample in df.iterrows():
-            train_dataset.append({"item.id": idx, "item.embed": sample["embeddings"]})
-
-        logger.info("Train dataset size: {}".format(len(train_dataset)))
-        logger.info("Test dataset size: {}".format(len(test_dataset)))
+            train_dataset.append({
+                'item.id': idx,
+                'item.embed': sample["embeddings"]
+            })
+            
+        logger.info('Train dataset size: {}'.format(len(train_dataset)))
+        logger.info('Test dataset size: {}'.format(len(test_dataset)))
 
         train_sampler = TrainSampler.create_from_config(
-            config["samplers"], dataset=train_dataset
+            config['samplers'],
+            dataset=train_dataset
         )
         validation_sampler = EvalSampler.create_from_config(
-            config["samplers"], dataset=validation_dataset
+            config['samplers'],
+            dataset=validation_dataset
         )
         test_sampler = EvalSampler.create_from_config(
-            config["samplers"], dataset=test_dataset
+            config['samplers'],
+            dataset=test_dataset
         )
 
         return cls(
             train_sampler=train_sampler,
             validation_sampler=validation_sampler,
             test_sampler=test_sampler,
-            num_items=len(df),
+            num_items=len(df)
         )
 
     def get_samplers(self):
@@ -1042,4 +938,7 @@ def max_sequence_length(self):
 
     @property
     def meta(self):
-        return {"num_items": self.num_items, "train_sampler": self._train_sampler}
+        return {
+            'num_items': self.num_items,
+            'train_sampler': self._train_sampler
+        }
diff --git a/modeling/dataset/negative_samplers/__init__.py b/modeling/dataset/negative_samplers/__init__.py
index 6cac99af..498de21f 100644
--- a/modeling/dataset/negative_samplers/__init__.py
+++ b/modeling/dataset/negative_samplers/__init__.py
@@ -2,4 +2,8 @@
 from .popular import PopularNegativeSampler
 from .random import RandomNegativeSampler
 
-__all__ = ["BaseNegativeSampler", "PopularNegativeSampler", "RandomNegativeSampler"]
+__all__ = [
+    'BaseNegativeSampler',
+    'PopularNegativeSampler',
+    'RandomNegativeSampler'
+]
diff --git a/modeling/dataset/negative_samplers/base.py b/modeling/dataset/negative_samplers/base.py
index ebaef60c..5483bd29 100644
--- a/modeling/dataset/negative_samplers/base.py
+++ b/modeling/dataset/negative_samplers/base.py
@@ -4,15 +4,21 @@
 
 
 class BaseNegativeSampler(metaclass=MetaParent):
-    def __init__(self, dataset, num_users, num_items):
+
+    def __init__(
+            self,
+            dataset,
+            num_users,
+            num_items
+    ):
         self._dataset = dataset
         self._num_users = num_users
         self._num_items = num_items
 
         self._seen_items = defaultdict(set)
         for sample in self._dataset:
-            user_id = sample["user.ids"][0]
-            items = list(sample["item.ids"])
+            user_id = sample['user.ids'][0]
+            items = list(sample['item.ids'])
             self._seen_items[user_id].update(items)
 
     def generate_negative_samples(self, sample, num_negatives):
diff --git a/modeling/dataset/negative_samplers/popular.py b/modeling/dataset/negative_samplers/popular.py
index 6e478e0c..68476651 100644
--- a/modeling/dataset/negative_samplers/popular.py
+++ b/modeling/dataset/negative_samplers/popular.py
@@ -1,34 +1,44 @@
+from dataset.negative_samplers.base import BaseNegativeSampler
+
 from collections import Counter
 
-from dataset.negative_samplers.base import BaseNegativeSampler
 
+class PopularNegativeSampler(BaseNegativeSampler, config_name='popular'):
 
-class PopularNegativeSampler(BaseNegativeSampler, config_name="popular"):
-    def __init__(self, dataset, num_users, num_items):
-        super().__init__(dataset=dataset, num_users=num_users, num_items=num_items)
+    def __init__(
+            self,
+            dataset,
+            num_users,
+            num_items
+    ):
+        super().__init__(
+            dataset=dataset,
+            num_users=num_users,
+            num_items=num_items
+        )
 
         self._popular_items = self._items_by_popularity()
 
     @classmethod
     def create_from_config(cls, _, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def _items_by_popularity(self):
         popularity = Counter()
 
         for sample in self._dataset:
-            for item_id in sample["item.ids"]:
+            for item_id in sample['item.ids']:
                 popularity[item_id] += 1
 
         popular_items = sorted(popularity, key=popularity.get, reverse=True)
         return popular_items
 
     def generate_negative_samples(self, sample, num_negatives):
-        user_id = sample["user.ids"][0]
+        user_id = sample['user.ids'][0]
         popularity_idx = 0
         negatives = []
         while len(negatives) < num_negatives:
diff --git a/modeling/dataset/negative_samplers/random.py b/modeling/dataset/negative_samplers/random.py
index 24055217..a816280c 100644
--- a/modeling/dataset/negative_samplers/random.py
+++ b/modeling/dataset/negative_samplers/random.py
@@ -1,9 +1,8 @@
 from collections import defaultdict
 
 import numpy as np
-from tqdm import tqdm
-
 from dataset.negative_samplers.base import BaseNegativeSampler
+from tqdm import tqdm
 
 
 class RandomNegativeSampler(BaseNegativeSampler, config_name="random"):
diff --git a/modeling/dataset/samplers/__init__.py b/modeling/dataset/samplers/__init__.py
index 40ffd454..6ed31eed 100644
--- a/modeling/dataset/samplers/__init__.py
+++ b/modeling/dataset/samplers/__init__.py
@@ -1,19 +1,10 @@
-from .base import EvalSampler, TrainSampler
-from .cl4srec import Cl4SRecEvalSampler, Cl4SRecTrainSampler
-from .duorec import DuoRecEvalSampler, DuorecTrainSampler
-from .identity import IdentityEvalSampler, IdentityTrainSampler
-from .last_item_prediction import (
-    LastItemPredictionEvalSampler,
-    LastItemPredictionTrainSampler,
-)
-from .masked_item_prediction import (
-    MaskedItemPredictionEvalSampler,
-    MaskedItemPredictionTrainSampler,
-)
-from .mclsr import MCLSRPredictionEvalSampler, MCLSRTrainSampler
-from .next_item_prediction import (
-    NextItemPredictionEvalSampler,
-    NextItemPredictionTrainSampler,
-)
-from .pop import PopEvalSampler, PopTrainSampler
-from .s3rec import S3RecPretrainEvalSampler, S3RecPretrainTrainSampler
+from .base import TrainSampler, EvalSampler
+from .cl4srec import Cl4SRecTrainSampler, Cl4SRecEvalSampler
+from .duorec import DuorecTrainSampler, DuoRecEvalSampler
+from .next_item_prediction import NextItemPredictionTrainSampler, NextItemPredictionEvalSampler
+from .last_item_prediction import LastItemPredictionTrainSampler, LastItemPredictionEvalSampler
+from .masked_item_prediction import MaskedItemPredictionTrainSampler, MaskedItemPredictionEvalSampler
+from .mclsr import MCLSRTrainSampler, MCLSRPredictionEvalSampler
+from .pop import PopTrainSampler, PopEvalSampler
+from .s3rec import S3RecPretrainTrainSampler, S3RecPretrainEvalSampler
+from .identity import IdentityTrainSampler, IdentityEvalSampler
diff --git a/modeling/dataset/samplers/base.py b/modeling/dataset/samplers/base.py
index 723abb0a..2dbfa705 100644
--- a/modeling/dataset/samplers/base.py
+++ b/modeling/dataset/samplers/base.py
@@ -1,9 +1,10 @@
-import copy
-
 from utils import MetaParent
 
+import copy
+
 
 class TrainSampler(metaclass=MetaParent):
+
     def __init__(self):
         self._dataset = None
 
@@ -19,6 +20,7 @@ def __getitem__(self, index):
 
 
 class EvalSampler(metaclass=MetaParent):
+
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -35,14 +37,16 @@ def __len__(self):
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item = sample["item.ids"][-1]
+        item_sequence = sample['item.ids'][:-1]
+        next_item = sample['item.ids'][-1]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "labels.ids": [next_item],
-            "labels.length": 1,
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'labels.ids': [next_item],
+            'labels.length': 1
         }
diff --git a/modeling/dataset/samplers/cl4srec.py b/modeling/dataset/samplers/cl4srec.py
index 6fbcd143..8aba1de1 100644
--- a/modeling/dataset/samplers/cl4srec.py
+++ b/modeling/dataset/samplers/cl4srec.py
@@ -1,19 +1,20 @@
-import copy
-
 import numpy as np
 
-from dataset.samplers.base import EvalSampler, TrainSampler
+from dataset.samplers.base import TrainSampler, EvalSampler
+
+import copy
+
 
+class Cl4SRecTrainSampler(TrainSampler, config_name='cl4srec'):
 
-class Cl4SRecTrainSampler(TrainSampler, config_name="cl4srec"):
     def __init__(
-        self,
-        dataset,
-        num_users,
-        num_items,
-        item_crop_portion,
-        item_mask_portion,
-        item_reorder_portion,
+            self,
+            dataset,
+            num_users,
+            num_items,
+            item_crop_portion,
+            item_mask_portion,
+            item_reorder_portion
     ):
         super().__init__()
         self._dataset = dataset
@@ -27,23 +28,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            item_crop_portion=config["item_crop_portion"],
-            item_mask_portion=config["item_mask_portion"],
-            item_reorder_portion=config["item_reorder_portion"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            item_crop_portion=config['item_crop_portion'],
+            item_mask_portion=config['item_mask_portion'],
+            item_reorder_portion=config['item_reorder_portion']
         )
 
     def _apply_crop_augmentation(self, item_sequence):
         num_elements_to_crop = max(1, int(self._item_crop_portion * len(item_sequence)))
-        crop_start_index = np.random.randint(
-            low=0, high=len(item_sequence) - num_elements_to_crop + 1
-        )
+        crop_start_index = np.random.randint(low=0, high=len(item_sequence) - num_elements_to_crop + 1)
         assert 0 <= crop_start_index <= len(item_sequence) - num_elements_to_crop
-        item_sequence = item_sequence[
-            crop_start_index : crop_start_index + num_elements_to_crop
-        ]
+        item_sequence = item_sequence[crop_start_index: crop_start_index + num_elements_to_crop]
         return item_sequence
 
     def _apply_mask_augmentation(self, item_sequence):
@@ -68,28 +65,22 @@ def _apply_mask_augmentation(self, item_sequence):
 
     def _apply_reorder_augmentation(self, item_sequence):
         num_elements_to_reorder = int(self._item_reorder_portion * len(item_sequence))
-        reorder_start_index = np.random.randint(
-            low=0, high=len(item_sequence) - num_elements_to_reorder + 1
-        )
+        reorder_start_index = np.random.randint(low=0, high=len(item_sequence) - num_elements_to_reorder + 1)
         assert 0 <= reorder_start_index <= len(item_sequence) - num_elements_to_reorder
-        reordered_subsequence = item_sequence[
-            reorder_start_index : reorder_start_index + num_elements_to_reorder
-        ]
+        reordered_subsequence = item_sequence[reorder_start_index: reorder_start_index + num_elements_to_reorder]
         np.random.shuffle(reordered_subsequence)  # This works in-place
-        item_sequence = (
-            item_sequence[:reorder_start_index]
-            + reordered_subsequence
-            + item_sequence[reorder_start_index + num_elements_to_reorder :]
-        )
+        item_sequence = item_sequence[:reorder_start_index] \
+                        + reordered_subsequence \
+                        + item_sequence[reorder_start_index + num_elements_to_reorder:]
         return item_sequence
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item = sample["item.ids"][-1]
+        item_sequence = sample['item.ids'][:-1]
+        next_item = sample['item.ids'][-1]
 
-        sample_items = set(sample["item.ids"])
+        sample_items = set(sample['item.ids'])
         negative = np.random.randint(low=1, high=self._num_items + 1)
         while negative in sample_items:
             negative = np.random.randint(low=1, high=self._num_items + 1)
@@ -97,7 +88,7 @@ def __getitem__(self, index):
         augmentation_list = [
             self._apply_crop_augmentation,
             self._apply_mask_augmentation,
-            self._apply_reorder_augmentation,
+            self._apply_reorder_augmentation
         ]
 
         fst_augmentation = np.random.choice(augmentation_list)
@@ -107,28 +98,35 @@ def __getitem__(self, index):
         snd_augmented_sequence = snd_augmentation(item_sequence)
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "fst_augmented_item.ids": fst_augmented_sequence,
-            "fst_augmented_item.length": len(fst_augmented_sequence),
-            "snd_augmented_item.ids": snd_augmented_sequence,
-            "snd_augmented_item.length": len(snd_augmented_sequence),
-            "labels.ids": [next_item],
-            "labels.length": 1,
-            "positive.ids": [next_item],
-            "positive.length": 1,
-            "negative.ids": [negative],
-            "negative.length": 1,
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'fst_augmented_item.ids': fst_augmented_sequence,
+            'fst_augmented_item.length': len(fst_augmented_sequence),
+
+            'snd_augmented_item.ids': snd_augmented_sequence,
+            'snd_augmented_item.length': len(snd_augmented_sequence),
+
+            'labels.ids': [next_item],
+            'labels.length': 1,
+
+            'positive.ids': [next_item],
+            'positive.length': 1,
+
+            'negative.ids': [negative],
+            'negative.length': 1
         }
 
 
-class Cl4SRecEvalSampler(EvalSampler, config_name="cl4srec"):
+class Cl4SRecEvalSampler(EvalSampler, config_name='cl4srec'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/duorec.py b/modeling/dataset/samplers/duorec.py
index f45ae100..a3067fdd 100644
--- a/modeling/dataset/samplers/duorec.py
+++ b/modeling/dataset/samplers/duorec.py
@@ -1,10 +1,12 @@
-import copy
 import random
 
-from dataset.samplers.base import EvalSampler, TrainSampler
+from dataset.samplers.base import TrainSampler, EvalSampler
+
+import copy
+
 
+class DuorecTrainSampler(TrainSampler, config_name='duorec'):
 
-class DuorecTrainSampler(TrainSampler, config_name="duorec"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -14,15 +16,15 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
 
         target_item = item_sequence[-1]
         item_sequence = item_sequence[:-1]
@@ -31,22 +33,26 @@ def __getitem__(self, index):
         semantic_similar_sequence = random.choice(self._target_2_sequences[target_item])
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "labels.ids": [target_item],
-            "labels.length": 1,
-            "semantic_similar_item.ids": semantic_similar_sequence,
-            "semantic_similar_item.length": len(semantic_similar_sequence),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'labels.ids': [target_item],
+            'labels.length': 1,
+
+            'semantic_similar_item.ids': semantic_similar_sequence,
+            'semantic_similar_item.length': len(semantic_similar_sequence)
         }
 
 
-class DuoRecEvalSampler(EvalSampler, config_name="duorec"):
+class DuoRecEvalSampler(EvalSampler, config_name='duorec'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/identity.py b/modeling/dataset/samplers/identity.py
index 4b34b03e..ffe01e23 100644
--- a/modeling/dataset/samplers/identity.py
+++ b/modeling/dataset/samplers/identity.py
@@ -1,30 +1,35 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class IdentityTrainSampler(TrainSampler, config_name='identity'):
 
-class IdentityTrainSampler(TrainSampler, config_name="identity"):
     def __init__(self, dataset):
         super().__init__()
         self._dataset = dataset
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(dataset=kwargs["dataset"])
+        return cls(
+            dataset=kwargs['dataset']
+        )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
         return sample
 
 
-class IdentityEvalSampler(EvalSampler, config_name="identity"):
+class IdentityEvalSampler(EvalSampler, config_name='identity'):
     def __init__(self, dataset):
         self._dataset = dataset
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(dataset=kwargs["dataset"])
-
+        return cls(
+            dataset=kwargs['dataset']
+        )
+    
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        return sample
+        return sample
\ No newline at end of file
diff --git a/modeling/dataset/samplers/last_item_prediction.py b/modeling/dataset/samplers/last_item_prediction.py
index c9da6570..e6f9ba73 100644
--- a/modeling/dataset/samplers/last_item_prediction.py
+++ b/modeling/dataset/samplers/last_item_prediction.py
@@ -1,8 +1,11 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
 
 from dataset.samplers.base import EvalSampler, TrainSampler
 from dataset.negative_samplers.base import BaseNegativeSampler
 
+class LastItemPredictionTrainSampler(TrainSampler, config_name='last_item_prediction'):
 
 class LastItemPredictionTrainSampler(TrainSampler, config_name="last_item_prediction"):
     def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives):
@@ -30,8 +33,8 @@ def create_from_config(cls, config, **kwargs):
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        last_item = sample["item.ids"][-1]
+        item_sequence = sample['item.ids'][:-1]
+        last_item = sample['item.ids'][-1]
 
         if self._num_negatives == 0:
             return {
@@ -59,11 +62,12 @@ def __getitem__(self, index):
             }
 
 
-class LastItemPredictionEvalSampler(EvalSampler, config_name="last_item_prediction"):
+class LastItemPredictionEvalSampler(EvalSampler, config_name='last_item_prediction'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/masked_item_prediction.py b/modeling/dataset/samplers/masked_item_prediction.py
index 2243e7ca..0100f731 100644
--- a/modeling/dataset/samplers/masked_item_prediction.py
+++ b/modeling/dataset/samplers/masked_item_prediction.py
@@ -1,13 +1,11 @@
-import copy
+from dataset.samplers.base import TrainSampler, EvalSampler
 
+import copy
 import numpy as np
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class MaskedItemPredictionTrainSampler(TrainSampler, config_name='masked_item_prediction'):
 
-class MaskedItemPredictionTrainSampler(
-    TrainSampler, config_name="masked_item_prediction"
-):
     def __init__(self, dataset, num_users, num_items, mask_prob=0.0):
         super().__init__()
         self._dataset = dataset
@@ -19,16 +17,16 @@ def __init__(self, dataset, num_users, num_items, mask_prob=0.0):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            mask_prob=config.get("mask_prob", 0.0),
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            mask_prob=config.get('mask_prob', 0.0)
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
 
         masked_sequence = []
         labels = []
@@ -56,18 +54,19 @@ def __getitem__(self, index):
         labels[-1] = item_sequence[-1]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": masked_sequence,
-            "item.length": len(masked_sequence),
-            "labels.ids": labels,
-            "labels.length": len(labels),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': masked_sequence,
+            'item.length': len(masked_sequence),
+
+            'labels.ids': labels,
+            'labels.length': len(labels)
         }
 
 
-class MaskedItemPredictionEvalSampler(
-    EvalSampler, config_name="masked_item_prediction"
-):
+class MaskedItemPredictionEvalSampler(EvalSampler, config_name='masked_item_prediction'):
+
     def __init__(self, dataset, num_users, num_items):
         super().__init__(dataset, num_users, num_items)
         self._mask_item_idx = self._num_items + 1
@@ -75,22 +74,24 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
         labels = [item_sequence[-1]]
         sequence = item_sequence[:-1] + [self._mask_item_idx]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": sequence,
-            "item.length": len(sequence),
-            "labels.ids": labels,
-            "labels.length": len(labels),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': sequence,
+            'item.length': len(sequence),
+
+            'labels.ids': labels,
+            'labels.length': len(labels)
         }
diff --git a/modeling/dataset/samplers/mclsr.py b/modeling/dataset/samplers/mclsr.py
index e3610547..2da0986c 100644
--- a/modeling/dataset/samplers/mclsr.py
+++ b/modeling/dataset/samplers/mclsr.py
@@ -1,9 +1,10 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class MCLSRTrainSampler(TrainSampler, config_name='mclsr'):
 
-class MCLSRTrainSampler(TrainSampler, config_name="mclsr"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__()
         self._dataset = dataset
@@ -13,35 +14,39 @@ def __init__(self, dataset, num_users, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item = sample["item.ids"][-1]
-        next_item_sequence = sample["item.ids"][1:]
+        item_sequence = sample['item.ids'][:-1]
+        next_item = sample['item.ids'][-1]
+        next_item_sequence = sample['item.ids'][1:]
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": item_sequence,
-            "item.length": len(item_sequence),
-            "positive.ids": next_item_sequence,
-            "positive.length": len(next_item_sequence),
-            "labels.ids": [next_item],
-            "labels.length": 1,
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': item_sequence,
+            'item.length': len(item_sequence),
+
+            'positive.ids': next_item_sequence,
+            'positive.length': len(next_item_sequence),
+
+            'labels.ids': [next_item],
+            'labels.length': 1
         }
 
 
-class MCLSRPredictionEvalSampler(EvalSampler, config_name="mclsr"):
+class MCLSRPredictionEvalSampler(EvalSampler, config_name='mclsr'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/next_item_prediction.py b/modeling/dataset/samplers/next_item_prediction.py
index cf9321ad..c141b065 100644
--- a/modeling/dataset/samplers/next_item_prediction.py
+++ b/modeling/dataset/samplers/next_item_prediction.py
@@ -1,13 +1,12 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+from dataset.negative_samplers.base import BaseNegativeSampler
+
 import copy
 
-from dataset.negative_samplers.base import BaseNegativeSampler
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class NextItemPredictionTrainSampler(TrainSampler, config_name='next_item_prediction'):
 
-class NextItemPredictionTrainSampler(TrainSampler, config_name="next_item_prediction"):
-    def __init__(
-        self, dataset, num_users, num_items, negative_sampler, num_negatives=0
-    ):
+    def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives=0):
         super().__init__()
         self._dataset = dataset
         self._num_users = num_users
@@ -17,32 +16,32 @@ def __init__(
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        negative_sampler = BaseNegativeSampler.create_from_config(
-            {"type": config["negative_sampler_type"]}, **kwargs
-        )
+        negative_sampler = BaseNegativeSampler.create_from_config({'type': config['negative_sampler_type']}, **kwargs)
 
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
             negative_sampler=negative_sampler,
-            num_negatives=config.get("num_negatives_train", 0),
+            num_negatives=config.get('num_negatives_train', 0)
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"][:-1]
-        next_item_sequence = sample["item.ids"][1:]
+        item_sequence = sample['item.ids'][:-1]
+        next_item_sequence = sample['item.ids'][1:]
 
         if self._num_negatives == 0:
             return {
-                "user.ids": sample["user.ids"],
-                "user.length": sample["user.length"],
-                "item.ids": item_sequence,
-                "item.length": len(item_sequence),
-                "positive.ids": next_item_sequence,
-                "positive.length": len(next_item_sequence),
+                'user.ids': sample['user.ids'],
+                'user.length': sample['user.length'],
+
+                'item.ids': item_sequence,
+                'item.length': len(item_sequence),
+
+                'positive.ids': next_item_sequence,
+                'positive.length': len(next_item_sequence)
             }
         else:
             negative_sequence = self._negative_sampler.generate_negative_samples(
@@ -50,22 +49,26 @@ def __getitem__(self, index):
             )
 
             return {
-                "user.ids": sample["user.ids"],
-                "user.length": sample["user.length"],
-                "item.ids": item_sequence,
-                "item.length": len(item_sequence),
-                "positive.ids": next_item_sequence,
-                "positive.length": len(next_item_sequence),
-                "negative.ids": negative_sequence,
-                "negative.length": len(negative_sequence),
+                'user.ids': sample['user.ids'],
+                'user.length': sample['user.length'],
+
+                'item.ids': item_sequence,
+                'item.length': len(item_sequence),
+
+                'positive.ids': next_item_sequence,
+                'positive.length': len(next_item_sequence),
+
+                'negative.ids': negative_sequence,
+                'negative.length': len(negative_sequence)
             }
 
 
-class NextItemPredictionEvalSampler(EvalSampler, config_name="next_item_prediction"):
+class NextItemPredictionEvalSampler(EvalSampler, config_name='next_item_prediction'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/dataset/samplers/pop.py b/modeling/dataset/samplers/pop.py
index 1d3ace34..afd8b208 100644
--- a/modeling/dataset/samplers/pop.py
+++ b/modeling/dataset/samplers/pop.py
@@ -1,12 +1,15 @@
+from dataset.samplers.base import TrainSampler, EvalSampler
+
 import copy
+
 from collections import Counter
 
-from dataset.samplers.base import EvalSampler, TrainSampler
 
 CANDIDATE_COUNTS = None
 
 
-class PopTrainSampler(TrainSampler, config_name="pop"):
+class PopTrainSampler(TrainSampler, config_name='pop'):
+
     def __init__(self, dataset, num_items):
         super().__init__()
 
@@ -16,48 +19,49 @@ def __init__(self, dataset, num_items):
             item_2_count = Counter()
 
             for sample in dataset:
-                items = sample["item.ids"]
+                items = sample['item.ids']
                 for item in items:
                     item_2_count[item] += 1
 
-            CANDIDATE_COUNTS = (
-                [0]
-                + [
-                    self._item_2_count[item_id]
-                    for item_id in range(1, self._num_items + 1)
-                ]
-                + [0]
-            )  # Mask + items + padding
-
+            CANDIDATE_COUNTS = [0] + [
+                self._item_2_count[item_id] for item_id in range(1, self._num_items + 1)
+            ] + [0]  # Mask + items + padding
+        
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(dataset=kwargs["dataset"], num_items=kwargs["num_items"])
+        return cls(
+            dataset=kwargs['dataset'],
+            num_items=kwargs['num_items']
+        )
+
 
+class PopEvalSampler(EvalSampler, config_name='pop'):
 
-class PopEvalSampler(EvalSampler, config_name="pop"):
     def __init__(self, dataset, num_users, num_items):
         super().__init__(dataset, num_users, num_items)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
-        labels = [sample["item.ids"][-1]]
+        labels = [sample['item.ids'][-1]]
 
         global CANDIDATE_COUNTS
         assert CANDIDATE_COUNTS is not None
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "labels.ids": labels,
-            "labels.length": len(labels),
-            "candidates_counts.ids": CANDIDATE_COUNTS,
-            "candidates_counts.length": len(CANDIDATE_COUNTS),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'labels.ids': labels,
+            'labels.length': len(labels),
+
+            'candidates_counts.ids': CANDIDATE_COUNTS,
+            'candidates_counts.length': len(CANDIDATE_COUNTS)
         }
diff --git a/modeling/dataset/samplers/s3rec.py b/modeling/dataset/samplers/s3rec.py
index 73bf9ff4..d1b424cf 100644
--- a/modeling/dataset/samplers/s3rec.py
+++ b/modeling/dataset/samplers/s3rec.py
@@ -1,12 +1,12 @@
-import copy
+from dataset.samplers.base import TrainSampler, EvalSampler
+from dataset.negative_samplers.base import BaseNegativeSampler
 
+import copy
 import numpy as np
 
-from dataset.negative_samplers.base import BaseNegativeSampler
-from dataset.samplers.base import EvalSampler, TrainSampler
 
+class S3RecPretrainTrainSampler(TrainSampler, config_name='s3rec_pretrain'):
 
-class S3RecPretrainTrainSampler(TrainSampler, config_name="s3rec_pretrain"):
     def __init__(self, dataset, num_users, num_items, negative_sampler, mask_prob=0.0):
         super().__init__()
         self._dataset = dataset
@@ -18,29 +18,27 @@ def __init__(self, dataset, num_users, num_items, negative_sampler, mask_prob=0.
 
         self._long_sequence = []
         for sample in self._dataset:
-            self._long_sequence.extend(copy.deepcopy(sample["item.ids"]))
+            self._long_sequence.extend(copy.deepcopy(sample['item.ids']))
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        negative_sampler = BaseNegativeSampler.create_from_config(
-            {"type": config["negative_sampler_type"]}, **kwargs
-        )
+        negative_sampler = BaseNegativeSampler.create_from_config({'type': config['negative_sampler_type']}, **kwargs)
 
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
             negative_sampler=negative_sampler,
-            mask_prob=config.get("mask_prob", 0.0),
+            mask_prob=config.get('mask_prob', 0.0)
         )
 
     def __getitem__(self, index):
         sample = copy.deepcopy(self._dataset[index])
 
-        item_sequence = sample["item.ids"]
+        item_sequence = sample['item.ids']
 
         if len(item_sequence) < 3:
-            assert False, "Something strange is happening"
+            assert False, 'Something strange is happening'
 
         # Masked Item Prediction
         masked_sequence = []
@@ -65,56 +63,48 @@ def __getitem__(self, index):
         # Mask last item
         masked_sequence[-1] = self._mask_item_idx
         positive_sequence[-1] = item_sequence[-1]
-        negative_sequence[-1] = self._negative_sampler.generate_negative_samples(
-            sample, 1
-        )[0]
-        assert (
-            len(positive_sequence)
-            == len(negative_sequence)
-            == len(masked_sequence)
-            == len(item_sequence)
-        )
+        negative_sequence[-1] = self._negative_sampler.generate_negative_samples(sample, 1)[0]
+        assert len(positive_sequence) == len(negative_sequence) == len(masked_sequence) == len(item_sequence)
 
         # Segment Prediction
         sample_length = np.random.randint(1, (len(item_sequence) + 1) // 2)
         start_id = np.random.randint(0, len(item_sequence) - sample_length)
-        negative_start_id = np.random.randint(
-            0, len(self._long_sequence) - sample_length
-        )
-        masked_segment_sequence = (
-            item_sequence[:start_id]
-            + [self._mask_item_idx] * sample_length
-            + item_sequence[start_id + sample_length :]
-        )
-        positive_segment = item_sequence[start_id : start_id + sample_length]
-        negative_segment = self._long_sequence[
-            negative_start_id : negative_start_id + sample_length
-        ]
+        negative_start_id = np.random.randint(0, len(self._long_sequence) - sample_length)
+        masked_segment_sequence = item_sequence[:start_id] + [self._mask_item_idx] * sample_length + item_sequence[start_id + sample_length:]
+        positive_segment = item_sequence[start_id: start_id + sample_length]
+        negative_segment = self._long_sequence[negative_start_id:negative_start_id + sample_length]
         assert len(positive_segment) == len(negative_segment) == sample_length
 
         return {
-            "user.ids": sample["user.ids"],
-            "user.length": sample["user.length"],
-            "item.ids": masked_sequence,
-            "item.length": len(masked_sequence),
-            "positive.ids": item_sequence,
-            "positive.length": len(item_sequence),
-            "negative.ids": negative_sequence,
-            "negative.length": len(negative_sequence),
+            'user.ids': sample['user.ids'],
+            'user.length': sample['user.length'],
+
+            'item.ids': masked_sequence,
+            'item.length': len(masked_sequence),
+
+            'positive.ids': item_sequence,
+            'positive.length': len(item_sequence),
+
+            'negative.ids': negative_sequence,
+            'negative.length': len(negative_sequence),
+
             "item_segment.ids": masked_segment_sequence,
             "item_segment.length": len(masked_segment_sequence),
-            "positive_segment.ids": positive_segment,
-            "positive_segment.length": len(positive_segment),
-            "negative_segment.ids": negative_segment,
-            "negative_segment.length": len(negative_segment),
+
+            'positive_segment.ids': positive_segment,
+            'positive_segment.length': len(positive_segment),
+
+            'negative_segment.ids': negative_segment,
+            'negative_segment.length': len(negative_segment)
         }
 
 
-class S3RecPretrainEvalSampler(EvalSampler, config_name="s3rec_pretrain"):
+class S3RecPretrainEvalSampler(EvalSampler, config_name='s3rec_pretrain'):
+
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            dataset=kwargs["dataset"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
+            dataset=kwargs['dataset'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items']
         )
diff --git a/modeling/infer.py b/modeling/infer.py
index 5bd4664e..c3abe16c 100644
--- a/modeling/infer.py
+++ b/modeling/infer.py
@@ -1,14 +1,15 @@
-import datetime
-import json
+from utils import parse_args, create_logger, fix_random_seed, DEVICE
+
+from dataset import BaseDataset
+from dataloader import BaseDataloader
+from models import BaseModel, TorchModel
+from metric import BaseMetric, StatefullMetric
 
+import json
 import numpy as np
 import torch
+import datetime
 
-from dataloader import BaseDataloader
-from dataset import BaseDataset
-from metric import BaseMetric, StatefullMetric
-from models import BaseModel, TorchModel
-from utils import DEVICE, create_logger, fix_random_seed, parse_args
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -24,6 +25,7 @@ def inference(dataloader, model, metrics, pred_prefix, labels_prefix):
 
     with torch.no_grad():
         for idx, batch in enumerate(dataloader):
+
             for key, value in batch.items():
                 batch[key] = value.to(DEVICE)
             batch[pred_prefix] = model(batch)
@@ -32,62 +34,57 @@ def inference(dataloader, model, metrics, pred_prefix, labels_prefix):
                 batch[key] = values.cpu()
 
             for metric_name, metric_function in metrics.items():
-                running_metrics[metric_name].extend(
-                    metric_function(
-                        inputs=batch,
-                        pred_prefix=pred_prefix,
-                        labels_prefix=labels_prefix,
-                    )
-                )
-
+                running_metrics[metric_name].extend(metric_function(
+                    inputs=batch,
+                    pred_prefix=pred_prefix,
+                    labels_prefix=labels_prefix,
+                ))
+            
         for metric_name, metric_function in metrics.items():
             if isinstance(metric_function, StatefullMetric):
-                running_metrics[metric_name] = metric_function.reduce(
-                    running_metrics[metric_name]
-                )
+                running_metrics[metric_name] = metric_function.reduce(running_metrics[metric_name])
 
-    logger.debug("Inference procedure has been finished!")
-    logger.debug("Metrics are the following:")
+    logger.debug('Inference procedure has been finished!')
+    logger.debug('Metrics are the following:')
     for metric_name, metric_value in running_metrics.items():
-        logger.info("{}: {}".format(metric_name, np.mean(metric_value)))
+        logger.info('{}: {}'.format(metric_name, np.mean(metric_value)))
 
 
 def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    logger.debug("Inference config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug('Inference config: \n{}'.format(json.dumps(config, indent=2)))
 
-    dataset = BaseDataset.create_from_config(config["dataset"])
+    dataset = BaseDataset.create_from_config(config['dataset'])
 
     _, _, eval_dataset = dataset.get_samplers()
 
     eval_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=eval_dataset
+        config['dataloader']['validation'],
+        dataset=eval_dataset
     )
 
-    model = BaseModel.create_from_config(config["model"], **dataset.meta)
+    model = BaseModel.create_from_config(config['model'], **dataset.meta)
 
     if isinstance(model, TorchModel):
         model = model.to(DEVICE)
-        checkpoint_path = "../checkpoints/{}_final_state.pth".format(
-            config["experiment_name"]
-        )
+        checkpoint_path = '../checkpoints/{}_final_state.pth'.format(config['experiment_name'])
         model.load_state_dict(torch.load(checkpoint_path))
 
     metrics = {
-        metric_name: BaseMetric.create_from_config(metric_cfg, **dataset.meta)
-        for metric_name, metric_cfg in config["metrics"].items()
+        metric_name: BaseMetric.create_from_config(metric_cfg , **dataset.meta)
+        for metric_name, metric_cfg in config['metrics'].items()
     }
 
     _ = inference(
-        dataloader=eval_dataloader,
-        model=model,
-        metrics=metrics,
-        pred_prefix=config["pred_prefix"],
-        labels_prefix=config["label_prefix"],
+        dataloader=eval_dataloader, 
+        model=model, 
+        metrics=metrics, 
+        pred_prefix=config['pred_prefix'], 
+        labels_prefix=config['label_prefix']
     )
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/loss/base.py b/modeling/loss/base.py
index e5a6ce5c..8ec91326 100644
--- a/modeling/loss/base.py
+++ b/modeling/loss/base.py
@@ -1,11 +1,11 @@
 import copy
 
+from utils import MetaParent, get_activation_function, maybe_to_list, DEVICE
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from utils import DEVICE, MetaParent, get_activation_function, maybe_to_list
-
 
 class BaseLoss(metaclass=MetaParent):
     pass
@@ -15,12 +15,14 @@ class TorchLoss(BaseLoss, nn.Module):
     pass
 
 
-class IdentityLoss(BaseLoss, config_name="identity"):
+class IdentityLoss(BaseLoss, config_name='identity'):
+
     def __call__(self, inputs):
         return inputs
 
 
-class CompositeLoss(TorchLoss, config_name="composite"):
+class CompositeLoss(TorchLoss, config_name='composite'):
+
     def __init__(self, losses, weights=None, output_prefix=None):
         super().__init__()
         self._losses = losses
@@ -32,16 +34,14 @@ def create_from_config(cls, config, **kwargs):
         losses = []
         weights = []
 
-        for loss_cfg in copy.deepcopy(config)["losses"]:
-            weight = loss_cfg.pop("weight") if "weight" in loss_cfg else 1.0
+        for loss_cfg in copy.deepcopy(config)['losses']:
+            weight = loss_cfg.pop('weight') if 'weight' in loss_cfg else 1.0
             loss_function = BaseLoss.create_from_config(loss_cfg)
 
             weights.append(weight)
             losses.append(loss_function)
 
-        return cls(
-            losses=losses, weights=weights, output_prefix=config.get("output_prefix")
-        )
+        return cls(losses=losses, weights=weights, output_prefix=config.get('output_prefix'))
 
     def forward(self, inputs):
         total_loss = 0.0
@@ -53,8 +53,7 @@ def forward(self, inputs):
 
         return total_loss
 
-
-class SampleLogSoftmaxLoss(TorchLoss, config_name="sample_logsoftmax"):
+class SampleLogSoftmaxLoss(TorchLoss, config_name='sample_logsoftmax'):
     def __init__(self, predictions_prefix, labels):
         super().__init__()
         self._predictions_prefix = predictions_prefix
@@ -63,38 +62,37 @@ def __init__(self, predictions_prefix, labels):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            predictions_prefix=config.get("predictions_prefix"),
-            labels=config.get("labels"),
+            predictions_prefix=config.get('predictions_prefix'),
+            labels=config.get('labels')
         )
 
     def forward(self, inputs):  # use log soft max
         logits = inputs[self._predictions_prefix]
         candidates = inputs[self._labels]
-
+        
         assert len(logits.shape) in [2, 3]
-
+        
         batch_size = logits.shape[0]
         seq_len = logits.shape[1]
-
+        
         if len(logits.shape) == 3:
             loss = -torch.gather(
-                torch.log_softmax(logits, dim=-1).reshape(
-                    batch_size * seq_len, logits.shape[-1]
-                ),
-                dim=-1,
-                index=candidates.reshape(batch_size * seq_len, 1),
+                torch.log_softmax(logits, dim=-1).reshape(batch_size * seq_len, logits.shape[-1]),
+                dim=-1, 
+                index=candidates.reshape(batch_size * seq_len, 1)
             ).mean()
         else:
             loss = -torch.gather(
                 torch.log_softmax(logits, dim=-1),
-                dim=-1,
-                index=candidates.reshape(batch_size, 1),
+                dim=-1, 
+                index=candidates.reshape(batch_size, 1)
             ).mean()
-
+            
         return loss
 
 
-class BatchLogSoftmaxLoss(TorchLoss, config_name="batch_logsoftmax"):
+class BatchLogSoftmaxLoss(TorchLoss, config_name='batch_logsoftmax'):
+
     def __init__(self, predictions_prefix, candidates_prefix):
         super().__init__()
         self._predictions_prefix = predictions_prefix
@@ -103,8 +101,8 @@ def __init__(self, predictions_prefix, candidates_prefix):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            predictions_prefix=config.get("predictions_prefix"),
-            candidates_prefix=config.get("candidates_prefix"),
+            predictions_prefix=config.get('predictions_prefix'),
+            candidates_prefix=config.get('candidates_prefix')
         )
 
     def forward(self, inputs):  # use log soft max
@@ -123,7 +121,8 @@ def forward(self, inputs):  # use log soft max
         return loss
 
 
-class CrossEntropyLoss(TorchLoss, config_name="ce"):
+class CrossEntropyLoss(TorchLoss, config_name='ce'):
+
     def __init__(self, predictions_prefix, labels_prefix, output_prefix=None):
         super().__init__()
         self._pred_prefix = predictions_prefix
@@ -134,7 +133,7 @@ def __init__(self, predictions_prefix, labels_prefix, output_prefix=None):
 
     def forward(self, inputs):
         all_logits = inputs[self._pred_prefix]  # (all_items, num_classes)
-        all_labels = inputs["{}.ids".format(self._labels_prefix)]  # (all_items)
+        all_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_items)
         assert all_logits.shape[0] == all_labels.shape[0]
 
         loss = self._loss(all_logits, all_labels)  # (1)
@@ -142,48 +141,55 @@ def forward(self, inputs):
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
+    
+class RqVaeLoss(TorchLoss, config_name='rqvae_loss'):
 
-
-class RqVaeLoss(TorchLoss, config_name="rqvae_loss"):
     def __init__(self, beta, output_prefix=None):
         super().__init__()
         self.beta = beta
         self._output_prefix = output_prefix
-
+        
         self._loss = nn.MSELoss()
-
+        
     @classmethod
     def create_from_config(cls, config, **kwargs):
         # 0.25 is default Beta in paper
         return cls(
-            beta=config.get("beta", 0.25),
-            output_prefix=config["output_prefix"],
+            beta = config.get('beta', 0.25),
+            output_prefix = config['output_prefix'],
         )
-
+                    
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
         embeddings_restored = inputs["embeddings_restored"]
         remainders = inputs["remainders"]
         codebooks_vectors = inputs["codebooks_vectors"]
-
+        
         rqvae_loss = 0
-
+        
         for remainder, codebook_vectors in zip(remainders, codebooks_vectors):
-            rqvae_loss += self.beta * self._loss(remainder, codebook_vectors.detach())
+            rqvae_loss += self.beta * self._loss(
+                remainder, codebook_vectors.detach()
+            )
             rqvae_loss += self._loss(codebook_vectors, remainder.detach())
-
+            
         recon_loss = self._loss(embeddings_restored, embeddings)
         loss = (recon_loss + rqvae_loss).mean(dim=0)
-
+        
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
-
+        
         return loss
 
 
-class BinaryCrossEntropyLoss(TorchLoss, config_name="bce"):
+class BinaryCrossEntropyLoss(TorchLoss, config_name='bce'):
+
     def __init__(
-        self, predictions_prefix, labels_prefix, with_logits=True, output_prefix=None
+            self,
+            predictions_prefix,
+            labels_prefix,
+            with_logits=True,
+            output_prefix=None
     ):
         super().__init__()
         self._pred_prefix = predictions_prefix
@@ -207,8 +213,14 @@ def forward(self, inputs):
         return loss
 
 
-class BPRLoss(TorchLoss, config_name="bpr"):
-    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
+class BPRLoss(TorchLoss, config_name='bpr'):
+
+    def __init__(
+            self,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
+    ):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -225,7 +237,8 @@ def forward(self, inputs):
         return loss
 
 
-class RegularizationLoss(TorchLoss, config_name="regularization"):
+class RegularizationLoss(TorchLoss, config_name='regularization'):
+
     def __init__(self, prefix, output_prefix=None):
         super().__init__()
         self._prefix = maybe_to_list(prefix)
@@ -234,7 +247,7 @@ def __init__(self, prefix, output_prefix=None):
     def forward(self, inputs):
         loss = 0.0
         for prefix in self._prefix:
-            loss += (1 / 2) * inputs[prefix].pow(2).mean()
+            loss += (1/2) * inputs[prefix].pow(2).mean()
 
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
@@ -242,23 +255,22 @@ def forward(self, inputs):
         return loss
 
 
-class FpsLoss(TorchLoss, config_name="fps"):
+class FpsLoss(TorchLoss, config_name='fps'):
+
     def __init__(
-        self,
-        fst_embeddings_prefix,
-        snd_embeddings_prefix,
-        tau=1.0,
-        normalize_embeddings=False,
-        use_mean=True,
-        output_prefix=None,
+            self,
+            fst_embeddings_prefix,
+            snd_embeddings_prefix,
+            tau=1.0,
+            normalize_embeddings=False,
+            use_mean=True,
+            output_prefix=None
     ):
         super().__init__()
         self._fst_embeddings_prefix = fst_embeddings_prefix
         self._snd_embeddings_prefix = snd_embeddings_prefix
         self._tau = tau
-        self._loss_function = nn.CrossEntropyLoss(
-            reduction="mean" if use_mean else "sum"
-        )
+        self._loss_function = nn.CrossEntropyLoss(reduction='mean' if use_mean else 'sum')
         self._normalize_embeddings = normalize_embeddings
         self._output_prefix = output_prefix
 
@@ -268,49 +280,34 @@ def forward(self, inputs):
 
         batch_size = fst_embeddings.shape[0]
 
-        combined_embeddings = torch.cat(
-            (fst_embeddings, snd_embeddings), dim=0
-        )  # (2 * x, embedding_dim)
+        combined_embeddings = torch.cat((fst_embeddings, snd_embeddings), dim=0)  # (2 * x, embedding_dim)
 
         if self._normalize_embeddings:
             combined_embeddings = torch.nn.functional.normalize(
                 combined_embeddings, p=2, dim=-1, eps=1e-6
             )  # (2 * x, embedding_dim)
 
-        similarity_scores = (
-            torch.mm(combined_embeddings, combined_embeddings.T) / self._tau
-        )  # (2 * x, 2 * x)
+        similarity_scores = torch.mm(
+            combined_embeddings,
+            combined_embeddings.T
+        ) / self._tau  # (2 * x, 2 * x)
 
         positive_samples = torch.cat(
-            (
-                torch.diag(similarity_scores, batch_size),
-                torch.diag(similarity_scores, -batch_size),
-            ),
-            dim=0,
+            (torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size)),
+            dim=0
         ).reshape(2 * batch_size, 1)  # (2 * x, 1)
-        assert torch.allclose(
-            torch.diag(similarity_scores, batch_size),
-            torch.diag(similarity_scores, -batch_size),
-        )
+        assert torch.allclose(torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size))
 
-        mask = torch.ones(
-            2 * batch_size, 2 * batch_size, dtype=torch.bool
-        )  # (2 * x, 2 * x)
+        mask = torch.ones(2 * batch_size, 2 * batch_size, dtype=torch.bool)  # (2 * x, 2 * x)
         mask = mask.fill_diagonal_(0)  # Remove equal embeddings scores
         for i in range(batch_size):  # Remove positives
             mask[i, batch_size + i] = 0
             mask[batch_size + i, i] = 0
 
-        negative_samples = similarity_scores[mask].reshape(
-            2 * batch_size, -1
-        )  # (2 * x, 2 * x - 2)
+        negative_samples = similarity_scores[mask].reshape(2 * batch_size, -1)  # (2 * x, 2 * x - 2)
 
-        labels = (
-            torch.zeros(2 * batch_size).to(positive_samples.device).long()
-        )  # (2 * x)
-        logits = torch.cat(
-            (positive_samples, negative_samples), dim=1
-        )  # (2 * x, 2 * x - 1)
+        labels = torch.zeros(2 * batch_size).to(positive_samples.device).long()  # (2 * x)
+        logits = torch.cat((positive_samples, negative_samples), dim=1)  # (2 * x, 2 * x - 1)
 
         loss = self._loss_function(logits, labels) / 2  # (1)
 
@@ -343,8 +340,14 @@ def forward(self, inputs):
         return loss
 
 
-class SASRecLoss(TorchLoss, config_name="sasrec"):
-    def __init__(self, positive_prefix, negative_prefix, output_prefix=None):
+class SASRecLoss(TorchLoss, config_name='sasrec'):
+
+    def __init__(
+            self,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
+    ):
         super().__init__()
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -354,40 +357,27 @@ def forward(self, inputs):
         positive_scores = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_scores = inputs[self._negative_prefix]  # (x, embedding_dim)
         sample_ids = inputs["sample_ids"]
-
+        
         num_items = negative_scores.shape[1] - 2
-
-        possible_indices = torch.arange(
-            1, num_items + 1, device=negative_scores.device
-        )  # 1, 2, ... num_items
-        mask = torch.ones_like(
-            possible_indices, dtype=torch.bool
-        )  # True, True, ... True
-        mask[sample_ids - 1] = False  # True, False, ... False, True, ... True
-        valid_indices = possible_indices[mask]  # 1, 2, ... num_items, except sample_ids
-
-        rand_idx = torch.randint(
-            0,
-            len(valid_indices),
-            size=(negative_scores.shape[0], 1),
-            device=negative_scores.device,
-        )
+        
+        possible_indices = torch.arange(1, num_items + 1, device=negative_scores.device) # 1, 2, ... num_items
+        mask = torch.ones_like(possible_indices, dtype=torch.bool) # True, True, ... True
+        mask[sample_ids - 1] = False # True, False, ... False, True, ... True
+        valid_indices = possible_indices[mask] # 1, 2, ... num_items, except sample_ids
+        
+        rand_idx = torch.randint(0, len(valid_indices), size=(negative_scores.shape[0], 1), device=negative_scores.device)
         index = valid_indices[rand_idx]
-
+        
         negative_scores = torch.gather(
             input=negative_scores,
             dim=1,
             index=index,
         )
-
+        
         assert positive_scores.shape[0] == negative_scores.shape[0]
 
-        positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(
-            dim=-1
-        )  # (x)
-        negative_loss = torch.log(
-            1.0 - nn.functional.sigmoid(negative_scores) + 1e-9
-        ).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
+        positive_loss = torch.log(nn.functional.sigmoid(positive_scores)).sum(dim=-1)  # (x)
+        negative_loss = torch.log(1.0 - nn.functional.sigmoid(negative_scores) + 1e-9).sum(dim=-1)  # (x), added 1e-9 for Tiger baseline
         loss = positive_loss + negative_loss  # (x)
         loss = -loss.sum()  # (1)
 
@@ -397,9 +387,14 @@ def forward(self, inputs):
         return loss
 
 
-class SamplesSoftmaxLoss(TorchLoss, config_name="sampled_softmax"):
+class SamplesSoftmaxLoss(TorchLoss, config_name='sampled_softmax'):
+
     def __init__(
-        self, queries_prefix, positive_prefix, negative_prefix, output_prefix=None
+            self,
+            queries_prefix,
+            positive_prefix,
+            negative_prefix,
+            output_prefix=None
     ):
         super().__init__()
         self._queries_prefix = queries_prefix
@@ -409,6 +404,7 @@ def __init__(
 
     def forward(self, inputs):
         queries_embeddings = inputs[self._queries_prefix]  # (batch_size, embedding_dim)
+        # TODOPK check
         positive_ids, positive_embeddings = inputs[
             self._positive_prefix
         ]  # (batch_size, embedding_dim)
@@ -418,13 +414,17 @@ def forward(self, inputs):
 
         # b -- batch_size, d -- embedding_dim
         positive_scores = torch.einsum(
-            "bd,bd->b", queries_embeddings, positive_embeddings
+            'bd,bd->b',
+            queries_embeddings,
+            positive_embeddings
         ).unsqueeze(-1)  # (batch_size, 1)
 
         if negative_embeddings.dim() == 2:  # (num_negatives, embedding_dim)
             # b -- batch_size, n -- num_negatives, d -- embedding_dim
             negative_scores = torch.einsum(
-                "bd,nd->bn", queries_embeddings, negative_embeddings
+                'bd,nd->bn',
+                queries_embeddings,
+                negative_embeddings
             )  # (batch_size, num_negatives)
 
             all_scores = torch.cat(
@@ -436,12 +436,12 @@ def forward(self, inputs):
             loss = (-logits)[:, 0]  # (batch_size)
             loss = loss.mean()  # (1)
         else:
-            assert (
-                negative_embeddings.dim() == 3
-            )  # (batch_size, num_negatives, embedding_dim)
+            assert negative_embeddings.dim() == 3  # (batch_size, num_negatives, embedding_dim)
             # b -- batch_size, n -- num_negatives, d -- embedding_dim
             negative_scores = torch.einsum(
-                "bd,bnd->bn", queries_embeddings, negative_embeddings
+                'bd,bnd->bn',
+                queries_embeddings,
+                negative_embeddings
             )  # (batch_size, num_negatives)
 
             assert False, "ask Vladimir wtf is it "
@@ -452,13 +452,14 @@ def forward(self, inputs):
         return loss
 
 
-class S3RecPretrainLoss(TorchLoss, config_name="s3rec_pretrain"):
+class S3RecPretrainLoss(TorchLoss, config_name='s3rec_pretrain'):
+
     def __init__(
-        self,
-        positive_prefix,
-        negative_prefix,
-        representation_prefix,
-        output_prefix=None,
+            self,
+            positive_prefix,
+            negative_prefix,
+            representation_prefix,
+            output_prefix=None
     ):
         super().__init__()
         self._positive_prefix = positive_prefix
@@ -471,39 +472,36 @@ def forward(self, inputs):
         positive_embeddings = inputs[self._positive_prefix]  # (x, embedding_dim)
         negative_embeddings = inputs[self._negative_prefix]  # (x, embedding_dim)
         current_embeddings = inputs[self._representation_prefix]  # (x, embedding_dim)
-        assert (
-            positive_embeddings.shape[0]
-            == negative_embeddings.shape[0]
-            == current_embeddings.shape[0]
-        )
+        assert positive_embeddings.shape[0] == negative_embeddings.shape[0] == current_embeddings.shape[0]
 
         positive_scores = torch.einsum(
-            "bd,bd->b", positive_embeddings, current_embeddings
+            'bd,bd->b',
+            positive_embeddings,
+            current_embeddings
         )  # (x)
 
         negative_scores = torch.einsum(
-            "bd,bd->b", negative_embeddings, current_embeddings
+            'bd,bd->b',
+            negative_embeddings,
+            current_embeddings
         )  # (x)
 
-        distance = torch.sigmoid(positive_scores) - torch.sigmoid(
-            negative_scores
-        )  # (x)
-        loss = torch.sum(
-            self._criterion(distance, torch.ones_like(distance, dtype=torch.float32))
-        )  # (1)
+        distance = torch.sigmoid(positive_scores) - torch.sigmoid(negative_scores)  # (x)
+        loss = torch.sum(self._criterion(distance, torch.ones_like(distance, dtype=torch.float32)))  # (1)
         if self._output_prefix is not None:
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
 
 
-class Cl4sRecLoss(TorchLoss, config_name="cl4srec"):
+class Cl4sRecLoss(TorchLoss, config_name='cl4srec'):
+
     def __init__(
-        self,
-        current_representation,
-        all_items_representation,
-        tau=1.0,
-        output_prefix=None,
+            self,
+            current_representation,
+            all_items_representation,
+            tau=1.0,
+            output_prefix=None
     ):
         super().__init__()
         self._current_representation = current_representation
@@ -513,9 +511,7 @@ def __init__(
         self._output_prefix = output_prefix
 
     def forward(self, inputs):
-        current_representation = inputs[
-            self._current_representation
-        ]  # (batch_size, embedding_dim)
+        current_representation = inputs[self._current_representation]  # (batch_size, embedding_dim)
         all_items_representation = inputs[
             self._all_items_representation
         ]  # (batch_size, num_negatives + 1, embedding_dim)
@@ -523,7 +519,9 @@ def forward(self, inputs):
         batch_size = current_representation.shape[0]
 
         logits = torch.einsum(
-            "bnd,bd->bn", all_items_representation, current_representation
+            'bnd,bd->bn',
+            all_items_representation,
+            current_representation
         )  # (batch_size, num_negatives + 1)
         labels = logits.new_zeros(batch_size)  # (batch_size)
 
@@ -535,15 +533,16 @@ def forward(self, inputs):
         return loss
 
 
-class DuorecSSLLoss(TorchLoss, config_name="duorec_ssl"):
+class DuorecSSLLoss(TorchLoss, config_name='duorec_ssl'):
+
     def __init__(
-        self,
-        original_embedding_prefix,
-        dropout_embedding_prefix,
-        similar_embedding_prefix,
-        normalize_embeddings=False,
-        tau=1.0,
-        output_prefix=None,
+            self,
+            original_embedding_prefix,
+            dropout_embedding_prefix,
+            similar_embedding_prefix,
+            normalize_embeddings=False,
+            tau=1.0,
+            output_prefix=None
     ):
         super().__init__()
         self._original_embedding_prefix = original_embedding_prefix
@@ -552,13 +551,14 @@ def __init__(
         self._normalize_embeddings = normalize_embeddings
         self._output_prefix = output_prefix
         self._tau = tau
-        self._loss_function = nn.CrossEntropyLoss(reduction="mean")
+        self._loss_function = nn.CrossEntropyLoss(reduction='mean')
 
     def _compute_partial_loss(self, fst_embeddings, snd_embeddings):
         batch_size = fst_embeddings.shape[0]
 
         combined_embeddings = torch.cat(
-            (fst_embeddings, snd_embeddings), dim=0
+            (fst_embeddings, snd_embeddings),
+            dim=0
         )  # (2 * x, embedding_dim)
 
         if self._normalize_embeddings:
@@ -566,76 +566,59 @@ def _compute_partial_loss(self, fst_embeddings, snd_embeddings):
                 combined_embeddings, p=2, dim=-1, eps=1e-6
             )
 
-        similarity_scores = (
-            torch.mm(combined_embeddings, combined_embeddings.T) / self._tau
-        )  # (2 * x, 2 * x)
+        similarity_scores = torch.mm(
+            combined_embeddings,
+            combined_embeddings.T
+        ) / self._tau  # (2 * x, 2 * x)
 
         positive_samples = torch.cat(
-            (
-                torch.diag(similarity_scores, batch_size),
-                torch.diag(similarity_scores, -batch_size),
-            ),
-            dim=0,
+            (torch.diag(similarity_scores, batch_size), torch.diag(similarity_scores, -batch_size)),
+            dim=0
         ).reshape(2 * batch_size, 1)  # (2 * x, 1)
 
         # TODO optimize
-        mask = torch.ones(
-            2 * batch_size, 2 * batch_size, dtype=torch.bool
-        )  # (2 * x, 2 * x)
+        mask = torch.ones(2 * batch_size, 2 * batch_size, dtype=torch.bool)  # (2 * x, 2 * x)
         mask = mask.fill_diagonal_(0)  # Remove equal embeddings scores
         for i in range(batch_size):  # Remove positives
             mask[i, batch_size + i] = 0
             mask[batch_size + i, i] = 0
 
-        negative_samples = similarity_scores[mask].reshape(
-            2 * batch_size, -1
-        )  # (2 * x, 2 * x - 2)
+        negative_samples = similarity_scores[mask].reshape(2 * batch_size, -1)  # (2 * x, 2 * x - 2)
 
-        labels = (
-            torch.zeros(2 * batch_size).to(positive_samples.device).long()
-        )  # (2 * x)
-        logits = torch.cat(
-            (positive_samples, negative_samples), dim=1
-        )  # (2 * x, 2 * x - 1)
+        labels = torch.zeros(2 * batch_size).to(positive_samples.device).long()  # (2 * x)
+        logits = torch.cat((positive_samples, negative_samples), dim=1)  # (2 * x, 2 * x - 1)
 
         loss = self._loss_function(logits, labels) / 2  # (1)
 
         return loss
 
     def forward(self, inputs):
-        original_embeddings = inputs[
-            self._original_embedding_prefix
-        ]  # (x, embedding_dim)
-        dropout_embeddings = inputs[
-            self._dropout_embedding_prefix
-        ]  # (x, embedding_dim)
-        similar_embeddings = inputs[
-            self._similar_embedding_prefix
-        ]  # (x, embedding_dim)
-
-        dropout_loss = self._compute_partial_loss(
-            original_embeddings, dropout_embeddings
-        )
+        original_embeddings = inputs[self._original_embedding_prefix]  # (x, embedding_dim)
+        dropout_embeddings = inputs[self._dropout_embedding_prefix]  # (x, embedding_dim)
+        similar_embeddings = inputs[self._similar_embedding_prefix]  # (x, embedding_dim)
+
+        dropout_loss = self._compute_partial_loss(original_embeddings, dropout_embeddings)
         ssl_loss = self._compute_partial_loss(original_embeddings, similar_embeddings)
 
         loss = dropout_loss + ssl_loss
 
         if self._output_prefix is not None:
-            inputs[f"{self._output_prefix}_dropout"] = dropout_loss.cpu().item()
-            inputs[f"{self._output_prefix}_ssl"] = ssl_loss.cpu().item()
+            inputs[f'{self._output_prefix}_dropout'] = dropout_loss.cpu().item()
+            inputs[f'{self._output_prefix}_ssl'] = ssl_loss.cpu().item()
             inputs[self._output_prefix] = loss.cpu().item()
 
         return loss
 
 
-class MCLSRLoss(TorchLoss, config_name="mclsr"):
+class MCLSRLoss(TorchLoss, config_name='mclsr'):
+
     def __init__(
-        self,
-        all_scores_prefix,
-        mask_prefix,
-        normalize_embeddings=False,
-        tau=1.0,
-        output_prefix=None,
+            self,
+            all_scores_prefix,
+            mask_prefix,
+            normalize_embeddings=False,
+            tau=1.0,
+            output_prefix=None
     ):
         super().__init__()
         self._all_scores_prefix = all_scores_prefix
@@ -645,9 +628,7 @@ def __init__(
         self._tau = tau
 
     def forward(self, inputs):
-        all_scores = inputs[
-            self._all_scores_prefix
-        ]  # (batch_size, batch_size, seq_len)
+        all_scores = inputs[self._all_scores_prefix]  # (batch_size, batch_size, seq_len)
         mask = inputs[self._mask_prefix]  # (batch_size)
 
         batch_size = mask.shape[0]
@@ -657,7 +638,8 @@ def forward(self, inputs):
 
         positive_scores = all_scores[positive_mask]  # (batch_size, seq_len)
         negative_scores = torch.reshape(
-            all_scores[~positive_mask], shape=(batch_size, batch_size - 1, seq_len)
+            all_scores[~positive_mask],
+            shape=(batch_size, batch_size - 1, seq_len)
         )  # (batch_size, batch_size - 1, seq_len)
         assert torch.allclose(all_scores[0, 1], negative_scores[0, 0])
         assert torch.allclose(all_scores[-1, -2], negative_scores[-1, -1])
diff --git a/modeling/metric/base.py b/modeling/metric/base.py
index e22c1d20..677f9086 100644
--- a/modeling/metric/base.py
+++ b/modeling/metric/base.py
@@ -1,18 +1,19 @@
-import torch
-
 from utils import MetaParent
 
+import torch
+
 
 class BaseMetric(metaclass=MetaParent):
     pass
 
 
 class StatefullMetric(BaseMetric):
+
     def reduce(self):
         raise NotImplementedError
 
 
-class StaticMetric(BaseMetric, config_name="dummy"):
+class StaticMetric(BaseMetric, config_name='dummy'):
     def __init__(self, name, value):
         self._name = name
         self._value = value
@@ -23,15 +24,17 @@ def __call__(self, inputs):
         return inputs
 
 
-class CompositeMetric(BaseMetric, config_name="composite"):
+class CompositeMetric(BaseMetric, config_name='composite'):
+
     def __init__(self, metrics):
         self._metrics = metrics
 
     @classmethod
     def create_from_config(cls, config):
-        return cls(
-            metrics=[BaseMetric.create_from_config(cfg) for cfg in config["metrics"]]
-        )
+        return cls(metrics=[
+            BaseMetric.create_from_config(cfg)
+            for cfg in config['metrics']
+        ])
 
     def __call__(self, inputs):
         for metric in self._metrics:
@@ -39,63 +42,57 @@ def __call__(self, inputs):
         return inputs
 
 
-class NDCGMetric(BaseMetric, config_name="ndcg"):
+class NDCGMetric(BaseMetric, config_name='ndcg'):
+
     def __init__(self, k):
         self._k = k
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][
-            :, : self._k
-        ].float()  # (batch_size, top_k_indices)
-        labels = inputs["{}.ids".format(labels_prefix)].float()  # (batch_size)
+        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
+        labels = inputs['{}.ids'.format(labels_prefix)].float()  # (batch_size)
 
         assert labels.shape[0] == predictions.shape[0]
 
-        hits = torch.eq(
-            predictions, labels[..., None]
-        ).float()  # (batch_size, top_k_indices)
-        discount_factor = 1 / torch.log2(
-            torch.arange(1, self._k + 1, 1).float() + 1.0
-        ).to(hits.device)  # (k)
-        dcg = torch.einsum("bk,k->b", hits, discount_factor)  # (batch_size)
+        hits = torch.eq(predictions, labels[..., None]).float()  # (batch_size, top_k_indices)
+        discount_factor = 1 / torch.log2(torch.arange(1, self._k + 1, 1).float() + 1.).to(hits.device)  # (k)
+        dcg = torch.einsum('bk,k->b', hits, discount_factor)  # (batch_size)
 
         return dcg.cpu().tolist()
 
 
-class RecallMetric(BaseMetric, config_name="recall"):
+class RecallMetric(BaseMetric, config_name='recall'):
+
     def __init__(self, k):
         self._k = k
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][
-            :, : self._k
-        ].float()  # (batch_size, top_k_indices)
-        labels = inputs["{}.ids".format(labels_prefix)].float()  # (batch_size)
+        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
+        labels = inputs['{}.ids'.format(labels_prefix)].float()  # (batch_size)
 
         assert labels.shape[0] == predictions.shape[0]
 
-        hits = torch.eq(
-            predictions, labels[..., None]
-        ).float()  # (batch_size, top_k_indices)
+        hits = torch.eq(predictions, labels[..., None]).float()  # (batch_size, top_k_indices)
         recall = hits.sum(dim=-1)  # (batch_size)
 
         return recall.cpu().tolist()
 
 
-class CoverageMetric(StatefullMetric, config_name="coverage"):
+class CoverageMetric(StatefullMetric, config_name='coverage'):
+
     def __init__(self, k, num_items):
         self._k = k
         self._num_items = num_items
-
+    
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(k=config["k"], num_items=kwargs["num_items"])
+        return cls(
+            k=config['k'],
+            num_items=kwargs['num_items']
+        )
 
     def __call__(self, inputs, pred_prefix, labels_prefix):
-        predictions = inputs[pred_prefix][
-            :, : self._k
-        ].float()  # (batch_size, top_k_indices)
+        predictions = inputs[pred_prefix][:, :self._k].float()  # (batch_size, top_k_indices)
         return predictions.view(-1).long().cpu().detach().tolist()  # (batch_size * k)
-
+    
     def reduce(self, values):
         return len(set(values)) / self._num_items
diff --git a/modeling/models/__init__.py b/modeling/models/__init__.py
index 377a703d..71fc9643 100644
--- a/modeling/models/__init__.py
+++ b/modeling/models/__init__.py
@@ -14,9 +14,9 @@
 from .random import RandomModel
 from .rqvae import RqVaeModel
 from .s3rec import S3RecModel
-from .sasrec_in_batch import SasRecInBatchModel
 from .sasrec_ce import SasRecCeModel
 from .sasrec_full import SasRecFullModel
+from .sasrec_in_batch import SasRecInBatchModel
 from .sasrec_real import SasRecRealModel
 from .sasrec_semantic import SasRecSemanticModel
 from .tiger import TigerModel
diff --git a/modeling/models/base.py b/modeling/models/base.py
index 786ef12c..a1384384 100644
--- a/modeling/models/base.py
+++ b/modeling/models/base.py
@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-
 from utils import DEVICE, MetaParent, create_masked_tensor, get_activation_function
 
 
@@ -9,42 +8,39 @@ class BaseModel(metaclass=MetaParent):
 
 
 class TorchModel(nn.Module, BaseModel):
+
     @torch.no_grad()
     def _init_weights(self, initializer_range):
         for key, value in self.named_parameters():
-            if "weight" in key:
-                if "norm" in key:
+            if 'weight' in key:
+                if 'norm' in key:
                     nn.init.ones_(value.data)
                 else:
                     nn.init.trunc_normal_(
                         value.data,
                         std=initializer_range,
                         a=-2 * initializer_range,
-                        b=2 * initializer_range,
+                        b=2 * initializer_range
                     )
-            elif "bias" in key:
+            elif 'bias' in key:
                 nn.init.zeros_(value.data)
-            elif "codebook" in key:
+            elif 'codebook' in key:
                 nn.init.trunc_normal_(
                     value.data,
                     std=initializer_range,
                     a=-2 * initializer_range,
-                    b=2 * initializer_range,
+                    b=2 * initializer_range
                 )
             else:
-                raise ValueError(f"Unknown transformer weight: {key}")
+                raise ValueError(f'Unknown transformer weight: {key}')
 
     @staticmethod
     def _get_last_embedding(embeddings, mask):
         lengths = torch.sum(mask, dim=-1)  # (batch_size)
-        lengths = lengths - 1  # (batch_size)
+        lengths = (lengths - 1)  # (batch_size)
         last_masks = mask.gather(dim=1, index=lengths[:, None])  # (batch_size, 1)
-        lengths = torch.tile(
-            lengths[:, None, None], (1, 1, embeddings.shape[-1])
-        )  # (batch_size, 1, emb_dim)
-        last_embeddings = embeddings.gather(
-            dim=1, index=lengths
-        )  # (batch_size, 1, emb_dim)
+        lengths = torch.tile(lengths[:, None, None], (1, 1, embeddings.shape[-1]))  # (batch_size, 1, emb_dim)
+        last_embeddings = embeddings.gather(dim=1, index=lengths)  # (batch_size, 1, emb_dim)
         last_embeddings = last_embeddings[last_masks]  # (batch_size, emb_dim)
         if not torch.allclose(embeddings[mask][-1], last_embeddings[-1]):
             print(embeddings)
@@ -56,18 +52,19 @@ def _get_last_embedding(embeddings, mask):
 
 
 class SequentialTorchModel(TorchModel):
+
     def __init__(
-        self,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        is_causal=True,
+            self,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            is_causal=True
     ):
         super().__init__()
         self._is_causal = is_causal
@@ -77,12 +74,11 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -95,10 +91,10 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True,
+            batch_first=True
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
-
+    
     def get_item_embeddings(self, events):
         return self._item_embeddings(events)
 
@@ -112,7 +108,8 @@ def _apply_sequential_encoder(
         assert embeddings.shape[0] == sum(lengths)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=lengths
+            data=embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
@@ -121,9 +118,7 @@ def _apply_sequential_encoder(
         position_embeddings = self._encoder_pos_embeddings(lengths, mask)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -134,10 +129,7 @@ def _apply_sequential_encoder(
             cls_token_tensor = self._cls_token.unsqueeze(0).unsqueeze(0)
             cls_token_expanded = torch.tile(cls_token_tensor, (batch_size, 1, 1))
             embeddings = torch.cat((cls_token_expanded, embeddings), dim=1)
-            mask = torch.cat(
-                (torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask),
-                dim=1,
-            )
+            mask = torch.cat((torch.ones((batch_size, 1), dtype=torch.bool, device=DEVICE), mask), dim=1)
 
         if user_embeddings is not None:
             embeddings = torch.cat((user_embeddings.unsqueeze(1), embeddings), dim=1)
@@ -148,15 +140,16 @@ def _apply_sequential_encoder(
             seq_len += 1  # TODOPK ask if this is correct
 
         if self._is_causal:
-            causal_mask = (
-                torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)
-            )  # (seq_len, seq_len)
+            causal_mask = torch.tril(torch.ones(seq_len, seq_len)).bool().to(DEVICE)  # (seq_len, seq_len)
             embeddings = self._encoder(
-                src=embeddings, mask=~causal_mask, src_key_padding_mask=~mask
+                src=embeddings,
+                mask=~causal_mask,
+                src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
         else:
             embeddings = self._encoder(
-                src=embeddings, src_key_padding_mask=~mask
+                src=embeddings,
+                src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
 
         return embeddings, mask
@@ -165,19 +158,16 @@ def _encoder_pos_embeddings(self, lengths, mask):
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
+            data=position_embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         return position_embeddings
 
@@ -187,18 +177,17 @@ def _add_cls_token(items, lengths, cls_token_id=0):
         batch_size = lengths.shape[0]
         num_new_items = num_items + batch_size
 
-        new_items = (
-            torch.ones(num_new_items, dtype=items.dtype, device=items.device)
-            * cls_token_id
-        )  # (num_new_items)
+        new_items = torch.ones(
+            num_new_items,
+            dtype=items.dtype,
+            device=items.device
+        ) * cls_token_id  # (num_new_items)
 
         old_items_mask = torch.zeros_like(new_items).bool()  # (num_new_items)
         old_items_mask = ~old_items_mask.scatter(
             src=torch.ones_like(lengths).bool(),
             dim=0,
-            index=torch.cat([torch.LongTensor([0]).to(DEVICE), lengths + 1]).cumsum(
-                dim=0
-            )[:-1],
+            index=torch.cat([torch.LongTensor([0]).to(DEVICE), lengths + 1]).cumsum(dim=0)[:-1]
         )  # (num_new_items)
         new_items[old_items_mask] = items
         new_length = lengths + 1
diff --git a/modeling/models/bert4rec.py b/modeling/models/bert4rec.py
index 69794fb6..40f1d331 100644
--- a/modeling/models/bert4rec.py
+++ b/modeling/models/bert4rec.py
@@ -1,24 +1,25 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 
+class Bert4RecModel(SequentialTorchModel, config_name='bert4rec'):
 
-class Bert4RecModel(SequentialTorchModel, config_name="bert4rec"):
     def __init__(
-        self,
-        sequence_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="gelu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='gelu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -30,77 +31,70 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=False,
+            is_causal=False
         )
         self._sequence_prefix = sequence_prefix
         self._labels_prefix = labels_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
+        )
 
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        embeddings = torch.nn.functional.gelu(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = torch.einsum(
-            "bsd,nd->bsn", embeddings, self._item_embeddings.weight
+            'bsd,nd->bsn', embeddings, self._item_embeddings.weight
         )  # (batch_size, seq_len, num_items)
         embeddings += self._bias[None, None, :]  # (batch_size, seq_len, num_items)
 
         if self.training:  # training mode
-            all_sample_labels = inputs[
-                "{}.ids".format(self._labels_prefix)
-            ]  # (all_batch_events)
+            all_sample_labels = inputs['{}.ids'.format(self._labels_prefix)]  # (all_batch_events)
             embeddings = embeddings[mask]  # (all_batch_events, num_items)
             labels_mask = (all_sample_labels != 0).bool()  # (all_batch_events)
 
             needed_logits = embeddings[labels_mask]  # (non_zero_events, num_items)
             needed_labels = all_sample_labels[labels_mask]  # (non_zero_events)
 
-            return {"logits": needed_logits, "labels.ids": needed_labels}
+            return {'logits': needed_logits, 'labels.ids': needed_labels}
         else:  # eval mode
-            candidate_scores = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, num_items)
+            candidate_scores = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/bert4rec_cls.py b/modeling/models/bert4rec_cls.py
index c0bbc61a..d6e59b0a 100644
--- a/modeling/models/bert4rec_cls.py
+++ b/modeling/models/bert4rec_cls.py
@@ -1,24 +1,25 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 
+class Bert4RecModelCLS(SequentialTorchModel, config_name='bert4rec_cls'):
 
-class Bert4RecModelCLS(SequentialTorchModel, config_name="bert4rec_cls"):
     def __init__(
-        self,
-        sequence_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="gelu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='gelu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -30,16 +31,20 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=False,
+            is_causal=False
         )
         self._sequence_prefix = sequence_prefix
         self._labels_prefix = labels_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
+        )
 
         self._init_weights(initializer_range)
 
@@ -48,50 +53,48 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
-            events=all_sample_events, lengths=all_sample_lengths, add_cls_token=True
+            events=all_sample_events,
+            lengths=all_sample_lengths,
+            add_cls_token=True
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
         predictions = embeddings[:, 0, :]  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             candidates = self._item_embeddings(
-                inputs["{}.ids".format(self._labels_prefix)]
-            )  # (batch_size, embedding_dim)
+                inputs['{}.ids'.format(self._labels_prefix)])  # (batch_size, embedding_dim)
 
-            return {"predictions": predictions, "candidates": candidates}
+            return {'predictions': predictions, 'candidates': candidates}
         else:  # eval mode
             candidate_scores = torch.einsum(
-                "bd,nd->bn", predictions, self._item_embeddings.weight
+                'bd,nd->bn',
+                predictions,
+                self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/cl4srec.py b/modeling/models/cl4srec.py
index e1bf7152..945c2a92 100644
--- a/modeling/models/cl4srec.py
+++ b/modeling/models/cl4srec.py
@@ -1,27 +1,28 @@
+from models.base import SequentialTorchModel
+
 import torch
 
-from models.base import SequentialTorchModel
 
+class Cl4SRecModel(SequentialTorchModel, config_name='cl4srec'):
 
-class Cl4SRecModel(SequentialTorchModel, config_name="cl4srec"):
     def __init__(
-        self,
-        sequence_prefix,
-        fst_augmented_sequence_prefix,
-        snd_augmented_sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            fst_augmented_sequence_prefix,
+            snd_augmented_sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -33,7 +34,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True
         )
         self._sequence_prefix = sequence_prefix
         self._fst_augmented_sequence_prefix = fst_augmented_sequence_prefix
@@ -46,56 +47,51 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            fst_augmented_sequence_prefix=config["fst_augmented_sequence_prefix"],
-            snd_augmented_sequence_prefix=config["snd_augmented_sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            num_layers=config["num_layers"],
-            num_heads=config["num_heads"],
-            embedding_dim=config["embedding_dim"],
-            dim_feedforward=config["dim_feedforward"],
-            dropout=config["dropout"],
-            activation=config["activation"],
-            layer_norm_eps=config["layer_norm_eps"],
-            initializer_range=config["initializer_range"],
+            sequence_prefix=config['sequence_prefix'],
+            fst_augmented_sequence_prefix=config['fst_augmented_sequence_prefix'],
+            snd_augmented_sequence_prefix=config['snd_augmented_sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            num_layers=config['num_layers'],
+            num_heads=config['num_heads'],
+            embedding_dim=config['embedding_dim'],
+            dim_feedforward=config['dim_feedforward'],
+            dropout=config['dropout'],
+            activation=config['activation'],
+            layer_norm_eps=config['layer_norm_eps'],
+            initializer_range=config['initializer_range']
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(
-            embeddings, mask
-        )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             items_logits = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items)
 
             # TODO remove this check
-            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
+            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
             assert torch.allclose(
-                self._item_embeddings(labels), self._item_embeddings.weight[labels]
+                self._item_embeddings(labels),
+                self._item_embeddings.weight[labels]
             )
 
             all_fst_aug_sample_events = inputs[
-                "{}.ids".format(self._fst_augmented_sequence_prefix)
+                '{}.ids'.format(self._fst_augmented_sequence_prefix)
             ]  # (all_batch_events)
-            all_fst_aug_sample_lengths = inputs[
-                "{}.length".format(self._fst_augmented_sequence_prefix)
-            ]  # (batch_size)
+            all_fst_aug_sample_lengths = inputs['{}.length'.format(self._fst_augmented_sequence_prefix)]  # (batch_size)
             fst_aug_embeddings, fst_aug_mask = self._apply_sequential_encoder(
                 all_fst_aug_sample_events, all_fst_aug_sample_lengths
             )  # (batch_size, fst_aug_seq_len, embedding_dim), (batch_size, fst_aug_seq_len)
@@ -104,11 +100,9 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             all_snd_aug_sample_events = inputs[
-                "{}.ids".format(self._snd_augmented_sequence_prefix)
+                '{}.ids'.format(self._snd_augmented_sequence_prefix)
             ]  # (all_batch_events)
-            all_snd_aug_sample_lengths = inputs[
-                "{}.length".format(self._snd_augmented_sequence_prefix)
-            ]  # (batch_size)
+            all_snd_aug_sample_lengths = inputs['{}.length'.format(self._snd_augmented_sequence_prefix)]  # (batch_size)
             snd_aug_embeddings, snd_aug_mask = self._apply_sequential_encoder(
                 all_snd_aug_sample_events, all_snd_aug_sample_lengths
             )  # (batch_size, snd_aug_seq_len, embedding_dim), (batch_size, snd_aug_seq_len)
@@ -117,23 +111,24 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             return {
-                "logits": items_logits,
-                "sequence_representation": last_embeddings,
-                "fst_aug_sequence_representation": last_fst_aug_embeddings,
-                "snd_aug_sequence_representation": last_snd_aug_embeddings,
+                'logits': items_logits,
+                'sequence_representation': last_embeddings,
+                'fst_aug_sequence_representation': last_fst_aug_embeddings,
+                'snd_aug_sequence_representation': last_snd_aug_embeddings
             }
         else:  # eval mode
-            candidate_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items, embedding_dim)
+            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, candidate_embeddings
+                'bd,nd->bn',
+                last_embeddings,
+                candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/duorec.py b/modeling/models/duorec.py
index 68f9df33..380a6675 100644
--- a/modeling/models/duorec.py
+++ b/modeling/models/duorec.py
@@ -1,27 +1,29 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 from utils import create_masked_tensor
 
 
-class DuoRecModel(SequentialTorchModel, config_name="duorec"):
+class DuoRecModel(SequentialTorchModel, config_name='duorec'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        augmented_sequence_prefix,
-        labels_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
-        is_causal=True,
+            self,
+            sequence_prefix,
+            augmented_sequence_prefix,
+            labels_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02,
+            is_causal=True
     ):
         super().__init__(
             num_items=num_items,
@@ -33,7 +35,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=is_causal,
+            is_causal=is_causal
         )
         self._sequence_prefix = sequence_prefix
         self._augmented_sequence_prefix = augmented_sequence_prefix
@@ -47,19 +49,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            augmented_sequence_prefix=config["augmented_sequence_prefix"],
-            labels_prefix=config["labels_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            num_layers=config["num_layers"],
-            num_heads=config["num_heads"],
-            embedding_dim=config["embedding_dim"],
-            dim_feedforward=config["dim_feedforward"],
-            dropout=config["dropout"],
-            activation=config["activation"],
-            layer_norm_eps=config["layer_norm_eps"],
-            initializer_range=config["initializer_range"],
+            sequence_prefix=config['sequence_prefix'],
+            augmented_sequence_prefix=config['augmented_sequence_prefix'],
+            labels_prefix=config['labels_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            num_layers=config['num_layers'],
+            num_heads=config['num_heads'],
+            embedding_dim=config['embedding_dim'],
+            dim_feedforward=config['dim_feedforward'],
+            dropout=config['dropout'],
+            activation=config['activation'],
+            layer_norm_eps=config['layer_norm_eps'],
+            initializer_range=config['initializer_range']
         )
 
     # TODO taken from duorec github
@@ -75,53 +77,47 @@ def _init_weights(self, module):
             module.bias.data.zero_()
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(
-            embeddings, mask
-        )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             items_logits = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items)
             training_output = {
-                "logits": items_logits,
-                "sequence_representation": last_embeddings,
+                'logits': items_logits,
+                'sequence_representation': last_embeddings
             }
 
             # TODO remove this check
-            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
+            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
             assert torch.allclose(
-                self._item_embeddings(labels), self._item_embeddings.weight[labels]
+                self._item_embeddings(labels),
+                self._item_embeddings.weight[labels]
             )
 
             # Unsupervised Augmentation
             embeddings_, mask_ = self._apply_sequential_encoder(
                 all_sample_events, all_sample_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-            last_embeddings_ = self._get_last_embedding(
-                embeddings_, mask_
-            )  # (batch_size, embedding_dim)
-            training_output["similar_sequence_representation"] = last_embeddings_
-            assert not torch.allclose(last_embeddings, last_embeddings_), (
-                "Embedding must be different because of dropout"
-            )
+            last_embeddings_ = self._get_last_embedding(embeddings_, mask_)  # (batch_size, embedding_dim)
+            training_output['similar_sequence_representation'] = last_embeddings_
+            assert not torch.allclose(last_embeddings, last_embeddings_), \
+                'Embedding must be different because of dropout'
 
             # Semantic Similarity
             all_sample_augmented_events = inputs[
-                "{}.ids".format(self._augmented_sequence_prefix)
+                '{}.ids'.format(self._augmented_sequence_prefix)
             ]  # (all_batch_events)
             all_sample_augmented_lengths = inputs[
-                "{}.length".format(self._augmented_sequence_prefix)
+                '{}.length'.format(self._augmented_sequence_prefix)
             ]  # (batch_size)
 
             augmented_embeddings, augmented_mask = self._apply_sequential_encoder(
@@ -130,23 +126,22 @@ def forward(self, inputs):
             last_augmented_embeddings = self._get_last_embedding(
                 augmented_embeddings, augmented_mask
             )  # (batch_size, embedding_dim)
-            training_output["augmented_sequence_representation"] = (
-                last_augmented_embeddings
-            )
+            training_output['augmented_sequence_representation'] = last_augmented_embeddings
 
             return training_output
         else:  # eval mode
-            candidate_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items, embedding_dim)
+            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, candidate_embeddings
+                'bd,nd->bn',
+                last_embeddings,
+                candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
-            return indices
+            return indices
\ No newline at end of file
diff --git a/modeling/models/graph_seq_rec.py b/modeling/models/graph_seq_rec.py
index caa7e2e0..b227f184 100644
--- a/modeling/models/graph_seq_rec.py
+++ b/modeling/models/graph_seq_rec.py
@@ -1,33 +1,35 @@
+from models.base import SequentialTorchModel
+
+from utils import create_masked_tensor, DEVICE
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
-from utils import DEVICE, create_masked_tensor
 
+class GraphSeqRecModel(SequentialTorchModel, config_name='graph_seq_rec'):
 
-class GraphSeqRecModel(SequentialTorchModel, config_name="graph_seq_rec"):
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        candidate_prefix,
-        common_graph,
-        user_graph,
-        item_graph,
-        num_hops,
-        graph_dropout,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        use_ce=False,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            candidate_prefix,
+            common_graph,
+            user_graph,
+            item_graph,
+            num_hops,
+            graph_dropout,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            use_ce=False,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -39,7 +41,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -55,34 +57,38 @@ def __init__(
         self._graph_dropout = graph_dropout
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
+        )
 
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            common_graph=kwargs["graph"],
-            user_graph=kwargs["user_graph"],
-            item_graph=kwargs["item_graph"],
-            num_hops=config["num_hops"],
-            graph_dropout=config["graph_dropout"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            use_ce=config.get("use_ce", False),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            common_graph=kwargs['graph'],
+            user_graph=kwargs['user_graph'],
+            item_graph=kwargs['item_graph'],
+            num_hops=config['num_hops'],
+            graph_dropout=config['graph_dropout'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            use_ce=config.get('use_ce', False),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _apply_graph_encoder(self, embeddings, graph):
@@ -104,45 +110,38 @@ def _apply_graph_encoder(self, embeddings, graph):
         return embeddings
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        lengths = inputs["{}.length".format(self._sequence_prefix)]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         common_graph_embeddings = self._apply_graph_encoder(
-            embeddings=self._item_embeddings.weight, graph=self._item_graph
+            embeddings=self._item_embeddings.weight,
+            graph=self._item_graph
         )  # (num_items + 2, embedding_dim)
 
-        embeddings = common_graph_embeddings[
-            all_sample_events
-        ]  # (all_batch_events, embedding_dim)
+        embeddings = common_graph_embeddings[all_sample_events]  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=lengths
+            data=embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
+            data=position_embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -150,52 +149,35 @@ def forward(self, inputs):
         embeddings[~mask] = 0
 
         if self._is_causal:
-            causal_mask = (
-                torch.tril(
-                    torch.tile(mask.unsqueeze(1), dims=[self._num_heads, seq_len, 1])
-                )
-                .bool()
-                .to(DEVICE)
-            )  # (seq_len, seq_len)
+            causal_mask = torch.tril(torch.tile(mask.unsqueeze(1), dims=[self._num_heads, seq_len, 1])).bool().to(DEVICE)  # (seq_len, seq_len)
             embeddings = self._encoder(
                 src=embeddings,
                 mask=~causal_mask,
             )  # (batch_size, seq_len, embedding_dim)
         else:
             embeddings = self._encoder(
-                src=embeddings, src_key_padding_mask=~mask
+                src=embeddings,
+                src_key_padding_mask=~mask
             )  # (batch_size, seq_len, embedding_dim)
 
         if self._use_ce:
-            embeddings = self._output_projection(
-                embeddings
-            )  # (batch_size, seq_len, embedding_dim)
-            embeddings = torch.nn.functional.gelu(
-                embeddings
-            )  # (batch_size, seq_len, embedding_dim)
+            embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+            embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
             embeddings = torch.einsum(
-                "bsd,nd->bsn", embeddings, self._item_embeddings.weight
+                'bsd,nd->bsn', embeddings, self._item_embeddings.weight
             )  # (batch_size, seq_len, num_items)
             embeddings += self._bias[None, None, :]  # (batch_size, seq_len, num_items)
         else:
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             if self._use_ce:
-                return {"logits": embeddings[mask]}
+                return {'logits': embeddings[mask]}
             else:
-                all_positive_sample_events = inputs[
-                    "{}.ids".format(self._positive_prefix)
-                ]  # (all_batch_events)
-                all_negative_sample_events = inputs[
-                    "{}.ids".format(self._negative_prefix)
-                ]  # (all_batch_events)
-
-                all_sample_embeddings = embeddings[
-                    mask
-                ]  # (all_batch_events, embedding_dim)
+                all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+                all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+
+                all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
                 all_positive_sample_embeddings = self._item_embeddings(
                     all_positive_sample_events
                 )  # (all_batch_events, embedding_dim)
@@ -204,27 +186,21 @@ def forward(self, inputs):
                 )  # (all_batch_events, embedding_dim)
 
                 return {
-                    "current_embeddings": all_sample_embeddings,
-                    "positive_embeddings": all_positive_sample_embeddings,
-                    "negative_embeddings": all_negative_sample_embeddings,
+                    'current_embeddings': all_sample_embeddings,
+                    'positive_embeddings': all_positive_sample_embeddings,
+                    'negative_embeddings': all_negative_sample_embeddings
                 }
         else:  # eval mode
             if self._use_ce:
-                last_embeddings = self._get_last_embedding(
-                    embeddings, mask
-                )  # (batch_size, num_items)
-
-                if "{}.ids".format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs[
-                        "{}.ids".format(self._candidate_prefix)
-                    ]  # (all_batch_candidates)
-                    candidate_lengths = inputs[
-                        "{}.length".format(self._candidate_prefix)
-                    ]  # (batch_size)
+                last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items)
+
+                if '{}.ids'.format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                     candidate_ids = torch.reshape(
                         candidate_events,
-                        (candidate_lengths.shape[0], candidate_lengths[0]),
+                        (candidate_lengths.shape[0], candidate_lengths[0])
                     )  # (batch_size, num_candidates)
                     candidate_scores = last_embeddings.gather(
                         dim=1, index=candidate_ids
@@ -232,35 +208,34 @@ def forward(self, inputs):
                 else:
                     candidate_scores = last_embeddings  # (batch_size, num_items + 2)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                    candidate_scores[:, self._num_items + 1:] = -torch.inf
             else:
-                if "{}.ids".format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs[
-                        "{}.ids".format(self._candidate_prefix)
-                    ]  # (all_batch_candidates)
-                    candidate_lengths = inputs[
-                        "{}.length".format(self._candidate_prefix)
-                    ]  # (batch_size)
+                if '{}.ids'.format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                     candidate_embeddings = self._item_embeddings(
                         candidate_events
                     )  # (all_batch_candidates, embedding_dim)
 
                     candidate_embeddings, _ = create_masked_tensor(
-                        data=candidate_embeddings, lengths=candidate_lengths
+                        data=candidate_embeddings,
+                        lengths=candidate_lengths
                     )  # (batch_size, num_candidates, embedding_dim)
 
                     candidate_scores = torch.einsum(
-                        "bd,bnd->bn", last_embeddings, candidate_embeddings
+                        'bd,bnd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_candidates)
                 else:
-                    candidate_embeddings = (
-                        self._item_embeddings.weight
-                    )  # (num_items, embedding_dim)
+                    candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
                     candidate_scores = torch.einsum(
-                        "bd,nd->bn", last_embeddings, candidate_embeddings
+                        'bd,nd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_items)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                    candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             return candidate_scores
diff --git a/modeling/models/gru4rec.py b/modeling/models/gru4rec.py
index 474743df..927445ac 100644
--- a/modeling/models/gru4rec.py
+++ b/modeling/models/gru4rec.py
@@ -1,20 +1,22 @@
-import torch
-from torch import nn
-
 from models.base import TorchModel
+
 from utils import create_masked_tensor, get_activation_function
 
+import torch
+from torch import nn
+
 
 class GRUModel(TorchModel):
+
     def __init__(
-        self,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
+            self,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5
     ):
         super().__init__()
         self._num_items = num_items
@@ -23,12 +25,11 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -40,7 +41,7 @@ def __init__(
             num_layers=num_layers,
             batch_first=True,
             dropout=dropout,
-            bidirectional=False,
+            bidirectional=False
         )
 
         self._hidden_to_output_projection = nn.Linear(embedding_dim, num_items)
@@ -50,31 +51,27 @@ def _apply_sequential_encoder(self, events, lengths):
         embeddings = self._item_embeddings(events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=lengths
+            data=embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
         positions_mask = positions < lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=lengths
+            data=position_embeddings,
+            lengths=lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
@@ -85,16 +82,14 @@ def _apply_sequential_encoder(self, events, lengths):
             input=embeddings,
             lengths=lengths.cpu(),
             batch_first=True,
-            enforce_sorted=False,
+            enforce_sorted=False
         )
 
         hidden = torch.zeros(
-            self._num_layers,
-            batch_size,
-            self._embedding_dim,
+            self._num_layers, batch_size, self._embedding_dim,
             dtype=embeddings.dtype,
             device=embeddings.device,
-            requires_grad=True,
+            requires_grad=True
         )  # (num_layers, batch_size, embedding_dim)
         out, hidden = self._encoder(packed_embeddings, hidden)
         embeddings, embedding_lengths = torch.nn.utils.rnn.pad_packed_sequence(
@@ -107,20 +102,21 @@ def _apply_sequential_encoder(self, events, lengths):
         return embeddings, mask
 
 
-class GRU4RecModel(GRUModel, config_name="gru4rec"):
+class GRU4RecModel(GRUModel, config_name='gru4rec'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -129,7 +125,7 @@ def __init__(
             num_layers=num_layers,
             dropout=dropout,
             activation=activation,
-            layer_norm_eps=layer_norm_eps,
+            layer_norm_eps=layer_norm_eps
         )
 
         self._sequence_prefix = sequence_prefix
@@ -140,42 +136,35 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_layers=config["num_layers"],
-            dropout=config.get("dropout", 0.0),
-            activation=config.get("activation", "tanh"),
-            layer_norm_eps=config.get("layer_norm_eps", 1e-5),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_layers=config['num_layers'],
+            dropout=config.get('dropout', 0.0),
+            activation=config.get('activation', 'tanh'),
+            layer_norm_eps=config.get('layer_norm_eps', 1e-5),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
-            events=all_sample_events, lengths=all_sample_lengths
+            events=all_sample_events,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim) (batch_size, seq_len)
 
         if self.training:  # training mode
-            all_positive_sample_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
+            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
             all_positive_sample_embeddings = self._item_embeddings(
                 all_positive_sample_events
             )  # (all_batch_events, embedding_dim)
 
-            all_sample_embeddings = embeddings[
-                mask
-            ]  # (all_batch_events, embedding_dim)
+            all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
 
             sample_end_idx = torch.cumsum(all_sample_lengths, dim=0)  # (batch_size)
             sample_begin_idx = sample_end_idx - all_sample_lengths  # (batch_size)
@@ -184,47 +173,44 @@ def forward(self, inputs):
             sample_begin_idx = sample_begin_idx[:, None]  # (batch_size, 1)
 
             negative_indices = torch.tile(
-                torch.arange(
-                    start=0,
-                    end=all_positive_sample_events.shape[0],
-                    device=all_sample_lengths.device,
-                ).long()[None],
-                dims=[all_sample_lengths.shape[0], 1],
+                torch.arange(start=0, end=all_positive_sample_events.shape[0], device=all_sample_lengths.device).long()[None],
+                dims=[all_sample_lengths.shape[0], 1]
             )  # (batch_size, all_batch_events)
 
-            negative_mask = (negative_indices >= sample_begin_idx) & (
-                negative_indices < sample_end_idx
-            )
+            negative_mask = (negative_indices >= sample_begin_idx) & (negative_indices < sample_end_idx)
             negative_mask = torch.repeat_interleave(
                 negative_mask, all_sample_lengths, dim=0
             )
 
             negative_scores = torch.einsum(
-                "ad,bd->ab",
+                'ad,bd->ab',
                 all_sample_embeddings,
-                self._item_embeddings(all_sample_events),
+                self._item_embeddings(all_sample_events)
             )  # (all_batch_events, all_batch_events)
-
+            
             positive_scores = torch.einsum(
-                "ad,ad->a", all_sample_embeddings, all_positive_sample_embeddings
+                'ad,ad->a',
+                all_sample_embeddings,
+                all_positive_sample_embeddings
             )  # (all_batch_events)
 
             return {
-                "positive_scores": positive_scores[..., None],
-                "negative_scores": negative_scores,
+                'positive_scores': positive_scores[..., None],
+                'negative_scores': negative_scores,
             }
         else:  # eval mode
-            last_embeddings = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, embedding_dim)
+            last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/gtorec.py b/modeling/models/gtorec.py
index d30e4616..dc29435f 100644
--- a/modeling/models/gtorec.py
+++ b/modeling/models/gtorec.py
@@ -1,39 +1,40 @@
+from models.base import SequentialTorchModel, TorchModel
+
+from utils import create_masked_tensor, get_activation_function
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.base import SequentialTorchModel, TorchModel
-from utils import create_masked_tensor, get_activation_function
-
 
-class GTOModel(SequentialTorchModel, config_name="gtorec"):
+class GTOModel(SequentialTorchModel, config_name='gtorec'):
     def __init__(
-        self,
-        # sequential params
-        sequence_prefix,  # =item_prefix
-        positive_prefix,
-        negative_prefix,
-        candidate_prefix,
-        source_domain,
-        num_users,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        # graph params
-        user_prefix,
-        graph,
-        graph_embedding_dim,
-        graph_num_layers,
-        # params with default values
-        dropout=0.0,
-        graph_dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
-        norm_first=True,
+            self,
+            # sequential params 
+            sequence_prefix, # =item_prefix
+            positive_prefix, 
+            negative_prefix, 
+            candidate_prefix,
+            source_domain, 
+            num_users,  
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            # graph params
+            user_prefix,
+            graph,
+            graph_embedding_dim,
+            graph_num_layers,
+            # params with default values
+            dropout=0.0,
+            graph_dropout=0.0,
+            activation='relu', 
+            layer_norm_eps=1e-9, 
+            initializer_range=0.02,
+            norm_first=True
     ):
         super().__init__(
             num_items=num_items,
@@ -45,9 +46,9 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True 
         )
-        # sequential part
+        # sequential part 
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
         self._negative_prefix = negative_prefix
@@ -55,9 +56,13 @@ def __init__(
         self._source_domain = source_domain
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
+        )
+        self._bias = nn.Parameter(
+            data=torch.zeros(num_items + 2),
+            requires_grad=True
         )
-        self._bias = nn.Parameter(data=torch.zeros(num_items + 2), requires_grad=True)
 
         # graph part
         self._user_prefix = user_prefix
@@ -68,13 +73,15 @@ def __init__(
         self._graph_dropout = graph_dropout
 
         self._graph_user_embeddings = nn.Embedding(
-            num_embeddings=num_users + 2, embedding_dim=self._graph_embedding_dim
+            num_embeddings=num_users + 2,
+            embedding_dim=self._graph_embedding_dim
         )
         self._graph_item_embeddings = nn.Embedding(
-            num_embeddings=num_items + 2, embedding_dim=self._graph_embedding_dim
+            num_embeddings=num_items + 2,
+            embedding_dim=self._graph_embedding_dim
         )
 
-        # cross_attention part
+        # cross_attention part 
         self._mha = nn.MultiheadAttention(
             embed_dim=embedding_dim,
             num_heads=num_heads,
@@ -100,39 +107,36 @@ def __init__(
             in_features=2 * embedding_dim,
             out_features=embedding_dim,
         )
-
+        
         self._init_weights(initializer_range)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
             # sequential part
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
-            norm_first=config.get("norm_first", True),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02),
+            norm_first=config.get('norm_first', True),
             # graph part
-            user_prefix=config["user_prefix"],
-            num_users=kwargs["num_users"],
+            user_prefix=config['user_prefix'],
+            num_users=kwargs['num_users'],
             graph_embedding_dim=config["graph_embedding_dim"],
             graph_num_layers=config["graph_num_layers"],
-            graph_dropout=config.get("graph_dropout", 0.0),
+            graph_dropout=config.get("graph_dropout", 0.0)
         )
-
+    
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat(
-            (self._graph_user_embeddings.weight, self._graph_item_embeddings.weight),
-            dim=0,
-        )
+        ego_embeddings = torch.cat((self._graph_user_embeddings.weight, self._graph_item_embeddings.weight), dim=0)
         all_embeddings = [ego_embeddings]
 
         if self._graph_dropout > 0:  # drop some edges
@@ -163,8 +167,8 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def _get_graph_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs["{}.ids".format(prefix)]  # (batch_size)
-        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
+        ids = inputs['{}.ids'.format(prefix)]  # (batch_size)
+        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (batch_size, emb_dim)
         ego_embeddings = ego_embeddings(ids)  # (batch_size, emb_dim)
@@ -175,15 +179,13 @@ def _get_graph_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings
         assert torch.all(mask == ego_mask)
 
         return padded_embeddings, padded_ego_embeddings, mask
-
+    
     def _ca_block(self, q, k, v, attn_mask=None, key_padding_mask=None):
         x = self._mha(
-            q,
-            k,
-            v,
+            q, k, v,
             attn_mask=attn_mask,
             key_padding_mask=key_padding_mask,
-            need_weights=False,
+            need_weights=False
         )[0]  # (batch_size, seq_len, embedding_dim)
         return self.dropout1(x)  # (batch_size, seq_len, embedding_dim)
 
@@ -193,19 +195,11 @@ def _ff_block(self, x):
 
     def forward(self, inputs):
         # target domain item sequence
-        all_sample_events_target = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths_target = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events_target = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths_target = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
         # source domain item sequence
-        all_sample_events_source = inputs[
-            "{}.{}.ids".format(self._sequence_prefix, self._source_domain)
-        ]  # (all_batch_events)
-        all_sample_lengths_source = inputs[
-            "{}.{}.length".format(self._sequence_prefix, self._source_domain)
-        ]  # (batch_size)
+        all_sample_events_source = inputs['{}.{}.ids'.format(self._sequence_prefix, self._source_domain)]  # (all_batch_events)
+        all_sample_lengths_source = inputs['{}.{}.length'.format(self._sequence_prefix, self._source_domain)]  # (batch_size)
 
         # sequential model encoder and target domain items embeddings from sequential model
         seq_embeddings_target, seq_mask_target = self._apply_sequential_encoder(
@@ -213,137 +207,81 @@ def forward(self, inputs):
         )  # (batch_size, target_seq_len, embedding_dim), (batch_size, target_seq_len)
 
         # target domain items encoder for graph model
-        all_final_user_embeddings_target, all_final_item_embeddings_target = (
-            self._apply_graph_encoder(
-                all_sample_events_target, all_sample_lengths_target
-            )
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings_target, all_final_item_embeddings_target = \
+            self._apply_graph_encoder(all_sample_events_target, all_sample_lengths_target)  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
         # source domain items encoder for graph model
-        all_final_user_embeddings_source, all_final_item_embeddings_source = (
-            self._apply_graph_encoder(
-                all_sample_events_source, all_sample_lengths_source
-            )
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
-
+        all_final_user_embeddings_source, all_final_item_embeddings_source = \
+            self._apply_graph_encoder(all_sample_events_source, all_sample_lengths_source)  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        
         # target domain items embeddings from graph model
-        (
-            graph_embeddings_target,
-            graph_item_ego_embeddings_target,
-            graph_item_mask_target,
-        ) = self._get_graph_embeddings(
-            inputs,
-            self._sequence_prefix,
-            self._graph_item_embeddings,
-            all_final_item_embeddings_target,
+        graph_embeddings_target, graph_item_ego_embeddings_target, graph_item_mask_target = self._get_graph_embeddings(
+            inputs, self._sequence_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
         )
-        graph_item_embeddings_target = graph_embeddings_target[
-            graph_item_mask_target
-        ]  # (batch_size, target_seq_len, embedding_dim)
+        graph_item_embeddings_target = graph_embeddings_target[graph_item_mask_target]  # (batch_size, target_seq_len, embedding_dim) 
         # source domain items embeddings from graph model
-        (
-            graph_embeddings_source,
-            graph_item_ego_embeddings_source,
-            graph_item_mask_source,
-        ) = self._get_graph_embeddings(
-            inputs,
-            self._sequence_prefix,
-            self._graph_item_embeddings,
-            all_final_item_embeddings_source,
+        graph_embeddings_source, graph_item_ego_embeddings_source, graph_item_mask_source = self._get_graph_embeddings(
+            inputs, self._sequence_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
         )
-        graph_item_embeddings_source = graph_embeddings_source[
-            graph_item_mask_source
-        ]  # (batch_size, source_seq_len, embedding_dim)
-
+        graph_item_embeddings_source = graph_embeddings_source[graph_item_mask_source]  # (batch_size, source_seq_len, embedding_dim) 
+        
         # embeddings + graph_embeddings_target -> cross-attention
         # query   = embeddings
         # keys    = graph_embeddings_target
         # values  = graph_embeddings_target
-        if self.norm_first:
-            graph_embeddings_target = graph_embeddings_target + self.norm1(
-                self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_target,
-                    v=graph_embeddings_target,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_target,
-                )
-            )  # (batch_size, target_seq_len, embedding_dim)
-            graph_embeddings_target = graph_embeddings_target + self.norm2(
-                self._ff_block(graph_embeddings_target)
-            )
+        if self.norm_first: 
+            graph_embeddings_target = graph_embeddings_target + self.norm1(self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_target,
+                v=graph_embeddings_target,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_target
+            ))  # (batch_size, target_seq_len, embedding_dim)
+            graph_embeddings_target = graph_embeddings_target + self.norm2(self._ff_block(graph_embeddings_target))
         else:
-            graph_embeddings_target = self.norm1(
-                graph_embeddings_target
-                + self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_target,
-                    v=graph_embeddings_target,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_target,
-                )
-            )  # (batch_size, target_seq_len, embedding_dim)
-            graph_embeddings_target = self.norm2(
-                graph_embeddings_target + self._ff_block(graph_embeddings_target)
-            )
+            graph_embeddings_target = self.norm1(graph_embeddings_target + self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_target,
+                v=graph_embeddings_target,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_target
+            ))  # (batch_size, target_seq_len, embedding_dim)
+            graph_embeddings_target = self.norm2(graph_embeddings_target + self._ff_block(graph_embeddings_target))
         # target-target cross-attention result
-        mha_embeddings_target = torch.cat(
-            [seq_embeddings_target, graph_embeddings_target], dim=-1
-        )
-        mha_embeddings_target = self._mha_output_projection(
-            mha_embeddings_target
-        )  # (batch_size, target_seq_len, embedding_dim)
+        mha_embeddings_target = torch.cat([seq_embeddings_target, graph_embeddings_target], dim=-1)
+        mha_embeddings_target = self._mha_output_projection(mha_embeddings_target)  # (batch_size, target_seq_len, embedding_dim)
 
         # embeddings + graph_embeddings_source -> cross-attention
         # query   = embeddings
         # keys    = graph_embeddings_source
         # values  = graph_embeddings_source
-        if self.norm_first:
-            graph_embeddings_source = graph_embeddings_source + self.norm1(
-                self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_source,
-                    v=graph_embeddings_source,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_source,
-                )
-            )  # (batch_size, seq_len, embedding_dim)
-            graph_embeddings_source = graph_embeddings_source + self.norm2(
-                self._ff_block(graph_embeddings_source)
-            )
+        if self.norm_first: 
+            graph_embeddings_source = graph_embeddings_source + self.norm1(self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_source,
+                v=graph_embeddings_source,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_source
+            ))  # (batch_size, seq_len, embedding_dim)
+            graph_embeddings_source = graph_embeddings_source + self.norm2(self._ff_block(graph_embeddings_source))
         else:
-            graph_embeddings_source = self.norm1(
-                graph_embeddings_source
-                + self._ca_block(
-                    q=seq_embeddings_target,
-                    k=graph_embeddings_source,
-                    v=graph_embeddings_source,
-                    attn_mask=None,
-                    key_padding_mask=~graph_item_mask_source,
-                )
-            )  # (batch_size, seq_len, embedding_dim)
-            graph_embeddings_source = self.norm2(
-                graph_embeddings_source + self._ff_block(graph_embeddings_source)
-            )
+            graph_embeddings_source = self.norm1(graph_embeddings_source + self._ca_block(
+                q=seq_embeddings_target,
+                k=graph_embeddings_source,
+                v=graph_embeddings_source,
+                attn_mask=None,
+                key_padding_mask=~graph_item_mask_source
+            ))  # (batch_size, seq_len, embedding_dim)
+            graph_embeddings_source = self.norm2(graph_embeddings_source + self._ff_block(graph_embeddings_source))
         # source-target cross-attention result
-        mha_embeddings_source = torch.cat(
-            [seq_embeddings_target, graph_embeddings_source], dim=-1
-        )
-        mha_embeddings_source = self._mha_output_projection(
-            mha_embeddings_source
-        )  # (batch_size, seq_len, embedding_dim)
+        mha_embeddings_source = torch.cat([seq_embeddings_target, graph_embeddings_source], dim=-1)
+        mha_embeddings_source = self._mha_output_projection(mha_embeddings_source)  # (batch_size, seq_len, embedding_dim)
 
         if self.training:  # training mode
             # sequential part
-            all_positive_sample_events = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            all_negative_sample_events = inputs[
-                "{}.ids".format(self._negative_prefix)
-            ]  # (all_batch_events)
-
-            all_sample_embeddings = seq_embeddings_target[
-                seq_mask_target
-            ]  # (all_batch_events, embedding_dim)
+            all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+
+            all_sample_embeddings = seq_embeddings_target[seq_mask_target]  # (all_batch_events, embedding_dim)
             all_positive_sample_embeddings = self._item_embeddings(
                 all_positive_sample_events
             )  # (all_batch_events, embedding_dim)
@@ -352,150 +290,96 @@ def forward(self, inputs):
             )  # (all_batch_events, embedding_dim)
 
             # graph part, target domain item embeddings
-            graph_positive_embeddings_target, _, graph_positive_mask_target = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._positive_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_target,
-                )
+            graph_positive_embeddings_target, _, graph_positive_mask_target = self._get_graph_embeddings(
+                inputs, self._positive_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
             )
-            graph_negative_embeddings_target, _, graph_negative_mask_target = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._negative_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_target,
-                )
+            graph_negative_embeddings_target, _, graph_negative_mask_target = self._get_graph_embeddings(
+                inputs, self._negative_prefix, self._graph_item_embeddings, all_final_item_embeddings_target
             )
             # b - batch_size, s - seq_len, d - embedding_dim
             graph_positive_scores_target = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_target,
-                graph_positive_embeddings_target,
+                'bd,bsd->bs', graph_item_embeddings_target, graph_positive_embeddings_target
             )  # (batch_size, target_seq_len)
             graph_negative_scores_target = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_target,
-                graph_negative_embeddings_target,
+                'bd,bsd->bs', graph_item_embeddings_target, graph_negative_embeddings_target
             )  # (batch_size, target_seq_len)
-            graph_positive_scores_target = graph_positive_scores_target[
-                graph_positive_mask_target
-            ]  # (all_batch_events)
-            graph_negative_scores_target = graph_negative_scores_target[
-                graph_negative_mask_target
-            ]  # (all_batch_events)
+            graph_positive_scores_target = graph_positive_scores_target[graph_positive_mask_target]  # (all_batch_events)
+            graph_negative_scores_target = graph_negative_scores_target[graph_negative_mask_target]  # (all_batch_events)
 
             # graph part, source domain item embeddings
-            graph_positive_embeddings_source, _, graph_positive_mask_source = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._positive_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_source,
-                )
+            graph_positive_embeddings_source, _, graph_positive_mask_source = self._get_graph_embeddings(
+                inputs, self._positive_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
             )
-            graph_negative_embeddings_source, _, graph_negative_mask_source = (
-                self._get_graph_embeddings(
-                    inputs,
-                    self._negative_prefix,
-                    self._graph_item_embeddings,
-                    all_final_item_embeddings_source,
-                )
+            graph_negative_embeddings_source, _, graph_negative_mask_source = self._get_graph_embeddings(
+                inputs, self._negative_prefix, self._graph_item_embeddings, all_final_item_embeddings_source
             )
             # b - batch_size, s - seq_len, d - embedding_dim
             graph_positive_scores_source = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_source,
-                graph_positive_embeddings_source,
+                'bd,bsd->bs', graph_item_embeddings_source, graph_positive_embeddings_source
             )  # (batch_size, source_seq_len)
             graph_negative_scores_source = torch.einsum(
-                "bd,bsd->bs",
-                graph_item_embeddings_source,
-                graph_negative_embeddings_source,
+                'bd,bsd->bs', graph_item_embeddings_source, graph_negative_embeddings_source
             )  # (batch_size, source_seq_len)
-            graph_positive_scores_source = graph_positive_scores_source[
-                graph_positive_mask_source
-            ]  # (all_batch_events)
-            graph_negative_scores_source = graph_negative_scores_source[
-                graph_negative_mask_source
-            ]  # (all_batch_events)
+            graph_positive_scores_source = graph_positive_scores_source[graph_positive_mask_source]  # (all_batch_events)
+            graph_negative_scores_source = graph_negative_scores_source[graph_negative_mask_source]  # (all_batch_events)
 
             # mha part
-            mha_all_sample_embeddings_target = mha_embeddings_target[
-                seq_mask_target
-            ]  # (all_batch_events, embedding_dim)
-            mha_all_sample_embeddings_source = mha_embeddings_source[
-                seq_mask_target
-            ]  # (all_batch_events, embedding_dim)
+            mha_all_sample_embeddings_target = mha_embeddings_target[seq_mask_target]  # (all_batch_events, embedding_dim)
+            mha_all_sample_embeddings_source = mha_embeddings_source[seq_mask_target]  # (all_batch_events, embedding_dim)
 
             return {
                 # sequential part
                 # target domain item embeddings
-                "current_embeddings": all_sample_embeddings,
-                "positive_embeddings": all_positive_sample_embeddings,
-                "negative_embeddings": all_negative_sample_embeddings,
+                'current_embeddings': all_sample_embeddings, 
+                'positive_embeddings': all_positive_sample_embeddings,
+                'negative_embeddings': all_negative_sample_embeddings,
+                    
                 # graph part
                 # target domain item embeddings
-                "graph_positive_embeddings_target": graph_positive_embeddings_target[
-                    graph_positive_mask_target
-                ],
-                "graph_negative_embeddings_target": graph_negative_embeddings_target[
-                    graph_negative_mask_target
-                ],
-                "graph_positive_scores_target": graph_positive_scores_target,
-                "graph_negative_scores_target": graph_negative_scores_target,
-                "graph_item_embeddings_target": graph_item_embeddings_target,
+                'graph_positive_embeddings_target': graph_positive_embeddings_target[graph_positive_mask_target],
+                'graph_negative_embeddings_target': graph_negative_embeddings_target[graph_negative_mask_target],
+                'graph_positive_scores_target': graph_positive_scores_target,
+                'graph_negative_scores_target': graph_negative_scores_target,
+                'graph_item_embeddings_target': graph_item_embeddings_target,
                 # source domain item embeddings
-                "graph_positive_embeddings_source": graph_positive_embeddings_source[
-                    graph_positive_mask_source
-                ],
-                "graph_negative_embeddings_source": graph_negative_embeddings_source[
-                    graph_negative_mask_source
-                ],
-                "graph_positive_scores_source": graph_positive_scores_source,
-                "graph_negative_scores_source": graph_negative_scores_source,
-                "graph_item_embeddings_source": graph_item_embeddings_source,
+                'graph_positive_embeddings_source': graph_positive_embeddings_source[graph_positive_mask_source],
+                'graph_negative_embeddings_source': graph_negative_embeddings_source[graph_negative_mask_source],
+                'graph_positive_scores_source': graph_positive_scores_source,
+                'graph_negative_scores_source': graph_negative_scores_source,
+                'graph_item_embeddings_source': graph_item_embeddings_source,
+
                 # mha part
                 # target domain item embeddings
-                "mha_embeddings_target": mha_all_sample_embeddings_target,
-                "mha_positive_embeddings_target": all_positive_sample_embeddings,
-                "mha_negative_embeddings_target": all_negative_sample_embeddings,
+                'mha_embeddings_target': mha_all_sample_embeddings_target, 
+                'mha_positive_embeddings_target': all_positive_sample_embeddings,
+                'mha_negative_embeddings_target': all_negative_sample_embeddings,
                 # source domain item embeddings
-                "mha_embeddings_source": mha_all_sample_embeddings_source,
-                "mha_positive_embeddings_source": all_positive_sample_embeddings,
-                "mha_negative_embeddings_source": all_negative_sample_embeddings,
+                'mha_embeddings_source': mha_all_sample_embeddings_source, 
+                'mha_positive_embeddings_source': all_positive_sample_embeddings,
+                'mha_negative_embeddings_source': all_negative_sample_embeddings
             }
         else:  # eval mode
-            seq_last_embeddings_target = self._get_last_embedding(
-                seq_embeddings_target, seq_mask_target
-            )  # (batch_size, embedding_dim)
-            mha_last_embeddings_target = self._get_last_embedding(
-                mha_embeddings_target, seq_mask_target
-            )  # (batch_size, embedding_dim)
-            mha_last_embeddings_source = self._get_last_embedding(
-                mha_embeddings_source, seq_mask_target
-            )  # (batch_size, embedding_dim)
+            seq_last_embeddings_target = self._get_last_embedding(seq_embeddings_target, seq_mask_target)  # (batch_size, embedding_dim)
+            mha_last_embeddings_target = self._get_last_embedding(mha_embeddings_target, seq_mask_target)  # (batch_size, embedding_dim)
+            mha_last_embeddings_source = self._get_last_embedding(mha_embeddings_source, seq_mask_target)  # (batch_size, embedding_dim)
 
             aggregated_last_embeddings = torch.maximum(
-                seq_last_embeddings_target,
-                torch.maximum(mha_last_embeddings_target, mha_last_embeddings_source),
+                seq_last_embeddings_target, 
+                torch.maximum(mha_last_embeddings_target, mha_last_embeddings_source)
             )  # (batch_size, embedding_dim)
 
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", aggregated_last_embeddings, self._item_embeddings.weight
+                'bd,nd->bn',
+                aggregated_last_embeddings,
+                self._item_embeddings.weight
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
-            if "{}.ids".format(self._candidate_prefix) in inputs:
-                candidate_events = inputs[
-                    "{}.ids".format(self._candidate_prefix)
-                ]  # (all_batch_candidates)
-                candidate_lengths = inputs[
-                    "{}.length".format(self._candidate_prefix)
-                ]  # (batch_size)
+            if '{}.ids'.format(self._candidate_prefix) in inputs:
+                candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                 batch_size = candidate_lengths.shape[0]
                 num_candidates = candidate_lengths[0]
@@ -503,11 +387,12 @@ def forward(self, inputs):
                 candidate_scores = torch.gather(
                     input=candidate_scores,
                     dim=1,
-                    index=torch.reshape(candidate_events, [batch_size, num_candidates]),
+                    index=torch.reshape(candidate_events, [batch_size, num_candidates])
                 )  # (batch_size, num_candidates)
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20), (batch_size, 20)
 
             return indices
diff --git a/modeling/models/lightgcn.py b/modeling/models/lightgcn.py
index 19d79b80..79e5f788 100644
--- a/modeling/models/lightgcn.py
+++ b/modeling/models/lightgcn.py
@@ -1,23 +1,25 @@
+from models.base import TorchModel
+
+from utils import create_masked_tensor, DEVICE
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.base import TorchModel
-from utils import DEVICE, create_masked_tensor
 
+class LightGCNModel(TorchModel, config_name='light_gcn'):
 
-class LightGCNModel(TorchModel, config_name="light_gcn"):
     def __init__(
-        self,
-        user_prefix,
-        positive_prefix,
-        graph,
-        num_users,
-        num_items,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        initializer_range=0.02,
+            self,
+            user_prefix,
+            positive_prefix,
+            graph,
+            num_users,
+            num_items,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            initializer_range=0.02
     ):
         super().__init__()
         self._user_prefix = user_prefix
@@ -30,11 +32,13 @@ def __init__(
         self._dropout_rate = dropout
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -42,21 +46,19 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config["user_prefix"],
-            positive_prefix=config["positive_prefix"],
-            graph=kwargs["graph"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            embedding_dim=config["embedding_dim"],
-            num_layers=config["num_layers"],
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            user_prefix=config['user_prefix'],
+            positive_prefix=config['positive_prefix'],
+            graph=kwargs['graph'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            embedding_dim=config['embedding_dim'],
+            num_layers=config['num_layers'],
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat(
-            (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-        )
+        ego_embeddings = torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
         all_embeddings = [ego_embeddings]
 
         if self._dropout_rate > 0:  # drop some edges
@@ -87,8 +89,8 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs["{}.ids".format(prefix)]  # (all_batch_events)
-        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
+        ids = inputs['{}.ids'.format(prefix)]  # (all_batch_events)
+        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (all_batch_events, embedding_dim)
         ego_embeddings = ego_embeddings(ids)  # (all_batch_events, embedding_dim)
@@ -106,9 +108,8 @@ def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
         return padded_embeddings, padded_ego_embeddings, mask
 
     def forward(self, inputs):
-        all_final_user_embeddings, all_final_item_embeddings = (
-            self._apply_graph_encoder()
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings, all_final_item_embeddings = \
+            self._apply_graph_encoder()  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
         user_embeddings, user_ego_embeddings, user_mask = self._get_embeddings(
             inputs, self._user_prefix, self._user_embeddings, all_final_user_embeddings
@@ -116,69 +117,63 @@ def forward(self, inputs):
         user_embeddings = user_embeddings[user_mask]  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
-            positive_item_ids = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            positive_item_lengths = inputs[
-                "{}.length".format(self._positive_prefix)
-            ]  # (batch_size)
+            positive_item_ids = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            positive_item_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
 
             batch_size = positive_item_lengths.shape[0]
             max_sequence_length = positive_item_lengths.max().item()
 
-            mask = (
-                torch.arange(end=max_sequence_length, device=DEVICE)[None].tile(
-                    [batch_size, 1]
-                )
-                < positive_item_lengths[:, None]
-            )  # (batch_size, max_seq_len)
-
-            positive_user_ids = (
-                torch.arange(batch_size, device=DEVICE)[None]
-                .tile([max_sequence_length, 1])
-                .T
-            )  # (batch_size, max_seq_len)
+            mask = torch.arange(
+                end=max_sequence_length,
+                device=DEVICE
+            )[None].tile([batch_size, 1]) < positive_item_lengths[:, None]  # (batch_size, max_seq_len)
+
+            positive_user_ids = torch.arange(
+                batch_size,
+                device=DEVICE
+            )[None].tile([max_sequence_length, 1]).T  # (batch_size, max_seq_len)
             positive_user_ids = positive_user_ids[mask]  # (all_batch_items)
-            user_embeddings = user_embeddings[
-                positive_user_ids
-            ]  # (all_batch_items, embedding_dim)
+            user_embeddings = user_embeddings[positive_user_ids]  # (all_batch_items, embedding_dim)
 
             all_scores = torch.einsum(
-                "ad,nd->an", user_embeddings, all_final_item_embeddings
+                'ad,nd->an',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (all_batch_items, num_items + 2)
 
-            negative_mask = torch.zeros(
-                self._num_items + 2, dtype=torch.bool, device=DEVICE
-            )  # (num_items + 2)
+            negative_mask = torch.zeros(self._num_items + 2, dtype=torch.bool, device=DEVICE)  # (num_items + 2)
             negative_mask[positive_item_ids] = 1
 
             positive_scores = torch.gather(
-                input=all_scores, dim=1, index=positive_item_ids[..., None]
+                input=all_scores,
+                dim=1,
+                index=positive_item_ids[..., None]
             )  # (all_batch_items, 1)
 
             all_scores = torch.scatter_add(
                 input=all_scores,
                 dim=1,
                 index=positive_item_ids[..., None],
-                src=torch.ones_like(positive_item_ids[..., None]).float(),
+                src=torch.ones_like(positive_item_ids[..., None]).float()
             )  # (all_batch_items, num_items + 2)
 
             return {
-                "positive_scores": positive_scores,
-                "negative_scores": all_scores,
-                "item_embeddings": torch.cat(
-                    (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-                ),
+                'positive_scores': positive_scores,
+                'negative_scores': all_scores,
+                'item_embeddings': torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
             }
         else:  # eval mode
             candidate_scores = torch.einsum(
-                "bd,nd->bn", user_embeddings, all_final_item_embeddings
+                'bd,nd->bn',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/mclsr.py b/modeling/models/mclsr.py
index c7fefd3c..8f78cda4 100644
--- a/modeling/models/mclsr.py
+++ b/modeling/models/mclsr.py
@@ -1,30 +1,32 @@
+from models.base import TorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import TorchModel
 from utils import create_masked_tensor
 
 
-class MCLSRModel(TorchModel, config_name="mclsr"):
+class MCLSRModel(TorchModel, config_name='mclsr'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        user_prefix,
-        labels_prefix,
-        candidate_prefix,
-        num_users,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_graph_layers,
-        common_graph,
-        user_graph,
-        item_graph,
-        dropout=0.0,
-        layer_norm_eps=1e-5,
-        graph_dropout=0.0,
-        alpha=0.5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            user_prefix,
+            labels_prefix,
+            candidate_prefix,
+            num_users,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_graph_layers,
+            common_graph,
+            user_graph,
+            item_graph,
+            dropout=0.0,
+            layer_norm_eps=1e-5,
+            graph_dropout=0.0,
+            alpha=0.5,
+            initializer_range=0.02
     ):
         super().__init__()
         self._sequence_prefix = sequence_prefix
@@ -48,17 +50,16 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._user_embeddings = nn.Embedding(
             num_embeddings=num_users + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -66,43 +67,39 @@ def __init__(
 
         # Current interest learning
         self._current_interest_learning_encoder = nn.Sequential(
-            nn.Linear(
-                in_features=embedding_dim, out_features=4 * embedding_dim, bias=False
-            ),
+            nn.Linear(in_features=embedding_dim, out_features=4 * embedding_dim, bias=False),
             nn.Tanh(),
-            nn.Linear(in_features=4 * embedding_dim, out_features=1, bias=False),
+            nn.Linear(in_features=4 * embedding_dim, out_features=1, bias=False)
         )
 
         # General interest learning
         self._general_interest_learning_encoder = nn.Sequential(
-            nn.Linear(
-                in_features=embedding_dim, out_features=embedding_dim, bias=False
-            ),
-            nn.Tanh(),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=False),
+            nn.Tanh()
         )
 
         # Cross-view contrastive learning
         self._sequential_projector = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
         self._graph_projector = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
 
         self._user_projection = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
 
         self._item_projection = nn.Sequential(
             nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
             nn.ELU(),
-            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True),
+            nn.Linear(in_features=embedding_dim, out_features=embedding_dim, bias=True)
         )
 
         self._init_weights(initializer_range)
@@ -110,22 +107,22 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            user_prefix=config["user_prefix"],
-            labels_prefix=config["labels_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_graph_layers=config["num_graph_layers"],
-            common_graph=kwargs["graph"],
-            user_graph=kwargs["user_graph"],
-            item_graph=kwargs["item_graph"],
-            dropout=config.get("dropout", 0.0),
-            layer_norm_eps=config.get("layer_norm_eps", 1e-5),
-            graph_dropout=config.get("graph_dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            user_prefix=config['user_prefix'],
+            labels_prefix=config['labels_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_graph_layers=config['num_graph_layers'],
+            common_graph=kwargs['graph'],
+            user_graph=kwargs['user_graph'],
+            item_graph=kwargs['item_graph'],
+            dropout=config.get('dropout', 0.0),
+            layer_norm_eps=config.get('layer_norm_eps', 1e-5),
+            graph_dropout=config.get('graph_dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _apply_graph_encoder(self, embeddings, graph, use_mean=False):
@@ -152,19 +149,14 @@ def _apply_graph_encoder(self, embeddings, graph, use_mean=False):
             return all_embeddings[-1]
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
-        user_ids = inputs["{}.ids".format(self._user_prefix)]  # (batch_size)
-
-        embeddings = self._item_embeddings(
-            all_sample_events
-        )  # (all_batch_events, embedding_dim)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
+        user_ids = inputs['{}.ids'.format(self._user_prefix)]  # (batch_size)
+
+        embeddings = self._item_embeddings(all_sample_events)  # (all_batch_events, embedding_dim)
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=all_sample_lengths
+            data=embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
 
         batch_size = mask.shape[0]
@@ -172,108 +164,88 @@ def forward(self, inputs):
 
         # Current interest learning
         # 1) get embeddings with positions
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
-        positions_mask = (
-            positions < all_sample_lengths[:, None]
-        )  # (batch_size, max_seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions_mask = positions < all_sample_lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=all_sample_lengths
+            data=position_embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        positioned_embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        positioned_embeddings = self._layernorm(
-            positioned_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        positioned_embeddings = self._dropout(
-            positioned_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = self._layernorm(positioned_embeddings)  # (batch_size, seq_len, embedding_dim)
+        positioned_embeddings = self._dropout(positioned_embeddings)  # (batch_size, seq_len, embedding_dim)
         positioned_embeddings[~mask] = 0
 
         sequential_attention_matrix = self._current_interest_learning_encoder(
             positioned_embeddings
         ).squeeze()  # (batch_size, seq_len)
         sequential_attention_matrix[~mask] = -torch.inf
-        sequential_attention_matrix = torch.softmax(
-            sequential_attention_matrix, dim=1
-        )  # (batch_size, seq_len)
+        sequential_attention_matrix = torch.softmax(sequential_attention_matrix, dim=1)  # (batch_size, seq_len)
         sequential_representation = torch.einsum(
-            "bs,bsd->bd", sequential_attention_matrix, embeddings
+            'bs,bsd->bd', sequential_attention_matrix, embeddings
         )  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
             # General interest learning
             all_embeddings = torch.cat(
-                [self._user_embeddings.weight, self._item_embeddings.weight], dim=0
+                [self._user_embeddings.weight, self._item_embeddings.weight],
+                dim=0
             )  # (num_users + 2 + num_items + 2, embedding_dim)
             common_graph_embeddings = self._apply_graph_encoder(
-                embeddings=all_embeddings, graph=self._graph
+                embeddings=all_embeddings,
+                graph=self._graph
             )  # (num_users + 2 + num_items + 2, embedding_dim)
             common_graph_user_embeddings, common_graph_item_embeddings = torch.split(
-                common_graph_embeddings, [self._num_users + 2, self._num_items + 2]
+                common_graph_embeddings,
+                [self._num_users + 2, self._num_items + 2]
             )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
-            common_graph_user_embeddings = common_graph_user_embeddings[
-                user_ids
-            ]  # (batch_size, embedding_dim)
-            common_graph_item_embeddings = common_graph_item_embeddings[
-                all_sample_events
-            ]  # (all_batch_events, embedding_dim)
+            common_graph_user_embeddings = common_graph_user_embeddings[user_ids]  # (batch_size, embedding_dim)
+            common_graph_item_embeddings = common_graph_item_embeddings[all_sample_events]  # (all_batch_events, embedding_dim)
             common_graph_item_embeddings, _ = create_masked_tensor(
-                data=common_graph_item_embeddings, lengths=all_sample_lengths
+                data=common_graph_item_embeddings,
+                lengths=all_sample_lengths
             )  # (batch_size, seq_len, embedding_dim)
 
             graph_attention_matrix = torch.einsum(
-                "bd,bsd->bs",
+                'bd,bsd->bs',
                 self._general_interest_learning_encoder(common_graph_user_embeddings),
-                common_graph_item_embeddings,
+                common_graph_item_embeddings
             )  # (batch_size, seq_len)
             graph_attention_matrix[~mask] = -torch.inf
-            graph_attention_matrix = torch.softmax(
-                graph_attention_matrix, dim=1
-            )  # (batch_size, seq_len)
+            graph_attention_matrix = torch.softmax(graph_attention_matrix, dim=1)  # (batch_size, seq_len)
             graph_representation = torch.einsum(
-                "bs,bsd->bd", graph_attention_matrix, common_graph_item_embeddings
+                'bs,bsd->bd', graph_attention_matrix, common_graph_item_embeddings
             )  # (batch_size, embedding_dim)
 
             # Get final representation
-            combined_representation = (
-                self._alpha * sequential_representation
-                + (1 - self._alpha) * graph_representation
-            )  # (batch_size, embedding_dim)
+            combined_representation = \
+                self._alpha * sequential_representation + \
+                (1 - self._alpha) * graph_representation  # (batch_size, embedding_dim)
 
-            labels = inputs["{}.ids".format(self._labels_prefix)]  # (batch_size)
-            labels_embeddings = self._item_embeddings(
-                labels
-            )  # (batch_size, embedding_dim)
+            labels = inputs['{}.ids'.format(self._labels_prefix)]  # (batch_size)
+            labels_embeddings = self._item_embeddings(labels)  # (batch_size, embedding_dim)
 
             # Cross-view contrastive learning
             sequential_representation = self._sequential_projector(
-                sequential_representation
-            )  # (batch_size, embedding_dim)
-            graph_representation = self._graph_projector(
-                graph_representation
-            )  # (batch_size, embedding_dim)
+                sequential_representation)  # (batch_size, embedding_dim)
+            graph_representation = self._graph_projector(graph_representation)  # (batch_size, embedding_dim)
 
             # Feature-level Contrastive Learning
             user_graph_user_embeddings = self._apply_graph_encoder(
-                embeddings=self._user_embeddings.weight, graph=self._user_graph
+                embeddings=self._user_embeddings.weight,
+                graph=self._user_graph
             )  # (num_users + 2, embedding_dim)
             user_graph_user_embeddings = torch.gather(
                 user_graph_user_embeddings,
                 dim=0,
-                index=user_ids[..., None].tile(1, self._embedding_dim),
+                index=user_ids[..., None].tile(1, self._embedding_dim)
             )  # (batch_size, embedding_dim)
 
             user_graph_user_embeddings = self._user_projection(
@@ -284,12 +256,13 @@ def forward(self, inputs):
             )  # (batch_size, embedding_dim)
 
             item_graph_item_embeddings = self._apply_graph_encoder(
-                embeddings=self._item_embeddings.weight, graph=self._item_graph
+                embeddings=self._item_embeddings.weight,
+                graph=self._item_graph
             )  # (num_items + 2, embedding_dim)
             item_graph_item_embeddings = torch.gather(
                 item_graph_item_embeddings,
                 dim=0,
-                index=all_sample_events[..., None].tile(1, self._embedding_dim),
+                index=all_sample_events[..., None].tile(1, self._embedding_dim)
             )  # (all_sample_events, embedding_dim)
 
             item_graph_item_embeddings = self._item_projection(
@@ -301,50 +274,51 @@ def forward(self, inputs):
 
             return {
                 # Downstream task
-                "combined_representation": combined_representation,
-                "label_representation": labels_embeddings,
+                'combined_representation': combined_representation,
+                'label_representation': labels_embeddings,
+
                 # Interest-level Contrastive Learning
-                "sequential_representation": sequential_representation,
-                "graph_representation": graph_representation,
+                'sequential_representation': sequential_representation,
+                'graph_representation': graph_representation,
+
                 # Feature-level Contrastive Learning (users)
-                "user_graph_user_embeddings": user_graph_user_embeddings,
-                "common_graph_user_embeddings": common_graph_user_embeddings,
+                'user_graph_user_embeddings': user_graph_user_embeddings,
+                'common_graph_user_embeddings': common_graph_user_embeddings,
+
                 # Feature-level Contrastive Learning (items)
-                "item_graph_item_embeddings": item_graph_item_embeddings,
-                "common_graph_item_embeddings": common_graph_item_embeddings,
+                'item_graph_item_embeddings': item_graph_item_embeddings,
+                'common_graph_item_embeddings': common_graph_item_embeddings
             }
         else:  # eval mode
-            if "{}.ids".format(self._candidate_prefix) in inputs:
-                candidate_events = inputs[
-                    "{}.ids".format(self._candidate_prefix)
-                ]  # (all_batch_candidates)
-                candidate_lengths = inputs[
-                    "{}.length".format(self._candidate_prefix)
-                ]  # (batch_size)
-
-                candidate_embeddings = self._item_embeddings(
-                    candidate_events
-                )  # (all_batch_candidates, embedding_dim)
+            if '{}.ids'.format(self._candidate_prefix) in inputs:
+                candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
+
+                candidate_embeddings = self._item_embeddings(candidate_events)  # (all_batch_candidates, embedding_dim)
 
                 candidate_embeddings, _ = create_masked_tensor(
-                    data=candidate_embeddings, lengths=candidate_lengths
+                    data=candidate_embeddings,
+                    lengths=candidate_lengths
                 )  # (batch_size, num_candidates, embedding_dim)
 
                 candidate_scores = torch.einsum(
-                    "bd,bnd->bn", sequential_representation, candidate_embeddings
+                    'bd,bnd->bn',
+                    sequential_representation,
+                    candidate_embeddings
                 )  # (batch_size, num_candidates)
             else:
-                candidate_embeddings = (
-                    self._item_embeddings.weight
-                )  # (num_items, embedding_dim)
+                candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
                 candidate_scores = torch.einsum(
-                    "bd,nd->bn", sequential_representation, candidate_embeddings
+                    'bd,nd->bn',
+                    sequential_representation,
+                    candidate_embeddings
                 )  # (batch_size, num_items)
                 candidate_scores[:, 0] = -torch.inf
-                candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             values, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 100), (batch_size, 100)
 
             return indices
diff --git a/modeling/models/mrgsrec.py b/modeling/models/mrgsrec.py
index f53861a0..488c580a 100644
--- a/modeling/models/mrgsrec.py
+++ b/modeling/models/mrgsrec.py
@@ -1,29 +1,32 @@
-import torch
-import torch.nn as nn
 from torch.nn import MultiheadAttention
 
 from models.base import TorchModel
+
 from utils import create_masked_tensor, get_activation_function
 
+import torch
+import torch.nn as nn
+
+
+class MRGSRecModel(TorchModel, config_name='mrgsrec'):
 
-class MRGSRecModel(TorchModel, config_name="mrgsrec"):
     def __init__(
-        self,
-        sequence_prefix,
-        user_prefix,
-        positive_prefix,
-        negative_prefix,
-        candidate_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            user_prefix,
+            positive_prefix,
+            negative_prefix,
+            candidate_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
         super().__init__()
         self._sequence_prefix = sequence_prefix
@@ -37,12 +40,11 @@ def __init__(
 
         self._item_embeddings = nn.Embedding(
             num_embeddings=num_items + 2,  # add zero embedding + mask embedding
-            embedding_dim=embedding_dim,
+            embedding_dim=embedding_dim
         )
         self._position_embeddings = nn.Embedding(
-            num_embeddings=max_sequence_length
-            + 1,  # in order to include `max_sequence_length` value
-            embedding_dim=embedding_dim,
+            num_embeddings=max_sequence_length + 1,  # in order to include `max_sequence_length` value
+            embedding_dim=embedding_dim
         )
 
         self._layernorm = nn.LayerNorm(embedding_dim, eps=layer_norm_eps)
@@ -55,69 +57,60 @@ def __init__(
             dropout=dropout,
             activation=get_activation_function(activation),
             layer_norm_eps=layer_norm_eps,
-            batch_first=True,
+            batch_first=True
         )
         self._encoder = nn.TransformerEncoder(transformer_encoder_layer, num_layers)
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
-        embeddings = self._item_embeddings(
-            all_sample_events
-        )  # (all_batch_events, embedding_dim)
+        embeddings = self._item_embeddings(all_sample_events)  # (all_batch_events, embedding_dim)
 
         embeddings, mask = create_masked_tensor(
-            data=embeddings, lengths=all_sample_lengths
+            data=embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
         batch_size = mask.shape[0]
         seq_len = mask.shape[1]
 
-        positions = (
-            torch.arange(start=seq_len - 1, end=-1, step=-1, device=mask.device)[None]
-            .tile([batch_size, 1])
-            .long()
-        )  # (batch_size, seq_len)
-        positions_mask = (
-            positions < all_sample_lengths[:, None]
-        )  # (batch_size, max_seq_len)
+        positions = torch.arange(
+            start=seq_len - 1, end=-1, step=-1, device=mask.device
+        )[None].tile([batch_size, 1]).long()  # (batch_size, seq_len)
+        positions_mask = positions < all_sample_lengths[:, None]  # (batch_size, max_seq_len)
 
         positions = positions[positions_mask]  # (all_batch_events)
-        position_embeddings = self._position_embeddings(
-            positions
-        )  # (all_batch_events, embedding_dim)
+        position_embeddings = self._position_embeddings(positions)  # (all_batch_events, embedding_dim)
         position_embeddings, _ = create_masked_tensor(
-            data=position_embeddings, lengths=all_sample_lengths
+            data=position_embeddings,
+            lengths=all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim)
         assert torch.allclose(position_embeddings[~mask], embeddings[~mask])
 
-        embeddings = (
-            embeddings + position_embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = embeddings + position_embeddings  # (batch_size, seq_len, embedding_dim)
 
         embeddings = self._layernorm(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = self._dropout(embeddings)  # (batch_size, seq_len, embedding_dim)
 
         embeddings[~mask] = 0
+
+
+
diff --git a/modeling/models/ngcf.py b/modeling/models/ngcf.py
index 8df917eb..2fcc9081 100644
--- a/modeling/models/ngcf.py
+++ b/modeling/models/ngcf.py
@@ -1,23 +1,25 @@
+from models.base import TorchModel
+
+from utils import create_masked_tensor, DEVICE
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.base import TorchModel
-from utils import DEVICE, create_masked_tensor
 
+class NgcfModel(TorchModel, config_name='ngcf'):
 
-class NgcfModel(TorchModel, config_name="ngcf"):
     def __init__(
-        self,
-        user_prefix,
-        positive_prefix,
-        graph,
-        num_users,
-        num_items,
-        embedding_dim,
-        num_layers,
-        dropout=0.0,
-        initializer_range=0.02,
+            self,
+            user_prefix,
+            positive_prefix,
+            graph,
+            num_users,
+            num_items,
+            embedding_dim,
+            num_layers,
+            dropout=0.0,
+            initializer_range=0.02
     ):
         super().__init__()
         self._user_prefix = user_prefix
@@ -38,11 +40,13 @@ def __init__(
             self.Bi_Linear_list.append(nn.Linear(embedding_dim, embedding_dim))
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -50,20 +54,20 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config["user_prefix"],
-            positive_prefix=config["positive_prefix"],
-            graph=kwargs["graph"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            embedding_dim=config["embedding_dim"],
-            num_layers=config["num_layers"],
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            user_prefix=config['user_prefix'],
+            positive_prefix=config['positive_prefix'],
+            graph=kwargs['graph'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            embedding_dim=config['embedding_dim'],
+            num_layers=config['num_layers'],
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
-        ids = inputs["{}.ids".format(prefix)]  # (all_batch_events)
-        lengths = inputs["{}.length".format(prefix)]  # (batch_size)
+        ids = inputs['{}.ids'.format(prefix)]  # (all_batch_events)
+        lengths = inputs['{}.length'.format(prefix)]  # (batch_size)
 
         final_embeddings = final_embeddings[ids]  # (all_batch_events, embedding_dim)
         ego_embeddings = ego_embeddings(ids)  # (all_batch_events, embedding_dim)
@@ -81,9 +85,7 @@ def _get_embeddings(self, inputs, prefix, ego_embeddings, final_embeddings):
         return padded_embeddings, padded_ego_embeddings, mask
 
     def _apply_graph_encoder(self):
-        ego_embeddings = torch.cat(
-            (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-        )
+        ego_embeddings = torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
         all_embeddings = [ego_embeddings]
 
         if self._dropout_rate > 0:  # drop some edges
@@ -120,82 +122,73 @@ def _apply_graph_encoder(self):
         return user_final_embeddings, item_final_embeddings
 
     def forward(self, inputs):
-        all_final_user_embeddings, all_final_item_embeddings = (
-            self._apply_graph_encoder()
-        )  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
+        all_final_user_embeddings, all_final_item_embeddings = \
+            self._apply_graph_encoder()  # (num_users + 2, embedding_dim), (num_items + 2, embedding_dim)
 
         user_embeddings, user_ego_embeddings, user_mask = self._get_embeddings(
             inputs, self._user_prefix, self._user_embeddings, all_final_user_embeddings
         )
-        user_embeddings = user_embeddings[
-            user_mask
-        ]  # (all_batch_events, embedding_dim)
+        user_embeddings = user_embeddings[user_mask]  # (all_batch_events, embedding_dim)
 
         if self.training:  # training mode
-            positive_item_ids = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            positive_item_lengths = inputs[
-                "{}.length".format(self._positive_prefix)
-            ]  # (batch_size)
+            positive_item_ids = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            positive_item_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
 
             batch_size = positive_item_lengths.shape[0]
             max_sequence_length = positive_item_lengths.max().item()
 
-            mask = (
-                torch.arange(end=max_sequence_length, device=DEVICE)[None].tile(
-                    [batch_size, 1]
-                )
-                < positive_item_lengths[:, None]
-            )  # (batch_size, max_seq_len)
-
-            positive_user_ids = (
-                torch.arange(batch_size, device=DEVICE)[None]
-                .tile([max_sequence_length, 1])
-                .T
-            )  # (batch_size, max_seq_len)
+            mask = torch.arange(
+                end=max_sequence_length,
+                device=DEVICE
+            )[None].tile([batch_size, 1]) < positive_item_lengths[:, None]  # (batch_size, max_seq_len)
+
+            positive_user_ids = torch.arange(
+                batch_size,
+                device=DEVICE
+            )[None].tile([max_sequence_length, 1]).T  # (batch_size, max_seq_len)
             positive_user_ids = positive_user_ids[mask]  # (all_batch_items)
-            user_embeddings = user_embeddings[
-                positive_user_ids
-            ]  # (all_batch_items, embedding_dim)
+            user_embeddings = user_embeddings[positive_user_ids]  # (all_batch_items, embedding_dim)
 
             all_scores = torch.einsum(
-                "ad,nd->an", user_embeddings, all_final_item_embeddings
+                'ad,nd->an',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (all_batch_items, num_items + 2)
 
-            negative_mask = torch.zeros(
-                self._num_items + 2, dtype=torch.bool, device=DEVICE
-            )  # (num_items + 2)
+            negative_mask = torch.zeros(self._num_items + 2, dtype=torch.bool, device=DEVICE)  # (num_items + 2)
             negative_mask[positive_item_ids] = 1
 
             positive_scores = torch.gather(
-                input=all_scores, dim=1, index=positive_item_ids[..., None]
+                input=all_scores,
+                dim=1,
+                index=positive_item_ids[..., None]
             )  # (all_batch_items, 1)
 
             all_scores = torch.scatter_add(
                 input=all_scores,
                 dim=1,
                 index=positive_item_ids[..., None],
-                src=torch.ones_like(positive_item_ids[..., None]).float(),
+                src=torch.ones_like(positive_item_ids[..., None]).float()
             )  # (all_batch_items, num_items + 2)
 
             return {
-                "positive_scores": positive_scores,
-                "negative_scores": all_scores,
-                "item_embeddings": torch.cat(
-                    (self._user_embeddings.weight, self._item_embeddings.weight), dim=0
-                ),
+                'positive_scores': positive_scores,
+                'negative_scores': all_scores,
+                'item_embeddings': torch.cat((self._user_embeddings.weight, self._item_embeddings.weight), dim=0)
             }
         else:  # eval mode
             # b - batch_size, n - num_candidates, d - embedding_dim
             candidate_scores = torch.einsum(
-                "bd,nd->bn", user_embeddings, all_final_item_embeddings
+                'bd,nd->bn',
+                user_embeddings,
+                all_final_item_embeddings
             )  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/pop.py b/modeling/models/pop.py
index ddf26881..bb0b6774 100644
--- a/modeling/models/pop.py
+++ b/modeling/models/pop.py
@@ -1,10 +1,16 @@
+from models.base import BaseModel
+
 import torch
 
-from models.base import BaseModel
 
+class PopModel(BaseModel, config_name='pop'):
 
-class PopModel(BaseModel, config_name="pop"):
-    def __init__(self, label_prefix, counts_prefix, num_items):
+    def __init__(
+            self,
+            label_prefix,
+            counts_prefix,
+            num_items
+    ):
         self._label_prefix = label_prefix
         self._counts_prefix = counts_prefix
         self._num_items = num_items
@@ -12,30 +18,26 @@ def __init__(self, label_prefix, counts_prefix, num_items):
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            label_prefix=config["label_prefix"],
-            counts_prefix=config["counts_prefix"],
-            num_items=kwargs["num_items"],
+            label_prefix=config['label_prefix'],
+            counts_prefix=config['counts_prefix'],
+            num_items=kwargs['num_items']
         )
 
     def __call__(self, inputs):
-        candidate_counts = inputs[
-            "{}.ids".format(self._counts_prefix)
-        ]  # (all_batch_candidates)
-        candidate_counts_lengths = inputs[
-            "{}.length".format(self._counts_prefix)
-        ]  # (batch_size)
+        candidate_counts = inputs['{}.ids'.format(self._counts_prefix)]  # (all_batch_candidates)
+        candidate_counts_lengths = inputs['{}.length'.format(self._counts_prefix)]  # (batch_size)
         batch_size = candidate_counts_lengths.shape[0]
 
         candidate_scores = torch.reshape(
-            candidate_counts, shape=(batch_size, self._num_items + 2)
+            candidate_counts,
+            shape=(batch_size, self._num_items + 2)
         ).float()  # (batch_size, num_items)
         candidate_scores[:, 0] = -torch.inf  # zero (padding) token
-        candidate_scores[
-            :, self._num_items + 1 :
-        ] = -torch.inf  # all not real items-related things
+        candidate_scores[:, self._num_items + 1:] = -torch.inf  # all not real items-related things
 
         _, indices = torch.topk(
-            candidate_scores, k=20, dim=-1, largest=True
+            candidate_scores,
+            k=20, dim=-1, largest=True
         )  # (batch_size, 20)
 
         return indices
diff --git a/modeling/models/pure_mf.py b/modeling/models/pure_mf.py
index dc0b691c..6cbe8dc5 100644
--- a/modeling/models/pure_mf.py
+++ b/modeling/models/pure_mf.py
@@ -1,20 +1,22 @@
+from models.base import TorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import TorchModel
 from utils import create_masked_tensor
 
 
-class PureMF(TorchModel, config_name="pure_mf"):
+class PureMF(TorchModel, config_name='pure_mf'):
+
     def __init__(
-        self,
-        user_prefix,
-        positive_prefix,
-        negative_prefix,
-        num_users,
-        num_items,
-        embedding_dim,
-        initializer_range,
+            self,
+            user_prefix,
+            positive_prefix,
+            negative_prefix,
+            num_users,
+            num_items,
+            embedding_dim,
+            initializer_range
     ):
         super().__init__()
 
@@ -27,11 +29,13 @@ def __init__(
         self._embedding_dim = embedding_dim
 
         self._user_embeddings = nn.Embedding(
-            num_embeddings=self._num_users + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_users + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._item_embeddings = nn.Embedding(
-            num_embeddings=self._num_items + 2, embedding_dim=self._embedding_dim
+            num_embeddings=self._num_items + 2,
+            embedding_dim=self._embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -39,73 +43,54 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            user_prefix=config["user_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            num_users=kwargs["num_users"],
-            num_items=kwargs["num_items"],
-            embedding_dim=config["embedding_dim"],
-            initializer_range=config.get("initializer_range", 0.02),
+            user_prefix=config['user_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            num_users=kwargs['num_users'],
+            num_items=kwargs['num_items'],
+            embedding_dim=config['embedding_dim'],
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        user_ids = inputs["{}.ids".format(self._user_prefix)]  # (batch_size)
+        user_ids = inputs['{}.ids'.format(self._user_prefix)]  # (batch_size)
         user_embeddings = self._user_embeddings(user_ids)  # (batch_size, embedding_dim)
 
         if self.training:  # training mode
-            all_positive = inputs[
-                "{}.ids".format(self._positive_prefix)
-            ]  # (all_batch_events)
-            all_positive_embeddings = self._item_embeddings(
-                all_positive
-            )  # (all_batch_events, embedding_dim)
-            positive_lengths = inputs[
-                "{}.length".format(self._positive_prefix)
-            ]  # (batch_size)
-
-            all_negative = inputs[
-                "{}.ids".format(self._negative_prefix)
-            ]  # (all_batch_events)
-            all_negative_embeddings = self._item_embeddings(
-                all_negative
-            )  # (all_batch_events, embedding_dim)
-            negative_lengths = inputs[
-                "{}.length".format(self._negative_prefix)
-            ]  # (batch_size)
-
-            positive_embeddings, positive_mask = create_masked_tensor(
-                all_positive_embeddings, positive_lengths
-            )
-            negative_embeddings, negative_mask = create_masked_tensor(
-                all_negative_embeddings, negative_lengths
-            )
-
-            positive_scores = torch.einsum(
-                "bd,bsd->bs", user_embeddings, positive_embeddings
-            )  # (batch_size, seq_len)
-            negative_scores = torch.einsum(
-                "bd,bsd->bs", user_embeddings, negative_embeddings
-            )  # (batch_size, seq_len)
+            all_positive = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+            all_positive_embeddings = self._item_embeddings(all_positive)  # (all_batch_events, embedding_dim)
+            positive_lengths = inputs['{}.length'.format(self._positive_prefix)]  # (batch_size)
+
+            all_negative = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+            all_negative_embeddings = self._item_embeddings(all_negative)  # (all_batch_events, embedding_dim)
+            negative_lengths = inputs['{}.length'.format(self._negative_prefix)]  # (batch_size)
+
+            positive_embeddings, positive_mask = create_masked_tensor(all_positive_embeddings, positive_lengths)
+            negative_embeddings, negative_mask = create_masked_tensor(all_negative_embeddings, negative_lengths)
+
+            positive_scores = torch.einsum('bd,bsd->bs', user_embeddings, positive_embeddings)  # (batch_size, seq_len)
+            negative_scores = torch.einsum('bd,bsd->bs', user_embeddings, negative_embeddings)  # (batch_size, seq_len)
 
             positive_scores = positive_scores[positive_mask]  # (all_batch_events)
             negative_scores = negative_scores[negative_mask]  # (all_batch_events)
 
             return {
-                "positive_scores": positive_scores,
-                "negative_scores": negative_scores,
+                'positive_scores': positive_scores,
+                'negative_scores': negative_scores
             }
         else:
-            candidate_embeddings = (
-                self._item_embeddings.weight
-            )  # (num_items, embedding_dim)
+            candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
             candidate_scores = torch.einsum(
-                "bd,nd->bn", user_embeddings, candidate_embeddings
+                'bd,nd->bn',
+                user_embeddings,
+                candidate_embeddings
             )  # (batch_size, num_items)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/pure_svd.py b/modeling/models/pure_svd.py
index fcb55a49..f705543b 100644
--- a/modeling/models/pure_svd.py
+++ b/modeling/models/pure_svd.py
@@ -1,14 +1,16 @@
 from models.base import BaseModel
 
 
-class SVDModel(BaseModel, config_name="pure_svd"):
+class SVDModel(BaseModel, config_name='pure_svd'):
+
     def __init__(self, rank):
         super().__init__()
 
         self._rank = rank
-        self._method = "PureSVD"
+        self._method = 'PureSVD'
         self._factors = {}
 
     @property
     def rank(self):
         return self._rank
+
diff --git a/modeling/models/random.py b/modeling/models/random.py
index cfa8d5fa..d5fae7c5 100644
--- a/modeling/models/random.py
+++ b/modeling/models/random.py
@@ -1,31 +1,36 @@
+from models.base import BaseModel
+
 import torch
 
-from models.base import BaseModel
 
+class RandomModel(BaseModel, config_name='random'):
 
-class RandomModel(BaseModel, config_name="random"):
-    def __init__(self, label_prefix, num_items):
+    def __init__(
+            self,
+            label_prefix,
+            num_items
+    ):
         self._label_prefix = label_prefix
         self._num_items = num_items
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        return cls(label_prefix=config["label_prefix"], num_items=kwargs["num_items"])
+        return cls(
+            label_prefix=config['label_prefix'],
+            num_items=kwargs['num_items']
+        )
 
     def __call__(self, inputs):
-        labels_lengths = inputs["{}.length".format(self._label_prefix)]  # (batch_size)
+        labels_lengths = inputs['{}.length'.format(self._label_prefix)]  # (batch_size)
         batch_size = labels_lengths.shape[0]
 
-        candidate_scores = torch.rand(
-            batch_size, self._num_items + 1
-        )  # (batch_size, num_items)
+        candidate_scores = torch.rand(batch_size, self._num_items + 1)  # (batch_size, num_items)
         candidate_scores[:, 0] = -torch.inf  # zero (padding) token
-        candidate_scores[
-            :, self._num_items + 1 :
-        ] = -torch.inf  # all not real items-related things
+        candidate_scores[:, self._num_items + 1:] = -torch.inf  # all not real items-related things
 
         _, indices = torch.topk(
-            candidate_scores, k=20, dim=-1, largest=True
+            candidate_scores,
+            k=20, dim=-1, largest=True
         )  # (batch_size, 20)
 
         return indices
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index d7d1606e..7e150421 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -1,23 +1,22 @@
 import functools
+from utils import DEVICE
+from models.base import TorchModel
 
-import faiss
 import torch
+import faiss
 
-from models.base import TorchModel
-from utils import DEVICE
-
+class RqVaeModel(TorchModel, config_name='rqvae'):
 
-class RqVaeModel(TorchModel, config_name="rqvae"):
     def __init__(
-        self,
-        train_sampler,
-        input_dim: int,
-        hidden_dim: int,
-        n_iter: int,
-        codebook_sizes: list[int],
-        should_init_codebooks,
-        should_reinit_unused_clusters,
-        initializer_range,
+            self,
+            train_sampler,
+            input_dim: int,
+            hidden_dim: int,
+            n_iter: int,
+            codebook_sizes: list[int],
+            should_init_codebooks,
+            should_reinit_unused_clusters,
+            initializer_range
     ):
         super().__init__()
 
@@ -35,39 +34,35 @@ def __init__(
 
         # Default initialization of codebook
         self.codebooks = torch.nn.ParameterList()
-
+        
         self.codebook_sizes = codebook_sizes
-
+        
         for codebook_size in codebook_sizes:
             cb = torch.FloatTensor(codebook_size, hidden_dim)
             self.codebooks.append(cb)
-
+         
         self._init_weights(initializer_range)
-
+        
         if self.should_init_codebooks:
             if train_sampler is None:
                 raise AttributeError("Train sampler is None")
-
-            embeddings = torch.stack(
-                [entry["item.embed"] for entry in train_sampler._dataset]
-            )
+            
+            embeddings = torch.stack([entry['item.embed'] for entry in train_sampler._dataset])
             self.init_codebooks(embeddings)
-            print("Codebooks initialized with Faiss Kmeans")
+            print('Codebooks initialized with Faiss Kmeans')
             self.should_init_codebooks = False
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            train_sampler=kwargs.get("train_sampler"),
-            input_dim=config["embedding_dim"],
-            hidden_dim=config["hidden_dim"],
-            n_iter=config["n_iter"],
-            codebook_sizes=config["codebook_sizes"],
-            should_init_codebooks=config.get("should_init_codebooks", False),
-            should_reinit_unused_clusters=config.get(
-                "should_reinit_unused_clusters", False
-            ),
-            initializer_range=config.get("initializer_range", 0.02),
+            train_sampler=kwargs.get('train_sampler'),
+            input_dim=config['embedding_dim'],
+            hidden_dim=config['hidden_dim'],
+            n_iter=config['n_iter'],
+            codebook_sizes=config['codebook_sizes'],
+            should_init_codebooks=config.get('should_init_codebooks', False),
+            should_reinit_unused_clusters=config.get('should_reinit_unused_clusters', False),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def make_encoding_tower(self, d1: int, d2: int):
@@ -107,21 +102,21 @@ def reinit_unused_clusters(remainder, codebook, codebook_indices):
             is_used[unique_indices] = True
             rand_input = torch.randint(0, remainder.shape[0], ((~is_used).sum(),))
             codebook[~is_used] = remainder[rand_input]
-
+    
     def train_pass(self, embeddings):
         latent_vector = self.encoder(embeddings)
 
         latent_restored = 0
-
+        
         num_unique_clusters = []
         remainder = latent_vector
-
+        
         remainders = []
         codebooks_vectors = []
-
+        
         for codebook in self.codebooks:
             remainders.append(remainder)
-
+            
             codebook_indices = self.get_codebook_indices(remainder, codebook)
             codebook_vectors = codebook[codebook_indices]
 
@@ -129,7 +124,7 @@ def train_pass(self, embeddings):
                 self.reinit_unused_clusters(remainder, codebook, codebook_indices)
 
             num_unique_clusters.append(codebook_indices.unique().shape[0])
-
+            
             codebooks_vectors.append(codebook_vectors)
 
             latent_restored = latent_restored + codebook_vectors
@@ -143,9 +138,9 @@ def train_pass(self, embeddings):
             "embeddings": embeddings,
             "embeddings_restored": embeddings_restored,
             "remainders": remainders,
-            "codebooks_vectors": codebooks_vectors,
+            "codebooks_vectors": codebooks_vectors
         }
-
+        
     def eval_pass(self, embeddings):
         ind_lists = []
         remainder = self.encoder(embeddings)
@@ -158,12 +153,12 @@ def eval_pass(self, embeddings):
 
     def forward(self, inputs):
         embeddings = inputs["embeddings"]
-
+        
         if self.training:  # training mode
             return self.train_pass(embeddings)
         else:  # eval mode
             return self.eval_pass(embeddings)
-
+        
     @functools.cache
     def get_single_embedding(self, codebook_idx: int, codebook_id: int):
         return self.codebooks[codebook_idx][codebook_id]
diff --git a/modeling/models/s3rec.py b/modeling/models/s3rec.py
index f9f361d4..e1fcffb4 100644
--- a/modeling/models/s3rec.py
+++ b/modeling/models/s3rec.py
@@ -1,31 +1,33 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 from utils import create_masked_tensor
 
 
-class S3RecModel(SequentialTorchModel, config_name="s3rec"):
+class S3RecModel(SequentialTorchModel, config_name='s3rec'):
+
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        negative_prefix,
-        sequence_segment_prefix,
-        positive_segment_prefix,
-        negative_segment_prefix,
-        candidate_prefix,
-        num_items,
-        max_sequence_length,
-        is_training,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-5,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            negative_prefix,
+            sequence_segment_prefix,
+            positive_segment_prefix,
+            negative_segment_prefix,
+            candidate_prefix,
+            num_items,
+            max_sequence_length,
+            is_training,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-5,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -37,7 +39,7 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=is_training,
+            is_causal=is_training
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
@@ -59,60 +61,50 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            negative_prefix=config["negative_prefix"],
-            sequence_segment_prefix=config["sequence_segment_prefix"],
-            positive_segment_prefix=config["positive_segment_prefix"],
-            negative_segment_prefix=config["negative_segment_prefix"],
-            candidate_prefix=config["candidate_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            is_training=config["is_training"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            negative_prefix=config['negative_prefix'],
+            sequence_segment_prefix=config['sequence_segment_prefix'],
+            positive_segment_prefix=config['positive_segment_prefix'],
+            negative_segment_prefix=config['negative_segment_prefix'],
+            candidate_prefix=config['candidate_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            is_training=config['is_training'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def masked_item_prediction(self, sequence_embeddings, sequence_mask, target_item):
         all_items = sequence_embeddings[sequence_mask]  # (all_batch_items, emb_dim)
-        score = torch.einsum("ad,ad->a", all_items, target_item)  # (all_batch_items)
+        score = torch.einsum(
+            'ad,ad->a', all_items, target_item
+        )  # (all_batch_items)
         return torch.sigmoid(score)  # (all_batch_items)
 
     def segment_prediction(self, context, segment):
-        score = torch.einsum("bd,bd->b", self.sp_norm(context), segment)  # (batch_size)
+        score = torch.einsum('bd,bd->b', self.sp_norm(context), segment)  # (batch_size)
         return torch.sigmoid(score)  # (batch_size)
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
-        last_embeddings = self._get_last_embedding(
-            embeddings, mask
-        )  # (batch_size, embedding_dim)
+        last_embeddings = self._get_last_embedding(embeddings, mask)  # (batch_size, embedding_dim)
 
         if self._is_training:
             if self.training:  # training mode
-                all_positive_sample_events = inputs[
-                    "{}.ids".format(self._positive_prefix)
-                ]  # (all_batch_events)
-                all_negative_sample_events = inputs[
-                    "{}.ids".format(self._negative_prefix)
-                ]  # (all_batch_events)
-
-                all_sample_embeddings = embeddings[
-                    mask
-                ]  # (all_batch_events, embedding_dim)
+                all_positive_sample_events = inputs['{}.ids'.format(self._positive_prefix)]  # (all_batch_events)
+                all_negative_sample_events = inputs['{}.ids'.format(self._negative_prefix)]  # (all_batch_events)
+
+                all_sample_embeddings = embeddings[mask]  # (all_batch_events, embedding_dim)
                 all_positive_sample_embeddings = self._item_embeddings(
                     all_positive_sample_events
                 )  # (all_batch_events, embedding_dim)
@@ -121,68 +113,53 @@ def forward(self, inputs):
                 )  # (all_batch_events, embedding_dim)
 
                 return {
-                    "current_embeddings": all_sample_embeddings,
-                    "positive_embeddings": all_positive_sample_embeddings,
-                    "negative_embeddings": all_negative_sample_embeddings,
+                    'current_embeddings': all_sample_embeddings,
+                    'positive_embeddings': all_positive_sample_embeddings,
+                    'negative_embeddings': all_negative_sample_embeddings
                 }
             else:  # eval mode
-                if "{}.ids".format(self._candidate_prefix) in inputs:
-                    candidate_events = inputs[
-                        "{}.ids".format(self._candidate_prefix)
-                    ]  # (all_batch_candidates)
-                    candidate_lengths = inputs[
-                        "{}.length".format(self._candidate_prefix)
-                    ]  # (batch_size)
+                if '{}.ids'.format(self._candidate_prefix) in inputs:
+                    candidate_events = inputs['{}.ids'.format(self._candidate_prefix)]  # (all_batch_candidates)
+                    candidate_lengths = inputs['{}.length'.format(self._candidate_prefix)]  # (batch_size)
 
                     candidate_embeddings = self._item_embeddings(
                         candidate_events
                     )  # (all_batch_candidates, embedding_dim)
 
                     candidate_embeddings, _ = create_masked_tensor(
-                        data=candidate_embeddings, lengths=candidate_lengths
+                        data=candidate_embeddings,
+                        lengths=candidate_lengths
                     )  # (batch_size, num_candidates, embedding_dim)
 
                     candidate_scores = torch.einsum(
-                        "bd,bnd->bn", last_embeddings, candidate_embeddings
+                        'bd,bnd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_candidates)
                 else:
-                    candidate_embeddings = (
-                        self._item_embeddings.weight
-                    )  # (num_items, embedding_dim)
+                    candidate_embeddings = self._item_embeddings.weight  # (num_items, embedding_dim)
                     candidate_scores = torch.einsum(
-                        "bd,nd->bn", last_embeddings, candidate_embeddings
+                        'bd,nd->bn',
+                        last_embeddings,
+                        candidate_embeddings
                     )  # (batch_size, num_items)
                     candidate_scores[:, 0] = -torch.inf
-                    candidate_scores[:, self._num_items + 1 :] = -torch.inf
+                    candidate_scores[:, self._num_items + 1:] = -torch.inf
 
                 return candidate_scores
         else:
             # Masked Item Prediction
-            mip_mask = (
-                all_sample_events == self._mask_item_idx
-            ).bool()  # (all_batch_events)
+            mip_mask = (all_sample_events == self._mask_item_idx).bool()  # (all_batch_events)
             embeddings = embeddings[mask][mip_mask]  # (all_batch_events, embedding_dim)
-            positive_item_events = inputs["{}.ids".format(self._positive_prefix)][
-                mip_mask
-            ]  # (all_batch_events)
-            negative_item_events = inputs["{}.ids".format(self._negative_prefix)][
-                mip_mask
-            ]  # (all_batch_events)
-
-            positive_item_embeddings = self._item_embeddings(
-                positive_item_events
-            )  # (all_batch_events, embedding_dim)
-            negative_item_embeddings = self._item_embeddings(
-                negative_item_events
-            )  # (all_batch_events, embedding_dim)
+            positive_item_events = inputs['{}.ids'.format(self._positive_prefix)][mip_mask]  # (all_batch_events)
+            negative_item_events = inputs['{}.ids'.format(self._negative_prefix)][mip_mask]  # (all_batch_events)
+
+            positive_item_embeddings = self._item_embeddings(positive_item_events)  # (all_batch_events, embedding_dim)
+            negative_item_embeddings = self._item_embeddings(negative_item_events)  # (all_batch_events, embedding_dim)
 
             # Sequence Prediction
-            all_segment_events = inputs[
-                "{}.ids".format(self._sequence_segment_prefix)
-            ]  # (all_batch_events)
-            all_segment_lengths = inputs[
-                "{}.length".format(self._sequence_segment_prefix)
-            ]  # (batch_size)
+            all_segment_events = inputs['{}.ids'.format(self._sequence_segment_prefix)]  # (all_batch_events)
+            all_segment_lengths = inputs['{}.length'.format(self._sequence_segment_prefix)]  # (batch_size)
             segment_embeddings, segment_mask = self._apply_sequential_encoder(
                 all_segment_events, all_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
@@ -190,41 +167,30 @@ def forward(self, inputs):
                 segment_embeddings, segment_mask
             )  # (batch_size, embedding_dim)
 
-            positive_segment_events = inputs[
-                "{}.ids".format(self._positive_segment_prefix)
-            ]  # (all_batch_events)
-            positive_segment_lengths = inputs[
-                "{}.length".format(self._positive_segment_prefix)
-            ]  # (batch_size)
-            positive_segment_embeddings, positive_segment_mask = (
-                self._apply_sequential_encoder(
-                    positive_segment_events, positive_segment_lengths
-                )
+            positive_segment_events = inputs['{}.ids'.format(self._positive_segment_prefix)]  # (all_batch_events)
+            positive_segment_lengths = inputs['{}.length'.format(self._positive_segment_prefix)]  # (batch_size)
+            positive_segment_embeddings, positive_segment_mask = self._apply_sequential_encoder(
+                positive_segment_events, positive_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
             last_positive_segment_embeddings = self._get_last_embedding(
                 positive_segment_embeddings, positive_segment_mask
             )  # (batch_size, embedding_dim)
 
-            negative_segment_events = inputs[
-                "{}.ids".format(self._negative_segment_prefix)
-            ]  # (all_batch_events)
-            negative_segment_lengths = inputs[
-                "{}.length".format(self._negative_segment_prefix)
-            ]  # (batch_size)
-            negative_segment_embeddings, negative_segment_mask = (
-                self._apply_sequential_encoder(
-                    negative_segment_events, negative_segment_lengths
-                )
+            negative_segment_events = inputs['{}.ids'.format(self._negative_segment_prefix)]  # (all_batch_events)
+            negative_segment_lengths = inputs['{}.length'.format(self._negative_segment_prefix)]  # (batch_size)
+            negative_segment_embeddings, negative_segment_mask = self._apply_sequential_encoder(
+                negative_segment_events, negative_segment_lengths
             )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
             last_negative_segment_embeddings = self._get_last_embedding(
                 negative_segment_embeddings, negative_segment_mask
             )  # (batch_size, embedding_dim)
 
             return {
-                "positive_representation": positive_item_embeddings,
-                "negative_representation": negative_item_embeddings,
-                "current_representation": embeddings,
-                "positive_segment_representation": last_positive_segment_embeddings,
-                "negative_segment_representation": last_negative_segment_embeddings,
-                "current_segment_representation": last_segment_embeddings,
+                'positive_representation': positive_item_embeddings,
+                'negative_representation': negative_item_embeddings,
+                'current_representation': embeddings,
+
+                'positive_segment_representation': last_positive_segment_embeddings,
+                'negative_segment_representation': last_negative_segment_embeddings,
+                'current_segment_representation': last_segment_embeddings
             }
diff --git a/modeling/models/sasrec_ce.py b/modeling/models/sasrec_ce.py
index d9eb882b..a0316853 100644
--- a/modeling/models/sasrec_ce.py
+++ b/modeling/models/sasrec_ce.py
@@ -1,24 +1,25 @@
+from models.base import SequentialTorchModel
+
 import torch
 import torch.nn as nn
 
-from models.base import SequentialTorchModel
 
+class SasRecCeModel(SequentialTorchModel, config_name='sasrec_ce'):
 
-class SasRecCeModel(SequentialTorchModel, config_name="sasrec_ce"):
     def __init__(
-        self,
-        sequence_prefix,
-        positive_prefix,
-        num_items,
-        max_sequence_length,
-        embedding_dim,
-        num_heads,
-        num_layers,
-        dim_feedforward,
-        dropout=0.0,
-        activation="relu",
-        layer_norm_eps=1e-9,
-        initializer_range=0.02,
+            self,
+            sequence_prefix,
+            positive_prefix,
+            num_items,
+            max_sequence_length,
+            embedding_dim,
+            num_heads,
+            num_layers,
+            dim_feedforward,
+            dropout=0.0,
+            activation='relu',
+            layer_norm_eps=1e-9,
+            initializer_range=0.02
     ):
         super().__init__(
             num_items=num_items,
@@ -30,13 +31,14 @@ def __init__(
             dropout=dropout,
             activation=activation,
             layer_norm_eps=layer_norm_eps,
-            is_causal=True,
+            is_causal=True
         )
         self._sequence_prefix = sequence_prefix
         self._positive_prefix = positive_prefix
 
         self._output_projection = nn.Linear(
-            in_features=embedding_dim, out_features=embedding_dim
+            in_features=embedding_dim,
+            out_features=embedding_dim
         )
 
         self._init_weights(initializer_range)
@@ -44,51 +46,42 @@ def __init__(
     @classmethod
     def create_from_config(cls, config, **kwargs):
         return cls(
-            sequence_prefix=config["sequence_prefix"],
-            positive_prefix=config["positive_prefix"],
-            num_items=kwargs["num_items"],
-            max_sequence_length=kwargs["max_sequence_length"],
-            embedding_dim=config["embedding_dim"],
-            num_heads=config.get("num_heads", int(config["embedding_dim"] // 64)),
-            num_layers=config["num_layers"],
-            dim_feedforward=config.get("dim_feedforward", 4 * config["embedding_dim"]),
-            dropout=config.get("dropout", 0.0),
-            initializer_range=config.get("initializer_range", 0.02),
+            sequence_prefix=config['sequence_prefix'],
+            positive_prefix=config['positive_prefix'],
+            num_items=kwargs['num_items'],
+            max_sequence_length=kwargs['max_sequence_length'],
+            embedding_dim=config['embedding_dim'],
+            num_heads=config.get('num_heads', int(config['embedding_dim'] // 64)),
+            num_layers=config['num_layers'],
+            dim_feedforward=config.get('dim_feedforward', 4 * config['embedding_dim']),
+            dropout=config.get('dropout', 0.0),
+            initializer_range=config.get('initializer_range', 0.02)
         )
 
     def forward(self, inputs):
-        all_sample_events = inputs[
-            "{}.ids".format(self._sequence_prefix)
-        ]  # (all_batch_events)
-        all_sample_lengths = inputs[
-            "{}.length".format(self._sequence_prefix)
-        ]  # (batch_size)
+        all_sample_events = inputs['{}.ids'.format(self._sequence_prefix)]  # (all_batch_events)
+        all_sample_lengths = inputs['{}.length'.format(self._sequence_prefix)]  # (batch_size)
 
         embeddings, mask = self._apply_sequential_encoder(
             all_sample_events, all_sample_lengths
         )  # (batch_size, seq_len, embedding_dim), (batch_size, seq_len)
 
-        embeddings = self._output_projection(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
-        embeddings = torch.nn.functional.gelu(
-            embeddings
-        )  # (batch_size, seq_len, embedding_dim)
+        embeddings = self._output_projection(embeddings)  # (batch_size, seq_len, embedding_dim)
+        embeddings = torch.nn.functional.gelu(embeddings)  # (batch_size, seq_len, embedding_dim)
         embeddings = torch.einsum(
-            "bsd,nd->bsn", embeddings, self._item_embeddings.weight
+            'bsd,nd->bsn', embeddings, self._item_embeddings.weight
         )  # (batch_size, seq_len, num_items + 2)
 
         if self.training:  # training mode
-            return {"logits": embeddings[mask]}
+            return {'logits': embeddings[mask]}
         else:  # eval mode
-            candidate_scores = self._get_last_embedding(
-                embeddings, mask
-            )  # (batch_size, num_items + 2)
+            candidate_scores = self._get_last_embedding(embeddings, mask)  # (batch_size, num_items + 2)
             candidate_scores[:, 0] = -torch.inf
-            candidate_scores[:, self._num_items + 1 :] = -torch.inf
+            candidate_scores[:, self._num_items + 1:] = -torch.inf
 
             _, indices = torch.topk(
-                candidate_scores, k=20, dim=-1, largest=True
+                candidate_scores,
+                k=20, dim=-1, largest=True
             )  # (batch_size, 20)
 
             return indices
diff --git a/modeling/models/sasrec_semantic.py b/modeling/models/sasrec_semantic.py
index c1089e23..0230f41e 100644
--- a/modeling/models/sasrec_semantic.py
+++ b/modeling/models/sasrec_semantic.py
@@ -1,9 +1,9 @@
 import torch
+from .tiger import TigerModel
 from models import SequentialTorchModel
 from torch import nn
 from utils import DEVICE, create_masked_tensor
-
-from .tiger import TigerModel
+from torch import nn
 
 
 class SasRecSemanticModel(SequentialTorchModel, config_name="sasrec_semantic"):
diff --git a/modeling/models/tiger.py b/modeling/models/tiger.py
index c44c13f3..81d5825d 100644
--- a/modeling/models/tiger.py
+++ b/modeling/models/tiger.py
@@ -1,10 +1,9 @@
 import json
 
 import torch
-from torch import nn
-
 from models.base import SequentialTorchModel
 from rqvae_utils import CollisionSolver, SimplifiedTree
+from torch import nn
 from utils import DEVICE, create_masked_tensor, get_activation_function
 
 from .rqvae import RqVaeModel
@@ -78,7 +77,7 @@ def __init__(
                 a=-2 * initializer_range,
                 b=2 * initializer_range,
             ),
-            requires_grad=True,
+            requires_grad=True, # TODOPK added for bos
         )
 
         self._codebook_embeddings = nn.Embedding(
@@ -353,17 +352,13 @@ def _apply_decoder_autoregressive(self, encoder_embeddings, encoder_mask):
             )
 
             assert last_position_embedding.shape == tgt_embeddings[:, -1, :].shape
-            assert tgt_embeddings.shape == torch.Size(
-                [batch_size, step + 1, embedding_dim]
-            )
+            assert tgt_embeddings.shape == torch.Size([batch_size, step + 1, embedding_dim])
 
             curr_step_embeddings = tgt_embeddings.clone()
             curr_step_embeddings[:, -1, :] = (
                 tgt_embeddings[:, -1, :] + last_position_embedding
             )
-            assert torch.allclose(
-                tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :]
-            )
+            assert torch.allclose(tgt_embeddings[:, :-1, :], curr_step_embeddings[:, :-1, :])
             tgt_embeddings = curr_step_embeddings
 
             # curr_embeddings[:, -1, :] = self._decoder_layernorm(curr_embeddings[:, -1, :])
diff --git a/modeling/optimizer/base.py b/modeling/optimizer/base.py
index 0e85cab8..86c63537 100644
--- a/modeling/optimizer/base.py
+++ b/modeling/optimizer/base.py
@@ -1,18 +1,17 @@
 import copy
 
 import torch
-
 from utils import MetaParent
 
 OPTIMIZERS = {
-    "sgd": torch.optim.SGD,
-    "adam": torch.optim.Adam,
-    "adamw": torch.optim.AdamW,
+    'sgd': torch.optim.SGD,
+    'adam': torch.optim.Adam,
+    'adamw': torch.optim.AdamW
 }
 
 SCHEDULERS = {
-    "step": torch.optim.lr_scheduler.StepLR,
-    "cyclic": torch.optim.lr_scheduler.CyclicLR,
+    'step': torch.optim.lr_scheduler.StepLR,
+    'cyclic': torch.optim.lr_scheduler.CyclicLR
 }
 
 
@@ -20,7 +19,7 @@ class BaseOptimizer(metaclass=MetaParent):
     pass
 
 
-class BasicOptimizer(BaseOptimizer, config_name="basic"):
+class BasicOptimizer(BaseOptimizer, config_name='basic'):
     def __init__(self, model, optimizer, scheduler=None, clip_grad_threshold=None):
         self._model = model
         self._optimizer = optimizer
@@ -29,24 +28,26 @@ def __init__(self, model, optimizer, scheduler=None, clip_grad_threshold=None):
 
     @classmethod
     def create_from_config(cls, config, **kwargs):
-        optimizer_cfg = copy.deepcopy(config["optimizer"])
-        optimizer = OPTIMIZERS[optimizer_cfg.pop("type")](
-            kwargs["model"].parameters(), **optimizer_cfg
+        optimizer_cfg = copy.deepcopy(config['optimizer'])
+        optimizer = OPTIMIZERS[optimizer_cfg.pop('type')](
+            kwargs['model'].parameters(),
+            **optimizer_cfg
         )
 
-        if "scheduler" in config:
-            scheduler_cfg = copy.deepcopy(config["scheduler"])
-            scheduler = SCHEDULERS[scheduler_cfg.pop("type")](
-                optimizer, **scheduler_cfg
+        if 'scheduler' in config:
+            scheduler_cfg = copy.deepcopy(config['scheduler'])
+            scheduler = SCHEDULERS[scheduler_cfg.pop('type')](
+                optimizer,
+                **scheduler_cfg
             )
         else:
             scheduler = None
 
         return cls(
-            model=kwargs["model"],
+            model=kwargs['model'],
             optimizer=optimizer,
             scheduler=scheduler,
-            clip_grad_threshold=config.get("clip_grad_threshold", None),
+            clip_grad_threshold=config.get('clip_grad_threshold', None)
         )
 
     def step(self, loss):
@@ -54,16 +55,14 @@ def step(self, loss):
         loss.backward()
 
         if self._clip_grad_threshold is not None:
-            torch.nn.utils.clip_grad_norm_(
-                self._model.parameters(), self._clip_grad_threshold
-            )
+            torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._clip_grad_threshold)
 
         self._optimizer.step()
         if self._scheduler is not None:
             self._scheduler.step()
 
     def state_dict(self):
-        state_dict = {"optimizer": self._optimizer.state_dict()}
+        state_dict = {'optimizer': self._optimizer.state_dict()}
         if self._scheduler is not None:
-            state_dict.update({"scheduler": self._scheduler.state_dict()})
+            state_dict.update({'scheduler': self._scheduler.state_dict()})
         return state_dict
diff --git a/modeling/pretrain.py b/modeling/pretrain.py
index 4221e62c..4597e3e8 100644
--- a/modeling/pretrain.py
+++ b/modeling/pretrain.py
@@ -1,16 +1,16 @@
-import copy
-import json
-
-import torch
-
 import utils
-from callbacks import BaseCallback
-from dataloader import BaseDataloader
+from utils import parse_args, create_logger, fix_random_seed, DEVICE
+
 from dataset import BaseDataset
-from loss import BaseLoss
+from dataloader import BaseDataloader
 from models import BaseModel
 from optimizer import BaseOptimizer
-from utils import DEVICE, create_logger, fix_random_seed, parse_args
+from loss import BaseLoss
+from callbacks import BaseCallback
+
+import copy
+import json
+import torch
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -20,10 +20,10 @@ def pretrain(dataloader, model, optimizer, loss_function, callback, epoch_cnt):
     step_num = 0
     best_checkpoint = None
 
-    logger.debug("Start pretraining...")
+    logger.debug('Start pretraining...')
 
     for epoch in range(epoch_cnt):
-        logger.debug(f"Start epoch {epoch}")
+        logger.debug(f'Start epoch {epoch}')
         for step, batch in enumerate(dataloader):
             model.train()
 
@@ -39,7 +39,7 @@ def pretrain(dataloader, model, optimizer, loss_function, callback, epoch_cnt):
 
             best_checkpoint = copy.deepcopy(model.state_dict())
 
-    logger.debug("Pretraining procedure has been finished!")
+    logger.debug('Pretraining procedure has been finished!')
     return best_checkpoint
 
 
@@ -47,38 +47,41 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(
-        config["experiment_name"]
-    )
+    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = \
+        utils.tensorboards.TensorboardWriter(config['experiment_name'])
 
-    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
 
-    dataset = BaseDataset.create_from_config(config["dataset"])
+    dataset = BaseDataset.create_from_config(config['dataset'])
 
     train_sampler, test_sampler = dataset.get_samplers()
 
     train_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
+        config['dataloader']['train'],
+        dataset=train_sampler,
+        **dataset.meta
     )
 
     validation_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=test_sampler, **dataset.meta
+        config['dataloader']['validation'],
+        dataset=test_sampler,
+        **dataset.meta
     )
 
-    model = BaseModel.create_from_config(config["model"], **dataset.meta).to(DEVICE)
+    model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
 
-    loss_function = BaseLoss.create_from_config(config["loss"])
+    loss_function = BaseLoss.create_from_config(config['loss'])
 
-    optimizer = BaseOptimizer.create_from_config(config["optimizer"], model=model)
+    optimizer = BaseOptimizer.create_from_config(config['optimizer'], model=model)
 
     callback = BaseCallback.create_from_config(
-        config["callback"],
+        config['callback'],
         model=model,
         dataloader=validation_dataloader,
-        optimizer=optimizer,
+        optimizer=optimizer
     )
 
-    logger.debug("Everything is ready for pretraining process!")
+    logger.debug('Everything is ready for pretraining process!')
 
     # Pretrain process
     pretrain(
@@ -87,16 +90,14 @@ def main():
         optimizer=optimizer,
         loss_function=loss_function,
         callback=callback,
-        epoch_cnt=config["pretrain_epochs_num"],
+        epoch_cnt=config['pretrain_epochs_num']
     )
 
-    logger.debug("Saving model...")
-    checkpoint_path = "../checkpoints/pretrain_{}_final_state.pth".format(
-        config["experiment_name"]
-    )
+    logger.debug('Saving model...')
+    checkpoint_path = '../checkpoints/pretrain_{}_final_state.pth'.format(config['experiment_name'])
     torch.save(model.state_dict(), checkpoint_path)
-    logger.debug("Saved model as {}".format(checkpoint_path))
+    logger.debug('Saved model as {}'.format(checkpoint_path))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/rqvae_utils/__init__.py b/modeling/rqvae_utils/__init__.py
index 33c649b9..2722515a 100644
--- a/modeling/rqvae_utils/__init__.py
+++ b/modeling/rqvae_utils/__init__.py
@@ -1,4 +1,4 @@
 from .collision_solver import CollisionSolver
-from .simplified_tree import SimplifiedTree
-from .tree import Tree
 from .trie import Trie
+from .tree import Tree
+from .simplified_tree import SimplifiedTree
\ No newline at end of file
diff --git a/modeling/rqvae_utils/collision_solver.py b/modeling/rqvae_utils/collision_solver.py
index 426cf1bf..f673c6dd 100644
--- a/modeling/rqvae_utils/collision_solver.py
+++ b/modeling/rqvae_utils/collision_solver.py
@@ -6,45 +6,32 @@
 
 
 class CollisionSolver:
-    def __init__(
-        self,
-        emb_dim: int,
-        sem_id_len: int,
-        codebook_size: int,
-        device: torch.device = DEVICE,
-    ):
+    def __init__(self,
+                 emb_dim: int,
+                 sem_id_len: int,
+                 codebook_size: int,
+                 device: torch.device=DEVICE):
         """
         :param emb_dim: Длина остатка
         :param codebook_size: Количество элементов в одном кодбуке
         :param sem_id_len: Длина semantic_id (без токена решающего коллизии)
         :param device: Устройство
         """
-        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty(
-            (0, 0)
-        )  # тензор группирирующий остатки по semantic_id
+        self._sem_ids_sparse_tensor: torch.Tensor = torch.empty((0, 0))  # тензор группирирующий остатки по semantic_id
         self.item_ids_sparse_tensor: torch.Tensor = torch.empty(
-            (0, 0)
-        )  # тензор группирирующий реальные айди айтемов по semantic_id
-        self.counts_dict: dict[int, int] = defaultdict(
-            int
-        )  # тензор храняющий количество коллизий по semantic_id
+            (0, 0))  # тензор группирирующий реальные айди айтемов по semantic_id
+        self.counts_dict: dict[int, int] = defaultdict(int)  # тензор храняющий количество коллизий по semantic_id
         self.emb_dim: int = emb_dim  # длина остатка
         self.sem_id_len: int = sem_id_len  # длина semantic_id
         self.codebook_size: int = codebook_size  # количество элементов в одном кодбуке
         self.device: torch.device = device  # девайс
 
-        self.key: torch.Tensor = torch.tensor(
-            [self.codebook_size**i for i in range(self.sem_id_len)],
-            dtype=torch.long,
-            device=self.device,
-        )  # ключ для сопоставления числа каждому semantic_id
-
-    def create_query_candidates_dict(
-        self,
-        item_ids: torch.Tensor,
-        semantic_ids: torch.Tensor,
-        residuals: torch.Tensor,
-    ) -> None:
+        self.key: torch.Tensor = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len)],
+                                              dtype=torch.long,
+                                              device=self.device)  # ключ для сопоставления числа каждому semantic_id
+
+    def create_query_candidates_dict(self, item_ids: torch.Tensor, semantic_ids: torch.Tensor,
+                                     residuals: torch.Tensor) -> None:
         """
         Создает разреженный тензор, который содержит сгруппированные по semantic id элементы
 
@@ -65,53 +52,35 @@ def create_query_candidates_dict(
         semantic_ids = semantic_ids.to(self.device)
 
         unique_id = (semantic_ids * self.key).sum(dim=1)  # хэши
-        unique_ids, inverse_indices, counts = torch.unique(
-            unique_id, return_inverse=True, return_counts=True
-        )
-        sorted_indices = torch.argsort(
-            inverse_indices
-        )  # сортированные индексы чтобы совпадающие хэши шли подряд
+        unique_ids, inverse_indices, counts = torch.unique(unique_id, return_inverse=True, return_counts=True)
+        sorted_indices = torch.argsort(inverse_indices)  # сортированные индексы чтобы совпадающие хэши шли подряд
 
         row_indices = inverse_indices[sorted_indices]  # отсортированные хэши
 
         offsets = torch.cumsum(counts, dim=0) - counts
-        col_indices = (
-            torch.arange(semantic_ids_count, device=self.device) - offsets[row_indices]
-        )  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
-
-        indices = torch.stack(
-            [unique_ids[row_indices], col_indices], dim=0
-        )  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
-
-        max_residuals_count = int(
-            counts.max().item()
-        )  # максимальное количество коллизий для одного sem_id
-        max_sid = int(
-            self.codebook_size**self.sem_id_len
-        )  # максимальный хэш sem_id который может быть
-
-        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(
-            indices,
-            residuals[sorted_indices],
-            size=(max_sid, max_residuals_count, self.emb_dim),
-            device=self.device,
-        )  # (max_sid, max_residuals_count, emb_dim)
-
-        self.counts_dict = defaultdict(
-            int, zip(unique_ids.tolist(), counts.tolist())
-        )  # sid -> collision count
-
-        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(
-            indices,
-            item_ids[sorted_indices],
-            size=(max_sid, max_residuals_count),
-            device=self.device,
-            dtype=torch.int32,
-        )  # (max_sid, max_residuals_count)
-
-    def get_residuals_by_semantic_id_batch(
-        self, semantic_ids: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+        col_indices = torch.arange(semantic_ids_count, device=self.device) - offsets[
+            row_indices]  # индексы от 0 до k внутри каждого набора из совпадающих хэшей
+
+        indices = torch.stack([
+            unique_ids[row_indices],
+            col_indices
+        ],
+            dim=0)  # индексы для разреженного тензора: 1 размерность хэш, 2 размерность индексы от 0 до k для коллизий каждого хэша
+
+        max_residuals_count = int(counts.max().item())  # максимальное количество коллизий для одного sem_id
+        max_sid = int(self.codebook_size ** self.sem_id_len)  # максимальный хэш sem_id который может быть
+
+        self._sem_ids_sparse_tensor = torch.sparse_coo_tensor(indices, residuals[sorted_indices],
+                                                              size=(max_sid, max_residuals_count, self.emb_dim),
+                                                              device=self.device)  # (max_sid, max_residuals_count, emb_dim)
+
+        self.counts_dict = defaultdict(int, zip(unique_ids.tolist(), counts.tolist()))  # sid -> collision count
+
+        self.item_ids_sparse_tensor = torch.sparse_coo_tensor(indices, item_ids[sorted_indices],
+                                                              size=(max_sid, max_residuals_count), device=self.device,
+                                                              dtype=torch.int32)  # (max_sid, max_residuals_count)
+
+    def get_residuals_by_semantic_id_batch(self, semantic_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         :param semantic_ids батч из semantic ids (batch_size, sem_id_len)
 
@@ -124,21 +93,13 @@ def get_residuals_by_semantic_id_batch(
         semantic_ids = semantic_ids.to(self.device)
         unique_ids = (semantic_ids * self.key).sum(dim=1)
 
-        candidates = torch.stack(
-            [self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids]
-        )
-        counts = torch.tensor(
-            [self.counts_dict[key.item()] for key in unique_ids], device=self.device
-        )
-        mask = torch.arange(candidates.shape[1], device=self.device).expand(
-            len(unique_ids), -1
-        ) < counts.view(-1, 1)
+        candidates = torch.stack([self._sem_ids_sparse_tensor[key].to_dense() for key in unique_ids])
+        counts = torch.tensor([self.counts_dict[key.item()] for key in unique_ids], device=self.device)
+        mask = torch.arange(candidates.shape[1], device=self.device).expand(len(unique_ids), -1) < counts.view(-1, 1)
 
         return candidates, mask
 
-    def get_pred_scores(
-        self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor
-    ) -> dict[str, torch.Tensor]:
+    def get_pred_scores(self, semantic_ids: torch.Tensor, pred_residuals: torch.Tensor) -> dict[str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param pred_residuals: [batch_size, emb_dim] предсказанные остатки
@@ -159,26 +120,19 @@ def get_pred_scores(
 
         candidates, mask = self.get_residuals_by_semantic_id_batch(semantic_ids)
 
-        pred_scores = torch.einsum(
-            "njk,nk->nj", candidates, pred_residuals
-        ).masked_fill(~mask, -torch.inf)
+        pred_scores = torch.einsum('njk,nk->nj', candidates, pred_residuals).masked_fill(~mask, -torch.inf)
         pred_indices = torch.argmax(pred_scores, dim=1)
         pred_item_ids = torch.stack(
-            [
-                self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]]
-                for i in range(semantic_ids.shape[0])
-            ]
-        )
+            [self.item_ids_sparse_tensor[unique_ids[i]][pred_indices[i]] for i in range(semantic_ids.shape[0])])
 
         return {
             "pred_scores_mask": mask,
             "pred_scores": pred_scores,
-            "pred_item_ids": pred_item_ids,
+            "pred_item_ids": pred_item_ids
         }
 
-    def get_true_dedup_tokens(
-        self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor
-    ) -> dict[str, torch.Tensor]:
+    def get_true_dedup_tokens(self, semantic_ids: torch.Tensor, true_residuals: torch.Tensor) -> dict[
+        str, torch.Tensor]:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токена решающего коллизии)
         :param true_residuals: [batch_size, emb_dim] реальные остатки
@@ -200,11 +154,11 @@ def get_true_dedup_tokens(
 
         assert matches.any(dim=1).all(), "Не у всех батчей есть совпадение"
 
-        return {"true_dedup_tokens": true_dedup_tokens}
+        return {
+            "true_dedup_tokens": true_dedup_tokens
+        }
 
-    def get_item_ids_batch(
-        self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor
-    ) -> torch.Tensor:
+    def get_item_ids_batch(self, semantic_ids: torch.Tensor, dedup_tokens: torch.Tensor) -> torch.Tensor:
         """
         :param semantic_id: [batch_size, sem_id_len] semantic ids (без токенов решающего коллизии)
         :param dedup_tokens: [batch_size] токены решающие коллизии
@@ -220,10 +174,6 @@ def get_item_ids_batch(
         unique_ids = (semantic_ids * self.key).sum(dim=1)
 
         item_ids = torch.stack(
-            [
-                self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]]
-                for i in range(semantic_ids.shape[0])
-            ]
-        )
+            [self.item_ids_sparse_tensor[unique_ids[i]][dedup_tokens[i]] for i in range(semantic_ids.shape[0])])
 
         return item_ids
diff --git a/modeling/rqvae_utils/rqvae_data.py b/modeling/rqvae_utils/rqvae_data.py
index 5ffec08e..47692cae 100644
--- a/modeling/rqvae_utils/rqvae_data.py
+++ b/modeling/rqvae_utils/rqvae_data.py
@@ -1,11 +1,11 @@
-import gzip
+import pandas as pd
 import json
+import gzip
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 import random
 
-import pandas as pd
-import torch
 from tqdm import tqdm
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 tqdm.pandas()
 
@@ -76,7 +76,7 @@ def get_data(cached=True):
             df["embeddings"] = df["combined_text"].progress_apply(encode_text)
     else:
         df = torch.load("../data/Beauty/data_full.pt", weights_only=False)
-
+        
     return df
 
 
@@ -91,3 +91,6 @@ def search_similar_items(items_with_tuples, clust2search, max_cnt=5):
         if cnt >= max_cnt:
             return similars
     return similars
+
+
+
diff --git a/modeling/rqvae_utils/rqvae_test.py b/modeling/rqvae_utils/rqvae_test.py
index 03f8e3e8..611e7739 100644
--- a/modeling/rqvae_utils/rqvae_test.py
+++ b/modeling/rqvae_utils/rqvae_test.py
@@ -6,7 +6,6 @@
 from models import RqVaeModel
 from utils import DEVICE
 
-
 def test(a, b):
     cos_sim = torch.nn.functional.cosine_similarity(a, b, dim=0)
     norm_a = torch.norm(a, p=2)
@@ -14,7 +13,6 @@ def test(a, b):
     l2_dist = torch.norm(a - b, p=2) / (norm_a + norm_b + 1e-8)
     return cos_sim, l2_dist
 
-
 if __name__ == "__main__":
     config = json.load(open("../configs/train/tiger_train_config.json"))
     config = config["model"]
@@ -26,16 +24,14 @@ def test(a, b):
     )
     df = torch.load(config["embs_extractor_path"], weights_only=False)
     embeddings_array = np.stack(df["embeddings"].values)
-    tensor_embeddings = torch.tensor(
-        embeddings_array, dtype=torch.float32, device=DEVICE
-    )
-    inputs = {"embeddings": tensor_embeddings}
+    tensor_embeddings = torch.tensor(embeddings_array, dtype=torch.float32, device=DEVICE)
+    inputs = {'embeddings': tensor_embeddings}
 
     rqvae_model.eval()
     sem_ids, residuals = rqvae_model.forward(inputs)
     scores = residuals.detach()
     print(torch.norm(residuals, p=2, dim=1).median())
-    for i, codebook in enumerate(rqvae_model.codebooks):
+    for (i, codebook) in enumerate(rqvae_model.codebooks):
         scores += codebook[sem_ids[:, i]].detach()
     decoder_output = rqvae_model.decoder(scores.detach()).detach()
 
@@ -45,14 +41,11 @@ def test(a, b):
     print("косинусное расстояние", cos_sim)
     print("евклидово расстояние", l2_dist)
 
-    cos_sim = torch.nn.functional.cosine_similarity(
-        tensor_embeddings, decoder_output, dim=1
-    )
+    cos_sim = torch.nn.functional.cosine_similarity(tensor_embeddings, decoder_output, dim=1)
     print("косинусное расстояние", cos_sim.mean(), cos_sim.min(), cos_sim.max())
 
-    norm_a = torch.norm(tensor_embeddings, p=2, dim=1)
-    norm_b = torch.norm(decoder_output, p=2, dim=1)
-    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim=1) / (
-        norm_a + norm_b + 1e-8
-    )
-    print("евклидово расстояние", l2_dist.median(), l2_dist.min(), l2_dist.max())
+    norm_a = torch.norm(tensor_embeddings, p=2, dim = 1)
+    norm_b = torch.norm(decoder_output, p=2, dim = 1)
+    l2_dist = torch.norm(decoder_output - tensor_embeddings, p=2, dim = 1) / (norm_a + norm_b + 1e-8)
+    print("евклидово расстояние",l2_dist.median(), l2_dist.min(), l2_dist.max())
+
diff --git a/modeling/rqvae_utils/simplified_tree.py b/modeling/rqvae_utils/simplified_tree.py
index 120f9a80..5a09dcb4 100644
--- a/modeling/rqvae_utils/simplified_tree.py
+++ b/modeling/rqvae_utils/simplified_tree.py
@@ -10,21 +10,14 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = (
-            embedding_table  # (semantic_id_len, codebook_size, emb_dim)
-        )
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.sem_ids_count: int = 0
         self.full_embeddings: torch.Tensor = torch.empty((0, 0))
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(
-        self,
-        semantic_ids: torch.Tensor,
-        residuals: torch.Tensor,
-        item_ids: torch.Tensor,
-        sum_with_residuals: bool = True,
-    ) -> None:
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor,
+                             sum_with_residuals: bool = True) -> None:
         """
         :param sum_with_residuals: флаг, отвечающий за то учитывать ли остатки при выборе кандидатов
         :param semantic_ids: (sem_ids_count, sem_id_len)
@@ -37,11 +30,9 @@ def build_tree_structure(
         assert item_ids.shape == (self.sem_ids_count,)
 
         semantic_ids = semantic_ids.to(self.device)
-        residuals = (
-            residuals.to(self.device).float()
-            if sum_with_residuals
-            else torch.zeros_like(residuals, device=self.device, dtype=torch.float)
-        )
+        residuals = residuals.to(self.device).float() if sum_with_residuals else torch.zeros_like(residuals,
+                                                                                                  device=self.device,
+                                                                                                  dtype=torch.float)
         self.full_embeddings = self.calculate_full(semantic_ids).float() + residuals
         self.item_ids = item_ids
 
@@ -53,17 +44,14 @@ def calculate_full(self, sem_ids: torch.Tensor) -> torch.Tensor:
         assert sem_ids.shape[1] == self.sem_id_len
         sem_ids = sem_ids.to(self.device)
 
-        expanded_emb_table = self.embedding_table.unsqueeze(0).expand(
-            sem_ids.shape[0], -1, -1, -1
-        )  # (count, sem_id_len, codebook_size, emb_dim)
+        expanded_emb_table = (self.embedding_table.unsqueeze(0)
+                              .expand(sem_ids.shape[0], -1, -1, -1))  # (count, sem_id_len, codebook_size, emb_dim)
 
-        index = (
-            sem_ids.unsqueeze(-1).expand(-1, -1, self.emb_dim).unsqueeze(2)
-        )  # (count, sem_id_len, 1, emb_dim)
+        index = (sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (count, sem_id_len, 1, emb_dim)
 
-        return (
-            torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)
-        )  # (count, emb_dim)
+        return torch.gather(input=expanded_emb_table, index=index, dim=2).sum(1).squeeze(1)  # (count, emb_dim)
 
     def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Tensor:
         """
@@ -75,21 +63,14 @@ def query(self, request_sem_ids: torch.Tensor, items_to_query: int) -> torch.Ten
         assert 0 < items_to_query <= self.sem_ids_count
 
         request_sem_ids = request_sem_ids.to(self.device)
-        request_embeddings = self.calculate_full(
-            request_sem_ids
-        )  # (batch_size, emb_dim)
+        request_embeddings = self.calculate_full(request_sem_ids)  # (batch_size, emb_dim)
 
-        request_embeddings = request_embeddings.unsqueeze(1).expand(
-            -1, self.sem_ids_count, -1
-        )  # (batch_size, sem_ids_count, emb_dim)
+        request_embeddings = (request_embeddings.unsqueeze(1)
+                              .expand(-1, self.sem_ids_count, -1))  # (batch_size, sem_ids_count, emb_dim)
 
-        diff_norm = torch.norm(
-            self.full_embeddings - request_embeddings, p=2, dim=2
-        )  # (batch_size, sem_ids_count)
+        diff_norm = torch.norm(self.full_embeddings - request_embeddings, p=2, dim=2)  # (batch_size, sem_ids_count)
 
-        indices = torch.argsort(diff_norm, descending=False, dim=1)[
-            :, :items_to_query
-        ]  # (batch_size, k)
+        indices = torch.argsort(diff_norm, descending=False, dim=1)[:, :items_to_query]  # (batch_size, k)
         return self.item_ids[indices]
 
     def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
@@ -103,21 +84,18 @@ def _query(self, request_sem_ids: torch.Tensor, k: int) -> torch.Tensor:
         assert 0 < k <= self.sem_ids_count
         request_sem_ids = request_sem_ids.to(self.device)
 
-        index = (
-            request_sem_ids.unsqueeze(-1).expand(-1, -1, self.emb_dim).unsqueeze(2)
-        )  # (batch_size, sem_id_len, 1, emb_dim)
+        index = (request_sem_ids.unsqueeze(-1)
+                 .expand(-1, -1, self.emb_dim)
+                 .unsqueeze(2))  # (batch_size, sem_id_len, 1, emb_dim)
 
         request_embeddings = torch.gather(
-            input=self.embedding_table.unsqueeze(0).expand(
-                request_sem_ids.shape[0], -1, -1, -1
-            ),
+            input=self.embedding_table.unsqueeze(0).expand(request_sem_ids.shape[0], -1, -1, -1),
             dim=2,
-            index=index,
+            index=index
         ).sum(1)  # (batch_size, emb_dim)
 
-        diff_norm = torch.cdist(
-            self.full_embeddings, request_embeddings.unsqueeze(1), p=2
-        ).squeeze(1)  # (batch_size, sem_ids_count)
+        diff_norm = torch.cdist(self.full_embeddings, request_embeddings.unsqueeze(1), p=2).squeeze(
+            1)  # (batch_size, sem_ids_count)
 
         _, indices = torch.topk(diff_norm, k=k, dim=1, largest=False)  # (batch_size, k)
         return self.item_ids[indices.squeeze(-1)]
diff --git a/modeling/rqvae_utils/tree.py b/modeling/rqvae_utils/tree.py
index 336f6dbe..b09cd049 100644
--- a/modeling/rqvae_utils/tree.py
+++ b/modeling/rqvae_utils/tree.py
@@ -11,9 +11,7 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         :param device: устройство
         """
         self.device: torch.device = device
-        self.embedding_table: torch.Tensor = (
-            embedding_table  # (semantic_id_len, codebook_size, emb_dim)
-        )
+        self.embedding_table: torch.Tensor = embedding_table  # (semantic_id_len, codebook_size, emb_dim)
         self.sem_id_len, self.codebook_size, self.emb_dim = self.embedding_table.shape
         self.key: torch.Tensor = torch.empty((0, 0))
         self.A: torch.Tensor = torch.empty((0, 0))  # будет (max_sem_id, )
@@ -22,12 +20,7 @@ def __init__(self, embedding_table: torch.Tensor, device: torch.device = DEVICE)
         self.sids: torch.Tensor = torch.empty((0, 0))  # будет (sem_id_len, )
         self.item_ids: torch.Tensor = torch.empty((0, 0))
 
-    def build_tree_structure(
-        self,
-        semantic_ids: torch.Tensor,
-        residuals: torch.Tensor,
-        item_ids: torch.Tensor,
-    ):
+    def build_tree_structure(self, semantic_ids: torch.Tensor, residuals: torch.Tensor, item_ids: torch.Tensor):
         """
         :param semantic_ids: (sem_ids_count, sem_id_len)
         :param residuals: (sem_ids_count, emb_dim)
@@ -41,41 +34,22 @@ def build_tree_structure(
         assert item_ids.shape == (self.sem_ids_count,)
 
         self.item_ids = item_ids
-        self.key = torch.tensor(
-            [self.codebook_size**i for i in range(self.sem_id_len - 1, -1, -1)],
-            dtype=torch.long,
-            device=self.device,
-        )
+        self.key = torch.tensor([self.codebook_size ** i for i in range(self.sem_id_len - 1, -1, -1)],
+                                dtype=torch.long, device=self.device)
         self.sids = self.get_sids(semantic_ids.float())  # (sem_id_len, )
         self.sem_ids_embs = self.calculate_full(semantic_ids, residuals)
 
-        result = torch.full(
-            size=[self.codebook_size**self.sem_id_len],
-            fill_value=0,
-            dtype=torch.int64,
-            device=self.device,
-        )
+        result = torch.full(size=[self.codebook_size ** self.sem_id_len], fill_value=0, dtype=torch.int64,
+                            device=self.device)
         temp_unique_id = self.sids * self.codebook_size
-        temp_sem_ids = torch.concat(
-            [
-                semantic_ids,
-                torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1),
-            ],
-            dim=-1,
-        )
+        temp_sem_ids = torch.concat([semantic_ids, torch.zeros(self.sem_ids_count, device=self.device).unsqueeze(1)],
+                                    dim=-1)
 
         for i in range(0, self.sem_id_len + 1):
-            temp_unique_id = (
-                temp_unique_id
-                - (self.codebook_size**i) * temp_sem_ids[:, self.sem_id_len - i]
-            )
-            temp_unique_ids, temp_inverse_indices = torch.unique(
-                temp_unique_id, return_inverse=True
-            )
+            temp_unique_id = temp_unique_id - (self.codebook_size ** i) * temp_sem_ids[:, self.sem_id_len - i]
+            temp_unique_ids, temp_inverse_indices = torch.unique(temp_unique_id, return_inverse=True)
             temp_counts = torch.bincount(temp_inverse_indices)
-            truncated_ids = torch.floor_divide(
-                input=temp_unique_id, other=(self.codebook_size ** (i + 1))
-            ).long()
+            truncated_ids = torch.floor_divide(input=temp_unique_id, other=(self.codebook_size ** (i + 1))).long()
             result[truncated_ids] = temp_counts[temp_inverse_indices]
 
         self.A = result
@@ -90,21 +64,13 @@ def get_counts(self, sem_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]
         offsets = torch.arange(self.sem_id_len + 1, device=self.device)
         i = torch.arange(self.sem_id_len, device=self.device)
 
-        mask_sem = (
-            i < (self.sem_id_len - offsets.unsqueeze(1))
-        ).long()  # (sem_id_len + 1, sem_id_len)
+        mask_sem = (i < (self.sem_id_len - offsets.unsqueeze(1))).long()  # (sem_id_len + 1, sem_id_len)
         divs = torch.pow(self.codebook_size, offsets)  # (sem_id_len + 1,)
 
-        C = (
-            sem_ids.unsqueeze(1)
-            * mask_sem.unsqueeze(0)
-            * self.key.unsqueeze(0).unsqueeze(1)
-        ).sum(dim=-1)
+        C = (sem_ids.unsqueeze(1) * mask_sem.unsqueeze(0) * self.key.unsqueeze(0).unsqueeze(1)).sum(dim=-1)
         B = C // divs.unsqueeze(0)
 
-        return C, self.A[
-            B
-        ]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
+        return C, self.A[B]  # (batch_size, sem_id_len + 1), (batch_size, sem_id_len + 1)
 
     def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
         """
@@ -112,11 +78,9 @@ def get_sids(self, sem_ids: torch.Tensor) -> torch.Tensor:
         :return: хэши sem_ids (sem_id_count,)
         """
         assert sem_ids.shape[1] == self.sem_id_len
-        return torch.einsum("nc,c->n", sem_ids, self.key.float())  # (sem_ids_count,)
+        return torch.einsum('nc,c->n', sem_ids, self.key.float())  # (sem_ids_count,)
 
-    def calc_ol(
-        self, batch_ids: torch.Tensor, k: int
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    def calc_ol(self, batch_ids: torch.Tensor, k: int) -> tuple[torch.Tensor, torch.Tensor]:
         """
         :param batch_ids: (batch_size, sem_id_len)
         :param k: int
@@ -130,8 +94,7 @@ def calc_ol(
         gather_ol = torch.gather(c, dim=1, index=ol.unsqueeze(1)).squeeze()  # (bs,)
 
         mask_ol = (gather_ol.unsqueeze(-1) <= self.sids) & (
-            self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1)
-        )
+                self.sids < (gather_ol + torch.pow(self.codebook_size, ol)).unsqueeze(-1))
         return ol, mask_ol  # (bs,) (bs, sem_ids_count)
 
     def calc_il(self, batch_ids, k):
@@ -145,27 +108,19 @@ def calc_il(self, batch_ids, k):
 
         batch_dim = batch_ids.shape[0]
         c, a = self.get_counts(batch_ids)
-        extended_c = torch.concat(
-            [torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c],
-            dim=1,
-        )
+        extended_c = torch.concat([torch.tensor(float("inf"), device=self.device).expand(batch_dim, 1), c], dim=1)
 
         il = torch.argmax((a > k).long(), dim=-1) - 1  # (bs,)
-        gather_il = torch.gather(
-            extended_c, dim=1, index=(il + 1).unsqueeze(1)
-        ).squeeze()  # (bs,)
+        gather_il = torch.gather(extended_c, dim=1, index=(il + 1).unsqueeze(1)).squeeze()  # (bs,)
 
         mask_il = (gather_il.unsqueeze(-1) <= self.sids) & (
-            self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1)
-        )
+                self.sids < (gather_il + torch.pow(self.codebook_size, il)).unsqueeze(-1))
         return il, mask_il  # (bs,) (bs, sem_ids_count)
 
     def get_repeated_sids(self, k: int) -> torch.Tensor:
         return self.sids.repeat(k, 1)  # (k, sem_ids_count)
 
-    def get_request_embeddings(
-        self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor
-    ) -> torch.Tensor:
+    def get_request_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
         """
         :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
         :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
@@ -174,16 +129,11 @@ def get_request_embeddings(
         assert decomposed_embeddings.shape[1:] == (self.sem_id_len + 1, self.emb_dim)
         assert levels.shape == (decomposed_embeddings.shape[0],)
 
-        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(
-            self.sem_id_len + 2, 0, -1, device=self.device
-        ).unsqueeze(1)
-        return torch.sum(
-            decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1
-        )  # (bs, emb_dim)
+        mask = torch.arange(1, self.sem_id_len + 2, device=self.device) >= torch.arange(self.sem_id_len + 2, 0, -1,
+                                                                                        device=self.device).unsqueeze(1)
+        return torch.sum(decomposed_embeddings * mask[levels + 1].unsqueeze(-1), dim=1)  # (bs, emb_dim)
 
-    def calculate_full(
-        self, sem_ids: torch.Tensor, residuals: torch.Tensor
-    ) -> torch.Tensor:
+    def calculate_full(self, sem_ids: torch.Tensor, residuals: torch.Tensor) -> torch.Tensor:
         """
         :param sem_ids: sem_ids (count, sem_id_len)
         :param residuals: остатки для каждого sem_id (count, emb_dim)
@@ -195,56 +145,31 @@ def calculate_full(
 
         count = residuals.shape[0]
         index = sem_ids.view(count, -1, 1, 1).expand(-1, -1, -1, self.emb_dim)
-        embs = torch.gather(
-            input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1),
-            dim=2,
-            index=index,
-        )  # expand бесплатный по памяти
-        decomposed_embs = torch.concat(
-            [embs.squeeze(2), residuals.unsqueeze(1)], dim=1
-        )  # (sem_ids_count, emb_dim)
-
-        assert decomposed_embs.shape == (
-            sem_ids.shape[0],
-            self.sem_id_len + 1,
-            self.emb_dim,
-        )
+        embs = torch.gather(input=self.embedding_table.unsqueeze(0).expand(count, -1, -1, -1), dim=2,
+                            index=index)  # expand бесплатный по памяти
+        decomposed_embs = torch.concat([embs.squeeze(2), residuals.unsqueeze(1)], dim=1)  # (sem_ids_count, emb_dim)
+
+        assert decomposed_embs.shape == (sem_ids.shape[0], self.sem_id_len + 1, self.emb_dim)
         return decomposed_embs
 
-    def calculate_level_embeddings(
-        self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor
-    ) -> torch.Tensor:
+    def calculate_level_embeddings(self, decomposed_embeddings: torch.Tensor, levels: torch.Tensor) -> torch.Tensor:
         """
         :param decomposed_embeddings: разложение sem_ids на эмбеддинги (count, sem_id_len +1, emb_dim)
         :param levels: сколько нужно взять эмбеддингов для суммы для каждого sem_id (count,)
         :return: эмбеддинги для всех sem_ids для нужных глубин (batch_size, sem_ids_count, emb_dim)
         """
-        assert decomposed_embeddings.shape == (
-            self.sem_ids_count,
-            self.sem_id_len + 1,
-            self.emb_dim,
-        )
-
-        mask = (
-            torch.arange(1, self.sem_id_len + 2, device=self.device)
-            >= torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)
-        ).float()
+        assert decomposed_embeddings.shape == (self.sem_ids_count, self.sem_id_len + 1, self.emb_dim)
+
+        mask = (torch.arange(1, self.sem_id_len + 2, device=self.device) >=
+                torch.arange(self.sem_id_len + 2, 0, -1, device=self.device).unsqueeze(1)).float()
         sids_mask = mask[levels + 1].unsqueeze(-1)  # (batch_size, sem_id_len + 1, 1)
-        return torch.einsum(
-            "nld,bld->bnd", decomposed_embeddings, sids_mask
-        )  # (batch_size, sem_ids_count, emb_dim)
+        return torch.einsum('nld,bld->bnd', decomposed_embeddings, sids_mask)  # (batch_size, sem_ids_count, emb_dim)
 
     def mask_result(self, result: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-        return torch.where(
-            mask, result, torch.tensor(float("-inf"), device=self.device)
-        )
-
-    def query(
-        self,
-        request_sem_ids: torch.Tensor,
-        request_residuals: torch.Tensor,
-        items_to_query: int,
-    ) -> torch.Tensor:
+        return torch.where(mask, result, torch.tensor(float('-inf'), device=self.device))
+
+    def query(self, request_sem_ids: torch.Tensor, request_residuals: torch.Tensor,
+              items_to_query: int) -> torch.Tensor:
         """
         :param request_sem_ids: батч из sem_ids (batch_size, sem_id_len)
         :param request_residuals: батч из остатков (batch_size, emb_dim)
@@ -272,31 +197,13 @@ def query(
         ol_request_embeddings = self.get_request_embeddings(request_embs, ol)
         il_request_embeddings = self.get_request_embeddings(request_embs, il)
 
-        ol_scores = (
-            torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1))
-            .squeeze(-1)
-            .detach()
-            .cpu()
-        )
-
-        il_scores = (
-            torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1))
-            .squeeze(-1)
-            .detach()
-            .cpu()
-        )
-
-        ids = np.lexsort(
-            keys=(
-                -torch.cat([il_scores, ol_scores], dim=1),
-                ~torch.cat(
-                    [torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1
-                ),
-                ~torch.cat([il_mask, ol_mask], dim=1),
-            )
-        )
-
-        ids = (ids % self.sem_ids_count)[:, : self.sem_ids_count][
-            :, :items_to_query
-        ]  # (batch_size, k)
+        ol_scores = torch.matmul(ol_sids_embeddings, ol_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
+
+        il_scores = torch.matmul(il_sids_embeddings, il_request_embeddings.unsqueeze(-1)).squeeze(-1).detach().cpu()
+
+        ids = np.lexsort(keys=(-torch.cat([il_scores, ol_scores], dim=1),
+                               ~torch.cat([torch.ones_like(il_mask), torch.zeros_like(ol_mask)], dim=1),
+                               ~torch.cat([il_mask, ol_mask], dim=1)))
+
+        ids = (ids % self.sem_ids_count)[:, :self.sem_ids_count][:, :items_to_query]  # (batch_size, k)
         return self.item_ids[ids]
diff --git a/modeling/rqvae_utils/tree_comparing.py b/modeling/rqvae_utils/tree_comparing.py
index f73db7c6..3f1d3f38 100644
--- a/modeling/rqvae_utils/tree_comparing.py
+++ b/modeling/rqvae_utils/tree_comparing.py
@@ -6,20 +6,20 @@
 import torch
 
 from models.rqvae import RqVaeModel
-from rqvae_utils import SimplifiedTree, Tree, Trie
+from rqvae_utils import Trie, SimplifiedTree, Tree
 from utils import DEVICE
 
 
 def memory_stats(k):
     process = psutil.Process(os.getpid())
-    memory_usage = process.memory_info().rss / 1024**2
+    memory_usage = process.memory_info().rss / 1024 ** 2
     print(f"{k}. Использование памяти: {memory_usage:.2f} MB")
 
 
 def calc_sid(sid, codebook_size):
     res = sid[-1]
     for i in range(1, sid.shape[0]):
-        res += sid[-i - 1] * (codebook_size**i)
+        res += sid[-i - 1] * (codebook_size ** i)
     return res
 
 
@@ -41,7 +41,9 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
     )
     rqvae_model.eval()
 
-    emb_table = torch.stack([cb for cb in rqvae_model.codebooks]).to(DEVICE)
+    emb_table = torch.stack(
+        [cb for cb in rqvae_model.codebooks]
+    ).to(DEVICE)
 
     trie = Trie(rqvae_model)
     tree = Tree(rqvae_model, DEVICE)
@@ -71,18 +73,14 @@ def stats(query_sem_id, codebook_size, sids, item_ids):
 
     now = time.time()
     simplified_tree_wr.build_tree_structure(semantic_ids, residuals, item_ids, False)
-    print(
-        f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms"
-    )
+    print(f"Time for simplified tree  without residuals init: {(time.time() - now) * 1000:.2f} ms")
 
     full_embeddings = tree.calculate_full(semantic_ids, residuals).sum(1)
     print(torch.all((full_embeddings == simplified_tree.full_embeddings) == True))
 
     items_to_query = 20
     batch_size = 256
-    q_semantic_ids = torch.randint(
-        0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE
-    )
+    q_semantic_ids = torch.randint(0, alphabet_size, (batch_size, K), dtype=torch.int64, device=DEVICE)
     q_residuals = torch.randn(batch_size, embedding_dim).to(DEVICE)
 
     total_time = 0
diff --git a/modeling/rqvae_utils/trie.py b/modeling/rqvae_utils/trie.py
index 4a4ca5b7..b7971e86 100644
--- a/modeling/rqvae_utils/trie.py
+++ b/modeling/rqvae_utils/trie.py
@@ -2,7 +2,6 @@
 import time
 
 import torch
-
 from models.rqvae import RqVaeModel
 from utils import DEVICE
 
diff --git a/modeling/train.py b/modeling/train.py
index 4919be6f..d865620d 100644
--- a/modeling/train.py
+++ b/modeling/train.py
@@ -1,32 +1,23 @@
-import copy
-import json
-import os
-
-import torch
-
 import utils
+from utils import parse_args, create_logger, DEVICE, fix_random_seed
+
 from callbacks import BaseCallback
-from dataloader import BaseDataloader
 from dataset import BaseDataset
+from dataloader import BaseDataloader
 from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
-from utils import DEVICE, create_logger, fix_random_seed, parse_args
+
+import copy
+import json
+import os
+import torch
 
 logger = create_logger(name=__name__)
 seed_val = 42
 
 
-def train(
-    dataloader,
-    model,
-    optimizer,
-    loss_function,
-    callback,
-    epoch_cnt=None,
-    step_cnt=None,
-    best_metric=None,
-):
+def train(dataloader, model, optimizer, loss_function, callback, epoch_cnt=None, step_cnt=None, best_metric=None):
     step_num = 0
     epoch_num = 0
     current_metric = 0
@@ -36,20 +27,14 @@ def train(
     best_epoch = 0
     best_checkpoint = None
 
-    logger.debug("Start training...")
+    logger.debug('Start training...')
 
-    while (epoch_cnt is None or epoch_num < epoch_cnt) and (
-        step_cnt is None or step_num < step_cnt
-    ):
+    while (epoch_cnt is None or epoch_num < epoch_cnt) and (step_cnt is None or step_num < step_cnt):
         if best_epoch + epochs_threshold < epoch_num:
-            logger.debug(
-                "There is no progress during {} epochs. Finish training".format(
-                    epochs_threshold
-                )
-            )
+            logger.debug('There is no progress during {} epochs. Finish training'.format(epochs_threshold))
             break
 
-        logger.debug(f"Start epoch {epoch_num}")
+        logger.debug(f'Start epoch {epoch_num}')
         for step, batch in enumerate(dataloader):
             batch_ = copy.deepcopy(batch)
 
@@ -69,18 +54,14 @@ def train(
                 # Take the last model
                 best_checkpoint = copy.deepcopy(model.state_dict())
                 best_epoch = epoch_num
-            elif (
-                best_checkpoint is None
-                or best_metric in batch_
-                and current_metric <= batch_[best_metric]
-            ):
+            elif best_checkpoint is None or best_metric in batch_ and current_metric <= batch_[best_metric]:
                 # If it is the first checkpoint, or it is the best checkpoint
                 current_metric = batch_[best_metric]
                 best_checkpoint = copy.deepcopy(model.state_dict())
                 best_epoch = epoch_num
 
         epoch_num += 1
-    logger.debug("Training procedure has been finished!")
+    logger.debug('Training procedure has been finished!')
     return best_checkpoint
 
 
@@ -88,54 +69,59 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(
-        config["experiment_name"]
-    )
+    utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = \
+        utils.tensorboards.TensorboardWriter(config['experiment_name'])
 
-    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
-    logger.debug("Current DEVICE: {}".format(DEVICE))
+    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
+    logger.debug('Current DEVICE: {}'.format(DEVICE))
 
-    dataset = BaseDataset.create_from_config(config["dataset"])
+    dataset = BaseDataset.create_from_config(config['dataset'])
 
     train_sampler, validation_sampler, test_sampler = dataset.get_samplers()
 
     train_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
+        config['dataloader']['train'],
+        dataset=train_sampler,
+        **dataset.meta
     )
 
     validation_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=validation_sampler, **dataset.meta
+        config['dataloader']['validation'],
+        dataset=validation_sampler,
+        **dataset.meta
     )
 
     eval_dataloader = BaseDataloader.create_from_config(
-        config["dataloader"]["validation"], dataset=test_sampler, **dataset.meta
+        config['dataloader']['validation'],
+        dataset=test_sampler,
+        **dataset.meta
     )
 
-    model = BaseModel.create_from_config(config["model"], **dataset.meta).to(DEVICE)
-    if "checkpoint" in config:
-        checkpoint_path = os.path.join("../checkpoints", f"{config['checkpoint']}.pth")
-        logger.debug("Loading checkpoint from {}".format(checkpoint_path))
+    model = BaseModel.create_from_config(config['model'], **dataset.meta).to(DEVICE)
+    if 'checkpoint' in config:
+        checkpoint_path = os.path.join('../checkpoints', f'{config["checkpoint"]}.pth')
+        logger.debug('Loading checkpoint from {}'.format(checkpoint_path))
         checkpoint = torch.load(checkpoint_path)
         print(checkpoint.keys())
         model.load_state_dict(checkpoint)
 
-    loss_function = BaseLoss.create_from_config(config["loss"])
+    loss_function = BaseLoss.create_from_config(config['loss'])
 
-    optimizer = BaseOptimizer.create_from_config(config["optimizer"], model=model)
+    optimizer = BaseOptimizer.create_from_config(config['optimizer'], model=model)
 
     callback = BaseCallback.create_from_config(
-        config["callback"],
+        config['callback'],
         model=model,
         train_dataloader=train_dataloader,
         validation_dataloader=validation_dataloader,
         eval_dataloader=eval_dataloader,
         optimizer=optimizer,
-        **dataset.meta,
+        **dataset.meta
     )
 
     # TODO add verbose option for all callbacks, multiple optimizer options (???)
     # TODO create pre/post callbacks
-    logger.debug("Everything is ready for training process!")
+    logger.debug('Everything is ready for training process!')
 
     # Train process
     _ = train(
@@ -144,18 +130,16 @@ def main():
         optimizer=optimizer,
         loss_function=loss_function,
         callback=callback,
-        epoch_cnt=config.get("train_epochs_num"),
-        step_cnt=config.get("train_steps_num"),
-        best_metric=config.get("best_metric"),
+        epoch_cnt=config.get('train_epochs_num'),
+        step_cnt=config.get('train_steps_num'),
+        best_metric=config.get('best_metric')
     )
 
-    logger.debug("Saving model...")
-    checkpoint_path = "../checkpoints/{}_final_state.pth".format(
-        config["experiment_name"]
-    )
+    logger.debug('Saving model...')
+    checkpoint_path = '../checkpoints/{}_final_state.pth'.format(config['experiment_name'])
     torch.save(model.state_dict(), checkpoint_path)
-    logger.debug("Saved model as {}".format(checkpoint_path))
+    logger.debug('Saved model as {}'.format(checkpoint_path))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/train_multiple.py b/modeling/train_multiple.py
index 8c118584..c42f77a6 100644
--- a/modeling/train_multiple.py
+++ b/modeling/train_multiple.py
@@ -1,26 +1,20 @@
 import itertools
 import json
 import random
-
 import torch
 
 import utils
+from utils import parse_args, create_logger, DEVICE, Params, dict_to_str, fix_random_seed
+
+from train import train
+from infer import inference
+
 from callbacks import BaseCallback, EvalCallback, ValidationCallback
-from dataloader import BaseDataloader
 from dataset import BaseDataset
-from infer import inference
+from dataloader import BaseDataloader
 from loss import BaseLoss
 from models import BaseModel
 from optimizer import BaseOptimizer
-from train import train
-from utils import (
-    DEVICE,
-    Params,
-    create_logger,
-    dict_to_str,
-    fix_random_seed,
-    parse_args,
-)
 
 logger = create_logger(name=__name__)
 seed_val = 42
@@ -30,23 +24,24 @@ def main():
     fix_random_seed(seed_val)
     config = parse_args()
 
-    logger.debug("Training config: \n{}".format(json.dumps(config, indent=2)))
+    logger.debug('Training config: \n{}'.format(json.dumps(config, indent=2)))
 
-    dataset_params = Params(config["dataset"], config["dataset_params"])
-    model_params = Params(config["model"], config["model_params"])
-    loss_function_params = Params(config["loss"], config["loss_params"])
-    optimizer_params = Params(config["optimizer"], config["optimizer_params"])
+    dataset_params = Params(config['dataset'], config['dataset_params'])
+    model_params = Params(config['model'], config['model_params'])
+    loss_function_params = Params(config['loss'], config['loss_params'])
+    optimizer_params = Params(config['optimizer'], config['optimizer_params'])
 
-    logger.debug("Everything is ready for training process!")
+    logger.debug('Everything is ready for training process!')
 
-    start_from = config.get("start_from", 0)
-    num = config.get("num_exps", None)
+    start_from = config.get('start_from', 0)
+    num = config.get('num_exps', None)
 
-    list_of_params = list(
-        itertools.product(
-            dataset_params, model_params, loss_function_params, optimizer_params
-        )
-    )
+    list_of_params = list(itertools.product(
+        dataset_params,
+        model_params,
+        loss_function_params,
+        optimizer_params
+    ))
 
     if num is None:
         num = len(list_of_params)
@@ -55,61 +50,59 @@ def main():
 
     cnt = 0
 
-    for dataset_param, model_param, loss_param, optimizer_param in list_of_params[
-        start_from:num
-    ]:
+    for dataset_param, model_param, loss_param, optimizer_param in list_of_params[start_from:num]:
         cnt += 1
         if cnt < start_from:
             continue
 
-        model_name = "_".join(
-            [
-                config["experiment_name"],
-                dict_to_str(dataset_param, config["dataset_params"]),
-                dict_to_str(model_param, config["model_params"]),
-                dict_to_str(loss_param, config["loss_params"]),
-                dict_to_str(optimizer_param, config["optimizer_params"]),
-            ]
-        )
+        model_name = '_'.join([
+            config['experiment_name'],
+            dict_to_str(dataset_param, config['dataset_params']),
+            dict_to_str(model_param, config['model_params']),
+            dict_to_str(loss_param, config['loss_params']),
+            dict_to_str(optimizer_param, config['optimizer_params'])
+        ])
 
-        logger.debug("Starting {}".format(model_name))
+        logger.debug('Starting {}'.format(model_name))
 
         dataset = BaseDataset.create_from_config(dataset_param)
 
         train_sampler, validation_sampler, eval_sampler = dataset.get_samplers()
 
         train_dataloader = BaseDataloader.create_from_config(
-            config["dataloader"]["train"], dataset=train_sampler, **dataset.meta
+            config['dataloader']['train'],
+            dataset=train_sampler,
+            **dataset.meta
         )
 
         validation_dataloader = BaseDataloader.create_from_config(
-            config["dataloader"]["validation"],
+            config['dataloader']['validation'],
             dataset=validation_sampler,
-            **dataset.meta,
+            **dataset.meta
         )
 
         eval_dataloader = BaseDataloader.create_from_config(
-            config["dataloader"]["validation"], dataset=eval_sampler, **dataset.meta
+            config['dataloader']['validation'],
+            dataset=eval_sampler,
+            **dataset.meta
         )
 
         if utils.tensorboards.GLOBAL_TENSORBOARD_WRITER is not None:
             utils.tensorboards.GLOBAL_TENSORBOARD_WRITER.close()
-        utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = (
-            utils.tensorboards.TensorboardWriter(model_name, use_time=False)
-        )
+        utils.tensorboards.GLOBAL_TENSORBOARD_WRITER = utils.tensorboards.TensorboardWriter(model_name, use_time=False)
 
         model = BaseModel.create_from_config(model_param, **dataset.meta).to(DEVICE)
         loss_function = BaseLoss.create_from_config(loss_param)
         optimizer = BaseOptimizer.create_from_config(optimizer_param, model=model)
 
         callback = BaseCallback.create_from_config(
-            config["callback"],
+            config['callback'],
             model=model,
             train_dataloader=train_dataloader,
             validation_dataloader=validation_dataloader,
             eval_dataloader=eval_dataloader,
             optimizer=optimizer,
-            **dataset.meta,
+            **dataset.meta
         )
 
         best_model_checkpoint = train(
@@ -118,13 +111,11 @@ def main():
             optimizer=optimizer,
             loss_function=loss_function,
             callback=callback,
-            epoch_cnt=config.get("train_epochs_num"),
-            best_metric=config.get("best_metric"),
+            epoch_cnt=config.get('train_epochs_num'),
+            best_metric=config.get('best_metric')
         )
 
-        eval_model = BaseModel.create_from_config(model_param, **dataset.meta).to(
-            DEVICE
-        )
+        eval_model = BaseModel.create_from_config(model_param, **dataset.meta).to(DEVICE)
         eval_model.load_state_dict(best_model_checkpoint)
 
         for cl in callback._callbacks:
@@ -145,11 +136,11 @@ def main():
 
         inference(eval_dataloader, eval_model, metrics, pred_prefix, labels_prefix)
 
-        logger.debug("Saving best model checkpoint...")
-        checkpoint_path = "../checkpoints/{}_final_state.pth".format(model_name)
+        logger.debug('Saving best model checkpoint...')
+        checkpoint_path = '../checkpoints/{}_final_state.pth'.format(model_name)
         torch.save(best_model_checkpoint, checkpoint_path)
-        logger.debug("Saved model as {}".format(checkpoint_path))
+        logger.debug('Saved model as {}'.format(checkpoint_path))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()
diff --git a/modeling/utils/__init__.py b/modeling/utils/__init__.py
index 1d62369e..8b96ac52 100644
--- a/modeling/utils/__init__.py
+++ b/modeling/utils/__init__.py
@@ -1,37 +1,39 @@
-import argparse
+from utils.registry import MetaParent
+from utils.grid_search import Params
+import utils.tensorboards
+
 import json
-import logging
 import random
-
+import logging
+import argparse
 import numpy as np
-import torch
 
-import utils.tensorboards
-from utils.grid_search import Params
-from utils.registry import MetaParent
+import torch
 
 if torch.cuda.is_available():
-    DEVICE = torch.device("cuda")
+    DEVICE = torch.device('cuda:0')
+# elif torch.backends.mps.is_available():
+#     DEVICE = torch.device("mps:0")
 else:
-    DEVICE = torch.device("cpu")
+    DEVICE = torch.device('cpu')
 
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--params", required=True)
+    parser.add_argument('--params', required=True)
     args = parser.parse_args()
 
     with open(args.params) as f:
         params = json.load(f)
-
+    
     return params
 
 
 def create_logger(
-    name,
-    level=logging.DEBUG,
-    format="[%(asctime)s] [%(levelname)s]: %(message)s",
-    datefmt="%Y-%m-%d %H:%M:%S",
+        name,
+        level=logging.DEBUG,
+        format='[%(asctime)s] [%(levelname)s]: %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S'
 ):
     logging.basicConfig(level=level, format=format, datefmt=datefmt)
     logger = logging.getLogger(name)
@@ -52,31 +54,26 @@ def maybe_to_list(values):
 
 
 def get_activation_function(name, **kwargs):
-    if name == "relu":
+    if name == 'relu':
         return torch.nn.ReLU()
-    elif name == "gelu":
+    elif name == 'gelu':
         return torch.nn.GELU()
-    elif name == "elu":
-        return torch.nn.ELU(alpha=float(kwargs.get("alpha", 1.0)))
-    elif name == "leaky":
-        return torch.nn.LeakyReLU(
-            negative_slope=float(kwargs.get("negative_slope", 1e-2))
-        )
-    elif name == "sigmoid":
+    elif name == 'elu':
+        return torch.nn.ELU(alpha=float(kwargs.get('alpha', 1.0)))
+    elif name == 'leaky':
+        return torch.nn.LeakyReLU(negative_slope=float(kwargs.get('negative_slope', 1e-2)))
+    elif name == 'sigmoid':
         return torch.nn.Sigmoid()
-    elif name == "tanh":
+    elif name == 'tanh':
         return torch.nn.Tanh()
-    elif name == "softmax":
+    elif name == 'softmax':
         return torch.nn.Softmax()
-    elif name == "softplus":
-        return torch.nn.Softplus(
-            beta=int(kwargs.get("beta", 1.0)),
-            threshold=int(kwargs.get("threshold", 20)),
-        )
-    elif name == "softmax_logit":
+    elif name == 'softplus':
+        return torch.nn.Softplus(beta=int(kwargs.get('beta', 1.0)), threshold=int(kwargs.get('threshold', 20)))
+    elif name == 'softmax_logit':
         return torch.nn.LogSoftmax()
     else:
-        raise ValueError("Unknown activation function name `{}`".format(name))
+        raise ValueError('Unknown activation function name `{}`'.format(name))
 
 
 def dict_to_str(x, params):
@@ -85,21 +82,19 @@ def dict_to_str(x, params):
         if k in params:
             if isinstance(v, dict):
                 # part = '_'.join([f'{k}-{sub_part}' for sub_part in dict_to_str(v, params[k]).split('_')])
-                part = "_".join(
-                    [f"{sub_part}" for sub_part in dict_to_str(v, params[k]).split("_")]
-                )
+                part = '_'.join([f'{sub_part}' for sub_part in dict_to_str(v, params[k]).split('_')])
             elif isinstance(v, tuple) or isinstance(v, list):
                 sub_strings = []
                 for i, sub_value in enumerate(v):
-                    sub_strings.append(f"({i})_{dict_to_str(v[i], params[k][i])}")
-                part = f"({'_'.join(sub_strings)})"
+                    sub_strings.append(f'({i})_{dict_to_str(v[i], params[k][i])}')
+                part = f'({"_".join(sub_strings)})'
             else:
                 # part = f'{k}-{v}'
-                part = f"{v}"
+                part = f'{v}'
             parts.append(part)
         else:
             continue
-    return "_".join(parts).replace(".", "-")
+    return '_'.join(parts).replace('.', '-')
 
 
 def create_masked_tensor(data, lengths):
@@ -108,22 +103,20 @@ def create_masked_tensor(data, lengths):
 
     if len(data.shape) == 1:  # only indices
         padded_tensor = torch.zeros(
-            batch_size, max_sequence_length, dtype=data.dtype, device=DEVICE
+            batch_size, max_sequence_length,
+            dtype=data.dtype, device=DEVICE
         )  # (batch_size, max_seq_len)
     else:
         assert len(data.shape) == 2  # embeddings
         padded_tensor = torch.zeros(
-            batch_size,
-            max_sequence_length,
-            data.shape[-1],
-            dtype=data.dtype,
-            device=DEVICE,
+            batch_size, max_sequence_length, data.shape[-1],
+            dtype=data.dtype, device=DEVICE
         )  # (batch_size, max_seq_len, emb_dim)
 
-    mask = (
-        torch.arange(end=max_sequence_length, device=DEVICE)[None].tile([batch_size, 1])
-        < lengths[:, None]
-    )  # (batch_size, max_seq_len)
+    mask = torch.arange(
+        end=max_sequence_length,
+        device=DEVICE
+    )[None].tile([batch_size, 1]) < lengths[:, None]  # (batch_size, max_seq_len)
 
     padded_tensor[mask] = data
 
diff --git a/modeling/utils/grid_search.py b/modeling/utils/grid_search.py
index d7330421..268d9458 100644
--- a/modeling/utils/grid_search.py
+++ b/modeling/utils/grid_search.py
@@ -3,6 +3,7 @@
 
 
 class Params:
+
     def __init__(self, config, params):
         self._initial_config = copy.deepcopy(config)
         self._initial_params = copy.deepcopy(params)
@@ -11,9 +12,7 @@ def __iter__(self):
         keys = []
         values = []
 
-        all_keys = set(self._initial_config.keys()).union(
-            set(self._initial_params.keys())
-        )
+        all_keys = set(self._initial_config.keys()).union(set(self._initial_params.keys()))
 
         for field_name in all_keys:
             keys.append(field_name)
@@ -22,28 +21,18 @@ def __iter__(self):
             params_fields_value = self._initial_params.get(field_name)
 
             if initial_field_value:
-                if (
-                    params_fields_value is None
-                ):  # We don't want to iterate through this field
+                if params_fields_value is None:  # We don't want to iterate through this field
                     values.append([initial_field_value])
-                elif isinstance(initial_field_value, list) and isinstance(
-                    initial_field_value, list
-                ):
+                elif isinstance(initial_field_value, list) and isinstance(initial_field_value, list):
                     assert len(initial_field_value) == len(params_fields_value)
                     list_values = []
                     for i in range(len(initial_field_value)):
-                        field_variations = list(
-                            Params(initial_field_value[i], params_fields_value[i])
-                        )
+                        field_variations = list(Params(initial_field_value[i], params_fields_value[i]))
                         list_values.append(field_variations)
                     list_values = [p for p in product(*list_values)]
                     values.append(list_values)
-                elif isinstance(
-                    initial_field_value, dict
-                ):  # It is composite param, need to go inside
-                    field_variations = list(
-                        Params(initial_field_value, params_fields_value)
-                    )
+                elif isinstance(initial_field_value, dict):  # It is composite param, need to go inside
+                    field_variations = list(Params(initial_field_value, params_fields_value))
                     values.append(field_variations)
                 else:  # Simple param, can take as it is
                     values.append([initial_field_value] + params_fields_value)
diff --git a/modeling/utils/registry.py b/modeling/utils/registry.py
index 3942269b..c2225480 100644
--- a/modeling/utils/registry.py
+++ b/modeling/utils/registry.py
@@ -2,6 +2,7 @@
 
 
 class MetaParent(type):
+
     def __init__(cls, name, base, params, **kwargs):
         super().__init__(name, base, params)
         is_base_class = cls.mro()[1] is object
@@ -11,10 +12,10 @@ def __init__(cls, name, base, params, **kwargs):
             base_class_found = False
             for key in cls.mro():
                 if isinstance(key, MetaParent) and key.mro()[1] is object:
-                    assert base_class_found is False, "multiple base classes(bug)"
+                    assert base_class_found is False, 'multiple base classes(bug)'
                     base_class = key
                     base_class_found = True
-            assert base_class_found is True, f"no base class for {name}"
+            assert base_class_found is True, f'no base class for {name}'
 
         if is_base_class:
             cls._subclasses = {}
@@ -24,9 +25,7 @@ def __init_subclass__(scls, config_name=None):
             super().__init_subclass__()
             if config_name is not None:
                 if config_name in base_class._subclasses:
-                    raise ValueError(
-                        "Class with name `{}` is already registered".format(config_name)
-                    )
+                    raise ValueError("Class with name `{}` is already registered".format(config_name))
                 scls.config_name = config_name
                 base_class._subclasses[config_name] = scls
 
@@ -34,19 +33,15 @@ def __init_subclass__(scls, config_name=None):
 
         @classmethod
         def parent_create_from_config(cls, config, **kwargs):
-            if "type" in config:
-                return cls._subclasses[config["type"]].create_from_config(
-                    config, **kwargs
-                )
+            if 'type' in config:
+                return cls._subclasses[config['type']].create_from_config(config, **kwargs)
             else:
-                raise ValueError(
-                    "There is no `type` provided for the `{}` class".format(name)
-                )
+                raise ValueError('There is no `type` provided for the `{}` class'.format(name))
 
         # Take kwargs for the last initialized baseclass
         init_kwargs = {}
         for bcls in cls.mro()[:-1]:  # Look into all base classes except object
-            if "__init__" not in bcls.__dict__:
+            if '__init__' not in bcls.__dict__:
                 continue
             init_kwargs = inspect.signature(bcls.__init__).parameters
             break
@@ -55,16 +50,14 @@ def parent_create_from_config(cls, config, **kwargs):
         def child_create_from_config(cls, config, **kwargs):
             kwargs = {}
             for key, argspec in init_kwargs.items():
-                if key == "self":
+                if key == 'self':
                     continue
                 value = config.get(key, argspec.default)
                 if value is inspect.Parameter.empty:
-                    msg = "There is no value for `{}.__init__` required field `{}` in config `{}`"
+                    msg = 'There is no value for `{}.__init__` required field `{}` in config `{}`'
                     raise ValueError(msg.format(cls, key, config))
                 kwargs[key] = value
             return cls(**kwargs)
 
-        if "create_from_config" not in cls.__dict__:
-            cls.create_from_config = (
-                parent_create_from_config if is_base_class else child_create_from_config
-            )
+        if 'create_from_config' not in cls.__dict__:
+            cls.create_from_config = parent_create_from_config if is_base_class else child_create_from_config
diff --git a/modeling/utils/tensorboards/__init__.py b/modeling/utils/tensorboards/__init__.py
index a2f8f279..6c470341 100644
--- a/modeling/utils/tensorboards/__init__.py
+++ b/modeling/utils/tensorboards/__init__.py
@@ -1 +1 @@
-from .tensorboard_writers import GLOBAL_TENSORBOARD_WRITER, LOGS_DIR, TensorboardWriter
+from .tensorboard_writers import TensorboardWriter, GLOBAL_TENSORBOARD_WRITER, LOGS_DIR
diff --git a/modeling/utils/tensorboards/tensorboard_writers.py b/modeling/utils/tensorboards/tensorboard_writers.py
index e788a6f8..770fae63 100644
--- a/modeling/utils/tensorboards/tensorboard_writers.py
+++ b/modeling/utils/tensorboards/tensorboard_writers.py
@@ -1,21 +1,19 @@
-import datetime
 import os
 import time
+import datetime
 
 from torch.utils.tensorboard import SummaryWriter
 
-LOGS_DIR = "../tensorboard_logs"
+LOGS_DIR = '../tensorboard_logs'
 GLOBAL_TENSORBOARD_WRITER = None
 
 
 class TensorboardWriter(SummaryWriter):
+
     def __init__(self, experiment_name, use_time=True):
         self._experiment_name = experiment_name
         super().__init__(
-            log_dir=os.path.join(
-                LOGS_DIR,
-                f"{experiment_name}_{datetime.datetime.now().strftime('%Y-%m-%dT%H:%M' if use_time else '')}",
-            )
+            log_dir=os.path.join(LOGS_DIR, f'{experiment_name}_{datetime.datetime.now().strftime("%Y-%m-%dT%H:%M" if use_time else "")}')
         )
 
     def add_scalar(self, *args, **kwargs):
@@ -23,6 +21,7 @@ def add_scalar(self, *args, **kwargs):
 
 
 class TensorboardTimer:
+
     def __init__(self, scope):
         super().__init__(LOGS_DIR)
         self._scope = scope

From 91b3f694dcbfc425a3b0e85ca49e3e8870b04344 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 8 Jun 2025 22:32:18 +0300
Subject: [PATCH 166/175] bump deps

---
 pyproject.toml |  18 +-
 uv.lock        | 579 ++++++++++++++++++++++++++-----------------------
 2 files changed, 317 insertions(+), 280 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 77289265..b342c5a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,23 +5,23 @@ description = "IRec framework"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
-    "faiss-cpu>=1.10.0",
-    "pandas>=2.2.3",
-    "scipy>=1.15.2",
+    "faiss-cpu>=1",
+    "pandas>=2",
+    "scipy>=1",
     "seaborn>=0.13.2",
-    "tensorboard>=2.19.0",
-    "torch>=2.6.0",
-    "transformers>=4.51.0",
+    "tensorboard>=2",
+    "torch>=2.7",
+    "transformers>=4.51",
 ]
 
 [tool.uv.sources]
 torch = [
-  { index = "pytorch-cu124" },
+  { index = "pytorch-cu128" },
 ]
 
 [[tool.uv.index]]
-name = "pytorch-cu124"
-url = "https://download.pytorch.org/whl/cu124"
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
 explicit = true
 
 [dependency-groups]
diff --git a/uv.lock b/uv.lock
index 24c7adf0..daa79a32 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4,42 +4,42 @@ requires-python = ">=3.13"
 
 [[package]]
 name = "absl-py"
-version = "2.2.2"
+version = "2.3.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b5/f0/e6342091061ed3a46aadc116b13edd7bb5249c3ab1b3ef07f24b0c248fc3/absl_py-2.2.2.tar.gz", hash = "sha256:bf25b2c2eed013ca456918c453d687eab4e8309fba81ee2f4c1a6aa2494175eb", size = 119982 }
+sdist = { url = "https://files.pythonhosted.org/packages/03/15/18693af986560a5c3cc0b84a8046b536ffb2cdb536e03cce897f2759e284/absl_py-2.3.0.tar.gz", hash = "sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f", size = 116400 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f6/d4/349f7f4bd5ea92dab34f5bb0fe31775ef6c311427a14d5a5b31ecb442341/absl_py-2.2.2-py3-none-any.whl", hash = "sha256:e5797bc6abe45f64fd95dc06394ca3f2bedf3b5d895e9da691c9ee3397d70092", size = 135565 },
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@@ -773,14 +802,14 @@ wheels = [
 
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 name = "sympy"
-version = "1.13.1"
+version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 ]
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+    { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353 },
 ]
 
 [[package]]
@@ -840,8 +869,8 @@ wheels = [
 
 [[package]]
 name = "torch"
-version = "2.6.0+cu124"
-source = { registry = "https://download.pytorch.org/whl/cu124" }
+version = "2.7.1+cu128"
+source = { registry = "https://download.pytorch.org/whl/cu128" }
 dependencies = [
     { name = "filelock" },
     { name = "fsspec" },
@@ -853,6 +882,7 @@ dependencies = [
     { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cufile-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
@@ -862,13 +892,16 @@ dependencies = [
     { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "setuptools" },
     { name = "sympy" },
-    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "triton", marker = "sys_platform == 'linux'" },
     { name = "typing-extensions" },
 ]
 wheels = [
-    { url = "https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp313-cp313-linux_x86_64.whl", hash = "sha256:0f3bc53c988ce9568cd876a2a5316761e84a8704135ec8068f5f81b4417979cb" },
-    { url = "https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp313-cp313-win_amd64.whl", hash = "sha256:519330eef09534acad8110b6f423d2fe58c1d8e9ada999ed077a637a0021f908" },
-    { url = "https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp313-cp313t-linux_x86_64.whl", hash = "sha256:35cba404c0d742406cdcba1609085874bc60facdfbc50e910c47a92405fef44c" },
+    { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:d56d29a6ad7758ba5173cc2b0c51c93e126e2b0a918e874101dc66545283967f" },
+    { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:9560425f9ea1af1791507e8ca70d5b9ecf62fed7ca226a95fcd58d0eb2cca78f" },
+    { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313-win_amd64.whl", hash = "sha256:500ad5b670483f62d4052e41948a3fb19e8c8de65b99f8d418d879cbb15a82d6" },
+    { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:f112465fdf42eb1297c6dddda1a8b7f411914428b704e1b8a47870c52e290909" },
+    { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:c355db49c218ada70321d5c5c9bb3077312738b99113c8f3723ef596b554a7b9" },
+    { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313t-win_amd64.whl", hash = "sha256:e27e5f7e74179fb5d814a0412e5026e4b50c9e0081e9050bc4c28c992a276eb1" },
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@@ -885,7 +918,7 @@ wheels = [
 
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 name = "transformers"
-version = "4.51.0"
+version = "4.52.4"
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@@ -899,26 +932,30 @@ dependencies = [
     { name = "tokenizers" },
     { name = "tqdm" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/38/75/6ebdae4d6f4574f47139a070445245537e43482d006f615af8e23d5bf05e/transformers-4.51.0.tar.gz", hash = "sha256:2d302563ff6c2cc2d0e88ef352cf059f9a21ce18102fd43662bb1246f70b8a84", size = 8925571 }
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 ]
 
 [[package]]
 name = "triton"
-version = "3.2.0"
+version = "3.3.1"
 source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "setuptools" },
+]
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/74/1f/dfb531f90a2d367d914adfee771babbd3f1a5b26c3f5fbc458dee21daa78/triton-3.3.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b89d846b5a4198317fec27a5d3a609ea96b6d557ff44b56c23176546023c4240", size = 155673035 },
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 [[package]]
 name = "typing-extensions"
-version = "4.13.1"
+version = "4.14.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/76/ad/cd3e3465232ec2416ae9b983f27b9e94dc8171d56ac99b345319a9475967/typing_extensions-4.13.1.tar.gz", hash = "sha256:98795af00fb9640edec5b8e31fc647597b4691f099ad75f469a2616be1a76dff", size = 106633 }
+sdist = { url = "https://files.pythonhosted.org/packages/d1/bc/51647cd02527e87d05cb083ccc402f93e441606ff1f01739a62c8ad09ba5/typing_extensions-4.14.0.tar.gz", hash = "sha256:8676b788e32f02ab42d9e7c61324048ae4c6d844a399eebace3d4979d75ceef4", size = 107423 }
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+    { url = "https://files.pythonhosted.org/packages/69/e0/552843e0d356fbb5256d21449fa957fa4eff3bbc135a74a691ee70c7c5da/typing_extensions-4.14.0-py3-none-any.whl", hash = "sha256:a1514509136dd0b477638fc68d6a91497af5076466ad0fa6c338e44e359944af", size = 43839 },
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 [[package]]
@@ -932,11 +969,11 @@ wheels = [
 
 [[package]]
 name = "urllib3"
-version = "2.3.0"
+version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/aa/63/e53da845320b757bf29ef6a9062f5c669fe997973f966045cb019c3f4b66/urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d", size = 307268 }
+sdist = { url = "https://files.pythonhosted.org/packages/8a/78/16493d9c386d8e60e442a35feac5e00f0913c0f4b7c217c11e8ec2ff53e0/urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466", size = 390672 }
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+    { url = "https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813", size = 128680 },
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 [[package]]

From d5c4abb7f2dc8a477551984f70bad7417f9055fd Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 8 Jun 2025 22:35:12 +0300
Subject: [PATCH 167/175] add conditional torch cuda deps

---
 pyproject.toml |  8 ++++++-
 uv.lock        | 62 +++++++++++++++++++++++++++++++++++++-------------
 2 files changed, 53 insertions(+), 17 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index b342c5a5..9cd165d8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,7 +16,8 @@ dependencies = [
 
 [tool.uv.sources]
 torch = [
-  { index = "pytorch-cu128" },
+  { index = "pytorch-cu128", marker = "sys_platform != 'darwin'" },
+  { index = "pytorch-cpu", marker = "sys_platform == 'darwin'" },
 ]
 
 [[tool.uv.index]]
@@ -24,6 +25,11 @@ name = "pytorch-cu128"
 url = "https://download.pytorch.org/whl/cu128"
 explicit = true
 
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
 [dependency-groups]
 dev = [
     "ruff>=0.11.4",
diff --git a/uv.lock b/uv.lock
index daa79a32..9e71d015 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,10 @@
 version = 1
 revision = 1
 requires-python = ">=3.13"
+resolution-markers = [
+    "sys_platform != 'darwin'",
+    "sys_platform == 'darwin'",
+]
 
 [[package]]
 name = "absl-py"
@@ -214,7 +218,8 @@ dependencies = [
     { name = "scipy" },
     { name = "seaborn" },
     { name = "tensorboard" },
-    { name = "torch" },
+    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.7.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform != 'darwin'" },
     { name = "transformers" },
 ]
 
@@ -230,7 +235,8 @@ requires-dist = [
     { name = "scipy", specifier = ">=1" },
     { name = "seaborn", specifier = ">=0.13.2" },
     { name = "tensorboard", specifier = ">=2" },
-    { name = "torch", specifier = ">=2.7", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torch", marker = "sys_platform != 'darwin'", specifier = ">=2.7", index = "https://download.pytorch.org/whl/cu128" },
+    { name = "torch", marker = "sys_platform == 'darwin'", specifier = ">=2.7", index = "https://download.pytorch.org/whl/cpu" },
     { name = "transformers", specifier = ">=4.51" },
 ]
 
@@ -438,7 +444,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.7.1.26"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/25/dc/dc825c4b1c83b538e207e34f48f86063c88deaa35d46c651c7c181364ba2/nvidia_cudnn_cu12-9.7.1.26-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:6d011159a158f3cfc47bf851aea79e31bcff60d530b70ef70474c84cac484d07", size = 726851421 },
@@ -449,7 +455,7 @@ name = "nvidia-cufft-cu12"
 version = "11.3.3.41"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/ac/26/b53c493c38dccb1f1a42e1a21dc12cba2a77fbe36c652f7726d9ec4aba28/nvidia_cufft_cu12-11.3.3.41-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:da650080ab79fcdf7a4b06aa1b460e99860646b176a43f6208099bdc17836b6a", size = 193118795 },
@@ -476,9 +482,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.7.2.55"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
-    { name = "nvidia-cusparse-cu12" },
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/c2/08/953675873a136d96bb12f93b49ba045d1107bc94d2551c52b12fa6c7dec3/nvidia_cusolver_cu12-11.7.2.55-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4d1354102f1e922cee9db51920dba9e2559877cf6ff5ad03a00d853adafb191b", size = 260373342 },
@@ -489,7 +495,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.5.7.53"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
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@@ -867,15 +873,39 @@ wheels = [
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+[[package]]
+name = "torch"
+version = "2.7.1"
+source = { registry = "https://download.pytorch.org/whl/cpu" }
+resolution-markers = [
+    "sys_platform == 'darwin'",
+]
+dependencies = [
+    { name = "filelock", marker = "sys_platform == 'darwin'" },
+    { name = "fsspec", marker = "sys_platform == 'darwin'" },
+    { name = "jinja2", marker = "sys_platform == 'darwin'" },
+    { name = "networkx", marker = "sys_platform == 'darwin'" },
+    { name = "setuptools", marker = "sys_platform == 'darwin'" },
+    { name = "sympy", marker = "sys_platform == 'darwin'" },
+    { name = "typing-extensions", marker = "sys_platform == 'darwin'" },
+]
+wheels = [
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:95af97e7b2cecdc89edc0558962a51921bf9c61538597dbec6b7cc48d31e2e13" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:7ecd868a086468e1bcf74b91db425c1c2951a9cfcd0592c4c73377b7e42485ae" },
+]
+
 [[package]]
 name = "torch"
 version = "2.7.1+cu128"
 source = { registry = "https://download.pytorch.org/whl/cu128" }
+resolution-markers = [
+    "sys_platform != 'darwin'",
+]
 dependencies = [
-    { name = "filelock" },
-    { name = "fsspec" },
-    { name = "jinja2" },
-    { name = "networkx" },
+    { name = "filelock", marker = "sys_platform != 'darwin'" },
+    { name = "fsspec", marker = "sys_platform != 'darwin'" },
+    { name = "jinja2", marker = "sys_platform != 'darwin'" },
+    { name = "networkx", marker = "sys_platform != 'darwin'" },
     { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
@@ -890,10 +920,10 @@ dependencies = [
     { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "setuptools" },
-    { name = "sympy" },
+    { name = "setuptools", marker = "sys_platform != 'darwin'" },
+    { name = "sympy", marker = "sys_platform != 'darwin'" },
     { name = "triton", marker = "sys_platform == 'linux'" },
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
     { url = "https://download.pytorch.org/whl/cu128/torch-2.7.1%2Bcu128-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:d56d29a6ad7758ba5173cc2b0c51c93e126e2b0a918e874101dc66545283967f" },
@@ -942,7 +972,7 @@ name = "triton"
 version = "3.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "setuptools" },
+    { name = "setuptools", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/74/1f/dfb531f90a2d367d914adfee771babbd3f1a5b26c3f5fbc458dee21daa78/triton-3.3.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b89d846b5a4198317fec27a5d3a609ea96b6d557ff44b56c23176546023c4240", size = 155673035 },

From ad67f7fe1c58ebc3f6add83c7822e212819ec1b3 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 8 Jun 2025 22:51:20 +0300
Subject: [PATCH 168/175] fix small issues after merge

---
 modeling/dataset/samplers/last_item_prediction.py | 4 ----
 modeling/models/rqvae.py                          | 1 -
 2 files changed, 5 deletions(-)

diff --git a/modeling/dataset/samplers/last_item_prediction.py b/modeling/dataset/samplers/last_item_prediction.py
index e6f9ba73..474ef4c5 100644
--- a/modeling/dataset/samplers/last_item_prediction.py
+++ b/modeling/dataset/samplers/last_item_prediction.py
@@ -1,12 +1,8 @@
-from dataset.samplers.base import TrainSampler, EvalSampler
-
 import copy
 
 from dataset.samplers.base import EvalSampler, TrainSampler
 from dataset.negative_samplers.base import BaseNegativeSampler
 
-class LastItemPredictionTrainSampler(TrainSampler, config_name='last_item_prediction'):
-
 class LastItemPredictionTrainSampler(TrainSampler, config_name="last_item_prediction"):
     def __init__(self, dataset, num_users, num_items, negative_sampler, num_negatives):
         super().__init__()
diff --git a/modeling/models/rqvae.py b/modeling/models/rqvae.py
index 7e150421..fc70b890 100644
--- a/modeling/models/rqvae.py
+++ b/modeling/models/rqvae.py
@@ -84,7 +84,6 @@ def init_codebooks(self, embeddings):
                     d=embeddings_np.shape[1],
                     k=n_clusters,
                     niter=self.n_iter,
-                    gpu=1,
                 )
                 kmeans.train(embeddings_np)
 

From a1b5b4553fbb4ea4d0c150b61dad04a7700ea004 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Sun, 8 Jun 2025 23:26:38 +0300
Subject: [PATCH 169/175] add debug lines for scientific dataset

---
 modeling/dataset/base.py | 26 ++++++++++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index ff040d6e..39bd47dd 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -272,8 +272,8 @@ def create_from_config(cls, config, **kwargs):
     def flatten_item_sequence(cls, item_ids):
         min_history_length = 3 # TODOPK make this configurable
         histories = []
-        for i in range(min_history_length-1, len(item_ids)):
-            histories.append(item_ids[:i+1])
+        for i in range(min_history_length, len(item_ids) + 1):
+            histories.append(item_ids[:i])
         return histories
     
     @classmethod
@@ -791,12 +791,31 @@ def create_from_config(cls, config, **kwargs):
             max_item_id = max(max_item_id, max(item_ids))
 
             assert len(item_ids) >= 5
+            
+            # item_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
 
+            # prefix_length: 5, 6, 7, 8, 9, 10
             for prefix_length in range(5, len(item_ids) + 1):
+                # prefix = [1, 2, 3, 4, 5]
+                # prefix = [1, 2, 3, 4, 5, 6]
+                # prefix = [1, 2, 3, 4, 5, 6, 7]
+                # prefix = [1, 2, 3, 4, 5, 6, 7, 8]
+                # prefix = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+                # prefix = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+                
+                
+                
                 prefix = item_ids[
                     :prefix_length
                 ]  # TODOPK no sliding window, only incrmenting sequence from last 50 items
 
+                # prefix[:-2] = [1, 2, 3]
+                # prefix[:-2] = [1, 2, 3, 4]
+                # prefix[:-2] = [1, 2, 3, 4, 5]
+                # prefix[:-2] = [1, 2, 3, 4, 5, 6]
+                # prefix[:-2] = [1, 2, 3, 4, 5, 6, 7]
+                # prefix[:-2] = [1, 2, 3, 4, 5, 6, 7, 8]
+
                 train_dataset.append(
                     {
                         "user.ids": [user_id],
@@ -809,6 +828,7 @@ def create_from_config(cls, config, **kwargs):
                     set(prefix[:-2][-max_sequence_length:])
                 )
 
+            # item_ids[:-1] = [1, 2, 3, 4, 5, 6, 7, 8, 9]
             validation_dataset.append(
                 {
                     "user.ids": [user_id],
@@ -820,6 +840,8 @@ def create_from_config(cls, config, **kwargs):
             assert len(item_ids[:-1][-max_sequence_length:]) == len(
                 set(item_ids[:-1][-max_sequence_length:])
             )
+            
+            # item_ids = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
             test_dataset.append(
                 {
                     "user.ids": [user_id],

From 26d724705c00191fa20d908bd5c43063a467887c Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 9 Jun 2025 17:12:51 +0300
Subject: [PATCH 170/175] add letter dataset

---
 configs/train/letter.json |  182 ++++++
 modeling/dataset/base.py  |   49 ++
 pyproject.toml            |    2 +
 uv.lock                   | 1174 ++++++++++++++++++++++++++++++++++++-
 4 files changed, 1390 insertions(+), 17 deletions(-)
 create mode 100644 configs/train/letter.json

diff --git a/configs/train/letter.json b/configs/train/letter.json
new file mode 100644
index 00000000..7a4aa5ae
--- /dev/null
+++ b/configs/train/letter.json
@@ -0,0 +1,182 @@
+{
+    "experiment_name": "letter_data",
+    "best_metric": "validation/ndcg@20",
+    "train_epochs_num": 100,
+    "dataset": {
+      "type": "letter_full",
+      "path_to_data_dir": "../data",
+      "name": "Beauty_letter",
+      "max_sequence_length": 50,
+      "samplers": {
+        "type": "letter_last_item_prediction",
+        "beauty_index_json": "../../LETTER/data/Beauty/Beauty.index.json"
+      },
+      "beauty_inter_json": "../../LETTER/data/Beauty/Beauty.inter.json"
+    },
+    "dataloader": {
+      "train": {
+        "type": "torch",
+        "batch_size": 256,
+        "batch_processor": {
+          "type": "basic"
+        },
+        "drop_last": true,
+        "shuffle": true
+      },
+      "validation": {
+        "type": "torch",
+        "batch_size": 256,
+        "batch_processor": {
+          "type": "basic"
+        },
+        "drop_last": false,
+        "shuffle": false
+      }
+    },
+    "model": {
+      "type": "tiger",
+      "rqvae_train_config_path": "../configs/train/rqvae_train_config.json",
+      "rqvae_checkpoint_path": "../checkpoints/rqvae_beauty_final_state.pth",
+      "embs_extractor_path": "../data/Beauty/rqvae/data_full.pt",
+      "sequence_prefix": "item",
+      "predictions_prefix": "logits",
+      "positive_prefix": "labels",
+      "labels_prefix": "labels",
+      "embedding_dim": 64,
+      "num_heads": 2,
+      "num_encoder_layers": 2,
+      "num_decoder_layers": 2,
+      "dim_feedforward": 256,
+      "dropout": 0.3,
+      "activation": "gelu",
+      "layer_norm_eps": 1e-9,
+      "initializer_range": 0.02
+    },
+    "optimizer": {
+      "type": "basic",
+      "optimizer": {
+        "type": "adam",
+        "lr": 0.001
+      },
+      "clip_grad_threshold": 5.0
+    },
+    "loss": {
+      "type": "composite",
+      "losses": [
+        {
+          "type": "ce",
+          "predictions_prefix": "logits",
+          "labels_prefix": "semantic.labels",
+          "weight": 1.0,
+          "output_prefix": "semantic_loss"
+        },
+        {
+          "type": "ce",
+          "predictions_prefix": "dedup.logits",
+          "labels_prefix": "dedup.labels",
+          "weight": 1.0,
+          "output_prefix": "dedup_loss"
+        }
+      ],
+      "output_prefix": "loss"
+    },
+    "callback": {
+      "type": "composite",
+      "callbacks": [
+        {
+          "type": "metric",
+          "on_step": 1,
+          "loss_prefix": "loss"
+        },
+        {
+          "type": "validation",
+          "on_step": 1024,
+          "pred_prefix": "logits",
+          "labels_prefix": "labels",
+          "metrics": {
+            "ndcg@5": {
+              "type": "ndcg",
+              "k": 5
+            },
+            "ndcg@10": {
+              "type": "ndcg",
+              "k": 10
+            },
+            "ndcg@20": {
+              "type": "ndcg",
+              "k": 20
+            },
+            "recall@5": {
+              "type": "recall",
+              "k": 5
+            },
+            "recall@10": {
+              "type": "recall",
+              "k": 10
+            },
+            "recall@20": {
+              "type": "recall",
+              "k": 20
+            },
+            "coverage@5": {
+              "type": "coverage",
+              "k": 5
+            },
+            "coverage@10": {
+              "type": "coverage",
+              "k": 10
+            },
+            "coverage@20": {
+              "type": "coverage",
+              "k": 20
+            }
+          }
+        },
+        {
+          "type": "eval",
+          "on_step": 2048,
+          "pred_prefix": "logits",
+          "labels_prefix": "labels",
+          "metrics": {
+            "ndcg@5": {
+              "type": "ndcg",
+              "k": 5
+            },
+            "ndcg@10": {
+              "type": "ndcg",
+              "k": 10
+            },
+            "ndcg@20": {
+              "type": "ndcg",
+              "k": 20
+            },
+            "recall@5": {
+              "type": "recall",
+              "k": 5
+            },
+            "recall@10": {
+              "type": "recall",
+              "k": 10
+            },
+            "recall@20": {
+              "type": "recall",
+              "k": 20
+            },
+            "coverage@5": {
+              "type": "coverage",
+              "k": 5
+            },
+            "coverage@10": {
+              "type": "coverage",
+              "k": 10
+            },
+            "coverage@20": {
+              "type": "coverage",
+              "k": 20
+            }
+          }
+        }
+      ]
+    }
+  }
+  
\ No newline at end of file
diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index 39bd47dd..a7983393 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -896,6 +896,55 @@ def create_from_config(cls, config, **kwargs):
         )
 
 
+class LetterFullDataset(ScientificFullDataset, config_name="letter_full"):
+    def __init__(
+        self,
+        train_sampler,
+        validation_sampler,
+        test_sampler,
+        num_users,
+        num_items,
+        max_sequence_length,
+    ):
+        self._train_sampler = train_sampler
+        self._validation_sampler = validation_sampler
+        self._test_sampler = test_sampler
+        self._num_users = num_users
+        self._num_items = num_items
+        self._max_sequence_length = max_sequence_length
+
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        user_interactions_path = os.path.join(config["beauty_inter_json"])
+        with open(user_interactions_path, "r") as f:
+            user_interactions = json.load(f)
+            
+        dir_path = os.path.join(config["path_to_data_dir"], config["name"])
+
+        os.makedirs(dir_path, exist_ok=True)
+        dataset_path = os.path.join(dir_path, "all_data.txt")
+        
+        logger.info(f"Saving data to {dataset_path}")
+        
+        # Map from LETTER format to Our format
+        with open(dataset_path, "w") as f:
+            for user_id, item_ids in user_interactions.items():
+                items_repr = map(str, item_ids)
+                f.write(f"{user_id} {' '.join(items_repr)}\n")
+
+        dataset = ScientificFullDataset.create_from_config(config, **kwargs)
+
+        return cls(
+            train_sampler=dataset._train_sampler,
+            validation_sampler=dataset._validation_sampler,
+            test_sampler=dataset._test_sampler,
+            num_users=dataset._num_users,
+            num_items=dataset._num_items,
+            max_sequence_length=dataset._max_sequence_length,
+        )
+            
+
+
 class RqVaeDataset(BaseDataset, config_name='rqvae'):
 
     def __init__(
diff --git a/pyproject.toml b/pyproject.toml
index 9cd165d8..f32390d0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,8 @@ dependencies = [
     "tensorboard>=2",
     "torch>=2.7",
     "transformers>=4.51",
+    "tqdm>=4",
+    "jupyter>=1",
 ]
 
 [tool.uv.sources]
diff --git a/uv.lock b/uv.lock
index 9e71d015..af2a3e74 100644
--- a/uv.lock
+++ b/uv.lock
@@ -15,6 +15,140 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/87/04/9d75e1d3bb4ab8ec67ff10919476ccdee06c098bcfcf3a352da5f985171d/absl_py-2.3.0-py3-none-any.whl", hash = "sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3", size = 135657 },
 ]
 
+[[package]]
+name = "anyio"
+version = "4.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916 },
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
+]
+
+[[package]]
+name = "argon2-cffi"
+version = "25.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "argon2-cffi-bindings" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657 },
+]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cffi" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b9/e9/184b8ccce6683b0aa2fbb7ba5683ea4b9c5763f1356347f1312c32e3c66e/argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3", size = 1779911 }
+wheels = [
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 version = "4.14.0"
@@ -997,6 +2083,15 @@ wheels = [
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+[[package]]
+name = "uri-template"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140 },
+]
+
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 name = "urllib3"
 version = "2.4.0"
@@ -1006,6 +2101,42 @@ wheels = [
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+wheels = [
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+]
+
+[[package]]
+name = "websocket-client"
+version = "1.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e6/30/fba0d96b4b5fbf5948ed3f4681f7da2f9f64512e1d303f94b4cc174c24a5/websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da", size = 54648 }
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 version = "3.1.3"
@@ -1017,3 +2148,12 @@ sdist = { url = "https://files.pythonhosted.org/packages/9f/69/83029f1f6300c5fb2
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+version = "4.0.14"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/53/2e0253c5efd69c9656b1843892052a31c36d37ad42812b5da45c62191f7e/widgetsnbextension-4.0.14.tar.gz", hash = "sha256:a3629b04e3edb893212df862038c7232f62973373869db5084aed739b437b5af", size = 1097428 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ca/51/5447876806d1088a0f8f71e16542bf350918128d0a69437df26047c8e46f/widgetsnbextension-4.0.14-py3-none-any.whl", hash = "sha256:4875a9eaf72fbf5079dc372a51a9f268fc38d46f767cbf85c43a36da5cb9b575", size = 2196503 },
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From 356c0721122e6d650d82d28b99cfe2a02ee5ef31 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 9 Jun 2025 20:02:58 +0300
Subject: [PATCH 171/175] add letter batch processor

---
 configs/train/letter.json               | 12 +++--
 modeling/dataloader/batch_processors.py | 60 +++++++++++++++++++++++++
 run.sh                                  |  5 +++
 3 files changed, 73 insertions(+), 4 deletions(-)
 create mode 100755 run.sh

diff --git a/configs/train/letter.json b/configs/train/letter.json
index 7a4aa5ae..576106b7 100644
--- a/configs/train/letter.json
+++ b/configs/train/letter.json
@@ -8,8 +8,8 @@
       "name": "Beauty_letter",
       "max_sequence_length": 50,
       "samplers": {
-        "type": "letter_last_item_prediction",
-        "beauty_index_json": "../../LETTER/data/Beauty/Beauty.index.json"
+        "type": "last_item_prediction",
+        "negative_sampler_type": "random"
       },
       "beauty_inter_json": "../../LETTER/data/Beauty/Beauty.inter.json"
     },
@@ -18,7 +18,9 @@
         "type": "torch",
         "batch_size": 256,
         "batch_processor": {
-          "type": "basic"
+          "type": "letter",
+          "beauty_index_json": "../../LETTER/data/Beauty/Beauty.index.json",
+          "semantic_length": 4
         },
         "drop_last": true,
         "shuffle": true
@@ -27,7 +29,9 @@
         "type": "torch",
         "batch_size": 256,
         "batch_processor": {
-          "type": "basic"
+          "type": "letter",
+          "beauty_index_json": "../../LETTER/data/Beauty/Beauty.index.json",
+          "semantic_length": 4
         },
         "drop_last": false,
         "shuffle": false
diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index 9991a073..c0134377 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,3 +1,6 @@
+import json
+import re
+
 import torch
 from utils import MetaParent
 
@@ -43,3 +46,60 @@ def __call__(self, batch):
             processed_batch[part] = torch.tensor(values, dtype=torch.long)
 
         return processed_batch
+
+
+class LetterBatchProcessor(BaseBatchProcessor, config_name='letter'):
+    def __init__(self, mapping, semantic_length):
+        self._mapping: dict[int, list[int]] = mapping
+        self._prefixes = ['item', 'labels', 'positive', 'negative']
+        self._semantic_length = semantic_length
+    
+    @classmethod
+    def create_from_config(cls, config, **kwargs):
+        mapping_path = config["beauty_index_json"]
+        with open(mapping_path, "r") as f:
+            mapping = json.load(f)
+            
+        semantic_length = config["semantic_length"]
+
+        parsed = {}
+            
+        for key, semantic_ids in mapping.items():
+            numbers = [int(re.search(r'\d+', item).group()) for item in semantic_ids]
+            assert len(numbers) == semantic_length
+            parsed[int(key)] = numbers
+            
+        return cls(mapping=parsed, semantic_length=semantic_length)
+    
+    def __call__(self, batch):
+        processed_batch = {}
+
+        for key in batch[0].keys():
+            if key.endswith('.ids'):
+                prefix = key.split('.')[0]
+                assert '{}.length'.format(prefix) in batch[0]
+
+                processed_batch[f'{prefix}.ids'] = []
+                processed_batch[f'{prefix}.length'] = []
+
+                for sample in batch:
+                    processed_batch[f'{prefix}.ids'].extend(sample[f'{prefix}.ids'])
+                    processed_batch[f'{prefix}.length'].append(sample[f'{prefix}.length'])
+                    
+        for prefix in self._prefixes:
+            if f"{prefix}.ids" in processed_batch:
+                ids = processed_batch[f"{prefix}.ids"]
+                lengths = processed_batch[f"{prefix}.length"]
+                
+                flattened_ids = []
+                
+                for _id in ids:
+                    flattened_ids.extend(self._mapping[_id])
+                    
+                processed_batch[f"semantic_{prefix}.ids"] = flattened_ids
+                processed_batch[f"semantic_{prefix}.length"] = [length * self._semantic_length for length in lengths]
+
+        for part, values in processed_batch.items():
+            processed_batch[part] = torch.tensor(values, dtype=torch.long)
+
+        return processed_batch
diff --git a/run.sh b/run.sh
new file mode 100755
index 00000000..894a3727
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,5 @@
+source .venv/bin/activate
+
+cd modeling
+
+python train.py --params ../configs/train/letter.json
\ No newline at end of file

From 488a94ca7fb3244378a507b0fc721af8e68439ef Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 9 Jun 2025 21:21:00 +0300
Subject: [PATCH 172/175] comment out dev thing

---
 .gitignore |    1 +
 run.sh     |    5 -
 uv.lock    | 2159 ----------------------------------------------------
 3 files changed, 1 insertion(+), 2164 deletions(-)
 delete mode 100755 run.sh
 delete mode 100644 uv.lock

diff --git a/.gitignore b/.gitignore
index 8e376638..ae0a51ea 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ saved_logs/*
 papers
 checkpoints/*
 *.prof
+uv.lock
diff --git a/run.sh b/run.sh
deleted file mode 100755
index 894a3727..00000000
--- a/run.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-source .venv/bin/activate
-
-cd modeling
-
-python train.py --params ../configs/train/letter.json
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
deleted file mode 100644
index af2a3e74..00000000
--- a/uv.lock
+++ /dev/null
@@ -1,2159 +0,0 @@
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-revision = 1
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From 7ecb8d9a71b98fa51308e6a8a14a69d0df06e937 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 9 Jun 2025 21:22:20 +0300
Subject: [PATCH 173/175] remove uv.lock file

---
 .gitignore |    1 +
 uv.lock    | 1019 ----------------------------------------------------
 2 files changed, 1 insertion(+), 1019 deletions(-)
 delete mode 100644 uv.lock

diff --git a/.gitignore b/.gitignore
index 8e376638..ae0a51ea 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ saved_logs/*
 papers
 checkpoints/*
 *.prof
+uv.lock
diff --git a/uv.lock b/uv.lock
deleted file mode 100644
index 9e71d015..00000000
--- a/uv.lock
+++ /dev/null
@@ -1,1019 +0,0 @@
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From 165a996b33da1831e10be2c5e1e84026a8bd3a14 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 9 Jun 2025 21:26:27 +0300
Subject: [PATCH 174/175] remove unneeded +1 call in range

---
 modeling/dataset/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modeling/dataset/base.py b/modeling/dataset/base.py
index a7983393..835c0578 100644
--- a/modeling/dataset/base.py
+++ b/modeling/dataset/base.py
@@ -272,7 +272,7 @@ def create_from_config(cls, config, **kwargs):
     def flatten_item_sequence(cls, item_ids):
         min_history_length = 3 # TODOPK make this configurable
         histories = []
-        for i in range(min_history_length, len(item_ids) + 1):
+        for i in range(min_history_length, len(item_ids)):
             histories.append(item_ids[:i])
         return histories
     

From b0cd74d2599610f3ee7db30f437dce6def422206 Mon Sep 17 00:00:00 2001
From: peterochek <peterkkor@gmail.com>
Date: Mon, 16 Jun 2025 16:42:11 +0300
Subject: [PATCH 175/175] add mapped tensor

---
 modeling/dataloader/batch_processors.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/modeling/dataloader/batch_processors.py b/modeling/dataloader/batch_processors.py
index c0134377..d7567bd3 100644
--- a/modeling/dataloader/batch_processors.py
+++ b/modeling/dataloader/batch_processors.py
@@ -1,6 +1,6 @@
 import json
 import re
-
+from itertools import chain
 import torch
 from utils import MetaParent
 
@@ -91,12 +91,13 @@ def __call__(self, batch):
                 ids = processed_batch[f"{prefix}.ids"]
                 lengths = processed_batch[f"{prefix}.length"]
                 
-                flattened_ids = []
+                mapped_ids = []
                 
                 for _id in ids:
-                    flattened_ids.extend(self._mapping[_id])
+                    mapped_ids.append(self._mapping[_id])
                     
-                processed_batch[f"semantic_{prefix}.ids"] = flattened_ids
+                processed_batch[f"semantic_{prefix}.ids"] = list(chain.from_iterable(mapped_ids))
+                processed_batch[f"semantic_{prefix}_tensor.ids"] = mapped_ids
                 processed_batch[f"semantic_{prefix}.length"] = [length * self._semantic_length for length in lengths]
 
         for part, values in processed_batch.items():