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diff --git a/DM2025-Lab1-Homework.ipynb b/DM2025-Lab1-Homework.ipynb
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+++ b/DM2025-Lab1-Homework.ipynb
@@ -5,11 +5,11 @@
"metadata": {},
"source": [
"### Student Information\n",
- "Name:\n",
+ "Name: Truong Ngoc Khoi Nguyen\n",
"\n",
- "Student ID:\n",
+ "Student ID: D142113032\n",
"\n",
- "GitHub ID:"
+ "GitHub ID: nguyentr0101"
]
},
{
@@ -102,12 +102,2013 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-05T02:51:44.093331Z",
+ "start_time": "2025-10-05T02:51:44.016393Z"
+ }
+ },
"source": [
- "### Begin Assignment Here"
- ]
+ "### Begin Assignment Here\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import nltk\n",
+ "nltk.download('punkt') # download the NLTK datasets\n",
+ "from sklearn.datasets import fetch_20newsgroups\n",
+ "from sklearn.feature_extraction.text import CountVectorizer\n",
+ "import plotly as py\n",
+ "import math\n",
+ "# If you get \"ModuleNotFoundError: No module named 'PAMI'\"\n",
+ "# run the following in a new Jupyter cell:\n",
+ "# !pip3 install PAMI\n",
+ "import PAMI\n",
+ "import umap"
+ ],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Error loading punkt: \n"
+ ]
+ }
+ ],
+ "execution_count": 8
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-05T03:10:45.842933Z",
+ "start_time": "2025-10-05T03:10:45.768713Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "DATA_PATH = \"newdataset/Reddit-stock-sentiment.csv\"\n",
+ "df= pd.read_csv(DATA_PATH)\n",
+ "\n",
+ "print(\"\\nFirst 5 records:\")\n",
+ "print(df.head())\n",
+ "\n",
+ "print(\"\\nData types:\")\n",
+ "print(df.dtypes)\n",
+ "\n",
+ "text_columns = df.select_dtypes(include=['object']).columns.tolist()\n",
+ "print(f\"\\nText columns found: {text_columns}\")\n",
+ "\n",
+ "# Keep only what we need\n",
+ "X = pd.DataFrame()\n",
+ "X['text'] = df['title'] + ' ' + df['text'] # Combine title and text\n",
+ "X['category'] = df['label'] # Numerical label\n",
+ "X['category_name'] = df['label'].map({-1: 'negative', 0: 'neutral', 1: 'positive'})\n",
+ "\n",
+ "print(f\"Dataset shape: {X.shape}\")\n",
+ "print(f\"\\nCategory distribution:\")\n",
+ "print(X['category_name'].value_counts())\n"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "First 5 records:\n",
+ " type datetime post_id subreddit \\\n",
+ "0 comment 2025-04-11 17:29:56 mmli62w wallstreetbets \n",
+ "1 comment 2025-04-12 1:12:19 mmnu7v9 wallstreetbets \n",
+ "2 comment 2025-04-10 15:09:41 mmeevio StockMarket \n",
+ "3 post 2023-08-30 17:12:55 165kllm stockstobuytoday \n",
+ "4 comment 2025-04-11 14:48:05 mmkl6bw StockMarket \n",
+ "\n",
+ " title author \\\n",
+ "0 Retardation is on the menu boys! WSB is so back StickyTip420 \n",
+ "1 Retail giant TARGET has now declined for 10 co... Comfortable-Dog-8437 \n",
+ "2 How do you feel about a sitting president maki... Btankersly66 \n",
+ "3 Who knows more? $VMAR emiljenfn \n",
+ "4 The Trump administration is begging Xi Jinping... Just-Big6411 \n",
+ "\n",
+ " url upvotes downvotes \\\n",
+ "0 https://i.redd.it/0yq2ftren8ue1.jpeg 0 NaN \n",
+ "1 https://i.redd.it/7tl6puv9waue1.jpeg -15 NaN \n",
+ "2 https://apnews.com/article/trump-truth-social-... 1 NaN \n",
+ "3 https://www.reddit.com/r/stockstobuytoday/comm... 30 0.0 \n",
+ "4 https://edition.cnn.com/2025/04/10/politics/tr... 1 NaN \n",
+ "\n",
+ " upvote_ratio text \\\n",
+ "0 NaN Calls on retards \n",
+ "1 NaN Stunt as in like why did they even make a big ... \n",
+ "2 NaN Seeing lots of red in the ticker. \n",
+ "3 0.98 Vision Marine Technologies Inc. is rewriting t... \n",
+ "4 NaN He didn’t say thank you. \n",
+ "\n",
+ " subjectivity polarity sentiment \\\n",
+ "0 1.000000 -0.900000 -1.0 \n",
+ "1 0.177778 0.083333 1.0 \n",
+ "2 0.000000 0.000000 0.0 \n",
+ "3 0.646970 0.216383 1.0 \n",
+ "4 0.000000 0.000000 0.0 \n",
+ "\n",
+ " entities label \n",
+ "0 [] -1.0 \n",
+ "1 ['Stunt', 'company', 'deal', 'place'] 0.0 \n",
+ "2 ['ticker'] 0.0 \n",
+ "3 ['watercraft', 'skill', 'power', ']', 'feat', ... 1.0 \n",
+ "4 [] -1.0 \n",
+ "\n",
+ "Data types:\n",
+ "type object\n",
+ "datetime object\n",
+ "post_id object\n",
+ "subreddit object\n",
+ "title object\n",
+ "author object\n",
+ "url object\n",
+ "upvotes int64\n",
+ "downvotes float64\n",
+ "upvote_ratio float64\n",
+ "text object\n",
+ "subjectivity float64\n",
+ "polarity float64\n",
+ "sentiment float64\n",
+ "entities object\n",
+ "label float64\n",
+ "dtype: object\n",
+ "\n",
+ "Text columns found: ['type', 'datetime', 'post_id', 'subreddit', 'title', 'author', 'url', 'text', 'entities']\n",
+ "Dataset shape: (847, 3)\n",
+ "\n",
+ "Category distribution:\n",
+ "category_name\n",
+ "neutral 423\n",
+ "negative 315\n",
+ "positive 109\n",
+ "Name: count, dtype: int64\n",
+ "\n",
+ "Sample texts from each category:\n",
+ "example 1\n",
+ "Retardation is on the menu boys! WSB is so back Calls on retards\n",
+ "\n",
+ "example 2\n",
+ "Retail giant TARGET has now declined for 10 consecutive weeks, its longest losing streak in history Stunt as in like why did they even make a big deal about starting it in the first place? No company should ever talk about politics ever.\n",
+ "\n",
+ "example 3\n",
+ "How do you feel about a sitting president making $415M in one day after pumping his own stock with social media and a policy decision? Seeing lots of red in the ticker.\n",
+ "\n",
+ "\n",
+ "Sample texts from each category:\n",
+ "\n",
+ "negative:\n",
+ "Retardation is on the menu boys! WSB is so back Calls on retards\n",
+ "\n",
+ "neutral:\n",
+ "Retail giant TARGET has now declined for 10 consecutive weeks, its longest losing streak in history Stunt as in like why did they even make a big deal about starting it in the first place? No company ...\n",
+ "\n",
+ "positive:\n",
+ "Who knows more? $VMAR Vision Marine Technologies Inc. is rewriting the watercraft rulebook. Their collaboration with Shaun Torrente has achieved the unthinkable – a blazing 116 mph on water! This isn'...\n"
+ ]
+ }
+ ],
+ "execution_count": 18
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-05T03:12:54.985458Z",
+ "start_time": "2025-10-05T03:12:54.943599Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# ==========================================\n",
+ "# Exercise 1\n",
+ "# ==========================================\n",
+ "# Print out the text data for the first three samples in the dataset\n",
+ "print(\"\\nFirst 3 samples in the dataset:\")\n",
+ "for i in range(3):\n",
+ " print(f\"example {i+1}\")\n",
+ " print(X['text'][i])\n",
+ " print()\n",
+ "\n",
+ "# Display sample texts\n",
+ "print(\"\\nSample texts from each category:\")\n",
+ "for category in X['category_name'].unique():\n",
+ " if pd.notna(category):\n",
+ " sample = X[X['category_name'] == category].iloc[0]['text']\n",
+ " print(f\"\\n{category}:\")\n",
+ " print(sample[:200] + \"...\" if len(str(sample)) > 200 else sample)"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "First 3 samples in the dataset:\n",
+ "example 1\n",
+ "Retardation is on the menu boys! WSB is so back Calls on retards\n",
+ "\n",
+ "example 2\n",
+ "Retail giant TARGET has now declined for 10 consecutive weeks, its longest losing streak in history Stunt as in like why did they even make a big deal about starting it in the first place? No company should ever talk about politics ever.\n",
+ "\n",
+ "example 3\n",
+ "How do you feel about a sitting president making $415M in one day after pumping his own stock with social media and a policy decision? Seeing lots of red in the ticker.\n",
+ "\n",
+ "\n",
+ "Sample texts from each category:\n",
+ "\n",
+ "negative:\n",
+ "Retardation is on the menu boys! WSB is so back Calls on retards\n",
+ "\n",
+ "neutral:\n",
+ "Retail giant TARGET has now declined for 10 consecutive weeks, its longest losing streak in history Stunt as in like why did they even make a big deal about starting it in the first place? No company ...\n",
+ "\n",
+ "positive:\n",
+ "Who knows more? $VMAR Vision Marine Technologies Inc. is rewriting the watercraft rulebook. Their collaboration with Shaun Torrente has achieved the unthinkable – a blazing 116 mph on water! This isn'...\n"
+ ]
+ }
+ ],
+ "execution_count": 20
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-05T03:16:08.214748Z",
+ "start_time": "2025-10-05T03:16:08.194613Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# ==========================================\n",
+ "# Exercise 2\n",
+ "# ==========================================\n",
+ "X.loc[0:10, [\"text\", \"category_name\"]]\n",
+ "\n",
+ "X[X[\"category_name\"] == \"positive\"].head(5)\n",
+ "\n",
+ "some_idx = [1, 81, 92, 100]\n",
+ "X.loc[some_idx, [\"text\", \"category_name\"]]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category_name\n",
+ "1 Retail giant TARGET has now declined for 10 co... neutral\n",
+ "81 Retardation is on the menu boys! WSB is so bac... positive\n",
+ "92 Data Shows US Allies—Not China—Dumping Treasur... neutral\n",
+ "100 The Trump administration is begging Xi Jinping... negative"
+ ],
+ "text/html": [
+ "
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+ "\n",
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+ "
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+ "
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Retail giant TARGET has now declined for 10 co...
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Retardation is on the menu boys! WSB is so bac...
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+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 24
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-05T03:20:38.245365Z",
+ "start_time": "2025-10-05T03:20:38.195959Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# ==========================================\n",
+ "# Exercise 3\n",
+ "# ==========================================\n",
+ "# Fetch records belonging to 'positive' sentiment, query every 10th record, show first 5\n",
+ "X[X[\"category_name\"] == \"positive\"][::10][0:5]\n",
+ "# Fetch records belonging to 'negative' sentiment, query every 10th record, show first 5\n",
+ "X[X[\"category_name\"] == \"negative\"][::10][0:5]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category category_name\n",
+ "0 Retardation is on the menu boys! WSB is so bac... -1.0 negative\n",
+ "25 Retail giant TARGET has now declined for 10 co... -1.0 negative\n",
+ "62 The Trump administration is begging Xi Jinping... -1.0 negative\n",
+ "95 Data Shows US Allies—Not China—Dumping Treasur... -1.0 negative\n",
+ "125 The Trump administration is begging Xi Jinping... -1.0 negative"
+ ],
+ "text/html": [
+ "
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+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 97
},
{
"cell_type": "markdown",
diff --git a/DM2025-Lab1-Master.ipynb b/DM2025-Lab1-Master.ipynb
index be46c3b4..908ff49e 100644
--- a/DM2025-Lab1-Master.ipynb
+++ b/DM2025-Lab1-Master.ipynb
@@ -10,19 +10,12 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package punkt to\n",
- "[nltk_data] C:\\Users\\ntone\\AppData\\Roaming\\nltk_data...\n",
- "[nltk_data] Package punkt is already up-to-date!\n"
- ]
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:18.897995Z",
+ "start_time": "2025-10-04T21:49:18.553587Z"
}
- ],
+ },
"source": [
"# test code for environment setup\n",
"import pandas as pd\n",
@@ -41,7 +34,19 @@
"\n",
"categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']\n",
"twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)"
- ]
+ ],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Error loading punkt: \n"
+ ]
+ }
+ ],
+ "execution_count": 600
},
{
"cell_type": "markdown",
@@ -159,34 +164,53 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:18.968462Z",
+ "start_time": "2025-10-04T21:49:18.931073Z"
+ }
+ },
"source": [
"# TEST necessary for when working with external scripts\n",
"%load_ext autoreload\n",
"%autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
+ ],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "C:\\Users\\ntone\\Documents\\DM2025Labs\\DM2025-Lab1-Exercise\\.venv\\Scripts\\python.exe\n",
- "3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)]\n"
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
]
}
],
+ "execution_count": 601
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.029430Z",
+ "start_time": "2025-10-04T21:49:19.004670Z"
+ }
+ },
"source": [
"import sys\n",
"print(sys.executable) # c:\\\\.venv\\Scripts\\python.exe\n",
"print(sys.version) #3.11.0"
- ]
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/Users/nguyentr/Library/CloudStorage/OneDrive-Personal/【】PhD TW 25-28/114-1/114-1-DATA mining/DM2025Labs/DM2025-Lab1-Exercise/.venv/bin/python3\n",
+ "3.13.3 (v3.13.3:6280bb54784, Apr 8 2025, 10:47:54) [Clang 15.0.0 (clang-1500.3.9.4)]\n"
+ ]
+ }
+ ],
+ "execution_count": 602
},
{
"cell_type": "markdown",
@@ -231,19 +255,27 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.091465Z",
+ "start_time": "2025-10-04T21:49:19.063922Z"
+ }
+ },
"source": [
"# categories\n",
"categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
- ]
+ ],
+ "outputs": [],
+ "execution_count": 603
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.220594Z",
+ "start_time": "2025-10-04T21:49:19.104385Z"
+ }
+ },
"source": [
"# obtain the documents containing the categories provided\n",
"from sklearn.datasets import fetch_20newsgroups\n",
@@ -253,7 +285,9 @@
"#This command also shuffles the data randomly, but with random_state we can bring the same distribution of data everytime \n",
"#if we choose the same number, in this case \"42\". This is good for us, it means we can reproduce the same results every time\n",
"#we want to run the code."
- ]
+ ],
+ "outputs": [],
+ "execution_count": 604
},
{
"cell_type": "markdown",
@@ -264,8 +298,15 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.259474Z",
+ "start_time": "2025-10-04T21:49:19.235122Z"
+ }
+ },
+ "source": [
+ "twenty_train.data[0:2]"
+ ],
"outputs": [
{
"data": {
@@ -274,14 +315,12 @@
" \"From: ani@ms.uky.edu (Aniruddha B. Deglurkar)\\nSubject: help: Splitting a trimming region along a mesh \\nOrganization: University Of Kentucky, Dept. of Math Sciences\\nLines: 28\\n\\n\\n\\n\\tHi,\\n\\n\\tI have a problem, I hope some of the 'gurus' can help me solve.\\n\\n\\tBackground of the problem:\\n\\tI have a rectangular mesh in the uv domain, i.e the mesh is a \\n\\tmapping of a 3d Bezier patch into 2d. The area in this domain\\n\\twhich is inside a trimming loop had to be rendered. The trimming\\n\\tloop is a set of 2d Bezier curve segments.\\n\\tFor the sake of notation: the mesh is made up of cells.\\n\\n\\tMy problem is this :\\n\\tThe trimming area has to be split up into individual smaller\\n\\tcells bounded by the trimming curve segments. If a cell\\n\\tis wholly inside the area...then it is output as a whole ,\\n\\telse it is trivially rejected. \\n\\n\\tDoes any body know how thiss can be done, or is there any algo. \\n\\tsomewhere for doing this.\\n\\n\\tAny help would be appreciated.\\n\\n\\tThanks, \\n\\tAni.\\n-- \\nTo get irritated is human, to stay cool, divine.\\n\"]"
]
},
- "execution_count": 6,
+ "execution_count": 605,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "twenty_train.data[0:2]"
- ]
+ "execution_count": 605
},
{
"cell_type": "markdown",
@@ -292,8 +331,15 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.323003Z",
+ "start_time": "2025-10-04T21:49:19.299129Z"
+ }
+ },
+ "source": [
+ "twenty_train.target_names"
+ ],
"outputs": [
{
"data": {
@@ -301,19 +347,24 @@
"['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian']"
]
},
- "execution_count": 7,
+ "execution_count": 606,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "twenty_train.target_names"
- ]
+ "execution_count": 606
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.387463Z",
+ "start_time": "2025-10-04T21:49:19.361821Z"
+ }
+ },
+ "source": [
+ "len(twenty_train.data)"
+ ],
"outputs": [
{
"data": {
@@ -321,19 +372,24 @@
"2257"
]
},
- "execution_count": 8,
+ "execution_count": 607,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "len(twenty_train.data)"
- ]
+ "execution_count": 607
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.425711Z",
+ "start_time": "2025-10-04T21:49:19.400601Z"
+ }
+ },
+ "source": [
+ "len(twenty_train.filenames)"
+ ],
"outputs": [
{
"data": {
@@ -341,14 +397,12 @@
"2257"
]
},
- "execution_count": 9,
+ "execution_count": 608,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "len(twenty_train.filenames)"
- ]
+ "execution_count": 608
},
{
"cell_type": "markdown",
@@ -359,8 +413,16 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.503857Z",
+ "start_time": "2025-10-04T21:49:19.465941Z"
+ }
+ },
+ "source": [
+ "# An example of what the subset contains\n",
+ "print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")))"
+ ],
"outputs": [
{
"name": "stdout",
@@ -390,10 +452,7 @@
]
}
],
- "source": [
- "# An example of what the subset contains\n",
- "print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")))"
- ]
+ "execution_count": 609
},
{
"cell_type": "markdown",
@@ -404,8 +463,15 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.546863Z",
+ "start_time": "2025-10-04T21:49:19.523475Z"
+ }
+ },
+ "source": [
+ "print(twenty_train.target_names[twenty_train.target[0]])"
+ ],
"outputs": [
{
"name": "stdout",
@@ -415,14 +481,19 @@
]
}
],
- "source": [
- "print(twenty_train.target_names[twenty_train.target[0]])"
- ]
+ "execution_count": 610
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.594509Z",
+ "start_time": "2025-10-04T21:49:19.568392Z"
+ }
+ },
+ "source": [
+ "twenty_train.target[0]"
+ ],
"outputs": [
{
"data": {
@@ -430,14 +501,12 @@
"np.int64(1)"
]
},
- "execution_count": 12,
+ "execution_count": 611,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "twenty_train.target[0]"
- ]
+ "execution_count": 611
},
{
"cell_type": "markdown",
@@ -448,8 +517,16 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.632457Z",
+ "start_time": "2025-10-04T21:49:19.608239Z"
+ }
+ },
+ "source": [
+ "# category of first 10 documents.\n",
+ "twenty_train.target[0:10]"
+ ],
"outputs": [
{
"data": {
@@ -457,15 +534,12 @@
"array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2])"
]
},
- "execution_count": 13,
+ "execution_count": 612,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "# category of first 10 documents.\n",
- "twenty_train.target[0:10]"
- ]
+ "execution_count": 612
},
{
"cell_type": "markdown",
@@ -485,8 +559,16 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.697224Z",
+ "start_time": "2025-10-04T21:49:19.669763Z"
+ }
+ },
+ "source": [
+ "for t in twenty_train.target[:10]:\n",
+ " print(twenty_train.target_names[t])"
+ ],
"outputs": [
{
"name": "stdout",
@@ -505,10 +587,7 @@
]
}
],
- "source": [
- "for t in twenty_train.target[:10]:\n",
- " print(twenty_train.target_names[t])"
- ]
+ "execution_count": 613
},
{
"cell_type": "markdown",
@@ -527,12 +606,154 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.766096Z",
+ "start_time": "2025-10-04T21:49:19.741686Z"
+ }
+ },
"source": [
- "# Answer here"
- ]
+ "# Answer here\n",
+ "for i in range(3):\n",
+ " print(f\"example {i+1}\")\n",
+ " print(\"\\n\".join(twenty_train.data[i].split(\"\\n\")))\n"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "example 1\n",
+ "From: sd345@city.ac.uk (Michael Collier)\n",
+ "Subject: Converting images to HP LaserJet III?\n",
+ "Nntp-Posting-Host: hampton\n",
+ "Organization: The City University\n",
+ "Lines: 14\n",
+ "\n",
+ "Does anyone know of a good way (standard PC application/PD utility) to\n",
+ "convert tif/img/tga files into LaserJet III format. We would also like to\n",
+ "do the same, converting to HPGL (HP plotter) files.\n",
+ "\n",
+ "Please email any response.\n",
+ "\n",
+ "Is this the correct group?\n",
+ "\n",
+ "Thanks in advance. Michael.\n",
+ "-- \n",
+ "Michael Collier (Programmer) The Computer Unit,\n",
+ "Email: M.P.Collier@uk.ac.city The City University,\n",
+ "Tel: 071 477-8000 x3769 London,\n",
+ "Fax: 071 477-8565 EC1V 0HB.\n",
+ "\n",
+ "example 2\n",
+ "From: ani@ms.uky.edu (Aniruddha B. Deglurkar)\n",
+ "Subject: help: Splitting a trimming region along a mesh \n",
+ "Organization: University Of Kentucky, Dept. of Math Sciences\n",
+ "Lines: 28\n",
+ "\n",
+ "\n",
+ "\n",
+ "\tHi,\n",
+ "\n",
+ "\tI have a problem, I hope some of the 'gurus' can help me solve.\n",
+ "\n",
+ "\tBackground of the problem:\n",
+ "\tI have a rectangular mesh in the uv domain, i.e the mesh is a \n",
+ "\tmapping of a 3d Bezier patch into 2d. The area in this domain\n",
+ "\twhich is inside a trimming loop had to be rendered. The trimming\n",
+ "\tloop is a set of 2d Bezier curve segments.\n",
+ "\tFor the sake of notation: the mesh is made up of cells.\n",
+ "\n",
+ "\tMy problem is this :\n",
+ "\tThe trimming area has to be split up into individual smaller\n",
+ "\tcells bounded by the trimming curve segments. If a cell\n",
+ "\tis wholly inside the area...then it is output as a whole ,\n",
+ "\telse it is trivially rejected. \n",
+ "\n",
+ "\tDoes any body know how thiss can be done, or is there any algo. \n",
+ "\tsomewhere for doing this.\n",
+ "\n",
+ "\tAny help would be appreciated.\n",
+ "\n",
+ "\tThanks, \n",
+ "\tAni.\n",
+ "-- \n",
+ "To get irritated is human, to stay cool, divine.\n",
+ "\n",
+ "example 3\n",
+ "From: djohnson@cs.ucsd.edu (Darin Johnson)\n",
+ "Subject: Re: harrassed at work, could use some prayers\n",
+ "Organization: =CSE Dept., U.C. San Diego\n",
+ "Lines: 63\n",
+ "\n",
+ "(Well, I'll email also, but this may apply to other people, so\n",
+ "I'll post also.)\n",
+ "\n",
+ ">I've been working at this company for eight years in various\n",
+ ">engineering jobs. I'm female. Yesterday I counted and realized that\n",
+ ">on seven different occasions I've been sexually harrassed at this\n",
+ ">company.\n",
+ "\n",
+ ">I dreaded coming back to work today. What if my boss comes in to ask\n",
+ ">me some kind of question...\n",
+ "\n",
+ "Your boss should be the person bring these problems to. If he/she\n",
+ "does not seem to take any action, keep going up higher and higher.\n",
+ "Sexual harrassment does not need to be tolerated, and it can be an\n",
+ "enormous emotional support to discuss this with someone and know that\n",
+ "they are trying to do something about it. If you feel you can not\n",
+ "discuss this with your boss, perhaps your company has a personnel\n",
+ "department that can work for you while preserving your privacy. Most\n",
+ "companies will want to deal with this problem because constant anxiety\n",
+ "does seriously affect how effectively employees do their jobs.\n",
+ "\n",
+ "It is unclear from your letter if you have done this or not. It is\n",
+ "not inconceivable that management remains ignorant of employee\n",
+ "problems/strife even after eight years (it's a miracle if they do\n",
+ "notice). Perhaps your manager did not bring to the attention of\n",
+ "higher ups? If the company indeed does seem to want to ignore the\n",
+ "entire problem, there may be a state agency willing to fight with\n",
+ "you. (check with a lawyer, a women's resource center, etc to find out)\n",
+ "\n",
+ "You may also want to discuss this with your paster, priest, husband,\n",
+ "etc. That is, someone you know will not be judgemental and that is\n",
+ "supportive, comforting, etc. This will bring a lot of healing.\n",
+ "\n",
+ ">So I returned at 11:25, only to find that ever single\n",
+ ">person had already left for lunch. They left at 11:15 or so. No one\n",
+ ">could be bothered to call me at the other building, even though my\n",
+ ">number was posted.\n",
+ "\n",
+ "This happens to a lot of people. Honest. I believe it may seem\n",
+ "to be due to gross insensitivity because of the feelings you are\n",
+ "going through. People in offices tend to be more insensitive while\n",
+ "working than they normally are (maybe it's the hustle or stress or...)\n",
+ "I've had this happen to me a lot, often because they didn't realize\n",
+ "my car was broken, etc. Then they will come back and wonder why I\n",
+ "didn't want to go (this would tend to make me stop being angry at\n",
+ "being ignored and make me laugh). Once, we went off without our\n",
+ "boss, who was paying for the lunch :-)\n",
+ "\n",
+ ">For this\n",
+ ">reason I hope good Mr. Moderator allows me this latest indulgence.\n",
+ "\n",
+ "Well, if you can't turn to the computer for support, what would\n",
+ "we do? (signs of the computer age :-)\n",
+ "\n",
+ "In closing, please don't let the hateful actions of a single person\n",
+ "harm you. They are doing it because they are still the playground\n",
+ "bully and enjoy seeing the hurt they cause. And you should not\n",
+ "accept the opinions of an imbecile that you are worthless - much\n",
+ "wiser people hold you in great esteem.\n",
+ "-- \n",
+ "Darin Johnson\n",
+ "djohnson@ucsd.edu\n",
+ " - Luxury! In MY day, we had to make do with 5 bytes of swap...\n",
+ "\n"
+ ]
+ }
+ ],
+ "execution_count": 614
},
{
"cell_type": "markdown",
@@ -566,9 +787,12 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.812908Z",
+ "start_time": "2025-10-04T21:49:19.776268Z"
+ }
+ },
"source": [
"import pandas as pd\n",
"\n",
@@ -577,12 +801,21 @@
"\n",
"# construct dataframe from a list\n",
"X = pd.DataFrame.from_records(dmh.format_rows(twenty_train), columns= ['text'])"
- ]
+ ],
+ "outputs": [],
+ "execution_count": 615
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.856381Z",
+ "start_time": "2025-10-04T21:49:19.831958Z"
+ }
+ },
+ "source": [
+ "len(X)"
+ ],
"outputs": [
{
"data": {
@@ -590,22 +823,32 @@
"2257"
]
},
- "execution_count": 17,
+ "execution_count": 616,
"metadata": {},
"output_type": "execute_result"
}
],
- "source": [
- "len(X)"
- ]
+ "execution_count": 616
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:19.925446Z",
+ "start_time": "2025-10-04T21:49:19.885581Z"
+ }
+ },
+ "source": [
+ "X[0:2]"
+ ],
"outputs": [
{
"data": {
+ "text/plain": [
+ " text\n",
+ "0 From: sd345@city.ac.uk (Michael Collier) Subje...\n",
+ "1 From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ..."
+ ],
"text/html": [
"
"
+ ]
+ },
+ "execution_count": 629,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 629
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Data Mining using Pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {},
"source": [
"Let's do some serious work now. Let's learn to program some of the ideas and concepts learned so far in the data mining course. This is the only way we can convince ourselves of the true power of Pandas dataframes. "
@@ -1307,11 +1933,35 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-04T21:49:20.612785Z",
+ "start_time": "2025-10-04T21:49:20.583572Z"
+ }
+ },
+ "source": [
+ "# check missing values\n",
+ "X.isnull()"
+ ],
"outputs": [
{
"data": {
+ "text/plain": [
+ " text category category_name\n",
+ "0 False False False\n",
+ "1 False False False\n",
+ "2 False False False\n",
+ "3 False False False\n",
+ "4 False False False\n",
+ "... ... ... ...\n",
+ "2252 False False False\n",
+ "2253 False False False\n",
+ "2254 False False False\n",
+ "2255 False False False\n",
+ "2256 False False False\n",
+ "\n",
+ "[2257 rows x 3 columns]"
+ ],
"text/html": [
"
\n",
"
LabelBinarizer()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 59
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:18:59.891619Z",
+ "start_time": "2025-09-29T15:18:59.868235Z"
+ }
+ },
+ "source": [
+ "for t in X[\"text\"][:2]:\n",
+ " print(t)"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "From: sd345@city.ac.uk (Michael Collier) Subject: Converting images to HP LaserJet III? Nntp-Posting-Host: hampton Organization: The City University Lines: 14 Does anyone know of a good way (standard PC application/PD utility) to convert tif/img/tga files into LaserJet III format. We would also like to do the same, converting to HPGL (HP plotter) files. Please email any response. Is this the correct group? Thanks in advance. Michael. -- Michael Collier (Programmer) The Computer Unit, Email: M.P.Collier@uk.ac.city The City University, Tel: 071 477-8000 x3769 London, Fax: 071 477-8565 EC1V 0HB. \n",
+ "From: ani@ms.uky.edu (Aniruddha B. Deglurkar) Subject: help: Splitting a trimming region along a mesh Organization: University Of Kentucky, Dept. of Math Sciences Lines: 28 \tHi, \tI have a problem, I hope some of the 'gurus' can help me solve. \tBackground of the problem: \tI have a rectangular mesh in the uv domain, i.e the mesh is a \tmapping of a 3d Bezier patch into 2d. The area in this domain \twhich is inside a trimming loop had to be rendered. The trimming \tloop is a set of 2d Bezier curve segments. \tFor the sake of notation: the mesh is made up of cells. \tMy problem is this : \tThe trimming area has to be split up into individual smaller \tcells bounded by the trimming curve segments. If a cell \tis wholly inside the area...then it is output as a whole , \telse it is trivially rejected. \tDoes any body know how thiss can be done, or is there any algo. \tsomewhere for doing this. \tAny help would be appreciated. \tThanks, \tAni. -- To get irritated is human, to stay cool, divine. \n"
+ ]
+ }
+ ],
+ "execution_count": 60
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Adding Columns"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One of the great advantages of a pandas dataframe is its flexibility. We can add columns to the current dataset programmatically with very little effort."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:19:02.670747Z",
+ "start_time": "2025-09-29T15:19:02.615035Z"
+ }
+ },
+ "source": [
+ "# add category to the dataframe\n",
+ "X['category'] = twenty_train.target"
+ ],
+ "outputs": [],
+ "execution_count": 61
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:19:05.129211Z",
+ "start_time": "2025-09-29T15:19:05.105304Z"
+ }
+ },
+ "source": [
+ "# add category label also\n",
+ "X['category_name'] = X.category.apply(lambda t: dmh.format_labels(t, twenty_train))"
+ ],
+ "outputs": [],
+ "execution_count": 62
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can print and see what our table looks like. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:19:06.652960Z",
+ "start_time": "2025-09-29T15:19:06.619494Z"
+ }
+ },
+ "source": [
+ "X[0:10]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "0 From: sd345@city.ac.uk (Michael Collier) Subje... 1 \n",
+ "1 From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ... 1 \n",
+ "2 From: djohnson@cs.ucsd.edu (Darin Johnson) Sub... 3 \n",
+ "3 From: s0612596@let.rug.nl (M.M. Zwart) Subject... 3 \n",
+ "4 From: stanly@grok11.columbiasc.ncr.com (stanly... 3 \n",
+ "5 From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B... 3 \n",
+ "6 From: jodfishe@silver.ucs.indiana.edu (joseph ... 3 \n",
+ "7 From: aldridge@netcom.com (Jacquelin Aldridge)... 2 \n",
+ "8 From: geb@cs.pitt.edu (Gordon Banks) Subject: ... 2 \n",
+ "9 From: libman@hsc.usc.edu (Marlena Libman) Subj... 2 \n",
+ "\n",
+ " category_name \n",
+ "0 comp.graphics \n",
+ "1 comp.graphics \n",
+ "2 soc.religion.christian \n",
+ "3 soc.religion.christian \n",
+ "4 soc.religion.christian \n",
+ "5 soc.religion.christian \n",
+ "6 soc.religion.christian \n",
+ "7 sci.med \n",
+ "8 sci.med \n",
+ "9 sci.med "
+ ],
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From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ...
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+ "
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+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 63
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Nice! Isn't it? With this format we can conduct many operations easily and efficiently since Pandas dataframes provide us with a wide range of built-in features/functionalities. These features are operations which can directly and quickly be applied to the dataset. These operations may include standard operations like **removing records with missing values** and **aggregating new fields** to the current table (hereinafter referred to as a dataframe), which is desirable in almost every data mining project. Go Pandas!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3.2 Familiarizing yourself with the Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To begin to show you the awesomeness of Pandas dataframes, let us look at how to run a simple query on our dataset. We want to query for the first 10 rows (documents), and we only want to keep the `text` and `category_name` attributes or fields."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:19:11.806390Z",
+ "start_time": "2025-09-29T15:19:11.743869Z"
+ }
+ },
+ "source": [
+ "# a simple query\n",
+ "X[:10][[\"text\",\"category_name\"]]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category_name\n",
+ "0 From: sd345@city.ac.uk (Michael Collier) Subje... comp.graphics\n",
+ "1 From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ... comp.graphics\n",
+ "2 From: djohnson@cs.ucsd.edu (Darin Johnson) Sub... soc.religion.christian\n",
+ "3 From: s0612596@let.rug.nl (M.M. Zwart) Subject... soc.religion.christian\n",
+ "4 From: stanly@grok11.columbiasc.ncr.com (stanly... soc.religion.christian\n",
+ "5 From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B... soc.religion.christian\n",
+ "6 From: jodfishe@silver.ucs.indiana.edu (joseph ... soc.religion.christian\n",
+ "7 From: aldridge@netcom.com (Jacquelin Aldridge)... sci.med\n",
+ "8 From: geb@cs.pitt.edu (Gordon Banks) Subject: ... sci.med\n",
+ "9 From: libman@hsc.usc.edu (Marlena Libman) Subj... sci.med"
+ ],
+ "text/html": [
+ "
"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 68
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4. Data Mining using Pandas"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's do some serious work now. Let's learn to program some of the ideas and concepts learned so far in the data mining course. This is the only way we can convince ourselves of the true power of Pandas dataframes. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.1 Missing Values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, let us consider that our dataset has some *missing values* and we want to remove those values. In its current state our dataset has no missing values, but for practice sake we will add some records with missing values and then write some code to deal with these objects that contain missing values. You will see for yourself how easy it is to deal with missing values once you have your data transformed into a Pandas dataframe.\n",
+ "\n",
+ "Before we jump into coding, let us do a quick review of what we have learned in the Data Mining course. Specifically, let's review the methods used to deal with missing values.\n",
+ "\n",
+ "The most common reasons for having missing values in datasets has to do with how the data was initially collected. A good example of this is when a patient comes into the ER room, the data is collected as quickly as possible and depending on the conditions of the patients, the personal data being collected is either incomplete or partially complete. In the former and latter cases, we are presented with a case of \"missing values\". Knowing that patients data is particularly critical and can be used by the health authorities to conduct some interesting analysis, we as the data miners are left with the tough task of deciding what to do with these missing and incomplete records. We need to deal with these records because they are definitely going to affect our analysis or learning algorithms. So what do we do? There are several ways to handle missing values, and some of the more effective ways are presented below (Note: You can reference the slides - Session 1 Handout for the additional information).\n",
+ "\n",
+ "- **Eliminate Data Objects** - Here we completely discard records once they contain some missing values. This is the easiest approach and the one we will be using in this notebook. The immediate drawback of going with this approach is that you lose some information, and in some cases too much of it. Now imagine that half of the records have at least one or more missing values. Here you are presented with the tough decision of quantity vs quality. In any event, this decision must be made carefully, hence the reason for emphasizing it here in this notebook. \n",
+ "\n",
+ "- **Estimate Missing Values** - Here we try to estimate the missing values based on some criteria. Although this approach may be proven to be effective, it is not always the case, especially when we are dealing with sensitive data, like **Gender** or **Names**. For fields like **Address**, there could be ways to obtain these missing addresses using some data aggregation technique or obtain the information directly from other databases or public data sources.\n",
+ "\n",
+ "- **Ignore the missing value during analysis** - Here we basically ignore the missing values and proceed with our analysis. Although this is the most naive way to handle missing values it may proof effective, especially when the missing values includes information that is not important to the analysis being conducted. But think about it for a while. Would you ignore missing values, especially when in this day and age it is difficult to obtain high quality datasets? Again, there are some tradeoffs, which we will talk about later in the notebook.\n",
+ "\n",
+ "- **Replace with all possible values** - As an efficient and responsible data miner, we sometimes just need to put in the hard hours of work and find ways to makes up for these missing values. This last option is a very wise option for cases where data is scarce (which is almost always) or when dealing with sensitive data. Imagine that our dataset has an **Age** field, which contains many missing values. Since **Age** is a continuous variable, it means that we can build a separate model for calculating the age for the incomplete records based on some rule-based approach or probabilistic approach. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As mentioned earlier, we are going to go with the first option but you may be asked to compute missing values, using a different approach, as an exercise. Let's get to it!\n",
+ "\n",
+ "First we want to add the dummy records with missing values since the dataset we have is perfectly composed and cleaned that it contains no missing values. First let us check for ourselves that indeed the dataset doesn't contain any missing values. We can do that easily by using the following built-in function provided by Pandas. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:29:20.548817Z",
+ "start_time": "2025-09-29T15:29:20.373545Z"
+ }
+ },
+ "source": [
+ "# check missing values\n",
+ "X.isnull()"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category category_name\n",
+ "0 False False False\n",
+ "1 False False False\n",
+ "2 False False False\n",
+ "3 False False False\n",
+ "4 False False False\n",
+ "... ... ... ...\n",
+ "2252 False False False\n",
+ "2253 False False False\n",
+ "2254 False False False\n",
+ "2255 False False False\n",
+ "2256 False False False\n",
+ "\n",
+ "[2257 rows x 3 columns]"
+ ],
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+ " \n",
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+ "
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+ "
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+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 69
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `isnull` function looks through the entire dataset for null values and returns `True` wherever it finds any missing field or record. As you will see above, and as we anticipated, our dataset looks clean and all values are present, since `isnull` returns **False** for all fields and records. But let us start to get our hands dirty and build a nice little function to check each of the records, column by column, and return a nice little message telling us the amount of missing records found. This excerice will also encourage us to explore other capabilities of pandas dataframes. In most cases, the build-in functions are good enough, but as you saw above when the entire table was printed, it is impossible to tell if there are missing records just by looking at preview of records manually, especially in cases where the dataset is huge. We want a more reliable way to achieve this. Let's get to it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:29:59.893444Z",
+ "start_time": "2025-09-29T15:29:59.688914Z"
+ }
+ },
+ "source": [
+ "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "0 The amoung of missing records is: The amoung of missing records is: \n",
+ "1 0 0 \n",
+ "\n",
+ " category_name \n",
+ "0 The amoung of missing records is: \n",
+ "1 0 "
+ ],
+ "text/html": [
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+ "
The amoung of missing records is:
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+ "
The amoung of missing records is:
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+ "
The amoung of missing records is:
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+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 70
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Okay, a lot happened there in that one line of code, so let's break it down. First, with the `isnull` we tranformed our table into the **True/False** table you see above, where **True** in this case means that the data is missing and **False** means that the data is present. We then take the transformed table and apply a function to each row that essentially counts to see if there are missing values in each record and print out how much missing values we found. In other words the `check_missing_values` function looks through each field (attribute or column) in the dataset and counts how many missing values were found. \n",
+ "\n",
+ "There are many other clever ways to check for missing data, and that is what makes Pandas so beautiful to work with. You get the control you need as a data scientist or just a person working in data mining projects. Indeed, Pandas makes your life easy!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 4 (Watch Video):** \n",
+ "Let's try something different. Instead of calculating missing values by column let's try to calculate the missing values in every record instead of every column. \n",
+ "$Hint$ : `axis` parameter. Check the documentation for more information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:32:32.209386Z",
+ "start_time": "2025-09-29T15:32:32.063578Z"
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "X.isnull().apply(lambda x: dmh.check_missing_values(x), axis=1)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 (The amoung of missing records is: , 0)\n",
+ "1 (The amoung of missing records is: , 0)\n",
+ "2 (The amoung of missing records is: , 0)\n",
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+ "4 (The amoung of missing records is: , 0)\n",
+ " ... \n",
+ "2252 (The amoung of missing records is: , 0)\n",
+ "2253 (The amoung of missing records is: , 0)\n",
+ "2254 (The amoung of missing records is: , 0)\n",
+ "2255 (The amoung of missing records is: , 0)\n",
+ "2256 (The amoung of missing records is: , 0)\n",
+ "Length: 2257, dtype: object"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 77
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have our function to check for missing records, now let us do something mischievous and insert some dummy data into the dataframe and test the reliability of our function. This dummy data is intended to corrupt the dataset. I mean this happens a lot today, especially when hackers want to hijack or corrupt a database.\n",
+ "\n",
+ "We will insert a `Series`, which is basically a \"one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index.\", into our current dataframe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:04.804535Z",
+ "start_time": "2025-09-29T15:33:04.751718Z"
+ }
+ },
+ "source": [
+ "dummy_series = pd.Series([\"dummy_record\", 1], index=[\"text\", \"category\"])"
+ ],
+ "outputs": [],
+ "execution_count": 78
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:07.018488Z",
+ "start_time": "2025-09-29T15:33:06.994097Z"
+ }
+ },
+ "source": [
+ "dummy_series"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "text dummy_record\n",
+ "category 1\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 79,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 79
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:18.305459Z",
+ "start_time": "2025-09-29T15:33:18.267451Z"
+ }
+ },
+ "source": [
+ "dummy_series.to_frame().T\n",
+ "# .to_frame() -> Convert Series to DataFrame\n",
+ "# .T -> Transpose"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category\n",
+ "0 dummy_record 1"
+ ],
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
text
\n",
+ "
category
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
dummy_record
\n",
+ "
1
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 80,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 80
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:27.454818Z",
+ "start_time": "2025-09-29T15:33:27.426214Z"
+ }
+ },
+ "source": [
+ "result_with_series = pd.concat([X, dummy_series.to_frame().T], ignore_index=True)"
+ ],
+ "outputs": [],
+ "execution_count": 81
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:30.107505Z",
+ "start_time": "2025-09-29T15:33:30.074749Z"
+ }
+ },
+ "source": [
+ "# check if the records was commited into result\n",
+ "len(result_with_series)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2258"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 82
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we that we have added the record with some missing values. Let try our function and see if it can detect that there is a missing value on the resulting dataframe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:36.223171Z",
+ "start_time": "2025-09-29T15:33:36.195948Z"
+ }
+ },
+ "source": [
+ "result_with_series.isnull().apply(lambda x: dmh.check_missing_values(x))"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "0 The amoung of missing records is: The amoung of missing records is: \n",
+ "1 0 0 \n",
+ "\n",
+ " category_name \n",
+ "0 The amoung of missing records is: \n",
+ "1 1 "
+ ],
+ "text/html": [
+ "
\n",
+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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\n",
+ "
category_name
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
The amoung of missing records is:
\n",
+ "
The amoung of missing records is:
\n",
+ "
The amoung of missing records is:
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
0
\n",
+ "
0
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+ "
1
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+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 83
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Indeed there is a missing value in this new dataframe. Specifically, the missing value comes from the `category_name` attribute. As I mentioned before, there are many ways to conduct specific operations on the dataframes. In this case let us use a simple dictionary and try to insert it into our original dataframe `X`. Notice that above we are not changing the `X` dataframe as results are directly applied to the assignment variable provided. But in the event that we just want to keep things simple, we can just directly apply the changes to `X` and assign it to itself as we will do below. This modification will create a need to remove this dummy record later on, which means that we need to learn more about Pandas dataframes. This is getting intense! But just relax, everything will be fine!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:33:59.804021Z",
+ "start_time": "2025-09-29T15:33:59.755240Z"
+ }
+ },
+ "source": [
+ "# dummy record as dictionary format\n",
+ "dummy_dict = [{'text': 'dummy_record',\n",
+ " 'category': 1\n",
+ " }]"
+ ],
+ "outputs": [],
+ "execution_count": 84
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:34:07.054468Z",
+ "start_time": "2025-09-29T15:34:07.006007Z"
+ }
+ },
+ "source": [
+ "X = pd.concat([X, pd.DataFrame(dummy_dict)], ignore_index=True)"
+ ],
+ "outputs": [],
+ "execution_count": 85
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:34:10.417007Z",
+ "start_time": "2025-09-29T15:34:10.384386Z"
+ }
+ },
+ "source": [
+ "len(X)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2258"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 86
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:34:45.695445Z",
+ "start_time": "2025-09-29T15:34:45.605030Z"
+ }
+ },
+ "source": [
+ "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "0 The amoung of missing records is: The amoung of missing records is: \n",
+ "1 0 0 \n",
+ "\n",
+ " category_name \n",
+ "0 The amoung of missing records is: \n",
+ "1 1 "
+ ],
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
category_name
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
The amoung of missing records is:
\n",
+ "
The amoung of missing records is:
\n",
+ "
The amoung of missing records is:
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
0
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+ "
0
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+ "
1
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+ "
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+ " \n",
+ "
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+ "
"
+ ]
+ },
+ "execution_count": 87,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 87
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So now that we can see that our data has missing values, we want to remove the records with missing values. The code to drop the record with missing that we just added, is the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:34:50.093434Z",
+ "start_time": "2025-09-29T15:34:50.063669Z"
+ }
+ },
+ "source": [
+ "X.dropna(inplace=True)"
+ ],
+ "outputs": [],
+ "execution_count": 88
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "... and now let us test to see if we gotten rid of the records with missing values. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:34:53.698639Z",
+ "start_time": "2025-09-29T15:34:53.667740Z"
+ }
+ },
+ "source": [
+ "X.isnull().apply(lambda x: dmh.check_missing_values(x))"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "0 The amoung of missing records is: The amoung of missing records is: \n",
+ "1 0 0 \n",
+ "\n",
+ " category_name \n",
+ "0 The amoung of missing records is: \n",
+ "1 0 "
+ ],
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
text
\n",
+ "
category
\n",
+ "
category_name
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
The amoung of missing records is:
\n",
+ "
The amoung of missing records is:
\n",
+ "
The amoung of missing records is:
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
+ "
0
\n",
+ "
0
\n",
+ "
0
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 89,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 89
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:35:01.800493Z",
+ "start_time": "2025-09-29T15:35:01.735189Z"
+ }
+ },
+ "source": [
+ "len(X)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2257"
+ ]
+ },
+ "execution_count": 90,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 90
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And we are back with our original dataset, clean and tidy as we want it. That's enough on how to deal with missing values, let us now move unto something more fun. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "But just in case you want to learn more about how to deal with missing data, refer to the official [Pandas documentation](http://pandas.pydata.org/pandas-docs/stable/missing_data.html#missing-data)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 5 (take home)** \n",
+ "There is an old saying that goes, \"The devil is in the details.\" When we are working with extremely large data, it's difficult to check records one by one (as we have been doing so far). And also, we don't even know what kind of missing values we are facing. Thus, \"debugging\" skills get sharper as we spend more time solving bugs. Let's focus on a different method to check for missing values and the kinds of missing values you may encounter. It's not easy to check for missing values as you will find out in a minute.\n",
+ "\n",
+ "Please check the data and the process below, describe what you observe and why it happened. \n",
+ "$Hint$ : why `.isnull()` didn't work?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:35:38.201219Z",
+ "start_time": "2025-09-29T15:35:38.084050Z"
+ }
+ },
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "NA_dict = [{ 'id': 'A', 'missing_example': np.nan },\n",
+ " { 'id': 'B' },\n",
+ " { 'id': 'C', 'missing_example': 'NaN' },\n",
+ " { 'id': 'D', 'missing_example': 'None' },\n",
+ " { 'id': 'E', 'missing_example': None },\n",
+ " { 'id': 'F', 'missing_example': '' }]\n",
+ "\n",
+ "NA_df = pd.DataFrame(NA_dict, columns = ['id','missing_example'])\n",
+ "NA_df"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " id missing_example\n",
+ "0 A NaN\n",
+ "1 B NaN\n",
+ "2 C NaN\n",
+ "3 D None\n",
+ "4 E None\n",
+ "5 F "
+ ],
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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D
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F
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+ "
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+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "execution_count": 91,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 91
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:35:49.427863Z",
+ "start_time": "2025-09-29T15:35:49.401434Z"
+ }
+ },
+ "source": [
+ "NA_df['missing_example'].isnull()"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 True\n",
+ "1 True\n",
+ "2 False\n",
+ "3 False\n",
+ "4 True\n",
+ "5 False\n",
+ "Name: missing_example, dtype: bool"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 92
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-09-29T15:46:33.830309Z",
+ "start_time": "2025-09-29T15:46:33.690941Z"
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "# .isnull() detects actual null values, not string representations.\n",
+ "# A: np.nan → Real NaN value → .isnull() = True\n",
+ "# B: Missing key → Fill with NaN → .isnull() = True\n",
+ "# C: 'NaN' → \"NaN\" as text → .isnull() = False\n",
+ "# D: 'None' → \"None\" as text → .isnull() = False\n",
+ "# E: None → Python's None object → .isnull() = True\n",
+ "# F: '' → Empty string is a valid string) → .isnull() = False\n"
+ ],
+ "outputs": [],
+ "execution_count": 94
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 4.2 Dealing with Duplicate Data\n",
+ "Dealing with duplicate data is just as painful as dealing with missing data. The worst case is that you have duplicate data that has missing values. But let us not get carried away. Let us stick with the basics. As we have learned in our Data Mining course, duplicate data can occur because of many reasons. The majority of the times it has to do with how we store data or how we collect and merge data. For instance, we may have collected and stored a tweet, and a retweet of that same tweet as two different records; this results in a case of data duplication; the only difference being that one is the original tweet and the other the retweeted one. Here you will learn that dealing with duplicate data is not as challenging as missing values. But this also all depends on what you consider as duplicate data, i.e., this all depends on your criteria for what is considered as a duplicate record and also what type of data you are dealing with. For textual data, it may not be so trivial as it is for numerical values or images. Anyhow, let us look at some code on how to deal with duplicate records in our `X` dataframe."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, let us check how many duplicates we have in our current dataset. Here is the line of code that checks for duplicates; it is very similar to the `isnull` function that we used to check for missing values. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:58:34.466245Z",
+ "start_time": "2025-10-01T14:58:34.295195Z"
+ }
+ },
+ "source": [
+ "X.duplicated()"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 False\n",
+ "1 False\n",
+ "2 False\n",
+ "3 False\n",
+ "4 False\n",
+ " ... \n",
+ "2252 False\n",
+ "2253 False\n",
+ "2254 False\n",
+ "2255 False\n",
+ "2256 False\n",
+ "Length: 2257, dtype: bool"
+ ]
+ },
+ "execution_count": 100,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 100
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also check the sum of duplicate records by simply doing:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:58:39.509742Z",
+ "start_time": "2025-10-01T14:58:39.459001Z"
+ }
+ },
+ "source": [
+ "sum(X.duplicated())"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 101
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Based on that output, you may be asking why did the `duplicated` operation only returned one single column that indicates whether there is a duplicate record or not. So yes, all the `duplicated()` operation does is to check per records instead of per column. That is why the operation only returns one value instead of three values for each column. It appears that we don't have any duplicates since none of our records resulted in `True`. If we want to check for duplicates as we did above for some particular column, instead of all columns, we do something as shown below. As you may have noticed, in the case where we select some columns instead of checking by all columns, we are kind of lowering the criteria of what is considered as a duplicate record. So let us only check for duplicates by only checking the `text` attribute. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:58:52.611871Z",
+ "start_time": "2025-10-01T14:58:52.581670Z"
+ }
+ },
+ "source": [
+ "sum(X.duplicated('text'))"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0"
+ ]
+ },
+ "execution_count": 102,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 102
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let us create some duplicated dummy records and append it to the main dataframe `X`. Subsequenlty, let us try to get rid of the duplicates."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:58:59.709614Z",
+ "start_time": "2025-10-01T14:58:59.689061Z"
+ }
+ },
+ "source": [
+ "dummy_duplicate_dict = [{\n",
+ " 'text': 'dummy record',\n",
+ " 'category': 1, \n",
+ " 'category_name': \"dummy category\"\n",
+ " },\n",
+ " {\n",
+ " 'text': 'dummy record',\n",
+ " 'category': 1, \n",
+ " 'category_name': \"dummy category\"\n",
+ " }]"
+ ],
+ "outputs": [],
+ "execution_count": 103
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:06.363061Z",
+ "start_time": "2025-10-01T14:59:06.337022Z"
+ }
+ },
+ "source": [
+ "X = pd.concat([X, pd.DataFrame(dummy_duplicate_dict)], ignore_index=True)"
+ ],
+ "outputs": [],
+ "execution_count": 104
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:08.932567Z",
+ "start_time": "2025-10-01T14:59:08.903641Z"
+ }
+ },
+ "source": [
+ "len(X)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2259"
+ ]
+ },
+ "execution_count": 105,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 105
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:11.087194Z",
+ "start_time": "2025-10-01T14:59:11.045811Z"
+ }
+ },
+ "source": [
+ "sum(X.duplicated())"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 106,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 106
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We have added the dummy duplicates to `X`. Now we are faced with the decision as to what to do with the duplicated records after we have found it. In our case, we want to get rid of all the duplicated records without preserving a copy. We can simply do that with the following line of code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:14.023414Z",
+ "start_time": "2025-10-01T14:59:13.984577Z"
+ }
+ },
+ "source": [
+ "X.drop_duplicates(keep=False, inplace=True) # inplace applies changes directly on our dataframe"
+ ],
+ "outputs": [],
+ "execution_count": 107
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:16.120365Z",
+ "start_time": "2025-10-01T14:59:16.056868Z"
+ }
+ },
+ "source": [
+ "len(X)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2257"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 108
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check out the Pandas [documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html?highlight=duplicate#duplicate-data) for more information on dealing with duplicate data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Data Preprocessing\n",
+ "In the Data Mining course we learned about the many ways of performing data preprocessing. In reality, the list is quiet general as the specifics of what data preprocessing involves is too much to cover in one course. This is especially true when you are dealing with unstructured data, as we are dealing with in this particular notebook. But let us look at some examples for each data preprocessing technique that we learned in the class. We will cover each item one by one, and provide example code for each category. You will learn how to perform each of the operations, using Pandas, that cover the essentials to Preprocessing in Data Mining. We are not going to follow any strict order, but the items we will cover in the preprocessing section of this notebook are as follows:\n",
+ "\n",
+ "- Aggregation\n",
+ "- Sampling\n",
+ "- Dimensionality Reduction\n",
+ "- Feature Subset Selection\n",
+ "- Feature Creation\n",
+ "- Discretization and Binarization\n",
+ "- Attribute Transformation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1 Sampling\n",
+ "The first concept that we are going to cover from the above list is sampling. Sampling refers to the technique used for selecting data. The functionalities that we use to selected data through queries provided by Pandas are actually basic methods for sampling. The reasons for sampling are sometimes due to the size of data -- we want a smaller subset of the data that is still representatitive enough as compared to the original dataset. \n",
+ "\n",
+ "We don't have a problem of size in our current dataset since it is just a couple thousand records long. But if we pay attention to how much content is included in the `text` field of each of those records, you will realize that sampling may not be a bad idea after all. In fact, we have already done some sampling by just reducing the records we are using here in this notebook; remember that we are only using four categories from the all the 20 categories available. Let us get an idea on how to sample using pandas operations."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:36.147493Z",
+ "start_time": "2025-10-01T14:59:35.918568Z"
+ }
+ },
+ "source": [
+ "X_sample = X.sample(n=1000) #random state"
+ ],
+ "outputs": [],
+ "execution_count": 109
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:37.683609Z",
+ "start_time": "2025-10-01T14:59:37.662387Z"
+ }
+ },
+ "source": [
+ "len(X_sample)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1000"
+ ]
+ },
+ "execution_count": 110,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 110
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T14:59:39.075218Z",
+ "start_time": "2025-10-01T14:59:39.039760Z"
+ }
+ },
+ "source": [
+ "X_sample[0:4]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "1714 From: eczcaw@mips.nott.ac.uk (A.Wainwright) Su... 0 \n",
+ "2140 From: jwindley@cheap.cs.utah.edu (Jay Windley)... 3 \n",
+ "1327 From: gmark@cbnewse.cb.att.com (gilbert.m.stew... 2 \n",
+ "2024 From: spbach@lerc.nasa.gov (James Felder) Subj... 0 \n",
+ "\n",
+ " category_name \n",
+ "1714 alt.atheism \n",
+ "2140 soc.religion.christian \n",
+ "1327 sci.med \n",
+ "2024 alt.atheism "
+ ],
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
text
\n",
+ "
category
\n",
+ "
category_name
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
1714
\n",
+ "
From: eczcaw@mips.nott.ac.uk (A.Wainwright) Su...
\n",
+ "
0
\n",
+ "
alt.atheism
\n",
+ "
\n",
+ "
\n",
+ "
2140
\n",
+ "
From: jwindley@cheap.cs.utah.edu (Jay Windley)...
\n",
+ "
3
\n",
+ "
soc.religion.christian
\n",
+ "
\n",
+ "
\n",
+ "
1327
\n",
+ "
From: gmark@cbnewse.cb.att.com (gilbert.m.stew...
\n",
+ "
2
\n",
+ "
sci.med
\n",
+ "
\n",
+ "
\n",
+ "
2024
\n",
+ "
From: spbach@lerc.nasa.gov (James Felder) Subj...
\n",
+ "
0
\n",
+ "
alt.atheism
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 111
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> Exercise 6 (take home):\n",
+ "Notice any changes from the `X` dataframe to the `X_sample` dataframe? What are they? Report every change you noticed as compared to the previous state of `X`. Feel free to query and look more closely at the dataframe for these changes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:11:58.520044Z",
+ "start_time": "2025-10-01T15:11:58.452966Z"
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "len(X), len(X_sample)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2257, 1000)"
+ ]
+ },
+ "execution_count": 114,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 114
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:15:26.421807Z",
+ "start_time": "2025-10-01T15:15:26.254725Z"
+ }
+ },
+ "cell_type": "code",
+ "source": "X.columns.equals(X_sample.columns)",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 118,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 118
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:23:26.849683Z",
+ "start_time": "2025-10-01T15:23:26.602027Z"
+ }
+ },
+ "cell_type": "code",
+ "source": "X_sample.index[:10]",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index([1714, 2140, 1327, 2024, 872, 1607, 2141, 76, 74, 471], dtype='int64')"
+ ]
+ },
+ "execution_count": 119,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 119
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:24:02.935111Z",
+ "start_time": "2025-10-01T15:24:02.833747Z"
+ }
+ },
+ "cell_type": "code",
+ "source": "X_sample.index.is_monotonic_increasing",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 120,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 120
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:25:38.229383Z",
+ "start_time": "2025-10-01T15:25:38.054921Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "X.category_name.value_counts(normalize=True), \\\n",
+ "X_sample.category_name.value_counts(normalize=True)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(category_name\n",
+ " soc.religion.christian 0.265397\n",
+ " sci.med 0.263181\n",
+ " comp.graphics 0.258751\n",
+ " alt.atheism 0.212672\n",
+ " Name: proportion, dtype: float64,\n",
+ " category_name\n",
+ " sci.med 0.271\n",
+ " comp.graphics 0.255\n",
+ " soc.religion.christian 0.248\n",
+ " alt.atheism 0.226\n",
+ " Name: proportion, dtype: float64)"
+ ]
+ },
+ "execution_count": 121,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 121
+ },
+ {
+ "metadata": {},
+ "cell_type": "code",
+ "outputs": [],
+ "execution_count": null,
+ "source": [
+ "# X_sample has 1000 lines, while X has 2257 lines, but they have the same columns\n",
+ "# Data types and column names are unchanged, because X_sample is the subset of X\n",
+ "# X_sample is chosen randomly, no replacement.\n",
+ "# Row order is shuffled, but index label is preserved.\n",
+ "# Category distribution of X_sample may different from X due to sample variability\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's do something cool here while we are working with sampling! Let us look at the distribution of categories in both the sample and original dataset. Let us visualize and analyze the disparity between the two datasets. To generate some visualizations, we are going to use `matplotlib` python library. With matplotlib, things are faster and compatability-wise it may just be the best visualization library for visualizing content extracted from dataframes and when using Jupyter notebooks. Let's take a loot at the magic of `matplotlib` below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:27:47.966782Z",
+ "start_time": "2025-10-01T15:27:47.947233Z"
+ }
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline"
+ ],
+ "outputs": [],
+ "execution_count": 123
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "categories"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:27:54.663094Z",
+ "start_time": "2025-10-01T15:27:54.321493Z"
+ }
+ },
+ "source": [
+ "print(X.category_name.value_counts())\n",
+ "\n",
+ "# plot barchart for X\n",
+ "X.category_name.value_counts().plot(kind = 'bar',\n",
+ " title = 'Category distribution',\n",
+ " ylim = [0, 700], \n",
+ " rot = 0, fontsize = 11, figsize = (8,3))"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "category_name\n",
+ "soc.religion.christian 599\n",
+ "sci.med 594\n",
+ "comp.graphics 584\n",
+ "alt.atheism 480\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 124,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 125
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can use following command to see other available styles to prettify your charts.\n",
+ "```python\n",
+ "print(plt.style.available)```"
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-01T15:31:00.453117Z",
+ "start_time": "2025-10-01T15:31:00.295878Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "print(plt.style.available)"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['Solarize_Light2', '_classic_test_patch', '_mpl-gallery', '_mpl-gallery-nogrid', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-v0_8', 'seaborn-v0_8-bright', 'seaborn-v0_8-colorblind', 'seaborn-v0_8-dark', 'seaborn-v0_8-dark-palette', 'seaborn-v0_8-darkgrid', 'seaborn-v0_8-deep', 'seaborn-v0_8-muted', 'seaborn-v0_8-notebook', 'seaborn-v0_8-paper', 'seaborn-v0_8-pastel', 'seaborn-v0_8-poster', 'seaborn-v0_8-talk', 'seaborn-v0_8-ticks', 'seaborn-v0_8-white', 'seaborn-v0_8-whitegrid', 'tableau-colorblind10']\n"
+ ]
+ }
+ ],
+ "execution_count": 126
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": "---"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 7 (Watch Video):**\n",
+ "Notice that for the `ylim` parameters we hardcoded the maximum value for y. Is it possible to automate this instead of hard-coding it? How would you go about doing that? (Hint: look at code above for clues)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T10:54:54.708634Z",
+ "start_time": "2025-10-03T10:54:53.525154Z"
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "X_sample.category_name.value_counts().plot(\n",
+ " kind='bar',\n",
+ " title='Category distribution',\n",
+ " ylim=[0, X_sample.category_name.value_counts().max() + 30],\n",
+ " rot=0, fontsize=12, figsize=(8, 3)\n",
+ ")"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 127,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 127
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 8 (take home):** \n",
+ "We can also do a side-by-side comparison of the distribution between the two datasets, but maybe you can try that as an excerise. Below we show you an snapshot of the type of chart we are looking for. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:04:32.453413Z",
+ "start_time": "2025-10-03T11:04:32.182374Z"
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "df = pd.DataFrame({\n",
+ " \"X\": X.category_name.value_counts(),\n",
+ " \"X_sample\": X_sample.category_name.value_counts()\n",
+ "}).fillna(0).astype(int)\n",
+ "\n",
+ "ax = df.plot(kind=\"bar\",\n",
+ " title=\"Category distribution\",\n",
+ " rot=0, fontsize=11, figsize=(8,5))\n",
+ "\n",
+ "ax.set_ylim(0, df.to_numpy().max() + 30)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 629.0)"
+ ]
+ },
+ "execution_count": 130,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 130
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "One thing that stood out from the both datasets, is that the distribution of the categories remain relatively the same, which is a good sign for us data scientist. There are many ways to conduct sampling on the dataset and still obtain a representative enough dataset. That is not the main focus in this notebook, but if you would like to know more about sampling and how the `sample` feature works, just reference the Pandas documentation and you will find interesting ways to conduct more advanced sampling."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 Feature Creation\n",
+ "The other operation from the list above that we are going to practise on is the so-called feature creation. As the name suggests, in feature creation we are looking at creating new interesting and useful features from the original dataset; a feature which captures the most important information from the raw information we already have access to. In our `X` table, we would like to create some features from the `text` field, but we are still not sure what kind of features we want to create. We can think of an interesting problem we want to solve, or something we want to analyze from the data, or some questions we want to answer. This is one process to come up with features -- this process is usually called `feature engineering` in the data science community. \n",
+ "\n",
+ "We know what feature creation is so let us get real involved with our dataset and make it more interesting by adding some special features or attributes if you will. First, we are going to obtain the **unigrams** for each text. (Unigram is just a fancy word we use in Text Mining which stands for 'tokens' or 'individual words'.) Yes, we want to extract all the words found in each text and append it as a new feature to the pandas dataframe. The reason for extracting unigrams is not so clear yet, but we can start to think of obtaining some statistics about the articles we have: something like **word distribution** or **word frequency**.\n",
+ "\n",
+ "Before going into any further coding, we will also introduce a useful text mining library called [NLTK](http://www.nltk.org/). The NLTK library is a natural language processing tool used for text mining tasks, so might as well we start to familiarize ourselves with it from now (It may come in handy for the final project!). In partcular, we are going to use the NLTK library to conduct tokenization because we are interested in splitting a sentence into its individual components, which we refer to as words, emojis, emails, etc. So let us go for it! We can call the `nltk` library as follows:\n",
+ "\n",
+ "```python\n",
+ "import nltk\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:00.672652Z",
+ "start_time": "2025-10-03T11:04:58.234058Z"
+ }
+ },
+ "source": [
+ "import nltk\n",
+ "nltk.download(\"punkt\")\n",
+ "nltk.download(\"punkt_tab\")"
+ ],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package punkt to /Users/nguyentr/nltk_data...\n",
+ "[nltk_data] Package punkt is already up-to-date!\n",
+ "[nltk_data] Downloading package punkt_tab to\n",
+ "[nltk_data] /Users/nguyentr/nltk_data...\n",
+ "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 131,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 131
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:14.179629Z",
+ "start_time": "2025-10-03T11:05:10.702598Z"
+ }
+ },
+ "source": [
+ "# takes a like a minute or two to process\n",
+ "\n",
+ "X['unigrams'] = X['text'].apply(lambda x: dmh.tokenize_text(x))"
+ ],
+ "outputs": [],
+ "execution_count": 132
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:17.398832Z",
+ "start_time": "2025-10-03T11:05:17.293173Z"
+ }
+ },
+ "source": [
+ "X[0:4][\"unigrams\"]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 [From, :, sd345, @, city.ac.uk, (, Michael, Co...\n",
+ "1 [From, :, ani, @, ms.uky.edu, (, Aniruddha, B....\n",
+ "2 [From, :, djohnson, @, cs.ucsd.edu, (, Darin, ...\n",
+ "3 [From, :, s0612596, @, let.rug.nl, (, M.M, ., ...\n",
+ "Name: unigrams, dtype: object"
+ ]
+ },
+ "execution_count": 134,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 134
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you take a closer look at the `X` table now, you will see the new columns `unigrams` that we have added. You will notice that it contains an array of tokens, which were extracted from the original `text` field. At first glance, you will notice that the tokenizer is not doing a great job, let us take a closer at a single record and see what was the exact result of the tokenization using the `nltk` library."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:23.624378Z",
+ "start_time": "2025-10-03T11:05:23.473598Z"
+ }
+ },
+ "source": [
+ "X[0:4]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " text category \\\n",
+ "0 From: sd345@city.ac.uk (Michael Collier) Subje... 1 \n",
+ "1 From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ... 1 \n",
+ "2 From: djohnson@cs.ucsd.edu (Darin Johnson) Sub... 3 \n",
+ "3 From: s0612596@let.rug.nl (M.M. Zwart) Subject... 3 \n",
+ "\n",
+ " category_name unigrams \n",
+ "0 comp.graphics [From, :, sd345, @, city.ac.uk, (, Michael, Co... \n",
+ "1 comp.graphics [From, :, ani, @, ms.uky.edu, (, Aniruddha, B.... \n",
+ "2 soc.religion.christian [From, :, djohnson, @, cs.ucsd.edu, (, Darin, ... \n",
+ "3 soc.religion.christian [From, :, s0612596, @, let.rug.nl, (, M.M, ., ... "
+ ],
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
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+ "
From: sd345@city.ac.uk (Michael Collier) Subje...
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+ "
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+ "
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+ "
[From, :, sd345, @, city.ac.uk, (, Michael, Co...
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ "
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+ "
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+ ]
+ },
+ "execution_count": 135,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 135
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:30.167081Z",
+ "start_time": "2025-10-03T11:05:30.078934Z"
+ }
+ },
+ "source": [
+ "list(X[0:1]['unigrams'])"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[['From',\n",
+ " ':',\n",
+ " 'sd345',\n",
+ " '@',\n",
+ " 'city.ac.uk',\n",
+ " '(',\n",
+ " 'Michael',\n",
+ " 'Collier',\n",
+ " ')',\n",
+ " 'Subject',\n",
+ " ':',\n",
+ " 'Converting',\n",
+ " 'images',\n",
+ " 'to',\n",
+ " 'HP',\n",
+ " 'LaserJet',\n",
+ " 'III',\n",
+ " '?',\n",
+ " 'Nntp-Posting-Host',\n",
+ " ':',\n",
+ " 'hampton',\n",
+ " 'Organization',\n",
+ " ':',\n",
+ " 'The',\n",
+ " 'City',\n",
+ " 'University',\n",
+ " 'Lines',\n",
+ " ':',\n",
+ " '14',\n",
+ " 'Does',\n",
+ " 'anyone',\n",
+ " 'know',\n",
+ " 'of',\n",
+ " 'a',\n",
+ " 'good',\n",
+ " 'way',\n",
+ " '(',\n",
+ " 'standard',\n",
+ " 'PC',\n",
+ " 'application/PD',\n",
+ " 'utility',\n",
+ " ')',\n",
+ " 'to',\n",
+ " 'convert',\n",
+ " 'tif/img/tga',\n",
+ " 'files',\n",
+ " 'into',\n",
+ " 'LaserJet',\n",
+ " 'III',\n",
+ " 'format',\n",
+ " '.',\n",
+ " 'We',\n",
+ " 'would',\n",
+ " 'also',\n",
+ " 'like',\n",
+ " 'to',\n",
+ " 'do',\n",
+ " 'the',\n",
+ " 'same',\n",
+ " ',',\n",
+ " 'converting',\n",
+ " 'to',\n",
+ " 'HPGL',\n",
+ " '(',\n",
+ " 'HP',\n",
+ " 'plotter',\n",
+ " ')',\n",
+ " 'files',\n",
+ " '.',\n",
+ " 'Please',\n",
+ " 'email',\n",
+ " 'any',\n",
+ " 'response',\n",
+ " '.',\n",
+ " 'Is',\n",
+ " 'this',\n",
+ " 'the',\n",
+ " 'correct',\n",
+ " 'group',\n",
+ " '?',\n",
+ " 'Thanks',\n",
+ " 'in',\n",
+ " 'advance',\n",
+ " '.',\n",
+ " 'Michael',\n",
+ " '.',\n",
+ " '--',\n",
+ " 'Michael',\n",
+ " 'Collier',\n",
+ " '(',\n",
+ " 'Programmer',\n",
+ " ')',\n",
+ " 'The',\n",
+ " 'Computer',\n",
+ " 'Unit',\n",
+ " ',',\n",
+ " 'Email',\n",
+ " ':',\n",
+ " 'M.P.Collier',\n",
+ " '@',\n",
+ " 'uk.ac.city',\n",
+ " 'The',\n",
+ " 'City',\n",
+ " 'University',\n",
+ " ',',\n",
+ " 'Tel',\n",
+ " ':',\n",
+ " '071',\n",
+ " '477-8000',\n",
+ " 'x3769',\n",
+ " 'London',\n",
+ " ',',\n",
+ " 'Fax',\n",
+ " ':',\n",
+ " '071',\n",
+ " '477-8565',\n",
+ " 'EC1V',\n",
+ " '0HB',\n",
+ " '.']]"
+ ]
+ },
+ "execution_count": 136,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 136
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The `nltk` library does a pretty decent job of tokenizing our text. There are many other tokenizers online, such as [spaCy](https://spacy.io/), and the built in libraries provided by [scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html). We are making use of the NLTK library because it is open source and because it does a good job of segmentating text-based data. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.3 Feature subset selection\n",
+ "Okay, so we are making some headway here. Let us now make things a bit more interesting. We are going to do something different from what we have been doing thus far. We are going use a bit of everything that we have learned so far. Briefly speaking, we are going to move away from our main dataset (one form of feature subset selection), and we are going to generate a document-term matrix from the original dataset. In other words we are going to be creating something like this. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Initially, it won't have the same shape as the table above, but we will get into that later. For now, let us use scikit learn built in functionalities to generate this document. You will see for yourself how easy it is to generate this table without much coding. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:49.639686Z",
+ "start_time": "2025-10-03T11:05:48.736319Z"
+ }
+ },
+ "source": [
+ "from sklearn.feature_extraction.text import CountVectorizer\n",
+ "\n",
+ "count_vect = CountVectorizer()\n",
+ "X_counts = count_vect.fit_transform(X.text) #learn the vocabulary and return document-term matrix\n",
+ "print(X_counts[0])"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " (np.int32(0), np.int32(14887))\t1\n",
+ " (np.int32(0), np.int32(29022))\t1\n",
+ " (np.int32(0), np.int32(8696))\t4\n",
+ " (np.int32(0), np.int32(4017))\t2\n",
+ " (np.int32(0), np.int32(33256))\t2\n",
+ " (np.int32(0), np.int32(21661))\t3\n",
+ " (np.int32(0), np.int32(9031))\t3\n",
+ " (np.int32(0), np.int32(31077))\t1\n",
+ " (np.int32(0), np.int32(9805))\t2\n",
+ " (np.int32(0), np.int32(17366))\t1\n",
+ " (np.int32(0), np.int32(32493))\t4\n",
+ " (np.int32(0), np.int32(16916))\t2\n",
+ " (np.int32(0), np.int32(19780))\t2\n",
+ " (np.int32(0), np.int32(17302))\t2\n",
+ " (np.int32(0), np.int32(23122))\t1\n",
+ " (np.int32(0), np.int32(25663))\t1\n",
+ " (np.int32(0), np.int32(16881))\t1\n",
+ " (np.int32(0), np.int32(16082))\t1\n",
+ " (np.int32(0), np.int32(23915))\t1\n",
+ " (np.int32(0), np.int32(32142))\t5\n",
+ " (np.int32(0), np.int32(33597))\t2\n",
+ " (np.int32(0), np.int32(20253))\t1\n",
+ " (np.int32(0), np.int32(587))\t1\n",
+ " (np.int32(0), np.int32(12051))\t1\n",
+ " (np.int32(0), np.int32(5201))\t1\n",
+ " :\t:\n",
+ " (np.int32(0), np.int32(25361))\t1\n",
+ " (np.int32(0), np.int32(25337))\t1\n",
+ " (np.int32(0), np.int32(12833))\t2\n",
+ " (np.int32(0), np.int32(5195))\t1\n",
+ " (np.int32(0), np.int32(27836))\t1\n",
+ " (np.int32(0), np.int32(18474))\t1\n",
+ " (np.int32(0), np.int32(32270))\t1\n",
+ " (np.int32(0), np.int32(9932))\t1\n",
+ " (np.int32(0), np.int32(15837))\t1\n",
+ " (np.int32(0), np.int32(32135))\t1\n",
+ " (np.int32(0), np.int32(17556))\t1\n",
+ " (np.int32(0), np.int32(4378))\t1\n",
+ " (np.int32(0), np.int32(26175))\t1\n",
+ " (np.int32(0), np.int32(9338))\t1\n",
+ " (np.int32(0), np.int32(33572))\t1\n",
+ " (np.int32(0), np.int32(31915))\t1\n",
+ " (np.int32(0), np.int32(177))\t2\n",
+ " (np.int32(0), np.int32(2326))\t2\n",
+ " (np.int32(0), np.int32(3062))\t1\n",
+ " (np.int32(0), np.int32(35416))\t1\n",
+ " (np.int32(0), np.int32(20459))\t1\n",
+ " (np.int32(0), np.int32(14085))\t1\n",
+ " (np.int32(0), np.int32(3166))\t1\n",
+ " (np.int32(0), np.int32(12541))\t1\n",
+ " (np.int32(0), np.int32(230))\t1\n"
+ ]
+ }
+ ],
+ "execution_count": 137
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now you can also see some examples of what each feature is based on their index in the vector:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:52.610842Z",
+ "start_time": "2025-10-03T11:05:52.522097Z"
+ }
+ },
+ "source": [
+ "count_vect.get_feature_names_out()[14887]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'from'"
+ ]
+ },
+ "execution_count": 138,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 138
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:55.688996Z",
+ "start_time": "2025-10-03T11:05:55.559129Z"
+ }
+ },
+ "source": [
+ "count_vect.get_feature_names_out()[29022]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'sd345'"
+ ]
+ },
+ "execution_count": 139,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 139
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:57.328934Z",
+ "start_time": "2025-10-03T11:05:57.232191Z"
+ }
+ },
+ "source": [
+ "count_vect.get_feature_names_out()[8696]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'city'"
+ ]
+ },
+ "execution_count": 140,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 140
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:05:59.329064Z",
+ "start_time": "2025-10-03T11:05:59.286893Z"
+ }
+ },
+ "source": [
+ "count_vect.get_feature_names_out()[4017]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'ac'"
+ ]
+ },
+ "execution_count": 141,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 141
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "What we did with those two lines of code is that we transformed the articles into a **term-document matrix**. Those lines of code tokenize each article using a built-in, default tokenizer (often referred to as an `analyzer`) and then produces the word frequency vector for each document. We can create our own analyzers or even use the nltk analyzer that we previously built. To keep things tidy and minimal we are going to use the default analyzer provided by `CountVectorizer`. Let us look closely at this analyzer. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:06:02.865349Z",
+ "start_time": "2025-10-03T11:06:02.795039Z"
+ }
+ },
+ "source": [
+ "analyze = count_vect.build_analyzer()\n",
+ "analyze(\"I am craving for a hawaiian pizza right now\")\n",
+ "\n",
+ "# tokenization, remove stop words (e.g i, a, the), create n-gram (or unigram)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['am', 'craving', 'for', 'hawaiian', 'pizza', 'right', 'now']"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 142
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **>>> Exercise 9 (Watch Video):**\n",
+ "Let's analyze the first record of our X dataframe with the new analyzer we have just built. Go ahead try it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:09:38.699005Z",
+ "start_time": "2025-10-03T11:09:38.661101Z"
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "# How do we turn our array[0] text document into a tokenized text using the build_analyzer()?\n",
+ "analyze(X.text[0])"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['from',\n",
+ " 'sd345',\n",
+ " 'city',\n",
+ " 'ac',\n",
+ " 'uk',\n",
+ " 'michael',\n",
+ " 'collier',\n",
+ " 'subject',\n",
+ " 'converting',\n",
+ " 'images',\n",
+ " 'to',\n",
+ " 'hp',\n",
+ " 'laserjet',\n",
+ " 'iii',\n",
+ " 'nntp',\n",
+ " 'posting',\n",
+ " 'host',\n",
+ " 'hampton',\n",
+ " 'organization',\n",
+ " 'the',\n",
+ " 'city',\n",
+ " 'university',\n",
+ " 'lines',\n",
+ " '14',\n",
+ " 'does',\n",
+ " 'anyone',\n",
+ " 'know',\n",
+ " 'of',\n",
+ " 'good',\n",
+ " 'way',\n",
+ " 'standard',\n",
+ " 'pc',\n",
+ " 'application',\n",
+ " 'pd',\n",
+ " 'utility',\n",
+ " 'to',\n",
+ " 'convert',\n",
+ " 'tif',\n",
+ " 'img',\n",
+ " 'tga',\n",
+ " 'files',\n",
+ " 'into',\n",
+ " 'laserjet',\n",
+ " 'iii',\n",
+ " 'format',\n",
+ " 'we',\n",
+ " 'would',\n",
+ " 'also',\n",
+ " 'like',\n",
+ " 'to',\n",
+ " 'do',\n",
+ " 'the',\n",
+ " 'same',\n",
+ " 'converting',\n",
+ " 'to',\n",
+ " 'hpgl',\n",
+ " 'hp',\n",
+ " 'plotter',\n",
+ " 'files',\n",
+ " 'please',\n",
+ " 'email',\n",
+ " 'any',\n",
+ " 'response',\n",
+ " 'is',\n",
+ " 'this',\n",
+ " 'the',\n",
+ " 'correct',\n",
+ " 'group',\n",
+ " 'thanks',\n",
+ " 'in',\n",
+ " 'advance',\n",
+ " 'michael',\n",
+ " 'michael',\n",
+ " 'collier',\n",
+ " 'programmer',\n",
+ " 'the',\n",
+ " 'computer',\n",
+ " 'unit',\n",
+ " 'email',\n",
+ " 'collier',\n",
+ " 'uk',\n",
+ " 'ac',\n",
+ " 'city',\n",
+ " 'the',\n",
+ " 'city',\n",
+ " 'university',\n",
+ " 'tel',\n",
+ " '071',\n",
+ " '477',\n",
+ " '8000',\n",
+ " 'x3769',\n",
+ " 'london',\n",
+ " 'fax',\n",
+ " '071',\n",
+ " '477',\n",
+ " '8565',\n",
+ " 'ec1v',\n",
+ " '0hb']"
+ ]
+ },
+ "execution_count": 144,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 144
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let us look at the term-document matrix we built above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:10:45.492525Z",
+ "start_time": "2025-10-03T11:10:45.339597Z"
+ }
+ },
+ "source": [
+ "# We can check the shape of this matrix by:\n",
+ "X_counts.shape"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2257, 35788)"
+ ]
+ },
+ "execution_count": 145,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 145
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:11:33.151311Z",
+ "start_time": "2025-10-03T11:11:33.106614Z"
+ }
+ },
+ "source": [
+ "# We can obtain the feature names of the vectorizer, i.e., the terms\n",
+ "# usually on the horizontal axis\n",
+ "count_vect.get_feature_names_out()[0:10]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['00', '000', '0000', '0000001200', '000005102000', '0001',\n",
+ " '000100255pixel', '00014', '000406', '0007'], dtype=object)"
+ ]
+ },
+ "execution_count": 148,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 148
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Above we can see the features found in the all the documents `X`, which are basically all the terms found in all the documents. As I said earlier, the transformation is not in the pretty format (table) we saw above -- the term-document matrix. We can do many things with the `count_vect` vectorizer and its transformation `X_counts`. You can find more information on other cool stuff you can do with the [CountVectorizer](http://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction). \n",
+ "\n",
+ "Now let us try to obtain something that is as close to the pretty table I provided above. Before jumping into the code for doing just that, it is important to mention that the reason for choosing the `fit_transform` for the `CountVectorizer` is that it efficiently learns the vocabulary dictionary and returns a term-document matrix.\n",
+ "\n",
+ "In the next bit of code, we want to extract the first five articles and transform them into document-term matrix, or in this case a 2-dimensional array. Here it goes. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:11:30.332040Z",
+ "start_time": "2025-10-03T11:11:30.137513Z"
+ }
+ },
+ "source": [
+ "X_counts.shape"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2257, 35788)"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 147
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:11:39.722978Z",
+ "start_time": "2025-10-03T11:11:39.652816Z"
+ }
+ },
+ "source": [
+ "# we convert from sparse array to normal array\n",
+ "X_counts[0:5, 0:100].toarray()"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 149
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T12:29:03.408690Z",
+ "start_time": "2025-10-03T11:11:47.286151Z"
+ }
+ },
+ "source": [
+ "count_vect.get_feature_names_out()[0:1]"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['00'], dtype=object)"
+ ]
+ },
+ "execution_count": 150,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 150
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As you can see the result is just this huge sparse matrix, which is computationally intensive to generate and difficult to visualize. But we can see that the fifth record, specifically, contains a `1` in the beginning, which from our feature names we can deduce that this article contains exactly one `00` term."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **>>> Exercise 10 (take home):**\n",
+ "We said that the `1` at the beginning of the fifth record represents the `00` term. Notice that there is another 1 in the same record. Can you provide code that can verify what word this 1 represents from the vocabulary. Try to do this as efficient as possible."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:28:28.539743Z",
+ "start_time": "2025-10-03T11:28:28.428715Z"
+ }
+ },
+ "source": [
+ "# Answer\n",
+ "# Find which word(s) are \"1\" in the 5th record (first 100 features)\n",
+ "row = X_counts[4, :100] # Pick the 5th record (0-based index -> 4) and keep only the first 100 features\n",
+ "cols_nonzero = row.nonzero()[1] # Find the column positions where the value is non-zero\n",
+ "vocab = count_vect.get_feature_names_out() # Map those positions back to words (vocabulary)\n",
+ "words_nonzero = [vocab[i] for i in cols_nonzero]\n",
+ "print(\"Non-zero words in 5th record (first 100 features):\", words_nonzero)\n",
+ "\n",
+ "second_word = words_nonzero[1] if len(words_nonzero) > 1 else None\n",
+ "second_word"
+ ],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Non-zero words in 5th record (first 100 features): ['00', '01']\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'01'"
+ ]
+ },
+ "execution_count": 159,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 159
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To get you started in thinking about how to better analyze your data or transformation, let us look at this nice little heat map of our term-document matrix. It may come as a surpise to see the gems you can mine when you start to look at the data from a different perspective. Visualization are good for this reason."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T12:29:03.399209Z",
+ "start_time": "2025-10-03T11:15:00.535781Z"
+ }
+ },
+ "source": [
+ "# first twenty features only\n",
+ "plot_x = [\"term_\"+str(i) for i in count_vect.get_feature_names_out()[0:20]]"
+ ],
+ "outputs": [],
+ "execution_count": 151
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T12:29:03.399973Z",
+ "start_time": "2025-10-03T11:15:02.445133Z"
+ }
+ },
+ "source": [
+ "# obtain document index\n",
+ "plot_y = [\"doc_\"+ str(i) for i in list(X.index)[0:20]]"
+ ],
+ "outputs": [],
+ "execution_count": 152
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T12:29:03.391961Z",
+ "start_time": "2025-10-03T11:15:03.915561Z"
+ }
+ },
+ "source": [
+ "plot_z = X_counts[0:20, 0:20].toarray() #X_counts[how many documents, how many terms]\n",
+ "plot_z"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+ " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
+ ]
+ },
+ "execution_count": 153,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 153
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For the heat map, we are going to use another visualization library called `seaborn`. It's built on top of matplotlib and closely integrated with pandas data structures. One of the biggest advantages of seaborn is that its default aesthetics are much more visually appealing than matplotlib. See comparison below."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The other big advantage of seaborn is that seaborn has some built-in plots that matplotlib does not support. Most of these can eventually be replicated by hacking away at matplotlib, but they’re not built in and require much more effort to build.\n",
+ "\n",
+ "So without further ado, let us try it now!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-03T11:29:14.451127Z",
+ "start_time": "2025-10-03T11:29:13.733331Z"
+ }
+ },
+ "source": [
+ "import seaborn as sns\n",
+ "\n",
+ "df_todraw = pd.DataFrame(plot_z, columns = plot_x, index = plot_y)\n",
+ "plt.subplots(figsize=(9, 7))\n",
+ "ax = sns.heatmap(df_todraw,\n",
+ " cmap=\"PuRd\",\n",
+ " vmin=0, vmax=1, annot=True)"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "
"
+ ],
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "execution_count": 160
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check out more beautiful color palettes here: https://python-graph-gallery.com/197-available-color-palettes-with-matplotlib/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **>>> Exercise 11 (take home):** \n",
+ "From the chart above, we can see how sparse the term-document matrix is; i.e., there is only one terms with **FREQUENCY** of `1` in the subselection of the matrix. By the way, you may have noticed that we only selected 20 articles and 20 terms to plot the histrogram. As an excersise you can try to modify the code above to plot the entire term-document matrix or just a sample of it. How would you do this efficiently? Remember there is a lot of words in the vocab. Report below what methods you would use to get a nice and useful visualization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "jupyter": {
+ "is_executing": true
+ }
+ },
+ "source": [
+ "# Answer here\n",
+ "n_docs = 50 # rows\n",
+ "n_terms = 50 # columns\n",
+ "plot_z = X_counts[0:n_docs, 0:n_terms].toarray()\n",
+ "plot_x = [f\"term_{i}\" for i in range(n_terms)]\n",
+ "plot_y = [f\"doc_{i}\" for i in range(n_docs)]\n",
+ "\n",
+ "# Heatmap\n",
+ "df_todraw = pd.DataFrame(plot_z, columns=plot_x, index=plot_y)\n",
+ "plt.subplots(figsize=(12, 8))\n",
+ "sns.heatmap(df_todraw, cmap=\"PuRd\", vmin=0, vmax=1, annot=True)\n",
+ "plt.title(f\"Term–Document Heatmap ({n_docs} docs × {n_terms} terms)\")\n",
+ "plt.show()\n"
+ ],
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The great thing about what we have done so far is that we now open doors to new problems. Let us be optimistic. Even though we have the problem of sparsity and a very high dimensional data, we are now closer to uncovering wonders from the data. You see, the price you pay for the hard work is worth it because now you are gaining a lot of knowledge from what was just a list of what appeared to be irrelevant articles. Just the fact that you can blow up the data and find out interesting characteristics about the dataset in just a couple lines of code, is something that truly inspires me to practise Data Science. That's the motivation right there!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.4 Attribute Transformation / Aggregation\n",
+ "We can do other things with the term-vector matrix besides applying dimensionality reduction technique to deal with sparsity problem. Here we are going to generate a simple distribution of the words found in all the entire set of articles. Intuitively, this may not make any sense, but in data science sometimes we take some things for granted, and we just have to explore the data first before making any premature conclusions. On the topic of attribute transformation, we will take the word distribution and put the distribution in a scale that makes it easy to analyze patterns in the distrubution of words. Let us get into it!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.4.1 Transform Text Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we need to compute these frequencies for each term in all documents. Visually speaking, we are seeking to add values of the 2D matrix, vertically; i.e., sum of each column. You can also refer to this process as aggregation, which we won't explore further in this notebook because of the type of data we are dealing with. But I believe you get the idea of what that includes. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# note this takes time to compute. You may want to reduce the amount of terms you want to compute frequencies for\n",
+ "term_frequencies = []\n",
+ "for j in range(0,X_counts.shape[1]):\n",
+ " term_frequencies.append(sum(X_counts[:,j].toarray()))\n",
+ "\n",
+ "#[3, 8, 5, 2, 5, 8, 2, 5, 3, 2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "term_frequencies = np.asarray(X_counts.sum(axis=0))[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.int64(134)"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "term_frequencies[0] #sum of first term: 00"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": true,
+ "jupyter": {
+ "is_executing": true
+ }
+ },
+ "source": [
+ "plt.subplots(figsize=(100, 10))\n",
+ "g = sns.barplot(x=count_vect.get_feature_names_out()[:300], \n",
+ " y=term_frequencies[:300])\n",
+ "g.set_xticklabels(count_vect.get_feature_names_out()[:300], rotation = 90);"
+ ],
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 12 (take home):**\n",
+ "If you want a nicer interactive visualization here, I would encourage you try to install and use plotly to achieve this."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 13 (take home):** \n",
+ "The chart above only contains 300 vocabulary in the documents, and it's already computationally intensive to both compute and visualize. Can you efficiently reduce the number of terms you want to visualize as an exercise. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 14 (take home):** \n",
+ "Additionally, you can attempt to sort the terms on the `x-axis` by frequency instead of in alphabetical order. This way the visualization is more meaninfgul and you will be able to observe the so called [long tail](https://en.wikipedia.org/wiki/Long_tail) (get familiar with this term since it will appear a lot in data mining and other statistics courses). see picture below\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer here\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since we already have those term frequencies, we can also transform the values in that vector into the log distribution. All we need is to import the `math` library provided by python and apply it to the array of values of the term frequency vector. This is a typical example of attribute transformation. Let's go for it. The log distribution is a technique to visualize the term frequency into a scale that makes you easily visualize the distribution in a more readable format. In other words, the variations between the term frequencies are now easy to observe. Let us try it out!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "term_frequencies_log = [math.log(i) for i in term_frequencies]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\2166548998.py:4: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+ " g.set_xticklabels(count_vect.get_feature_names_out()[:300], rotation = 90);\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ ],
+ "source": [
+ "plt.subplots(figsize=(100, 10))\n",
+ "g = sns.barplot(x=count_vect.get_feature_names_out()[:300],\n",
+ " y=term_frequencies_log[:300])\n",
+ "g.set_xticklabels(count_vect.get_feature_names_out()[:300], rotation = 90);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Besides observing a complete transformation on the disrtibution, notice the scale on the y-axis. The log distribution in our unsorted example has no meaning, but try to properly sort the terms by their frequency, and you will see an interesting effect. Go for it!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 15 (take home):** \n",
+ "You can copy the code from the previous exercise and change the 'term_frequencies' variable for the 'term_frequencies_log', comment about the differences that you observe and talk about other possible insights that we can get from a log distribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "End of Phase 1\n",
+ "\n",
+ "The phase 1 exercises and homeworks should be committed and submitted before September 28th"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Phase 2** "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.4.2 Finding frequent patterns\n",
+ "Perfect, so now that we know how to interpret a document-term matrix from our text data, we will see how to get extra insight from it, we will do this by mining frequent patterns. For this we will be using the PAMI library that we previously installed."
+ ]
+ },
+ {
+ "attachments": {
+ "freq_patterns_alg.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Introduction to PAMI**\n",
+ "\n",
+ "PAMI (PAttern MIning) is a Python-based library designed to empower data scientists by providing the necessary tools to uncover hidden patterns within large datasets. Unlike other pattern mining libraries that are Java-based (such as WEKA and SPMF), PAMI caters specifically to the Python environment, making it more accessible for data scientists working with Big Data. The goal of PAMI is to streamline the process of discovering patterns that are often hidden within large datasets, offering a unified platform for applying various pattern mining techniques. In the library you can find a lot of implementations from current state-of-the-art algorithms, all of them cater to different type of data, they can be: transactional data, temporal data, utility data and some others. You can find more information in the following github: [PAMI](https://github.com/UdayLab/PAMI?tab=readme-ov-file). For the purpose of our lab we will be modeling our text data as a transactional type. So let's get into it.\n",
+ "\n",
+ "\n",
+ "Some code cells might have changed slightly from last year's explanation due to some updates of PAMI or fixes during the lab period of time\n",
+ "\n",
+ "\n",
+ "**Transactional Data**\n",
+ "\n",
+ "In order to apply pattern mining techniques, we first need to convert our text data into transactional data. A transactional database is a set of transactions where each transaction consists of a unique identifier (TID) and a set of items. For instance, think of a transaction as a basket of items purchased by a customer, and the TID is like the receipt number. Each transaction could contain items such as \"apple\", \"banana\", and \"orange\".\n",
+ "\n",
+ "Here's an example of a transactional database:\n",
+ "\n",
+ "TID\tTransactions\n",
+ "1\ta, b, c\n",
+ "2\td, e\n",
+ "3\ta, e, f\n",
+ "\n",
+ "In this structure:\n",
+ "TID refers to the unique identifier of each transaction (often ignored by PAMI to save storage space).\n",
+ "Items refer to the elements in each transaction, which could be either integers or strings (e.g., products, words, etc.).\n",
+ "When preparing text data, we need to transform sentences or documents into a similar format, where each sentence or document becomes a transaction, and the words within it become the items.\n",
+ "\n",
+ "**Frequent Pattern Mining**\n",
+ "\n",
+ "After converting the text into a transactional format, we can then apply frequent pattern mining. This process identifies patterns or combinations of items that occur frequently across the dataset. For example, in text data, frequent patterns might be common word pairs or phrases that appear together across multiple documents. Important term to learn: **Minimum Support**: It refers to the minimum frequency that a transaction has to have to be considered a pattern in our scenario.\n",
+ "\n",
+ "PAMI allows us to mine various types of patterns, but for the purpuse of this lab we will explore the following types:\n",
+ "\n",
+ "\n",
+ "**Patterns Above Minimum Support:** These are all patterns that meet a specified minimum support threshold. The result set can be quite large as it includes all frequent patterns, making it ideal for comprehensive analysis but potentially complex.\n",
+ "\n",
+ "**Maximal Frequent Patterns:** These are the largest frequent patterns that cannot be extended by adding more items without reducing their frequency below the minimum support threshold. The result set is smaller and more concise, as it only includes the largest patterns, reducing redundancy.\n",
+ "\n",
+ "**Top-K Frequent Patterns:** These patterns represent the K most frequent patterns, regardless of the minimum support threshold. The result set is highly focused and concise, with a fixed number of patterns, making it ideal when prioritizing the most frequent patterns.\n",
+ "\n",
+ "\n",
+ "\n",
+ "In the following steps, we will guide you through how to convert text data into transactional form and mine frequent patterns from it.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In our scenario, what we need is to mine patterns that can be representative to **each category**, in this way we will be able to differentiate each group of data more easily, for that we will need to first modify our document-term matrix to be able to work for each category, for this we will do the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.feature_extraction.text import CountVectorizer\n",
+ "\n",
+ "#Create separate DataFrames for each category\n",
+ "categories = X['category_name'].unique() # Get unique category labels\n",
+ "category_dfs = {} # Dictionary to store DataFrames for each category\n",
+ "\n",
+ "for category in categories:\n",
+ " # Filter the original DataFrame by category\n",
+ " category_dfs[category] = X[X['category_name'] == category].copy()\n",
+ "\n",
+ "# Function to create term-document frequency DataFrame for each category\n",
+ "def create_term_document_df(df):\n",
+ " count_vect = CountVectorizer() # Initialize the CountVectorizer\n",
+ " X_counts = count_vect.fit_transform(df['text']) # Transform the text data into word counts\n",
+ " \n",
+ " # Get the unique words (vocabulary) from the vectorizer\n",
+ " words = count_vect.get_feature_names_out()\n",
+ " \n",
+ " # Create a DataFrame where rows are documents and columns are words\n",
+ " term_document_df = pd.DataFrame(X_counts.toarray(), columns=words)\n",
+ " \n",
+ " return term_document_df\n",
+ "\n",
+ "# Create term-document frequency DataFrames for each category\n",
+ "filt_term_document_dfs = {} # Dictionary to store term-document DataFrames for each category\n",
+ "\n",
+ "for category in categories:\n",
+ " filt_term_document_dfs[category] = create_term_document_df(category_dfs[category])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Filtered Term-Document Frequency DataFrame for Category comp.graphics:\n"
+ ]
+ },
+ {
+ "data": {
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+ },
+ "execution_count": 101,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Display the filtered DataFrame for one of the categories, feel free to change the number in the vector\n",
+ "category_number=0 #You can change it from 0 to 3\n",
+ "print(f\"Filtered Term-Document Frequency DataFrame for Category {categories[category_number]}:\")\n",
+ "filt_term_document_dfs[categories[category_number]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can see the number of unique words per category based on the column number in the new dataframe, feel free to **explore the changes of each category changing the vector number at the end**."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the past sections we saw the behaviour of each word frequency in the documents, but we still want to generalize a little bit more so we can observe and determine the data that we are going to use to mine the patterns. For this we will group the terms in bins and we are going to plot their frequency. Again, feel free to change the category number to explore the different results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ " # Sum over all documents to get total frequency for each word\n",
+ "category_number=0 #You can change it from 0 to 3\n",
+ "word_counts = filt_term_document_dfs[categories[category_number]].sum(axis=0).to_numpy()\n",
+ " \n",
+ "# Visualize the frequency distribution\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.hist(word_counts, bins=5000, color='blue', edgecolor='black')\n",
+ "plt.title(f'Term Frequency Distribution for Category {categories[category_number]}')\n",
+ "plt.xlabel('Frequency')\n",
+ "plt.ylabel('Number of Terms')\n",
+ "plt.xlim(1, 200)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From this graph, we can see that most of the words appear very infrequently across the entire dataset, while a small number of words appear quite often. When we're trying to find patterns in text data, we focus on combinations of words that are most helpful for classifying the documents. However, very rare words or extremely common words (like stopwords: 'the,' 'in,' 'a,' 'of,' etc.) don’t usually give us much useful information. To improve our results, we can filter out these words. Specifically, we'll remove the **bottom 1%** of the least frequent words and the **top 5%** of the most frequent ones. This helps us focus on words that might reveal more valuable patterns.\n",
+ "\n",
+ "In this case, the choice of filtering the top 5% and bottom 1% is **arbitrary**, but in other applications, domain knowledge might guide us to filter words differently, depending on the type of text classification we're working on.\n",
+ "\n",
+ "Let us look first at the words that we will be filtering based on the set percentage threshold."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Category: comp.graphics\n",
+ "Number of terms in top 5%: 687\n",
+ "Filtered terms: ['the', 'to', 'of', 'and', 'is', 'in', 'for', 'it', 'from', 'you', 'edu', 'that', 'on', 'this', 'or', 'be', 'with', 'have', 'lines', 'can', 'subject', 'are', 'graphics', 'if', 'organization', 'image', 'as', 'not', 'but', 'at', 'there', 'com', 'an', 'any', 'will', 'by', 'university', 're', 'about', 'some', 'posting', 'file', 'do', 'would', 'all', 'host', 'jpeg', 'what', 'so', 'has', 'nntp', 'files', 'which', 'one', 'also', 'me', 'use', 'software', 'was', 'images', 'my', 'writes', 'other', 'article', 'out', 'data', 'program', 'know', 'like', 'version', 'more', 'color', 'ftp', 'your', 'get', 'computer', 'don', '3d', 'does', 'no', 'mail', 'format', 'they', 'available', 'need', 'we', 'ca', 'thanks', 'just', 'bit', 'gif', 'how', 'help', 'am', 'please', 'package', 'pub', 'anyone', 'very', 'information', 'using', 'code', 'than', 'line', 'system', 'find', 'only', 'time', 'where', '24', 'windows', 'good', 'cs', 'uk', 'display', 'points', 'then', 'up', 'ac', '___', 'these', 'etc', 'when', 'see', 've', 'think', 'video', 'could', 'reply', 'new', 'mac', 'looking', 'info', 'systems', 'should', 'email', 'its', 'them', 'free', 'distribution', 'been', 'want', 'used', 'world', 'much', 'point', 'send', 'into', 'ibm', 'comp', 'polygon', 'well', 'sgi', 'may', 'two', 'tiff', 'number', 'many', 'keywords', 'gov', 'work', 'such', 'problem', 'screen', 'fax', 'read', 'pc', 'library', 'programs', 'ray', 'unix', 'support', 'center', 'au', 'now', 'de', 'research', 'processing', 'their', '16', 'directory', 'way', 'same', 'source', 'll', 'hi', 'group', 'dos', 'under', 'mode', 'based', 'address', 'vga', 'algorithm', 'animation', 'formats', 'those', 'sun', '256', 'here', 'cc', 'user', 'inc', 'even', '10', 'set', 'amiga', 'look', 'card', 'fast', 'he', 'ch', 'most', 'write', 'run', 'postscript', 'since', 'quality', 'better', 'book', '128', 'contact', 'another', 'news', 'lot', 'different', 'few', 'nasa', 'who', 'over', 'cview', 'real', 'hardware', 'first', 'convert', 'colors', 'without', 'both', 'full', 'were', 'objects', '20', 'programming', 'surface', 'stuff', 'analysis', 'called', 'had', 'washington', 'space', 'robert', 'got', 'net', 'standard', 'science', 'something', 'site', 'vesa', 'post', 'quicktime', '15', 'make', 'too', 'server', 'phone', 'question', 'include', 'internet', 'original', 'anybody', 'people', 'sphere', 'able', '42', 'virtual', 'advance', 'interested', 'either', 'via', 'because', 'between', 'viewer', 'technology', 'nl', 'co', 'current', 'pov', 'archive', 'david', '11', 'xv', 'say', 'each', 'ms', 'try', 'last', 'tar', 'anything', 'our', 'found', 'wrote', 'newsreader', 'tin', 'uucp', 'name', 'umich', '17', 'high', 'sure', 'siggraph', 'works', 'den', 'shareware', 'best', 'view', 'appreciated', 'must', 'cd', 'per', 'else', 'sites', 'faq', 'interface', 'type', 'access', 'non', 'running', '12', 'check', 'mark', '30', 'still', 'p2', 'list', 'note', 'doesn', 'visualization', 'drawing', 'around', 'pixel', '1993', 'mil', 'above', 'users', 'probably', 'steve', 'simple', '__', 'imagine', 'p3', 'tools', 'written', 'least', 'take', 'back', 'output', 'following', 'zip', 'before', 'give', 'object', 'nice', 'go', 'memory', 'routines', 'dept', 'driver', '3do', 'off', '13', 'through', '21', 'however', 'might', 'change', 'needed', 'things', 'rayshade', 'little', 'machine', 'let', 'newsgroup', 'conversion', 'possible', 'author', 'fi', 'text', 'disk', 'radius', 'reality', 'packages', 'bits', 'p1', 'actually', 'institute', 'digital', 'interactive', 'michael', 'picture', 'several', 'end', 'public', 'reference', '14', 'years', 'call', 'ed', 'navy', 'us', 'usa', 'technical', 'thing', 'australia', 'archie', 'bbs', 'john', 'reading', 'routine', 'why', 'message', 'right', 'product', 'project', 'drivers', '32', 'engin', 'summary', 'dec', 'size', 'applications', 'input', 'case', 'problems', 'kind', 'national', 'compression', 'create', 'frame', 'being', 'state', 'hp', 'far', 'split', 'level', 'including', 'sources', '18', 'while', 'form', 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'nfotis', 'wanted', 'volume', 'easy', 'bad', 'example', 'documentation', 'college', 'yet', 'linux', 'scientific', 'peter', 'board', 'writing', 'circle', 'complete', 'fine', 'tracing', 'said', 'faster', 'supports', 'int', 'domain', 'menu', 'gl', 'map', 'department', 'made', 'art', 'general', 'otis', 'st', 'greatly', 'function', 'listing', 'sean', 'rpi', 'usenet', 'apple', 'second', 'tu', 'jfif', 'cis', 'manipulation', 'uses', 'thomas', 'means', 'questions', 'groups', 'performance', 'aspects', 'his', 'understand', 'craig', 'value', 'ricardo', 'save', 'scale', 'values', 'photoshop', 'buy', 'details', 'lab', 'process', 'trying', 'edges', 'utilities', 'ab', 'allows', 'developed', 'programmer', 'come', 'start', 'workstations', 'black', 'diablo', 'containing', 'result', 'mit', 'network', 'versions', 'viewing', 'macintosh', 'machines', 'printer', 'known', 'viewers', 'heard', 'bitmap', 'seen', 'year', 'cards', 'never', 'row', 'thank', 'op_cols', 'related', 'silicon', 'univ', '____', 'similar', '50', 'day', 'request', 'hidden', 'making', 'demo', '130', 'op_rows', 'james', 'speedstar', 'utexas', 'mirror', 'california', 'search', 'thus', 'mpeg', 'test', 'posted', 'raster', 'converter', 'getting', 'pcx', 'paper', 'studio', 'hello', 'maybe', 'setting', 'place', 'distributed', 'multi', 'martin', '27', 'ii', 'further']\n"
+ ]
+ }
+ ],
+ "source": [
+ "category_number=0 #You can change it from 0 to 3\n",
+ "word_counts = filt_term_document_dfs[categories[category_number]].sum(axis=0).to_numpy()\n",
+ "\n",
+ "# Sort the term frequencies in descending order\n",
+ "sorted_indices = np.argsort(word_counts)[::-1] # Get indices of sorted frequencies\n",
+ "sorted_counts = np.sort(word_counts)[::-1] # Sort frequencies in descending order\n",
+ "\n",
+ "# Calculate the index corresponding to the top 5% most frequent terms\n",
+ "total_terms = len(sorted_counts)\n",
+ "top_5_percent_index = int(0.05 * total_terms)\n",
+ "\n",
+ "# Get the indices of the top 5% most frequent terms\n",
+ "top_5_percent_indices = sorted_indices[:top_5_percent_index]\n",
+ "\n",
+ "# Filter terms that belong to the top 5% based on their rank\n",
+ "filtered_words = [filt_term_document_dfs[categories[category_number]].iloc[:, i].name for i in top_5_percent_indices]\n",
+ "\n",
+ "print(f\"Category: {categories[category_number]}\")\n",
+ "print(f\"Number of terms in top 5%: {top_5_percent_index}\")\n",
+ "print(f\"Filtered terms: {filtered_words}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we can explore the frequencies of the **top 5%** words:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([4537, 2775, 2470, ..., 1, 1, 1])"
+ ]
+ },
+ "execution_count": 104,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_counts #We can see the frequencies sorted in a descending order"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([12266, 12390, 9021, ..., 10, 11, 12])"
+ ]
+ },
+ "execution_count": 105,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_indices #This are the indices corresponding to the words after being sorted in a descending order"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.int64(4537)"
+ ]
+ },
+ "execution_count": 106,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "filt_term_document_dfs[categories[category_number]].loc[:,'the'].sum(axis=0) #Here we can sum up the column corresponding to the top 5% words, we just specify which one first."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Category: comp.graphics\n",
+ "Number of terms in bottom 1%: 137\n",
+ "Filtered terms: ['0039', '0038', '0028', '00196', '001200201pixel', 'zillions', 'ziedman', 'zero_', '0199', '01890', '01854', '01852', '01821', '0179', '01752', '0150', 'zen', 'zeit', '02139', '020751', '020637', '020', 'zool', 'zoo', 'zirkel', 'zipped', '013846', 'zvi', '011605', '0100', 'ªl', '0049', '051201', '04g', '044946', '0410', '040819', '040493161915', '040286', 'zcat', 'zc', 'zbuffering', 'zaphod', 'zamenhof', 'z_c', 'yyqi', 'yy', 'yutani', '07653', '07410', '0729', '0704', '068', '0649', '060493161931', 'yr', 'youve', 'yourdon', 'yost', '0600', '054600', '05446', '05402', '053250', '0901', '0900', '08934', '08786', 'yktvmh', 'yk4', 'yingyong', 'yields', 'yielded', 'yield', 'yhe', 'yevtechenko', 'yevgeny', '083731', '080719', '077', '1001', '0xc018', '0xc010', '0x3d4', '0x1f', '0x00', '0hb', '0e9', '0b', '0a', '0______________________________________________________________________0', '0_', '0943', '0926', '092051', '0903', '101h', '101747', '1015', '1010', '100megs', '100lez', '100c', 'yc', 'yayayay', 'yasp', 'yarra', 'yar', '10038', '10036', '1003', '10012', '104107', '1039', '1030', '1027', '1024x768x65000', '1024x768x24', '1024x728', 'yabiku', 'yabi', 'y_c', 'y14', 'xyz21', 'xyvision', '1024x512', '1024x1024', '1023', '110mbytes', '10mhz', '10k', '10h', '10fps', '10665', '1066', 'xwindow', 'xwdtopnm']\n"
+ ]
+ }
+ ],
+ "source": [
+ "category_number=0 #You can change it from 0 to 3\n",
+ "word_counts = filt_term_document_dfs[categories[category_number]].sum(axis=0).to_numpy()\n",
+ "\n",
+ "# Sort the term frequencies in ascending order and get sorted indices\n",
+ "sorted_indices = np.argsort(word_counts) # Get indices of sorted frequencies\n",
+ "sorted_counts = word_counts[sorted_indices] # Sort frequencies\n",
+ "\n",
+ "# Calculate the index corresponding to the bottom 1% least frequent terms\n",
+ "total_terms = len(sorted_counts)\n",
+ "bottom_1_percent_index = int(0.01 * total_terms)\n",
+ "\n",
+ "# Get the indices of the bottom 1% least frequent terms\n",
+ "bottom_1_percent_indices = sorted_indices[:bottom_1_percent_index]\n",
+ "\n",
+ "# Filter terms that belong to the bottom 1% based on their rank\n",
+ "filtered_words = [filt_term_document_dfs[categories[category_number]].iloc[:, i].name for i in bottom_1_percent_indices]\n",
+ "\n",
+ "print(f\"Category: {categories[category_number]}\")\n",
+ "print(f\"Number of terms in bottom 1%: {bottom_1_percent_index}\")\n",
+ "print(f\"Filtered terms: {filtered_words}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here we can explore the frequencies of the **bottom 1%** words:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 1, 1, ..., 2470, 2775, 4537])"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_counts #We can see the frequencies sorted in an ascending order"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 12, 11, 10, ..., 9021, 12390, 12266])"
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_indices #This are the indices corresponding to the words after being sorted in an ascending order"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "np.int64(1)"
+ ]
+ },
+ "execution_count": 110,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "filt_term_document_dfs[categories[category_number]].loc[:,'l14h11'].sum(axis=0) #Here we can sum up the column corresponding to the bottom 1% words, we just specify which one first."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Well done, now that we have seen what type of words are inside the thresholds we set, then we can procede to **filter them out of the dataframe**. If you want to experiment after you complete the lab, you can return to try different percentages to filter, or not filter at all to do all the subsequent tasks for the pattern minings, and see if there is a significant change in the result."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "For category comp.graphics we filter the following words:\n",
+ "Bottom 1.0% words: \n",
+ "0039 1\n",
+ "0038 1\n",
+ "0028 1\n",
+ "00196 1\n",
+ "001200201pixel 1\n",
+ " ..\n",
+ "10fps 1\n",
+ "10665 1\n",
+ "1066 1\n",
+ "xwindow 1\n",
+ "xwdtopnm 1\n",
+ "Length: 137, dtype: int64\n",
+ "Top 5.0% words: \n",
+ "further 27\n",
+ "ii 27\n",
+ "27 27\n",
+ "martin 27\n",
+ "multi 27\n",
+ " ... \n",
+ "is 1751\n",
+ "and 2382\n",
+ "of 2470\n",
+ "to 2775\n",
+ "the 4537\n",
+ "Length: 687, dtype: int64\n",
+ "\n",
+ "For category soc.religion.christian we filter the following words:\n",
+ "Bottom 1.0% words: \n",
+ "0706 1\n",
+ "zeitschrift 1\n",
+ "zeal 1\n",
+ "zara 1\n",
+ "yves 1\n",
+ " ..\n",
+ "prevented__ 1\n",
+ "prevented 1\n",
+ "prevelant 1\n",
+ "preeminence 1\n",
+ "predicts 1\n",
+ "Length: 138, dtype: int64\n",
+ "Top 5.0% words: \n",
+ "pagan 36\n",
+ "mmalt 36\n",
+ "reject 36\n",
+ "friend 36\n",
+ "parents 37\n",
+ " ... \n",
+ "that 4393\n",
+ "and 4409\n",
+ "to 6113\n",
+ "of 6377\n",
+ "the 11200\n",
+ "Length: 693, dtype: int64\n",
+ "\n",
+ "For category sci.med we filter the following words:\n",
+ "Bottom 1.0% words: \n",
+ "zz 1\n",
+ "zubkoff 1\n",
+ "zooid 1\n",
+ "zone 1\n",
+ "zonal 1\n",
+ " ..\n",
+ "1263 1\n",
+ "125920 1\n",
+ "125545 1\n",
+ "1356 1\n",
+ "134848 1\n",
+ "Length: 162, dtype: int64\n",
+ "Top 5.0% words: \n",
+ "genetic 29\n",
+ "nye 29\n",
+ "haven 29\n",
+ "experimental 29\n",
+ "appreciated 29\n",
+ " ... \n",
+ "in 2993\n",
+ "and 3450\n",
+ "to 4085\n",
+ "of 4560\n",
+ "the 6854\n",
+ "Length: 812, dtype: int64\n",
+ "\n",
+ "For category alt.atheism we filter the following words:\n",
+ "Bottom 1.0% words: \n",
+ "050750 1\n",
+ "0511 1\n",
+ "051246 1\n",
+ "06 1\n",
+ "011255 1\n",
+ " ..\n",
+ "128 1\n",
+ "yohan 1\n",
+ "yoke 1\n",
+ "yorktown 1\n",
+ "youngster 1\n",
+ "Length: 119, dtype: int64\n",
+ "Top 5.0% words: \n",
+ "gmt 36\n",
+ "place 37\n",
+ "days 37\n",
+ "jewish 37\n",
+ "beyond 37\n",
+ " ... \n",
+ "that 3158\n",
+ "is 3530\n",
+ "to 4249\n",
+ "of 4253\n",
+ "the 7234\n",
+ "Length: 598, dtype: int64\n",
+ "Filtered Term-Document Frequency DataFrame for Category comp.graphics:\n"
+ ]
+ },
+ {
+ "data": {
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+ "text/plain": [
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+ ]
+ },
+ "execution_count": 111,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "category_number=0 #You can change it from 0 to 3\n",
+ "\n",
+ "# Filter the bottom 1% and top 5% words based on their sum across all documents\n",
+ "def filter_top_bottom_words_by_sum(term_document_df, top_percent=0.05, bottom_percent=0.01):\n",
+ " # Calculate the sum of each word across all documents\n",
+ " word_sums = term_document_df.sum(axis=0)\n",
+ " \n",
+ " # Sort the words by their total sum\n",
+ " sorted_words = word_sums.sort_values()\n",
+ " \n",
+ " # Calculate the number of words to remove\n",
+ " total_words = len(sorted_words)\n",
+ " top_n = int(top_percent * total_words)\n",
+ " bottom_n = int(bottom_percent * total_words)\n",
+ " \n",
+ " # Get the words to remove from the top 5% and bottom 1%\n",
+ " words_to_remove = pd.concat([sorted_words.head(bottom_n), sorted_words.tail(top_n)]).index\n",
+ " print(f'Bottom {bottom_percent*100}% words: \\n{sorted_words.head(bottom_n)}') #Here we print which words correspond to the bottom percentage we filter\n",
+ " print(f'Top {top_percent*100}% words: \\n{sorted_words.tail(top_n)}') #Here we print which words correspond to the top percentage we filter\n",
+ " # Return the DataFrame without the filtered words\n",
+ " return term_document_df.drop(columns=words_to_remove)\n",
+ "\n",
+ "# Apply the filtering function to each category\n",
+ "term_document_dfs = {}\n",
+ "\n",
+ "for category in categories:\n",
+ " print(f'\\nFor category {category} we filter the following words:')\n",
+ " term_document_dfs[category] = filter_top_bottom_words_by_sum(filt_term_document_dfs[category])\n",
+ "\n",
+ "# Example: Display the filtered DataFrame for one of the categories\n",
+ "print(f\"Filtered Term-Document Frequency DataFrame for Category {categories[category_number]}:\")\n",
+ "term_document_dfs[categories[category_number]]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 16 (take home):** \n",
+ "Review the words that were filtered in each category and comment about the differences and similarities that you can see."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Great! Now that our document-term frequency dataframe is ready, we can proceed with the frequent pattern mining process. To do this, we first need to convert our dataframe into a transactional database that the PAMI library can work with. We will generate a CSV file for each category to create this database.\n",
+ "\n",
+ "A key step in this process is defining the threshold that determines when a value in the data is considered a transaction. As we observed in the previous cell, there are **many zeros** in our dataframe, which indicate that certain words do not appear in specific documents. With this in mind, we'll set the transactional threshold to be **greater than or equal to 1**. This means that for each document/transaction, we will include all the words that occur at least once (after filtering), ensuring that only relevant words are included in the pattern mining process. For your reference you can also check the following real world example that the PAMI library provides to review how they chose the threshold to generate the transactional data: [Air Pollution Analytics - Japan](https://colab.research.google.com/github/udayLab/PAMI/blob/main/notebooks/airPollutionAnalytics.ipynb). \n",
+ "\n",
+ "#### The next part of the code will take a couple of minutes to execute, for simplicity I already shared the resulting files from it, to continue onwards\n",
+ "\n",
+ "#### Given that some students have been experiencing some errors with recent newer versions of PAMI after Oct 11, where they changed some directories of these functions, you can try to run the following block of code or uncomment the lines indicated inside to run the older version of the functions:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from PAMI.extras.convert.DF2DB import DF2DB \n",
+ "\n",
+ "# Loop through the dictionary of term-document DataFrames\n",
+ "for category in term_document_dfs:\n",
+ " # Replace dots with underscores in the category name to avoid errors in the file creation\n",
+ " category_safe = category.replace('.', '_')\n",
+ " \n",
+ " # Create the DenseFormatDF object and convert to a transactional database\n",
+ " obj = DF2DB(term_document_dfs[category]) \n",
+ " \n",
+ " obj.convert2TransactionalDatabase(f'td_freq_db_{category_safe}.csv', '>=', 1)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you encounter errors when running the above codes, try commenting out the above codes and running the code in this box. This error may comes from the update of the source code from the PAMI library. \n",
+ "\n",
+ "```python\n",
+ "from PAMI.extras.DF2DB import DenseFormatDF as db \n",
+ "\n",
+ "# Loop through the dictionary of term-document DataFrames\n",
+ "for category in term_document_dfs:\n",
+ " # Replace dots with underscores in the category name to avoid errors in the file creation\n",
+ " category_safe = category.replace('.', '_')\n",
+ " \n",
+ " # Create the DenseFormatDF object and convert to a transactional database\n",
+ " obj = db.DenseFormatDF(term_document_dfs[category]) \n",
+ " \n",
+ " obj.convert2TransactionalDatabase(f'td_freq_db_{category_safe}.csv', '>=', 1)\n",
+ "```\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let us look into the stats of our newly created transactional databases, we will observe the following:\n",
+ "\n",
+ "- **Database Size (Total Number of Transactions)**: Total count of transactions in the dataset.\n",
+ "\n",
+ "- **Number of Items**: Total count of unique items available across all transactions.\n",
+ "\n",
+ "- **Minimum Transaction Size**: Smallest number of items in any transaction, indicating the simplest transaction.\n",
+ "\n",
+ "- **Average Transaction Size**: Mean number of items per transaction, showing the typical complexity.\n",
+ "\n",
+ "- **Maximum Transaction Size**: Largest number of items in a transaction, representing the most complex scenario.\n",
+ "\n",
+ "- **Standard Deviation of Transaction Size**: Measures variability in transaction sizes; higher values indicate greater diversity.\n",
+ "\n",
+ "- **Variance in Transaction Sizes**: Square of the standard deviation, providing a broader view of transaction size spread.\n",
+ "\n",
+ "- **Sparsity**: Indicates the proportion of possible item combinations that do not occur, with values close to 1 showing high levels of missing combinations.\n",
+ "\n",
+ "With regards to the graphs we will have: \n",
+ "\n",
+ "- **Item Frequency Distribution**\n",
+ " - Y-axis (Frequency): Number of transactions an item appears in.\n",
+ " - X-axis (Number of Items): Items ranked by frequency.\n",
+ "\n",
+ "- **Transaction Length Distribution**\n",
+ " - Y-axis (Frequency): Occurrence of transaction lengths.\n",
+ " - X-axis (Transaction Length): Number of items per transaction."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you encounter errors when running the subsequent codes due to UTF-8 encoding, try running the codes in this box first\n",
+ "\n",
+ "```python\n",
+ "import builtins\n",
+ "\n",
+ "_orig_open = open\n",
+ "\n",
+ "def safe_open(*args, **kwargs):\n",
+ " if len(args) > 0 and isinstance(args[0], str) and args[0].endswith('.csv'):\n",
+ " kwargs['encoding'] = 'latin-1' # Force Latin-1\n",
+ " kwargs['errors'] = 'ignore' # Ignore bad characters\n",
+ " return _orig_open(*args, **kwargs)\n",
+ "\n",
+ "\n",
+ "builtins.open = safe_open\n",
+ "```\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Database size (total no of transactions) : 584\n",
+ "Number of items : 12932\n",
+ "Minimum Transaction Size : 4\n",
+ "Average Transaction Size : 56.41609589041096\n",
+ "Maximum Transaction Size : 2061\n",
+ "Standard Deviation Transaction Size : 152.701494376309\n",
+ "Variance in Transaction Sizes : 23357.742519208627\n",
+ "Sparsity : 0.9956374809858946\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from PAMI.extras.dbStats import TransactionalDatabase as tds\n",
+ "obj = tds.TransactionalDatabase('td_freq_db_comp_graphics.csv')\n",
+ "obj.run()\n",
+ "obj.printStats()\n",
+ "obj.plotGraphs()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you see that in the graph the numbers are sticked together (unlike what you see in the tutorial video) it is normal. This may be due to the update of the PAMI library. Our TA have reported the issue to the PAMI developers, if there are any update that fixes this issue we will let you know of it during the period of time of this lab."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Database size (total no of transactions) : 594\n",
+ "Number of items : 15283\n",
+ "Minimum Transaction Size : 9\n",
+ "Average Transaction Size : 74.51515151515152\n",
+ "Maximum Transaction Size : 1069\n",
+ "Standard Deviation Transaction Size : 115.25322993083026\n",
+ "Variance in Transaction Sizes : 13305.707189943278\n",
+ "Sparsity : 0.9951243112271706\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from PAMI.extras.dbStats import TransactionalDatabase as tds\n",
+ "obj = tds.TransactionalDatabase('td_freq_db_alt_atheism.csv')\n",
+ "obj.run()\n",
+ "obj.printStats()\n",
+ "obj.plotGraphs()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we have reviewed the stats of our databases, there are some things to notice from them, the total number of transactions refer to the amount of documents per category, the number of items refer to the amount of unique words encountered in each category, the transaction size refers to the amount of words per document that it can be found, and we can see that our databases are very sparse, this is the result of having many zeros in the first place when making the document-term matrix. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Why are these stats important? It is because we are going to use the FPGrowth algorithm from PAMI, and for that we need to determine the *minimum support* (frequency) that our algorithm will use to mine for patterns in our transactions. \n",
+ "\n",
+ "When we set a minimum support threshold (minSup) for finding frequent patterns, we are looking for a good balance. We want to capture important patterns that show real connections in the data, but we also want to avoid too many unimportant patterns. For this dataset, we've chosen a minSup of 9. We have done this after observing the following:\n",
+ "\n",
+ "- **Item Frequency**: The first graph shows that most items don't appear very often in transactions. There's a sharp drop in how frequently items appear, which means our data has many items that aren't used much.\n",
+ "\n",
+ "- **Transaction Length**: The second graph shows that most transactions involve a small number of items. The most common transaction sizes are small, which matches our finding that the dataset does not group many items together often.\n",
+ "\n",
+ "By setting minSup at 9, we focus on combinations of items that show up in these smaller, more common transactions. This level is low enough to include items that show up more than just a few times, but it's high enough to leave out patterns that don't appear often enough to be meaningful. This helps us keep our results clear and makes sure the patterns we find are useful and represent what's really happening in the dataset. \n",
+ "\n",
+ "**This value works for all categories**. Now let's get into mining those patterns. For more information you can visit the FPGrowth example in PAMI for transactional data: [FPGrowth Example](https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/basic/FPGrowth.ipynb#scrollTo=pLV84IYcDHe3)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Frequent patterns were generated successfully using frequentPatternGrowth algorithm\n",
+ "Total No of patterns: 9994\n",
+ "Runtime: 0.2876615524291992\n"
+ ]
+ }
+ ],
+ "source": [
+ "from PAMI.frequentPattern.basic import FPGrowth as alg\n",
+ "minSup=9\n",
+ "obj1 = alg.FPGrowth(iFile='td_freq_db_sci_med.csv', minSup=minSup)\n",
+ "obj1.mine()\n",
+ "frequentPatternsDF_sci_med= obj1.getPatternsAsDataFrame()\n",
+ "print('Total No of patterns: ' + str(len(frequentPatternsDF_sci_med))) #print the total number of patterns\n",
+ "print('Runtime: ' + str(obj1.getRuntime())) #measure the runtime"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ " Patterns Support\n",
+ "0 latest 9\n",
+ "1 san 9\n",
+ "2 seven 9\n",
+ "3 schools 9\n",
+ "4 chose 9\n",
+ "... ... ...\n",
+ "11208 34 33\n",
+ "11209 institute 33\n",
+ "11210 ways 33\n",
+ "11211 oh 34\n",
+ "11212 send 34\n",
+ "\n",
+ "[11213 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 126,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "obj4.save('freq_patterns_soc_religion_minSup9.txt') #save the patterns\n",
+ "frequentPatternsDF_soc_religion_christian"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now that we've extracted the transactional patterns from our databases, the next step is to integrate them effectively with our initial data for further analysis. One effective method is to identify and use only the unique patterns that are specific to each category. This involves filtering out any patterns that are common across multiple categories.\n",
+ "\n",
+ "The reason for focusing on **unique patterns** is that they can **significantly improve the classification process**. When a document contains these distinctive patterns, it provides clear, category-specific signals that help our model more accurately determine the document's category. This approach ensures that the patterns we use enhance the model's ability to distinguish between different types of content."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Patterns Support\n",
+ "19908 gov 33\n",
+ "19909 institute 33\n",
+ "57110 snm6394 32\n",
+ "19906 form 31\n",
+ "19907 claims 31\n",
+ "... ... ...\n",
+ "14 newsreader\\tpl8\\ttin 9\n",
+ "13 pl8\\ttin 9\n",
+ "12 pl8\\tnewsreader 9\n",
+ "11 holland 9\n",
+ "10 deeply 9\n",
+ "\n",
+ "[57111 rows x 2 columns]\n",
+ "Number of patterns discarded: 2298\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "#We group together all of the dataframes related to our found patterns\n",
+ "dfs = [frequentPatternsDF_sci_med, frequentPatternsDF_soc_religion_christian, frequentPatternsDF_comp_graphics, frequentPatternsDF_alt_atheism]\n",
+ "\n",
+ "\n",
+ "# Identify patterns that appear in more than one category\n",
+ "# Count how many times each pattern appears across all dataframes\n",
+ "pattern_counts = {}\n",
+ "for df in dfs:\n",
+ " for pattern in df['Patterns']:\n",
+ " if pattern not in pattern_counts:\n",
+ " pattern_counts[pattern] = 1\n",
+ " else:\n",
+ " pattern_counts[pattern] += 1\n",
+ "\n",
+ "# Filter out patterns that appear in more than one dataframe\n",
+ "unique_patterns = {pattern for pattern, count in pattern_counts.items() if count == 1}\n",
+ "# Calculate the total number of patterns across all categories\n",
+ "total_patterns_count = sum(len(df) for df in dfs)\n",
+ "# Calculate how many patterns were discarded\n",
+ "discarded_patterns_count = total_patterns_count - len(unique_patterns)\n",
+ "\n",
+ "# For each category, filter the patterns to keep only the unique ones\n",
+ "filtered_dfs = []\n",
+ "for df in dfs:\n",
+ " filtered_df = df[df['Patterns'].isin(unique_patterns)]\n",
+ " filtered_dfs.append(filtered_df)\n",
+ "\n",
+ "# Merge the filtered dataframes into a final dataframe\n",
+ "final_pattern_df = pd.concat(filtered_dfs, ignore_index=True)\n",
+ "\n",
+ "# Sort by support\n",
+ "final_pattern_df = final_pattern_df.sort_values(by='Support', ascending=False)\n",
+ "\n",
+ "# Display the final result\n",
+ "print(final_pattern_df)\n",
+ "# Print the number of discarded patterns\n",
+ "print(f\"Number of patterns discarded: {discarded_patterns_count}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We observed a significant number of patterns that were common across different categories, which is why we chose to discard them. The next step is to integrate these now category-specific patterns into our data. How will we do this? By converting the patterns into binary data within the columns of our document-term matrix. Specifically, we will check each document for the presence of each pattern. If a pattern is found in the document, we'll mark it with a '1'; if it's not present, we'll mark it with a '0'. This binary encoding allows us to effectively augment our data, enhancing its utility for subsequent classification tasks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "
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+ ],
+ "text/plain": [
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+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "from sklearn.feature_extraction.text import CountVectorizer\n",
+ "\n",
+ "# Convert 'text' column into term-document matrix using CountVectorizer\n",
+ "count_vect = CountVectorizer()\n",
+ "X_tdm = count_vect.fit_transform(X['text']) # X['text'] contains your text data\n",
+ "terms = count_vect.get_feature_names_out() # Original terms in the vocabulary\n",
+ "\n",
+ "# Tokenize the sentences into sets of unique words\n",
+ "X['tokenized_text'] = X['text'].str.split().apply(set)\n",
+ "\n",
+ "# Initialize the pattern matrix\n",
+ "pattern_matrix = pd.DataFrame(0, index=X.index, columns=final_pattern_df['Patterns'])\n",
+ "\n",
+ "# Iterate over each pattern and check if all words in the pattern are present in the tokenized sentence\n",
+ "for pattern in final_pattern_df['Patterns']:\n",
+ " pattern_words = set(pattern.split()) # Tokenize pattern into words\n",
+ " pattern_matrix[pattern] = X['tokenized_text'].apply(lambda x: 1 if pattern_words.issubset(x) else 0)\n",
+ "\n",
+ "# Convert the term-document matrix to a DataFrame for easy merging\n",
+ "tdm_df = pd.DataFrame(X_tdm.toarray(), columns=terms, index=X.index)\n",
+ "\n",
+ "# Concatenate the original TDM and the pattern matrix to augment the features\n",
+ "augmented_df = pd.concat([tdm_df, pattern_matrix], axis=1)\n",
+ "\n",
+ "augmented_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 17 (take home):** \n",
+ "Implement the FAE Top-K and MaxFPGrowth algorithms from the PAMI library to analyze the 'comp.graphics' category in our processed database. **Only implement the mining part of the algorithm and display the resulting patterns**, like we did with the FPGrowth algorithm after creating the new databases. For the FAE Top-K, run trials with k values of 500, 1000, and 1500, recording the runtime for each. For MaxFPGrowth, test minimum support thresholds of 3, 6, and 9, noting the runtime for these settings as well. Compare the patterns these algorithms extract with those from the previously implemented FPGrowth algorithm. Document your findings, focusing on differences and similarities in the outputs and performance. For this you can find the following google collabs for reference provided by their github repository here: [FAE Top-K](https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/topk/FAE.ipynb) and [MaxFPGrowth](https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/maximal/MaxFPGrowth.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer Here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.5 Dimensionality Reduction\n",
+ "Dimensionality reduction is a powerful technique for tackling the \"curse of dimensionality,\" which commonly arises due to data sparsity. This technique is not only beneficial for visualizing data more effectively but also simplifies the data by reducing the number of dimensions without losing significant information. For a deeper understanding, please refer to the additional notes provided.\n",
+ "\n",
+ "We will start with **Principal Component Analysis (PCA)**, which is focused on finding a projection that captures the largest amount of variation in the data. PCA is excellent for linear dimensionality reduction and works well when dealing with Gaussian distributed data. However, its effectiveness diminishes with non-linear data structures.\n",
+ "\n",
+ "Additionally, we will explore two advanced techniques suited for non-linear dimensionality reductions:\n",
+ "\n",
+ "- **t-Distributed Stochastic Neighbor Embedding (t-SNE)**:\n",
+ " - Pros:\n",
+ " - Effective at revealing local data structures at many scales.\n",
+ " - Great for identifying clusters in data.\n",
+ " - Cons:\n",
+ " - Computationally intensive, especially with large datasets.\n",
+ " - Sensitive to parameter settings and might require tuning (e.g., perplexity).\n",
+ " \n",
+ "- **Uniform Manifold Approximation and Projection (UMAP)**:\n",
+ " - Pros:\n",
+ " - Often faster than t-SNE and can handle larger datasets.\n",
+ " - Less sensitive to the choice of parameters compared to t-SNE.\n",
+ " - Preserves more of the global data structure while also revealing local structure.\n",
+ " - Cons:\n",
+ " - Results can still vary based on parameter settings and random seed.\n",
+ " - May require some experimentation to find the optimal settings for specific datasets.\n",
+ " \n",
+ "These methods will be applied to visualize our data more effectively, each offering unique strengths to mitigate the issue of sparsity and allowing us to observe underlying patterns in our dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[PCA Algorithm](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)\n",
+ "[t-SNE Algorithm](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)\n",
+ "[UMAP Algorithm](https://umap-learn.readthedocs.io/en/latest/basic_usage.html)\n",
+ "\n",
+ "**Input:** Raw term-vector matrix\n",
+ "\n",
+ "**Output:** Projections "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So, let's experiment with something interesting, from our previous work we have our data with only the document-term frequency data and also the one with both the document-term frequency and the pattern derived data, let's try to create a 2D plot after applying these algorithms to our dataframes and see what comes out."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Applying dimensionality reduction with only the document-term frequency data\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.manifold import TSNE\n",
+ "import umap\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#This might take a couple of minutes to execute\n",
+ "# Apply PCA, t-SNE, and UMAP to the data\n",
+ "X_pca_tdm = PCA(n_components=2).fit_transform(tdm_df.values)\n",
+ "X_tsne_tdm = TSNE(n_components=2).fit_transform(tdm_df.values)\n",
+ "X_umap_tdm = umap.UMAP(n_components=2).fit_transform(tdm_df.values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2257, 2)"
+ ]
+ },
+ "execution_count": 131,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_pca_tdm.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2257, 2)"
+ ]
+ },
+ "execution_count": 132,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_tsne_tdm.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2257, 2)"
+ ]
+ },
+ "execution_count": 133,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_umap_tdm.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the results in subplots\n",
+ "col = ['coral', 'blue', 'black', 'orange']\n",
+ "categories = X['category_name'].unique() \n",
+ "\n",
+ "fig, axes = plt.subplots(1, 3, figsize=(30, 10)) # Create 3 subplots for PCA, t-SNE, and UMAP\n",
+ "fig.suptitle('PCA, t-SNE, and UMAP Comparison')\n",
+ "\n",
+ "# Define a function to create a scatter plot for each method\n",
+ "def plot_scatter(ax, X_reduced, title):\n",
+ " for c, category in zip(col, categories):\n",
+ " xs = X_reduced[X['category_name'] == category].T[0]\n",
+ " ys = X_reduced[X['category_name'] == category].T[1]\n",
+ " ax.scatter(xs, ys, c=c, marker='o', label=category)\n",
+ " \n",
+ " ax.grid(color='gray', linestyle=':', linewidth=2, alpha=0.2)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel('X')\n",
+ " ax.set_ylabel('Y')\n",
+ " ax.legend(loc='upper right')\n",
+ "\n",
+ "# Step 4: Create scatter plots for PCA, t-SNE, and UMAP\n",
+ "plot_scatter(axes[0], X_pca_tdm, 'PCA')\n",
+ "plot_scatter(axes[1], X_tsne_tdm, 't-SNE')\n",
+ "plot_scatter(axes[2], X_umap_tdm, 'UMAP')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The plots generated by the above code may looks slightly different from those in the tutorial video. It is normal behavior due to some update of the PAMI library"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From the 2D PCA visualization above, we can see a slight \"hint of separation in the data\"; i.e., they might have some special grouping by category, but it is not immediately clear. In the t-SNE graph we observe a more scattered distribution, but still intermixing with all the categories. And with the UMAP graph, the limits for the data seem pretty well defined, two categories seem to have some points well differentiated from the other classes, but most of them remain intermixed. The algorithms were applied to the raw frequencies and this is considered a very naive approach as some words are not really unique to a document. Only categorizing by word frequency is considered a \"bag of words\" approach. Later on in the course you will learn about different approaches on how to create better features from the term-vector matrix, such as term-frequency inverse document frequency so-called TF-IDF."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's try in tandem with our pattern augmented data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#This might take a couple of minutes to execute\n",
+ "#Applying dimensionality reduction with both the document-term frequency data and the pattern derived data\n",
+ "# Apply PCA, t-SNE, and UMAP to the data\n",
+ "X_pca_aug = PCA(n_components=2).fit_transform(augmented_df.values)\n",
+ "X_tsne_aug = TSNE(n_components=2).fit_transform(augmented_df.values)\n",
+ "X_umap_aug = umap.UMAP(n_components=2).fit_transform(augmented_df.values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the results in subplots\n",
+ "col = ['coral', 'blue', 'black', 'orange']\n",
+ "categories = X['category_name'].unique() \n",
+ "\n",
+ "fig, axes = plt.subplots(1, 3, figsize=(30, 10)) # Create 3 subplots for PCA, t-SNE, and UMAP\n",
+ "fig.suptitle('PCA, t-SNE, and UMAP Comparison')\n",
+ "\n",
+ "# Define a function to create a scatter plot for each method\n",
+ "def plot_scatter(ax, X_reduced, title):\n",
+ " for c, category in zip(col, categories):\n",
+ " xs = X_reduced[X['category_name'] == category].T[0]\n",
+ " ys = X_reduced[X['category_name'] == category].T[1]\n",
+ " ax.scatter(xs, ys, c=c, marker='o', label=category)\n",
+ " \n",
+ " ax.grid(color='gray', linestyle=':', linewidth=2, alpha=0.2)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel('X')\n",
+ " ax.set_ylabel('Y')\n",
+ " ax.legend(loc='upper right')\n",
+ "\n",
+ "# Create scatter plots for PCA, t-SNE, and UMAP\n",
+ "plot_scatter(axes[0], X_pca_aug, 'PCA')\n",
+ "plot_scatter(axes[1], X_tsne_aug, 't-SNE')\n",
+ "plot_scatter(axes[2], X_umap_aug, 'UMAP')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The plots generated by the above code may looks slightly different from those in the tutorial video. It is normal behavior due to some update of the PAMI library"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that our PCA visualization hasn't changed much from the previous version. This is likely because the original document-term matrix still dominates what the algorithm captures, overshadowing the new binary pattern data we added.\n",
+ "\n",
+ "Looking at the t-SNE graph, it might seem different at first glance. However, upon closer inspection, it's almost the same but mirrored along the y-axis, with only slight changes in how the data points are placed. This similarity might be due to the stability of the t-SNE algorithm. Even small changes in the data can result in embeddings that look different but are structurally similar, indicating that the binary patterns may not have significantly altered the relationships among the data points in high-dimensional space.\n",
+ "\n",
+ "The UMAP visualization shows the most noticeable changes—it appears more compact. This compactness could be because UMAP uses a more complex distance metric, which might be making it easier to see differences between closer and further points. The binary patterns could also be helping to reduce noise within categories, resulting in clearer, more coherent groups. However, the categories still appear quite mixed together.\n",
+ "\n",
+ "Remember, just because you can't see clear groups in these visualizations doesn’t mean that a machine learning model won’t be able to classify the data correctly. These techniques are mainly used to help us see and understand complex data in a simpler two or three-dimensional space. However, they have their limits and might not show everything a computer model can find in the data. So, while these tools are great for getting a first look at your data, always use more methods and analyses to get the full picture."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> Exercise 18 (take home):\n",
+ "Please try to reduce the dimension to 3, and plot the result use 3-D plot. Use at least 3 different angle (camera position) to check your result and describe what you found.\n",
+ "\n",
+ "$Hint$: you can refer to Axes3D in the documentation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Answer Here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.6 Discretization and Binarization\n",
+ "In this section we are going to discuss a very important pre-preprocessing technique used to transform the data, specifically categorical values, into a format that satisfies certain criteria required by particular algorithms. Given our current original dataset, we would like to transform one of the attributes, `category_name`, into four binary attributes. In other words, we are taking the category name and replacing it with a `n` asymmetric binary attributes. The logic behind this transformation is discussed in detail in the recommended Data Mining text book (please refer to it on page 58). People from the machine learning community also refer to this transformation as one-hot encoding, but as you may become aware later in the course, these concepts are all the same, we just have different prefrence on how we refer to the concepts. Let us take a look at what we want to achieve in code. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn import preprocessing, metrics, decomposition, pipeline, dummy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mlb = preprocessing.LabelBinarizer()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
LabelBinarizer()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"
+ ],
+ "text/plain": [
+ " text category \\\n",
+ "0 From: sd345@city.ac.uk (Michael Collier) Subje... 1 \n",
+ "1 From: ani@ms.uky.edu (Aniruddha B. Deglurkar) ... 1 \n",
+ "2 From: djohnson@cs.ucsd.edu (Darin Johnson) Sub... 3 \n",
+ "3 From: s0612596@let.rug.nl (M.M. Zwart) Subject... 3 \n",
+ "4 From: stanly@grok11.columbiasc.ncr.com (stanly... 3 \n",
+ "5 From: vbv@lor.eeap.cwru.edu (Virgilio (Dean) B... 3 \n",
+ "6 From: jodfishe@silver.ucs.indiana.edu (joseph ... 3 \n",
+ "7 From: aldridge@netcom.com (Jacquelin Aldridge)... 2 \n",
+ "8 From: geb@cs.pitt.edu (Gordon Banks) Subject: ... 2 \n",
+ "\n",
+ " category_name unigrams \\\n",
+ "0 comp.graphics [From, :, sd345, @, city.ac.uk, (, Michael, Co... \n",
+ "1 comp.graphics [From, :, ani, @, ms.uky.edu, (, Aniruddha, B.... \n",
+ "2 soc.religion.christian [From, :, djohnson, @, cs.ucsd.edu, (, Darin, ... \n",
+ "3 soc.religion.christian [From, :, s0612596, @, let.rug.nl, (, M.M, ., ... \n",
+ "4 soc.religion.christian [From, :, stanly, @, grok11.columbiasc.ncr.com... \n",
+ "5 soc.religion.christian [From, :, vbv, @, lor.eeap.cwru.edu, (, Virgil... \n",
+ "6 soc.religion.christian [From, :, jodfishe, @, silver.ucs.indiana.edu,... \n",
+ "7 sci.med [From, :, aldridge, @, netcom.com, (, Jacqueli... \n",
+ "8 sci.med [From, :, geb, @, cs.pitt.edu, (, Gordon, Bank... \n",
+ "\n",
+ " tokenized_text bin_category \n",
+ "0 {HP, convert, know, Nntp-Posting-Host:, HPGL, ... [0, 1, 0, 0] \n",
+ "1 {3d, human,, know, for, divine., doing, would,... [0, 1, 0, 0] \n",
+ "2 {djohnson@ucsd.edu, for, due, Most, Luxury!, d... [0, 0, 0, 1] \n",
+ "3 {recent, Can, english,, Hello,, this,, for, 19... [0, 0, 0, 1] \n",
+ "4 {that, 15, for, man, tells, which, it, writes:... [0, 0, 0, 1] \n",
+ "5 {that, matters, they, Case, step, Most, whose,... [0, 0, 0, 1] \n",
+ "6 {that, choices, Paul, giving, [insert, know, e... [0, 0, 0, 1] \n",
+ "7 {for, softened, which, used, it, Taking, figur... [0, 0, 1, 0] \n",
+ "8 {that, <19213@pitt.UUCP>,, types, chastity, >T... [0, 0, 1, 0] "
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X[0:9]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Take a look at the new attribute we have added to the `X` table. You can see that the new attribute, which is called `bin_category`, contains an array of 0's and 1's. The `1` is basically to indicate the position of the label or category we binarized. If you look at the first two records, the one is places in slot 2 in the array; this helps to indicate to any of the algorithms which we are feeding this data to, that the record belong to that specific category. \n",
+ "\n",
+ "Attributes with **continuous values** also have strategies to tranform the data; this is usually called **Discretization** (please refer to the text book for more inforamation)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 19 (take home):**\n",
+ "Try to generate the binarization using the `category_name` column instead. Does it work?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer here\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 6. Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sometimes you need to take a peek at your data to understand the relationships in your dataset. Here, we will focus in a similarity example. Let's take 3 documents and compare them."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We retrieve 3 sentences for a random record\n",
+ "document_to_transform_1 = []\n",
+ "random_record_1 = X.iloc[10]\n",
+ "random_record_1 = random_record_1['text']\n",
+ "document_to_transform_1.append(random_record_1)\n",
+ "\n",
+ "document_to_transform_2 = []\n",
+ "random_record_2 = X.iloc[100]\n",
+ "random_record_2 = random_record_2['text']\n",
+ "document_to_transform_2.append(random_record_2)\n",
+ "\n",
+ "document_to_transform_3 = []\n",
+ "random_record_3 = X.iloc[1000]\n",
+ "random_record_3 = random_record_3['text']\n",
+ "document_to_transform_3.append(random_record_3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's look at our emails."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['From: anasaz!karl@anasazi.com (Karl Dussik) Subject: Re: Is \"Christian\" a dirty word? Organization: Anasazi Inc Phx Az USA Lines: 73 In article @usceast.cs.scarolina.edu:moss@cs.scarolina.edu (James Moss) writes: >I was brought up christian, but I am not christian any longer. >I also have a bad taste in my mouth over christianity. I (in >my own faith) accept and live my life by many if not most of the >teachings of christ, but I cannot let myself be called a christian, >beacuse to me too many things are done on the name of christianity, >that I can not be associated with. A question for you - can you give me the name of an organization or a philosophy or a political movement, etc., which has never had anything evil done in its name? You\\'re missing a central teaching of Christianity - man is inherently sinful. We are saved through faith by grace. Knowing that, believing that, does not make us without sin. Furthermore, not all who consider themselves \"christians\" are (even those who manage to head their own \"churches\"). \"Not everyone who says to me, \\'Lord, Lord,\\' will enter the kingdom of heaven, but only he who does the will of my Father who is in heaven.\" - Matt. 7:21. >I also have a problem with the inconsistancies in the Bible, and >how it seems to me that too many people have edited the original >documents to fit their own world views, thereby leaving the Bible >an unbelievable source. Again, what historical documents do you trust? Do you think Hannibal crossed the Alps? How do you know? How do you know for sure? What historical documents have stood the scrutiny and the attempts to dis- credit it as well as the Bible has? >I don\\'t have dislike of christians (except for a few who won\\'t >quit witnessing to me, no matter how many times I tell them to stop), >but the christian faith/organized religion will never (as far as i can >see at the moment) get my support. Well, it\\'s really a shame you feel this way. No one can browbeat you into believing, and those who try will probably only succeed in driving you further away. You need to ask yourself some difficult questions: 1) is there an afterlife, and if so, does man require salvation to attain it. If the answer is yes, the next question is 2) how does man attain this salvation - can he do it on his own as the eastern religions and certain modern offshoots like the \"new age movement\" teach or does he require God\\'s help? 3) If the latter, in what form does - indeed, in what form can such help come? Needless to say, this discussion could take a lifetime, and for some people it did comprise their life\\'s writings, so I am hardly in a position to offer the answers here - merely pointers to what to ask. Few, of us manage to have an unshaken faith our entire lives (certainly not me). The spritual life is a difficult journey (if you\\'ve never read \"A Pilgrim\\'s Progress,\" I highly recommend this greatest allegory of the english language). >Peace and Love >In God(ess)\\'s name >James Moss Now I see by your close that one possible source of trouble for you may be a conflict between your politcal beliefs and your religious upbringing. You wrote that \"I (in my own faith) accept and live my life by many if not most of the teachings of christ\". Well, Christ referred to God as \"My Father\", not \"My Mother\", and while the \"maleness\" of God is not the same as the maleness of those of us humans who possess a Y chromosome, it does not honor God to refer to Him as female purely to be trendy, non-discriminatory, or politically correct. This in no way disparages women (nor is it my intent to do so by my use of the male pronoun to refer to both men and women - english just does not have a decent neuter set of pronouns). After all, God chose a woman as his only human partner in bringing Christ into the human population. Well, I\\'m not about to launch into a detailed discussion of the role of women in Christianity at 1am with only 6 hours of sleep in the last 63, and for that reason I also apologize for any shortcomings in this article. I just happened across yours and felt moved to reply. I hope I may have given you, and anyone else who finds himself in a similar frame of mind, something to contemplate. Karl Dussik ']\n",
+ "['From: mathew Subject: Re: university violating separation of church/state? Organization: Mantis Consultants, Cambridge. UK. X-Newsreader: rusnews v1.01 Lines: 29 dmn@kepler.unh.edu (...until kings become philosophers or philosophers become kings) writes: > Recently, RAs have been ordered (and none have resisted or cared about > it apparently) to post a religious flyer entitled _The Soul Scroll: Thoughts > on religion, spirituality, and matters of the soul_ on the inside of bathroom > stall doors. (at my school, the University of New Hampshire) It is some sort > of newsletter assembled by a Hall Director somewhere on campus. It poses a > question about \\'spirituality\\' each issue, and solicits responses to be > included in the next \\'issue.\\' It\\'s all pretty vague. I assume it\\'s put out > by a Christian, but they\\'re very careful not to mention Jesus or the bible. > I\\'ve heard someone defend it, saying \"Well it doesn\\'t support any one religion. > \" So what??? This is a STATE university, and as a strong supporter of the > separation of church and state, I was enraged. > > What can I do about this? It sounds to me like it\\'s just SCREAMING OUT for parody. Give a copy to your friendly neighbourhood SubGenius preacher; with luck, he\\'ll run it through the mental mincer and hand you back an outrageously offensive and gut-bustingly funny parody you can paste over the originals. I can see it now: The Stool Scroll Thoughts on Religion, Spirituality, and Matters of the Colon (You can use this text to wipe) mathew ']\n",
+ "['From: bobs@thnext.mit.edu (Robert Singleton) Subject: Re: Americans and Evolution Organization: Massachvsetts Institvte of Technology Lines: 138 Distribution: world NNTP-Posting-Host: thnext.mit.edu In article <16BA8C4AC.I3150101@dbstu1.rz.tu-bs.de> I3150101@dbstu1.rz.tu-bs.de (Benedikt Rosenau) writes: > In article <1pq47tINN8lp@senator-bedfellow.MIT.EDU> > bobs@thnext.mit.edu (Robert Singleton) writes: > > (Deletion) > > > >I will argue that your latter statement, \"I believe that no gods exist\" > >does rest upon faith - that is, if you are making a POSITIVE statement > >that \"no gods exist\" (strong atheism) rather than merely saying I don\\'t > >know and therefore don\\'t believe in them and don\\'t NOT believe in then > >(weak atheism). Once again, to not believe in God is different than > >saying I BELIEVE that God does not exist. I still maintain the > >position, even after reading the FAQs, that strong atheism requires > >faith. > > > > No it in the way it is usually used. In my view, you are saying here > that driving a car requires faith that the car drives. > I\\'m not saying this at all - it requires no faith on my part to say the car drives because I\\'ve seen it drive - I\\'ve done more than at in fact - I\\'ve actually driven it. (now what does require some faith is the belief that my senses give an accurate representation of what\\'s out there....) But there is NO evidence - pro or con - for the existence or non-existence of God (see what I have to say below on this). > For me it is a conclusion, and I have no more faith in it than I > have in the premises and the argument used. > Sorry if I remain skeptical - I don\\'t believe it\\'s entirely a conclusion. That you have seen no evidence that there IS a God is correct - neither have I. But lack of evidence for the existence of something is in NO WAY evidence for the non-existence of something (the creationist have a similar mode of argumentation in which if they disprove evolution the establish creation). You (personally) have never seen a neutrino before, but they exist. The \"pink unicorn\" analogy breaks down and is rather naive. I have a scientific theory that explains the appearance of animal life - evolution. When I draw the conclusion that \"pink unicorns\" don\\'t exist because I haven\\'t seen them, this conclusion has it\\'s foundation in observation and theory. A \"pink unicorn\", if it did exist, would be qualitatively similar to other known entities. That is to say, since there is good evidence that all life on earth has evolved from \"more primitive\" ancestors these pink unicorns would share a common anscestory with horses and zebras and such. God, however, has no such correspondence with anything (IMO). There is no physical frame work of observation to draw ANY conclusions FROM. > >But first let me say the following. > >We might have a language problem here - in regards to \"faith\" and > >\"existence\". I, as a Christian, maintain that God does not exist. > >To exist means to have being in space and time. God does not HAVE > >being - God IS Being. Kierkegaard once said that God does not > >exist, He is eternal. With this said, I feel it\\'s rather pointless > >to debate the so called \"existence\" of God - and that is not what > >I\\'m doing here. I believe that God is the source and ground of > >being. When you say that \"god does not exist\", I also accept this > >statement - but we obviously mean two different things by it. However, > >in what follows I will use the phrase \"the existence of God\" in it\\'s > >\\'usual sense\\' - and this is the sense that I think you are using it. > >I would like a clarification upon what you mean by \"the existence of > >God\". > > > > No, that\\'s a word game. I disagree with you profoundly on this. I haven\\'t defined God as existence - in fact, I haven\\'t defined God. But this might be getting off the subject - although if you think it\\'s relevant we can come back to it. > > Further, saying god is existence is either a waste of time, existence is > already used and there is no need to replace it by god, or you are > implying more with it, in which case your definition and your argument > so far are incomplete, making it a fallacy. > You are using wrong categories here - or perhaps you misunderstand what I\\'m saying. I\\'m making no argument what so ever and offering no definition so there is no fallacy. I\\'m not trying to convince you of anything. *I* Believe - and that rests upon Faith. And it is inappropriate to apply the category of logic in this realm (unless someone tells you that they can logically prove God or that they have \"evidence\" or ..., then the use of logic to disprove their claims if fine and necessary). BTW, an incomplete argument is not a fallacy - some things are not EVEN wrong. > > (Deletion) > >One can never prove that God does or does not exist. When you say > >that you believe God does not exist, and that this is an opinion > >\"based upon observation\", I will have to ask \"what observtions are > >you refering to?\" There are NO observations - pro or con - that > >are valid here in establishing a POSITIVE belief. > (Deletion) > > Where does that follow? Aren\\'t observations based on the assumption > that something exists? > I don\\'t follow you here. Certainly one can make observations of things that they didn\\'t know existed. I still maintain that one cannot use observation to infer that \"God does not exist\". Such a positive assertion requires a leap. > And wouldn\\'t you say there is a level of definition that the assumption > \"god is\" is meaningful. If not, I would reject that concept anyway. > > So, where is your evidence for that \"god is\" is meaningful at some > level? Once again you seem to completely misunderstand me. I have no EVIDENCE that \"\\'god is\\' is meaningful\" at ANY level. Maybe such a response as you gave just comes naturally to you because so many people try to run their own private conception of God down your throat. I, however, am not doing this. I am arguing one, and only one, thing - that to make a positive assertion about something for which there can in principle be no evidence for or against requires a leap - it requires faith. I am, as you would say, a \"theist\"; however, there is a form of atheism that I can respect - but it must be founded upon honesty. > Benedikt -- bob singleton bobs@thnext.mit.edu ']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(document_to_transform_1)\n",
+ "print(document_to_transform_2)\n",
+ "print(document_to_transform_3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Let's take a look at the count vectors:\n",
+ "[[0 0 0 ... 0 0 0]]\n",
+ "[[0 0 0 ... 0 0 0]]\n",
+ "[[0 0 0 ... 0 0 0]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import binarize\n",
+ "\n",
+ "# Transform sentence with Vectorizers\n",
+ "document_vector_count_1 = count_vect.transform(document_to_transform_1)\n",
+ "document_vector_count_2 = count_vect.transform(document_to_transform_2)\n",
+ "document_vector_count_3 = count_vect.transform(document_to_transform_3)\n",
+ "\n",
+ "# Binarize vectors to simplify: 0 for abscence, 1 for prescence\n",
+ "document_vector_count_1_bin = binarize(document_vector_count_1)\n",
+ "document_vector_count_2_bin = binarize(document_vector_count_2)\n",
+ "document_vector_count_3_bin = binarize(document_vector_count_3)\n",
+ "\n",
+ "# print vectors\n",
+ "print(\"Let's take a look at the count vectors:\")\n",
+ "print(document_vector_count_1.todense())\n",
+ "print(document_vector_count_2.todense())\n",
+ "print(document_vector_count_3.todense())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cosine Similarity using count bw 1 and 2: 0.627571\n",
+ "Cosine Similarity using count bw 1 and 3: 0.713666\n",
+ "Cosine Similarity using count bw 2 and 3: 0.526166\n",
+ "Cosine Similarity using count bw 1 and 1: 1.000000\n",
+ "Cosine Similarity using count bw 2 and 2: 1.000000\n",
+ "Cosine Similarity using count bw 3 and 3: 1.000000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\452772485.py:13: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+ " print(\"Cosine Similarity using count bw 1 and 2: %(x)f\" %{\"x\":cos_sim_count_1_2})\n",
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\452772485.py:14: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+ " print(\"Cosine Similarity using count bw 1 and 3: %(x)f\" %{\"x\":cos_sim_count_1_3})\n",
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\452772485.py:15: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+ " print(\"Cosine Similarity using count bw 2 and 3: %(x)f\" %{\"x\":cos_sim_count_2_3})\n",
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\452772485.py:17: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+ " print(\"Cosine Similarity using count bw 1 and 1: %(x)f\" %{\"x\":cos_sim_count_1_1})\n",
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\452772485.py:18: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+ " print(\"Cosine Similarity using count bw 2 and 2: %(x)f\" %{\"x\":cos_sim_count_2_2})\n",
+ "C:\\Users\\ntone\\AppData\\Local\\Temp\\ipykernel_4204\\452772485.py:19: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+ " print(\"Cosine Similarity using count bw 3 and 3: %(x)f\" %{\"x\":cos_sim_count_3_3})\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "# Calculate Cosine Similarity\n",
+ "cos_sim_count_1_2 = cosine_similarity(document_vector_count_1, document_vector_count_2, dense_output=True)\n",
+ "cos_sim_count_1_3 = cosine_similarity(document_vector_count_1, document_vector_count_3, dense_output=True)\n",
+ "cos_sim_count_2_3 = cosine_similarity(document_vector_count_2, document_vector_count_3, dense_output=True)\n",
+ "\n",
+ "cos_sim_count_1_1 = cosine_similarity(document_vector_count_1, document_vector_count_1, dense_output=True)\n",
+ "cos_sim_count_2_2 = cosine_similarity(document_vector_count_2, document_vector_count_2, dense_output=True)\n",
+ "cos_sim_count_3_3 = cosine_similarity(document_vector_count_3, document_vector_count_3, dense_output=True)\n",
+ "\n",
+ "# Print \n",
+ "print(\"Cosine Similarity using count bw 1 and 2: %(x)f\" %{\"x\":cos_sim_count_1_2})\n",
+ "print(\"Cosine Similarity using count bw 1 and 3: %(x)f\" %{\"x\":cos_sim_count_1_3})\n",
+ "print(\"Cosine Similarity using count bw 2 and 3: %(x)f\" %{\"x\":cos_sim_count_2_3})\n",
+ "\n",
+ "print(\"Cosine Similarity using count bw 1 and 1: %(x)f\" %{\"x\":cos_sim_count_1_1})\n",
+ "print(\"Cosine Similarity using count bw 2 and 2: %(x)f\" %{\"x\":cos_sim_count_2_2})\n",
+ "print(\"Cosine Similarity using count bw 3 and 3: %(x)f\" %{\"x\":cos_sim_count_3_3})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### >>> **Exercise 20 (take home):**\n",
+ "Try changing the texts reference for Text 1, Text 2, and Text 3. What do you observe from the Cosine Similarity results of different text references? following the modifications to texts reference, how can the results of the cosine similarity be interpreted?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Answer"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 7. Data Classification\n",
+ "Data classification is one of the most critical steps in the final stages of the data mining process. After uncovering patterns, trends, or insights from raw data, classification helps organize and label the data into predefined categories. This step is crucial in making the mined data actionable, as it allows for accurate predictions and decision-making. For example, in text mining, classification can be used to categorize documents based on their content, like classifying news articles into categories such as sports, politics, or technology.\n",
+ "Among various classification techniques, the **Naive Bayes classifier** is a simple yet powerful algorithm commonly used for text classification tasks. Specifically, the Multinomial Naive Bayes classifier is particularly suited for datasets where features are represented by term frequencies, such as a document-term matrix, like the one we have.\n",
+ "\n",
+ "- **Multinomial Naive Bayes:**\n",
+ " The Multinomial Naive Bayes classifier works by assuming that the features (words or terms in text data) follow a multinomial distribution. In simple terms, it calculates the probability of a document belonging to a particular category based on the frequency of words in that document, assuming independence between words (the \"naive\" part of Naive Bayes). Despite this assumption, it often performs remarkably well for text data, especially when working with word count features. Now, when incorporating the binary matrix of patterns we have, it remains compatible because the binary values can be seen as a count of pattern occurrences (1 for present, 0 for absent). Although binary features are not true \"counts,\" the Multinomial Naive Bayes classifier can still handle them without issue. For more information you can go to: [NB Classifier](https://hub.packtpub.com/implementing-3-naive-bayes-classifiers-in-scikit-learn/)\n",
+ " \n",
+ "We will implement a Multinomial Naive Bayes, for that we first choose how to split our data, in this case we will follow a typical **70/30 split for the training and test set**. Let's see a comparison of what we obtain when classifying our data without patterns vs our data with the patterns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Model with only the document-term frequency data\n",
+ "import pandas as pd\n",
+ "from sklearn.feature_extraction.text import CountVectorizer\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.naive_bayes import MultinomialNB\n",
+ "from sklearn.metrics import classification_report, accuracy_score\n",
+ "\n",
+ "# Create a mapping from numerical labels to category names\n",
+ "category_mapping = dict(X[['category', 'category_name']].drop_duplicates().values)\n",
+ "\n",
+ "# Convert the numerical category labels to text labels\n",
+ "target_names = [category_mapping[label] for label in sorted(category_mapping.keys())]\n",
+ "\n",
+ "# Split the data into training and testing sets (70% train, 30% test)\n",
+ "X_train, X_test, y_train, y_test = train_test_split(tdm_df, X['category'], test_size=0.3, random_state=42)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "