Baseline experiment files and organization

.gitignore

@@ -1,8 +1,17 @@
 Dataset/*
 !Dataset/.gitkeep
 .venv/*
+ControlDataset/*
+Models/*
+NotInUseModels/*
+Result/*
+Scalers/*
+TestDataset/*
+inference_results.csv
+FeatureWeights/*
+CombinedFeatureWeights.csv
 Bladder/Dataset/*
 Breast/Dataset/*
 Brain/Dataset/*
 Liver/Dataset/*
-Test_data/*
+Test_data/*

Baseline/baseline_aggregate_and_infer.ipynb

@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import joblib\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_models(models_folder=\"Models\"):\n",
+    "    \"\"\"Load all saved models from the specified folder.\"\"\"\n",
+    "    models = {}\n",
+    "    for file_name in os.listdir(models_folder):\n",
+    "        if file_name.endswith(\".joblib\"):\n",
+    "            model_name, cancer_type = file_name.split(\"_\")\n",
+    "            model_path = os.path.join(models_folder, file_name)\n",
+    "            models[(model_name, cancer_type)] = joblib.load(model_path)\n",
+    "            print(f\"Loaded model: {model_name} for cancer type: {cancer_type}\")\n",
+    "    return models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_data(new_data_path, scaler=None):\n",
+    "    \"\"\"Load and preprocess new data.\"\"\"\n",
+    "    # Load the data\n",
+    "    data = pd.read_csv(new_data_path)\n",
+    "    X = data.drop(['cancer_type', 'type'], axis=1, errors='ignore')\n",
+    "    \n",
+    "    # standardize the data\n",
+    "    if scaler is None:\n",
+    "        scaler = StandardScaler()\n",
+    "        X_scaled = scaler.fit_transform(X)\n",
+    "    else:\n",
+    "        X_scaled = scaler.transform(X)\n",
+    "    \n",
+    "    return X_scaled, data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_inference(models, test_data_path, scalers_folder=\"Scalers\"):\n",
+    "    # Load the test data\n",
+    "    test_df = pd.read_csv(test_data_path)\n",
+    "    test_features = test_df.drop(['cancer_type', 'type'], axis=1)\n",
+    "\n",
+    "    # List all scaler files in the Scalers folder\n",
+    "    scaler_files = [f for f in os.listdir(scalers_folder) if f.endswith('.joblib')]\n",
+    "    \n",
+    "    results = []\n",
+    "    \n",
+    "    for index, row in test_df.iterrows():\n",
+    "        row_predictions = {}  # Store model predictions and their confidence\n",
+    "        \n",
+    "        for (model_name, cancer_type), model in models.items():\n",
+    "            # Find the scaler corresponding to the cancer type\n",
+    "            cancer_type, _ = cancer_type.split(\".\")\n",
+    "            scaler_filename = f\"{cancer_type}_scaler.joblib\"\n",
+    "            if scaler_filename in scaler_files:\n",
+    "                scaler_path = os.path.join(scalers_folder, scaler_filename)\n",
+    "                scaler = joblib.load(scaler_path)\n",
+    "                test_features_scaled = scaler.transform([test_features.iloc[index]])  # Transform a single row\n",
+    "                \n",
+    "                # Run inference with the model\n",
+    "                probabilities = model.predict_proba(test_features_scaled)\n",
+    "                confidence = probabilities[0][1]\n",
+    "                \n",
+    "                # Store the model's prediction and confidence\n",
+    "                row_predictions[f\"{model_name}-{cancer_type}\"] = {\n",
+    "                    'cancer_type': cancer_type,\n",
+    "                    'predicted_class': model.predict(test_features_scaled)[0],\n",
+    "                    'confidence': confidence,\n",
+    "                    'probabilities': probabilities[0].tolist()\n",
+    "                }\n",
+    "            else:\n",
+    "                print(f\"couldn't find {cancer_type}_scaler.joblib\")\n",
+    "\n",
+    "        # Determine the final prediction based on the highest confidence\n",
+    "        final_prediction = \"normal\"\n",
+    "        max_confidence = -1\n",
+    "        \n",
+    "        for model_name, prediction_info in row_predictions.items():\n",
+    "            if prediction_info['confidence'] > max_confidence and prediction_info['confidence'] > 0.5:\n",
+    "                max_confidence = prediction_info['confidence']\n",
+    "                final_prediction = prediction_info['cancer_type']\n",
+    "\n",
+    "        # Append the final prediction for the current data point\n",
+    "        results.append({\n",
+    "            'index': index,\n",
+    "            'cancer_type': final_prediction,\n",
+    "            'confidence': max_confidence if final_prediction is not \"normal\" else -1.0,\n",
+    "            'predictions': row_predictions\n",
+    "        })\n",
+    "\n",
+    "    return results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_inference_results(results, output_file=\"inference_results.csv\"):\n",
+    "    # Convert the results to a DataFrame\n",
+    "    df_results = pd.DataFrame(results)\n",
+    "    df_results.to_csv(output_file, index=False)\n",
+    "    print(f\"Inference results saved to {output_file}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the updated function\n",
+    "models_folder = \"Models\"\n",
+    "new_data_path = \"TestDataset/test_data.csv\"\n",
+    "\n",
+    "# Load models\n",
+    "models = load_models(models_folder=models_folder)\n",
+    "\n",
+    "\n",
+    "# Run inference\n",
+    "inference_results = run_inference(models, new_data_path)\n",
+    "\n",
+    "# Save results\n",
+    "save_inference_results(inference_results, output_file=\"inference_results.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 88.24%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_df = pd.read_csv(\"TestDataset/test_data.csv\")\n",
+    "\n",
+    "predictions_df = pd.read_csv(\"inference_results.csv\")\n",
+    "\n",
+    "\n",
+    "accuracy = (predictions_df[\"cancer_type\"] == test_df[\"cancer_type\"]).mean()\n",
+    "print(f\"Accuracy: {accuracy:.2%}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}