notebooks/model_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation\n",
    "\n",
    "This notebook covers the evaluation of the trained machine learning models using various metrics and visualizations for the Wisconsin Breast Cancer dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries and functions\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# Get the current notebook's directory\n",
    "current_dir = os.path.dirname(os.path.abspath(\"__file__\"))\n",
    "\n",
    "# Construct the path to the scripts directory\n",
    "utils_dir = os.path.join(current_dir, '..', 'scripts')\n",
    "\n",
    "# Add the utils directory to the system path\n",
    "sys.path.append(utils_dir)\n",
    "\n",
    "#Load functions from the script evaluate.py\n",
    "from evaluate import load_features_data, load_model, load_metrics, plot_confusion_matrices, plot_roc_curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Preprocessed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the preprocessed data\n",
    "X_test, y_test = load_features_data('../data/selected_features_data.npz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Trained Models and evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>confusion_matrix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.964912</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.997707</td>\n",
       "      <td>[[69, 2], [2, 41]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.995414</td>\n",
       "      <td>[[68, 3], [3, 40]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLS Regression</th>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.964706</td>\n",
       "      <td>0.994759</td>\n",
       "      <td>[[70, 1], [2, 41]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Network</th>\n",
       "      <td>0.991228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.988863</td>\n",
       "      <td>[[71, 0], [1, 42]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   accuracy  precision    recall  f1_score   roc_auc  \\\n",
       "Random Forest      0.964912   0.953488  0.953488  0.953488  0.997707   \n",
       "Gradient Boosting  0.947368   0.930233  0.930233  0.930233  0.995414   \n",
       "PLS Regression     0.973684   0.976190  0.953488  0.964706  0.994759   \n",
       "Neural Network     0.991228   1.000000  0.976744  0.988235  0.988863   \n",
       "\n",
       "                     confusion_matrix  \n",
       "Random Forest      [[69, 2], [2, 41]]  \n",
       "Gradient Boosting  [[68, 3], [3, 40]]  \n",
       "PLS Regression     [[70, 1], [2, 41]]  \n",
       "Neural Network     [[71, 0], [1, 42]]  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load trained models\n",
    "model_names = ['random_forest_model.pkl', 'gradient_boosting_model.pkl', 'pls_regression_model.pkl', 'neural_network_model.pkl']\n",
    "models = {\n",
    "    'Random Forest': load_model('../models/random_forest_model.pkl'),\n",
    "    'Gradient Boosting': load_model('../models/gradient_boosting_model.pkl'),\n",
    "    'PLS Regression': load_model('../models/pls_regression_model.pkl'),\n",
    "    'Neural Network': load_model('../models/neural_network_model.pkl')\n",
    "}\n",
    "\n",
    "# Load evaluation metrics\n",
    "metrics = load_metrics('../results/evaluation_metrics.csv')\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot confusion matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrices\n",
    "plot_confusion_matrices(metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot ROC curves\n",
    "The ROC curve illustrates the performance of each classifier across all classification thresholds. The ROC AUC (Area Under the Curve) is a measure of the model's ability to distinguish between classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curves\n",
    "plot_roc_curves(models, X_test, y_test, metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- The Neural Network model demonstrated the highest accuracy (0.991228) and perfect precision (1.000000), making it the most reliable model for this dataset. It also had the highest recall (0.976744), indicating its effectiveness in identifying malignant cases correctly.\n",
    "\n",
    "- PLS Regression performed very well, with high accuracy and F1 score, and an excellent ROC AUC score. It serves as a strong contender but slightly lagged behind the Neural Network in terms of precision and reca\n",
    "  \n",
    "- The Random Forest and Gradient Boosting models both performed exceptionally well, with perfect ROC AUC scores. However, Random Forest had a slightly better balance between precision and recall.\n",
    "\n",
    "## Conclusion\n",
    "In conclusion, while all models demonstrated high performance, the Neural Network model stands out as the best-performing model for the Wisconsin Breast Cancer dataset due to its highest accuracy and precision. The Random Forest model also shows robust performance, making it a strong alternative. However, PLS Regression is a noteworthy option for scenarios where computational efficiency and ease of model tuning are important considerations. The importance of high recall in cancer detection cannot be overstated, as it ensures that malignant cases are identified and treated promptly, reducing the risk of missed diagnoses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}