class_imbalance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Class Balancing"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c4988819950108f1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Import Dependencies**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a3d07c3ad6faadf4"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:13.315148711Z",
     "start_time": "2023-08-16T09:07:12.864147755Z"
    }
   },
   "id": "initial_id"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Load the dataset**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1cadadbe86d5ebbf"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id       RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
      "0   1  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
      "1   2  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
      "2   3  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
      "3   4  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
      "4   5  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1\n",
      "Id      0\n",
      "RI      0\n",
      "Na      0\n",
      "Mg      0\n",
      "Al      0\n",
      "Si      0\n",
      "K       0\n",
      "Ca      0\n",
      "Ba      0\n",
      "Fe      0\n",
      "Type    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data\"\n",
    "names = [\"Id\", \"RI\", \"Na\", \"Mg\", \"Al\", \"Si\", \"K\", \"Ca\", \"Ba\", \"Fe\", \"Type\"]\n",
    "data = pd.read_csv(url, names=names)\n",
    "\n",
    "print(data.head())\n",
    "print(data.isnull().sum())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.490364872Z",
     "start_time": "2023-08-16T09:07:13.356465988Z"
    }
   },
   "id": "20854c977237c34f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Separate features (X) and labels (y)**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "73f63ccd90101f74"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "X = data.drop([\"Id\", \"Type\"], axis=1)\n",
    "y = data[\"Type\"]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.496307988Z",
     "start_time": "2023-08-16T09:07:14.490992609Z"
    }
   },
   "id": "77eeeddb45af01af"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Feature Engineering**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c704ec430afe4ab6"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "X[\"Light_elements\"] = X[\"Na\"] + X[\"Mg\"]\n",
    "X[\"Heavy_elements\"] = X[\"Al\"] + X[\"Si\"] + X[\"K\"] + X[\"Ca\"] + X[\"Ba\"] + X[\"Fe\"]\n",
    "X[\"Total_elements\"] = (\n",
    "    X[\"Na\"] + X[\"Mg\"] + X[\"Al\"] + X[\"Si\"] + X[\"K\"] + X[\"Ca\"] + X[\"Ba\"] + X[\"Fe\"]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.508039450Z",
     "start_time": "2023-08-16T09:07:14.496824537Z"
    }
   },
   "id": "585dabe6b3ba6ddd"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Scaling**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "609a51e68158cdb0"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.553994081Z",
     "start_time": "2023-08-16T09:07:14.505289849Z"
    }
   },
   "id": "a89b197e03bf6e23"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Split the data into training, validation, and test sets**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "20ba955208a8cd83"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "X_train, X_temp, y_train, y_temp = train_test_split(\n",
    "    X_scaled, y, test_size=0.3, random_state=42\n",
    ")\n",
    "X_val, X_test, y_val, y_test = train_test_split(\n",
    "    X_temp, y_temp, test_size=0.5, random_state=42\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.573014371Z",
     "start_time": "2023-08-16T09:07:14.513359927Z"
    }
   },
   "id": "ab7f62dd9687108f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Apply RandomOverSampler to balance the class distribution**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5b1a1a43a9b0089b"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.574115636Z",
     "start_time": "2023-08-16T09:07:14.554359703Z"
    }
   },
   "id": "78b451c2c300c5c6"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Train a Logistic Regression model on the resampled training data**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ece37896e51ecd54"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression(max_iter=1000, random_state=42, solver='saga')",
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(solver=\"saga\", penalty=\"l2\", max_iter=1000, random_state=42)\n",
    "model.fit(X_train_resampled, y_train_resampled)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:14.600459325Z",
     "start_time": "2023-08-16T09:07:14.554566574Z"
    }
   },
   "id": "45a472bc4cfb3c53"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Define hyperparameter grid and find the best model**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "faf25dbc2c02fb9a"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "param_grid = {\"C\": [0.01, 0.1, 1, 10, 100]}\n",
    "grid_search = GridSearchCV(\n",
    "    LogisticRegression(random_state=42, max_iter=1000), param_grid, cv=5, n_jobs=-1\n",
    ")\n",
    "grid_search.fit(X_train_resampled, y_train_resampled)\n",
    "\n",
    "best_model = grid_search.best_estimator_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:16.686163382Z",
     "start_time": "2023-08-16T09:07:14.596618656Z"
    }
   },
   "id": "4b1fee65729fe1c8"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Evaluate the model on the validation set**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "a4edb340a147c1f7"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy:\"\n",
      "0.875\n",
      "\n",
      "\"Confusion Matrix:\"\n",
      "[[9 0 1 0 0 0]\n",
      " [2 6 1 0 0 0]\n",
      " [0 0 3 0 0 0]\n",
      " [0 0 0 3 0 0]\n",
      " [0 0 0 0 2 0]\n",
      " [0 0 0 0 0 5]]\n",
      "\n",
      "\"Classification Report:\"\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.90      0.86        10\n",
      "           2       1.00      0.67      0.80         9\n",
      "           3       0.60      1.00      0.75         3\n",
      "           5       1.00      1.00      1.00         3\n",
      "           6       1.00      1.00      1.00         2\n",
      "           7       1.00      1.00      1.00         5\n",
      "\n",
      "    accuracy                           0.88        32\n",
      "   macro avg       0.90      0.93      0.90        32\n",
      "weighted avg       0.91      0.88      0.88        32\n"
     ]
    }
   ],
   "source": [
    "y_pred_val = best_model.predict(X_val)\n",
    "accuracy_val = accuracy_score(y_val, y_pred_val)\n",
    "confusion_val = confusion_matrix(y_val, y_pred_val)\n",
    "classification_report_val = classification_report(y_val, y_pred_val)\n",
    "\n",
    "print(\n",
    "    f\"\"\"Validation Accuracy:\"\n",
    "{accuracy_val}\n",
    "\n",
    "\"Confusion Matrix:\"\n",
    "{confusion_val}\n",
    "\n",
    "\"Classification Report:\"\n",
    "{classification_report_val}\n",
    "\"\"\"\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:16.735117019Z",
     "start_time": "2023-08-16T09:07:16.692024085Z"
    }
   },
   "id": "7d2dc9dec907a81f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Evaluate the model on the test set**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bfad1d89f548f1a2"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy:\"\n",
      "0.5454545454545454\n",
      "\n",
      "\"Confusion Matrix:\"\n",
      "[[4 3 2 0 0 0]\n",
      " [2 7 3 1 1 0]\n",
      " [1 0 0 0 0 0]\n",
      " [0 0 1 2 0 0]\n",
      " [0 0 0 0 1 0]\n",
      " [1 0 0 0 0 4]]\n",
      "\n",
      "\"Classification Report:\"\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.50      0.44      0.47         9\n",
      "           2       0.70      0.50      0.58        14\n",
      "           3       0.00      0.00      0.00         1\n",
      "           5       0.67      0.67      0.67         3\n",
      "           6       0.50      1.00      0.67         1\n",
      "           7       1.00      0.80      0.89         5\n",
      "\n",
      "    accuracy                           0.55        33\n",
      "   macro avg       0.56      0.57      0.55        33\n",
      "weighted avg       0.66      0.55      0.59        33\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_test = best_model.predict(X_test)\n",
    "accuracy_test = accuracy_score(y_test, y_pred_test)\n",
    "confusion_test = confusion_matrix(y_test, y_pred_test)\n",
    "classification_report_test = classification_report(y_test, y_pred_test)\n",
    "\n",
    "print(\n",
    "    f\"\"\"Test Accuracy:\"\n",
    "{accuracy_test}\n",
    "\n",
    "\"Confusion Matrix:\"\n",
    "{confusion_test}\n",
    "\n",
    "\"Classification Report:\"\n",
    "{classification_report_test}\n",
    "\"\"\"\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:16.735939685Z",
     "start_time": "2023-08-16T09:07:16.734592926Z"
    }
   },
   "id": "dd4984fda0cdd697"
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Display class distribution before and after resampling**"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "415cf7ebda5eb68a"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples number in classes before resampling:\n",
      "Class 1: 51 samples\n",
      "Class 2: 53 samples\n",
      "Class 3: 13 samples\n",
      "Class 5: 7 samples\n",
      "Class 6: 6 samples\n",
      "Class 7: 19 samples\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 800x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples number in classes after resampling:\n",
      "Class 1: 53 samples\n",
      "Class 2: 53 samples\n",
      "Class 3: 53 samples\n",
      "Class 5: 53 samples\n",
      "Class 6: 53 samples\n",
      "Class 7: 53 samples\n"
     ]
    }
   ],
   "source": [
    "unique_classes, counts = np.unique(y_train, return_counts=True)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(unique_classes, counts)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Samples\")\n",
    "plt.title(\"Samples number in classes before resampling\")\n",
    "plt.xticks(unique_classes)\n",
    "plt.show()\n",
    "\n",
    "print(\"Samples number in classes before resampling:\")\n",
    "for cls, count in zip(unique_classes, counts):\n",
    "    print(f\"Class {cls}: {count} samples\")\n",
    "\n",
    "unique_classes, counts = np.unique(y_train_resampled, return_counts=True)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.bar(unique_classes, counts)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Samples\")\n",
    "plt.title(\"Samples number in classes after resampling\")\n",
    "plt.xticks(unique_classes)\n",
    "plt.show()\n",
    "\n",
    "print(\"Samples number in classes after resampling:\")\n",
    "for cls, count in zip(unique_classes, counts):\n",
    "    print(f\"Class {cls}: {count} samples\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-16T09:07:17.111292011Z",
     "start_time": "2023-08-16T09:07:16.734854134Z"
    }
   },
   "id": "1ddc4ae97db1332e"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}