{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>row</th>\n",
       "      <th>col</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Map Data</th>\n",
       "      <th>label</th>\n",
       "      <th>label_ground</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.89433</td>\n",
       "      <td>72.794102</td>\n",
       "      <td>Prongs Reef is a Natural;</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>18.89433</td>\n",
       "      <td>72.803607</td>\n",
       "      <td>United Services Club Golf Course is a Leisure ...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>18.89433</td>\n",
       "      <td>72.813112</td>\n",
       "      <td>Indian Meterological Department is a Commercia...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>18.89433</td>\n",
       "      <td>72.908163</td>\n",
       "      <td>Uran Naval Base is a Landuse: Military;</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>18.89433</td>\n",
       "      <td>72.917668</td>\n",
       "      <td>Uran Naval Base is a Landuse: Military;</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row  col  latitude  longitude  \\\n",
       "0    0    1  18.89433  72.794102   \n",
       "1    0    2  18.89433  72.803607   \n",
       "2    0    3  18.89433  72.813112   \n",
       "3    0   13  18.89433  72.908163   \n",
       "4    0   14  18.89433  72.917668   \n",
       "\n",
       "                                            Map Data  label  label_ground  \n",
       "0                         Prongs Reef is a Natural;       2             2  \n",
       "1  United Services Club Golf Course is a Leisure ...      2             2  \n",
       "2  Indian Meterological Department is a Commercia...      4             4  \n",
       "3           Uran Naval Base is a Landuse: Military;       3             3  \n",
       "4           Uran Naval Base is a Landuse: Military;       3             3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(r'C:\\Users\\Akhil PC\\Documents\\projects\\research\\Marauders-Map\\data\\MMR_DATA_CLEAN_LABELLED_GROUND.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = df['label']\n",
    "y_true = df['label_ground']\n",
    "\n",
    "if y_pred.shape != y_true.shape:\n",
    "    print(\"Error: The shape of the prediction and true labels do not match.\")\n",
    "    exit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9705159705159705\n",
      "Precision:  0.9706854194661688\n",
      "Recall:  0.9705159705159705\n",
      "F1 Score:  0.9704914288808899\n"
     ]
    }
   ],
   "source": [
    "## accuracy, precision, recall, f1 score\n",
    "\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "precision = precision_score(y_true, y_pred, average='weighted')\n",
    "recall = recall_score(y_true, y_pred, average='weighted')\n",
    "f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"Precision: \", precision)\n",
    "print(\"Recall: \", recall)\n",
    "print(\"F1 Score: \", f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Define the labels\n",
    "labels = ['Residential', 'Natural', 'Industrial', 'Commercial']\n",
    "\n",
    "# Create confusion matrix\n",
    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "# Normalize the confusion matrix\n",
    "conf_matrix_norm = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix_norm, \n",
    "            annot=True, \n",
    "            fmt='.2f',\n",
    "            cmap='Blues',\n",
    "            xticklabels=labels,\n",
    "            yticklabels=labels,\n",
    "            square=True)\n",
    "\n",
    "plt.title('Normalized Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "\n",
    "# Rotate the tick labels\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "# Adjust layout to prevent label cutoff\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c9fb0476b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## confusion matrix\n",
    "\n",
    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "plt.matshow(conf_matrix, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}