BDT_XGBoost_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>label</th>\n",
       "      <th>L1_centre_sum</th>\n",
       "      <th>L1_wide_centre_sum</th>\n",
       "      <th>L1_wider_centre_sum</th>\n",
       "      <th>L2_centre_sum</th>\n",
       "      <th>L2_wide_centre_sum</th>\n",
       "      <th>L2_spread_sum</th>\n",
       "      <th>L2_vertical_sum</th>\n",
       "      <th>L3_centre_sum</th>\n",
       "      <th>Had_centre_sum</th>\n",
       "      <th>Had_peripheral_sum</th>\n",
       "      <th>Had_out_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>3.300</td>\n",
       "      <td>2.025</td>\n",
       "      <td>7.700</td>\n",
       "      <td>3.425</td>\n",
       "      <td>0.775</td>\n",
       "      <td>2.550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4.20</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.025</td>\n",
       "      <td>3.650</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5.60</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.125</td>\n",
       "      <td>2.475</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.475</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>11.300</td>\n",
       "      <td>1.700</td>\n",
       "      <td>5.350</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.575</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.400</td>\n",
       "      <td>2.750</td>\n",
       "      <td>1.100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  L1_centre_sum  L1_wide_centre_sum  L1_wider_centre_sum  \\\n",
       "0      0           2.85               3.300                2.025   \n",
       "1      0           4.20               0.825                0.000   \n",
       "2      1           5.60               0.000                0.000   \n",
       "3      1           0.00               4.475                0.000   \n",
       "4      1           0.00               4.575                0.000   \n",
       "\n",
       "   L2_centre_sum  L2_wide_centre_sum  L2_spread_sum  L2_vertical_sum  \\\n",
       "0          7.700               3.425          0.775            2.550   \n",
       "1         11.025               3.650          0.000            3.975   \n",
       "2         23.125               2.475          0.000            0.000   \n",
       "3          0.000              11.300          1.700            5.350   \n",
       "4          0.000              14.400          2.750            1.100   \n",
       "\n",
       "   L3_centre_sum  Had_centre_sum  Had_peripheral_sum  Had_out_sum  \n",
       "0            0.0             0.0                 0.0          0.0  \n",
       "1            0.0             0.0                 0.0          0.0  \n",
       "2            0.0             0.0                 0.0          0.0  \n",
       "3            0.0             0.0                 1.5          0.0  \n",
       "4            0.0             0.0                 0.0          0.0  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "DS = pd.read_csv(r'\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\QNN_Ideas\\New_prams_1.csv')\n",
    "TEST = pd.read_csv(r'\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\Muon_data_1.csv')\n",
    "\n",
    "#DS = pd.read_csv(r\"C:\\Users\\oscar\\OneDrive\\Documents\\Year4_Project\\QNN_Ideas\\Above_16GeV_Train_DATA.csv\")\n",
    "#DF = DS.drop(['TOB_ET', 'offline_ele_pt','ID', 'TOB_eta'], axis=1)\n",
    "#columns_to_keep = ['label', 'Had_2', 'Had_5', 'L0_5', 'L1_17', 'L1_18', 'L1_19', 'L1_20', 'L2_17', 'L2_18', 'L2_19', 'L2_20']\n",
    "\n",
    "#columns_to_keep = ['label', 'Had_5', 'L2_18', 'L2_19', 'L1_18', 'L1_19', 'L1_20', 'L2_20', 'L1_17', 'L2_17', 'L0_5', 'Had_8', 'Had_2', 'L2_21', 'Had_4', 'Had_6', 'L2_16', 'L3_5', 'L2_15', 'Had_7', 'Had_9', 'Had_3', 'L2_22', 'L2_7', 'L1_16', 'L1_21', 'L2_32', 'Had_1', 'L2_8', 'L2_31', 'L2_30', 'L3_8', 'L3_2', 'L2_5', 'L1_29', 'L2_6', 'L1_30', 'L2_29', 'L1_32', 'L1_22', 'L2_4', 'L2_33', 'L2_27', 'L2_10', 'L1_15', 'L1_7', 'L1_6', 'L2_28', 'L1_31', 'L2_23', 'L1_8']\n",
    "#columns_to_keep = ['label' , 'L0_centre_sum',\t'L1_centre_sum',\t'L1_wide_centre_sum',\t'L2_centre_sum',\t'L2_wide_centre_sum', 'L2_spread_sum',\t'L2_vertical_sum',\t'L3_centre_sum',\t'Had_centre_sum',\t'Had_peripheral_sum',\t'Had_out_sum','TOB_eta']\n",
    "#columns_to_keep = ['label' ,'L1_centre_sum', 'L1_wide_centre_sum', 'L1_wider_centre_sum','L2_centre_sum', 'L2_wide_centre_sum', 'L2_spread_sum', 'L2_vertical_sum', 'L3_centre_sum', 'Had_centre_sum', 'Had_peripheral_sum', 'Had_out_sum', 'TOB_eta']\n",
    "columns_to_keep = ['label' ,'L1_centre_sum', 'L1_wide_centre_sum', 'L1_wider_centre_sum','L2_centre_sum', 'L2_wide_centre_sum', 'L2_spread_sum', 'L2_vertical_sum', 'L3_centre_sum', 'Had_centre_sum', 'Had_peripheral_sum', 'Had_out_sum']\n",
    "DF = DS[columns_to_keep]\n",
    "\n",
    "DF.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(TEST['TOB_ET'], TEST['TOB_eta'], s=1)  \n",
    "plt.ylabel('TOB_eta', fontsize=14)\n",
    "plt.xlabel('TOB_ET GeV' , fontsize=14)\n",
    "plt.title('Scatter Plot of TOB_ET vs TOB_eta' , fontsize=16)\n",
    "plt.axhline(y=1.4, color='r', linestyle=':', linewidth=2, label='TOB_eta = 1.4')\n",
    "plt.axhline(y=-1.4, color='r', linestyle=':', linewidth=2, label='TOB_eta = -1.4')\n",
    "plt.tick_params(axis='both', which='major', labelsize=11) \n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(TEST['TOB_ET'], TEST['TOB_phi'], s=1)  # 's' controls the size of the dots\n",
    "plt.ylabel('TOB_phi', fontsize=14)\n",
    "plt.xlabel('TOB_ET GeV', fontsize=14)\n",
    "plt.title('Scatter Plot of TOB_ET vs TOB_Phi', fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.tick_params(axis='both', which='major', labelsize=11) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x =\"label\", data = DF)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56274, 11)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = DF.drop([\"label\"], axis=1)  \n",
    "Y = DF[\"label\"]  \n",
    "\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=0)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.46728\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalidation_0-logloss:0.41863\n",
      "[2]\tvalidation_0-logloss:0.35873\n",
      "[3]\tvalidation_0-logloss:0.32477\n",
      "[4]\tvalidation_0-logloss:0.31139\n",
      "[5]\tvalidation_0-logloss:0.30448\n",
      "[6]\tvalidation_0-logloss:0.29887\n",
      "[7]\tvalidation_0-logloss:0.29326\n",
      "[8]\tvalidation_0-logloss:0.28790\n",
      "[9]\tvalidation_0-logloss:0.28464\n",
      "[10]\tvalidation_0-logloss:0.28078\n",
      "[11]\tvalidation_0-logloss:0.27939\n",
      "[12]\tvalidation_0-logloss:0.27739\n",
      "[13]\tvalidation_0-logloss:0.27402\n",
      "[14]\tvalidation_0-logloss:0.27126\n",
      "[15]\tvalidation_0-logloss:0.26980\n",
      "[16]\tvalidation_0-logloss:0.26719\n",
      "[17]\tvalidation_0-logloss:0.26673\n",
      "[18]\tvalidation_0-logloss:0.26580\n",
      "[19]\tvalidation_0-logloss:0.26417\n",
      "[20]\tvalidation_0-logloss:0.26379\n",
      "[21]\tvalidation_0-logloss:0.26338\n",
      "[22]\tvalidation_0-logloss:0.26259\n",
      "[23]\tvalidation_0-logloss:0.26148\n",
      "[24]\tvalidation_0-logloss:0.26022\n",
      "[25]\tvalidation_0-logloss:0.25957\n",
      "[26]\tvalidation_0-logloss:0.25895\n",
      "[27]\tvalidation_0-logloss:0.25820\n",
      "[28]\tvalidation_0-logloss:0.25765\n",
      "[29]\tvalidation_0-logloss:0.25718\n",
      "[30]\tvalidation_0-logloss:0.25734\n",
      "[31]\tvalidation_0-logloss:0.25713\n",
      "Accuracy: 0.8931122720972385\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.86      0.89      9376\n",
      "           1       0.87      0.93      0.90      9382\n",
      "\n",
      "    accuracy                           0.89     18758\n",
      "   macro avg       0.89      0.89      0.89     18758\n",
      "weighted avg       0.89      0.89      0.89     18758\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[21:14:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "[21:14:50] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "XGBoost = XGBClassifier(\n",
    "    n_estimators=32,  # Reduced number of estimators\n",
    "    learning_rate=1.15,\n",
    "    verbose=1,\n",
    "    validation_fraction=0.2,  # For early stopping\n",
    "    n_iter_no_change=10,# Number of iterations with no improvement to stop\n",
    "    \n",
    "    max_depth= 2\n",
    ")\n",
    "\n",
    "# Fit the model to your training data\n",
    "XGBoost.fit(\n",
    "    X_train, \n",
    "    Y_train, \n",
    "    eval_set=[(X_test, Y_test)], # Use the test set for early stopping\n",
    "    verbose=1,\n",
    "    early_stopping_rounds=10 # Set early stopping rounds\n",
    ")\n",
    "\n",
    "XGBoost.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = XGBoost.predict(X_test)\n",
    "print(\"Accuracy:\", accuracy_score(Y_test, Y_pred))\n",
    "print(\"Classification Report:\\n\", classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x22d2ccb1880>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tr+TIgRBaXHqSYf+3naDgChbMigPAz89GSYk/HyxpyXU3513oRxQvsmLAipvlGDfvr81qRXg1d+5cWrZsSVBQEF26dDlrkxTxnO8/DyPu1gLa3lxIg2altLvjBJd1K+TglyFAZRZk05uRdB96mPgeBTSNO82fX9rHyXwjOz9q4NBXYGgFYU3K7IexvvWs76vfoNyhjX89Hy52Sp2QlRnBP+a3IXNd1Dmvr191CdmfNSbvcH1y94Xx+ow4QkLLib3sZOX9m5swY3J7vtjShLzD9dnySSTvLWrFH7r/EmyUFAfwyt+vZNV/m3Pix/PvcilysfB6ELJkyRJGjhzJ008/zeeff0779u1JSkpy2CRFPKdFx5Ps/dTED/uCADiyM5jvPgvj8psKAThxIJCTx4xcer3Ffk+QqYJmHYrI/TzUoa/185oy+eqOzE6+gg2vRlFxjiTHPwZdxrOdr+bVP8exKyPcY88l4gkBAVZu73WAopMB7P/2t1cphISWcdLi3ooI8Q1V27a7e/gqr5djXn75ZQYNGsTDDz8MwPz580lPT+fNN99k7NixXh6d77thyBGKi/yZntgOg78NW4WBW584SIdePwJw8ljl/5GGNi5zuC+0cRlFx375P9k/PJRP9JWnCDaXk/t5KKumxXDymJHkcbkAGOtbueOpXFp0OonBD75e2YB//vUy7n91N3G3FlyYhxX5na65Pp8xU7IJDKrg+A+BjBt6LZZC4znbNm12ijvv+Z4FM9te4FFKbaQ5Ic55NQgpLS0lKyuLJ5980n7Oz8+PxMTEc74OuKSkxOHVxRaL5aw24prt6Q358r+NuGfmXiIvO82RnfVZMbkFpsgyOvb5odr9XD/wl9Rz07jT+NezseypliSNOkBAoI2QhuUObZq1P8XJfCMbXmuqIERqva+2NWLY/ddjCi/ltl4HGDv1C0Y+/AcKTziWVho1KWbSzM/YuCaKVf9t7qXRitQdXg2vfvjhByoqKs75OuCq1wWfaerUqQ6vMY6JiblQQ/VZK6fGcMPgI7S/8zhRbU9zde8fua5/HuteaQpAWJPKDEjRD46p5aIf6hHapOys/qrEdDiFtdyPEwd/u/7drEMRx79XfVxqv5LiAI4cDCHn6wbMnHIVFeUGevzpgEObho2LmTpvM7u2N2D2c+28NFKpbazUwLtjNDG1dnjyyScpLCy0HwcOHDj/TeJU6Wl/DL/6U+Dnb8NmrfxD3yCmhLAmpez99Jf6d/FJPw5mh9K8Y9Fv9ntkZ30Mfrazyji/bhMW8dvXRWorPz+oZ/xl4nWjJsU8P38ze3aZmTHpKmw+XMMX19h+Xh3jzmHz4SDEq+WYxo0b4+/vf87XAVe9LvhMgYGB9pfnSM2Iu+UE6+ZGEx5dQuTlpzm8I4SNC6Lo/OfKpbYGA/yhfz4fz4mmcctiGsSUkPFyM8IiS4nvcQKA3M9DOZAdQquuFgJDreR+Hkr6lOZ06PUjwebKZbqf/6cx/vWsNL3iJwB2rmxA1rtN6P38fu88uMjPgoLLiW72k/1zVPRpWl1m4aSlHpbCevzl4b1s+SSC4z8EYQ4vJfnu72nUpJiNayqzhY2aVGZAjuUFs2BWHOYGpfa+zlwJExN7knoBNsJMZQTXL6fVZZXl5H27fWsbbhFXeDUIMRqNdOrUiTVr1tCrVy+g8s1/a9asYejQod4c2kXjzonfk/FyMz4Y35KiHys3K7v23qPc/Nhhe5sb/nqE0p/8eP//WlJsCaDFNSd5OO1b+x4h/kYrXy1vxJoZl1Be6keDmBKu65/H9QMcS2prZ19CwSEjfgE2mrQqpu/sPbS748QFfV6RX7ssrpDn52+xfx40YhcAq1dcwpznrySmZRG3JB/EHF6GpbAeu3eaGf1IV3L3hQFw9bU/cEnzn7ik+U+8nb7Woe/ka++w//cz07cRGf3LHjmzF208q434nqqSirt9+Cqvv8BuyZIlpKSk8Oqrr3LttdcyY8YMli5dyjfffHPWXJFf0wvs5GKnF9jJxawuvMDuroyHqRdy7pVU1VV2qpT3b11YK5/TXV5fovuXv/yFY8eOMWHCBPLy8ujQoQMrV648bwAiIiIidZvXgxCAoUOHqvwiIiI+R+UY52pFECIiIuKL9O4Y5zSRQkRERLxCmRAREREPUTnGOQUhIiIiHqIgxDmVY0RERMQrlAkRERHxEGVCnFMmRERERLxCmRAREREPUSbEOQUhIiIiHmLD/X0+vPpuFQ9TOUZERES8QpkQERERD1E5xjkFISIiIh6iIMQ5lWNERETEK5QJERER8RBlQpxTECIiIuIhCkKcUzlGREREvEKZEBEREQ+x2QzY3MxkuHt/baYgRERExEOsGNzerMzd+2szlWNERETEK5QJERER8RBNTHVOQYiIiIiHaE6IcyrHiIiIiFcoCBEREfGQqnKMu4erDh06xP3330+jRo0IDg6mXbt2bNu2zX7dZrMxYcIEmjZtSnBwMImJiezevduhj+PHj9OvXz9MJhPh4eEMGDCAoqIihzZfffUV3bp1IygoiJiYGKZNm+bSOBWEiIiIeEhVOcbdwxUnTpzguuuuo169enz44Yfs3LmTl156iQYNGtjbTJs2jVmzZjF//ny2bNlCSEgISUlJFBcX29v069ePHTt2kJGRwYoVK9iwYQOPPPKI/brFYqFHjx60aNGCrKwsXnjhBSZOnMhrr71W7bFqToiIiIgP+fvf/05MTAwLFy60n4uNjbX/t81mY8aMGYwbN46ePXsC8PbbbxMZGcmyZcvo27cvu3btYuXKlXz22Wd07twZgNmzZ3PHHXfw4osvEh0dzaJFiygtLeXNN9/EaDRyxRVXkJ2dzcsvv+wQrDijTIiIiIiH2GqgFFOVCbFYLA5HSUnJOb/zgw8+oHPnzvz5z38mIiKCq6++mtdff91+ff/+/eTl5ZGYmGg/Zzab6dKlC5mZmQBkZmYSHh5uD0AAEhMT8fPzY8uWLfY2N9xwA0aj0d4mKSmJnJwcTpw4Ua2fj4IQERERD7EBNpubx899xcTEYDab7cfUqVPP+Z379u1j3rx5XHbZZaxatYohQ4bw2GOP8dZbbwGQl5cHQGRkpMN9kZGR9mt5eXlEREQ4XA8ICKBhw4YObc7Vx5nfcT4qx4iIiNQBBw4cwGQy2T8HBgaes53VaqVz584899xzAFx99dV8/fXXzJ8/n5SUlAsy1upSJkRERMRDqrZtd/cAMJlMDsdvBSFNmzYlPj7e4VxcXBy5ubkAREVFAZCfn+/QJj8/334tKiqKo0ePOlwvLy/n+PHjDm3O1ceZ33E+CkJEREQ8xBurY6677jpycnIczn377be0aNECqJykGhUVxZo1a+zXLRYLW7ZsISEhAYCEhAQKCgrIysqyt1m7di1Wq5UuXbrY22zYsIGysjJ7m4yMDNq0aeOwEscZBSEiIiI+ZMSIEWzevJnnnnuOPXv2sHjxYl577TVSU1MBMBgMDB8+nClTpvDBBx+wfft2HnzwQaKjo+nVqxdQmTm57bbbGDRoEFu3buXTTz9l6NCh9O3bl+joaADuu+8+jEYjAwYMYMeOHSxZsoSZM2cycuTIao9Vc0JEREQ8xGozYLjA74655ppreP/993nyySeZNGkSsbGxzJgxg379+tnbjB49mlOnTvHII49QUFDA9ddfz8qVKwkKCrK3WbRoEUOHDuWWW27Bz8+PPn36MGvWLPt1s9nMRx99RGpqKp06daJx48ZMmDCh2stzAQw2m812/ma1k8ViwWw2s3Z7M0LDlNSRi8+Ebnd5ewgiXlNuLWH1ofkUFhY6TNisDap+P12xZBT+9c89d6O6Kn4qYcdfXqiVz+ku/eYWERERr1A5RkRExEP0Fl3nFISIiIh4iIIQ51SOEREREa9QJkRERMRDvLE6pi5RECIiIuIhVe9/cbcPX6VyjIiIiHiFMiEiIiIeUpkJcXdiag0NphZSECIiIuIhWh3jnMoxIiIi4hXKhIiIiHiI7efD3T58lYIQERERD1E5xjmVY0RERMQrlAkRERHxFNVjnFIQIiIi4ik1UI5B5RgRERGRmqVMiIiIiIdo23bnFISIiIh4iFbHOKdyjIiIiHiFMiEiIiKeYjO4P7HUhzMhCkJEREQ8RHNCnFM5RkRERLxCmRARERFP0WZlTikIERER8RCtjnGuWkHIBx98UO0O//SnP/3uwYiIiMjFo1pBSK9evarVmcFgoKKiwp3xiIiI+BYfLqe4q1pBiNVq9fQ4REREfI7KMc65tTqmuLi4psYhIiIiFxmXg5CKigomT57MJZdcQmhoKPv27QNg/PjxLFiwoMYHKCIiUmfZaujwUS4HIc8++yxpaWlMmzYNo9FoP3/llVfyxhtv1OjgRERE6jZDDR2+yeUg5O233+a1116jX79++Pv728+3b9+eb775pkYHJyIiIr7L5X1CDh06ROvWrc86b7VaKSsrq5FBiYiI+ARtVuaUy5mQ+Ph4Pvnkk7PO//vf/+bqq6+ukUGJiIj4BM0JccrlTMiECRNISUnh0KFDWK1W3nvvPXJycnj77bdZsWKFJ8YoIiIiPsjlTEjPnj1Zvnw5q1evJiQkhAkTJrBr1y6WL1/Orbfe6okxioiI1E02Q80cPup3vTumW7duZGRk1PRYREREfIrNVnm424ev+t0vsNu2bRu7du0CKueJdOrUqcYGJSIiIr7P5XLMwYMH6datG9deey2PP/44jz/+ONdccw3XX389Bw8e9MQYRURE6iYvTEydOHEiBoPB4Wjbtq39enFxMampqTRq1IjQ0FD69OlDfn6+Qx+5ubkkJydTv359IiIiGDVqFOXl5Q5t1q1bR8eOHQkMDKR169akpaW5NlB+RxAycOBAysrK2LVrF8ePH+f48ePs2rULq9XKwIEDXR6AiIiIz/LSnJArrriCI0eO2I+NGzfar40YMYLly5fz7rvvsn79eg4fPkzv3r3t1ysqKkhOTqa0tJRNmzbx1ltvkZaWxoQJE+xt9u/fT3JyMt27dyc7O5vhw4czcOBAVq1a5dI4XS7HrF+/nk2bNtGmTRv7uTZt2jB79my6devmanciIiJSwwICAoiKijrrfGFhIQsWLGDx4sXcfPPNACxcuJC4uDg2b95M165d+eijj9i5cyerV68mMjKSDh06MHnyZMaMGcPEiRMxGo3Mnz+f2NhYXnrpJQDi4uLYuHEj06dPJykpqdrjdDkTEhMTc85NySoqKoiOjna1OxEREZ9lsNXMAWCxWByOkpKS3/ze3bt3Ex0dTatWrejXrx+5ubkAZGVlUVZWRmJior1t27Ztad68OZmZmQBkZmbSrl07IiMj7W2SkpKwWCzs2LHD3ubMPqraVPVRXS4HIS+88ALDhg1j27Zt9nPbtm3j8ccf58UXX3S1OxEREd9Vg3NCYmJiMJvN9mPq1Knn/MouXbqQlpbGypUrmTdvHvv376dbt26cPHmSvLw8jEYj4eHhDvdERkaSl5cHQF5enkMAUnW96pqzNhaLhdOnT1f7x1OtckyDBg0wGH6pSZ06dYouXboQEFB5e3l5OQEBAfTv359evXpV+8tFRESkeg4cOIDJZLJ/DgwMPGe722+/3f7fV111FV26dKFFixYsXbqU4OBgj4/TFdUKQmbMmOHhYYiIiPigmths7Of7TSaTQxBSXeHh4Vx++eXs2bOHW2+9ldLSUgoKChyyIfn5+fY5JFFRUWzdutWhj6rVM2e2+fWKmvz8fEwmk0uBTrWCkJSUlGp3KCIiIj+rBS+wKyoqYu/evTzwwAN06tSJevXqsWbNGvr06QNATk4Oubm5JCQkAJCQkMCzzz7L0aNHiYiIACAjIwOTyUR8fLy9zf/+9z+H78nIyLD3UV2/e7MyqFxrXFpa6nDu90RpIiIiUjOeeOIJ7rzzTlq0aMHhw4d5+umn8ff3595778VsNjNgwABGjhxJw4YNMZlMDBs2jISEBLp27QpAjx49iI+P54EHHmDatGnk5eUxbtw4UlNT7SWgwYMHM2fOHEaPHk3//v1Zu3YtS5cuJT093aWxuhyEnDp1ijFjxrB06VJ+/PHHs65XVFS42qWIiIhv8kIm5ODBg9x77738+OOPNGnShOuvv57NmzfTpEkTAKZPn46fnx99+vShpKSEpKQkXnnlFfv9/v7+rFixgiFDhpCQkEBISAgpKSlMmjTJ3iY2Npb09HRGjBjBzJkzadasGW+88YZLy3PhdwQho0eP5uOPP2bevHk88MADzJ07l0OHDvHqq6/y/PPPu9qdiIiI7/JCEPLOO+84vR4UFMTcuXOZO3fub7Zp0aLFWeWWX7vpppv44osvXBvcr7gchCxfvpy3336bm266iYcffphu3brRunVrWrRowaJFi+jXr59bAxIREZGLg8v7hBw/fpxWrVoBlfM/jh8/DsD111/Phg0banZ0IiIidZmXtm2vK1wOQlq1asX+/fuByl3Wli5dClRmSH69+YmIiMjFrCZ3TPVFLgchDz/8MF9++SUAY8eOZe7cuQQFBTFixAhGjRpV4wMUERER3+TynJARI0bY/zsxMZFvvvmGrKwsWrduzVVXXVWjgxMREanTasE+IbWZW/uEQOUM2hYtWtTEWEREROQiUq0gZNasWdXu8LHHHvvdgxEREZGLR7WCkOnTp1erM4PBoCBERETkZwbcn1jqu2tjqhmEVK2Gqa0mtetEgKGet4chcsGtOuzaFskivsRy0kqDy709ivOowRfY+SKXV8eIiIiI1AS3J6aKiIjIb9DqGKcUhIiIiHiKghCnVI4RERERr1AmRERExENqYtt1bdv+K5988gn3338/CQkJHDp0CIB//OMfbNy4sUYHJyIiUqfZaujwUS4HIf/5z39ISkoiODiYL774gpKSEgAKCwt57rnnanyAIiIi4ptcDkKmTJnC/Pnzef3116lX75e9Oa677jo+//zzGh2ciIhInaZMiFMuzwnJycnhhhtuOOu82WymoKCgJsYkIiLiEzQnxDmXMyFRUVHs2bPnrPMbN26kVatWNTIoERER8X0uByGDBg3i8ccfZ8uWLRgMBg4fPsyiRYt44oknGDJkiCfGKCIiUjdVbdvu7uGjXC7HjB07FqvVyi233MJPP/3EDTfcQGBgIE888QTDhg3zxBhFRETqJm1W5pTLQYjBYOCpp55i1KhR7Nmzh6KiIuLj4wkNDfXE+ERERMRH/e7NyoxGI/Hx8TU5FhEREZ+iianOuRyEdO/eHYPht+tTa9eudWtAIiIiPkPlGKdcDkI6dOjg8LmsrIzs7Gy+/vprUlJSampcIiIi4uNcDkKmT59+zvMTJ06kqKjI7QGJiIj4jBoox/hyJqTG3qJ7//338+abb9ZUdyIiInWfdkx1qsaCkMzMTIKCgmqqOxEREfFxLpdjevfu7fDZZrNx5MgRtm3bxvjx42tsYCIiInWeJqY65XIQYjabHT77+fnRpk0bJk2aRI8ePWpsYCIiInWdlug651IQUlFRwcMPP0y7du1o0KCBp8YkIiIiFwGX5oT4+/vTo0cPvS1XRERE3ObyxNQrr7ySffv2eWIsIiIivkWrY5xyOQiZMmUKTzzxBCtWrODIkSNYLBaHQ0RERKQ6qj0nZNKkSfztb3/jjjvuAOBPf/qTw/btNpsNg8FARUVFzY9SRESkDtLEVOeqHYQ888wzDB48mI8//tiT4xEREfEtPhxEuKvaQYjNVvlTvPHGGz02GBEREbl4uDQnxNnbc0VERORXasHE1Oeffx6DwcDw4cPt54qLi0lNTaVRo0aEhobSp08f8vPzHe7Lzc0lOTmZ+vXrExERwahRoygvL3dos27dOjp27EhgYCCtW7cmLS3NpbG5tE/I5Zdfft5A5Pjx4y4NQERExFd5e07IZ599xquvvspVV13lcH7EiBGkp6fz7rvvYjabGTp0KL179+bTTz8FKvcFS05OJioqik2bNnHkyBEefPBB6tWrx3PPPQfA/v37SU5OZvDgwSxatIg1a9YwcOBAmjZtSlJSUrXG51IQ8swzz5y1Y6qIiIjUPkVFRfTr14/XX3+dKVOm2M8XFhayYMECFi9ezM033wzAwoULiYuLY/PmzXTt2pWPPvqInTt3snr1aiIjI+nQoQOTJ09mzJgxTJw4EaPRyPz584mNjeWll14CIC4ujo0bNzJ9+nTPBCF9+/YlIiLClVtEREQuXjX47phfb4MRGBhIYGDgb96WmppKcnIyiYmJDkFIVlYWZWVlJCYm2s+1bduW5s2bk5mZSdeuXcnMzKRdu3ZERkba2yQlJTFkyBB27NjB1VdfTWZmpkMfVW3OLPucT7XnhGg+iIiIiGuqyjHuHgAxMTGYzWb7MXXq1N/83nfeeYfPP//8nG3y8vIwGo2Eh4c7nI+MjCQvL8/e5swApOp61TVnbSwWC6dPn67Wz8fl1TEiIiJy4R04cACTyWT//FtZkAMHDvD444+TkZFBUFDQhRre71LtTIjValUpRkRExBU1uDrGZDI5HL8VhGRlZXH06FE6duxIQEAAAQEBrF+/nlmzZhEQEEBkZCSlpaVnvQcuPz+fqKgoAKKios5aLVP1+XxtTCYTwcHB1frxuLxtu4iIiFSTF5bo3nLLLWzfvp3s7Gz70blzZ/r162f/73r16rFmzRr7PTk5OeTm5pKQkABAQkIC27dv5+jRo/Y2GRkZmEwm4uPj7W3O7KOqTVUf1eHSxFQRERGp3cLCwrjyyisdzoWEhNCoUSP7+QEDBjBy5EgaNmyIyWRi2LBhJCQk0LVrVwB69OhBfHw8DzzwANOmTSMvL49x48aRmppqz8AMHjyYOXPmMHr0aPr378/atWtZunQp6enp1R6rghAREREP8fY+Ib9l+vTp+Pn50adPH0pKSkhKSuKVV16xX/f392fFihUMGTKEhIQEQkJCSElJYdKkSfY2sbGxpKenM2LECGbOnEmzZs144403qr08F8Bgq8MzTi0WC2azmZvoSYChnreHI3LBrTqc7e0hiHiN5aSVBpfvo7Cw0GHCZm1Q9fupzfDn8A90b3JoRUkxOTP+r1Y+p7s0J0RERES8QuUYERERT6nBzcp8kYIQERERD6mtc0JqC5VjRERExCuUCREREfEUlWOcUhAiIiLiISrHOKdyjIiIiHiFMiEiIiKeonKMUwpCREREPEVBiFMqx4iIiIhXKBMiIiLiIYafD3f78FUKQkRERDxF5RinVI4RERERr1AmRERExEO0T4hzCkJEREQ8ReUYp1SOEREREa9QJkRERMSTfDiT4S4FISIiIh6iOSHOqRwjIiIiXqFMiIiIiKdoYqpTCkJEREQ8ROUY51SOEREREa9QJkRERMRTVI5xSkGIiIiIh6gc45zKMSIiIuIVyoSIiIh4isoxTikIERER8RQFIU6pHCMiIiJeoUyIiIiIh2hiqnMKQkRERDxF5RinVI4RERERr1AmRERExEMMNhsGm3upDHfvr80UhIiIiHiKyjFOqRwjIiIiXqFMiIiIiIdodYxzCkJEREQ8ReUYp1SOEREREa9QECIiIuIhVeUYdw9XzJs3j6uuugqTyYTJZCIhIYEPP/zQfr24uJjU1FQaNWpEaGgoffr0IT8/36GP3NxckpOTqV+/PhEREYwaNYry8nKHNuvWraNjx44EBgbSunVr0tLSXP75KAgRERHxFFsNHS5o1qwZzz//PFlZWWzbto2bb76Znj17smPHDgBGjBjB8uXLeffdd1m/fj2HDx+md+/e9vsrKipITk6mtLSUTZs28dZbb5GWlsaECRPsbfbv309ycjLdu3cnOzub4cOHM3DgQFatWuXSWA02W91dgGyxWDCbzdxETwIM9bw9HJELbtXhbG8PQcRrLCetNLh8H4WFhZhMJm8Px0HV76eOfZ/F3xjkVl8VpcV8/s5Tbj1nw4YNeeGFF7j77rtp0qQJixcv5u677wbgm2++IS4ujszMTLp27cqHH37IH//4Rw4fPkxkZCQA8+fPZ8yYMRw7dgyj0ciYMWNIT0/n66+/tn9H3759KSgoYOXKldUelzIhIiIiHlKT5RiLxeJwlJSUnPf7KyoqeOeddzh16hQJCQlkZWVRVlZGYmKivU3btm1p3rw5mZmZAGRmZtKuXTt7AAKQlJSExWKxZ1MyMzMd+qhqU9VHdSkIERER8ZQaLMfExMRgNpvtx9SpU3/za7dv305oaCiBgYEMHjyY999/n/j4ePLy8jAajYSHhzu0j4yMJC8vD4C8vDyHAKTqetU1Z20sFgunT5+u9o9HS3RFRETqgAMHDjiUYwIDA3+zbZs2bcjOzqawsJB///vfpKSksH79+gsxTJcoCBEREfGgmtpsrGq1S3UYjUZat24NQKdOnfjss8+YOXMmf/nLXygtLaWgoMAhG5Kfn09UVBQAUVFRbN261aG/qtUzZ7b59Yqa/Px8TCYTwcHB1X4mlWNEREQ8xWarmcNNVquVkpISOnXqRL169VizZo39Wk5ODrm5uSQkJACQkJDA9u3bOXr0qL1NRkYGJpOJ+Ph4e5sz+6hqU9VHdSkTIiIi4kOefPJJbr/9dpo3b87JkydZvHgx69atY9WqVZjNZgYMGMDIkSNp2LAhJpOJYcOGkZCQQNeuXQHo0aMH8fHxPPDAA0ybNo28vDzGjRtHamqqvQQ0ePBg5syZw+jRo+nfvz9r165l6dKlpKenuzRWBSEiIiIe4o13xxw9epQHH3yQI0eOYDabueqqq1i1ahW33norANOnT8fPz48+ffpQUlJCUlISr7zyiv1+f39/VqxYwZAhQ0hISCAkJISUlBQmTZpkbxMbG0t6ejojRoxg5syZNGvWjDfeeIOkpCQXn037hIjUWdonRC5mdWGfkM59phBQz719QsrLitn2n3G18jndpTkhIiIi4hUqx4iIiHiIwVp5uNuHr1IQIiIi4im/490v5+zDRykIkbM0iipjwFOHuab7SQKDrRz+LpCXRsSw+6v6AATVr2DAU0dISLJgalBO3gEj/13QmPR/ND5Hbzam/HM/19x8kon9W5K50nxhH0bEiYoK+OdLUaz5TwNOHKtHo8gybr3nOPcNz8dg+KVd7u5AFkyJ5qvNoVSUQ4vLSxj/+n4impUBcPg7I69PimbH1lDKSg106m4hdcohGjT55a2jB/cG8vrkaHZ+FkJ5mYHYuNM8ODqPDtcVXejHFqk1vDonZMOGDdx5551ER0djMBhYtmyZN4cjQKi5nJf/u5uKcgPj7m/FoJva8NqkaIoK/e1t/jrxMJ1vOsm0Yc0ZdGNb3n+9CanPHqJrj8Kz+rtr0A81scRdxCOWzo1gxVuNSX32EK+v/4YBTx3m3Vci+O+CXwLqw98ZGdnrMmJaF/PCv/cwf00O9w3PwxhU+Qe7+Cc//u/eSzEY4O/v7uHl/+6mvNSPCSmxWM9Io09IicVaUdlmzsocWsWfZsKDsRw/qn8L+rKafHeML/JqEHLq1Cnat2/P3LlzvTkMOcM9qUf54bCRl0Y0Jye7PvkHAvl8fRhHvv9le+D4zj+R8W5DvsoMJf+gkQ8XNWLfzmDadPjJoa9WV5ymz1+P8fLImAv9GCLVsnNbCAlJhXRJtBAVU0q3PxbS8caT5GTXt7dJe74p195sYeD4I7Rud5rolqUkJFkIb1yZ5dixNYT8A0b+NiOX2LhiYuOKGTXze3Z/WZ/sjaEAFP7oz6F9Qdwz9Cit4ou5pFUp/Z86Qslpf777xr2VE1LL1ZLNymorrwYht99+O1OmTOGuu+7y5jDkDF17WPj2y2CeevU7lny1g7kf5XD7fT86tNm5rT5dexTSKKoMsNH+D0Vc0qqErPVh9jaBwVbGzv2euU9dwoljWj4ttVN851Nkbwzj4N7KIHvvjiB2bA3hmptPAmC1wtY1Ji5pVcL/3duKe9pdwWPJl7Hpw1/KimWlBjBAPeMvvyjqBdow+MGOrZVBiKlhBc0uLWb1uw0p/smPinJI/0cjwhuXcdlV1X/Zl4ivqVN5wJKSEodXF1ssFi+Oxjc1bV7KHx/8kfdea8I7syO4vP1phkw+RFmZgdXvNgTglXGX8Pi0gyz+fCflZWC1Gpg5qhlfbwm19/PXiYfYuS2EzFWaAyK111+GHuWnk/4MvKEtfv5grYCHxh7h5t4nACj4IYDTp/xZMieCh8bkMeCpI2z7OIxJA1sy7d97uCrhFG07nSKovpUFz0bz8NjDgIEFzzbFWmGwl1oMBnh+yV6e6R9Lr8vaYfCD8MblPLtoH2HhFV78CYineWOzsrqkTgUhU6dO5ZlnnvH2MHyawQ92fxXMwuebArD36/q0bFtM8gM/2oOQnv1/oG2nn5iQ0pKjB42063qK1OcO8WN+Pb74JIyuPQrpcF0Rj/a43JuPInJeGz4IZ+17DRg793tatClm745g5j99yc8TVE9g+3lOR0KShd6PHAPg0itPs3NbCOlvN+aqhFOEN6pg3KvfMfvJZvx3QWMMftC91wlat/sJw8+5ZpsN5vxfM8Ibl/PS+3swBllZ+a9GPP1QLLP+9y2NIst/Y4RS52l1jFN1Kgh58sknGTlypP2zxWIhJkbzDWrS8aMBfP+tY436wO5Arr+jAABjkJWHxuYxaUBLtq6p3Llv/65gWl1xmrsHH+OLT8LocF0RTVuW8t43Xzv0M/717/h6Swij7259QZ5F5HxenxzNX4Ye5aZeBQDExhVz9KCRd2ZHcus9JzA1rMA/wEaLy4sd7ou5rJgdW0PsnzvddJK0zF0U/uiPfwCEmivo2/4KmjavzNxmbwxl62oT/961nZCwysjmsqsO8vmGOFYvbchfhh1F5GJUp4KQwMBA+8tzxDN2fhZCzKUlDucuaVXC0UNGAAICbNQz2hxm/UNlGtvgVxmuL5kTwYeLGzpcf+3jb3l1YjSbP/KtLYelbisp9rP/ua3i52+zzwOsZ7Rxefuf7HNGqhzaF2hfnnsmc6PK0kr2xlAKfgiga4/KknHJ6cqUiN+vZuH5GWxYffhfuaJyzPnUqSBEPO+915ow/YPd9B2Wz4bl4bS5+ifuuP84M0Y1A+CnIn++3BTCoPFHKC32I/9gPa5KOEXi3Sd47ZloAE4cq3fOyahHDxnJP6AgUmqPrrdaeGdWJBGXlFWWY74O5r1XI+jR95fJ2H9+9CjPDW7BlV2LaP+HIrZ9bGJzhpkX/r3H3mbVOw1pflkx5kbl7MoKYd6ES7jrkWPEtK4M6OM6nSLUXMELjzen34g8AoNsfLioEXkHjFx7i+a2+bSaWN3iw6tjvBqEFBUVsWfPL3+R9+/fT3Z2Ng0bNqR58+ZeHNnF69sv6zNpQCwPP3mEfiPyyTtgZP6EaD5+v4G9zdQhLej/f0cYM+d7wsIrOHrISNrfm7Li7UZeHLmI6x6dcpC3pjVlzpPNKPgxgEaRZdzxwA/0G5Fvb3Pd7YU89vxB3pkTybzxzWjWqnKjsiu7nLK3Obg3kIVTm3KywJ/ImFLufSzfPocEKjMkzy7eS9rzTRlzT2sqygy0aFPMxIX7ufQKx1KPyMXEq2/RXbduHd27dz/rfEpKCmlpaee9X2/RlYud3qIrF7O68BbdhNsn1chbdDM/nFArn9NdXs2E3HTTTXgxBhIREfEsrY5xyqublYmIiMjFSxNTRUREPESrY5xTECIiIuIpVhtur8P24XXcKseIiIiIVygTIiIi4imamOqUMiEiIiLiFcqEiIiIeIiBGpiYWiMjqZ0UhIiIiHiKtm13SuUYERER8QplQkRERDxE+4Q4pyBERETEU7Q6ximVY0RERMQrlAkRERHxEIPNhsHNiaXu3l+bKQgRERHxFOvPh7t9+CiVY0RERMQrlAkRERHxEJVjnFMQIiIi4ilaHeOUyjEiIiLiFcqEiIiIeIq2bXdKQYiIiIiHaMdU51SOEREREa9QJkRERMRTVI5xSpkQERERDzFYa+ZwxdSpU7nmmmsICwsjIiKCXr16kZOT49CmuLiY1NRUGjVqRGhoKH369CE/P9+hTW5uLsnJydSvX5+IiAhGjRpFeXm5Q5t169bRsWNHAgMDad26NWlpaS6NVUGIiIiID1m/fj2pqals3ryZjIwMysrK6NGjB6dOnbK3GTFiBMuXL+fdd99l/fr1HD58mN69e9uvV1RUkJycTGlpKZs2beKtt94iLS2NCRMm2Nvs37+f5ORkunfvTnZ2NsOHD2fgwIGsWrWq2mM12Gx1N89jsVgwm83cRE8CDPW8PRyRC27V4WxvD0HEaywnrTS4fB+FhYWYTCZvD8eB/ffTtU8REBDkVl/l5cWs2/rs737OY8eOERERwfr167nhhhsoLCykSZMmLF68mLvvvhuAb775hri4ODIzM+natSsffvghf/zjHzl8+DCRkZEAzJ8/nzFjxnDs2DGMRiNjxowhPT2dr7/+2v5dffv2paCggJUrV1ZrbMqEiIiIeIqthg43FBYWAtCwYUMAsrKyKCsrIzEx0d6mbdu2NG/enMzMTAAyMzNp166dPQABSEpKwmKxsGPHDnubM/uoalPVR3VoYqqIiEgdYLFYHD4HBgYSGBjo9B6r1crw4cO57rrruPLKKwHIy8vDaDQSHh7u0DYyMpK8vDx7mzMDkKrrVdectbFYLJw+fZrg4ODzPpMyISIiIh5S9e4Ydw+AmJgYzGaz/Zg6dep5vz81NZWvv/6ad955x9OP+rsoEyIiIuIpNbhE98CBAw5zQs6XBRk6dCgrVqxgw4YNNGvWzH4+KiqK0tJSCgoKHLIh+fn5REVF2dts3brVob+q1TNntvn1ipr8/HxMJlO1siCgTIiIiEidYDKZHI7fCkJsNhtDhw7l/fffZ+3atcTGxjpc79SpE/Xq1WPNmjX2czk5OeTm5pKQkABAQkIC27dv5+jRo/Y2GRkZmEwm4uPj7W3O7KOqTVUf1aFMiIiIiKfYABf3+ThnHy5ITU1l8eLF/Pe//yUsLMw+h8NsNhMcHIzZbGbAgAGMHDmShg0bYjKZGDZsGAkJCXTt2hWAHj16EB8fzwMPPMC0adPIy8tj3LhxpKam2oOfwYMHM2fOHEaPHk3//v1Zu3YtS5cuJT09vdpjVRAiIiLiIWfO6XCnD1fMmzcPgJtuusnh/MKFC3nooYcAmD59On5+fvTp04eSkhKSkpJ45ZVX7G39/f1ZsWIFQ4YMISEhgZCQEFJSUpg0aZK9TWxsLOnp6YwYMYKZM2fSrFkz3njjDZKSklx5Nu0TIlJXaZ8QuZjVhX1Cbr56LAH+bu4TUlHM2i+er5XP6S5lQkRERDzFRg1MTK2RkdRKCkJEREQ8RS+wc0qrY0RERMQrlAkRERHxFCtgqIE+fJSCEBEREQ/xxuqYukTlGBEREfEKZUJEREQ8RRNTnVIQIiIi4ikKQpxSOUZERES8QpkQERERT1EmxCkFISIiIp6iJbpOqRwjIiIiXqFMiIiIiIdonxDnFISIiIh4iuaEOKVyjIiIiHiFMiEiIiKeYrWBwc1MhtV3MyEKQkRERDxF5RinVI4RERERr1AmRERExGNqIBOC72ZCFISIiIh4isoxTqkcIyIiIl6hTIiIiIinWG24XU7R6hgRERFxmc1aebjbh49SOUZERES8QpkQERERT9HEVKcUhIiIiHiK5oQ4pXKMiIiIeIUyISIiIp6icoxTCkJEREQ8xUYNBCE1MpJaSeUYERER8QplQkRERDxF5RinFISIiIh4itUKuLnZmFWblYmIiIjUKGVCREREPEXlGKcUhIiIiHiKghCnVI4RERERr1AmRERExFO0bbtTyoSIiIh4iM1mrZHDFRs2bODOO+8kOjoag8HAsmXLfjUmGxMmTKBp06YEBweTmJjI7t27HdocP36cfv36YTKZCA8PZ8CAARQVFTm0+eqrr+jWrRtBQUHExMQwbdo0l38+CkJERER8yKlTp2jfvj1z58495/Vp06Yxa9Ys5s+fz5YtWwgJCSEpKYni4mJ7m379+rFjxw4yMjJYsWIFGzZs4JFHHrFft1gs9OjRgxYtWpCVlcULL7zAxIkTee2111waq8oxIiIinmKzuV9OcXFi6u23387tt9/+G13ZmDFjBuPGjaNnz54AvP3220RGRrJs2TL69u3Lrl27WLlyJZ999hmdO3cGYPbs2dxxxx28+OKLREdHs2jRIkpLS3nzzTcxGo1cccUVZGdn8/LLLzsEK+ejTIiIiIinVK2OcfeoIfv37ycvL4/ExET7ObPZTJcuXcjMzAQgMzOT8PBwewACkJiYiJ+fH1u2bLG3ueGGGzAajfY2SUlJ5OTkcOLEiWqPR5kQERGROsBisTh8DgwMJDAw0KU+8vLyAIiMjHQ4HxkZab+Wl5dHRESEw/WAgAAaNmzo0CY2NvasPqquNWjQoFrjUSZERETEU6zWmjmAmJgYzGaz/Zg6daqXH859yoSIiIh4iq0Gluj+XI45cOAAJpPJftrVLAhAVFQUAPn5+TRt2tR+Pj8/nw4dOtjbHD161OG+8vJyjh8/br8/KiqK/Px8hzZVn6vaVIcyISIiInWAyWRyOH5PEBIbG0tUVBRr1qyxn7NYLGzZsoWEhAQAEhISKCgoICsry95m7dq1WK1WunTpYm+zYcMGysrK7G0yMjJo06ZNtUsxoCBERETEY2xWa40crigqKiI7O5vs7GygcjJqdnY2ubm5GAwGhg8fzpQpU/jggw/Yvn07Dz74INHR0fTq1QuAuLg4brvtNgYNGsTWrVv59NNPGTp0KH379iU6OhqA++67D6PRyIABA9ixYwdLlixh5syZjBw50qWxqhwjIiLiKTVYjqmubdu20b17d/vnqsAgJSWFtLQ0Ro8ezalTp3jkkUcoKCjg+uuvZ+XKlQQFBdnvWbRoEUOHDuWWW27Bz8+PPn36MGvWLPt1s9nMRx99RGpqKp06daJx48ZMmDDBpeW5AAabre6+GcdisWA2m7mJngQY6nl7OCIX3KrD2d4egojXWE5aaXD5PgoLCx3mStQGVb+fbg7+CwEG4/lvcKLcVsra00tq5XO6S5kQERERT7HawKC36P4WBSEiIiKeYrMBrs3pOHcfvkkTU0VERMQrlAkRERHxEJvVhs3Nckwdnrp5XgpCREREPMVmxf1yjJv312Iqx4iIiIhXKBMiIiLiISrHOKcgRERExFNUjnGqTgchVdFhOWVub0gnUhdZTvru/zmJnI+lqPLPf23OFNTE76dyys7fqI6q00HIyZMnAdjI/7w8EhHvaHC5t0cg4n0nT57EbDZ7exgOjEYjUVFRbMyrmd9PUVFRGI3u7bxaG9XpbdutViuHDx8mLCwMg8Hg7eFcdCwWCzExMWe9XlrkYqG/A95ls9k4efIk0dHR+PnVvnUWxcXFlJaW1khfRqPR4d0uvqJOZ0L8/Pxo1qyZt4dx0at6rbTIxUp/B7yntmVAzhQUFOSTgUNNqn2ho4iIiFwUFISIiIiIVygIkd8tMDCQp59+msDAQG8PRcQr9HdAxD11emKqiIiI1F3KhIiIiIhXKAgRERERr1AQIiIiIl6hIERERES8QkGI/G5z586lZcuWBAUF0aVLF7Zu3ertIYlcEBs2bODOO+8kOjoag8HAsmXLvD0kkTpJQYj8LkuWLGHkyJE8/fTTfP7557Rv356kpCSOHj3q7aGJeNypU6do3749c+fO9fZQROo0LdGV36VLly5cc801zJkzB6h8j09MTAzDhg1j7NixXh6dyIVjMBh4//336dWrl7eHIlLnKBMiListLSUrK4vExET7OT8/PxITE8nMzPTiyEREpC5RECIu++GHH6ioqCAyMtLhfGRkJHl5eV4alYiI1DUKQkRERMQrFISIyxo3boy/vz/5+fkO5/Pz84mKivLSqEREpK5RECIuMxqNdOrUiTVr1tjPWa1W1qxZQ0JCghdHJiIidUmAtwcgddPIkSNJSUmhc+fOXHvttcyYMYNTp07x8MMPe3toIh5XVFTEnj177J/3799PdnY2DRs2pHnz5l4cmUjdoiW68rvNmTOHF154gby8PDp06MCsWbPo0qWLt4cl4nHr1q2je/fuZ51PSUkhLS3twg9IpI5SECIiIiJeoTkhIiIi4hUKQkRERMQrFISIiIiIVygIEREREa9QECIiIiJeoSBEREREvEJBiIiIiHiFghARD3vooYfo1auX/fNNN93E8OHDL/g41q1bh8FgoKCg4DfbGAwGli1bVu0+J06cSIcOHdwa13fffYfBYCA7O9utfkSk7lEQIhelhx56CIPBgMFgwGg00rp1ayZNmkR5ebnHv/u9995j8uTJ1WpbncBBRKSu0rtj5KJ12223sXDhQkpKSvjf//5Hamoq9erV48knnzyrbWlpKUajsUa+t2HDhjXSj4hIXadMiFy0AgMDiYqKokWLFgwZMoTExEQ++OAD4JcSyrPPPkt0dDRt2rQB4MCBA9xzzz2Eh4fTsGFDevbsyXfffWfvs6KigpEjRxIeHk6jRo0YPXo0v34zwq/LMSUlJYwZM4aYmBgCAwNp3bo1CxYs4LvvvrO/n6RBgwYYDAYeeughoPKtxVOnTiU2Npbg4GDat2/Pv//9b4fv+d///sfll19OcHAw3bt3dxhndY0ZM4bLL7+c+vXr06pVK8aPH09ZWdlZ7V599VViYmKoX78+99xzD4WFhQ7X33jjDeLi4ggKCqJt27a88sorLo9FRHyPghCRnwUHB1NaWmr/vGbNGnJycsjIyGDFihWUlZWRlJREWFgYn3zyCZ9++imhoaHcdttt9vteeukl0tLSePPNN9m4cSPHjx/n/fffd/q9Dz74IP/617+YNWsWu3bt4tVXXyU0NJSYmBj+85//AJCTk8ORI0eYOXMmAFOnTuXtt99m/vz57NixgxEjRnD//fezfv16oDJY6t27N3feeSfZ2dkMHDiQsWPHuvwzCQsLIy0tjZ07dzJz5kxef/11pk+f7tBmz549LF26lOXLl7Ny5Uq++OILHn30Ufv1RYsWMWHCBJ599ll27drFc889x/jx43nrrbdcHo+I+BibyEUoJSXF1rNnT5vNZrNZrVZbRkaGLTAw0PbEE0/Yr0dGRtpKSkrs9/zjH/+wtWnTxma1Wu3nSkpKbMHBwbZVq1bZbDabrWnTprZp06bZr5eVldmaNWtm/y6bzWa78cYbbY8//rjNZrPZcnJybIAtIyPjnOP8+OOPbYDtxIkT9nPFxcW2+vXr2zZt2uTQdsCAAbZ7773XZrPZbE8++aQtPj7e4fqYMWPO6uvXANv777//m9dfeOEFW6dOneyfn376aZu/v7/t4MGD9nMffvihzc/Pz3bkyBGbzWazXXrppbbFixc79DN58mRbQkKCzWaz2fbv328DbF988cVvfq+I+CbNCZGL1ooVKwgNDaWsrAyr1cp9993HxIkT7dfbtWvnMA/kyy+/ZM+ePYSFhTn0U1xczN69eyksLOTIkSN06dLFfi0gIIDOnTufVZKpkp2djb+/PzfeeGO1x71nzx5++uknbr31VofzpaWlXH311QDs2rXLYRwACQkJ1f6OKkuWLGHWrFns3buXoqIiysvLMZlMDm2aN2/OJZdc4vA9VquVnJwcwsLC2Lt3LwMGDGDQoEH2NuXl5ZjNZpfHIyK+RUGIXLS6d+/OvHnzMBqNREdHExDg+NchJCTE4XNRURGdOnVi0aJFZ/XVpEmT3zWG4OBgl+8pKioCID093eGXP1TOc6kpmZmZ9OvXj2eeeYakpCTMZjPvvPMOL730kstjff31188Kivz9/WtsrCJSNykIkYtWSEgIrVu3rnb7jh07smTJEiIiIs7KBlRp2rQpW7Zs4YYbbgAq/8WflZVFx44dz9m+Xbt2WK1W1q9fT2Ji4lnXqzIxFRUV9nPx8fEEBgaSm5v7mxmUuLg4+yTbKps3bz7/Q55h06ZNtGjRgqeeesp+7vvvvz+rXW5uLocPHyY6Otr+PX5+frRp04bIyEiio6PZt28f/fr1c+n7RcT3aWKqSDX169ePxo0b07NnTz755BP279/PunXreOyxxzh48CAAjz/+OM8//zzLli3jm2++4dFHH3W6x0fLli1JSUmhf//+LFu2zN7n0qVLAWjRogUGg4EVK1Zw7NgxioqKCAsL44knnmDEiBG89dZb7N27l88//5zZs2fbJ3sOHjyY3bt3M2rUKHJycli8eDFpaWkuPe9ll11Gbm4u77zzDnv37mXWrFnnnGQbFBRESkoKX375JZ988gmPPfYY99xzD1FRUQA888wzTJ06lVmzZvHtt9+yfft2Fi5cyMsvv+zSeETE9ygIEamm+vXrs2HDBpo3b07v3r2Ji4tjwIABFBcX2zMjf/vb33jggQdISUkhISGBsLAw7rrrLqf9zps3j7vvvptHH32Utm3bMmjQIE6dOgXAJZdcwjPPPMPYsWOJjIxk6NChAEyePJnx48czdepU4uLiuO2220hPTyc2NhaonKfxn//8h2XLltG+fXvmz5/Pc88959Lz/ulPf2LEiBEMHTqUDh06sGnTJsaPH39Wu9atW9O7d2/uuOMOevTowVVXXeWwBHfgwIG88cYbLFy4kHbt2nHjjTeSlpZmH6uIXLwMtt+aMSciIiLiQcqEiIiIiFcoCBERERGvUBAiIiIiXqEgRERERLxCQYiIiIh4hYIQERER8QoFISIiIuIVCkJERETEKxSEiIiIiFcoCBERERGvUBAiIiIiXqEgRERERLzi/wGlHJRyAoeNVAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "cm = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = cm)\n",
    "\n",
    "cm_display.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                feature  importance\n",
      "9    Had_peripheral_sum    1.235178\n",
      "5         L2_spread_sum    1.164493\n",
      "1    L1_wide_centre_sum    1.085463\n",
      "8        Had_centre_sum    0.960995\n",
      "0         L1_centre_sum    0.803050\n",
      "3         L2_centre_sum    0.723700\n",
      "2   L1_wider_centre_sum    0.687853\n",
      "4    L2_wide_centre_sum    0.641656\n",
      "10          Had_out_sum    0.587324\n",
      "7         L3_centre_sum    0.357355\n",
      "6       L2_vertical_sum    0.327456\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Assuming `model` is your trained XGBoost model (e.g., after fitting with your training data)\n",
    "# Make sure to replace `model` with the actual variable name of your trained model\n",
    "\n",
    "# Create a SHAP explainer\n",
    "explainer = shap.TreeExplainer(XGBoost)\n",
    "shap_values = explainer.shap_values(X_train)\n",
    "\n",
    "# Sum the SHAP values for each feature across all samples to get a measure of overall importance\n",
    "shap_sum = np.abs(shap_values).mean(axis=0)\n",
    "importance_df = pd.DataFrame({'feature': X_train.columns, 'importance': shap_sum})\n",
    "importance_df.sort_values(by='importance', ascending=False, inplace=True)\n",
    "\n",
    "# Print the top 30 features\n",
    "top_50_features = importance_df.head(50)\n",
    "print(top_50_features)\n",
    "\n",
    "# Plot summary plot for the top 30 features\n",
    "#shap.summary_plot(shap_values, X_train, feature_names=X_train.columns, plot_type=\"bar\", max_display=15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the SHAP values for the top features as a bar chart\n",
    "top_features = top_50_features.head(15)  # Assuming you want the top 15 features\n",
    "shap_values_summary = np.abs(shap_values).mean(0)\n",
    "top_shap_values_summary = shap_values_summary[top_features.index]\n",
    "\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.barh(top_features['feature'], top_features['importance'], color='navy')\n",
    "plt.gca().invert_yaxis()  # To display the most important feature on top\n",
    "plt.xlabel('Mean Absolute SHAP Value (Impact on Model Output)')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Ranking of SHAP Values for 11 supercell sumations')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot summary plot for the top features using a bee swarm plot\n",
    "#shap.summary_plot(shap_values, X_train, feature_names=X_train.columns, plot_type='dot', max_display=15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8966548116076491"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(Y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.9548425003073929\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "y_scores = XGBoost.predict_proba(X_test)[:, 1]\n",
    "\n",
    "roc_auc = roc_auc_score(Y_test, y_scores)\n",
    "print(f\"ROC AUC Score: {roc_auc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BDT_model_11prams_32trees_2depth_XGBoost_1.joblib']"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "\n",
    "model_filename = 'BDT_model_11prams_32trees_2depth_XGBoost_1.joblib'\n",
    "dump(XGBoost, model_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model contains 32 trees.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "# Get the number of boosting rounds from the model\n",
    "number_of_trees = XGBoost.get_booster().num_boosted_rounds()\n",
    "\n",
    "print(f\"The model contains {number_of_trees} trees.\")\n",
    "\n",
    "# Visualize the first tree (index 0)\n",
    "# Optionally, you can change the index to visualize other trees\n",
    "xgb.plot_tree(XGBoost, num_trees=0)\n",
    "plt.rcParams['figure.figsize'] = [10,5]  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Qu(t),null;if(r=0!=(128&t.flags),null===(l=o.rendering))if(r)qu(o,!1);else{if(0!==Fl||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(l=lo(e))){for(t.flags|=128,qu(o,!1),null!==(r=l.updateQueue)&&(t.updateQueue=r,t.flags|=4),t.subtreeFlags=0,r=n,n=t.child;null!==n;)e=r,(o=n).flags&=14680066,null===(l=o.alternate)?(o.childLanes=0,o.lanes=e,o.child=null,o.subtreeFlags=0,o.memoizedProps=null,o.memoizedState=null,o.updateQueue=null,o.dependencies=null,o.stateNode=null):(o.childLanes=l.childLanes,o.lanes=l.lanes,o.child=l.child,o.subtreeFlags=0,o.deletions=null,o.memoizedProps=l.memoizedProps,o.memoizedState=l.memoizedState,o.updateQueue=l.updateQueue,o.type=l.type,e=l.dependencies,o.dependencies=null===e?null:{lanes:e.lanes,firstContext:e.firstContext}),n=n.sibling;return 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t=void 0;\"string\"==typeof this.props.plot_cmap?this.props.plot_cmap in je.colors?t=je.colors[this.props.plot_cmap]:(console.log(\"Invalid color map name, reverting to default.\"),t=je.colors.RdBu):Array.isArray(this.props.plot_cmap)&&(t=this.props.plot_cmap),this.colors=t.map((function(e){return q(e)})),this.brighterColors=[1.45,1.6].map((function(t,n){return e.colors[n].brighter(t)}));var n=ze(\",.4\");null!=this.props.ordering_keys&&null!=this.props.ordering_keys_time_format?(this.parseTime=Tr(this.props.ordering_keys_time_format),this.formatTime=Cr(this.props.ordering_keys_time_format),this.xtickFormat=function(e){return\"object\"==Za(e)?this.formatTime(e):n(e)}):(this.parseTime=null,this.formatTime=null,this.xtickFormat=n),this.xscale=De(),this.xaxis=dn().scale(this.xscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.xtickFormat(t)})).tickPadding(-18),this.ytickFormat=n,this.yscale=De(),this.yaxis=pn(an,undefined).scale(this.yscale).tickSizeInner(4).tickSizeOuter(0).tickFormat((function(t){return e.ytickFormat(e.invLinkFunction(t))})).tickPadding(2),this.xlabel.node().onchange=function(){return e.internalDraw()},this.ylabel.node().onchange=function(){return e.internalDraw()},this.svg.on(\"mousemove\",(function(t){return e.mouseMoved(t)})),this.svg.on(\"click\",(function(){return alert(\"This original index of the sample you clicked is \"+e.nearestExpIndex)})),this.svg.on(\"mouseout\",(function(t){return e.mouseOut(t)})),window.addEventListener(\"resize\",this.redraw),window.setTimeout(this.redraw,50)}},{key:\"componentDidUpdate\",value:function(){this.draw()}},{key:\"mouseOut\",value:function(){this.hoverLine.attr(\"display\",\"none\"),this.hoverx.attr(\"display\",\"none\"),this.hoverxOutline.attr(\"display\",\"none\"),this.hoverxTitle.attr(\"display\",\"none\"),this.hovery.attr(\"display\",\"none\"),this.hoveryOutline.attr(\"display\",\"none\"),this.hoverGroup1.attr(\"display\",\"none\"),this.hoverGroup2.attr(\"display\",\"none\")}},{key:\"mouseMoved\",value:function(e){var t,n,r=this;this.hoverLine.attr(\"display\",\"\"),this.hoverx.attr(\"display\",\"\"),this.hoverxOutline.attr(\"display\",\"\"),this.hoverxTitle.attr(\"display\",\"\"),this.hovery.attr(\"display\",\"\"),this.hoveryOutline.attr(\"display\",\"\"),this.hoverGroup1.attr(\"display\",\"\"),this.hoverGroup2.attr(\"display\",\"\");var a=function(e,t){if(e=function(e){let t;for(;t=e.sourceEvent;)e=t;return e}(e),void 0===t&&(t=e.currentTarget),t){var n=t.ownerSVGElement||t;if(n.createSVGPoint){var r=n.createSVGPoint();return r.x=e.clientX,r.y=e.clientY,[(r=r.matrixTransform(t.getScreenCTM().inverse())).x,r.y]}if(t.getBoundingClientRect){var a=t.getBoundingClientRect();return[e.clientX-a.left-t.clientLeft,e.clientY-a.top-t.clientTop]}}return[e.pageX,e.pageY]}(e,this.svg.node())[0];if(this.props.explanations){for(t=0;t<this.currExplanations.length;++t)(!n||Math.abs(n.xmapScaled-a)>Math.abs(this.currExplanations[t].xmapScaled-a))&&(n=this.currExplanations[t]);this.nearestExpIndex=n.origInd,this.hoverLine.attr(\"x1\",n.xmapScaled).attr(\"x2\",n.xmapScaled).attr(\"y1\",0+this.topOffset).attr(\"y2\",this.height),this.hoverx.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxOutline.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-5).text(this.xtickFormat(n.xmap)),this.hoverxTitle.attr(\"x\",n.xmapScaled).attr(\"y\",this.topOffset-18).text(n.count>1?n.count+\" averaged samples\":\"\"),this.hovery.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint))),this.hoveryOutline.attr(\"x\",this.leftOffset-6).attr(\"y\",n.joinPointy).text(this.ytickFormat(this.invLinkFunction(n.joinPoint)));for(var i,o,u=[],l=this.currPosOrderedFeatures.length-1;l>=0;--l){var s=this.currPosOrderedFeatures[l],c=n.features[s];o=5+(c.posyTop+c.posyBottom)/2,(!i||o-i>=15)&&c.posyTop-c.posyBottom>=6&&(u.push(c),i=o)}var f=[];i=void 0;var p,d=Ga(this.currNegOrderedFeatures);try{for(d.s();!(p=d.n()).done;){var h=p.value,v=n.features[h];o=5+(v.negyTop+v.negyBottom)/2,(!i||i-o>=15)&&v.negyTop-v.negyBottom>=6&&(f.push(v),i=o)}}catch(e){d.e(e)}finally{d.f()}var g=function(e){var t=\"\";return null!==e.value&&void 0!==e.value&&(t=\" = \"+(isNaN(e.value)?e.value:r.ytickFormat(e.value))),n.count>1?\"mean(\"+r.props.featureNames[e.ind]+\")\"+t:r.props.featureNames[e.ind]+t},y=this.hoverGroup1.selectAll(\".pos-values\").data(u);y.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(y).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),y.exit().remove();var m=this.hoverGroup2.selectAll(\".pos-values\").data(u);m.enter().append(\"text\").attr(\"class\",\"pos-values\").merge(m).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.posyTop+e.posyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[0]).text(g),m.exit().remove();var b=this.hoverGroup1.selectAll(\".neg-values\").data(f);b.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(b).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"stroke\",\"#fff\").attr(\"fill\",\"#fff\").attr(\"stroke-width\",\"4\").attr(\"stroke-linejoin\",\"round\").attr(\"opacity\",1).text(g),b.exit().remove();var _=this.hoverGroup2.selectAll(\".neg-values\").data(f);_.enter().append(\"text\").attr(\"class\",\"neg-values\").merge(_).attr(\"x\",n.xmapScaled+5).attr(\"y\",(function(e){return 4+(e.negyTop+e.negyBottom)/2})).attr(\"text-anchor\",\"start\").attr(\"font-size\",12).attr(\"fill\",this.colors[1]).text(g),_.exit().remove()}}},{key:\"draw\",value:function(){var e=this;if(this.props.explanations&&0!==this.props.explanations.length){(0,Re.each)(this.props.explanations,(function(e,t){return e.origInd=t}));var t,n={},r={},a={},i=Ga(this.props.explanations);try{for(i.s();!(t=i.n()).done;){var o=t.value;for(var u in o.features)void 0===n[u]&&(n[u]=0,r[u]=0,a[u]=0),o.features[u].effect>0?n[u]+=o.features[u].effect:r[u]-=o.features[u].effect,null!==o.features[u].value&&void 0!==o.features[u].value&&(a[u]+=1)}}catch(e){i.e(e)}finally{i.f()}this.usedFeatures=(0,Re.sortBy)((0,Re.keys)(n),(function(e){return-(n[e]+r[e])})),console.log(\"found \",this.usedFeatures.length,\" used features\"),this.posOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return n[e]})),this.negOrderedFeatures=(0,Re.sortBy)(this.usedFeatures,(function(e){return-r[e]})),this.singleValueFeatures=(0,Re.filter)(this.usedFeatures,(function(e){return a[e]>0}));var l=[\"sample order by similarity\",\"sample order by output value\",\"original sample ordering\"].concat(this.singleValueFeatures.map((function(t){return e.props.featureNames[t]})));null!=this.props.ordering_keys&&l.unshift(\"sample order by key\");var s=this.xlabel.selectAll(\"option\").data(l);s.enter().append(\"option\").merge(s).attr(\"value\",(function(e){return e})).text((function(e){return e})),s.exit().remove();var c=this.props.outNames[0]?this.props.outNames[0]:\"model output value\";(l=(0,Re.map)(this.usedFeatures,(function(t){return[e.props.featureNames[t],e.props.featureNames[t]+\" effects\"]}))).unshift([\"model output value\",c]);var f=this.ylabel.selectAll(\"option\").data(l);f.enter().append(\"option\").merge(f).attr(\"value\",(function(e){return e[0]})).text((function(e){return e[1]})),f.exit().remove(),this.ylabel.style(\"top\",(this.height-10-this.topOffset)/2+this.topOffset+\"px\").style(\"left\",10-this.ylabel.node().offsetWidth/2+\"px\"),this.internalDraw()}}},{key:\"internalDraw\",value:function(){var e,t,n=this,r=Ga(this.props.explanations);try{for(r.s();!(e=r.n()).done;){var a,i=e.value,o=Ga(this.usedFeatures);try{for(o.s();!(a=o.n()).done;){var u=a.value;i.features.hasOwnProperty(u)||(i.features[u]={effect:0,value:0}),i.features[u].ind=u}}catch(e){o.e(e)}finally{o.f()}}}catch(e){r.e(e)}finally{r.f()}var l=this.xlabel.node().value,s=\"sample order by key\"===l&&null!=this.props.ordering_keys_time_format;if(this.xscale=s?Ya():De(),this.xaxis.scale(this.xscale),\"sample order by similarity\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.simIndex})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by output value\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return-e.outValue})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"original sample ordering\"===l)t=(0,Re.sortBy)(this.props.explanations,(function(e){return e.origInd})),(0,Re.each)(t,(function(e,t){return e.xmap=t}));else if(\"sample order by key\"===l)t=this.props.explanations,s?(0,Re.each)(t,(function(e,t){return e.xmap=n.parseTime(n.props.ordering_keys[t])})):(0,Re.each)(t,(function(e,t){return e.xmap=n.props.ordering_keys[t]})),t=(0,Re.sortBy)(t,(function(e){return e.xmap}));else{var c=(0,Re.findKey)(this.props.featureNames,(function(e){return e===l}));(0,Re.each)(this.props.explanations,(function(e,t){return e.xmap=e.features[c].value}));var f=(0,Re.sortBy)(this.props.explanations,(function(e){return e.xmap})),p=(0,Re.map)(f,(function(e){return e.xmap}));if(\"string\"==typeof p[0])return void alert(\"Ordering by category names is not yet supported.\");var d,h,v=(0,Re.min)(p),g=((0,Re.max)(p)-v)/100;t=[];for(var y=0;y<f.length;++y){var m=f[y];if(d&&!h&&m.xmap-d.xmap<=g||h&&m.xmap-h.xmap<=g){h||((h=(0,Re.cloneDeep)(d)).count=1);var b,_=Ga(this.usedFeatures);try{for(_.s();!(b=_.n()).done;){var w=b.value;h.features[w].effect+=m.features[w].effect,h.features[w].value+=m.features[w].value}}catch(e){_.e(e)}finally{_.f()}h.count+=1}else if(d)if(h){var x,k=Ga(this.usedFeatures);try{for(k.s();!(x=k.n()).done;){var S=x.value;h.features[S].effect/=h.count,h.features[S].value/=h.count}}catch(e){k.e(e)}finally{k.f()}t.push(h),h=void 0}else t.push(d);d=m}d.xmap-t[t.length-1].xmap>g&&t.push(d)}this.currUsedFeatures=this.usedFeatures,this.currPosOrderedFeatures=this.posOrderedFeatures,this.currNegOrderedFeatures=this.negOrderedFeatures;var E=this.ylabel.node().value;if(\"model output value\"!==E){var C=t;t=(0,Re.cloneDeep)(t);for(var T=(0,Re.findKey)(this.props.featureNames,(function(e){return e===E})),M=0;M<t.length;++M){var N=t[M].features[T];t[M].features={},t[M].features[T]=N,C[M].remapped_version=t[M]}this.currUsedFeatures=[T],this.currPosOrderedFeatures=[T],this.currNegOrderedFeatures=[T]}this.currExplanations=t,\"identity\"===this.props.link?this.invLinkFunction=function(e){return n.props.baseValue+e}:\"logit\"===this.props.link?this.invLinkFunction=function(e){return 1/(1+Math.exp(-(n.props.baseValue+e)))}:console.log(\"ERROR: Unrecognized link function: 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e.effect<0})),(function(e){return-e.effect})))||0;A=Math.max(A,2.2*Math.max(R,j))}this.yscale.domain([-A/2,A/2]).range([this.height-10,this.topOffset]),this.yaxisElement.attr(\"transform\",\"translate(\"+this.leftOffset+\",0)\").call(this.yaxis);for(var U=0;U<O;++U){var I,$=t[U].features,B=-((0,Re.sum)((0,Re.map)((0,Re.filter)($,(function(e){return e.effect<0})),(function(e){return-e.effect})))||0),W=void 0,V=Ga(this.currPosOrderedFeatures);try{for(V.s();!(I=V.n()).done;)$[W=I.value].posyTop=this.yscale(B),$[W].effect>0&&(B+=$[W].effect),$[W].posyBottom=this.yscale(B),$[W].ind=W}catch(e){V.e(e)}finally{V.f()}var H,q=B,Q=Ga(this.currNegOrderedFeatures);try{for(Q.s();!(H=Q.n()).done;)$[W=H.value].negyTop=this.yscale(B),$[W].effect<0&&(B-=$[W].effect),$[W].negyBottom=this.yscale(B)}catch(e){Q.e(e)}finally{Q.f()}t[U].joinPoint=q,t[U].joinPointy=this.yscale(q)}var Y=En().x((function(e){return e[0]})).y((function(e){return e[1]})),G=this.mainGroup.selectAll(\".force-bar-array-area-pos\").data(this.currUsedFeatures);G.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-pos\").merge(G).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[0]),G.exit().remove();var K=this.mainGroup.selectAll(\".force-bar-array-area-neg\").data(this.currUsedFeatures);K.enter().append(\"path\").attr(\"class\",\"force-bar-array-area-neg\").merge(K).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]})),r=(0,Re.map)((0,Re.rangeRight)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyBottom]}));return Y(n.concat(r))})).attr(\"fill\",this.colors[1]),K.exit().remove();var Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var i,o,u,l;\"pos\"===a?(i=e[n].features[t].posyBottom,o=e[n].features[t].posyTop):(i=e[n].features[t].negyBottom,o=e[n].features[t].negyTop);for(var s=n+1;s<=r;++s)\"pos\"===a?(u=e[s].features[t].posyBottom,l=e[s].features[t].posyTop):(u=e[s].features[t].negyBottom,l=e[s].features[t].negyTop),u>i&&(i=u),l<o&&(o=l);return{top:i,bottom:o}},ee=[],te=0,ne=[\"pos\",\"neg\"];te<ne.length;te++){var re,ae=ne[te],ie=Ga(this.currUsedFeatures);try{for(ie.s();!(re=ie.n()).done;)for(var oe=re.value,ue=0,le=0,se=0,ce={top:0,bottom:0},fe=void 0;le<O-1;){for(;se<100&&le<O-1;)++le,se=t[le].xmapScaled-t[ue].xmapScaled;for(ce=J(t,oe,ue,le,ae);ce.bottom-ce.top<20&&ue<le;)++ue,ce=J(t,oe,ue,le,ae);if(se=t[le].xmapScaled-t[ue].xmapScaled,ce.bottom-ce.top>=20&&se>=100){for(;le<O-1;){if(++le,!((fe=J(t,oe,ue,le,ae)).bottom-fe.top>20)){--le;break}ce=fe}se=t[le].xmapScaled-t[ue].xmapScaled,ee.push([(t[le].xmapScaled+t[ue].xmapScaled)/2,(ce.top+ce.bottom)/2,this.props.featureNames[oe]]);var pe=t[le].xmapScaled;for(ue=le;pe+100>t[ue].xmapScaled&&ue<O-1;)++ue;le=ue}}}catch(e){ie.e(e)}finally{ie.f()}}var de=this.onTopGroup.selectAll(\".force-bar-array-flabels\").data(ee);de.enter().append(\"text\").attr(\"class\",\"force-bar-array-flabels\").merge(de).attr(\"x\",(function(e){return e[0]})).attr(\"y\",(function(e){return e[1]+4})).text((function(e){return e[2]})),de.exit().remove()}},{key:\"componentWillUnmount\",value:function(){window.removeEventListener(\"resize\",this.redraw)}},{key:\"render\",value:function(){var t=this;return e.createElement(\"div\",{ref:function(e){return t.wrapper=Jt(e)},style:{textAlign:\"center\"}},e.createElement(\"style\",{dangerouslySetInnerHTML:{__html:\"\\n          .force-bar-array-wrapper {\\n            text-align: center;\\n          }\\n          .force-bar-array-xaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-xaxis .domain {\\n            opacity: 0;\\n          }\\n          .force-bar-array-xaxis paths {\\n            display: none;\\n          }\\n          .force-bar-array-yaxis path {\\n            fill: none;\\n            opacity: 0.4;\\n          }\\n          .force-bar-array-yaxis paths {\\n            display: none;\\n          }\\n          .tick line {\\n            stroke: #000;\\n            stroke-width: 1px;\\n            opacity: 0.4;\\n          }\\n          .tick text {\\n            fill: #000;\\n            opacity: 0.5;\\n            font-size: 12px;\\n            padding: 0px;\\n          }\\n          .force-bar-array-flabels {\\n            font-size: 12px;\\n            fill: #fff;\\n            text-anchor: middle;\\n          }\\n          .additive-force-array-xlabel {\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            margin-left: 80px;\\n            max-width: 300px;\\n          }\\n          .additive-force-array-xlabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-ylabel {\\n            position: relative;\\n            top: 0px;\\n            left: 0px;\\n            transform: rotate(-90deg);\\n            background: none;\\n            border: 1px solid #ccc;\\n            opacity: 0.5;\\n            margin-bottom: 0px;\\n            font-size: 12px;\\n            font-family: arial;\\n            max-width: 150px;\\n          }\\n          .additive-force-array-ylabel:focus {\\n            outline: none;\\n          }\\n          .additive-force-array-hoverLine {\\n            stroke-width: 1px;\\n            stroke: #fff;\\n            opacity: 1;\\n          }\"}}),e.createElement(\"select\",{className:\"additive-force-array-xlabel\"}),e.createElement(\"div\",{style:{height:\"0px\",textAlign:\"left\"}},e.createElement(\"select\",{className:\"additive-force-array-ylabel\"})),e.createElement(\"svg\",{ref:function(e){return t.svg=Jt(e)},style:{userSelect:\"none\",display:\"block\",fontFamily:\"arial\",sansSerif:!0}}))}}])&&Xa(n.prototype,r),Object.defineProperty(n,\"prototype\",{writable:!1}),u}(e.Component);ni.defaultProps={plot_cmap:\"RdBu\",ordering_keys:null,ordering_keys_time_format:null};const ri=ni;window.SHAP={SimpleListVisualizer:He,AdditiveForceVisualizer:Ln,AdditiveForceArrayVisualizer:ri,React:e,ReactDom:t}})()})();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id='iQ8XQ5AYO0GAFUP7A8VLS'>\n",
       "<div style='color: #900; text-align: center;'>\n",
       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
       "</div></div>\n",
       " <script>\n",
       "   if (window.SHAP) SHAP.ReactDom.render(\n",
       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": -0.02504182979464531, \"outValue\": -1.2019506692886353, \"link\": \"identity\", \"featureNames\": [\"L1_centre_sum\", \"L1_wide_centre_sum\", \"L1_wider_centre_sum\", \"L2_centre_sum\", \"L2_wide_centre_sum\", \"L2_spread_sum\", \"L2_vertical_sum\", \"L3_centre_sum\", \"Had_centre_sum\", \"Had_peripheral_sum\", \"Had_out_sum\"], \"features\": {\"0\": {\"effect\": -0.9750726819038391, \"value\": 0.95}, \"1\": {\"effect\": -1.0841329097747803, \"value\": 1.65}, \"2\": {\"effect\": 0.42337071895599365, \"value\": 0.0}, \"3\": {\"effect\": -0.1289074569940567, \"value\": 10.55}, \"4\": {\"effect\": -0.8047330379486084, \"value\": 4.275}, \"5\": {\"effect\": 0.46989625692367554, \"value\": 0.0}, \"6\": {\"effect\": -0.17424727976322174, \"value\": 0.0}, \"7\": {\"effect\": -0.158375546336174, \"value\": 0.75}, \"8\": {\"effect\": 0.20779713988304138, \"value\": 0.0}, \"9\": {\"effect\": 0.811146080493927, \"value\": 0.0}, \"10\": {\"effect\": 0.23634997010231018, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
       "    document.getElementById('iQ8XQ5AYO0GAFUP7A8VLS')\n",
       "  );\n",
       "</script>"
      ],
      "text/plain": [
       "<shap.plots._force.AdditiveForceVisualizer at 0x22d27f556d0>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import shap\n",
    "import xgboost as xgb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Initialize SHAP Explainer with your XGBoost model\n",
    "explainer = shap.Explainer(XGBoost)\n",
    "\n",
    "# Select a sample from the test set for explanation\n",
    "sample = X_test.iloc[[1]]  # Ensuring input is in the expected format\n",
    "\n",
    "# Compute SHAP values for the selected sample\n",
    "shap_values = explainer.shap_values(sample)\n",
    "\n",
    "# Visualize the SHAP values for the sample\n",
    "shap.initjs()  \n",
    "# For a single sample, use force_plot for an interactive plot, or summary_plot for static\n",
    "shap.force_plot(explainer.expected_value, shap_values[0], sample)\n",
    "\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:51] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:54] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"max_features\", \"n_iter_no_change\", \"validation_fraction\", \"verbose\" } are not used.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Learning Rate: 0.3500000000000001, Best F1-Score for Label 1: 0.9091471589151284\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, f1_score\n",
    "from xgboost import XGBClassifier\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Initialize variables to store the best score and corresponding learning rate\n",
    "best_f1 = 0\n",
    "best_lr = 0\n",
    "\n",
    "# Define the range of learning rates to test\n",
    "learning_rates = np.arange(0.1, 2.1, 0.05)\n",
    "\n",
    "for lr in learning_rates:\n",
    "    XGBoost = XGBClassifier(\n",
    "        n_estimators=64,\n",
    "        learning_rate=lr,\n",
    "        verbose=0,\n",
    "        validation_fraction=0.2,\n",
    "        n_iter_no_change=100,\n",
    "        max_features='sqrt',\n",
    "        max_depth=4\n",
    "    )\n",
    "    \n",
    "    # Fit the model to your training data\n",
    "    XGBoost.fit(\n",
    "        X_train, \n",
    "        Y_train, \n",
    "        eval_set=[(X_test, Y_test)],\n",
    "        verbose=0,\n",
    "        early_stopping_rounds=10\n",
    "    )\n",
    "    \n",
    "    # Predict and evaluate\n",
    "    Y_pred = XGBoost.predict(X_test)\n",
    "    f1 = f1_score(Y_test, Y_pred, average='binary', pos_label=1)  # Assuming label 1 is the positive class\n",
    "    #print(f\"Learning Rate: {lr}, F1-Score for Label 1: {f1}\")\n",
    "    \n",
    "    # Update best score and learning rate if current model is better\n",
    "    if f1 > best_f1:\n",
    "        best_f1 = f1\n",
    "        best_lr = lr\n",
    "\n",
    "print(f\"Best Learning Rate: {best_lr}, Best F1-Score for Label 1: {best_f1}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"verbose\" } are not used.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Learning Rate: 0.6, Best Average F1-Score: 0.9053963130482322\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"verbose\" } are not used.\n",
      "\n",
      "`early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "[21:14:56] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:742: \n",
      "Parameters: { \"verbose\" } are not used.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from xgboost import XGBClassifier\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Initialize variables to store the best average F1-score and corresponding learning rate\n",
    "best_avg_f1 = 0\n",
    "best_lr = 0\n",
    "\n",
    "# Define the range of learning rates to test\n",
    "learning_rates = np.arange(0.1, 2.1, 0.5)\n",
    "\n",
    "for lr in learning_rates:\n",
    "    XGBoost = XGBClassifier(\n",
    "        n_estimators=64,\n",
    "        learning_rate=lr,\n",
    "        verbose=0,\n",
    "        max_depth=4\n",
    "    )\n",
    "    \n",
    "    # Fit the model to your training data\n",
    "    XGBoost.fit(\n",
    "        X_train, \n",
    "        Y_train, \n",
    "        eval_set=[(X_test, Y_test)],\n",
    "        verbose=0,\n",
    "        early_stopping_rounds=10\n",
    "    )\n",
    "    \n",
    "    # Predict and evaluate\n",
    "    Y_pred = XGBoost.predict(X_test)\n",
    "    f1_label_1 = f1_score(Y_test, Y_pred, average='binary', pos_label=1)  # F1-score for label 1\n",
    "    f1_label_0 = f1_score(Y_test, Y_pred, average='binary', pos_label=0)  # F1-score for label 0\n",
    "    avg_f1 = (f1_label_1 + f1_label_0) / 2  # Average F1-score for labels 1 and 0\n",
    "    \n",
    "    #print(f\"Learning Rate: {lr}, Average F1-Score: {avg_f1}\")\n",
    "    \n",
    "    # Update best score and learning rate if current model is better\n",
    "    if avg_f1 > best_avg_f1:\n",
    "        best_avg_f1 = avg_f1\n",
    "        best_lr = lr\n",
    "\n",
    "print(f\"Best Learning Rate: {best_lr}, Best Average F1-Score: {best_avg_f1}\")\n"
   ]
  }
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