XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6f9406c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install xgboost\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV,RandomizedSearchCV\n",
    "import xgboost as xgb\n",
    "import timeit\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,f1_score,precision_score,recall_score,RocCurveDisplay\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7553693",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_resampled=pd.read_csv('resampled_without_encoding.csv')\n",
    "x_resampled_encoding=pd.read_csv('resampled_with_encoding.csv')\n",
    "y_resampled=pd.read_csv('y_resampled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c1d3f2",
   "metadata": {},
   "source": [
    "### Data with Resampling and without OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a092be13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  42.1936776\n",
      "Accuracy score: 0.9809\n",
      "F1 score : 0.9806\n",
      "Recall score : 0.9620\n",
      "Precision score : 0.9999\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test1,y_train,y_test1=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test1)\n",
    "\n",
    "model2 =xgb.XGBClassifier(random_state=48)\n",
    "start_time = timeit.default_timer()\n",
    "model2.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "y_pred2 = model2.predict(test_sc)\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "print(f\"Accuracy score: {accuracy_score(y_test1, y_pred2):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test1, y_pred2):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test1, y_pred2):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test1, y_pred2):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "642b5a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test1,y_pred2),annot=True,fmt='', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f363bf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98    114386\n",
      "           1       1.00      0.96      0.98    115022\n",
      "\n",
      "    accuracy                           0.98    229408\n",
      "   macro avg       0.98      0.98      0.98    229408\n",
      "weighted avg       0.98      0.98      0.98    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test1, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fa46e13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1aebbdd7f10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_predictions(y_test1, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53118eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1aec0713c40>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(model2, X_test1, y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a52ba0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.DataFrame({\"Col\": x_resampled.columns})\n",
    "feat_imp[\"Coef\"] = model2.feature_importances_\n",
    "feat_imp = feat_imp.sort_values(\"Coef\", ascending=False)\n",
    "#Scaling Weights\n",
    "feat_imp[\"FeatImp\"] = (\n",
    "        (feat_imp[\"Coef\"] - feat_imp[\"Coef\"].min())/ (feat_imp[\"Coef\"].max() - feat_imp[\"Coef\"].min())).round(4)\n",
    "\n",
    "num_features = 10 \n",
    "pyplot.figure(figsize=(10,10))\n",
    "pyplot.title('Feature Importances')\n",
    "# only plot the customized number of features\n",
    "pyplot.barh(range(num_features), feat_imp['FeatImp'][:num_features][::-1], color='b', align='center')\n",
    "pyplot.yticks(range(num_features), feat_imp['Col'][:num_features][::-1])\n",
    "pyplot.xlabel('Relative Importance')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dab314c",
   "metadata": {},
   "source": [
    "### Data with Resampling and with OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0cdd43aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  113.06205469999998\n",
      "Accuracy score: 0.9809\n",
      "F1 score : 0.9806\n",
      "Recall score : 0.9620\n",
      "Precision score : 1.0000\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled_encoding.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test)\n",
    "\n",
    "model=xgb.XGBClassifier(random_state=48)\n",
    "start_time = timeit.default_timer()\n",
    "model.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "y_pred1=model.predict(test_sc)\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "print(f\"Accuracy score: {accuracy_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_pred1):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8d5bbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:logistic',\n",
       " 'use_label_encoder': None,\n",
       " 'base_score': 0.5,\n",
       " 'booster': 'gbtree',\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': 1,\n",
       " 'colsample_bynode': 1,\n",
       " 'colsample_bytree': 1,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': 0,\n",
       " 'gpu_id': -1,\n",
       " 'grow_policy': 'depthwise',\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': '',\n",
       " 'learning_rate': 0.300000012,\n",
       " 'max_bin': 256,\n",
       " 'max_cat_threshold': 64,\n",
       " 'max_cat_to_onehot': 4,\n",
       " 'max_delta_step': 0,\n",
       " 'max_depth': 6,\n",
       " 'max_leaves': 0,\n",
       " 'min_child_weight': 1,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': '()',\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': 0,\n",
       " 'num_parallel_tree': 1,\n",
       " 'predictor': 'auto',\n",
       " 'random_state': 48,\n",
       " 'reg_alpha': 0,\n",
       " 'reg_lambda': 1,\n",
       " 'sampling_method': 'uniform',\n",
       " 'scale_pos_weight': 1,\n",
       " 'subsample': 1,\n",
       " 'tree_method': 'exact',\n",
       " 'validate_parameters': 1,\n",
       " 'verbosity': None}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "566e9e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_pred1),annot=True,fmt='',cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "58466eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98    114386\n",
      "           1       1.00      0.96      0.98    115022\n",
      "\n",
      "    accuracy                           0.98    229408\n",
      "   macro avg       0.98      0.98      0.98    229408\n",
      "weighted avg       0.98      0.98      0.98    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d376f5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1ae80dd9c70>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_estimator(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76801457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1ae805aebb0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_predictions(y_test, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5af2b47f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.DataFrame({\"Col\": x_resampled_encoding.columns})\n",
    "feat_imp[\"Coef\"] = model.feature_importances_\n",
    "feat_imp[\"Col\"] = feat_imp[\"Col\"].apply(lambda x: (x.split('cat')[0]+'cat') if 'cat' in x else x)\n",
    "feat_imp = feat_imp.groupby(\"Col\",as_index=False).sum().sort_values(\"Coef\", ascending=False)\n",
    "feat_imp[\"FeatImp\"] = (\n",
    "        (feat_imp[\"Coef\"] - feat_imp[\"Coef\"].min())\n",
    "        / (feat_imp[\"Coef\"].max() - feat_imp[\"Coef\"].min())\n",
    "    ).round(4)\n",
    "\n",
    "# customized number \n",
    "num_features = 10 \n",
    "pyplot.figure(figsize=(10,10))\n",
    "pyplot.title('Feature Importances')\n",
    "# only plot the customized number of features\n",
    "pyplot.barh(range(num_features), feat_imp['FeatImp'][:num_features][::-1], color='b', align='center')\n",
    "pyplot.yticks(range(num_features), feat_imp['Col'][:num_features][::-1])\n",
    "pyplot.xlabel('Relative Importance')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fefd8c83",
   "metadata": {},
   "source": [
    "### Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cc912a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "params ={\n",
    "    'min_child_weight':[1,3,5],\n",
    "    'max_depth':[3,4,5,6,8],\n",
    "    'learning_rate':[0.05,0.1,0.15],\n",
    "    'gamma':[0,0.1,0.2,0.3],\n",
    "    'colsample_bytree' :[0.3,0.4,0.5]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0823f35a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 540 candidates, totalling 2700 fits\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.927 total time=  21.4s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.925 total time=  20.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.928 total time=  23.1s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.923 total time=  18.7s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=1;, score=0.936 total time=  18.8s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.927 total time=  18.6s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.925 total time=  18.7s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.928 total time=  18.9s\n",
      "[CV 4/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.923 total time=  18.6s\n",
      "[CV 5/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=3;, score=0.936 total time=  18.5s\n",
      "[CV 1/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.927 total time=  18.6s\n",
      "[CV 2/5] END colsample_bytree=0.3, gamma=0, learning_rate=0.05, max_depth=3, min_child_weight=5;, score=0.925 total time=  18.8s\n",
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    },
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     ]
    },
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     "output_type": "stream",
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      "[CV 5/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.1, max_depth=4, min_child_weight=1;, score=0.974 total time=  21.5s\n",
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     ]
    },
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     "output_type": "stream",
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      "[CV 1/5] END colsample_bytree=0.3, gamma=0.1, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=  37.1s\n",
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      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.977 total time=  25.0s\n",
      "[CV 3/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=3;, score=0.978 total time=  24.8s\n",
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      "[CV 1/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=5, min_child_weight=5;, score=0.976 total time=  25.0s\n",
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     ]
    },
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     "output_type": "stream",
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      "[CV 2/5] END colsample_bytree=0.3, gamma=0.2, learning_rate=0.1, max_depth=6, min_child_weight=1;, score=0.980 total time=  29.4s\n",
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "[CV 3/5] END colsample_bytree=0.3, gamma=0.3, learning_rate=0.05, max_depth=4, min_child_weight=3;, score=0.951 total time=  21.8s\n",
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     ]
    },
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     ]
    },
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     "output_type": "stream",
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    },
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     ]
    },
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     ]
    },
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     ]
    },
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     "output_type": "stream",
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      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.981 total time=  53.3s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.980 total time=  53.3s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.981 total time=  53.0s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=3;, score=0.981 total time=  51.3s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.980 total time=  53.1s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.981 total time=  52.9s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.980 total time=  51.9s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.981 total time=  51.6s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.1, max_depth=8, min_child_weight=5;, score=0.981 total time=  50.9s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.969 total time=  24.2s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.971 total time=  24.5s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.971 total time=  24.3s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.972 total time=  24.6s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=1;, score=0.971 total time=  24.5s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.969 total time=  23.8s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.971 total time=  23.3s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.971 total time=  24.6s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.972 total time=  24.6s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=3;, score=0.971 total time=  24.2s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.969 total time=  24.3s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.971 total time=  24.3s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.971 total time=  23.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.972 total time=  24.3s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=3, min_child_weight=5;, score=0.971 total time=  24.4s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.979 total time=  29.6s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.980 total time=  28.5s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.980 total time=  29.7s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.979 total time=  29.5s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=1;, score=0.980 total time=  29.7s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.979 total time=  29.6s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.980 total time=  29.6s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.980 total time=  28.9s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.979 total time=  29.3s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=3;, score=0.980 total time=  29.7s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.979 total time=  29.6s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.980 total time=  29.8s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.979 total time=  29.1s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.979 total time=  29.7s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=4, min_child_weight=5;, score=0.980 total time=  29.5s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.980 total time=  34.6s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.981 total time=  33.4s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.980 total time=  35.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.981 total time=  34.7s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=1;, score=0.981 total time=  34.7s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.980 total time=  35.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=  33.8s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.980 total time=  34.2s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=  34.8s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=3;, score=0.981 total time=  34.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.980 total time=  34.9s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=  34.6s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.980 total time=  34.0s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=  33.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=5, min_child_weight=5;, score=0.981 total time=  34.8s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.980 total time=  40.4s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.981 total time=  39.5s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.981 total time=  40.6s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.981 total time=  40.2s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=1;, score=0.981 total time=  38.8s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.980 total time=  40.2s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=  40.5s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.980 total time=  40.6s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=  39.8s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=3;, score=0.981 total time=  39.0s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.980 total time=  40.6s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=  40.5s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.980 total time=  40.6s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=  39.7s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=6, min_child_weight=5;, score=0.981 total time=  40.3s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=  53.9s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=  53.8s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=  53.8s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=  53.8s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=1;, score=0.981 total time=  53.1s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=  53.0s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=  53.4s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=  53.2s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=  53.1s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=3;, score=0.981 total time=  52.6s\n",
      "[CV 1/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=  51.2s\n",
      "[CV 2/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=  52.8s\n",
      "[CV 3/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=  53.5s\n",
      "[CV 4/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=  52.9s\n",
      "[CV 5/5] END colsample_bytree=0.5, gamma=0.3, learning_rate=0.15, max_depth=8, min_child_weight=5;, score=0.981 total time=  53.0s\n",
      "Time taken to fit the model in sec is  82239.2026109\n"
     ]
    }
   ],
   "source": [
    "clf=xgb.XGBClassifier()\n",
    "grid_search=GridSearchCV(estimator=clf,param_grid=params,n_jobs=None,cv=5,verbose=3)\n",
    "start_time = timeit.default_timer()\n",
    "grid_search.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "#Time taken to fit the model in sec is  82239.2026109"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6220668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.5,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.15, max_bin=256,\n",
       "              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "              max_depth=8, max_leaves=0, min_child_weight=3, missing=nan,\n",
       "              monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.5,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy=&#x27;depthwise&#x27;, importance_type=None,\n",
       "              interaction_constraints=&#x27;&#x27;, learning_rate=0.15, max_bin=256,\n",
       "              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "              max_depth=8, max_leaves=0, min_child_weight=3, missing=nan,\n",
       "              monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.5,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, gpu_id=-1,\n",
       "              grow_policy='depthwise', importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.15, max_bin=256,\n",
       "              max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,\n",
       "              max_depth=8, max_leaves=0, min_child_weight=3, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0, ...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "228ff205",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.5,\n",
       " 'gamma': 0,\n",
       " 'learning_rate': 0.15,\n",
       " 'max_depth': 8,\n",
       " 'min_child_weight': 3}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05793630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.981010824116163"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4fd7a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  64.20180049999908\n"
     ]
    }
   ],
   "source": [
    "clf1=grid_search.best_estimator_\n",
    "start_time = timeit.default_timer()\n",
    "clf1.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2473b45a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  78.91370189999998\n",
      "Accuracy score: 0.9810\n",
      "F1 score : 0.9806\n",
      "Recall score : 0.9620\n",
      "Precision score : 1.0000\n"
     ]
    }
   ],
   "source": [
    "clf1=xgb.XGBClassifier(colsample_bytree = 0.5,\n",
    " gamma= 0,\n",
    " learning_rate=0.15,\n",
    " max_depth=8,\n",
    " min_child_weight=3,random_state=0)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "clf1.fit(train_sc, y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "y_pred = clf1.predict(test_sc)\n",
    "\n",
    "print(f\"Accuracy score: {accuracy_score(y_test, y_pred):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test, y_pred):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_pred):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e919fdd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_pred),annot=True,fmt='',cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7dd203cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98    114386\n",
      "           1       1.00      0.96      0.98    115022\n",
      "\n",
      "    accuracy                           0.98    229408\n",
      "   macro avg       0.98      0.98      0.98    229408\n",
      "weighted avg       0.98      0.98      0.98    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd7282f",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b34b427b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.DataFrame({\"Col\": x_resampled_encoding.columns})\n",
    "feat_imp[\"Coef\"] = clf1.feature_importances_\n",
    "feat_imp[\"Col\"] = feat_imp[\"Col\"].apply(lambda x: (x.split('cat')[0]+'cat') if 'cat' in x else x)\n",
    "feat_imp = feat_imp.groupby(\"Col\",as_index=False).sum().sort_values(\"Coef\", ascending=False)\n",
    "feat_imp[\"FeatImp\"] = (\n",
    "        (feat_imp[\"Coef\"] - feat_imp[\"Coef\"].min())\n",
    "        / (feat_imp[\"Coef\"].max() - feat_imp[\"Coef\"].min())\n",
    "    ).round(4)\n",
    "\n",
    "# customized number \n",
    "num_features = 10 \n",
    "pyplot.figure(figsize=(10,10))\n",
    "pyplot.title('Feature Importances')\n",
    "# only plot the customized number of features\n",
    "pyplot.barh(range(num_features), feat_imp['FeatImp'][:num_features][::-1], color='b', align='center')\n",
    "pyplot.yticks(range(num_features), feat_imp['Col'][:num_features][::-1])\n",
    "pyplot.xlabel('Relative Importance')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f839e0a9",
   "metadata": {},
   "source": [
    "* https://machinelearningmastery.com/feature-importance-and-feature-selection-with-xgboost-in-python/#:~:text=The%20XGBoost%20library%20provides%20a%20built-in%20function%20to,Indians%20dataset%20using%20the%20built-in%20plot_importance%20%28%29%20function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d3e55e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "pyplot.bar(range(len(clf1.feature_importances_)), clf1.feature_importances_)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e37e36c",
   "metadata": {},
   "source": [
    "### Selected dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c92da300",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "selection = SelectFromModel(clf1, threshold=0.03, prefit=True)\n",
    "selected_dataset = selection.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "85d02fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(229408, 12)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cefce5ed",
   "metadata": {},
   "source": [
    "* you will get a dataset with only the features of which the importance pass the threshold, as Numpy array. Point that the threshold is relative to the total importance, so it goes from 0 to 1."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}