Hyperparameter Tuning Gridsearch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as pl\n",
    "def plot_confusion(TN, FP, FN, TP, title):\n",
    "    confusion_mat = [[TN, FN], [FP, TP]]\n",
    "    ax = sns.heatmap(confusion_mat, annot=True, fmt='d', cmap=\"YlGnBu\", cbar=False)\n",
    "    plt.xlabel('Actual')\n",
    "    plt.ylabel('Predicted')\n",
    "    plt.title(\"Confusion Matrix \\n\" + title)\n",
    "    ax.set_xticklabels(['No Fraud', 'Fraud'])\n",
    "    ax.set_yticklabels(['No Fraud', 'Fraud'])\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "\n",
    "def display_scores(Y, yhat):\n",
    "    print('Accuracy Score : ' + str(accuracy_score(Y, yhat)))\n",
    "    print('Precision Score : ' + str(precision_score(Y, yhat)))\n",
    "    print('Recall Score : ' + str(recall_score(Y, yhat)))\n",
    "    print('F1 Score : ' + str(f1_score(Y, yhat)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfcc = pd.read_csv('C:/Users/Damien/Desktop/Aquis/Resources/creditcardfraud_normalised.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfcc.drop('class', axis=1)\n",
    "Y = dfcc['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=404)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(random_state=1, verbosity=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "        'min_child_weight': [1, 5, 10],\n",
    "        'gamma': [0, 0.5, 1],\n",
    "        'subsample': [0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [3, 4, 5, 6]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = 5\n",
    "skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 404)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(estimator = xgb,\n",
    "                    param_grid = params,\n",
    "                    scoring = 'f1',\n",
    "                    cv=skf.split(X_train,y_train),\n",
    "                   verbose = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8, score=0.833, total=   6.9s\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8, score=0.763, total=   7.2s\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   14.0s remaining:    0.0s\n"
     ]
    },
    {
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      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8, score=0.899, total=   7.6s\n",
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     ]
    },
    {
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      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=4, min_child_weight=5, subsample=0.8, score=0.846, total=   8.1s\n",
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      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=1, subsample=1.0, score=0.892, total=   7.2s\n",
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      "[CV] colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, score=0.884, total=   7.0s\n",
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      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.892, total=   7.3s\n",
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      "[CV] colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=1.0, gamma=0.5, max_depth=4, min_child_weight=1, subsample=1.0, score=0.836, total=  10.0s\n",
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      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.885, total=   9.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.870, total=   8.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.829, total=   8.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.831, total=   8.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.793, total=   7.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.908, total=   8.8s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.864, total=   8.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.823, total=   8.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1080 out of 1080 | elapsed: 154.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator:\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1.0, gamma=1, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.300000012, max_delta_step=0, max_depth=4,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=100, n_jobs=12, num_parallel_tree=1, random_state=1,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=0.8,\n",
      "              tree_method='exact', validate_parameters=1, verbosity=0)\n",
      "\n",
      " Best score:\n",
      "0.859840082644628\n",
      "\n",
      " Best parameters:\n",
      "{'colsample_bytree': 1.0, 'gamma': 1, 'max_depth': 4, 'min_child_weight': 1, 'subsample': 0.8}\n"
     ]
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)\n",
    "print('Best estimator:')\n",
    "print(grid.best_estimator_)\n",
    "print('\\n Best score:')\n",
    "print(grid.best_score_)\n",
    "print('\\n Best parameters:')\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(estimator = xgb,\n",
    "                    param_grid = params,\n",
    "                    scoring = 'precision',\n",
    "                    cv=skf.split(X_train,y_train),\n",
    "                   verbose = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "y_pred = grid.predict(X_test)\n",
    "end_time = time.time()\n",
    "print('Execution time for prediction:' + str(end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "plot_confusion(tn,fp,fn,tp, title='XGBoost (Dev set)')\n",
    "print('Model on Development Set \\n')\n",
    "display_scores(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "yhat=grid.predict(X)\n",
    "end_time = time.time()\n",
    "print('Execution time for prediction:' + str(end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(Y, yhat).ravel()\n",
    "plot_confusion(tn,fp,fn,tp, title='XGBoost (All data)')\n",
    "print('Model on all data \\n')\n",
    "display_scores(Y, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "print('Accuracy Score : ' + str(accuracy_score(y_test,y_pred)))\n",
    "print('Precision Score : ' + str(precision_score(y_test,y_pred)))\n",
    "print('Recall Score : ' + str(recall_score(y_test,y_pred)))\n",
    "print('F1 Score : ' + str(f1_score(y_test,y_pred)))\n",
    "plot_confusion(tn,fp,fn,tp, title='XGBoost (Dev set)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = grid.predict(X)\n",
    "tn, fp, fn, tp = confusion_matrix(Y, yhat).ravel()\n",
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "print('Accuracy Score : ' + str(accuracy_score(Y, yhat)))\n",
    "print('Precision Score : ' + str(precision_score(Y, yhat)))\n",
    "print('Recall Score : ' + str(recall_score(Y, yhat)))\n",
    "print('F1 Score : ' + str(f1_score(Y, yhat)))\n",
    "plot_confusion(tn,fp,fn,tp, title='XGBoost (All data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimize for Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8, score=0.962, total=   6.8s\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    6.7s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8, score=0.900, total=   6.7s\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   13.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8, score=0.951, total=   7.0s\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=3, min_child_weight=1, subsample=0.8 \n",
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      "[CV] colsample_bytree=0.6, gamma=0, max_depth=4, min_child_weight=5, subsample=0.8 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=4, min_child_weight=5, subsample=0.8, score=0.929, total=   8.1s\n",
      "[CV] colsample_bytree=0.6, gamma=0, max_depth=4, min_child_weight=5, subsample=1.0 \n",
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      "[CV] colsample_bytree=0.6, gamma=0, max_depth=4, min_child_weight=5, subsample=1.0 \n",
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      "[CV] colsample_bytree=0.6, gamma=0, max_depth=4, min_child_weight=5, subsample=1.0 \n",
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      "[CV] colsample_bytree=0.6, gamma=0, max_depth=5, min_child_weight=10, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.6, gamma=0, max_depth=5, min_child_weight=10, subsample=1.0, score=0.885, total=   6.5s\n",
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     ]
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      "[CV]  colsample_bytree=0.6, gamma=0.5, max_depth=6, min_child_weight=1, subsample=0.8, score=0.943, total=  11.1s\n",
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      "[CV]  colsample_bytree=0.6, gamma=1, max_depth=3, min_child_weight=5, subsample=0.8, score=0.934, total=   6.5s\n",
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     ]
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      "[CV]  colsample_bytree=0.8, gamma=0, max_depth=3, min_child_weight=5, subsample=1.0, score=0.918, total=   6.4s\n",
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      "[CV]  colsample_bytree=0.8, gamma=0, max_depth=6, min_child_weight=5, subsample=0.8, score=0.921, total=   9.1s\n",
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      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=1, subsample=0.8 \n",
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      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8 \n",
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      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, score=0.921, total=   6.4s\n",
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      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=0.8, score=0.903, total=   6.2s\n",
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      "[CV] colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  colsample_bytree=0.8, gamma=1, max_depth=3, min_child_weight=10, subsample=1.0, score=0.891, total=   5.6s\n",
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     ]
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     ]
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     ]
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      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=4, min_child_weight=5, subsample=0.8, score=0.932, total=  13.3s\n",
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      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=0.8, score=0.943, total=  20.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0, score=0.923, total=  20.6s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0, score=0.906, total=  20.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0, score=0.951, total=  21.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0, score=0.919, total=  21.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=1, subsample=1.0, score=0.941, total=  20.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8, score=0.941, total=  14.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8, score=0.906, total=  14.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8, score=0.935, total=  14.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8, score=0.903, total=  15.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=0.8, score=0.929, total=  14.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0, score=0.960, total=  15.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0, score=0.922, total=  15.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0, score=0.921, total=  16.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0, score=0.933, total=  15.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=5, subsample=1.0, score=0.944, total=  15.7s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.980, total=  11.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.904, total=   9.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.921, total=  10.0s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.905, total=  10.4s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=0.8, score=0.911, total=  10.5s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.980, total=  10.1s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.906, total=  10.9s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.952, total=  11.2s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.891, total=  11.3s\n",
      "[CV] colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0 \n",
      "[CV]  colsample_bytree=1.0, gamma=1, max_depth=6, min_child_weight=10, subsample=1.0, score=0.895, total=  10.5s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1080 out of 1080 | elapsed: 162.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator:\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=0.6, gamma=1, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.300000012, max_delta_step=0, max_depth=5,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=100, n_jobs=12, num_parallel_tree=1, random_state=1,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1.0,\n",
      "              tree_method='exact', validate_parameters=1, verbosity=0)\n",
      "\n",
      " Best score:\n",
      "0.9463951599990852\n",
      "\n",
      " Best parameters:\n",
      "{'colsample_bytree': 0.6, 'gamma': 1, 'max_depth': 5, 'min_child_weight': 1, 'subsample': 1.0}\n"
     ]
    }
   ],
   "source": [
    "grid.fit(X_train,y_train)\n",
    "print('Best estimator:')\n",
    "print(grid.best_estimator_)\n",
    "print('\\n Best score:')\n",
    "print(grid.best_score_)\n",
    "print('\\n Best parameters:')\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = grid.best_params_\n",
    "xgb_score = grid.best_score_\n",
    "xgb_estimator = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model on Development Set \n",
      "\n",
      "Accuracy Score : 0.999602073897218\n",
      "Precision Score : 0.968503937007874\n",
      "Recall Score : 0.803921568627451\n",
      "F1 Score : 0.8785714285714286\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "plot_confusion(tn,fp,fn,tp, title='XGBoost (Dev set)')\n",
    "print('Model on Development Set \\n')\n",
    "display_scores(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time for prediction:0.20844054222106934\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "yhat=grid.predict(X)\n",
    "end_time = time.time()\n",
    "print('Execution time for prediction:' + str(end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model on all data \n",
      "\n",
      "Accuracy Score : 0.999870087462738\n",
      "Precision Score : 0.9913606911447084\n",
      "Recall Score : 0.9329268292682927\n",
      "F1 Score : 0.9612565445026178\n"
     ]
    }
   ],
   "source": [
    "tn, fp, fn, tp = confusion_matrix(Y, yhat).ravel()\n",
    "plot_confusion(tn,fp,fn,tp, title='XGBoost (All data)')\n",
    "print('Model on all data \\n')\n",
    "display_scores(Y, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1.0, gamma=1, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=4,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=12, num_parallel_tree=1, random_state=1,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=0.8,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 1.0,\n",
       " 'gamma': 1,\n",
       " 'max_depth': 4,\n",
       " 'min_child_weight': 1,\n",
       " 'subsample': 0.8}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}