xgboost_template.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGboost template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the model\n",
    "xgb_model = XGBClassifier(learning_rate=0.1, max_depth=6,\n",
    "              min_child_weight=1,\n",
    "              n_estimators=180, \n",
    "              objective='binary:logistic', \n",
    "                random_state=42, nthread=1)\n",
    "\n",
    "# fit model to training data\n",
    "xgb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "        'min_child_weight': [1, 5, 10],\n",
    "        'gamma': [0.5, 1, 1.5, 2, 5],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [3, 4, 5]\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = 5\n",
    "param_comb = 5\n",
    "skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)\n",
    "\n",
    "grid = GridSearchCV(estimator=xgb_model, param_grid=params, scoring='roc_auc', n_jobs=4, cv=skf.split(X,Y), verbose=3 )\n",
    "grid.fit(X, Y)\n",
    "print('\\n All results:')\n",
    "print(grid.cv_results_)\n",
    "print('\\n Best estimator:')\n",
    "print(grid.best_estimator_)\n",
    "print('\\n Best score:')\n",
    "print(grid.best_score_ * 2 - 1)\n",
    "print('\\n Best parameters:')\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions for test data\n",
    "y_pred = xgb_model.predict(X_test)\n",
    "predictions = [round(value) for value in y_pred]\n",
    "\n",
    "# evaluate predictions\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find optimal threshold\n",
    "thresholds=[0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5]\n",
    "for t in thresholds:\n",
    "    predictions=xgb_model.predict_proba(X_test)[:,1]>t\n",
    "    print(\"AUC for threshold\",t,\":\",\n",
    "         roc_auc_score(y_test, predictions))\n",
    "    accuracy = accuracy_score(y_test, predictions)\n",
    "    print(\"XGB Classifier accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the most important features\n",
    "importance = xgb_model.get_booster().get_score(importance_type= 'gain')\n",
    "sorted(importance.items(), key=lambda x:x[1],reverse=True)[:3]"
   ]
  }
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