3 - Sınıflandırma Modelleri/8 - XGBoost.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[\"Outcome\"]\n",
    "X = df.drop(\"Outcome\", axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[07:30:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7359307359307359"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier().fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 648 candidates, totalling 6480 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    3.5s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:   43.1s\n",
      "[Parallel(n_jobs=-1)]: Done 624 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 989 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done 1434 tasks      | elapsed:  5.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1961 tasks      | elapsed:  7.4min\n",
      "[Parallel(n_jobs=-1)]: Done 2568 tasks      | elapsed:  9.3min\n",
      "[Parallel(n_jobs=-1)]: Done 3257 tasks      | elapsed: 12.3min\n",
      "[Parallel(n_jobs=-1)]: Done 4026 tasks      | elapsed: 17.1min\n",
      "[Parallel(n_jobs=-1)]: Done 4877 tasks      | elapsed: 20.9min\n",
      "[Parallel(n_jobs=-1)]: Done 5808 tasks      | elapsed: 25.9min\n",
      "[Parallel(n_jobs=-1)]: Done 6480 out of 6480 | elapsed: 31.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:01:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "tuned_parameters = {\n",
    "    \"n_estimators\": [100,200,300,400,500,1000,1500,2000],\n",
    "    \"subsample\": [0.6,0.8,1],\n",
    "    \"max_depth\": [2,3,4,5,6,7,8,9,10],\n",
    "    \"learning_rate\": [0.1,0.01,0.001],\n",
    "}\n",
    "model = XGBClassifier()\n",
    "model_cv = GridSearchCV(model, tuned_parameters, cv=10, n_jobs=-1, verbose=2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.001, 'max_depth': 5, 'n_estimators': 2000, 'subsample': 1}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[08:01:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7575757575757576"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tuned = XGBClassifier(n_estimators = model_cv.best_estimator_.n_estimators, \n",
    "                            subsample = model_cv.best_estimator_.subsample, \n",
    "                            max_depth = model_cv.best_estimator_.max_depth, \n",
    "                            learning_rate = model_cv.best_estimator_.learning_rate).fit(X_train, y_train)\n",
    "\n",
    "y_pred = model_tuned.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "feature_imp = pd.Series(model_tuned.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.title(\"Değişken Önem Düzeyleri\")\n",
    "plt.xlabel(\"Değişken Önem Skorları\")\n",
    "plt.ylabel(\"Değişkenler\")\n",
    "plt.show()"
   ]
  },
  {
   "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"
  },
  "nbTranslate": {
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