Day 34/XGBoost_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>P3</th>\n",
       "      <th>P4</th>\n",
       "      <th>P4.1</th>\n",
       "      <th>P5</th>\n",
       "      <th>P6</th>\n",
       "      <th>P7</th>\n",
       "      <th>T</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     P1   P2  P3  P4  P4.1    P5     P6  P7  T\n",
       "0     6  148  72  35     0  33.6  0.627  50  1\n",
       "1     1   85  66  29     0  26.6  0.351  31  0\n",
       "2     8  183  64   0     0  23.3  0.672  32  1\n",
       "3     1   89  66  23    94  28.1  0.167  21  0\n",
       "4     0  137  40  35   168  43.1  2.288  33  1\n",
       "..   ..  ...  ..  ..   ...   ...    ...  .. ..\n",
       "763  10  101  76  48   180  32.9  0.171  63  0\n",
       "764   2  122  70  27     0  36.8  0.340  27  0\n",
       "765   5  121  72  23   112  26.2  0.245  30  0\n",
       "766   1  126  60   0     0  30.1  0.349  47  1\n",
       "767   1   93  70  31     0  30.4  0.315  23  0\n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"diabetes.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P1</th>\n",
       "      <th>P2</th>\n",
       "      <th>P3</th>\n",
       "      <th>P4</th>\n",
       "      <th>P4.1</th>\n",
       "      <th>P5</th>\n",
       "      <th>P6</th>\n",
       "      <th>P7</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               P1          P2          P3          P4        P4.1          P5  \\\n",
       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "               P6          P7           T  \n",
       "count  768.000000  768.000000  768.000000  \n",
       "mean     0.471876   33.240885    0.348958  \n",
       "std      0.331329   11.760232    0.476951  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.243750   24.000000    0.000000  \n",
       "50%      0.372500   29.000000    0.000000  \n",
       "75%      0.626250   41.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   P1      768 non-null    int64  \n",
      " 1   P2      768 non-null    int64  \n",
      " 2   P3      768 non-null    int64  \n",
      " 3   P4      768 non-null    int64  \n",
      " 4   P4.1    768 non-null    int64  \n",
      " 5   P5      768 non-null    float64\n",
      " 6   P6      768 non-null    float64\n",
      " 7   P7      768 non-null    int64  \n",
      " 8   T       768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            P1        P2        P3        P4      P4.1        P5        P6  \\\n",
      "P1    1.000000  0.129459  0.141282 -0.081672 -0.073535  0.017683 -0.033523   \n",
      "P2    0.129459  1.000000  0.152590  0.057328  0.331357  0.221071  0.137337   \n",
      "P3    0.141282  0.152590  1.000000  0.207371  0.088933  0.281805  0.041265   \n",
      "P4   -0.081672  0.057328  0.207371  1.000000  0.436783  0.392573  0.183928   \n",
      "P4.1 -0.073535  0.331357  0.088933  0.436783  1.000000  0.197859  0.185071   \n",
      "P5    0.017683  0.221071  0.281805  0.392573  0.197859  1.000000  0.140647   \n",
      "P6   -0.033523  0.137337  0.041265  0.183928  0.185071  0.140647  1.000000   \n",
      "P7    0.544341  0.263514  0.239528 -0.113970 -0.042163  0.036242  0.033561   \n",
      "T     0.221898  0.466581  0.065068  0.074752  0.130548  0.292695  0.173844   \n",
      "\n",
      "            P7         T  \n",
      "P1    0.544341  0.221898  \n",
      "P2    0.263514  0.466581  \n",
      "P3    0.239528  0.065068  \n",
      "P4   -0.113970  0.074752  \n",
      "P4.1 -0.042163  0.130548  \n",
      "P5    0.036242  0.292695  \n",
      "P6    0.033561  0.173844  \n",
      "P7    1.000000  0.238356  \n",
      "T     0.238356  1.000000  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadcc57ee50>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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     "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": [
    "corr = df.corr()\n",
    "print(corr)\n",
    "sns.heatmap(corr, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into X and y\n",
    "X = df.iloc[:, 0:8]\n",
    "y = df[\"T\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "predictions = [round(value) for value in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 73.59%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 6, 8]\n",
      "Best: -0.481847 using {'max_depth': 2, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)\n",
    "n_estimators = [50, 100, 150, 200]\n",
    "max_depth = [2, 3, 4, 6, 8]\n",
    "print(max_depth)\n",
    "param_grid = dict(max_depth=max_depth, n_estimators=n_estimators)\n",
    "grid_search = GridSearchCV(model, param_grid, scoring=\"neg_log_loss\", n_jobs=-1, cv=kfold)\n",
    "grid_result = grid_search.fit(X, y)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.11826812 0.35275036 0.04312551 0.06407958 0.06926734 0.15376033\n",
      " 0.06070097 0.1380478 ]\n"
     ]
    },
    {
     "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": [
    "model = XGBClassifier(max_depth=2, n_estimators=50)\n",
    "model.fit(X_train, y_train)\n",
    "print(model.feature_importances_)\n",
    "# plot\n",
    "plt.bar(range(len(model.feature_importances_)), model.feature_importances_)\n",
    "plt.show()\n",
    "y_pred = model.predict(X_test)\n",
    "predictions = [round(value) for value in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 76.19%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}