Week_5/Day_3/ensemble_technique.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn import metrics \n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz\n",
    "from io import StringIO\n",
    "from IPython.display import Image  \n",
    "import pydotplus\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    }
   ],
   "source": [
    "print(boston.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n"
     ]
    }
   ],
   "source": [
    "print(boston.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
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       "      <th>LSTAT</th>\n",
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       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
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       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
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       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
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       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['PRICE'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>21.6</td>\n",
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       "      <td>34.7</td>\n",
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       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  PRICE  \n",
       "0       15.3  396.90   4.98   24.0  \n",
       "1       17.8  396.90   9.14   21.6  \n",
       "2       17.8  392.83   4.03   34.7  \n",
       "3       18.7  394.63   2.94   33.4  \n",
       "4       18.7  396.90   5.33   36.2  \n",
       "..       ...     ...    ...    ...  \n",
       "501     21.0  391.99   9.67   22.4  \n",
       "502     21.0  396.90   9.08   20.6  \n",
       "503     21.0  396.90   5.64   23.9  \n",
       "504     21.0  393.45   6.48   22.0  \n",
       "505     21.0  396.90   7.88   11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS       float64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD        float64\n",
       "TAX        float64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "PRICE      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = data.iloc[:,:-1],data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dmatrix = xgb.DMatrix(data=X,label=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xgboost.core.DMatrix at 0x7fef6cedbfa0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,\n",
    "                max_depth = 5, alpha = 10, n_estimators = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16:28:21] WARNING: /workspace/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[16:28:21] WARNING: /workspace/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "xg_reg.fit(X_train,y_train)\n",
    "preds = xg_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.49602144216164"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_test, preds))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.49602144216164"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sum((y_test - preds)**2)/len(y_test))**(1/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-fold Cross Validation using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"objective\":\"reg:squarederror\",'colsample_bytree': 0.3,'learning_rate': 0.1,'max_depth': 5, 'alpha': 10}\n",
    "\n",
    "cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,\n",
    "                    num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>train-rmse-mean</th>\n",
       "      <th>train-rmse-std</th>\n",
       "      <th>test-rmse-mean</th>\n",
       "      <th>test-rmse-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.679235</td>\n",
       "      <td>0.025628</td>\n",
       "      <td>21.677615</td>\n",
       "      <td>0.050615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.772293</td>\n",
       "      <td>0.016054</td>\n",
       "      <td>19.773320</td>\n",
       "      <td>0.020567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.049566</td>\n",
       "      <td>0.058903</td>\n",
       "      <td>18.065856</td>\n",
       "      <td>0.082746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.430115</td>\n",
       "      <td>0.013614</td>\n",
       "      <td>16.492198</td>\n",
       "      <td>0.020921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.025979</td>\n",
       "      <td>0.062131</td>\n",
       "      <td>15.132644</td>\n",
       "      <td>0.073106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
       "0        21.679235        0.025628       21.677615       0.050615\n",
       "1        19.772293        0.016054       19.773320       0.020567\n",
       "2        18.049566        0.058903       18.065856       0.082746\n",
       "3        16.430115        0.013614       16.492198       0.020921\n",
       "4        15.025979        0.062131       15.132644       0.073106"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49    3.975679\n",
      "Name: test-rmse-mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((cv_results[\"test-rmse-mean\"]).tail(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-fold Cross Validation using XGBoost¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg = xgb.train(params=params, dtrain=data_dmatrix, num_boost_round=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "xgb.plot_tree(xg_reg,num_trees=0)\n",
    "plt.rcParams['figure.figsize'] = [50, 10]\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(xg_reg)\n",
    "plt.rcParams['figure.figsize'] = [10, 10]\n",
    "plt.show()"
   ]
  }
 ],
 "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
}