Submit/5-2.Modeling [Task1].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "wound-blocking",
   "metadata": {},
   "source": [
    "# 5-2.Modeling [Task1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-southeast",
   "metadata": {},
   "source": [
    "## 모델링 진행 과정:  \n",
    "- 10가지 ML 모델의 MSE 검증 결과 비교  \n",
    "`Linear Regression` , `Lasso` , `Ridge` , `Elastic` , `SVR` , `Regression Tree` , `Random Forest` , `Gradient Boost` , `AdaBoost` , `XGBoost`  \n",
    "  \n",
    "---  \n",
    "\n",
    "- 5-1.Modeling [Task1] 파일에서는 R언어를 활용하여 8가지 모델  \n",
    "`Linear Regression` , `Lasso` , `Ridge` , `Elastic` , `SVR` , `Regression Tree` , `Random Forest` , `Gradient Boost` 생성 및 성능을 확인  \n",
    "  \n",
    "  \n",
    "- 5-2.Modeling [Task1] 파일에서는 Python언어를 활용하여 2가지 모델(Compas Jupyter 의 R버전에 따른 패키지 호환성 때문에 python으로 진행)  \n",
    "`AdaBoost` , `XGBoost` 생성 및 성능을 확인 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "athletic-american",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler , StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_squared_error,make_scorer\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "upper-funds",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Font\n",
    "import matplotlib\n",
    "from matplotlib import font_manager\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "matplotlib.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "font_fname = '/opt/app-root/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/malgun.ttf'\n",
    "font_family = font_manager.FontProperties(fname=font_fname).get_name()\n",
    "plt.rcParams[\"font.family\"] = font_family\n",
    "#sns.set(font=font_family)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "spatial-moisture",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    .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>accident_cnt</th>\n",
       "      <th>고령인구_count_100</th>\n",
       "      <th>생산가능인구_count_100</th>\n",
       "      <th>신호등_sum</th>\n",
       "      <th>유동인구_sum</th>\n",
       "      <th>유소년인구_count_100</th>\n",
       "      <th>유소년인구_count_200</th>\n",
       "      <th>주정차_count_300</th>\n",
       "      <th>차량등록_count_200</th>\n",
       "      <th>학원수_count_100</th>\n",
       "      <th>...</th>\n",
       "      <th>학구.raw오산원일초통학구역</th>\n",
       "      <th>학구.raw오산초가수초공동통학구역</th>\n",
       "      <th>학구.raw오산초통학구역</th>\n",
       "      <th>학구.raw운산초통학구역</th>\n",
       "      <th>학구.raw운암초통학구역</th>\n",
       "      <th>학구.raw원동초통학구역</th>\n",
       "      <th>학구.raw필봉초통학구역</th>\n",
       "      <th>학구.raw화성초통학구역</th>\n",
       "      <th>정류장_count_200</th>\n",
       "      <th>총인구_count_100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>31.521083</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>81.903417</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     accident_cnt  고령인구_count_100  생산가능인구_count_100  신호등_sum   유동인구_sum  \\\n",
       "195             5              25                64        3  31.521083   \n",
       "84              1               0               366        0  81.903417   \n",
       "\n",
       "     유소년인구_count_100  유소년인구_count_200  주정차_count_300  차량등록_count_200  \\\n",
       "195                6               24              9               0   \n",
       "84                 6               45              9               0   \n",
       "\n",
       "     학원수_count_100  ...  학구.raw오산원일초통학구역  학구.raw오산초가수초공동통학구역  학구.raw오산초통학구역  \\\n",
       "195              0  ...                0                   0              0   \n",
       "84               0  ...                0                   0              0   \n",
       "\n",
       "     학구.raw운산초통학구역  학구.raw운암초통학구역  학구.raw원동초통학구역  학구.raw필봉초통학구역  \\\n",
       "195              0              0              0              0   \n",
       "84               0              0              0              0   \n",
       "\n",
       "     학구.raw화성초통학구역  정류장_count_200  총인구_count_100  \n",
       "195              0              5             98  \n",
       "84               0              4            383  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = pd.read_csv('./data/total_sub.csv',index_col='Unnamed: 0')\n",
    "total.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "plastic-peter",
   "metadata": {},
   "source": [
    "## __Generate X , Y data__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fourth-escape",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((345, 80), (345,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(total.drop(['accident_cnt'],axis=1))\n",
    "Y = pd.Series(total[\"accident_cnt\"])\n",
    "X.shape , Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rational-auction",
   "metadata": {},
   "source": [
    "## __Train | Test set split__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "reasonable-slave",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((276, 80), (69, 80))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "m_scaler = MinMaxScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=2021)\n",
    "x_train.shape , x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sudden-ontario",
   "metadata": {},
   "source": [
    "## __Make validator__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "explicit-border",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_scorer = make_scorer(mean_squared_error,greater_is_better=False)\n",
    "\n",
    "kf = KFold(n_splits=10,shuffle=True,random_state=2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fluid-burke",
   "metadata": {},
   "source": [
    "# 9. AdaBoost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "tracked-stake",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost(linear) MSE : 0.5345846963982313\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "ada = AdaBoostRegressor(n_estimators=200,learning_rate=0.1,loss=\"linear\",random_state=2021)\n",
    "ada.fit(x_train, y_train)\n",
    "\n",
    "score = cross_val_score(ada,x_test,y_test,cv=kf,scoring=my_scorer)  \n",
    "print(\"AdaBoost(linear) MSE :\",score.mean()*(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "coupled-royal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost(exponential) MSE : 0.47954329369814125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "ada = AdaBoostRegressor(n_estimators=200,learning_rate=0.1,loss=\"exponential\",random_state=2021)\n",
    "ada.fit(x_train, y_train)\n",
    "\n",
    "score = cross_val_score(ada,x_test,y_test,cv=kf,scoring=my_scorer)  \n",
    "print(\"AdaBoost(exponential) MSE :\",score.mean()*(-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "downtown-invasion",
   "metadata": {},
   "source": [
    "# 10. XGBoost Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "loved-execution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost MSE : 0.5918947791711247\n"
     ]
    }
   ],
   "source": [
    "import xgboost\n",
    "xgb_model = xgboost.XGBRegressor(n_estimators=200, learning_rate=0.08, gamma=0, subsample=0.75,colsample_bytree=1, max_depth=7)\n",
    "xgb_model.fit(x_train,y_train)\n",
    "\n",
    "score = cross_val_score(xgb_model,x_test,y_test,cv=kf,scoring=my_scorer)  \n",
    "print(\"XGBoost MSE :\",score.mean()*(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "mighty-thanks",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "xgboost.plot_importance(xgb_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlimited-overhead",
   "metadata": {},
   "source": [
    "# 전체 모델들의 MSE 비교 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "mediterranean-roommate",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [['Linear Regression',1.0499],['Ridge Regression',0.97336],['Lasso Regression', 0.94607],['Eleastic Regerssion',0.88615],\n",
    "       ['SVM -linear',1.4186],['Regression Tree',0.99607],['Random Forest',0.98421],['Gradient Boosting',1.0391],\n",
    "       ['AdaBoost',0.47954],['XGBoost', 0.59189]]\n",
    "\n",
    "df = pd.DataFrame(data,columns=['Model','MSE'])\n",
    "df = df.sort_values(by='MSE',axis=0,ascending=False)\n",
    "\n",
    "colors_ = sns.color_palette('GnBu',df.shape[0])\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.bar(df.Model,df.MSE, align='edge', color=colors_,edgecolor=\"red\", linewidth=1.5)\n",
    "plt.xticks(rotation = 45)\n",
    "plt.title(\"모델 MSE 성능 비교\",fontsize=20)\n",
    "plt.xlabel(\"Model\",fontsize=13)\n",
    "plt.ylabel(\"MSE\",fontsize=13)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "professional-reputation",
   "metadata": {},
   "source": [
    "> ##  MSE가 가장 낮은 `AdaBoost` 모델을 최종 모델로 선정"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}