Dacon_JejuBusRide_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 기본 라이브러리\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# geopy 지리 정보 만들기 위한 라이브러리\n",
    "from geopy.geocoders import Nominatim\n",
    "from geopy.distance import geodesic\n",
    "from geopy.distance import great_circle\n",
    "import folium\n",
    "from folium.features import CustomIcon\n",
    "from folium.plugins import MarkerCluster\n",
    "import geopy\n",
    "\n",
    "# 인코딩을 위한 라벨인코딩\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# linear 모델 돌리기 위한 라이브러리\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# 랜덤포레스트 돌리기 위한 라이브러리\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import random\n",
    "\n",
    "# 부스팅 모델 돌리기 위한 라이브러리\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이거는 자꾸 맷플롯립 그래프에서 한글이 깨지는 문제로 폰트 설치해주는 코드\n",
    "\n",
    "from matplotlib import font_manager, rc\n",
    "\n",
    "font = r\"C:\\Users\\thinp\\fonts\\NanumGothic.ttf\"\n",
    "font = font_manager.FontProperties(fname=font).get_name()\n",
    "plt.style.use('seaborn-white')\n",
    "rc('font', family=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_train = pd.read_csv(r\"C:\\Users\\thinp\\Desktop\\Graduate School\\data\\bus\\train.csv\")\n",
    "bus_test = pd.read_csv(r\"C:\\Users\\thinp\\Desktop\\Graduate School\\data\\bus\\test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "bus_bts = pd.read_csv(r\"C:\\Users\\thinp\\Desktop\\Graduate School\\data\\bus\\bus_bts.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "raining = pd.read_csv(r\"C:\\Users\\thinp\\python\\Kaggle\\jeju_bus\\weather.csv\") # 기상청에서 가져온 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 415423 entries, 0 to 415422\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count   Dtype  \n",
      "---  ------         --------------   -----  \n",
      " 0   id             415423 non-null  int64  \n",
      " 1   date           415423 non-null  object \n",
      " 2   bus_route_id   415423 non-null  int64  \n",
      " 3   in_out         415423 non-null  object \n",
      " 4   station_code   415423 non-null  int64  \n",
      " 5   station_name   415423 non-null  object \n",
      " 6   latitude       415423 non-null  float64\n",
      " 7   longitude      415423 non-null  float64\n",
      " 8   6~7_ride       415423 non-null  float64\n",
      " 9   7~8_ride       415423 non-null  float64\n",
      " 10  8~9_ride       415423 non-null  float64\n",
      " 11  9~10_ride      415423 non-null  float64\n",
      " 12  10~11_ride     415423 non-null  float64\n",
      " 13  11~12_ride     415423 non-null  float64\n",
      " 14  6~7_takeoff    415423 non-null  float64\n",
      " 15  7~8_takeoff    415423 non-null  float64\n",
      " 16  8~9_takeoff    415423 non-null  float64\n",
      " 17  9~10_takeoff   415423 non-null  float64\n",
      " 18  10~11_takeoff  415423 non-null  float64\n",
      " 19  11~12_takeoff  415423 non-null  float64\n",
      " 20  18~20_ride     415423 non-null  float64\n",
      "dtypes: float64(15), int64(3), object(3)\n",
      "memory usage: 66.6+ MB\n"
     ]
    }
   ],
   "source": [
    "bus_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 내부 데이터 전처리"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 전처리: date 값 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_date(bus_train, bus_test):\n",
    "    # \"date\" 칼럼을 판다스의 datetime으로 바꿔줍니다. from 구글링.\n",
    "    bus_train['date2'] = pd.to_datetime(bus_train['date'], errors='coerce')\n",
    "    bus_test['date2'] = pd.to_datetime(bus_test['date'], errors='coerce')\n",
    "    \n",
    "    # 일을 나타내는 새로운 컬럼을 생성합니다.\n",
    "    # 년,월은 의미가 없습니다.\n",
    "    bus_train[\"date_day\"] = bus_train[\"date2\"].dt.day\n",
    "    bus_test[\"date_day\"] = bus_test[\"date2\"].dt.day\n",
    "    \n",
    "    # dayofweek는 날짜에서 요일(월~일)을 가져오는 기능입니다.\n",
    "    # 값은 0(월), 1(화), 2(수), 3(목), 4(금), 5(토), 6(일) 을 나타냅니다.\n",
    "    bus_train[\"date-dayofweek\"] = bus_train[\"date2\"].dt.dayofweek\n",
    "    bus_test[\"date-dayofweek\"] = bus_test[\"date2\"].dt.dayofweek\n",
    "    \n",
    "    \n",
    "    # 다 쓴 \"date2\" 칼럼 버리기\n",
    "    bus_train.drop(\"date2\", axis=1, inplace=True)\n",
    "    bus_test.drop(\"date2\", axis=1, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 전처리: in_out 값 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_inout(bus_train, bus_test):\n",
    "    # 시내는 0 시외는 1로 바꿔준다.\n",
    "    bus_train['in_out'] = bus_train['in_out'].map({'시내':0,'시외':1})\n",
    "    bus_test['in_out'] = bus_test['in_out'].map({'시내':0,'시외':1})\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 전처리: ride, off 값 합쳐주기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_time(bus_train, bus_test):\n",
    "    # y값이 2시간 단위이니 x값도 2시간으로 합쳐줍니다.\n",
    "    bus_train['6~8_on']=bus_train['6~7_ride']+bus_train['7~8_ride'] \n",
    "    bus_train['8~10_on']=bus_train['8~9_ride']+bus_train['9~10_ride']\n",
    "    bus_train['10~12_on']=bus_train['10~11_ride']+bus_train['11~12_ride']\n",
    "\n",
    "    bus_train['6~8_off']=bus_train['6~7_takeoff']+bus_train['7~8_takeoff'] \n",
    "    bus_train['8~10_off']=bus_train['8~9_takeoff']+bus_train['9~10_takeoff']\n",
    "    bus_train['10~12_off']=bus_train['10~11_takeoff']+bus_train['11~12_takeoff']\n",
    "\n",
    "    bus_test['6~8_on']=bus_test['6~7_ride']+bus_test['7~8_ride']\n",
    "    bus_test['8~10_on']=bus_test['8~9_ride']+bus_test['9~10_ride']\n",
    "    bus_test['10~12_on']=bus_test['10~11_ride']+bus_test['11~12_ride']\n",
    "\n",
    "    bus_test['6~8_off']=bus_test['6~7_takeoff']+bus_test['7~8_takeoff']\n",
    "    bus_test['8~10_off']=bus_test['8~9_takeoff']+bus_test['9~10_takeoff']\n",
    "    bus_test['10~12_off']=bus_test['10~11_takeoff']+bus_test['11~12_takeoff']\n",
    "    \n",
    "    # 기존 시간변수들은 drop 해줍니다.\n",
    "    bus_train.drop([\"6~7_ride\", \"7~8_ride\", \"8~9_ride\", \"9~10_ride\", \"10~11_ride\", \"11~12_ride\",\n",
    "                   \"6~7_takeoff\", \"7~8_takeoff\", \"8~9_takeoff\", \"9~10_takeoff\", \"10~11_takeoff\", \"11~12_takeoff\"], axis=1, inplace=True)\n",
    "    bus_test.drop([\"6~7_ride\", \"7~8_ride\", \"8~9_ride\", \"9~10_ride\", \"10~11_ride\", \"11~12_ride\",\n",
    "                   \"6~7_takeoff\", \"7~8_takeoff\", \"8~9_takeoff\", \"9~10_takeoff\", \"10~11_takeoff\", \"11~12_takeoff\"], axis=1, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.9 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>bus_route_id</th>\n",
       "      <th>in_out</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>18~20_ride</th>\n",
       "      <th>date_day</th>\n",
       "      <th>date-dayofweek</th>\n",
       "      <th>6~8_on</th>\n",
       "      <th>8~10_on</th>\n",
       "      <th>10~12_on</th>\n",
       "      <th>6~8_off</th>\n",
       "      <th>8~10_off</th>\n",
       "      <th>10~12_off</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>4270000</td>\n",
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       "      <td>344</td>\n",
       "      <td>제주썬호텔</td>\n",
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       "      <td>1</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>4270000</td>\n",
       "      <td>1</td>\n",
       "      <td>357</td>\n",
       "      <td>한라병원</td>\n",
       "      <td>33.48944</td>\n",
       "      <td>126.48508</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>4270000</td>\n",
       "      <td>1</td>\n",
       "      <td>432</td>\n",
       "      <td>정존마을</td>\n",
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       "      <td>2019-09-01</td>\n",
       "      <td>4270000</td>\n",
       "      <td>0</td>\n",
       "      <td>1579</td>\n",
       "      <td>제주국제공항(600번)</td>\n",
       "      <td>33.50577</td>\n",
       "      <td>126.49252</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2019-09-01</td>\n",
       "      <td>4270000</td>\n",
       "      <td>0</td>\n",
       "      <td>1646</td>\n",
       "      <td>중문관광단지입구</td>\n",
       "      <td>33.25579</td>\n",
       "      <td>126.41260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   id        date  bus_route_id  in_out  station_code  station_name  latitude  \\\n",
       "0   0  2019-09-01       4270000       1           344         제주썬호텔  33.48990   \n",
       "1   1  2019-09-01       4270000       1           357          한라병원  33.48944   \n",
       "2   2  2019-09-01       4270000       1           432          정존마을  33.48181   \n",
       "3   3  2019-09-01       4270000       0          1579  제주국제공항(600번)  33.50577   \n",
       "4   4  2019-09-01       4270000       0          1646      중문관광단지입구  33.25579   \n",
       "\n",
       "   longitude  18~20_ride  date_day  date-dayofweek  6~8_on  8~10_on  10~12_on  \\\n",
       "0  126.49373         0.0         1               6     1.0      7.0       8.0   \n",
       "1  126.48508         5.0         1               6     5.0      6.0      11.0   \n",
       "2  126.47352         2.0         1               6     2.0      2.0       0.0   \n",
       "3  126.49252        53.0         1               6    17.0     32.0      30.0   \n",
       "4  126.41260         0.0         1               6     0.0      0.0       0.0   \n",
       "\n",
       "   6~8_off  8~10_off  10~12_off  \n",
       "0      0.0       0.0        0.0  \n",
       "1      0.0       0.0        0.0  \n",
       "2      0.0       0.0        0.0  \n",
       "3      0.0       0.0        0.0  \n",
       "4      0.0       1.0        0.0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr, te = pre_date(bus_train, bus_test)\n",
    "tr, te = pre_inout(tr, te)\n",
    "tr, te = pre_time(tr, te)\n",
    "\n",
    "tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 칼럼별로 상관관계를 살펴봅시다.\n",
    "\n",
    "cor=tr.corr()\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "mask=np.zeros_like(cor,dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)]=True\n",
    "\n",
    "f,ax=plt.subplots(figsize=(20,15))\n",
    "cmap=sns.diverging_palette(200,10,as_cmap=True)\n",
    "sns.heatmap(cor,mask=mask,cmap=cmap,center=0,square=True,linewidths=0.5,cbar_kws={\"shrink\":1},annot=True); #히트맵 생성\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20,rotation=0)\n",
    "plt.title(\"arrive and leave correlation graph\",size=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c622c2dd88>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 날짜별, 요일별 하차인원 차이를 알아봅시다.\n",
    "\n",
    "# 2x2으로 총 4개의 시각화를 한 화면에 띄웁니다.\n",
    "figure, ((ax1, ax2)) = plt.subplots(nrows=1, ncols=2)\n",
    "\n",
    "# 시각화의 전체 사이즈는 18x8로 설정합니다.\n",
    "figure.set_size_inches(18, 4)\n",
    "\n",
    "sns.barplot(data=tr, x=\"date-dayofweek\", y=\"10~12_on\", ax=ax1)\n",
    "sns.barplot(data=tr, x=\"date-dayofweek\", y=\"18~20_ride\", ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "값은 0(월), 1(화), 2(수), 3(목), 4(금), 5(토), 6(일) 을 나타냅니다. 10-12시 탑승객에 비해 18-20 탑승객은 차이가 다소 있습니다.\n",
    "    - 월요일-수요일까지는 탑승객 수가 증가합니다.\n",
    "    - 목요일부터 하락하기 시작해서 금,토,일요일은 차이가 다소 납니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c62647d808>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 날짜별, 요일별 하차인원 차이를 알아봅시다.\n",
    "\n",
    "# 2x2으로 총 4개의 시각화를 한 화면에 띄웁니다.\n",
    "figure, ((ax1, ax2)) = plt.subplots(nrows=2, ncols=1)\n",
    "\n",
    "# 시각화의 전체 사이즈는 18x8로 설정합니다.\n",
    "figure.set_size_inches(18, 8)\n",
    "\n",
    "sns.barplot(data=tr, x=\"date_day\", y=\"10~12_on\", ax=ax1)\n",
    "sns.barplot(data=tr, x=\"date_day\", y=\"18~20_ride\", ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "날짜별로도 차이가 다소 느껴집니다. 2019년 9월의 달력을 살펴보겠습니다.\n",
    "    - 1일은 일요일이었습니다.\n",
    "    - 6일은 금요일이었습니다. 차이가 많이 납니다.\n",
    "    - 8일은 일요일이었습니다.\n",
    "    - 12일은 목요일이면서 추석연휴의 시작입니다. 추석은 14일까지 이어집니다.\n",
    "    - 14일은 추석연휴이자 토요일임에도 18-20탑승객이 많습니다. 이는 15일 일요일도 마찬가지입니다.\n",
    "    - 21일은 토요일입니다.\n",
    "    - 29일도 토요일입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c61c1e0908>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 시내외 버스 여부를 확인해볼까요?\n",
    "\n",
    "figure, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "figure.set_size_inches(18, 8)\n",
    "\n",
    "sns.barplot(data=tr, x=\"in_out\", y=\"10~12_on\", ax=ax1) \n",
    "sns.barplot(data=tr, x=\"in_out\", y=\"10~12_off\", ax=ax2) \n",
    "sns.barplot(data=tr, x=\"in_out\", y=\"8~10_on\", ax=ax3) \n",
    "sns.barplot(data=tr, x=\"in_out\", y=\"18~20_ride\", ax=ax4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시내는 0 시외는 1로 변환해주었습니다. 이 비율은 크게 차이가 보이지는 않습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 모델링"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre_encode(bus_train, bus_test):\n",
    "    # 원핫인코딩 진행해줍니다.\n",
    "    bus_train = pd.get_dummies(bus_train,columns=['date-dayofweek'],prefix='wd_')\n",
    "    bus_test = pd.get_dummies(bus_test,columns=['date-dayofweek'],prefix='wd_')\n",
    "    \n",
    "    # 의미없는 칼럼 날려줍니다.\n",
    "    bus_train.drop([\"date\", \"station_name\", \"date_day\"], axis=1, inplace=True)\n",
    "    bus_test.drop([\"date\", \"station_name\", \"date_day\"], axis=1, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_pre_encode, te_pre_encode = pre_encode(tr, te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_pre_encode.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 독립변수들을 정해줍니다.\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', 'latitude', 'longitude',\n",
    "       '6~8_on', '8~10_on', '10~12_on', '6~8_off', '8~10_off',\n",
    "       '10~12_off', 'wd__0', 'wd__1', 'wd__2', 'wd__3', 'wd__4', 'wd__5',\n",
    "       'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = \"18~20_ride\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr 모델들은 스케일링을 해줍니다.\n",
    "\n",
    "scaler = StandardScaler()\n",
    "tr_scaled = scaler.fit_transform(tr_pre_encode.values)\n",
    "te_scaled = scaler.fit_transform(te_pre_encode.values)\n",
    "tr_scaled2 = pd.DataFrame(tr_scaled, index=tr_pre_encode.index, columns=tr_pre_encode.columns)\n",
    "te_scaled2 = pd.DataFrame(te_scaled, index=te_pre_encode.index, columns=te_pre_encode.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((415423, 19), (415423,), (228170, 19))"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train, test 셋을 세팅해줍니다.\n",
    "X_train = tr_scaled2[features]\n",
    "X_test = te_scaled2[features]\n",
    "y_train = tr_scaled2[label]\n",
    "\n",
    "X_train.shape, y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.50148909e-04 -2.44071772e-02 -2.61600631e-03 -8.59280026e-03\n",
      "  1.68887170e-02 -1.18070919e-02  6.95880584e-02  1.86073513e-01\n",
      "  4.09178248e-01  7.65158772e-02  7.34666431e-02  3.11741794e-02\n",
      "  2.67752070e+10  2.47262063e+10  2.44621648e+10  2.39953618e+10\n",
      "  2.34245915e+10  2.14882921e+10  2.28446718e+10]\n",
      "0.43959222869949166\n"
     ]
    }
   ],
   "source": [
    "lr_pre = linear_model.LinearRegression()\n",
    "model_pre = lr_pre.fit(X_train, y_train)\n",
    "\n",
    "print(lr_pre.coef_)\n",
    "print(model_pre.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elastic Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.1, 'l1_ratio': 0.1}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {'alpha': [0.1,1,10],\n",
    "              'l1_ratio': [0.1, 0.3, 0.7, 0.9]\n",
    "}\n",
    "# Create a based model\n",
    "en = ElasticNet(random_state=42) # 랜덤포레스트 모델을 정의한다.\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = en, param_grid = param_grid) # GridSearchCV를 정의한다.\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "grid_search.best_params_ #학습 이후 최적의 paramter를 출력\n",
    "\n",
    "# {'alpha': 0.1, 'l1_ratio': 0.1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3553151659327355"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 규제를 줘봅니다.\n",
    "\n",
    "en_pre = ElasticNet(alpha = 1, l1_ratio=0.1)\n",
    "en_pre.fit(X_train,y_train)\n",
    "en_pre.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((415423, 19), (415423,), (228170, 19))"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 부스팅 모델은 다시 전체 데이터로 적용\n",
    "X_train=tr_pre_encode[features]\n",
    "y_train=tr_pre_encode[label]\n",
    "\n",
    "X_test=te_pre_encode[features]\n",
    "\n",
    "X_train.shape, y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48214114551665976"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt_pre = GradientBoostingRegressor(max_depth = 2,\n",
    "                                     n_estimators= 20,\n",
    "                                     learning_rate=0.5,)\n",
    "gbrt_pre.fit(X_train,y_train)\n",
    "gbrt_pre.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8504220129592986"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_pre = RandomForestRegressor(max_features=3,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=20)\n",
    "\n",
    "rf_pre.fit(X_train,y_train)\n",
    "rf_pre.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "랜덤 포레스트가 압도적으로 높은 성능을 내고 있습니다. 85%는 심지어 나쁜 스코어가 아니니 랜덤 포레스트로 파라미터 튜닝을 들어가겠습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 파생변수생성"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.0 외부 데이터 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:30: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n"
     ]
    }
   ],
   "source": [
    "# geopy 데이터를 만들어줍니다.\n",
    "# 본 데이터는 기상청 측정소를 기준으로 정류장을 맵핑해주기 위함이며, 마찬가지로 기상 데이터의 정확도를 높이기 위함입니다.\n",
    "\n",
    "# 해당 주요 장소의 임의 지역 위도, 경도\n",
    "jeju=(33.51411, 126.52969) # 제주 측정소 근처\n",
    "gosan=(33.29382, 126.16283) #고산 측정소 근처\n",
    "seongsan=(33.38677, 126.8802) #성산 측정소 근처\n",
    "po=(33.24616, 126.5653) #서귀포 측정소 근처\n",
    "\n",
    "#제주도 지역이 보일 수 있는 위치의 위도, 경도를 표시한 뒤, folium.Map에 변수로 넣고, map_osm에 할당\n",
    "map_osm= folium.Map((33.399835, 126.506031),zoom_start=9)\n",
    "mc = MarkerCluster()\n",
    "    \n",
    "#각 주요 장소에 마커 추가\n",
    "mc.add_child(folium.Marker(location=jeju,popup='제주 측정소',icon=folium.Icon(color='red',icon='info-sign') ) ) \n",
    "#마커를 map_osm에 추가\n",
    "map_osm.add_child(mc) \n",
    "\n",
    "mc.add_child(folium.Marker(location=gosan,popup='고산 측정소',icon=folium.Icon(color='red',icon='info-sign') ) )\n",
    "map_osm.add_child(mc) \n",
    "\n",
    "mc.add_child(folium.Marker(location=seongsan,popup='성산 측정소',icon=folium.Icon(color='red',icon='info-sign') ) )\n",
    "map_osm.add_child(mc) \n",
    "\n",
    "mc.add_child(folium.Marker(location=po,popup='서귀포 측정소',icon=folium.Icon(color='red',icon='info-sign') ) )\n",
    "map_osm.add_child(mc)\n",
    "\n",
    "#정류장의 위치만 확인하기 위해 groupby를 실행함\n",
    "flag_data=bus_train[['latitude','longitude','station_name']].drop_duplicates(keep='first')\n",
    "flag_data2=flag_data.groupby(['station_name'])['latitude','longitude'].mean()\n",
    "flag_data2.to_csv(\"folium.csv\")\n",
    "flag_data2=pd.read_csv(\"folium.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rain 데이터를 만들어줍니다.\n",
    "\n",
    "#외부데이터에서 나오는 지점명들을 변경\n",
    "raining['지점'] = [str(i) for i in raining['지점']]\n",
    "\n",
    "raining['지점'] = ['jeju' if i=='184' else i for i in raining['지점']]  # 위도 : 33.51411 경도 : 126.52969\n",
    "raining['지점'] = ['gosan' if i=='185' else i for i in raining['지점']]  # 위도 : 33.29382 경도 : 126.16283\n",
    "raining['지점'] = ['seongsan' if i=='188' else i for i in raining['지점']]  # 위도 : 33.38677 경도 : 126.8802\n",
    "raining['지점'] = ['po' if i=='189' else i for i in raining['지점']]  # 위도 : 33.24616 경도 : 126.5653\n",
    "\n",
    "raining[\"강수량(mm)\"].fillna(value=0, inplace=True)\n",
    "raining[\"풍속(m/s)\"].fillna(value=0, inplace=True)\n",
    "\n",
    "# data merge를 위하여, 일시와 시간대를 분리\n",
    "raining['time'] = [int( i.split(' ')[1].split(':')[0]) for i in raining['일시']] \n",
    "\n",
    "raining['일시'] = [i.split(' ')[0] for i in raining['일시']] \n",
    "\n",
    "# 실제 측정 데이터이기 때문에, 12시 이전의 시간대만 사용\n",
    "rain2 = raining[(raining['time']>12)]\n",
    "\n",
    "# 해당 시간대의 평균 기온 및 강수량을 변수로 사용할 예정이기 때문에, groupby를 실행\n",
    "\n",
    "rain3 = rain2.groupby(['지점','일시'])[['기온(°C)','강수량(mm)','풍속(m/s)']].mean()\n",
    "\n",
    "rain3.to_csv(\"rain3.csv\")\n",
    "\n",
    "rain3=pd.read_csv(\"rain3.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 휴일 여부 칼럼 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_holiday(bus_train, bus_test):\n",
    "    # 9월 10월에 공휴일이 있었습니다. 공휴일은 holiday 칼럼을 넣어주겠습니다.\n",
    "    # 다만 주말과 평일공휴일을 구분해보기 위해서 값을 다르게 줘보겠습니다.\n",
    "    # 평일 공휴일은 \"holiday\", 주말은 \"weekend\", 평일은 \"weekday\" 로 가겠습니다.\n",
    "    \n",
    "    # 2019년 9월 12, 13, 14 일은 추석연휴였습니다. 14일은 참고로 토요일.\n",
    "    # 2019년 9월 1,8,15,22,29 은 일요일, 7, 14, 21, 28은 토요일\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 12, \"date_holiday\"] = \"holiday\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 13, \"date_holiday\"] = \"holiday\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 14, \"date_holiday\"] = \"holiday\"\n",
    "\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 1, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 8, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 15, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 22, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 29, \"date_holiday\"] = \"weekend\"\n",
    "\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 7, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 14, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 21, \"date_holiday\"] = \"weekend\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 28, \"date_holiday\"] = \"weekend\"\n",
    "    \n",
    "    bus_train[\"date_holiday\"].fillna(value=\"weekday\",inplace=True)\n",
    "    bus_train.loc[bus_train[\"date_holiday\"] == 0, \"date_holiday\"] = \"weekday\"\n",
    "    \n",
    "    # 2019년 10월 3일은 개천절, 10월 9일은 한글날, 각각 목요일과 수요일.\n",
    "    # 2019년 10월 3일은 개천절, 10월 9일은 한글날, 각각 목요일과 수요일.\n",
    "    # 2019년 10월 5,12는 토요일, 6,13은 일요일\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 3, \"date_holiday\"] = \"holiday\"\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 9, \"date_holiday\"] = \"holiday\"\n",
    "\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 5, \"date_holiday\"] = \"weekend\"\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 12, \"date_holiday\"] = \"weekend\"\n",
    "\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 6, \"date_holiday\"] = \"weekend\"\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 13, \"date_holiday\"] = \"weekend\"\n",
    "\n",
    "\n",
    "    bus_test[\"date_holiday\"].fillna(value=\"weekday\",inplace=True)\n",
    "    bus_test.loc[bus_test[\"date_holiday\"] == 0, \"date_holiday\"] = \"weekday\"\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_holiday2(bus_train, bus_test):\n",
    "    # pre_holiday2 는 holiday 칼럼에 대한 새로운 접근입니다.\n",
    "    # 완벽하게 모두가 쉬는 공휴일은 off로,\n",
    "    # 출근 할 가능성이 있는 poss(ible)로,\n",
    "    # 완벽하게 출근 하는 날은 on 으로, 구분해보겠습니다.\n",
    "    \n",
    "    # 2019년 9월 12, 13, 14 일은 추석연휴였지만, 13일만 off로, 13, 14는 poss로 하겠습니다.\n",
    "    # 2019년 9월 1,8,15,22,29 은 일요일, 7, 14, 21, 28은 토요일\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 12, \"date_holiday2\"] = \"poss\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 13, \"date_holiday2\"] = \"off\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 14, \"date_holiday2\"] = \"poss\"\n",
    "\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 1, \"date_holiday2\"] = \"off\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 8, \"date_holiday2\"] = \"off\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 15, \"date_holiday2\"] = \"off\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 22, \"date_holiday2\"] = \"off\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 29, \"date_holiday2\"] = \"off\"\n",
    "\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 7, \"date_holiday2\"] = \"poss\"\n",
    "    #bus_train.loc[bus_train[\"date_day\"] == 14, \"date_holiday2\"] = \"poss\" # 추석 연휴에 포함됩니다.\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 21, \"date_holiday2\"] = \"poss\"\n",
    "    bus_train.loc[bus_train[\"date_day\"] == 28, \"date_holiday2\"] = \"poss\"\n",
    "    \n",
    "    bus_train[\"date_holiday2\"].fillna(value=\"on\",inplace=True)\n",
    "    bus_train.loc[bus_train[\"date_holiday2\"] == 0, \"date_holiday2\"] = \"on\"\n",
    "    \n",
    "    # 2019년 10월 3일은 개천절, 10월 9일은 한글날, 각각 목요일과 수요일.\n",
    "    # 2019년 10월 3일은 개천절, 10월 9일은 한글날, 각각 목요일과 수요일.\n",
    "    # 2019년 10월 5,12는 토요일, 6,13은 일요일\n",
    "    # 공휴일\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 3, \"date_holiday2\"] = \"off\"\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 9, \"date_holiday2\"] = \"off\"\n",
    "    # 토요일\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 5, \"date_holiday2\"] = \"poss\"\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 12, \"date_holiday2\"] = \"poss\"\n",
    "    # 일요일\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 6, \"date_holiday2\"] = \"off\"\n",
    "    bus_test.loc[bus_test[\"date_day\"] == 13, \"date_holiday2\"] = \"off\"\n",
    "\n",
    "\n",
    "    bus_test[\"date_holiday2\"].fillna(value=\"on\",inplace=True)\n",
    "    bus_test.loc[bus_test[\"date_holiday2\"] == 0, \"date_holiday2\"] = \"on\"\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 정류장 그룹화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_latlong(bus_train, bus_test):\n",
    "    # lat과 long, route_id, station name과 code까지 엮어준다.\n",
    "    bus_train['bus_route_id'] = bus_train['bus_route_id'].apply(lambda x: str(x)[:-4]).astype(int)\n",
    "    bus_test['bus_route_id'] = bus_test['bus_route_id'].apply(lambda x: str(x)[:-4]).astype(int)\n",
    "    #bus_train['station_name2'] = bus_train['station_name'].apply(lambda x: str(x)[:2])\n",
    "    #bus_test['station_name2'] = bus_test['station_name'].apply(lambda x: str(x)[:2])\n",
    "    #bus_train['station_name'] = bus_train['station_name'].apply(lambda x: x.replace(' ', ''))\n",
    "    #bus_test['station_name'] = bus_test['station_name'].apply(lambda x: x.replace(' ', ''))\n",
    "\n",
    "    # lat_long을 합쳐서 그룹핑을 해준다.\n",
    "    bus_train['lat_long'] = np.round(bus_train['latitude'], 2).astype(str) + np.round(bus_train['longitude'], 2).astype(str)\n",
    "    bus_test['lat_long'] = np.round(bus_test['latitude'], 2).astype(str) + np.round(bus_test['longitude'], 2).astype(str)\n",
    "    le = LabelEncoder().fit(pd.concat([bus_train['lat_long'], bus_test['lat_long']]))\n",
    "    bus_train['station_lat_long'] = le.transform(bus_train['lat_long'])\n",
    "    bus_test['station_lat_long'] = le.transform(bus_test['lat_long'])\n",
    "\n",
    "    # 좀 더 디테일하게 한 번 더 한다.\n",
    "    bus_train['lat_long'] = np.round(bus_train['latitude'], 3).astype(str) + np.round(bus_train['longitude'], 2).astype(str)\n",
    "    bus_test['lat_long'] = np.round(bus_test['latitude'], 3).astype(str) + np.round(bus_test['longitude'], 2).astype(str)\n",
    "    le = LabelEncoder().fit(pd.concat([bus_train['lat_long'], bus_test['lat_long']]))\n",
    "    bus_train['station_lat_long2'] = le.transform(bus_train['lat_long'])\n",
    "    bus_test['station_lat_long2'] = le.transform(bus_test['lat_long'])\n",
    "\n",
    "    # 루트와 코드, 그룹핑을 교차 합쳐준다.\n",
    "    bus_train['bus_route_id_station_code'] = ((bus_train['bus_route_id']).astype(str) + \n",
    "                                              (bus_train['station_code']).astype(str)).astype('category')\n",
    "    bus_test['bus_route_id_station_code'] = ((bus_test['bus_route_id']).astype(str) + \n",
    "                                             (bus_test['station_code']).astype(str)).astype('category')\n",
    "    bus_train['bus_route_id_station_lat_long'] = ((bus_train['bus_route_id']).astype(str) +\n",
    "                                                  (bus_train['station_lat_long']).astype(str)).astype('category')\n",
    "    bus_test['bus_route_id_station_lat_long'] = ((bus_test['bus_route_id']).astype(str) + \n",
    "                                                 (bus_test['station_lat_long']).astype(str)).astype('category')\n",
    "    \n",
    "    # station_name2와 lat_long 은 인코딩도, 형변환도 불가능하니 버려줍니다.\n",
    "    #bus_train.drop(\"station_name2\", axis=1, inplace=True)\n",
    "    #bus_test.drop(\"station_name2\", axis=1, inplace=True)\n",
    "    \n",
    "    bus_train.drop(\"lat_long\", axis=1, inplace=True)\n",
    "    bus_test.drop(\"lat_long\", axis=1, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 정류장 위치 칼럼 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def add_dist(bus_train, bus_test):\n",
    "    # 만들어 둔 geopy 데이터 불러옵니다.\n",
    "    flag_data2=pd.read_csv(\"folium.csv\")\n",
    "\n",
    "    #정류장의 대략적인 위치를 확인하기 위하여, folium map에 해당 정류장을 표시\n",
    "    for row in flag_data2.itertuples():\n",
    "        mc.add_child(folium.Marker(location=[row.latitude,  row.longitude], popup=row.station_name)) #마커 생성\n",
    "        map_osm.add_child(mc) #마커를 map_osm에 추가\n",
    "\n",
    "    t1 = [geopy.distance.vincenty( (i,j), jeju).km for i,j in list( zip( bus_train['latitude'],bus_train['longitude'] )) ]\n",
    "    t2 = [geopy.distance.vincenty( (i,j), gosan).km for i,j in list( zip( bus_train['latitude'],bus_train['longitude'] )) ]\n",
    "    t3 = [geopy.distance.vincenty( (i,j), seongsan).km for i,j in list( zip( bus_train['latitude'],bus_train['longitude'] )) ]\n",
    "    t4 = [geopy.distance.vincenty( (i,j), po).km for i,j in list( zip( bus_train['latitude'],bus_train['longitude'] )) ]\n",
    "\n",
    "    bus_train['dis_jeju']=t1\n",
    "    bus_train['dis_gosan']=t2\n",
    "    bus_train['dis_seongsan']=t3\n",
    "    bus_train['dis_po']=t4\n",
    "\n",
    "    total=pd.DataFrame(list(zip( t1,t2,t3,t4)),columns=['jeju','gosan','seongsan','po'])\n",
    "    bus_train['dist_name'] = total.apply(lambda x: x.argmin(), axis=1)\n",
    "\n",
    "    data22=bus_train[['station_name','latitude','longitude','dist_name']].drop_duplicates(keep='first')\n",
    "\n",
    "    t1 = [geopy.distance.vincenty( (i,j), jeju).km for i,j in list( zip( bus_test['latitude'],bus_test['longitude'] )) ]\n",
    "    t2 = [geopy.distance.vincenty( (i,j), gosan).km for i,j in list( zip( bus_test['latitude'],bus_test['longitude'] )) ]\n",
    "    t3 = [geopy.distance.vincenty( (i,j), seongsan).km for i,j in list( zip( bus_test['latitude'],bus_test['longitude'] )) ]\n",
    "    t4 = [geopy.distance.vincenty( (i,j), po).km for i,j in list( zip( bus_test['latitude'],bus_test['longitude'] )) ]\n",
    "\n",
    "    bus_test['dis_jeju']=t1\n",
    "    bus_test['dis_gosan']=t2\n",
    "    bus_test['dis_seongsan']=t3\n",
    "    bus_test['dis_po']=t4\n",
    "\n",
    "    total=pd.DataFrame( list(zip( t1,t2,t3,t4)),columns=['jeju','gosan','seongsan','po'] )\n",
    "    bus_test['dist_name'] = total.apply(lambda x: x.argmin(), axis=1)\n",
    "\n",
    "    bus_train.loc[bus_train[\"dist_name\"] == 0,\"dist_name\"] = \"jeju\"\n",
    "    bus_train.loc[bus_train[\"dist_name\"] == 1,\"dist_name\"] = \"gosan\"\n",
    "    bus_train.loc[bus_train[\"dist_name\"] == 2,\"dist_name\"] = \"seongsan\"\n",
    "    bus_train.loc[bus_train[\"dist_name\"] == 3,\"dist_name\"] = \"po\"\n",
    "\n",
    "    bus_test.loc[bus_test[\"dist_name\"] == 0,\"dist_name\"] = \"jeju\"\n",
    "    bus_test.loc[bus_test[\"dist_name\"] == 1,\"dist_name\"] = \"gosan\"\n",
    "    bus_test.loc[bus_test[\"dist_name\"] == 2,\"dist_name\"] = \"seongsan\"\n",
    "    bus_test.loc[bus_test[\"dist_name\"] == 3,\"dist_name\"] = \"po\"\n",
    "\n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 기상 데이터 머지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_weather(bus_train, bus_test):\n",
    "    # rain 데이터를 불러옵니다.\n",
    "    rain3=pd.read_csv(\"rain3.csv\")\n",
    "    \n",
    "    # train, test의 변수명과 통일\n",
    "    rain3 = rain3.rename(columns={\"일시\":\"date\",\"지점\":\"dist_name\"})\n",
    "\n",
    "    # 최종적으로 완성이 된 외부데이터와 현재 만들어진 train, test data에 각각 merge 한다.\n",
    "    bus_train=pd.merge(bus_train,rain3,how='left',on=['dist_name','date'])\n",
    "    bus_test=pd.merge(bus_test,rain3,how='left',on=['dist_name','date'])\n",
    "    \n",
    "    bus_train.rename(columns={'풍속(m/s)':\"wind\",\n",
    "                              \"강수량(mm)\":\"rain\",\n",
    "                              \"기온(°C)\":\"temp\"}, inplace=True)\n",
    "    bus_test.rename(columns={'풍속(m/s)':\"wind\",\n",
    "                              \"강수량(mm)\":\"rain\",\n",
    "                              \"기온(°C)\":\"temp\"}, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 기상 데이터 추가 칼럼 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_weather2(bus_train, bus_test):\n",
    "    # 강풍 주의보 : 육상에서 풍속 14m/s 이상, 그러나 이건 엄청 정말 쎈 바람이고, 보통 10m/s 이상이면 우산이 뒤집힌다.\n",
    "    # 비오는 날 : 강수량이 많지 않으나 비가 온 날과 오지 않은 날을 구분해보자.\n",
    "    # 체감온도 : tf＝t－4√v＋12I 이 식에서 tf는 체감온도, t는 기온, v는 풍속, I는 지표면이 받는 복사량\n",
    "    # 체감온도는 복사량이 없으니, 기온-4(루트풍속) 으로 구해보자.\n",
    "\n",
    "    # 만들 feature는 \"wind_power\", \"rain_or_not\", \"feel_temp\"\n",
    "    \n",
    "    bus_train.loc[bus_train['wind'] >= 10, \"wind_power\"] = 1\n",
    "    bus_train.loc[bus_train['wind'] < 10, \"wind_power\"] = 0\n",
    "    \n",
    "    bus_train.loc[bus_train[\"rain\"] == 0 , \"rain_or_not\"] = 0\n",
    "    bus_train.loc[bus_train[\"rain\"] > 0 , \"rain_or_not\"] = 1\n",
    "    \n",
    "    temp = bus_train[\"temp\"]\n",
    "    feel_temp = []\n",
    "\n",
    "    for i in range(len(temp)):\n",
    "        feel_temp.append(round((bus_train[\"temp\"].iloc[i]-4*np.sqrt(bus_train['wind'].iloc[i])),1))\n",
    "\n",
    "    bus_train[\"feel_temp\"] = feel_temp\n",
    "    \n",
    "    # test 데이터에도 동일한 작업\n",
    "\n",
    "    bus_test.loc[bus_test['wind'] >= 10, \"wind_power\"] = 1\n",
    "    bus_test.loc[bus_test['wind'] < 10, \"wind_power\"] = 0\n",
    "\n",
    "    bus_test.loc[bus_test[\"rain\"] == 0 , \"rain_or_not\"] = 0\n",
    "    bus_test.loc[bus_test[\"rain\"] > 0 , \"rain_or_not\"] = 1\n",
    "\n",
    "    temp = bus_test[\"temp\"]\n",
    "    feel_temp = []\n",
    "\n",
    "    for i in range(len(temp)):\n",
    "        feel_temp.append(round((bus_test[\"temp\"].iloc[i]-4*np.sqrt(bus_test['wind'].iloc[i])),1))\n",
    "    \n",
    "    bus_test[\"feel_temp\"] = feel_temp\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:11: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:12: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:13: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "  del sys.path[0]\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:25: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:26: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:27: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:28: DeprecationWarning: Vincenty is deprecated and is going to be removed in geopy 2.0. Use `geopy.distance.geodesic` (or the default `geopy.distance.distance`) instead, which is more accurate and always converges.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>bus_route_id</th>\n",
       "      <th>in_out</th>\n",
       "      <th>station_code</th>\n",
       "      <th>station_name</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>18~20_ride</th>\n",
       "      <th>date_day</th>\n",
       "      <th>...</th>\n",
       "      <th>dis_gosan</th>\n",
       "      <th>dis_seongsan</th>\n",
       "      <th>dis_po</th>\n",
       "      <th>dist_name</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain</th>\n",
       "      <th>wind</th>\n",
       "      <th>wind_power</th>\n",
       "      <th>rain_or_not</th>\n",
       "      <th>feel_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>427</td>\n",
       "      <td>1</td>\n",
       "      <td>344</td>\n",
       "      <td>제주썬호텔</td>\n",
       "      <td>33.48990</td>\n",
       "      <td>126.49373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>37.692766</td>\n",
       "      <td>37.713630</td>\n",
       "      <td>27.841879</td>\n",
       "      <td>jeju</td>\n",
       "      <td>22.727273</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>427</td>\n",
       "      <td>1</td>\n",
       "      <td>357</td>\n",
       "      <td>한라병원</td>\n",
       "      <td>33.48944</td>\n",
       "      <td>126.48508</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>37.008492</td>\n",
       "      <td>38.465822</td>\n",
       "      <td>27.996179</td>\n",
       "      <td>jeju</td>\n",
       "      <td>22.727273</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.6</td>\n",
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       "    <tr>\n",
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       "      <td>2019-09-01</td>\n",
       "      <td>427</td>\n",
       "      <td>1</td>\n",
       "      <td>432</td>\n",
       "      <td>정존마을</td>\n",
       "      <td>33.48181</td>\n",
       "      <td>126.47352</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>35.642126</td>\n",
       "      <td>39.259820</td>\n",
       "      <td>27.496551</td>\n",
       "      <td>jeju</td>\n",
       "      <td>22.727273</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>1579</td>\n",
       "      <td>제주국제공항(600번)</td>\n",
       "      <td>33.50577</td>\n",
       "      <td>126.49252</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>38.643401</td>\n",
       "      <td>38.386802</td>\n",
       "      <td>29.579404</td>\n",
       "      <td>jeju</td>\n",
       "      <td>22.727273</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>2.318182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>1646</td>\n",
       "      <td>중문관광단지입구</td>\n",
       "      <td>33.25579</td>\n",
       "      <td>126.41260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>23.648361</td>\n",
       "      <td>45.899140</td>\n",
       "      <td>14.269792</td>\n",
       "      <td>po</td>\n",
       "      <td>22.127273</td>\n",
       "      <td>1.627273</td>\n",
       "      <td>2.109091</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id        date  bus_route_id  in_out  station_code  station_name  latitude  \\\n",
       "0   0  2019-09-01           427       1           344         제주썬호텔  33.48990   \n",
       "1   1  2019-09-01           427       1           357          한라병원  33.48944   \n",
       "2   2  2019-09-01           427       1           432          정존마을  33.48181   \n",
       "3   3  2019-09-01           427       0          1579  제주국제공항(600번)  33.50577   \n",
       "4   4  2019-09-01           427       0          1646      중문관광단지입구  33.25579   \n",
       "\n",
       "   longitude  18~20_ride  date_day  ...  dis_gosan  dis_seongsan     dis_po  \\\n",
       "0  126.49373         0.0         1  ...  37.692766     37.713630  27.841879   \n",
       "1  126.48508         5.0         1  ...  37.008492     38.465822  27.996179   \n",
       "2  126.47352         2.0         1  ...  35.642126     39.259820  27.496551   \n",
       "3  126.49252        53.0         1  ...  38.643401     38.386802  29.579404   \n",
       "4  126.41260         0.0         1  ...  23.648361     45.899140  14.269792   \n",
       "\n",
       "   dist_name       temp      rain      wind wind_power rain_or_not  feel_temp  \n",
       "0       jeju  22.727273  2.272727  2.318182        0.0         1.0       16.6  \n",
       "1       jeju  22.727273  2.272727  2.318182        0.0         1.0       16.6  \n",
       "2       jeju  22.727273  2.272727  2.318182        0.0         1.0       16.6  \n",
       "3       jeju  22.727273  2.272727  2.318182        0.0         1.0       16.6  \n",
       "4         po  22.127273  1.627273  2.109091        0.0         1.0       16.3  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr, te = add_holiday(tr, te)\n",
    "tr, te = add_holiday2(tr, te)\n",
    "tr, te = add_latlong(tr, te)\n",
    "tr, te = add_dist(tr, te)\n",
    "tr, te = add_weather(tr, te)\n",
    "tr, te = add_weather2(tr, te)\n",
    "tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 추가 EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 칼럼별로 상관관계를 살펴봅시다.\n",
    "\n",
    "cor=tr.corr()\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "mask=np.zeros_like(cor,dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)]=True\n",
    "\n",
    "f,ax=plt.subplots(figsize=(20,15))\n",
    "cmap=sns.diverging_palette(200,10,as_cmap=True)\n",
    "sns.heatmap(cor,mask=mask,cmap=cmap,center=0,square=True,linewidths=0.5,cbar_kws={\"shrink\":1},annot=True); #히트맵 생성\n",
    "plt.xticks(size=20)\n",
    "plt.yticks(size=20,rotation=0)\n",
    "plt.title(\"arrive and leave correlation graph\",size=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- date_day와 id, latitude와 longitude, latlong1과 latlong2, holiday1과 holiday2의 변수들은 강력한 양의 상관관계를 보입니다.\n",
    "- 변수 삭제\n",
    "    - date_day는 train data(9월)과 test data(10월)의 상관관계를 분석할 수 없으므로 date_day를 삭제해줍니다.\n",
    "    - latitude와 longitude는 latlong과의 양의 상관관계가 있지만, latlong을 남기고 latitude와 longitude를 삭제해줍니다.\n",
    "    \n",
    "- 성능 비교\n",
    "    - latlong1과 latlong2는 강력한 양의 상관관계를 보입니다. 모델 튜닝 단계에서 feature selection을 통해 성능을 비교하겠습니다.\n",
    "    - holiday1과 holiday2도 모델 튜닝 단계에서 feature selection을 통해 성능을 비교하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_encode(bus_train, bus_test):\n",
    "    # 원핫인코딩 진행해줍니다.\n",
    "    bus_train = pd.get_dummies(bus_train,columns=['dist_name'])\n",
    "    bus_test = pd.get_dummies(bus_test,columns=['dist_name'])\n",
    "    \n",
    "    bus_train = pd.get_dummies(bus_train,columns=['date_holiday'])\n",
    "    bus_test = pd.get_dummies(bus_test,columns=['date_holiday'])\n",
    "    bus_train = pd.get_dummies(bus_train,columns=['date_holiday2'])\n",
    "    bus_test = pd.get_dummies(bus_test,columns=['date_holiday2'])\n",
    "    \n",
    "    bus_train = pd.get_dummies(bus_train,columns=['date-dayofweek'],prefix='wd_')\n",
    "    bus_test = pd.get_dummies(bus_test,columns=['date-dayofweek'],prefix='wd_')\n",
    "    \n",
    "    # 의미없는 칼럼 날려줍니다.\n",
    "    bus_train.drop([\"date\", \"station_name\"], axis=1, inplace=True)\n",
    "    bus_test.drop([\"date\", \"station_name\"], axis=1, inplace=True)\n",
    "    \n",
    "    # 위에서 언급한 삭제 할 변수들을 제거해줍니다.\n",
    "    bus_train.drop([\"date_day\", \"latitude\", \"longitude\"], axis=1, inplace=True)\n",
    "    bus_test.drop([\"date_day\", \"latitude\", \"longitude\"], axis=1, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bus_route_id</th>\n",
       "      <th>in_out</th>\n",
       "      <th>station_code</th>\n",
       "      <th>18~20_ride</th>\n",
       "      <th>6~8_on</th>\n",
       "      <th>8~10_on</th>\n",
       "      <th>10~12_on</th>\n",
       "      <th>6~8_off</th>\n",
       "      <th>8~10_off</th>\n",
       "      <th>...</th>\n",
       "      <th>date_holiday2_off</th>\n",
       "      <th>date_holiday2_on</th>\n",
       "      <th>date_holiday2_poss</th>\n",
       "      <th>wd__0</th>\n",
       "      <th>wd__1</th>\n",
       "      <th>wd__2</th>\n",
       "      <th>wd__3</th>\n",
       "      <th>wd__4</th>\n",
       "      <th>wd__5</th>\n",
       "      <th>wd__6</th>\n",
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       "      <td>5.0</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>1579</td>\n",
       "      <td>53.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>1646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  bus_route_id  in_out  station_code  18~20_ride  6~8_on  8~10_on  \\\n",
       "0   0           427       1           344         0.0     1.0      7.0   \n",
       "1   1           427       1           357         5.0     5.0      6.0   \n",
       "2   2           427       1           432         2.0     2.0      2.0   \n",
       "3   3           427       0          1579        53.0    17.0     32.0   \n",
       "4   4           427       0          1646         0.0     0.0      0.0   \n",
       "\n",
       "   10~12_on  6~8_off  8~10_off  ...  date_holiday2_off  date_holiday2_on  \\\n",
       "0       8.0      0.0       0.0  ...                  1                 0   \n",
       "1      11.0      0.0       0.0  ...                  1                 0   \n",
       "2       0.0      0.0       0.0  ...                  1                 0   \n",
       "3      30.0      0.0       0.0  ...                  1                 0   \n",
       "4       0.0      0.0       1.0  ...                  1                 0   \n",
       "\n",
       "   date_holiday2_poss wd__0 wd__1  wd__2  wd__3  wd__4  wd__5  wd__6  \n",
       "0                   0     0     0      0      0      0      0      1  \n",
       "1                   0     0     0      0      0      0      0      1  \n",
       "2                   0     0     0      0      0      0      0      1  \n",
       "3                   0     0     0      0      0      0      0      1  \n",
       "4                   0     0     0      0      0      0      0      1  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr, te = add_encode(tr, te)\n",
    "tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr.to_csv(\"tr_preprocessed_200517.csv\")\n",
    "te.to_csv(\"te_preprocessed_200517.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~--------------------------------------------------------------------------------------------------------------------------~"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr = pd.read_csv(\"tr_preprocessed_200517.csv\", index_col=0)\n",
    "te = pd.read_csv(\"te_preprocessed_200517.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>1579</td>\n",
       "      <td>53.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>427</td>\n",
       "      <td>0</td>\n",
       "      <td>1646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  bus_route_id  in_out  station_code  18~20_ride  6~8_on  8~10_on  \\\n",
       "0   0           427       1           344         0.0     1.0      7.0   \n",
       "1   1           427       1           357         5.0     5.0      6.0   \n",
       "2   2           427       1           432         2.0     2.0      2.0   \n",
       "3   3           427       0          1579        53.0    17.0     32.0   \n",
       "4   4           427       0          1646         0.0     0.0      0.0   \n",
       "\n",
       "   10~12_on  6~8_off  8~10_off  ...  date_holiday2_off  date_holiday2_on  \\\n",
       "0       8.0      0.0       0.0  ...                  1                 0   \n",
       "1      11.0      0.0       0.0  ...                  1                 0   \n",
       "2       0.0      0.0       0.0  ...                  1                 0   \n",
       "3      30.0      0.0       0.0  ...                  1                 0   \n",
       "4       0.0      0.0       1.0  ...                  1                 0   \n",
       "\n",
       "   date_holiday2_poss  wd__0  wd__1  wd__2  wd__3  wd__4  wd__5  wd__6  \n",
       "0                   0      0      0      0      0      0      0      1  \n",
       "1                   0      0      0      0      0      0      0      1  \n",
       "2                   0      0      0      0      0      0      0      1  \n",
       "3                   0      0      0      0      0      0      0      1  \n",
       "4                   0      0      0      0      0      0      0      1  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 모델 튜닝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'bus_route_id', 'in_out', 'station_code', '18~20_ride', '6~8_on',\n",
      "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
      "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
      "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
      "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
      "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
      "       'dist_name_po', 'dist_name_seongsan', 'date_holiday_holiday',\n",
      "       'date_holiday_weekday', 'date_holiday_weekend', 'date_holiday2_off',\n",
      "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
      "       'wd__3', 'wd__4', 'wd__5', 'wd__6'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 415423 entries, 0 to 415422\n",
      "Data columns (total 42 columns):\n",
      " #   Column                         Non-Null Count   Dtype   \n",
      "---  ------                         --------------   -----   \n",
      " 0   id                             415423 non-null  int64   \n",
      " 1   bus_route_id                   415423 non-null  int32   \n",
      " 2   in_out                         415423 non-null  int64   \n",
      " 3   station_code                   415423 non-null  int64   \n",
      " 4   18~20_ride                     415423 non-null  float64 \n",
      " 5   6~8_on                         415423 non-null  float64 \n",
      " 6   8~10_on                        415423 non-null  float64 \n",
      " 7   10~12_on                       415423 non-null  float64 \n",
      " 8   6~8_off                        415423 non-null  float64 \n",
      " 9   8~10_off                       415423 non-null  float64 \n",
      " 10  10~12_off                      415423 non-null  float64 \n",
      " 11  station_lat_long               415423 non-null  int32   \n",
      " 12  station_lat_long2              415423 non-null  int32   \n",
      " 13  bus_route_id_station_code      415423 non-null  category\n",
      " 14  bus_route_id_station_lat_long  415423 non-null  category\n",
      " 15  dis_jeju                       415423 non-null  float64 \n",
      " 16  dis_gosan                      415423 non-null  float64 \n",
      " 17  dis_seongsan                   415423 non-null  float64 \n",
      " 18  dis_po                         415423 non-null  float64 \n",
      " 19  temp                           415423 non-null  float64 \n",
      " 20  rain                           415423 non-null  float64 \n",
      " 21  wind                           415423 non-null  float64 \n",
      " 22  wind_power                     415423 non-null  float64 \n",
      " 23  rain_or_not                    415423 non-null  float64 \n",
      " 24  feel_temp                      415423 non-null  float64 \n",
      " 25  dist_name_gosan                415423 non-null  uint8   \n",
      " 26  dist_name_jeju                 415423 non-null  uint8   \n",
      " 27  dist_name_po                   415423 non-null  uint8   \n",
      " 28  dist_name_seongsan             415423 non-null  uint8   \n",
      " 29  date_holiday_holiday           415423 non-null  uint8   \n",
      " 30  date_holiday_weekday           415423 non-null  uint8   \n",
      " 31  date_holiday_weekend           415423 non-null  uint8   \n",
      " 32  date_holiday2_off              415423 non-null  uint8   \n",
      " 33  date_holiday2_on               415423 non-null  uint8   \n",
      " 34  date_holiday2_poss             415423 non-null  uint8   \n",
      " 35  wd__0                          415423 non-null  uint8   \n",
      " 36  wd__1                          415423 non-null  uint8   \n",
      " 37  wd__2                          415423 non-null  uint8   \n",
      " 38  wd__3                          415423 non-null  uint8   \n",
      " 39  wd__4                          415423 non-null  uint8   \n",
      " 40  wd__5                          415423 non-null  uint8   \n",
      " 41  wd__6                          415423 non-null  uint8   \n",
      "dtypes: category(2), float64(17), int32(3), int64(3), uint8(17)\n",
      "memory usage: 81.8 MB\n"
     ]
    }
   ],
   "source": [
    "print(tr.columns)\n",
    "tr.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 최적의 파라미터 값 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전체 칼럼을 넣어줍니다.\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday_holiday',\n",
    "       'date_holiday_weekday', 'date_holiday_weekend', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4154, 41), (4154, 1))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train=tr[features]\n",
    "random.seed(1217) #동일한 샘플링하기 위한 시드번호\n",
    "train_list=random.sample(list(range(X_train.shape[0])), int(round(X_train.shape[0]*0.01,0)) )\n",
    "\n",
    "X_train=tr[features]\n",
    "X_train=X_train.iloc[train_list,:]\n",
    "y_train=tr[label]\n",
    "y_train=y_train.iloc[train_list,:]\n",
    "\n",
    "X_test=te[features]\n",
    "\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\sklearn\\model_selection\\_search.py:765: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'max_features': 5,\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 200}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the parameter grid based on the results of random search \n",
    "param_grid = {\n",
    "    'max_features': [2,3,5],\n",
    "    'min_samples_leaf': [2,3],\n",
    "    'min_samples_split': [2,4,6],\n",
    "    'n_estimators': [100,200,500]\n",
    "}\n",
    "# Create a based model\n",
    "rf = RandomForestRegressor(random_state=1217) # 랜덤포레스트 모델을 정의한다.\n",
    "# Instantiate the grid search model\n",
    "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid) # GridSearchCV를 정의한다.\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "grid_search.best_params_ #학습 이후 최적의 paramter를 출력\n",
    "\n",
    "#해당 코드 실행시간 5분 소요\n",
    "\n",
    "#{'max_features': 5,\n",
    "# 'min_samples_leaf': 2,\n",
    "# 'min_samples_split': 2,\n",
    "# 'n_estimators': 200}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 변수별 모델 성능 비교"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2.1 전체 변수 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전체 칼럼을 넣어줍니다.\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday_holiday',\n",
    "       'date_holiday_weekday', 'date_holiday_weekend', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((415423, 41), (415423, 1), (228170, 41))"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "\n",
    "X_test=te[features]\n",
    "\n",
    "X_train.shape, y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:10: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8983880737871013"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_all = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_all.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_all.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_all.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_all.score(X_train, y_train)\n",
    "\n",
    "# 0.8983880737871013"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2.2 휴일 변수별 성능 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:29: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.899002585350873"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# holiday1만을 적용한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday_holiday',\n",
    "       'date_holiday_weekday', 'date_holiday_weekend', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_hol1 = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_hol1.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_hol1.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_hol1.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_hol1.score(X_train, y_train)\n",
    "\n",
    "#0.899002585350873"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:29: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.899716804402078"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# holiday2만을 적용한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_hol2 = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_hol2.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_hol2.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_hol2.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_hol2.score(X_train, y_train)\n",
    "\n",
    "#0.899716804402078"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2.3 latlong 변수별 성능 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:29: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8974656643398884"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# holiday2*latlong1 을 적용한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_latlong1 = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_latlong1.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_latlong1.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_latlong1.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_latlong1.score(X_train, y_train)\n",
    "\n",
    "#0.8974656643398884"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:29: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8973693314457125"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# holiday2*latlong2 을 적용한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_latlong2 = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_latlong2.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_latlong2.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_latlong2.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_latlong2.score(X_train, y_train)\n",
    "\n",
    "#0.8973693314457125"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 후진선택법을 통한 모델별 성능 비교"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 전체변수선택: 0.8983880737871013\n",
    "- hol1변수만 선택: 0.899002585350873\n",
    "- hol2변수만 선택: 0.899716804402078 -> 가장 높은 스코어\n",
    "- hol2*latlong1 선택: 0.8974656643398884\n",
    "- hol2*latlong2 선택: 0.8973693314457125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 특성 중요도 추출을 위해 다시 세트 적용\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'rain_or_not', 'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features sorted by their score:\n",
      "0.2191 10~12_on\n",
      "0.121 8~10_on\n",
      "0.0674 6~8_on\n",
      "0.0476 10~12_off\n",
      "0.041 dis_jeju\n",
      "0.0406 bus_route_id_station_lat_long\n",
      "0.0404 8~10_off\n",
      "0.04 temp\n",
      "0.04 bus_route_id\n",
      "0.0388 dis_gosan\n",
      "0.0368 dis_po\n",
      "0.0335 dis_seongsan\n",
      "0.0332 bus_route_id_station_code\n",
      "0.031 6~8_off\n",
      "0.0308 station_code\n",
      "0.0277 station_lat_long2\n",
      "0.0192 id\n",
      "0.015 wind_power\n",
      "0.0143 dist_name_gosan\n",
      "0.0135 rain\n",
      "0.0092 wind\n",
      "0.009 date_holiday2_poss\n",
      "0.004 feel_temp\n",
      "0.0033 wd__1\n",
      "0.0032 date_holiday2_on\n",
      "0.0029 wd__3\n",
      "0.0025 wd__2\n",
      "0.0023 wd__0\n",
      "0.002 wd__5\n",
      "0.0018 dist_name_po\n",
      "0.0017 wd__4\n",
      "0.0013 wd__6\n",
      "0.0008 in_out\n",
      "0.0008 dist_name_seongsan\n",
      "0.0006 date_holiday2_off\n",
      "0.0003 dist_name_jeju\n",
      "0.0 rain_or_not\n"
     ]
    }
   ],
   "source": [
    "# hol2 모델의 특성 중요도를 추출해보겠습니다.\n",
    "\n",
    "print (\"Features sorted by their score:\")\n",
    "\n",
    "fi = sorted(zip(map(lambda x: round(x, 4), rf_hol2.feature_importances_), features), \n",
    "             reverse=True)\n",
    "\n",
    "for i in range(len(fi)):\n",
    "    print(fi[i][0], fi[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 그래프로 보겠습니다.\n",
    "\n",
    "importances = rf_hol2.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in rf_hol2.estimators_],axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(X_train.shape[1]), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
    "plt.xticks(range(X_train.shape[1]), indices)\n",
    "plt.xlim([-1, X_train.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:29: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8998036397146723"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가장 중요도가 낮은 rain or not 만을 제외한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', 'wind_power',\n",
    "       'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_hol2_ron = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_hol2_ron.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_hol2_ron.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_hol2_ron.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_hol2_ron.score(X_train, y_train)\n",
    "\n",
    "# 0.899716804402078\n",
    "# 0.8998036397146723 소폭 상승. 이건 제출해서 적합 여부를 확인해봐얄듯.\n",
    "\n",
    "# \t2.3705331267\t그러나 실제 RMSE는 떨어짐."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features sorted by their score:\n",
      "0.1967 10~12_on\n",
      "0.1417 8~10_on\n",
      "0.0662 6~8_on\n",
      "0.0511 10~12_off\n",
      "0.0454 bus_route_id_station_lat_long\n",
      "0.0425 8~10_off\n",
      "0.0411 dis_seongsan\n",
      "0.0406 dis_po\n",
      "0.0401 bus_route_id_station_code\n",
      "0.0397 bus_route_id\n",
      "0.0364 dis_jeju\n",
      "0.0351 dis_gosan\n",
      "0.0335 station_lat_long2\n",
      "0.0318 station_code\n",
      "0.0271 6~8_off\n",
      "0.0247 station_lat_long\n",
      "0.0192 id\n",
      "0.0144 temp\n",
      "0.0141 wind\n",
      "0.0141 feel_temp\n",
      "0.0092 rain\n",
      "0.0079 date_holiday2_on\n",
      "0.0035 date_holiday2_off\n",
      "0.0033 wd__6\n",
      "0.003 wd__0\n",
      "0.0029 wd__2\n",
      "0.0025 date_holiday2_poss\n",
      "0.0024 wd__1\n",
      "0.0022 wd__4\n",
      "0.0021 dist_name_jeju\n",
      "0.0018 wd__3\n",
      "0.0016 wd__5\n",
      "0.0009 in_out\n",
      "0.0006 dist_name_po\n",
      "0.0005 dist_name_seongsan\n",
      "0.0003 dist_name_gosan\n",
      "0.0 wind_power\n"
     ]
    }
   ],
   "source": [
    "# 위 모델의 특성 중요도 추출\n",
    "\n",
    "print (\"Features sorted by their score:\")\n",
    "\n",
    "fi = sorted(zip(map(lambda x: round(x, 4), rf_hol2_ron.feature_importances_), features), \n",
    "             reverse=True)\n",
    "\n",
    "for i in range(len(fi)):\n",
    "    print(fi[i][0], fi[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:29: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9002048084772074"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가장 중요도가 낮은 rain_or_not에 wind_power도 제거한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', \n",
    "       'feel_temp', 'dist_name_gosan', 'dist_name_jeju',\n",
    "       'dist_name_po', 'dist_name_seongsan', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_hol2_ron_wp = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_hol2_ron_wp.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_hol2_ron_wp.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_hol2_ron_wp.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_hol2_ron_wp.score(X_train, y_train)\n",
    "\n",
    "# 0.899716804402078\n",
    "# 0.8998036397146723 ron 모델.\n",
    "# 0.9002048084772074 ron-wp 모델\n",
    "\n",
    "# 2.3760604556\t위 모델보다 더 떨어짐."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features sorted by their score:\n",
      "0.201 10~12_on\n",
      "0.1346 8~10_on\n",
      "0.0708 6~8_on\n",
      "0.0521 10~12_off\n",
      "0.0444 8~10_off\n",
      "0.0406 bus_route_id_station_lat_long\n",
      "0.0402 dis_po\n",
      "0.0397 dis_seongsan\n",
      "0.0396 bus_route_id\n",
      "0.0382 dis_jeju\n",
      "0.0374 bus_route_id_station_code\n",
      "0.0354 dis_gosan\n",
      "0.033 station_lat_long2\n",
      "0.0305 station_code\n",
      "0.0303 6~8_off\n",
      "0.0252 station_lat_long\n",
      "0.0195 id\n",
      "0.0148 feel_temp\n",
      "0.0147 wind\n",
      "0.0138 temp\n",
      "0.0091 date_holiday2_on\n",
      "0.0089 rain\n",
      "0.0035 date_holiday2_off\n",
      "0.0031 wd__6\n",
      "0.003 wd__2\n",
      "0.003 wd__0\n",
      "0.0024 wd__1\n",
      "0.0023 date_holiday2_poss\n",
      "0.002 wd__4\n",
      "0.002 dist_name_jeju\n",
      "0.0017 wd__3\n",
      "0.0015 wd__5\n",
      "0.0008 in_out\n",
      "0.0005 dist_name_po\n",
      "0.0003 dist_name_seongsan\n",
      "0.0003 dist_name_gosan\n"
     ]
    }
   ],
   "source": [
    "# 위 모델의 특성 중요도 추출\n",
    "\n",
    "print (\"Features sorted by their score:\")\n",
    "\n",
    "fi = sorted(zip(map(lambda x: round(x, 4), rf_hol2_ron_wp.feature_importances_), features), \n",
    "             reverse=True)\n",
    "\n",
    "for i in range(len(fi)):\n",
    "    print(fi[i][0], fi[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thinp\\anaconda3\\envs\\Wyatt37\\lib\\site-packages\\ipykernel_launcher.py:28: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9030724589010164"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가장 중요도가 낮은 rain_or_not에 wind_power에 dist3형제 제거한 모델\n",
    "features=['id', 'bus_route_id', 'in_out', 'station_code', '6~8_on',\n",
    "       '8~10_on', '10~12_on', '6~8_off', '8~10_off', '10~12_off',\n",
    "       'station_lat_long', 'station_lat_long2', 'bus_route_id_station_code',\n",
    "       'bus_route_id_station_lat_long', 'dis_jeju', 'dis_gosan',\n",
    "       'dis_seongsan', 'dis_po', 'temp', 'rain', 'wind', \n",
    "       'feel_temp','dist_name_jeju', 'date_holiday2_off',\n",
    "       'date_holiday2_on', 'date_holiday2_poss', 'wd__0', 'wd__1', 'wd__2',\n",
    "       'wd__3', 'wd__4', 'wd__5', 'wd__6']\n",
    "\n",
    "# 18~20ride 를 label로 지정합니다.\n",
    "label = [\"18~20_ride\"]\n",
    "\n",
    "#전체 데이터로 적용\n",
    "X_train=tr[features]\n",
    "y_train=tr[label]\n",
    "X_test=te[features]\n",
    "\n",
    "# best_params_를 통해서 나온 값을 투입\n",
    "rf_hol2_ron_wp_dist = RandomForestRegressor(max_features=5,\n",
    "                           min_samples_leaf=2,\n",
    "                           min_samples_split=2,\n",
    "                           n_estimators=100,\n",
    "                           random_state=1217,\n",
    "                           n_jobs=-1)\n",
    "\n",
    "#학습 \n",
    "rf_hol2_ron_wp_dist.fit(X_train,y_train)\n",
    "\n",
    "#예측값 생성 후, test['18~20_ride']에 집어 넣는다.\n",
    "te['18~20_ride'] = rf_hol2_ron_wp_dist.predict(X_test)\n",
    "\n",
    "# id와 18~20_ride만 선택 후 csv 파일로 내보낸다\n",
    "te[['id','18~20_ride']].to_csv(\"dacon_bus_submission_rf_hol2_ron_wp_dist.csv\",index=False) \n",
    "\n",
    "# 비교를 위해 간단히 스코어를 추출한다.\n",
    "rf_hol2_ron_wp_dist.score(X_train, y_train)\n",
    "\n",
    "# 0.899716804402078\n",
    "# 0.8998036397146723 ron 모델.\n",
    "# 0.9002048084772074 ron-wp 모델\n",
    "# 0.9030724589010164 ron-wp-dist 모델\n",
    "\n",
    "# score는 계속 올라가는데, 과연 적합도 할지\n",
    "# 2.3779167967 가장 안 좋게 나옴."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결과적으로 후진선택법으로 변수를 제거할 때마다 RMSE값이 올라가는 걸 알 수 있었다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 평가:RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "평가 기준은 RMSE 값으로 이루어진다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.7 교통카드 데이터 머지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bts데이터 전처리\n",
    "def pre_bts(bus_bts):\n",
    "    # bts 데이터 date로 만들어주기\n",
    "    geton_datetime = []\n",
    "    length = len(bus_bts[\"user_card_id\"])\n",
    "\n",
    "    for i in range(length):\n",
    "        geton_datetime.append((str(bus_bts[\"geton_date\"].iloc[i])+\"-\"+str((bus_bts['geton_time'].iloc[i]))))\n",
    "    \n",
    "    bus_bts[\"geton_datetime\"] = geton_datetime\n",
    "    \n",
    "    # \"date\" 칼럼을 판다스의 datetime으로 바꿔줍니다. from 구글링.\n",
    "    bus_bts[\"geton_datetime\"] = pd.to_datetime(bus_bts[\"geton_datetime\"], errors='coerce')\n",
    "    \n",
    "    # 어차피 날짜와 시간만 쓴다.\n",
    "    bus_bts[\"geton_datetime_day\"] = bus_bts[\"geton_datetime\"].dt.day\n",
    "    bus_bts[\"geton_datetime_hour\"] = bus_bts[\"geton_datetime\"].dt.hour\n",
    "    \n",
    "    # 키값을 맞추기 위해 노선을 동일하게 뒤 4자리를 없애줍니다.\n",
    "    #bus_bts['bus_route_id'] = bus_bts['bus_route_id'].apply(lambda x: str(x)[:-4]).astype(int)\n",
    "    \n",
    "    return bus_bts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_bts(bus_train, bus_test, bus_bts):\n",
    "    # merge를 위한 key값을 만들어줍니다.\n",
    "    bus_train[\"route_station_date\"] = bus_train[\"bus_route_id\"].astype(str)+bus_train[\"station_code\"].astype(str)+bus_train[\"date\"].astype(str)\n",
    "    bus_test[\"route_station_date\"] = bus_test[\"bus_route_id\"].astype(str)+bus_test[\"station_code\"].astype(str)+bus_test[\"date\"].astype(str)\n",
    "    bus_bts[\"route_station_date\"] = bus_bts[\"bus_route_id\"].astype(str)+bus_bts[\"geton_station_code\"].astype(str)+bus_bts[\"geton_date\"].astype(str)\n",
    "\n",
    "    # bts의 user_category를 만들어준다.\n",
    "    merge1 = bus_bts.groupby([\"route_station_date\", \"user_category\"], sort=False)[\"user_count\"].count().unstack().add_prefix(\"user_\").reset_index()\n",
    "    merge1.fillna(value=0, inplace=True)\n",
    "    merge1[\"user_old\"] = merge1[\"user_6\"]+merge1[\"user_27\"]+merge1[\"user_28\"]+merge1[\"user_29\"]+merge1[\"user_30\"]\n",
    "    merge1 = merge1.drop([\"user_6\", \"user_27\", \"user_28\", \"user_29\", \"user_30\"], axis=1)\n",
    "    merge1 = merge1.rename(columns={\"user_1\":\"user_adult\", \"user_2\":\"user_kid\", \"user_4\":\"user_youth\"})\n",
    "    \n",
    "    # ratio를 잡아줍니다.\n",
    "    merge1[\"total\"] = merge1[\"user_adult\"] + merge1[\"user_kid\"] + merge1[\"user_old\"] + merge1[\"user_youth\"]\n",
    "    merge1[\"adult_ratio\"] = round((merge1[\"user_adult\"] / merge1[\"total\"]), 2)\n",
    "    merge1[\"youth_ratio\"] = round((merge1[\"user_youth\"] / merge1[\"total\"]), 2)\n",
    "    # bts의 on 트랜잭션을 만들어준다.\n",
    "    #merge2 = bus_bts.groupby([\"route_station_date\", \"geton_datetime_hour\"], sort=False)[\"user_count\"].count().unstack().add_prefix(\"trx_\").reset_index()\n",
    "    #merge2.fillna(value=0, inplace=True)\n",
    "    #merge2[\"6~8_trx\"] = merge2[\"trx_6\"]+merge2[\"trx_7\"]\n",
    "    #merge2[\"8~10_trx\"] = merge2[\"trx_8\"]+merge2[\"trx_9\"]\n",
    "    #merge2[\"10~12_trx\"] = merge2[\"trx_10\"]+merge2[\"trx_11\"]\n",
    "    #merge2 = merge2.drop([\"trx_6\",\"trx_7\",\"trx_9\",\"trx_8\",\"trx_10\",\"trx_11\"],axis=1)\n",
    "    \n",
    "    # 두 데이터를 합치고.\n",
    "    #merge_all=pd.merge(merge2,merge1,how='left',on=['route_station_date'])\n",
    "    \n",
    "    # 각각 tr, te에 머지해준다.\n",
    "    bus_train=pd.merge(bus_train,merge1,how='left',on=['route_station_date'])\n",
    "    bus_test=pd.merge(bus_test,merge1,how='left',on=['route_station_date'])\n",
    "  \n",
    "    bus_train.drop([\"route_station_date\"], axis=1, inplace=True)\n",
    "    bus_test.drop([\"route_station_date\"], axis=1, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_na(bus_train, bus_test):\n",
    "    #bus_train[\"6~8_trx\"].fillna(value=0, inplace=True)\n",
    "    #bus_train[\"8~10_trx\"].fillna(value=0, inplace=True)\n",
    "    #bus_train[\"10~12_trx\"].fillna(value=0, inplace=True)\n",
    "    bus_train[\"user_adult\"].fillna(value=0, inplace=True)\n",
    "    bus_train[\"user_youth\"].fillna(value=0, inplace=True)\n",
    "    bus_train[\"user_kid\"].fillna(value=0, inplace=True)\n",
    "    bus_train[\"user_old\"].fillna(value=0, inplace=True)\n",
    "\n",
    "    #bus_test[\"6~8_trx\"].fillna(value=0, inplace=True)\n",
    "    #bus_test[\"8~10_trx\"].fillna(value=0, inplace=True)\n",
    "    #bus_test[\"10~12_trx\"].fillna(value=0, inplace=True)\n",
    "    bus_test[\"user_adult\"].fillna(value=0, inplace=True)\n",
    "    bus_test[\"user_youth\"].fillna(value=0, inplace=True)\n",
    "    bus_test[\"user_kid\"].fillna(value=0, inplace=True)\n",
    "    bus_test[\"user_old\"].fillna(value=0, inplace=True)\n",
    "    \n",
    "    return bus_train, bus_test"
   ]
  }
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