210519 Shuffle& 최적 모델  .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 변수는 다음과 같이 변환하다.\n",
    "- cloud dummy추가\n",
    "- 화씨전환\n",
    "- month dummy 추가 -> good!!\n",
    "- WindDirection은 제거해준다(plot근거로)\n",
    "- temp^2변수를 추가해준다(plot 기준 abs보다 타당하다고 고려)\n",
    "- temp^3변수를 추가해보자\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data의 AR을 제거해주기 위해 shuffle하였다\n",
    "## 각 시간별 지역별 모델은 crossvalidation기준으로 선정\n",
    "### us\n",
    "- 18,19 -> RF        /  나머지 ->  LR\n",
    "\n",
    "### dj_or\n",
    "- 17 -> RF           /       나머지 ->  LR\n",
    "\n",
    "\n",
    "### dj_wh\n",
    "- 17,18 -> RF           /              나머지 ->  LR\n",
    "\n",
    "### dj_ft\n",
    "- 17,18 -> RF            /        나머지 ->LR\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train 및 test 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "from datetime import timedelta\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcst_us=pd.read_csv(\"ulsan_fcst_best_new.csv\")\n",
    "fcst_dj=pd.read_csv(\"DJ_fcst_best_new.csv\")\n",
    "energy=pd.read_csv('energy.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>dangjin_floating</th>\n",
       "      <th>dangjin_warehouse</th>\n",
       "      <th>dangjin</th>\n",
       "      <th>ulsan</th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_int</th>\n",
       "      <th>day_hour_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-01 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20180301</td>\n",
       "      <td>2018030101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-01 02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20180301</td>\n",
       "      <td>2018030102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-01 03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20180301</td>\n",
       "      <td>2018030103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20180301</td>\n",
       "      <td>2018030104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01 05:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>20180301</td>\n",
       "      <td>2018030105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25627</th>\n",
       "      <td>2021-01-31 20:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021013120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25628</th>\n",
       "      <td>2021-01-31 21:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021013121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25629</th>\n",
       "      <td>2021-01-31 22:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021013122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25630</th>\n",
       "      <td>2021-01-31 23:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021013123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25631</th>\n",
       "      <td>2021-02-01 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20210201</td>\n",
       "      <td>2021020100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25632 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  dangjin_floating  dangjin_warehouse  dangjin  \\\n",
       "0     2018-03-01 01:00:00               0.0                0.0        0   \n",
       "1     2018-03-01 02:00:00               0.0                0.0        0   \n",
       "2     2018-03-01 03:00:00               0.0                0.0        0   \n",
       "3     2018-03-01 04:00:00               0.0                0.0        0   \n",
       "4     2018-03-01 05:00:00               0.0                0.0        0   \n",
       "...                   ...               ...                ...      ...   \n",
       "25627 2021-01-31 20:00:00               0.0                0.0        0   \n",
       "25628 2021-01-31 21:00:00               0.0                0.0        0   \n",
       "25629 2021-01-31 22:00:00               0.0                0.0        0   \n",
       "25630 2021-01-31 23:00:00               0.0                0.0        0   \n",
       "25631 2021-02-01 00:00:00               0.0                0.0        0   \n",
       "\n",
       "       ulsan        date  year  month  day  hour   day_int  day_hour_int  \n",
       "0          0  2018-03-01  2018      3    1     1  20180301    2018030101  \n",
       "1          0  2018-03-01  2018      3    1     2  20180301    2018030102  \n",
       "2          0  2018-03-01  2018      3    1     3  20180301    2018030103  \n",
       "3          0  2018-03-01  2018      3    1     4  20180301    2018030104  \n",
       "4          0  2018-03-01  2018      3    1     5  20180301    2018030105  \n",
       "...      ...         ...   ...    ...  ...   ...       ...           ...  \n",
       "25627      0  2021-01-31  2021      1   31    20  20210131    2021013120  \n",
       "25628      0  2021-01-31  2021      1   31    21  20210131    2021013121  \n",
       "25629      0  2021-01-31  2021      1   31    22  20210131    2021013122  \n",
       "25630      0  2021-01-31  2021      1   31    23  20210131    2021013123  \n",
       "25631      0  2021-01-31  2021      2    1     0  20210201    2021020100  \n",
       "\n",
       "[25632 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 시간으로 분류를해본다\n",
    "\n",
    "energy['date'] = energy['time'].apply(lambda x: x.split()[0])\n",
    "energy['time'] = energy['time'].apply(lambda x: x.split()[1])\n",
    "energy['time'] = energy['time'].str.rjust(8,'0') # 한자릿수 시간 앞에 0 추가 ex) 3시 -> 03시\n",
    "\n",
    "# 24시를 00시로 바꿔주기\n",
    "energy.loc[energy['time']=='24:00:00','time'] = '00:00:00'\n",
    "energy['time'] = energy['date'] + ' ' + energy['time']\n",
    "energy['time'] = pd.to_datetime(energy['time'])\n",
    "energy.loc[energy['time'].dt.hour==0,'time'] += timedelta(days=1)\n",
    "\n",
    "\n",
    "\n",
    "energy['year']=energy['time'].dt.year\n",
    "energy['month']=energy['time'].dt.month\n",
    "energy['day']=energy['time'].dt.day\n",
    "energy['hour']=energy['time'].dt.hour\n",
    "\n",
    "energy['day_int']=energy['year']*10000+energy['month']*100+energy['day']\n",
    "energy['day_hour_int']=energy['day_int']*100+energy['hour']\n",
    "\n",
    "energy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#소수점 맞춤\n",
    "fcst_dj['Temperature']=round(fcst_dj['Temperature'])\n",
    "fcst_us['Temperature']=round(fcst_us['Temperature'])\n",
    "\n",
    "fcst_dj['Humidity']=round(fcst_dj['Humidity'])\n",
    "fcst_us['Humidity']=round(fcst_us['Humidity'])\n",
    "\n",
    "\n",
    "fcst_dj['WindSpeed']=round(fcst_dj['WindSpeed'],1)\n",
    "fcst_us['WindSpeed']=round(fcst_us['WindSpeed'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forecast_time</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Cloud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-01 15:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-01 16:00:00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-01 17:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01 18:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01 19:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26285</th>\n",
       "      <td>2021-02-28 20:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26286</th>\n",
       "      <td>2021-02-28 21:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26287</th>\n",
       "      <td>2021-02-28 22:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26288</th>\n",
       "      <td>2021-02-28 23:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26289</th>\n",
       "      <td>2021-03-01 00:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26290 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Forecast_time  Temperature  Humidity  WindSpeed  Cloud\n",
       "0      2018-03-01 15:00:00          0.0      60.0        7.3    2.0\n",
       "1      2018-03-01 16:00:00         -1.0      60.0        7.2    2.0\n",
       "2      2018-03-01 17:00:00         -2.0      60.0        7.2    1.0\n",
       "3      2018-03-01 18:00:00         -2.0      60.0        7.1    1.0\n",
       "4      2018-03-01 19:00:00         -2.0      61.0        7.2    1.0\n",
       "...                    ...          ...       ...        ...    ...\n",
       "26285  2021-02-28 20:00:00          7.0      67.0        1.1    4.0\n",
       "26286  2021-02-28 21:00:00          7.0      70.0        1.3    4.0\n",
       "26287  2021-02-28 22:00:00          7.0      73.0        1.3    4.0\n",
       "26288  2021-02-28 23:00:00          6.0      76.0        1.3    4.0\n",
       "26289  2021-03-01 00:00:00          6.0      80.0        1.3    4.0\n",
       "\n",
       "[26290 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcst_dj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시간(hour) 변수 생성\n",
    "fcst_dj['hour']= fcst_dj['Forecast_time'].str.split(' ').str[1].str.split(':').str[0].astype(int)\n",
    "fcst_dj['date']= fcst_dj['Forecast_time'].str.split(' ').str[0]\n",
    "\n",
    "# year montj day추가\n",
    "year_fcst = fcst_dj['date'].str.split('-').str[0].astype(int)\n",
    "month_fcst = fcst_dj['date'].str.split('-').str[1].astype(int)\n",
    "day_fcst = fcst_dj['date'].str.split('-').str[2].astype(int)\n",
    "fcst_dj['day_int']=year_fcst*10000+month_fcst*100+day_fcst\n",
    "\n",
    "fcst_dj['year']=year_fcst\n",
    "fcst_dj['month']=month_fcst\n",
    "fcst_dj['day']=day_fcst\n",
    "\n",
    "# 정렬을 쉽게하기위한 day_hour_int를 생성\n",
    "fcst_dj['day_int']=year_fcst*10000+month_fcst*100+day_fcst\n",
    "fcst_dj['day_hour_int']=fcst_dj['day_int']*100+fcst_dj['hour']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시간(hour) 변수 생성\n",
    "fcst_us['hour']= fcst_us['Forecast_time'].str.split(' ').str[1].str.split(':').str[0].astype(int)\n",
    "fcst_us['date']= fcst_us['Forecast_time'].str.split(' ').str[0]\n",
    "\n",
    "# year montj day추가\n",
    "year_fcst = fcst_us['date'].str.split('-').str[0].astype(int)\n",
    "month_fcst = fcst_us['date'].str.split('-').str[1].astype(int)\n",
    "day_fcst = fcst_us['date'].str.split('-').str[2].astype(int)\n",
    "\n",
    "fcst_us['year']=year_fcst\n",
    "fcst_us['month']=month_fcst\n",
    "fcst_us['day']=day_fcst\n",
    "\n",
    "\n",
    "# 정렬을 쉽게하기위한 day_hour_int를 생성\n",
    "fcst_us['day_int']=year_fcst*10000+month_fcst*100+day_fcst\n",
    "fcst_us['day_hour_int']=fcst_us['day_int']*100+fcst_us['hour']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 변수변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 화씨 생성\n",
    "fcst_dj['Temp_F']=fcst_dj['Temperature']*9/5+32\n",
    "fcst_us['Temp_F']=fcst_us['Temperature']*9/5+32\n",
    "\n",
    "## 제곱 온도 생성  &  3제곱 온도 생성\n",
    "fcst_dj['Temp2']=fcst_dj['Temperature']**2\n",
    "fcst_us['Temp2']=fcst_us['Temperature']**2\n",
    "\n",
    "fcst_dj['Temp3']=fcst_dj['Temperature']**3\n",
    "fcst_us['Temp3']=fcst_us['Temperature']**3\n",
    "\n",
    "\n",
    "#구름 더미화\n",
    "fcst_incoded_dj_1=pd.get_dummies(fcst_dj,columns=['Cloud'])\n",
    "fcst_incoded_us_1=pd.get_dummies(fcst_us,columns=['Cloud'])\n",
    "\n",
    "# 월별 더미화\n",
    "fcst_incoded_dj=pd.get_dummies(fcst_incoded_dj_1,columns=['month'])\n",
    "fcst_incoded_us=pd.get_dummies(fcst_incoded_us_1,columns=['month'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Forecast_time', 'Temperature', 'Humidity', 'WindSpeed', 'hour', 'date',\n",
       "       'day_int', 'year', 'day', 'day_hour_int', 'Temp_F', 'Temp2', 'Temp3',\n",
       "       'Cloud_1.0', 'Cloud_2.0', 'Cloud_3.0', 'Cloud_4.0', 'month_1',\n",
       "       'month_2', 'month_3', 'month_4', 'month_5', 'month_6', 'month_7',\n",
       "       'month_8', 'month_9', 'month_10', 'month_11', 'month_12'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fcst_incoded_dj.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결합해서 데이터 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_X\n",
    "\n",
    "\n",
    "train_us=fcst_incoded_us[(fcst_incoded_us['day_int']<=20210131)&(fcst_incoded_us['day_int']>=20180302)]\n",
    "train_us=train_us.reset_index().drop(['index'],axis=1)\n",
    "\n",
    "train_dj=fcst_incoded_dj[(fcst_incoded_dj['day_int']<=20210131)&(fcst_incoded_dj['day_int']>=20180302)]\n",
    "train_dj=train_dj.reset_index().drop(['index'],axis=1)\n",
    "\n",
    "\n",
    "train_dj_or=train_dj.copy()\n",
    "train_dj_wh=train_dj.copy()\n",
    "train_dj_ft=train_dj.copy()\n",
    "\n",
    "\n",
    "#y값 추가 및 na 값 제거\n",
    "train_y=energy[(energy['day_int']<=20210131)&(energy['day_int']>=20180302)]\n",
    "train_y=train_y.reset_index(drop=True)\n",
    "\n",
    "\n",
    "train_us['energy']=train_y.ulsan\n",
    "train_us=train_us.dropna(axis=0)\n",
    "\n",
    "train_dj_or['energy']=train_y.dangjin\n",
    "train_dj_or=train_dj_or.dropna(axis=0)\n",
    "\n",
    "train_dj_wh['energy']=train_y.dangjin_warehouse\n",
    "train_dj_wh=train_dj_wh.dropna(axis=0)\n",
    "\n",
    "train_dj_ft['energy']=train_y.dangjin_floating\n",
    "train_dj_ft=train_dj_ft.dropna(axis=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Forecast_time</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>hour</th>\n",
       "      <th>date</th>\n",
       "      <th>day_int</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "      <th>day_hour_int</th>\n",
       "      <th>...</th>\n",
       "      <th>month_4</th>\n",
       "      <th>month_5</th>\n",
       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>month_12</th>\n",
       "      <th>energy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-02 00:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>20180302</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2018030200</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-02 01:00:00</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>20180302</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2018030201</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-02 02:00:00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>20180302</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2018030202</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-02 03:00:00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>20180302</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2018030203</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-02 04:00:00</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>20180302</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>2018030204</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25603</th>\n",
       "      <td>2021-01-31 19:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
       "      <td>2021013119</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25604</th>\n",
       "      <td>2021-01-31 20:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>20</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
       "      <td>2021013120</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25605</th>\n",
       "      <td>2021-01-31 21:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
       "      <td>2021013121</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25606</th>\n",
       "      <td>2021-01-31 22:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>22</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
       "      <td>2021013122</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25607</th>\n",
       "      <td>2021-01-31 23:00:00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>23</td>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>20210131</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
       "      <td>2021013123</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25608 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Forecast_time  Temperature  Humidity  WindSpeed  hour  \\\n",
       "0      2018-03-02 00:00:00         -2.0      50.0        5.8     0   \n",
       "1      2018-03-02 01:00:00         -2.0      50.0        5.5     1   \n",
       "2      2018-03-02 02:00:00         -3.0      53.0        5.3     2   \n",
       "3      2018-03-02 03:00:00         -3.0      55.0        5.0     3   \n",
       "4      2018-03-02 04:00:00         -3.0      57.0        4.7     4   \n",
       "...                    ...          ...       ...        ...   ...   \n",
       "25603  2021-01-31 19:00:00          6.0      78.0        3.5    19   \n",
       "25604  2021-01-31 20:00:00          6.0      80.0        3.8    20   \n",
       "25605  2021-01-31 21:00:00          6.0      80.0        4.0    21   \n",
       "25606  2021-01-31 22:00:00          6.0      80.0        4.7    22   \n",
       "25607  2021-01-31 23:00:00          7.0      79.0        5.3    23   \n",
       "\n",
       "             date   day_int  year  day  day_hour_int  ...  month_4  month_5  \\\n",
       "0      2018-03-02  20180302  2018    2    2018030200  ...        0        0   \n",
       "1      2018-03-02  20180302  2018    2    2018030201  ...        0        0   \n",
       "2      2018-03-02  20180302  2018    2    2018030202  ...        0        0   \n",
       "3      2018-03-02  20180302  2018    2    2018030203  ...        0        0   \n",
       "4      2018-03-02  20180302  2018    2    2018030204  ...        0        0   \n",
       "...           ...       ...   ...  ...           ...  ...      ...      ...   \n",
       "25603  2021-01-31  20210131  2021   31    2021013119  ...        0        0   \n",
       "25604  2021-01-31  20210131  2021   31    2021013120  ...        0        0   \n",
       "25605  2021-01-31  20210131  2021   31    2021013121  ...        0        0   \n",
       "25606  2021-01-31  20210131  2021   31    2021013122  ...        0        0   \n",
       "25607  2021-01-31  20210131  2021   31    2021013123  ...        0        0   \n",
       "\n",
       "       month_6  month_7  month_8  month_9  month_10  month_11  month_12  \\\n",
       "0            0        0        0        0         0         0         0   \n",
       "1            0        0        0        0         0         0         0   \n",
       "2            0        0        0        0         0         0         0   \n",
       "3            0        0        0        0         0         0         0   \n",
       "4            0        0        0        0         0         0         0   \n",
       "...        ...      ...      ...      ...       ...       ...       ...   \n",
       "25603        0        0        0        0         0         0         0   \n",
       "25604        0        0        0        0         0         0         0   \n",
       "25605        0        0        0        0         0         0         0   \n",
       "25606        0        0        0        0         0         0         0   \n",
       "25607        0        0        0        0         0         0         0   \n",
       "\n",
       "       energy  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "...       ...  \n",
       "25603       0  \n",
       "25604       0  \n",
       "25605       0  \n",
       "25606       0  \n",
       "25607       0  \n",
       "\n",
       "[25608 rows x 30 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dj_or"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SHUFFLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_us=train_us.sample(frac=1).reset_index(drop=True)\n",
    "train_dj_or=train_dj_or.sample(frac=1).reset_index(drop=True)\n",
    "train_dj_wh=train_dj_wh.sample(frac=1).reset_index(drop=True)\n",
    "train_dj_ft=train_dj_ft.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 각 train data 내보내기\n",
    "#train_us.to_csv(\"train_month_us.csv\",index=False)\n",
    "#train_dj_or.to_csv(\"train_month_dj_or.csv\",index=False)\n",
    "#train_dj_wh.to_csv(\"train_month_dj_wh.csv\",index=False)\n",
    "#train_dj_ft.to_csv(\"train_month_dj_ft.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test 생성\n",
    "dummy변수의 마지막 변수를 제외-> 최종 변수: 습도,풍속,기온,기온^2,구름-1&2&3, month-1~11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_us=fcst_incoded_us[(fcst_incoded_us['day_hour_int']>=2021020100)&(fcst_incoded_us['day_hour_int']<=2021022823)]\n",
    "test_us=test_us.reset_index().drop(['index'],axis=1)\n",
    "test_us['energy']=0\n",
    "\n",
    "test_X_us=test_us[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11','day_hour_int','hour','energy']]\n",
    "\n",
    "\n",
    "\n",
    "test_dj=fcst_incoded_dj[(fcst_incoded_dj['day_hour_int']>=2021020100)&(fcst_incoded_dj['day_hour_int']<=2021022823)]\n",
    "test_dj=test_dj.reset_index().drop(['index'],axis=1)\n",
    "test_dj['energy']=0\n",
    "\n",
    "test_X_dj=test_dj[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11','day_hour_int','hour','energy']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X_dj.columns==test_X_us.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 변수선택 및 최적모형 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor # k-최근접 이웃 회귀 #분류대신 값을 예측\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#개별 모델 성능 (MAE) 확인\n",
    "\n",
    "knn_model = KNeighborsRegressor()\n",
    "svr_model = SVR()\n",
    "lr_model = LinearRegression()\n",
    "rf_model = RandomForestRegressor()\n",
    "lgb_model = LGBMRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED_us=pd.DataFrame(np.zeros((28,24)))\n",
    "\n",
    "### 18,19 -> RF     /  나머지 ->  LR\n",
    "\n",
    "\n",
    "for h in range(24):\n",
    "########################  평가기준 아닌 시간은 0########################3    \n",
    "    if h<=7:\n",
    "        PRED_us.iloc[:,h]=0\n",
    "    elif h>=20:\n",
    "        PRED_us.iloc[:,h]=0\n",
    "\n",
    "#######################  18,19시 ->  RF ############################        \n",
    "\n",
    "    elif h>=18:\n",
    "        data=train_us[train_us['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_us[test_X_us['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "        rf_model = RandomForestRegressor()\n",
    "        rf_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=rf_model.predict(test)\n",
    "        PRED_us.iloc[:,h]=energy\n",
    "        \n",
    "        \n",
    "####################### 나머지-> LR ###############################\n",
    "    else:\n",
    "        data=train_us[train_us['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_us[test_X_us['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "        lr_model = LinearRegression()\n",
    "        lr_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=lr_model.predict(test)\n",
    "        PRED_us.iloc[:,h]=energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED_dj_or=pd.DataFrame(np.zeros((28,24)))\n",
    "# 17-> RF       /       나머지 ->  LR\n",
    "\n",
    "\n",
    "for h in range(24):\n",
    "    if h<=7:\n",
    "        PRED_dj_or.iloc[:,h]=0\n",
    "    elif h>=20:\n",
    "        PRED_dj_or.iloc[:,h]=0\n",
    "\n",
    "#######################  17시 ->  RF ############################        \n",
    "    elif h==17:\n",
    "        data=train_dj_or[train_dj_or['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        rf_model = RandomForestRegressor()\n",
    "        rf_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=rf_model.predict(test)\n",
    "        PRED_dj_or.iloc[:,h]=energy\n",
    "\n",
    "         \n",
    "####################### 나머지-> LR ###############################\n",
    "    \n",
    "    else:\n",
    "        data=train_dj_or[train_dj_or['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "        \n",
    "\n",
    "        lr_model = LinearRegression()\n",
    "        lr_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=lr_model.predict(test)\n",
    "        PRED_dj_or.iloc[:,h]=energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED_dj_wh=pd.DataFrame(np.zeros((28,24)))\n",
    "# 17,18 -> RF           /              나머지 ->  LR\n",
    "\n",
    "\n",
    "for h in range(24):\n",
    "    if h<=7:\n",
    "        PRED_dj_wh.iloc[:,h]=0\n",
    "    elif h>=20:\n",
    "        PRED_dj_wh.iloc[:,h]=0\n",
    "#######################  17,18시 ->  RF ############################        \n",
    "    elif h==17:\n",
    "        data=train_dj_wh[train_dj_wh['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "        \n",
    "\n",
    "\n",
    "        rf_model = RandomForestRegressor()\n",
    "        rf_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=rf_model.predict(test)\n",
    "        PRED_dj_wh.iloc[:,h]=energy\n",
    "        \n",
    "    elif h==18:\n",
    "        data=train_dj_wh[train_dj_wh['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "        rf_model = RandomForestRegressor()\n",
    "        rf_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=rf_model.predict(test)\n",
    "        PRED_dj_wh.iloc[:,h]=energy\n",
    "\n",
    "         \n",
    "####################### 나머지-> LR ###############################\n",
    "    else:\n",
    "        data=train_dj_wh[train_dj_wh['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "        lr_model = LinearRegression()\n",
    "        lr_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=lr_model.predict(test)\n",
    "        PRED_dj_wh.iloc[:,h]=energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED_dj_ft=pd.DataFrame(np.zeros((28,24)))\n",
    "# 17,18 -> RF            /        나머지 ->LR\n",
    "\n",
    "\n",
    "for h in range(24):\n",
    "    if h<=7:\n",
    "        PRED_dj_ft.iloc[:,h]=0\n",
    "    elif h>=20:\n",
    "        PRED_dj_ft.iloc[:,h]=0\n",
    "\n",
    "#######################  17시,18시 ->  RF ############################        \n",
    "\n",
    "    elif h==17:\n",
    "        data=train_dj_ft[train_dj_ft['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "        rf_model = RandomForestRegressor()\n",
    "        rf_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=rf_model.predict(test)\n",
    "        PRED_dj_ft.iloc[:,h]=energy\n",
    "\n",
    "\n",
    "    elif h==18:\n",
    "        data=train_dj_ft[train_dj_ft['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "        rf_model = RandomForestRegressor()\n",
    "        rf_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=rf_model.predict(test)\n",
    "        PRED_dj_ft.iloc[:,h]=energy\n",
    "####################### 나머지-> LR ###############################\n",
    "    else:\n",
    "        data=train_dj_ft[train_dj_ft['hour']==h].reset_index(drop=True)\n",
    "        data=data[data['energy']>0]\n",
    "        \n",
    "        test_h=test_X_dj[test_X_dj['hour']==h].reset_index(drop=True)\n",
    "\n",
    "\n",
    "        train_X=data[['Humidity','WindSpeed','Temperature','Temp2','Temp3','Cloud_1.0','Cloud_2.0','Cloud_3.0',\n",
    "                  'month_1','month_2','month_3','month_4','month_5','month_6','month_7','month_8','month_9','month_10','month_11']]\n",
    "\n",
    "        train_y=data.energy.reset_index(drop=True)\n",
    "        test=test_h.iloc[:,:-3]\n",
    "\n",
    "\n",
    "\n",
    "        lr_model = LinearRegression()\n",
    "        lr_model.fit(train_X,train_y)\n",
    "\n",
    "        #예측값\n",
    "        energy=lr_model.predict(test)\n",
    "        PRED_dj_ft.iloc[:,h]=energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "PRED_np_us=PRED_us.to_numpy()\n",
    "PRED_np_dj_or=PRED_dj_or.to_numpy()\n",
    "PRED_np_dj_ft=PRED_dj_ft.to_numpy()\n",
    "PRED_np_dj_wh=PRED_dj_wh.to_numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X_dj['dangjin']=PRED_np_dj_or.reshape(-1,1)\n",
    "test_X_dj['dangjin_warehouse']=PRED_np_dj_wh.reshape(-1,1)\n",
    "test_X_dj['dangjin_floating']=PRED_np_dj_ft.reshape(-1,1)\n",
    "test_X_us['ulsan']=PRED_np_us.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Humidity</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Temp2</th>\n",
       "      <th>Temp3</th>\n",
       "      <th>Cloud_1.0</th>\n",
       "      <th>Cloud_2.0</th>\n",
       "      <th>Cloud_3.0</th>\n",
       "      <th>month_1</th>\n",
       "      <th>month_2</th>\n",
       "      <th>...</th>\n",
       "      <th>month_6</th>\n",
       "      <th>month_7</th>\n",
       "      <th>month_8</th>\n",
       "      <th>month_9</th>\n",
       "      <th>month_10</th>\n",
       "      <th>month_11</th>\n",
       "      <th>day_hour_int</th>\n",
       "      <th>hour</th>\n",
       "      <th>energy</th>\n",
       "      <th>ulsan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021020100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021020101</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021020102</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021020103</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>79.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021020104</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>72.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021022819</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>74.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021022820</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>75.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021022821</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>77.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021022822</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>78.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2021022823</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>672 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Humidity  WindSpeed  Temperature  Temp2   Temp3  Cloud_1.0  Cloud_2.0  \\\n",
       "0        65.0        4.7         11.0  121.0  1331.0          0          0   \n",
       "1        75.0        6.2         11.0  121.0  1331.0          0          0   \n",
       "2        75.0        6.2         11.0  121.0  1331.0          0          0   \n",
       "3        75.0        6.2         11.0  121.0  1331.0          0          0   \n",
       "4        79.0        6.7         12.0  144.0  1728.0          0          0   \n",
       "..        ...        ...          ...    ...     ...        ...        ...   \n",
       "667      72.0        3.5         14.0  196.0  2744.0          0          0   \n",
       "668      74.0        3.9         14.0  196.0  2744.0          0          0   \n",
       "669      75.0        4.3         14.0  196.0  2744.0          0          0   \n",
       "670      77.0        4.0         14.0  196.0  2744.0          0          0   \n",
       "671      78.0        3.6         13.0  169.0  2197.0          0          0   \n",
       "\n",
       "     Cloud_3.0  month_1  month_2  ...  month_6  month_7  month_8  month_9  \\\n",
       "0            0        0        1  ...        0        0        0        0   \n",
       "1            0        0        1  ...        0        0        0        0   \n",
       "2            0        0        1  ...        0        0        0        0   \n",
       "3            0        0        1  ...        0        0        0        0   \n",
       "4            0        0        1  ...        0        0        0        0   \n",
       "..         ...      ...      ...  ...      ...      ...      ...      ...   \n",
       "667          0        0        1  ...        0        0        0        0   \n",
       "668          0        0        1  ...        0        0        0        0   \n",
       "669          0        0        1  ...        0        0        0        0   \n",
       "670          0        0        1  ...        0        0        0        0   \n",
       "671          0        0        1  ...        0        0        0        0   \n",
       "\n",
       "     month_10  month_11  day_hour_int  hour  energy  ulsan  \n",
       "0           0         0    2021020100     0       0   0.00  \n",
       "1           0         0    2021020101     1       0   0.00  \n",
       "2           0         0    2021020102     2       0   0.00  \n",
       "3           0         0    2021020103     3       0   0.00  \n",
       "4           0         0    2021020104     4       0   0.00  \n",
       "..        ...       ...           ...   ...     ...    ...  \n",
       "667         0         0    2021022819    19       0   4.11  \n",
       "668         0         0    2021022820    20       0   0.00  \n",
       "669         0         0    2021022821    21       0   0.00  \n",
       "670         0         0    2021022822    22       0   0.00  \n",
       "671         0         0    2021022823    23       0   0.00  \n",
       "\n",
       "[672 rows x 23 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X_us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>dangjin_floating</th>\n",
       "      <th>dangjin_warehouse</th>\n",
       "      <th>dangjin</th>\n",
       "      <th>ulsan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-02-01 01:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-02-01 02:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-01 03:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-02-01 04:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-01 05:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-02-01 06:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-02-01 07:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-02-01 08:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-02-01 09:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-02-01 10:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  dangjin_floating  dangjin_warehouse  dangjin  ulsan\n",
       "0  2021-02-01 01:00:00                 0                  0        0      0\n",
       "1  2021-02-01 02:00:00                 0                  0        0      0\n",
       "2  2021-02-01 03:00:00                 0                  0        0      0\n",
       "3  2021-02-01 04:00:00                 0                  0        0      0\n",
       "4  2021-02-01 05:00:00                 0                  0        0      0\n",
       "5  2021-02-01 06:00:00                 0                  0        0      0\n",
       "6  2021-02-01 07:00:00                 0                  0        0      0\n",
       "7  2021-02-01 08:00:00                 0                  0        0      0\n",
       "8  2021-02-01 09:00:00                 0                  0        0      0\n",
       "9  2021-02-01 10:00:00                 0                  0        0      0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub=pd.read_csv('sample_submission.csv')\n",
    "sub_2=sub.iloc[:671,:]\n",
    "sub_2\n",
    "sub_67=sub.iloc[671:,:]\n",
    "sub.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_2['dangjin']=list(test_X_dj.iloc[1:,:].dangjin)\n",
    "sub_2['dangjin_warehouse']=list(test_X_dj.iloc[1:,:].dangjin_warehouse)\n",
    "sub_2['dangjin_floating']=list(test_X_dj.iloc[1:,:].dangjin_floating)\n",
    "sub_2['ulsan']=list(test_X_us.iloc[1:,:].ulsan)\n",
    "\n",
    "\n",
    "\n",
    "sub_final=pd.concat([sub_2,sub_67],axis=0)\n",
    "sub_final.to_csv('sub_shuffled_best_new.csv',index=False)"
   ]
  },
  {
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}