建模算法/预测/kaggle预测题/Rossmann_Store_Sales.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f1b7d31",
   "metadata": {},
   "source": [
    "## 1.导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb4bc3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost import plot_tree\n",
    "from matplotlib.pylab import rcParams\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import KFold\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1a7101b",
   "metadata": {},
   "source": [
    "## 2.读取文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1ae5e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda3\\envs\\pytorch\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3441: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "store_df = pd.read_csv(\"F:/学习/数学建模/Rossmann Store/store.csv\")\n",
    "train_df = pd.read_csv(\"F:/学习/数学建模/Rossmann Store/train.csv\")           \n",
    "test_df = pd.read_csv(\"F:/学习/数学建模/Rossmann Store/test.csv\")\n",
    "submission_df = pd.read_csv(\"F:/学习/数学建模/Rossmann Store/sample_submission.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc58590a",
   "metadata": {},
   "source": [
    "## 3.数据展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "929a8624",
   "metadata": {},
   "outputs": [
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       "         Store  DayOfWeek        Date  Sales  Customers  Open  Promo  \\\n",
       "0            1          5  2015-07-31   5263        555     1      1   \n",
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       "\n",
       "        StateHoliday  SchoolHoliday  \n",
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     "execution_count": 3,
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   "source": [
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  {
   "cell_type": "code",
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   "id": "64cdca95",
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   "outputs": [
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       "          Id  Store  DayOfWeek        Date  Open  Promo StateHoliday  \\\n",
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       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>1112</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>1113</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>9260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>1114</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>1115</td>\n",
       "      <td>d</td>\n",
       "      <td>c</td>\n",
       "      <td>5350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mar,Jun,Sept,Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Store StoreType Assortment  CompetitionDistance  \\\n",
       "0         1         c          a               1270.0   \n",
       "1         2         a          a                570.0   \n",
       "2         3         a          a              14130.0   \n",
       "3         4         c          c                620.0   \n",
       "4         5         a          a              29910.0   \n",
       "...     ...       ...        ...                  ...   \n",
       "1110   1111         a          a               1900.0   \n",
       "1111   1112         c          c               1880.0   \n",
       "1112   1113         a          c               9260.0   \n",
       "1113   1114         a          c                870.0   \n",
       "1114   1115         d          c               5350.0   \n",
       "\n",
       "      CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "0                           9.0                    2008.0       0   \n",
       "1                          11.0                    2007.0       1   \n",
       "2                          12.0                    2006.0       1   \n",
       "3                           9.0                    2009.0       0   \n",
       "4                           4.0                    2015.0       0   \n",
       "...                         ...                       ...     ...   \n",
       "1110                        6.0                    2014.0       1   \n",
       "1111                        4.0                    2006.0       0   \n",
       "1112                        NaN                       NaN       0   \n",
       "1113                        NaN                       NaN       0   \n",
       "1114                        NaN                       NaN       1   \n",
       "\n",
       "      Promo2SinceWeek  Promo2SinceYear     PromoInterval  \n",
       "0                 NaN              NaN               NaN  \n",
       "1                13.0           2010.0   Jan,Apr,Jul,Oct  \n",
       "2                14.0           2011.0   Jan,Apr,Jul,Oct  \n",
       "3                 NaN              NaN               NaN  \n",
       "4                 NaN              NaN               NaN  \n",
       "...               ...              ...               ...  \n",
       "1110             31.0           2013.0   Jan,Apr,Jul,Oct  \n",
       "1111              NaN              NaN               NaN  \n",
       "1112              NaN              NaN               NaN  \n",
       "1113              NaN              NaN               NaN  \n",
       "1114             22.0           2012.0  Mar,Jun,Sept,Dec  \n",
       "\n",
       "[1115 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d7add18",
   "metadata": {},
   "source": [
    "## 4.数据合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fcbe249",
   "metadata": {},
   "outputs": [],
   "source": [
    "##通过分析数据可知，商店的详细信息在store_df中，需要将商店的详细信息根据商店的编号与训练集和测试集合并，以增加训练集和测试集的特征\n",
    "## Store 为商店编号，向左合并\n",
    "merge_train_df = train_df.merge(store_df, how=\"left\",on=\"Store\")\n",
    "merge_test_df = test_df.merge(store_df, how=\"left\",on=\"Store\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7706d8c0",
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>570.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>620.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017204</th>\n",
       "      <td>1111</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017205</th>\n",
       "      <td>1112</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>c</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017206</th>\n",
       "      <td>1113</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>9260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017207</th>\n",
       "      <td>1114</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>870.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017208</th>\n",
       "      <td>1115</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>d</td>\n",
       "      <td>c</td>\n",
       "      <td>5350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mar,Jun,Sept,Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1017209 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Store  DayOfWeek        Date  Sales  Customers  Open  Promo  \\\n",
       "0            1          5  2015-07-31   5263        555     1      1   \n",
       "1            2          5  2015-07-31   6064        625     1      1   \n",
       "2            3          5  2015-07-31   8314        821     1      1   \n",
       "3            4          5  2015-07-31  13995       1498     1      1   \n",
       "4            5          5  2015-07-31   4822        559     1      1   \n",
       "...        ...        ...         ...    ...        ...   ...    ...   \n",
       "1017204   1111          2  2013-01-01      0          0     0      0   \n",
       "1017205   1112          2  2013-01-01      0          0     0      0   \n",
       "1017206   1113          2  2013-01-01      0          0     0      0   \n",
       "1017207   1114          2  2013-01-01      0          0     0      0   \n",
       "1017208   1115          2  2013-01-01      0          0     0      0   \n",
       "\n",
       "        StateHoliday  SchoolHoliday StoreType Assortment  CompetitionDistance  \\\n",
       "0                  0              1         c          a               1270.0   \n",
       "1                  0              1         a          a                570.0   \n",
       "2                  0              1         a          a              14130.0   \n",
       "3                  0              1         c          c                620.0   \n",
       "4                  0              1         a          a              29910.0   \n",
       "...              ...            ...       ...        ...                  ...   \n",
       "1017204            a              1         a          a               1900.0   \n",
       "1017205            a              1         c          c               1880.0   \n",
       "1017206            a              1         a          c               9260.0   \n",
       "1017207            a              1         a          c                870.0   \n",
       "1017208            a              1         d          c               5350.0   \n",
       "\n",
       "         CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
       "0                              9.0                    2008.0       0   \n",
       "1                             11.0                    2007.0       1   \n",
       "2                             12.0                    2006.0       1   \n",
       "3                              9.0                    2009.0       0   \n",
       "4                              4.0                    2015.0       0   \n",
       "...                            ...                       ...     ...   \n",
       "1017204                        6.0                    2014.0       1   \n",
       "1017205                        4.0                    2006.0       0   \n",
       "1017206                        NaN                       NaN       0   \n",
       "1017207                        NaN                       NaN       0   \n",
       "1017208                        NaN                       NaN       1   \n",
       "\n",
       "         Promo2SinceWeek  Promo2SinceYear     PromoInterval  \n",
       "0                    NaN              NaN               NaN  \n",
       "1                   13.0           2010.0   Jan,Apr,Jul,Oct  \n",
       "2                   14.0           2011.0   Jan,Apr,Jul,Oct  \n",
       "3                    NaN              NaN               NaN  \n",
       "4                    NaN              NaN               NaN  \n",
       "...                  ...              ...               ...  \n",
       "1017204             31.0           2013.0   Jan,Apr,Jul,Oct  \n",
       "1017205              NaN              NaN               NaN  \n",
       "1017206              NaN              NaN               NaN  \n",
       "1017207              NaN              NaN               NaN  \n",
       "1017208             22.0           2012.0  Mar,Jun,Sept,Dec  \n",
       "\n",
       "[1017209 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d55503",
   "metadata": {},
   "source": [
    "## 5.数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b994ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1017209 entries, 0 to 1017208\n",
      "Data columns (total 18 columns):\n",
      " #   Column                     Non-Null Count    Dtype  \n",
      "---  ------                     --------------    -----  \n",
      " 0   Store                      1017209 non-null  int64  \n",
      " 1   DayOfWeek                  1017209 non-null  int64  \n",
      " 2   Date                       1017209 non-null  object \n",
      " 3   Sales                      1017209 non-null  int64  \n",
      " 4   Customers                  1017209 non-null  int64  \n",
      " 5   Open                       1017209 non-null  int64  \n",
      " 6   Promo                      1017209 non-null  int64  \n",
      " 7   StateHoliday               1017209 non-null  object \n",
      " 8   SchoolHoliday              1017209 non-null  int64  \n",
      " 9   StoreType                  1017209 non-null  object \n",
      " 10  Assortment                 1017209 non-null  object \n",
      " 11  CompetitionDistance        1014567 non-null  float64\n",
      " 12  CompetitionOpenSinceMonth  693861 non-null   float64\n",
      " 13  CompetitionOpenSinceYear   693861 non-null   float64\n",
      " 14  Promo2                     1017209 non-null  int64  \n",
      " 15  Promo2SinceWeek            509178 non-null   float64\n",
      " 16  Promo2SinceYear            509178 non-null   float64\n",
      " 17  PromoInterval              509178 non-null   object \n",
      "dtypes: float64(5), int64(8), object(5)\n",
      "memory usage: 147.5+ MB\n"
     ]
    }
   ],
   "source": [
    "merge_train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6f964d",
   "metadata": {},
   "source": [
    "### 5.1 提取时间信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cbcd5179",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_time(df):\n",
    "    df['Date'] = pd.to_datetime(df['Date'])\n",
    "    df['Year'] = df['Date'].dt.year\n",
    "    df['Month'] = df[\"Date\"].dt.month\n",
    "    df['Day'] = df['Date'].dt.day\n",
    "    df['WeekOfYear'] = df['Date'].dt.isocalendar().week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4de1db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_time(merge_train_df)\n",
    "extract_time(merge_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0326ff80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>14.0</td>\n",
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       "      <td>Jan,Apr,Jul,Oct</td>\n",
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       "      <td>31</td>\n",
       "      <td>31</td>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1017206</th>\n",
       "      <td>1113</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1017208</th>\n",
       "      <td>1115</td>\n",
       "      <td>2</td>\n",
       "      <td>2013-01-01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Mar,Jun,Sept,Dec</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1017209 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Store  DayOfWeek       Date  Sales  Customers  Open  Promo  \\\n",
       "0            1          5 2015-07-31   5263        555     1      1   \n",
       "1            2          5 2015-07-31   6064        625     1      1   \n",
       "2            3          5 2015-07-31   8314        821     1      1   \n",
       "3            4          5 2015-07-31  13995       1498     1      1   \n",
       "4            5          5 2015-07-31   4822        559     1      1   \n",
       "...        ...        ...        ...    ...        ...   ...    ...   \n",
       "1017204   1111          2 2013-01-01      0          0     0      0   \n",
       "1017205   1112          2 2013-01-01      0          0     0      0   \n",
       "1017206   1113          2 2013-01-01      0          0     0      0   \n",
       "1017207   1114          2 2013-01-01      0          0     0      0   \n",
       "1017208   1115          2 2013-01-01      0          0     0      0   \n",
       "\n",
       "        StateHoliday  SchoolHoliday StoreType  ... CompetitionOpenSinceMonth  \\\n",
       "0                  0              1         c  ...                       9.0   \n",
       "1                  0              1         a  ...                      11.0   \n",
       "2                  0              1         a  ...                      12.0   \n",
       "3                  0              1         c  ...                       9.0   \n",
       "4                  0              1         a  ...                       4.0   \n",
       "...              ...            ...       ...  ...                       ...   \n",
       "1017204            a              1         a  ...                       6.0   \n",
       "1017205            a              1         c  ...                       4.0   \n",
       "1017206            a              1         a  ...                       NaN   \n",
       "1017207            a              1         a  ...                       NaN   \n",
       "1017208            a              1         d  ...                       NaN   \n",
       "\n",
       "         CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear  \\\n",
       "0                          2008.0       0              NaN              NaN   \n",
       "1                          2007.0       1             13.0           2010.0   \n",
       "2                          2006.0       1             14.0           2011.0   \n",
       "3                          2009.0       0              NaN              NaN   \n",
       "4                          2015.0       0              NaN              NaN   \n",
       "...                           ...     ...              ...              ...   \n",
       "1017204                    2014.0       1             31.0           2013.0   \n",
       "1017205                    2006.0       0              NaN              NaN   \n",
       "1017206                       NaN       0              NaN              NaN   \n",
       "1017207                       NaN       0              NaN              NaN   \n",
       "1017208                       NaN       1             22.0           2012.0   \n",
       "\n",
       "            PromoInterval  Year Month  Day  WeekOfYear  \n",
       "0                     NaN  2015     7   31          31  \n",
       "1         Jan,Apr,Jul,Oct  2015     7   31          31  \n",
       "2         Jan,Apr,Jul,Oct  2015     7   31          31  \n",
       "3                     NaN  2015     7   31          31  \n",
       "4                     NaN  2015     7   31          31  \n",
       "...                   ...   ...   ...  ...         ...  \n",
       "1017204   Jan,Apr,Jul,Oct  2013     1    1           1  \n",
       "1017205               NaN  2013     1    1           1  \n",
       "1017206               NaN  2013     1    1           1  \n",
       "1017207               NaN  2013     1    1           1  \n",
       "1017208  Mar,Jun,Sept,Dec  2013     1    1           1  \n",
       "\n",
       "[1017209 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "857ed491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    844392\n",
       "0    172817\n",
       "Name: Open, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除训练集中商店关门的日子\n",
    "merge_train_df.Open.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc427f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_train_df = merge_train_df[merge_train_df['Open']==1].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1715701",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 844392 entries, 0 to 1017190\n",
      "Data columns (total 22 columns):\n",
      " #   Column                     Non-Null Count   Dtype         \n",
      "---  ------                     --------------   -----         \n",
      " 0   Store                      844392 non-null  int64         \n",
      " 1   DayOfWeek                  844392 non-null  int64         \n",
      " 2   Date                       844392 non-null  datetime64[ns]\n",
      " 3   Sales                      844392 non-null  int64         \n",
      " 4   Customers                  844392 non-null  int64         \n",
      " 5   Open                       844392 non-null  int64         \n",
      " 6   Promo                      844392 non-null  int64         \n",
      " 7   StateHoliday               844392 non-null  object        \n",
      " 8   SchoolHoliday              844392 non-null  int64         \n",
      " 9   StoreType                  844392 non-null  object        \n",
      " 10  Assortment                 844392 non-null  object        \n",
      " 11  CompetitionDistance        842206 non-null  float64       \n",
      " 12  CompetitionOpenSinceMonth  575773 non-null  float64       \n",
      " 13  CompetitionOpenSinceYear   575773 non-null  float64       \n",
      " 14  Promo2                     844392 non-null  int64         \n",
      " 15  Promo2SinceWeek            421085 non-null  float64       \n",
      " 16  Promo2SinceYear            421085 non-null  float64       \n",
      " 17  PromoInterval              421085 non-null  object        \n",
      " 18  Year                       844392 non-null  int64         \n",
      " 19  Month                      844392 non-null  int64         \n",
      " 20  Day                        844392 non-null  int64         \n",
      " 21  WeekOfYear                 844392 non-null  UInt32        \n",
      "dtypes: UInt32(1), datetime64[ns](1), float64(5), int64(11), object(4)\n",
      "memory usage: 145.8+ MB\n"
     ]
    }
   ],
   "source": [
    "merge_train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cee9b84",
   "metadata": {},
   "source": [
    "### 5.2缺失值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffde850f",
   "metadata": {},
   "source": [
    "- 首先分析 CompetitionOpenSinceMonth 和 CompetitionOpenSinceYear 这两列数据，这两列的含义是出现竞争店铺的年份和月份，存在缺失值的原因是该商店附近不存在竞争店铺，可以用 0 填补。\n",
    "- 其次可以通过上一步求得的年份和月份创建一列新数据来展示竞争者出现了多久"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0b4cd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_during_compitition(df):\n",
    "    df[\"CompetitionOpen\"] = 12*(df['Year']-df['CompetitionOpenSinceYear'])+(df['Month']-df['CompetitionOpenSinceMonth'])\n",
    "    df[\"CompetitionOpen\"] = df[\"CompetitionOpen\"].apply(lambda x:0 if x<0 else x).fillna(0)                                          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "541cc8e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_during_compitition(merge_train_df)\n",
    "cal_during_compitition(merge_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c2960b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>CompetitionOpen</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016776</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016827</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016863</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017042</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017190</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>844392 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CompetitionOpenSinceMonth  CompetitionOpenSinceYear  CompetitionOpen\n",
       "0                              9.0                    2008.0             82.0\n",
       "1                             11.0                    2007.0             92.0\n",
       "2                             12.0                    2006.0            103.0\n",
       "3                              9.0                    2009.0             70.0\n",
       "4                              4.0                    2015.0              3.0\n",
       "...                            ...                       ...              ...\n",
       "1016776                        9.0                    2006.0             76.0\n",
       "1016827                       10.0                    1999.0            159.0\n",
       "1016863                        NaN                       NaN              0.0\n",
       "1017042                        NaN                       NaN              0.0\n",
       "1017190                        3.0                    2002.0            130.0\n",
       "\n",
       "[844392 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_train_df[['CompetitionOpenSinceMonth','CompetitionOpenSinceYear','CompetitionOpen']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd62ba05",
   "metadata": {},
   "source": [
    "- 接下来分析 Promo2，Promo2SinceWeek，Promo2SinceYear，PromoInterval 这四列数据\n",
    " - Promo2表示该店铺是否有促销：0表示从未参加过促销，1表示参加过促销\n",
    " - Promo2SinceWeek/Promo2SinceYear表示该店铺 ××年、第×周开始参加促销\n",
    " - PromoInterval表示该店铺促销的时间间隔： \"Feb,May,Aug,Nov\"表示该店铺每年的1月，5月，8月，11月开始搞促销\n",
    "- 然后就可以创建两列数据分别表示促销开始了多久，当前的月份是否有搞促销"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c8296941",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_promo_month(row):#检查当前月份是否有搞促销\n",
    "    month2str = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun',              \n",
    "                 7:'Jul', 8:'Aug', 9:'Sept', 10:'Oct', 11:'Nov', 12:'Dec'}\n",
    "    try:\n",
    "        months = (row['PromoInterval'] or '').split(',')\n",
    "        if(row['Promo2Open'] and month2str[row['Month']] in months):\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "    except:\n",
    "        return 0\n",
    "    \n",
    "def cal_during_promo(data): #计算促销持续了几个月\n",
    "    data['Promo2Open'] = 12 * (data.Year - data.Promo2SinceYear) + (data.WeekOfYear - data.Promo2SinceWeek)*7/30.5\n",
    "    data['Promo2Open'] = data['Promo2Open'].apply(lambda x: 0 if x < 0 else x).fillna(0)*data['Promo2']#only when there is promo\n",
    "    #创建新列表示当前月份是否有搞促销\n",
    "    data['IsPromo2Month'] = data.apply(check_promo_month, axis=1) * data['Promo2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "819a4ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cal_during_promo(merge_train_df)\n",
    "cal_during_promo(merge_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d9ce404",
   "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>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "      <th>Promo2Open</th>\n",
       "      <th>IsPromo2Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>64.131148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>51.901639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016776</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016827</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016863</th>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>1.213115</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017042</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017190</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>844392 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Promo2  Promo2SinceWeek  Promo2SinceYear    PromoInterval  \\\n",
       "0             0              NaN              NaN              NaN   \n",
       "1             1             13.0           2010.0  Jan,Apr,Jul,Oct   \n",
       "2             1             14.0           2011.0  Jan,Apr,Jul,Oct   \n",
       "3             0              NaN              NaN              NaN   \n",
       "4             0              NaN              NaN              NaN   \n",
       "...         ...              ...              ...              ...   \n",
       "1016776       0              NaN              NaN              NaN   \n",
       "1016827       0              NaN              NaN              NaN   \n",
       "1016863       1             48.0           2012.0  Jan,Apr,Jul,Oct   \n",
       "1017042       0              NaN              NaN              NaN   \n",
       "1017190       0              NaN              NaN              NaN   \n",
       "\n",
       "         Promo2Open  IsPromo2Month  \n",
       "0          0.000000              0  \n",
       "1         64.131148              1  \n",
       "2         51.901639              1  \n",
       "3          0.000000              0  \n",
       "4          0.000000              0  \n",
       "...             ...            ...  \n",
       "1016776    0.000000              0  \n",
       "1016827    0.000000              0  \n",
       "1016863    1.213115              1  \n",
       "1017042    0.000000              0  \n",
       "1017190    0.000000              0  \n",
       "\n",
       "[844392 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_train_df[['Promo2','Promo2SinceWeek','Promo2SinceYear','PromoInterval','Promo2Open','IsPromo2Month']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150b12c7",
   "metadata": {},
   "source": [
    "## 6.特征提取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6727ffe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_cols = ['Store', 'DayOfWeek', 'Promo', 'StateHoliday', 'SchoolHoliday', \n",
    "              'StoreType', 'Assortment', 'CompetitionDistance', 'CompetitionOpen', \n",
    "              'Day', 'Month', 'Year', 'WeekOfYear',  'Promo2', \n",
    "              'Promo2Open', 'IsPromo2Month']\n",
    "target_col = 'Sales'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca75118a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 844392 entries, 0 to 1017190\n",
      "Data columns (total 25 columns):\n",
      " #   Column                     Non-Null Count   Dtype         \n",
      "---  ------                     --------------   -----         \n",
      " 0   Store                      844392 non-null  int64         \n",
      " 1   DayOfWeek                  844392 non-null  int64         \n",
      " 2   Date                       844392 non-null  datetime64[ns]\n",
      " 3   Sales                      844392 non-null  int64         \n",
      " 4   Customers                  844392 non-null  int64         \n",
      " 5   Open                       844392 non-null  int64         \n",
      " 6   Promo                      844392 non-null  int64         \n",
      " 7   StateHoliday               844392 non-null  object        \n",
      " 8   SchoolHoliday              844392 non-null  int64         \n",
      " 9   StoreType                  844392 non-null  object        \n",
      " 10  Assortment                 844392 non-null  object        \n",
      " 11  CompetitionDistance        842206 non-null  float64       \n",
      " 12  CompetitionOpenSinceMonth  575773 non-null  float64       \n",
      " 13  CompetitionOpenSinceYear   575773 non-null  float64       \n",
      " 14  Promo2                     844392 non-null  int64         \n",
      " 15  Promo2SinceWeek            421085 non-null  float64       \n",
      " 16  Promo2SinceYear            421085 non-null  float64       \n",
      " 17  PromoInterval              421085 non-null  object        \n",
      " 18  Year                       844392 non-null  int64         \n",
      " 19  Month                      844392 non-null  int64         \n",
      " 20  Day                        844392 non-null  int64         \n",
      " 21  WeekOfYear                 844392 non-null  UInt32        \n",
      " 22  CompetitionOpen            844392 non-null  float64       \n",
      " 23  Promo2Open                 844392 non-null  float64       \n",
      " 24  IsPromo2Month              844392 non-null  int64         \n",
      "dtypes: UInt32(1), datetime64[ns](1), float64(7), int64(12), object(4)\n",
      "memory usage: 165.1+ MB\n"
     ]
    }
   ],
   "source": [
    "merge_train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ab956d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs = merge_train_df[input_cols].copy()\n",
    "targets = merge_train_df[target_col].copy()\n",
    "test_inputs = merge_test_df[input_cols].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57247311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 844392 entries, 0 to 1017190\n",
      "Data columns (total 16 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   Store                844392 non-null  int64  \n",
      " 1   DayOfWeek            844392 non-null  int64  \n",
      " 2   Promo                844392 non-null  int64  \n",
      " 3   StateHoliday         844392 non-null  object \n",
      " 4   SchoolHoliday        844392 non-null  int64  \n",
      " 5   StoreType            844392 non-null  object \n",
      " 6   Assortment           844392 non-null  object \n",
      " 7   CompetitionDistance  842206 non-null  float64\n",
      " 8   CompetitionOpen      844392 non-null  float64\n",
      " 9   Day                  844392 non-null  int64  \n",
      " 10  Month                844392 non-null  int64  \n",
      " 11  Year                 844392 non-null  int64  \n",
      " 12  WeekOfYear           844392 non-null  UInt32 \n",
      " 13  Promo2               844392 non-null  int64  \n",
      " 14  Promo2Open           844392 non-null  float64\n",
      " 15  IsPromo2Month        844392 non-null  int64  \n",
      "dtypes: UInt32(1), float64(3), int64(9), object(3)\n",
      "memory usage: 107.1+ MB\n"
     ]
    }
   ],
   "source": [
    "train_inputs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f24e6d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "##划分数值特征和分类特征\n",
    "numeric_cols = ['Store', 'Promo', 'SchoolHoliday', 'CompetitionDistance', 'CompetitionOpen', 'Promo2', 'Promo2Open', 'IsPromo2Month',\n",
    "              'Day', 'Month', 'Year', 'WeekOfYear',  ]\n",
    "categorical_cols = ['StateHoliday', 'StoreType', 'Assortment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f6afdba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                     0\n",
       "Promo                     0\n",
       "SchoolHoliday             0\n",
       "CompetitionDistance    2186\n",
       "CompetitionOpen           0\n",
       "Promo2                    0\n",
       "Promo2Open                0\n",
       "IsPromo2Month             0\n",
       "Day                       0\n",
       "Month                     0\n",
       "Year                      0\n",
       "WeekOfYear                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_inputs[numeric_cols].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35fabd22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                   0\n",
       "Promo                   0\n",
       "SchoolHoliday           0\n",
       "CompetitionDistance    96\n",
       "CompetitionOpen         0\n",
       "Promo2                  0\n",
       "Promo2Open              0\n",
       "IsPromo2Month           0\n",
       "Day                     0\n",
       "Month                   0\n",
       "Year                    0\n",
       "WeekOfYear              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_inputs[numeric_cols].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ccc1ff6",
   "metadata": {},
   "source": [
    "- 采用最大距离插补缺失距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "14065085",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_distance = train_inputs.CompetitionDistance.max()\n",
    "train_inputs['CompetitionDistance'].fillna(max_distance, inplace=True)\n",
    "test_inputs['CompetitionDistance'].fillna(max_distance, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba606a3",
   "metadata": {},
   "source": [
    "- 归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "16234c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler().fit(train_inputs[numeric_cols])\n",
    "train_inputs[numeric_cols] = scaler.transform(train_inputs[numeric_cols])\n",
    "test_inputs[numeric_cols] = scaler.transform(test_inputs[numeric_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "01359058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store                  0\n",
       "Promo                  0\n",
       "SchoolHoliday          0\n",
       "CompetitionDistance    0\n",
       "CompetitionOpen        0\n",
       "Promo2                 0\n",
       "Promo2Open             0\n",
       "IsPromo2Month          0\n",
       "Day                    0\n",
       "Month                  0\n",
       "Year                   0\n",
       "WeekOfYear             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_inputs[numeric_cols].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d12bec1",
   "metadata": {},
   "source": [
    "- 独热编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1529648",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_inputs[categorical_cols] = train_inputs[categorical_cols].astype('str')\n",
    "encoder = OneHotEncoder(sparse=False, handle_unknown='ignore').fit(train_inputs[categorical_cols])\n",
    "encoded_cols = list(encoder.get_feature_names(categorical_cols))\n",
    "train_inputs[encoded_cols] = encoder.transform(train_inputs[categorical_cols])\n",
    "test_inputs[encoded_cols] = encoder.transform(test_inputs[categorical_cols])\n",
    "# train_inputs[categorical_cols].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aadc3b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_inputs[numeric_cols + encoded_cols]\n",
    "X_test = test_inputs[numeric_cols + encoded_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2106ab",
   "metadata": {},
   "source": [
    "## 7.XGBoost回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "efdac19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBRegressor(random_state=42, n_jobs=-1, n_estimators=20, max_depth=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae3c6c1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.61 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_delta_step=0, max_depth=4,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=20, n_jobs=-1, num_parallel_tree=1, random_state=42,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(X, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b1dae95",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed426d8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2436.070232904147"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rmse(a, b):\n",
    "    return mean_squared_error(a, b, squared=False)\n",
    "rmse(preds, targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c8093e",
   "metadata": {},
   "source": [
    "- 特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1eaf9618",
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Promo</td>\n",
       "      <td>0.291977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>StoreType_b</td>\n",
       "      <td>0.103151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Promo2</td>\n",
       "      <td>0.096431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Assortment_a</td>\n",
       "      <td>0.058846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CompetitionDistance</td>\n",
       "      <td>0.051998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Assortment_c</td>\n",
       "      <td>0.049951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Store</td>\n",
       "      <td>0.044921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Day</td>\n",
       "      <td>0.039690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Month</td>\n",
       "      <td>0.036663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>StoreType_d</td>\n",
       "      <td>0.036597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                feature  importance\n",
       "1                 Promo    0.291977\n",
       "17          StoreType_b    0.103151\n",
       "5                Promo2    0.096431\n",
       "20         Assortment_a    0.058846\n",
       "3   CompetitionDistance    0.051998\n",
       "22         Assortment_c    0.049951\n",
       "0                 Store    0.044921\n",
       "8                   Day    0.039690\n",
       "9                 Month    0.036663\n",
       "19          StoreType_d    0.036597"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance_df = pd.DataFrame({\n",
    "    'feature': X.columns,\n",
    "    'importance': model.feature_importances_\n",
    "}).sort_values('importance', ascending=False)\n",
    "importance_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9b7f8b04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Feature Importance')\n",
    "sns.barplot(data=importance_df.head(10), x='importance', y='feature');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d5fd89",
   "metadata": {},
   "source": [
    "- k折叠交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "75ce47fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_evaluate(X_train, train_targets, X_val, val_targets, **params):\n",
    "    model = XGBRegressor(random_state=42, n_jobs=-1, **params)\n",
    "    model.fit(X_train, train_targets)\n",
    "    train_rmse = rmse(model.predict(X_train), train_targets)\n",
    "    val_rmse = rmse(model.predict(X_val), val_targets)\n",
    "    return model, train_rmse, val_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1f52ed5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ec0e5498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 2414.7936312858537, Validation RMSE: 2467.171620065982\n",
      "Train RMSE: 2404.0534016734114, Validation RMSE: 2441.6522708946354\n",
      "Train RMSE: 2422.521349581155, Validation RMSE: 2403.6346008232986\n",
      "Train RMSE: 2356.396019856885, Validation RMSE: 2465.678539017347\n",
      "Train RMSE: 2413.4193964653396, Validation RMSE: 2479.248192000345\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "\n",
    "for train_idxs, val_idxs in kfold.split(X):\n",
    "    X_train, train_targets = X.iloc[train_idxs], targets.iloc[train_idxs]\n",
    "    X_val, val_targets = X.iloc[val_idxs], targets.iloc[val_idxs]\n",
    "    model, train_rmse, val_rmse = train_and_evaluate(X_train, \n",
    "                                                     train_targets, \n",
    "                                                     X_val, \n",
    "                                                     val_targets, \n",
    "                                                     max_depth=4, \n",
    "                                                     n_estimators=20)\n",
    "    models.append(model)\n",
    "    print('Train RMSE: {}, Validation RMSE: {}'.format(train_rmse, val_rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2cc93638",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_avg(models, inputs):\n",
    "    return np.mean([model.predict(inputs) for model in models], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0114f05a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8144.918 , 7517.7554, 8913.873 , ..., 8067.2563, 9229.947 ,\n",
       "       8738.672 ], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = predict_avg(models, X_train)\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf04b622",
   "metadata": {},
   "source": [
    "- 超参数调整和正则化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3f779422",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_params_kfold(n_splits, **params):\n",
    "    train_rmses, val_rmses, models = [], [], []\n",
    "    kfold = KFold(n_splits)\n",
    "    for train_idxs, val_idxs in kfold.split(X):\n",
    "        X_train, train_targets = X.iloc[train_idxs], targets.iloc[train_idxs]\n",
    "        X_val, val_targets = X.iloc[val_idxs], targets.iloc[val_idxs]\n",
    "        model, train_rmse, val_rmse = train_and_evaluate(X_train, train_targets, X_val, val_targets, **params)\n",
    "        models.append(model)\n",
    "        train_rmses.append(train_rmse)\n",
    "        val_rmses.append(val_rmse)\n",
    "    print('Train RMSE: {}, Validation RMSE: {}'.format(np.mean(train_rmses), np.mean(val_rmses)))\n",
    "    return models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7852af7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, train_targets, val_targets = train_test_split(X, targets, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0ed1f6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_params(**params):\n",
    "    model = XGBRegressor(n_jobs=-1, random_state=42, **params)\n",
    "    model.fit(X_train, train_targets)\n",
    "    train_rmse = rmse(model.predict(X_train), train_targets)\n",
    "    val_rmse = rmse(model.predict(X_val), val_targets)\n",
    "    print('Train RMSE: {}, Validation RMSE: {}'.format(train_rmse, val_rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "55c075e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 2412.206862830845, Validation RMSE: 2428.2967201925658\n"
     ]
    }
   ],
   "source": [
    "test_params(n_estimators=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "478cc32d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 2416.2372927698643, Validation RMSE: 2429.450532771219\n"
     ]
    }
   ],
   "source": [
    "test_params(max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ff6c962b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 1503.4743795063553, Validation RMSE: 1518.1417137048907\n"
     ]
    }
   ],
   "source": [
    "test_params(max_depth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8dba4986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 5050.9249946985965, Validation RMSE: 5056.824977594182\n"
     ]
    }
   ],
   "source": [
    "test_params(n_estimators=50, learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9ec6ddaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 2203.568029826842, Validation RMSE: 2222.8839609391002\n"
     ]
    }
   ],
   "source": [
    "test_params(n_estimators=50, learning_rate=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "52361f64-bcf7-4108-b0cd-616bafded160",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_params(booster='gblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d2bbdff1-7306-4606-bfc8-937ba4264b0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 2369.130317834184, Validation RMSE: 3369.7144918390945\n"
     ]
    }
   ],
   "source": [
    "test_params(n_estimators=1000, learning_rate=0.2, max_depth=10, subsample=0.9, colsample_bytree=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a5d4dddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBRegressor(n_jobs=-1, random_state=42, n_estimators=1000, \n",
    "                     learning_rate=0.2, max_depth=10, subsample=0.9, \n",
    "                     colsample_bytree=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ebf2ffeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.7, gamma=0, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.2, max_delta_step=0, max_depth=10,\n",
       "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=1000, n_jobs=-1, num_parallel_tree=1, random_state=42,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=0.9,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "96cd7d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "348b44d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df['Sales']  = test_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "efd39e27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.Open.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f9784491",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_df['Sales'] = submission_df['Sales'] * test_df.Open.fillna(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1ad09467",
   "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>Id</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4341.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8693.640625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9301.455078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6786.824219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6893.714844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41083</th>\n",
       "      <td>41084</td>\n",
       "      <td>3420.328369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41084</th>\n",
       "      <td>41085</td>\n",
       "      <td>7441.502441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41085</th>\n",
       "      <td>41086</td>\n",
       "      <td>7421.017090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41086</th>\n",
       "      <td>41087</td>\n",
       "      <td>23765.673828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41087</th>\n",
       "      <td>41088</td>\n",
       "      <td>7068.877930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41088 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id         Sales\n",
       "0          1   4341.630859\n",
       "1          2   8693.640625\n",
       "2          3   9301.455078\n",
       "3          4   6786.824219\n",
       "4          5   6893.714844\n",
       "...      ...           ...\n",
       "41083  41084   3420.328369\n",
       "41084  41085   7441.502441\n",
       "41085  41086   7421.017090\n",
       "41086  41087  23765.673828\n",
       "41087  41088   7068.877930\n",
       "\n",
       "[41088 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5165686e-4615-4782-863f-31cc1896e6e5",
   "metadata": {},
   "source": [
    "## 8.神经网络集成学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "13263c59-0346-4122-a2b2-9de95ddd2af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from torchensemble import GradientBoostingRegressor,BaggingRegressor,FusionRegressor,VotingRegressor,SnapshotEnsembleRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3f3cdb14-6491-4488-b721-0e682828751b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.linear1 = nn.Linear(23, 128)\n",
    "        self.linear2 = nn.Linear(128, 128)\n",
    "        self.linear3 = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(x.size()[0], -1)\n",
    "        x = F.relu(self.linear1(x))\n",
    "        x = F.relu(self.linear2(x))\n",
    "        x = self.linear3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d615ed0b-d226-4687-8d17-753a121afb2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ensemble_model():\n",
    "    def __init__(self,ensemble,network,estimators=10,lr=1e-3,weight_decay=5e-4,epoch=50,use_cuda=False):\n",
    "        super(ensemble_model, self).__init__()\n",
    "        self.epoch=epoch\n",
    "        self.model=ensemble(\n",
    "                        estimator=network,\n",
    "                        n_estimators=10,cuda=use_cuda)\n",
    "        #设置优化器optimizer\n",
    "        self.model.set_optimizer(\"Adam\", lr=lr, weight_decay=weight_decay)\n",
    "    def fit(self,X_train,Y_train):\n",
    "        X_train=torch.FloatTensor(np.array(X_train))\n",
    "        Y_train=torch.FloatTensor(np.array(Y_train)).reshape(-1,1)\n",
    "        # Tensor -> Data loader\n",
    "        train_data = TensorDataset(X_train, Y_train)\n",
    "        train_loader = DataLoader(train_data, batch_size=100, shuffle=True)  \n",
    "        self.model.fit(train_loader, epochs=self.epoch)\n",
    "    def predict(self,X_test):\n",
    "        X_test=torch.FloatTensor(np.array(X_test))\n",
    "        # 预测\n",
    "        Y_pred = self.model.predict(X_test)\n",
    "        return np.array(Y_pred)\n",
    "    def score(self,X_test,Y_test):\n",
    "        X_test = torch.FloatTensor(np.array(X_test))\n",
    "        Y_test = torch.FloatTensor(np.array(Y_test)).reshape(-1, 1)\n",
    "        test_data = TensorDataset(X_test, Y_test)\n",
    "        test_loader = DataLoader(test_data, batch_size=10, shuffle=False)\n",
    "        testing_mse = self.model.evaluate(test_loader)\n",
    "        return testing_mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b025f577-a2d9-43e5-895d-97590a709fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_model=ensemble_model(GradientBoostingRegressor,MLP,epoch=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e70e8dea-b8c5-43e7-8b12-f480b6b366d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844392, 23)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c87fb292-918b-499a-8ad3-2f73389e2d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844392,)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "39e38415-3832-4f46-9599-2094c7891422",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "神经网梯度提升集成学习 , MSE:1178782.559139635,mae:775.1471195999563,train_r2:0.8879223618521294,test_r2:0.8774864421275211\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, targets, test_size = 0.1)\n",
    "mlp_model.fit(X_train,Y_train)\n",
    "Y_pred=mlp_model.predict(X_test)\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(Y_test, Y_pred)\n",
    "# Get Mean Absolute Error (MAE)\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "mae = mean_absolute_error(Y_test, Y_pred)\n",
    "from  sklearn.metrics import r2_score\n",
    "train_r2=r2_score( Y_train,mlp_model.predict(X_train))\n",
    "test_r2=r2_score( Y_test,Y_pred)\n",
    "print(f\"神经网梯度提升集成学习 , MSE:{mse},mae:{mae},train_r2:{train_r2},test_r2:{test_r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8808960b-1875-4cc5-a3f5-5c359bf1ffb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd3e15f-f82c-4a4e-976b-b6cf59a7271e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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