LightGBM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "28dc652c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd          \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime    \n",
    "from pandas import Series \n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import statsmodels\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a024efdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一些全局变量 \n",
    "is_need_log = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d7a77895",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = pd.read_csv(\"./示例数据/order_train2.csv\")\n",
    "test  = pd.read_csv(\"./示例数据/predict_sku1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "97522bcb",
   "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>order_date</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>sales_chan_name</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>104</td>\n",
       "      <td>22069</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>offline</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>104</td>\n",
       "      <td>20028</td>\n",
       "      <td>301</td>\n",
       "      <td>405</td>\n",
       "      <td>offline</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>105</td>\n",
       "      <td>21183</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>online</td>\n",
       "      <td>428.0</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>105</td>\n",
       "      <td>20448</td>\n",
       "      <td>308</td>\n",
       "      <td>404</td>\n",
       "      <td>online</td>\n",
       "      <td>962.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>104</td>\n",
       "      <td>21565</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>offline</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_date  sales_region_code  item_code  first_cate_code  \\\n",
       "0  2015-09-01                104      22069              307   \n",
       "1  2015-09-01                104      20028              301   \n",
       "2  2015-09-02                105      21183              307   \n",
       "3  2015-09-02                105      20448              308   \n",
       "4  2015-09-02                104      21565              307   \n",
       "\n",
       "   second_cate_code sales_chan_name  item_price  ord_qty  \n",
       "0               403         offline      1114.0       19  \n",
       "1               405         offline      1012.0       12  \n",
       "2               403          online       428.0      109  \n",
       "3               404          online       962.0        3  \n",
       "4               403         offline      1400.0        3  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c9c4a9d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 597694 entries, 0 to 597693\n",
      "Data columns (total 8 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   order_date         597694 non-null  object \n",
      " 1   sales_region_code  597694 non-null  int64  \n",
      " 2   item_code          597694 non-null  int64  \n",
      " 3   first_cate_code    597694 non-null  int64  \n",
      " 4   second_cate_code   597694 non-null  int64  \n",
      " 5   sales_chan_name    597694 non-null  object \n",
      " 6   item_price         597694 non-null  float64\n",
      " 7   ord_qty            597694 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(2)\n",
      "memory usage: 36.5+ MB\n"
     ]
    }
   ],
   "source": [
    "sales_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "89449d5e",
   "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>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>597694.000000</td>\n",
       "      <td>597694.000000</td>\n",
       "      <td>597694.000000</td>\n",
       "      <td>597694.000000</td>\n",
       "      <td>597694.000000</td>\n",
       "      <td>597694.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>102.934557</td>\n",
       "      <td>21008.015983</td>\n",
       "      <td>304.932094</td>\n",
       "      <td>406.457174</td>\n",
       "      <td>1076.241578</td>\n",
       "      <td>91.650505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.536694</td>\n",
       "      <td>590.896347</td>\n",
       "      <td>2.004195</td>\n",
       "      <td>3.107329</td>\n",
       "      <td>1167.511104</td>\n",
       "      <td>199.843336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>20001.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>401.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>20518.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>598.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103.000000</td>\n",
       "      <td>20976.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>883.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>21521.000000</td>\n",
       "      <td>306.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>1291.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>22084.000000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>412.000000</td>\n",
       "      <td>260014.000000</td>\n",
       "      <td>16308.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sales_region_code      item_code  first_cate_code  second_cate_code  \\\n",
       "count      597694.000000  597694.000000    597694.000000     597694.000000   \n",
       "mean          102.934557   21008.015983       304.932094        406.457174   \n",
       "std             1.536694     590.896347         2.004195          3.107329   \n",
       "min           101.000000   20001.000000       301.000000        401.000000   \n",
       "25%           102.000000   20518.000000       303.000000        404.000000   \n",
       "50%           103.000000   20976.000000       306.000000        407.000000   \n",
       "75%           105.000000   21521.000000       306.000000        408.000000   \n",
       "max           105.000000   22084.000000       308.000000        412.000000   \n",
       "\n",
       "          item_price        ord_qty  \n",
       "count  597694.000000  597694.000000  \n",
       "mean     1076.241578      91.650505  \n",
       "std      1167.511104     199.843336  \n",
       "min         1.000000       1.000000  \n",
       "25%       598.000000      10.000000  \n",
       "50%       883.000000      29.000000  \n",
       "75%      1291.000000     101.000000  \n",
       "max    260014.000000   16308.000000  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bfdd6d",
   "metadata": {},
   "source": [
    "# 1. Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d71fa102",
   "metadata": {},
   "source": [
    "## Remove outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1423d1df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='item_price'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TuXSXzMxMJSUleeZ/+PDhgOsiVGqydetWTz1SU1M1ceJErV27NmTXSk1NjZKTk3X8+HFJnZ9veXm5lixZojlz5uinP/2pamtrJUnnz5/Xgw8+qLlz52rJkiU6c+ZM70+ui9rWJC8vT8nJyUpJSdHKlSvV1NQk6eJzzsyZMz1rxv146myt+ou2demux0x/rkvrmuzdu9fruWXy5MlaunSppNBZK/5eh/v0OcW049SpU2bmzJmmurra1NbWmpSUFHP06NH2Tuu39u/fb9LT001jY6NpamoymZmZ5p133jHJycnm9OnTXm3r6+vNXXfdZcrKykxzc7PJysoye/bsMcYYk5SUZA4dOmSMMWblypUmJyent6fSbVwul5k6dappbm727Au0LkKlJm39+9//NgkJCebs2bMhuVb+/ve/m+TkZHPTTTeZY8eOdWm+Dz74oHn77beNMca88sorZv369cYYY5555hmzadMmY4wxhYWFZtmyZb07uS5qW5Mvv/zSJCQkmAsXLhiXy2WeeOIJs3nzZmOMMUuXLjWffPKJTx+drVV/0LYuxphue8z017r4q4lbRUWFueeee0xJSYkxJjTWir/X4bfeeqtPn1PafWepuLhYkydPVlxcnGJiYjR79mzt3r27K0GxXxg2bJhWrFihqKgoRUZGaty4cSovL1d5ebnWrFmjlJQUvfzyy3K5XPr00081evRojRw5Ug6HQykpKdq9e7dOnDihhoYG3XrrrZKk+fPn9+uaffnllwoLC9NPfvITzZs3T2+++WbAdREqNWnr6aef1iOPPKLo6OiQXCtbtmzRU089peHDh0tSp+fb3Nysjz76SLNnz/baL0l79uxRSkqKJCk5OVn79u1Tc3Nz70+yk9rWJCoqSk8//bRiY2MVFham733veyovL5ck/eMf/9Crr76qlJQUrV27Vo2NjV2qVX/Qti51dXXd8pjpz3VpW5PW1q9fr4yMDI0ZM0ZSaKwVf6/DpaWlffqc0m5Yqqio0LBhwzzbw4cP1+nTp7tUgP7g+uuv9xS9tLRURUVFuvPOOzV58mQ999xz2rJliw4ePKj8/PyAtWm7f9iwYf26ZufPn9cdd9yhjRs36o033lBubq7Ky8s7NPfLtSatFRcXq6GhQXPnztXZs2dDcq2sW7dOkyZN8mx3dr7V1dWKjY2Vw+Hw2t+2L4fDodjYWFVVVfXGtC5J25qMGDFCU6ZMkSRVVVUpJydH99xzj2pra3XjjTdq+fLlKiws1Pnz5/Wb3/ymS7XqD9rWpbseM/25Lm1r4lZaWqoPP/xQmZmZkhQya8Xf63BYWFifPqe0G5aMMT77wsLC2jut3zt69KiysrK0fPlyjR07Vhs3btSQIUM0cOBA/fCHP9TevXsD1uZyq9ltt92m9evXKyYmRvHx8Vq4cKFefvlln3a2uV9uNWktNzdXP/rRjyRJI0eODOm14tbZ+Xa2DuHh/fe7KadPn9b999+vBQsW6Pbbb9egQYP06quvavTo0XI4HMrKygqpNdNdj5nLrS7Sxc+4LV68WFFRUZIUcmul9evwqFGjfI735nNKu8843/nOd1RZWenZrqio8PtW4eXk448/1gMPPKDHHntMaWlpOnLkiP785z97jhtj5HA4Atam7f4zZ87065odPHhQBw4c8GwbYzRixIgOzf1yrYlbU1OTPvroI919992SFPJrxa2z842Pj1dNTY1aWlq89ksX/wvSfY7T6VRNTY3i4uJ6bzLd6IsvvtB9992ntLQ0ZWdnS7r4IdT8/HxPm0BrpiO16o+66zFzudVFkv76178qMTHRsx1Ka6Xt63BfP6e0G5amTJmiAwcOqKqqSvX19XrnnXc0ffr0Tk+8vzh58qSys7P1i1/8QklJSZIuLsjnnntOX3/9tZqbm5WXl6eEhARNmDBBJSUl+uqrr9TS0qK3335b06dP14gRIzRgwAB9/PHHkqTt27f365pduHBB69evV2Njo2pqalRYWKgNGzb4XRehUhO3I0eOaMyYMYqJiZHEWnHr7HwjIyM1adIkFRUVee2XpLvuukvbt2+XJBUVFWnSpEmKjIzsk3ldipqaGv34xz/WsmXLlJWV5dkfHR2tDRs26NixYzLGKCcnRwkJCV2qVX/UXY+Zy60uVVVVamho0MiRIz37QmWt+Hsd7vPnlI58Mn3nzp0mKSnJzJo1y/z+97/vyCn91rPPPmtuvfVWM2/ePM///vjHP5o333zTzJ071yQkJJgNGzZ42hcXF5uUlBQza9Yss27dOuNyuYwxxnz++edmwYIFZs6cOebRRx81jY2NfTWlbvHiiy+aOXPmmFmzZpk33njDGBN4XYRKTYwxZteuXebhhx/22hfKa2XmzJmeb/N0dr7Hjx83P/jBD8zcuXNNVlaWOXfunDHGmOrqarN06VKTmJho0tPTfb4tFOzcNdm8ebO56aabvJ5bfv3rXxtjjNm9e7fnsbRixQpPTTpbq/6k9VrprsdMf69L65ocPnzYLFq0yKdNKKyVQK/DffmcEmaMn1/sAQAAQBL/D94AAABWhCUAAAALwhIAAIAFYQkAAMCCsAQAAGBBWALQo5YuXapt27Z1+fw9e/bopZde6sYRAUDnEJYABLXPPvtMX3/9dV8PA0AIc/T1AAD0H3l5efrDH/6g8PBwDR06VGvWrNGmTZt07tw5HTt2TDNmzFBmZqZWrFihiooKffe739XZs2fb7bepqUnr1q1TcXGxhgwZohtvvFH19fW67777lJubq5aWFl1xxRX69NNPNWfOHKWnp0uSfvvb36q6ulqrVq3q6akDCGGEJQAdcuDAAb322mvKy8tTfHy8tm3bpuzsbN1yyy1qaGjQrl27JEnZ2dmaMGGCHn74YX311Ve699572+07JydHpaWl2rVrl5qbm7VkyRKNHz9eEyZMUEZGhqqrq/XII4/o3Xff1e9+9zulp6fL5XJp69ateu2113p45gBCHb+GA9Ah7733nhITExUfHy9Jmj9/vk6fPi1JmjhxoqddcXGx5s+fL0kaPXq0br/99nb7/uCDD5ScnKyoqCgNGjQoYMCaOXOmKisr9a9//Uvvvfeerr32Wo0dO/YSZwYAdryzBKBD/P1lJGOMnE6n5w8JS1JYWJhXW4ej/aeZAQMGeG0H+qOWERERysjIUH5+vioqKpSRkdHR4QNAl/HOEoAOmTZtmoqKilRVVSVJKigoUFxcnCIiIrza3XnnncrLy5MklZeX629/+1u7fc+YMUPbtm1TY2OjmpqaPH8pXLoYkJxOp2d70aJFevfdd/XPf/5TCQkJ3TE1ALDinSUAHTJ16lQ98MADuv/+++VyuRQfH69Nmzbp9ddf92r31FNPaeXKlZo7d66uvvpqjR8/vt2+09LSdOzYMaWlpSkmJsbzqz5JuuOOO/Szn/1MkZGRWrNmjYYMGaKbb75Z48aNC/gOFAB0pzDj7711AOhDr7/+uo4eParnn3/e51hVVZUWLlyonJwcXXPNNX0wOgChhneWAPSKxYsXq7a21u+xnJwcxcbGttvHli1b9Ktf/UoPPfQQQQlAr+GdJQAAAAs+4A0AAGBBWAIAALAgLAEAAFgQlgAAACwISwAAABaEJQAAAIv/B/8IYBDlS/P2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 销售量\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.xlim(-100, 20000)\n",
    "flierprops = dict(marker='o', markerfacecolor='purple', markersize=6,\n",
    "                  linestyle='none', markeredgecolor='black')\n",
    "sns.boxplot(x=sales_data.ord_qty, flierprops=flierprops)\n",
    "\n",
    "# 价格\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.xlim(sales_data.item_price.min(), sales_data.item_price.max()*1.1)\n",
    "sns.boxplot(x=sales_data.item_price, flierprops=flierprops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f3ba4beb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260014.0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.item_price.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0b7cdb7",
   "metadata": {},
   "source": [
    "- 去除销量大于4000的以及价格大于30000的\n",
    "    - 其实这里还需要验证一下去除的那几条记录，是不是好的，因为可能有些商品就是卖这么多或者怎样\n",
    "    - 验证一下test集里有没有出现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2b74a57d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_data = sales_data[(sales_data.item_price < 30000 )& (sales_data.ord_qty < 6000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ed7582e",
   "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>order_date</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>sales_chan_name</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>104</td>\n",
       "      <td>22069</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>offline</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>104</td>\n",
       "      <td>20028</td>\n",
       "      <td>301</td>\n",
       "      <td>405</td>\n",
       "      <td>offline</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>105</td>\n",
       "      <td>21183</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>online</td>\n",
       "      <td>428.0</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>105</td>\n",
       "      <td>20448</td>\n",
       "      <td>308</td>\n",
       "      <td>404</td>\n",
       "      <td>online</td>\n",
       "      <td>962.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>104</td>\n",
       "      <td>21565</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>offline</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_date  sales_region_code  item_code  first_cate_code  \\\n",
       "0  2015-09-01                104      22069              307   \n",
       "1  2015-09-01                104      20028              301   \n",
       "2  2015-09-02                105      21183              307   \n",
       "3  2015-09-02                105      20448              308   \n",
       "4  2015-09-02                104      21565              307   \n",
       "\n",
       "   second_cate_code sales_chan_name  item_price  ord_qty  \n",
       "0               403         offline      1114.0       19  \n",
       "1               405         offline      1012.0       12  \n",
       "2               403          online       428.0      109  \n",
       "3               404          online       962.0        3  \n",
       "4               403         offline      1400.0        3  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3360481b",
   "metadata": {},
   "source": [
    "## 数据类型处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c36924e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 时间 object->datetime格式\n",
    "sales_data[\"order_date\"]=sales_data[\"order_date\"].apply(pd.to_datetime,format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d4ecf444",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 线上0 线下1\n",
    "dic = {\"offline\":1,\"online\":0}\n",
    "sales_data['sales_chan_name'] = sales_data['sales_chan_name'].map(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a1e602d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([20001, 20002, 20003, ..., 22082, 22083, 22084]),\n",
       " array([  13,  694, 2767, ...,  159,  311,  468]))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 单独的item代码，以及其对应的记录条数\n",
    "train_item = np.unique(sales_data[\"item_code\"],return_counts=True)\n",
    "train_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "833c2454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique item codes: 1754\n",
      "大类编码 [307 301 308 305 303 306 302 304]\n",
      "细类编码 [403 405 404 412 410 401 407 408 402 409 406 411]\n",
      "地区编码 [104 105 102 101 103]\n"
     ]
    }
   ],
   "source": [
    "# 当然这是做了异常值处理之后的\n",
    "print('Unique item codes:', len(sales_data['item_code'].unique()))  # 1421种\n",
    "print('大类编码', sales_data[\"first_cate_code\"].unique())  # 8大类\n",
    "print('细类编码', sales_data[\"second_cate_code\"].unique()) # 12小类\n",
    "print('地区编码', sales_data[\"sales_region_code\"].unique()) # 5个地区\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87701d6f",
   "metadata": {},
   "source": [
    "## 检验这四类code是否一一对应\n",
    "对后续如何处理很关键"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "98116680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "first_cate_code  second_cate_code\n",
       "301              405                  21457\n",
       "302              408                  94785\n",
       "303              401                  60167\n",
       "                 406                   5187\n",
       "                 410                   5011\n",
       "                 411                   1437\n",
       "304              409                   9996\n",
       "305              412                  75165\n",
       "306              402                  12262\n",
       "                 407                 207185\n",
       "307              403                  50855\n",
       "308              404                  54072\n",
       "dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 大类和细类是否一一对应\n",
    "grouped_data = sales_data.groupby(['first_cate_code', 'second_cate_code']).size() #.reset_index(name='counts')\n",
    "print(len(grouped_data))\n",
    "grouped_data\n",
    "# 发现大类和细类是一一对应的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8897938c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有 1754 种商品只属于一个second_cate_code\n",
      "每个second_cate_code中包含的商品数量：\n",
      "    second_cate_code  counts\n",
      "0                401     183\n",
      "1                402      15\n",
      "2                403     163\n",
      "3                404     140\n",
      "4                405      36\n",
      "5                406      14\n",
      "6                407     498\n",
      "7                408     404\n",
      "8                409      71\n",
      "9                410      31\n",
      "10               411       7\n",
      "11               412     192\n"
     ]
    }
   ],
   "source": [
    "# 统计每个second_cate_code中包含的商品种类数量\n",
    "second_cate_count = sales_data.groupby('second_cate_code')['item_code'].nunique().reset_index(name='counts')\n",
    "# second_cate_count\n",
    "# 统计每个商品所属的second_cate_code数量，并检查是否有某个商品只属于一个second_cate_code\n",
    "item_counts = sales_data.groupby('item_code')['second_cate_code'].nunique()\n",
    "unique_items = item_counts[item_counts == 1].index.tolist()\n",
    "if len(unique_items) > 0:\n",
    "    print(f\"有 {len(unique_items)} 种商品只属于一个second_cate_code\")\n",
    "else:\n",
    "    print(\"所有商品都属于多个second_cate_code\")\n",
    "\n",
    "# 输出每个second_cate_code中包含的商品数量\n",
    "print(\"每个second_cate_code中包含的商品数量：\")\n",
    "print(second_cate_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3a0f310b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有 374 种商品只在一个地区销售\n",
      "有 234 种商品在两个地区销售\n",
      "有 216 种商品在三个地区销售\n",
      "有 861 种商品在四个地区销售\n",
      "有 69 种商品在五个地区销售\n",
      "每个sales_region_codee中包含的商品数量：\n",
      "   sales_region_code  counts\n",
      "0                101    1290\n",
      "1                102    1250\n",
      "2                103    1203\n",
      "3                104     185\n",
      "4                105    1351\n"
     ]
    }
   ],
   "source": [
    "## 地区和商品\n",
    "# 统计每个sales_region_code中包含的商品种类数量\n",
    "sales_region_count = sales_data.groupby('sales_region_code')['item_code'].nunique().reset_index(name='counts')\n",
    "# second_cate_count\n",
    "# 统计每个商品所属的sales_region_code数量，并检查是否有某个商品只属于一个sales_region_code\n",
    "item_counts = sales_data.groupby('item_code')['sales_region_code'].nunique()\n",
    "unique_items = item_counts[item_counts == 1].index.tolist()\n",
    "two_items = item_counts[item_counts == 2].index.tolist()\n",
    "three_items = item_counts[item_counts == 3].index.tolist()\n",
    "four_items = item_counts[item_counts == 4].index.tolist()\n",
    "five_items = item_counts[item_counts == 5].index.tolist()\n",
    "if len(unique_items) > 0:\n",
    "    print(f\"有 {len(unique_items)} 种商品只在一个地区销售\")\n",
    "if len(two_items) > 0:\n",
    "    print(f\"有 {len(two_items)} 种商品在两个地区销售\")\n",
    "if len(three_items) > 0:  \n",
    "    print(f\"有 {len(three_items)} 种商品在三个地区销售\")\n",
    "if len(four_items) > 0:\n",
    "    print(f\"有 {len(four_items)} 种商品在四个地区销售\")\n",
    "if len(five_items) > 0:\n",
    "    print(f\"有 {len(five_items)} 种商品在五个地区销售\")\n",
    "\n",
    "# 输出每个sales_region_code中包含的商品数量\n",
    "print(\"每个sales_region_codee中包含的商品数量：\")\n",
    "print(sales_region_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08431a5b",
   "metadata": {},
   "source": [
    "- 简单总结\n",
    "   - 一级分类和二级分类是一对多的\n",
    "   - 每个商品只有一个类别，不会对用多种\n",
    "   - 1421 种商品中，只有 332 种商品是只在一个地区销售的，其他的都在多个地区销售"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03df2f16",
   "metadata": {},
   "source": [
    "## 编码label_encoding\n",
    "- 将地区、item_code、类别进行顺序编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ea93feb8",
   "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>order_date</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>sales_chan_name</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>104</td>\n",
       "      <td>22069</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>1</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>104</td>\n",
       "      <td>20028</td>\n",
       "      <td>301</td>\n",
       "      <td>405</td>\n",
       "      <td>1</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>105</td>\n",
       "      <td>21183</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>105</td>\n",
       "      <td>20448</td>\n",
       "      <td>308</td>\n",
       "      <td>404</td>\n",
       "      <td>0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>104</td>\n",
       "      <td>21565</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>1</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date  sales_region_code  item_code  first_cate_code  second_cate_code  \\\n",
       "0 2015-09-01                104      22069              307               403   \n",
       "1 2015-09-01                104      20028              301               405   \n",
       "2 2015-09-02                105      21183              307               403   \n",
       "3 2015-09-02                105      20448              308               404   \n",
       "4 2015-09-02                104      21565              307               403   \n",
       "\n",
       "   sales_chan_name  item_price  ord_qty  \n",
       "0                1      1114.0       19  \n",
       "1                1      1012.0       12  \n",
       "2                0       428.0      109  \n",
       "3                0       962.0        3  \n",
       "4                1      1400.0        3  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ef4ed32",
   "metadata": {},
   "source": [
    "这里test有train中没有的商品类别（没有更新）\n",
    "包括\n",
    "> y contains previously unseen labels: [20011, 20025, 20198, 20273, 20324, 20401, 20449, 20456, 20469, 20503, 20549, 20701, 20721, 20736, 20750, 20793, 20841, 20869, 20951, 20999, 21085, 21184, 21393, 21416, 21523, 21606, 21667, 21687, 21823, 21867, 21879, 21954, 22075]\n",
    "\n",
    "所以就先不对item code进行编码了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "741f97ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# 地区\n",
    "LE1 = LabelEncoder()\n",
    "sales_data[\"sales_region_code\"] = LE1.fit_transform(sales_data.sales_region_code )\n",
    "test[\"sales_region_code\"] = LE1.transform(test.sales_region_code)\n",
    "# itemcode\n",
    "# LE2 = LabelEncoder()\n",
    "# sales_data[\"item_code\"] = LE2.fit_transform(sales_data.item_code )\n",
    "# test[\"item_code\"] = LE2.transform( test.item_code )\n",
    "# 大类\n",
    "LE3 = LabelEncoder()\n",
    "sales_data[\"first_cate_code\"] = LE3.fit_transform(sales_data.first_cate_code )\n",
    "test[\"first_cate_code\"] = LE3.transform( test.first_cate_code )\n",
    "# 细类\n",
    "LE4 = LabelEncoder()\n",
    "sales_data[\"second_cate_code\"] = LE4.fit_transform(sales_data.second_cate_code )\n",
    "test[\"second_cate_code\"] = LE4.transform( test.second_cate_code )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "93d24b41",
   "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>order_date</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>sales_chan_name</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>3</td>\n",
       "      <td>22069</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>3</td>\n",
       "      <td>20028</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>4</td>\n",
       "      <td>21183</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>4</td>\n",
       "      <td>20448</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>3</td>\n",
       "      <td>21565</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date  sales_region_code  item_code  first_cate_code  second_cate_code  \\\n",
       "0 2015-09-01                  3      22069                6                 2   \n",
       "1 2015-09-01                  3      20028                0                 4   \n",
       "2 2015-09-02                  4      21183                6                 2   \n",
       "3 2015-09-02                  4      20448                7                 3   \n",
       "4 2015-09-02                  3      21565                6                 2   \n",
       "\n",
       "   sales_chan_name  item_price  ord_qty  \n",
       "0                1      1114.0       19  \n",
       "1                1      1012.0       12  \n",
       "2                0       428.0      109  \n",
       "3                0       962.0        3  \n",
       "4                1      1400.0        3  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b040271",
   "metadata": {},
   "source": [
    "# 2. Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df98009b",
   "metadata": {},
   "source": [
    "- 标签平滑处理：取对数，其实应该放在不同的时间精度不同，比如按月预测的话，应该先按月合成再取对数\n",
    "- 按照月份递增的顺序，创建一个包含月份、地区和商品的组合的矩阵df。\n",
    "    - Item_cnt_day（ord_qty）被求和为Item_cnt_month。\n",
    "- product()函数也可以用于求取一个可迭代对象中自己各个元素的全排列组合\n",
    "\n",
    "- 因为每一个月，每个region种所出售的item情况不同，很多时候可能是0（没有这个记录），但是这都要参与到我们的建模中。所以我们要先将所有的情况都列出来。然后再与现有数据合并，缺失的数据就填补为0，这样构建数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6303889",
   "metadata": {},
   "source": [
    "- ？？？\n",
    "- 这里暂时按月合成的时候，先忽略线上、线下的因素，全部一股脑合并先，需要补充eda，对不同商品线上线下销售情况进行EDA，再进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "abff876f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 销量取对数\n",
    "# import math\n",
    "# sales_data[\"ord_qty\"] = list(map(lambda x : x if x== np.NaN else math.log(x+1,2),sales_data[\"ord_qty\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e1f13693",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成一列月份编号\n",
    "sales_data[\"month\"] = sales_data.order_date.dt.month\n",
    "sales_data[\"year\"] = sales_data.order_date.dt.year\n",
    "sales_data[\"date_block_num\"] = (sales_data[\"year\"]-sales_data[\"year\"].min())*12+sales_data[\"month\"]-9 # 减8是因为第一个月是9月份\n",
    "\n",
    "# 输出结果\n",
    "del sales_data[\"month\"],sales_data[\"year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d7e78511",
   "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>order_date</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>sales_chan_name</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "      <th>date_block_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>597689</th>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>1</td>\n",
       "      <td>20994</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>843.0</td>\n",
       "      <td>59</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597690</th>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>1</td>\n",
       "      <td>21875</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>762.0</td>\n",
       "      <td>502</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597691</th>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>1</td>\n",
       "      <td>20215</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>106</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597692</th>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>1</td>\n",
       "      <td>20195</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>187</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597693</th>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>1</td>\n",
       "      <td>20321</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>205</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order_date  sales_region_code  item_code  first_cate_code  \\\n",
       "597689 2018-12-20                  1      20994                1   \n",
       "597690 2018-12-20                  1      21875                1   \n",
       "597691 2018-12-20                  1      20215                1   \n",
       "597692 2018-12-20                  1      20195                1   \n",
       "597693 2018-12-20                  1      20321                1   \n",
       "\n",
       "        second_cate_code  sales_chan_name  item_price  ord_qty  date_block_num  \n",
       "597689                 7                1       843.0       59              39  \n",
       "597690                 7                1       762.0      502              39  \n",
       "597691                 7                1      2013.0      106              39  \n",
       "597692                 7                1      2120.0      187              39  \n",
       "597693                 7                1      1244.0      205              39  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sales_data.head()\n",
    "sales_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a295d9",
   "metadata": {},
   "source": [
    "尝试直接剔除104地区\n",
    "实际上应该不需要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130f989b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "af7a93dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.259052038192749"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import product\n",
    "import time\n",
    "ts = time.time()\n",
    "matrix = []\n",
    "cols  = [\"date_block_num\",\"sales_region_code\", \"item_code\"]\n",
    "for i in range(40):\n",
    "    sales = sales_data[sales_data.date_block_num == i]\n",
    "    region_list = sales.sales_region_code.unique() # （不）剔除了104地区\n",
    "    item_list = sales.item_code.unique()\n",
    "    matrix.append( np.array(list( product( [i],region_list , item_list) ), dtype = np.int16) )\n",
    "\n",
    "matrix = pd.DataFrame( np.vstack(matrix), columns = cols ) # 堆叠\n",
    "matrix[\"date_block_num\"] = matrix[\"date_block_num\"].astype(np.int8)\n",
    "matrix[\"sales_region_code\"] = matrix[\"sales_region_code\"].astype(np.int8)\n",
    "matrix[\"item_code\"] = matrix[\"item_code\"].astype(np.int16)\n",
    "matrix.sort_values( cols, inplace = True )\n",
    "time.time()- ts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "688cc900",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add revenue to train df 收入\n",
    "sales_data[\"revenue\"] = sales_data[\"item_price\"] * sales_data[\"ord_qty\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b2fb3d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1559619903564453"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "group = sales_data.groupby( [\"date_block_num\", \"sales_region_code\", \"item_code\"] ).agg( {\"ord_qty\": [\"sum\"]} )\n",
    "group.columns = [\"item_cnt_month\"]\n",
    "group.reset_index( inplace = True) # 这一步重置索引，直接把层级索引转换为列\n",
    "matrix = pd.merge( matrix, group, on = cols, how = \"left\" ) # 转换后，二者就可以merge了\n",
    "matrix[\"item_cnt_month\"] = matrix[\"item_cnt_month\"].fillna(0).astype(np.float16) # 填补缺失值\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f4222e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>item_cnt_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20006</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20007</td>\n",
       "      <td>3092.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20008</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20009</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date_block_num  sales_region_code  item_code  item_cnt_month\n",
       "0               0                  0      20003             0.0\n",
       "1               0                  0      20006             0.0\n",
       "2               0                  0      20007          3092.0\n",
       "3               0                  0      20008           308.0\n",
       "4               0                  0      20009           260.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrix就是每月、每个地区、每个商品的销量\n",
    "matrix.head()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de81c6a1",
   "metadata": {},
   "source": [
    "- 为第40个月设置一下test集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "77a66e3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对测试集添加一个月份列，同时设置一下三列的数据格式\n",
    "test[\"date_block_num\"] = 40\n",
    "test[\"date_block_num\"] = test[\"date_block_num\"].astype(np.int8)\n",
    "test[\"sales_region_code\"] = test.sales_region_code.astype(np.int8)\n",
    "test[\"item_code\"] = test.item_code.astype(np.int16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c15bc0",
   "metadata": {},
   "source": [
    "- 合并训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ab2cba33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>item_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>22069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>20028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>21183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>20448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>21565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_cate_code  second_cate_code  item_code\n",
       "0                6                 2      22069\n",
       "1                0                 4      20028\n",
       "2                6                 2      21183\n",
       "3                7                 3      20448\n",
       "4                6                 2      21565"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_cate_df = sales_data[[\"first_cate_code\",\"second_cate_code\",\"item_code\"]].drop_duplicates()\n",
    "map_cate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "be0d090e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "69b494f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 给matrix补充上类别属性\n",
    "matrix = pd.merge(matrix, map_cate_df, on = [\"item_code\"], how = \"left\")\n",
    "matrix[\"first_cate_code\"] = matrix[\"first_cate_code\"].astype(np.int8)\n",
    "matrix[\"second_cate_code\"] = matrix[\"second_cate_code\"].astype(np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b30d72a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5ebac8fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20007</td>\n",
       "      <td>3092.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20008</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20009</td>\n",
       "      <td>260.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</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>107696</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>22080</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107697</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>22081</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107698</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>22082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107699</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>22083</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107700</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>22084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107701 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date_block_num  sales_region_code  item_code  item_cnt_month  \\\n",
       "0                    0                  0      20003             0.0   \n",
       "1                    0                  0      20006             0.0   \n",
       "2                    0                  0      20007          3092.0   \n",
       "3                    0                  0      20008           308.0   \n",
       "4                    0                  0      20009           260.0   \n",
       "...                ...                ...        ...             ...   \n",
       "107696              39                  4      22080            16.0   \n",
       "107697              39                  4      22081             7.0   \n",
       "107698              39                  4      22082             0.0   \n",
       "107699              39                  4      22083          1124.0   \n",
       "107700              39                  4      22084             0.0   \n",
       "\n",
       "        first_cate_code  second_cate_code  \n",
       "0                     0                 4  \n",
       "1                     6                 2  \n",
       "2                     4                11  \n",
       "3                     2                 0  \n",
       "4                     5                 6  \n",
       "...                 ...               ...  \n",
       "107696                5                 6  \n",
       "107697                2                 0  \n",
       "107698                4                11  \n",
       "107699                2                 0  \n",
       "107700                1                 7  \n",
       "\n",
       "[107701 rows x 6 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "42cf3c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006705045700073242"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 合并训练和测试\n",
    "ts = time.time()\n",
    "# cols  = [\"date_block_num\",\"sales_region_code\", \"item_code\"]\n",
    "\n",
    "matrix = pd.concat([matrix, test], ignore_index=True, sort=False, keys=cols) # 按三列，添加？\n",
    "matrix.fillna( 0, inplace = True )\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "20a9ca0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20007</td>\n",
       "      <td>3092.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20008</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20009</td>\n",
       "      <td>260.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</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>110315</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110316</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110317</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110318</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110319</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110320 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date_block_num  sales_region_code  item_code  item_cnt_month  \\\n",
       "0                    0                  0      20003             0.0   \n",
       "1                    0                  0      20006             0.0   \n",
       "2                    0                  0      20007          3092.0   \n",
       "3                    0                  0      20008           308.0   \n",
       "4                    0                  0      20009           260.0   \n",
       "...                ...                ...        ...             ...   \n",
       "110315              40                  4      22066             0.0   \n",
       "110316              40                  4      22072             0.0   \n",
       "110317              40                  4      22075             0.0   \n",
       "110318              40                  4      22083             0.0   \n",
       "110319              40                  4      22084             0.0   \n",
       "\n",
       "        first_cate_code  second_cate_code  \n",
       "0                     0                 4  \n",
       "1                     6                 2  \n",
       "2                     4                11  \n",
       "3                     2                 0  \n",
       "4                     5                 6  \n",
       "...                 ...               ...  \n",
       "110315                6                 2  \n",
       "110316                4                11  \n",
       "110317                6                 2  \n",
       "110318                2                 0  \n",
       "110319                1                 7  \n",
       "\n",
       "[110320 rows x 6 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58615d0b",
   "metadata": {},
   "source": [
    "取对数，+1是因为销量有可能为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "58d4f19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 销量取对数\n",
    "import math\n",
    "if is_need_log:\n",
    "    matrix[\"item_cnt_month\"] = list(map(lambda x : x if x== np.NaN else math.log(x+1,2),matrix[\"item_cnt_month\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0442ddfb",
   "metadata": {},
   "source": [
    "#  3. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40d13b7a",
   "metadata": {},
   "source": [
    "## 3.1 Add lag features to matrix df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ae5b5a9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a lag feature function\n",
    "def lag_feature( df,lags, cols ):\n",
    "    for col in cols:\n",
    "        print(col)\n",
    "        tmp = df[[\"date_block_num\", \"sales_region_code\",\"item_code\",col ]]\n",
    "        for i in lags:\n",
    "            shifted = tmp.copy()\n",
    "            shifted.columns = [\"date_block_num\", \"sales_region_code\",\"item_code\", col + \"_lag_\"+str(i)]\n",
    "            shifted.date_block_num = shifted.date_block_num + i\n",
    "            df = pd.merge(df, shifted, on=[\"date_block_num\", \"sales_region_code\",\"item_code\"], how='left')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d551ce2b",
   "metadata": {},
   "source": [
    "- Add item_cnt_month lag features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ce12809a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item_cnt_month\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.13857722282409668"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "matrix = lag_feature( matrix, [1,2,3], [\"item_cnt_month\"] ) # 添加该地区的该商品前一个月、两个月、三个月的销量\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7902376e",
   "metadata": {},
   "source": [
    "- Add the previous month's average item_cnt. 上个月的平均销量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "256ccd7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_avg_item_cnt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.09653782844543457"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "# 计算每个月的平均销量（这个月的所有大杂烩）\n",
    "group = matrix.groupby( [\"date_block_num\"] ).agg({\"item_cnt_month\" : [\"mean\"]})\n",
    "group.columns = [\"date_avg_item_cnt\"]\n",
    "group.reset_index(inplace = True)\n",
    "\n",
    "# 在matrix中添加  前一个月的平均销量 作为一个lag feature\n",
    "matrix = pd.merge(matrix, group, on = [\"date_block_num\"], how = \"left\")\n",
    "matrix.date_avg_item_cnt = matrix[\"date_avg_item_cnt\"].astype(np.float16)\n",
    "matrix = lag_feature( matrix, [1], [\"date_avg_item_cnt\"] )\n",
    "matrix.drop( [\"date_avg_item_cnt\"], axis = 1, inplace = True )\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde4eee4",
   "metadata": {},
   "source": [
    "- Add lag values of item_cnt_month for month / item_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "920a962f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_item_avg_item_cnt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.21156978607177734"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "# 计算每个月该商品的平均销量（这个月的该商品）\n",
    "group = matrix.groupby(['date_block_num', 'item_code']).agg({'item_cnt_month': ['mean']})\n",
    "group.columns = [ 'date_item_avg_item_cnt' ]\n",
    "group.reset_index(inplace=True)\n",
    "\n",
    "# 添加 前三个月该商品的平均销量\n",
    "matrix = pd.merge(matrix, group, on=['date_block_num','item_code'], how='left')\n",
    "matrix.date_item_avg_item_cnt = matrix['date_item_avg_item_cnt'].astype(np.float16)\n",
    "matrix = lag_feature(matrix, [1,2,3], ['date_item_avg_item_cnt'])\n",
    "matrix.drop(['date_item_avg_item_cnt'], axis=1, inplace=True)\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e874b14",
   "metadata": {},
   "source": [
    "- Add lag values for item_cnt_month for every month / region combination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "22a65673",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_region_avg_item_cnt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-116-1f9a7b212985>:8: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  matrix.date_avg_item_cnt = matrix[\"date_region_avg_item_cnt\"].astype(np.float16)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.22776174545288086"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "# 计算 该地区的平均销量\n",
    "group = matrix.groupby( [\"date_block_num\",\"sales_region_code\"] ).agg({\"item_cnt_month\" : [\"mean\"]})\n",
    "group.columns = [\"date_region_avg_item_cnt\"]\n",
    "group.reset_index(inplace = True)\n",
    "\n",
    "matrix = pd.merge(matrix, group, on = [\"date_block_num\",\"sales_region_code\"], how = \"left\")\n",
    "matrix.date_avg_item_cnt = matrix[\"date_region_avg_item_cnt\"].astype(np.float16)\n",
    "matrix = lag_feature( matrix, [1,2,3], [\"date_region_avg_item_cnt\"] )\n",
    "matrix.drop( [\"date_region_avg_item_cnt\"], axis = 1, inplace = True )\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2369b88c",
   "metadata": {},
   "source": [
    "- Add lag values for item_cnt_month for month/region/item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "18bf6c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-117-4c9cee418e72>:8: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  matrix.date_avg_item_cnt = matrix[\"date_region_item_avg_item_cnt\"].astype(np.float16)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_region_item_avg_item_cnt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.3253819942474365"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "# 计算每月该地区、该商品的平均销量\n",
    "group = matrix.groupby( [\"date_block_num\",\"sales_region_code\",\"item_code\"] ).agg({\"item_cnt_month\" : [\"mean\"]})\n",
    "group.columns = [\"date_region_item_avg_item_cnt\"]\n",
    "group.reset_index(inplace = True)\n",
    "\n",
    "matrix = pd.merge(matrix, group, on = [\"date_block_num\",\"sales_region_code\",\"item_code\"], how = \"left\")\n",
    "matrix.date_avg_item_cnt = matrix[\"date_region_item_avg_item_cnt\"].astype(np.float16)\n",
    "matrix = lag_feature( matrix, [1,2,3], [\"date_region_item_avg_item_cnt\"] )\n",
    "matrix.drop( [\"date_region_item_avg_item_cnt\"], axis = 1, inplace = True )\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db025b26",
   "metadata": {},
   "source": [
    "- Add lag values for item_cnt_month for month/region/item second_cate(subtype)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "da595deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_region_subtype_avg_item_cnt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.1800858974456787"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "# 计算每月该地区、该子类商品的 销售量\n",
    "group = matrix.groupby(['date_block_num', 'sales_region_code', 'second_cate_code']).agg({'item_cnt_month': ['mean']})\n",
    "group.columns = ['date_region_subtype_avg_item_cnt']\n",
    "group.reset_index(inplace=True)\n",
    "\n",
    "# 添加上一月的\n",
    "matrix = pd.merge(matrix, group, on=['date_block_num', 'sales_region_code', 'second_cate_code'], how='left')\n",
    "matrix.date_region_subtype_avg_item_cnt = matrix['date_region_subtype_avg_item_cnt'].astype(np.float16)\n",
    "matrix = lag_feature(matrix, [1], ['date_region_subtype_avg_item_cnt'])\n",
    "matrix.drop(['date_region_subtype_avg_item_cnt'], axis=1, inplace=True)\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3d0d2a",
   "metadata": {},
   "source": [
    "- 其实还可以补充一个大类商品的销售情况（有需要再说吧）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e988a3d0",
   "metadata": {},
   "source": [
    "## 3.2 add price trend feature\n",
    "* Add average item price on to matix df. \n",
    "* Add lag values of item price per month.\n",
    "* Add delta price values - how current month average pirce relates to global average.\n",
    "\n",
    "前面算的一直是销量，这里开始添加的特征是价格\n",
    "虽然要求预测的是销量，但是价格与销量息息相关，也是重要的特征\n",
    "\n",
    "Price trend features，最后只有一个'delta_price_lag'特征，具体看代码注释"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "9d0bb94d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date_block_num', 'sales_region_code', 'item_code', 'item_cnt_month',\n",
       "       'first_cate_code', 'second_cate_code', 'item_cnt_month_lag_1',\n",
       "       'item_cnt_month_lag_2', 'item_cnt_month_lag_3',\n",
       "       'date_avg_item_cnt_lag_1', 'date_item_avg_item_cnt_lag_1',\n",
       "       'date_item_avg_item_cnt_lag_2', 'date_item_avg_item_cnt_lag_3',\n",
       "       'date_region_avg_item_cnt_lag_1', 'date_region_avg_item_cnt_lag_2',\n",
       "       'date_region_avg_item_cnt_lag_3', 'date_region_item_avg_item_cnt_lag_1',\n",
       "       'date_region_item_avg_item_cnt_lag_2',\n",
       "       'date_region_item_avg_item_cnt_lag_3',\n",
       "       'date_region_subtype_avg_item_cnt_lag_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0467b8bd",
   "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>order_date</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>sales_chan_name</th>\n",
       "      <th>item_price</th>\n",
       "      <th>ord_qty</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>3</td>\n",
       "      <td>22069</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>21166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>3</td>\n",
       "      <td>20028</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>4</td>\n",
       "      <td>21183</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>46652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>4</td>\n",
       "      <td>20448</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>962.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>3</td>\n",
       "      <td>21565</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date  sales_region_code  item_code  first_cate_code  second_cate_code  \\\n",
       "0 2015-09-01                  3      22069                6                 2   \n",
       "1 2015-09-01                  3      20028                0                 4   \n",
       "2 2015-09-02                  4      21183                6                 2   \n",
       "3 2015-09-02                  4      20448                7                 3   \n",
       "4 2015-09-02                  3      21565                6                 2   \n",
       "\n",
       "   sales_chan_name  item_price  ord_qty  date_block_num  revenue  \n",
       "0                1      1114.0       19               0  21166.0  \n",
       "1                1      1012.0       12               0  12144.0  \n",
       "2                0       428.0      109               0  46652.0  \n",
       "3                0       962.0        3               0   2886.0  \n",
       "4                1      1400.0        3               0   4200.0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e4f96073",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_item_avg_item_price\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.7292048931121826"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "# 添加该商品的平均价格\n",
    "group = sales_data.groupby( [\"item_code\"] ).agg({\"item_price\": [\"mean\"]})\n",
    "group.columns = [\"item_avg_item_price\"]\n",
    "group.reset_index(inplace = True)\n",
    "\n",
    "matrix = matrix.merge( group, on = [\"item_code\"], how = \"left\" )\n",
    "matrix[\"item_avg_item_price\"] = matrix.item_avg_item_price.astype(np.float16)\n",
    "\n",
    "\n",
    "\n",
    "# 添加该商品每月的平均价格，前三个月的\n",
    "group = sales_data.groupby( [\"date_block_num\",\"item_code\"] ).agg( {\"item_price\": [\"mean\"]} )\n",
    "group.columns = [\"date_item_avg_item_price\"]\n",
    "group.reset_index(inplace = True)\n",
    "\n",
    "matrix = matrix.merge(group, on = [\"date_block_num\",\"item_code\"], how = \"left\")\n",
    "matrix[\"date_item_avg_item_price\"] = matrix.date_item_avg_item_price.astype(np.float16)\n",
    "lags = [1, 2, 3]\n",
    "matrix = lag_feature( matrix, lags, [\"date_item_avg_item_price\"] ) # 添加价格\n",
    "for i in lags: # 添加价格趋势\n",
    "    matrix[\"delta_price_lag_\" + str(i) ] = (matrix[\"date_item_avg_item_price_lag_\" + str(i)]- matrix[\"item_avg_item_price\"] )/ matrix[\"item_avg_item_price\"]\n",
    "\n",
    "\n",
    "def select_trends(row) :\n",
    "    for i in lags:\n",
    "        if row[\"delta_price_lag_\" + str(i)]:\n",
    "            return row[\"delta_price_lag_\" + str(i)]\n",
    "    return 0\n",
    "\n",
    "# 这个函数看起来像是，该商品有上一个月的趋势，就取上一个月的。有上上个月的，就取上上个月的。。。如果前三个月都没有销售的价格，那么就取0\n",
    "matrix[\"delta_price_lag\"] = matrix.apply(select_trends, axis = 1)\n",
    "matrix[\"delta_price_lag\"] = matrix.delta_price_lag.astype( np.float16 )\n",
    "matrix[\"delta_price_lag\"].fillna( 0 ,inplace = True)\n",
    "\n",
    "# 删除该商品的平均价格，商品的每月平均价格，和每月价格趋势。最后仅仅保留了一个delta_price_lag\n",
    "features_to_drop = [\"item_avg_item_price\", \"date_item_avg_item_price\"]\n",
    "for i in lags:\n",
    "    features_to_drop.append(\"date_item_avg_item_price_lag_\" + str(i) )\n",
    "    features_to_drop.append(\"delta_price_lag_\" + str(i) )\n",
    "matrix.drop(features_to_drop, axis = 1, inplace = True)\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58508ba6",
   "metadata": {},
   "source": [
    "* Add total shop revenue per month to matix df. \n",
    "* Add lag values of revenue per month.\n",
    "* Add delta revenue values - how current month revenue relates to global average.\n",
    "\n",
    "我们已经知道validation set和test set都会出现train set所没有的新商品，借助商店revenue趋势特征可以帮助算法预测新商品在现有店铺的销量\n",
    "\n",
    "该商店的特征同上，但是泰迪杯没有这样的商店列，所以可以用大类或者小类的商品销售趋势替代\n",
    "暂时用的是细类，其实可以优化为细类和地区的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "62622529",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>sales_region_code</th>\n",
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       "      <th>date_region_avg_item_cnt_lag_2</th>\n",
       "      <th>date_region_avg_item_cnt_lag_3</th>\n",
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       "      <th>date_region_item_avg_item_cnt_lag_2</th>\n",
       "      <th>date_region_item_avg_item_cnt_lag_3</th>\n",
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       "      <td>488.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>300.75</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>110319</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "        date_block_num  sales_region_code  item_code  item_cnt_month  \\\n",
       "0                    0                  0      20003             0.0   \n",
       "1                    0                  0      20006             0.0   \n",
       "2                    0                  0      20007          3092.0   \n",
       "3                    0                  0      20008           308.0   \n",
       "4                    0                  0      20009           260.0   \n",
       "...                ...                ...        ...             ...   \n",
       "110315              40                  4      22066             0.0   \n",
       "110316              40                  4      22072             0.0   \n",
       "110317              40                  4      22075             0.0   \n",
       "110318              40                  4      22083             0.0   \n",
       "110319              40                  4      22084             0.0   \n",
       "\n",
       "        first_cate_code  second_cate_code  item_cnt_month_lag_1  \\\n",
       "0                     0                 4                   NaN   \n",
       "1                     6                 2                   NaN   \n",
       "2                     4                11                   NaN   \n",
       "3                     2                 0                   NaN   \n",
       "4                     5                 6                   NaN   \n",
       "...                 ...               ...                   ...   \n",
       "110315                6                 2                7224.0   \n",
       "110316                4                11                 488.0   \n",
       "110317                6                 2                   NaN   \n",
       "110318                2                 0                1124.0   \n",
       "110319                1                 7                   0.0   \n",
       "\n",
       "        item_cnt_month_lag_2  item_cnt_month_lag_3  date_avg_item_cnt_lag_1  \\\n",
       "0                        NaN                   NaN                      NaN   \n",
       "1                        NaN                   NaN                      NaN   \n",
       "2                        NaN                   NaN                      NaN   \n",
       "3                        NaN                   NaN                      NaN   \n",
       "4                        NaN                   NaN                      NaN   \n",
       "...                      ...                   ...                      ...   \n",
       "110315               10536.0                5556.0                   300.75   \n",
       "110316                1842.0                1082.0                   300.75   \n",
       "110317                   NaN                   NaN                      NaN   \n",
       "110318                1344.0                7420.0                   300.75   \n",
       "110319                   0.0                  11.0                   300.75   \n",
       "\n",
       "        ...  date_item_avg_item_cnt_lag_2  date_item_avg_item_cnt_lag_3  \\\n",
       "0       ...                           NaN                           NaN   \n",
       "1       ...                           NaN                           NaN   \n",
       "2       ...                           NaN                           NaN   \n",
       "3       ...                           NaN                           NaN   \n",
       "4       ...                           NaN                           NaN   \n",
       "...     ...                           ...                           ...   \n",
       "110315  ...                       4584.00                        3054.0   \n",
       "110316  ...                        460.50                         270.5   \n",
       "110317  ...                           NaN                           NaN   \n",
       "110318  ...                       2326.00                        5108.0   \n",
       "110319  ...                        239.25                         146.0   \n",
       "\n",
       "        date_region_avg_item_cnt_lag_1  date_region_avg_item_cnt_lag_2  \\\n",
       "0                                  NaN                             NaN   \n",
       "1                                  NaN                             NaN   \n",
       "2                                  NaN                             NaN   \n",
       "3                                  NaN                             NaN   \n",
       "4                                  NaN                             NaN   \n",
       "...                                ...                             ...   \n",
       "110315                          337.75                           486.0   \n",
       "110316                          337.75                           486.0   \n",
       "110317                             NaN                             NaN   \n",
       "110318                          337.75                           486.0   \n",
       "110319                          337.75                           486.0   \n",
       "\n",
       "        date_region_avg_item_cnt_lag_3  date_region_item_avg_item_cnt_lag_1  \\\n",
       "0                                  NaN                                  NaN   \n",
       "1                                  NaN                                  NaN   \n",
       "2                                  NaN                                  NaN   \n",
       "3                                  NaN                                  NaN   \n",
       "4                                  NaN                                  NaN   \n",
       "...                                ...                                  ...   \n",
       "110315                           900.5                               7224.0   \n",
       "110316                           900.5                                488.0   \n",
       "110317                             NaN                                  NaN   \n",
       "110318                           900.5                               1124.0   \n",
       "110319                           900.5                                  0.0   \n",
       "\n",
       "        date_region_item_avg_item_cnt_lag_2  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "...                                     ...   \n",
       "110315                              10536.0   \n",
       "110316                               1842.0   \n",
       "110317                                  NaN   \n",
       "110318                               1344.0   \n",
       "110319                                  0.0   \n",
       "\n",
       "        date_region_item_avg_item_cnt_lag_3  \\\n",
       "0                                       NaN   \n",
       "1                                       NaN   \n",
       "2                                       NaN   \n",
       "3                                       NaN   \n",
       "4                                       NaN   \n",
       "...                                     ...   \n",
       "110315                               5556.0   \n",
       "110316                               1082.0   \n",
       "110317                                  NaN   \n",
       "110318                               7420.0   \n",
       "110319                                 11.0   \n",
       "\n",
       "        date_region_subtype_avg_item_cnt_lag_1  delta_price_lag  \n",
       "0                                          NaN         0.000000  \n",
       "1                                          NaN         0.000000  \n",
       "2                                          NaN         0.000000  \n",
       "3                                          NaN         0.000000  \n",
       "4                                          NaN         0.000000  \n",
       "...                                        ...              ...  \n",
       "110315                                 433.750         0.091797  \n",
       "110316                                 278.250        -0.006069  \n",
       "110317                                     NaN         0.000000  \n",
       "110318                                 217.375        -0.006493  \n",
       "110319                                 197.125        -0.010872  \n",
       "\n",
       "[110320 rows x 21 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5c7ddaf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "delta_revenue\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.24373102188110352"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "group = sales_data.groupby( [\"date_block_num\",\"second_cate_code\"] ).agg({\"revenue\": [\"sum\"] })\n",
    "group.columns = [\"date_subtype_revenue\"]\n",
    "group.reset_index(inplace = True)\n",
    "\n",
    "matrix = matrix.merge( group , on = [\"date_block_num\", \"second_cate_code\"], how = \"left\" )\n",
    "matrix['date_subtype_revenue'] = matrix['date_subtype_revenue'].astype(np.float32)\n",
    "\n",
    "group = group.groupby([\"second_cate_code\"]).agg({ \"date_block_num\":[\"mean\"] })\n",
    "group.columns = [\"subtype_avg_revenue\"]\n",
    "group.reset_index(inplace = True )\n",
    "\n",
    "matrix = matrix.merge( group, on = [\"second_cate_code\"], how = \"left\" )\n",
    "matrix[\"subtype_avg_revenue\"] = matrix.subtype_avg_revenue.astype(np.float32)\n",
    "matrix[\"delta_revenue\"] = (matrix['date_subtype_revenue'] - matrix['subtype_avg_revenue']) / matrix['subtype_avg_revenue']\n",
    "matrix[\"delta_revenue\"] = matrix[\"delta_revenue\"]. astype(np.float32)\n",
    "\n",
    "matrix = lag_feature(matrix, [1], [\"delta_revenue\"])\n",
    "matrix[\"delta_revenue_lag_1\"] = matrix[\"delta_revenue_lag_1\"].astype(np.float32)\n",
    "matrix.drop( [\"date_subtype_revenue\", \"subtype_avg_revenue\", \"delta_revenue\"] ,axis = 1, inplace = True)\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44869c9f",
   "metadata": {},
   "source": [
    "- Add month and number of days in each month to matrix df.\n",
    "\n",
    "添加月份、和该月有多少天的两个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "8560c680",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix[\"month\"] = (matrix[\"date_block_num\"]+9) % 12 # 9个月开始\n",
    "days = pd.Series([31,28,31,30,31,30,31,31,30,31,30,31])\n",
    "matrix[\"days\"] = matrix[\"month\"].map(days).astype(np.int8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6331a7cd",
   "metadata": {},
   "source": [
    "Add the month of each shop and item first sale.\n",
    "\n",
    "'item_subtype_last_sale'和'item_subtype_sale'用以纪录距离最近一次销售之间隔了几个月，通过它可以和之前月份的数据建立关联。'item_subtype_first_sale'和'item_first_sale'则是用于表示新商品的特征\n",
    "\n",
    "该商品从第一个销售到现在有几个月了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "fb01e770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01840806007385254"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "matrix[\"item_subtype_first_sale\"] = matrix[\"date_block_num\"] - matrix.groupby([\"item_code\",\"second_cate_code\"])[\"date_block_num\"].transform('min')\n",
    "matrix[\"item_first_sale\"] = matrix[\"date_block_num\"] - matrix.groupby([\"item_code\"])[\"date_block_num\"].transform('min')\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51ff0f6",
   "metadata": {},
   "source": [
    "Delete first three months from matrix. They don't have lag values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "754b950a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01947021484375"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = time.time()\n",
    "matrix = matrix[matrix[\"date_block_num\"] > 3]\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "cc7403d7",
   "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>9940</th>\n",
       "      <th>9941</th>\n",
       "      <th>9942</th>\n",
       "      <th>9943</th>\n",
       "      <th>9944</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>date_block_num</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales_region_code</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_code</th>\n",
       "      <td>20003.000000</td>\n",
       "      <td>2.000500e+04</td>\n",
       "      <td>20006.000000</td>\n",
       "      <td>2.000800e+04</td>\n",
       "      <td>2.000900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_cnt_month</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.570000e+02</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_cate_code</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second_cate_code</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_cnt_month_lag_1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.300000e+02</td>\n",
       "      <td>2.060000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_cnt_month_lag_2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_cnt_month_lag_3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.620000e+02</td>\n",
       "      <td>4.230000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_avg_item_cnt_lag_1</th>\n",
       "      <td>573.500000</td>\n",
       "      <td>5.735000e+02</td>\n",
       "      <td>573.500000</td>\n",
       "      <td>5.735000e+02</td>\n",
       "      <td>5.735000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_item_avg_item_cnt_lag_1</th>\n",
       "      <td>408.250000</td>\n",
       "      <td>1.020312e+01</td>\n",
       "      <td>81.812500</td>\n",
       "      <td>5.870000e+02</td>\n",
       "      <td>2.867500e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_item_avg_item_cnt_lag_2</th>\n",
       "      <td>304.500000</td>\n",
       "      <td>3.199219e+00</td>\n",
       "      <td>376.500000</td>\n",
       "      <td>2.042500e+02</td>\n",
       "      <td>7.218750e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_item_avg_item_cnt_lag_3</th>\n",
       "      <td>1060.000000</td>\n",
       "      <td>1.799805e+00</td>\n",
       "      <td>701.000000</td>\n",
       "      <td>2.392500e+02</td>\n",
       "      <td>7.445000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_avg_item_cnt_lag_1</th>\n",
       "      <td>772.000000</td>\n",
       "      <td>7.720000e+02</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>7.720000e+02</td>\n",
       "      <td>7.720000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_avg_item_cnt_lag_2</th>\n",
       "      <td>366.500000</td>\n",
       "      <td>3.665000e+02</td>\n",
       "      <td>366.500000</td>\n",
       "      <td>3.665000e+02</td>\n",
       "      <td>3.665000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_avg_item_cnt_lag_3</th>\n",
       "      <td>379.750000</td>\n",
       "      <td>3.797500e+02</td>\n",
       "      <td>379.750000</td>\n",
       "      <td>3.797500e+02</td>\n",
       "      <td>3.797500e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_item_avg_item_cnt_lag_1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.300000e+02</td>\n",
       "      <td>2.060000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_item_avg_item_cnt_lag_2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_item_avg_item_cnt_lag_3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.620000e+02</td>\n",
       "      <td>4.230000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_region_subtype_avg_item_cnt_lag_1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.211000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.185000e+02</td>\n",
       "      <td>1.211000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_price_lag</th>\n",
       "      <td>-0.057953</td>\n",
       "      <td>3.256226e-02</td>\n",
       "      <td>0.070618</td>\n",
       "      <td>-3.829956e-02</td>\n",
       "      <td>-3.530884e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_revenue_lag_1</th>\n",
       "      <td>389511.656250</td>\n",
       "      <td>2.354960e+07</td>\n",
       "      <td>628721.562500</td>\n",
       "      <td>5.590607e+06</td>\n",
       "      <td>2.354960e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.800000e+01</td>\n",
       "      <td>2.800000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_subtype_first_sale</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_first_sale</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 9940          9941  \\\n",
       "date_block_num                               4.000000  4.000000e+00   \n",
       "sales_region_code                            0.000000  0.000000e+00   \n",
       "item_code                                20003.000000  2.000500e+04   \n",
       "item_cnt_month                               0.000000  3.000000e+00   \n",
       "first_cate_code                              0.000000  5.000000e+00   \n",
       "second_cate_code                             4.000000  6.000000e+00   \n",
       "item_cnt_month_lag_1                         0.000000  0.000000e+00   \n",
       "item_cnt_month_lag_2                         0.000000  7.000000e+00   \n",
       "item_cnt_month_lag_3                         0.000000  0.000000e+00   \n",
       "date_avg_item_cnt_lag_1                    573.500000  5.735000e+02   \n",
       "date_item_avg_item_cnt_lag_1               408.250000  1.020312e+01   \n",
       "date_item_avg_item_cnt_lag_2               304.500000  3.199219e+00   \n",
       "date_item_avg_item_cnt_lag_3              1060.000000  1.799805e+00   \n",
       "date_region_avg_item_cnt_lag_1             772.000000  7.720000e+02   \n",
       "date_region_avg_item_cnt_lag_2             366.500000  3.665000e+02   \n",
       "date_region_avg_item_cnt_lag_3             379.750000  3.797500e+02   \n",
       "date_region_item_avg_item_cnt_lag_1          0.000000  0.000000e+00   \n",
       "date_region_item_avg_item_cnt_lag_2          0.000000  7.000000e+00   \n",
       "date_region_item_avg_item_cnt_lag_3          0.000000  0.000000e+00   \n",
       "date_region_subtype_avg_item_cnt_lag_1       0.000000  1.211000e+03   \n",
       "delta_price_lag                             -0.057953  3.256226e-02   \n",
       "delta_revenue_lag_1                     389511.656250  2.354960e+07   \n",
       "month                                        1.000000  1.000000e+00   \n",
       "days                                        28.000000  2.800000e+01   \n",
       "item_subtype_first_sale                      4.000000  3.000000e+00   \n",
       "item_first_sale                              4.000000  3.000000e+00   \n",
       "\n",
       "                                                 9942          9943  \\\n",
       "date_block_num                               4.000000  4.000000e+00   \n",
       "sales_region_code                            0.000000  0.000000e+00   \n",
       "item_code                                20006.000000  2.000800e+04   \n",
       "item_cnt_month                               0.000000  3.570000e+02   \n",
       "first_cate_code                              6.000000  2.000000e+00   \n",
       "second_cate_code                             2.000000  0.000000e+00   \n",
       "item_cnt_month_lag_1                         0.000000  2.300000e+02   \n",
       "item_cnt_month_lag_2                         0.000000  8.100000e+01   \n",
       "item_cnt_month_lag_3                         0.000000  2.620000e+02   \n",
       "date_avg_item_cnt_lag_1                    573.500000  5.735000e+02   \n",
       "date_item_avg_item_cnt_lag_1                81.812500  5.870000e+02   \n",
       "date_item_avg_item_cnt_lag_2               376.500000  2.042500e+02   \n",
       "date_item_avg_item_cnt_lag_3               701.000000  2.392500e+02   \n",
       "date_region_avg_item_cnt_lag_1             772.000000  7.720000e+02   \n",
       "date_region_avg_item_cnt_lag_2             366.500000  3.665000e+02   \n",
       "date_region_avg_item_cnt_lag_3             379.750000  3.797500e+02   \n",
       "date_region_item_avg_item_cnt_lag_1          0.000000  2.300000e+02   \n",
       "date_region_item_avg_item_cnt_lag_2          0.000000  8.100000e+01   \n",
       "date_region_item_avg_item_cnt_lag_3          0.000000  2.620000e+02   \n",
       "date_region_subtype_avg_item_cnt_lag_1       0.000000  7.185000e+02   \n",
       "delta_price_lag                              0.070618 -3.829956e-02   \n",
       "delta_revenue_lag_1                     628721.562500  5.590607e+06   \n",
       "month                                        1.000000  1.000000e+00   \n",
       "days                                        28.000000  2.800000e+01   \n",
       "item_subtype_first_sale                      4.000000  4.000000e+00   \n",
       "item_first_sale                              4.000000  4.000000e+00   \n",
       "\n",
       "                                                9944  \n",
       "date_block_num                          4.000000e+00  \n",
       "sales_region_code                       0.000000e+00  \n",
       "item_code                               2.000900e+04  \n",
       "item_cnt_month                          1.000000e+01  \n",
       "first_cate_code                         5.000000e+00  \n",
       "second_cate_code                        6.000000e+00  \n",
       "item_cnt_month_lag_1                    2.060000e+02  \n",
       "item_cnt_month_lag_2                    9.000000e+00  \n",
       "item_cnt_month_lag_3                    4.230000e+02  \n",
       "date_avg_item_cnt_lag_1                 5.735000e+02  \n",
       "date_item_avg_item_cnt_lag_1            2.867500e+02  \n",
       "date_item_avg_item_cnt_lag_2            7.218750e+01  \n",
       "date_item_avg_item_cnt_lag_3            7.445000e+02  \n",
       "date_region_avg_item_cnt_lag_1          7.720000e+02  \n",
       "date_region_avg_item_cnt_lag_2          3.665000e+02  \n",
       "date_region_avg_item_cnt_lag_3          3.797500e+02  \n",
       "date_region_item_avg_item_cnt_lag_1     2.060000e+02  \n",
       "date_region_item_avg_item_cnt_lag_2     9.000000e+00  \n",
       "date_region_item_avg_item_cnt_lag_3     4.230000e+02  \n",
       "date_region_subtype_avg_item_cnt_lag_1  1.211000e+03  \n",
       "delta_price_lag                        -3.530884e-02  \n",
       "delta_revenue_lag_1                     2.354960e+07  \n",
       "month                                   1.000000e+00  \n",
       "days                                    2.800000e+01  \n",
       "item_subtype_first_sale                 4.000000e+00  \n",
       "item_first_sale                         4.000000e+00  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d3e4b594",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>date_block_num</th>\n",
       "      <th>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>item_cnt_month</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>item_cnt_month_lag_1</th>\n",
       "      <th>item_cnt_month_lag_2</th>\n",
       "      <th>item_cnt_month_lag_3</th>\n",
       "      <th>date_avg_item_cnt_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>date_region_item_avg_item_cnt_lag_1</th>\n",
       "      <th>date_region_item_avg_item_cnt_lag_2</th>\n",
       "      <th>date_region_item_avg_item_cnt_lag_3</th>\n",
       "      <th>date_region_subtype_avg_item_cnt_lag_1</th>\n",
       "      <th>delta_price_lag</th>\n",
       "      <th>delta_revenue_lag_1</th>\n",
       "      <th>month</th>\n",
       "      <th>days</th>\n",
       "      <th>item_subtype_first_sale</th>\n",
       "      <th>item_first_sale</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9940</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>573.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.057953</td>\n",
       "      <td>3.895117e+05</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9941</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20005</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>573.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1211.000</td>\n",
       "      <td>0.032562</td>\n",
       "      <td>2.354960e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9942</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>573.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.070618</td>\n",
       "      <td>6.287216e+05</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9943</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20008</td>\n",
       "      <td>357.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>573.50</td>\n",
       "      <td>...</td>\n",
       "      <td>230.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>718.500</td>\n",
       "      <td>-0.038300</td>\n",
       "      <td>5.590607e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9944</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20009</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>206.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>573.50</td>\n",
       "      <td>...</td>\n",
       "      <td>206.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>423.0</td>\n",
       "      <td>1211.000</td>\n",
       "      <td>-0.035309</td>\n",
       "      <td>2.354960e+07</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>110315</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7224.0</td>\n",
       "      <td>10536.0</td>\n",
       "      <td>5556.0</td>\n",
       "      <td>300.75</td>\n",
       "      <td>...</td>\n",
       "      <td>7224.0</td>\n",
       "      <td>10536.0</td>\n",
       "      <td>5556.0</td>\n",
       "      <td>433.750</td>\n",
       "      <td>0.091797</td>\n",
       "      <td>3.829482e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110316</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>488.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>300.75</td>\n",
       "      <td>...</td>\n",
       "      <td>488.0</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>278.250</td>\n",
       "      <td>-0.006069</td>\n",
       "      <td>2.064940e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110317</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110318</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>7420.0</td>\n",
       "      <td>300.75</td>\n",
       "      <td>...</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>7420.0</td>\n",
       "      <td>217.375</td>\n",
       "      <td>-0.006493</td>\n",
       "      <td>3.506226e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110319</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>22084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>197.125</td>\n",
       "      <td>-0.010872</td>\n",
       "      <td>6.008424e+06</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100380 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date_block_num  sales_region_code  item_code  item_cnt_month  \\\n",
       "9940                 4                  0      20003             0.0   \n",
       "9941                 4                  0      20005             3.0   \n",
       "9942                 4                  0      20006             0.0   \n",
       "9943                 4                  0      20008           357.0   \n",
       "9944                 4                  0      20009            10.0   \n",
       "...                ...                ...        ...             ...   \n",
       "110315              40                  4      22066             0.0   \n",
       "110316              40                  4      22072             0.0   \n",
       "110317              40                  4      22075             0.0   \n",
       "110318              40                  4      22083             0.0   \n",
       "110319              40                  4      22084             0.0   \n",
       "\n",
       "        first_cate_code  second_cate_code  item_cnt_month_lag_1  \\\n",
       "9940                  0                 4                   0.0   \n",
       "9941                  5                 6                   0.0   \n",
       "9942                  6                 2                   0.0   \n",
       "9943                  2                 0                 230.0   \n",
       "9944                  5                 6                 206.0   \n",
       "...                 ...               ...                   ...   \n",
       "110315                6                 2                7224.0   \n",
       "110316                4                11                 488.0   \n",
       "110317                6                 2                   0.0   \n",
       "110318                2                 0                1124.0   \n",
       "110319                1                 7                   0.0   \n",
       "\n",
       "        item_cnt_month_lag_2  item_cnt_month_lag_3  date_avg_item_cnt_lag_1  \\\n",
       "9940                     0.0                   0.0                   573.50   \n",
       "9941                     7.0                   0.0                   573.50   \n",
       "9942                     0.0                   0.0                   573.50   \n",
       "9943                    81.0                 262.0                   573.50   \n",
       "9944                     9.0                 423.0                   573.50   \n",
       "...                      ...                   ...                      ...   \n",
       "110315               10536.0                5556.0                   300.75   \n",
       "110316                1842.0                1082.0                   300.75   \n",
       "110317                   0.0                   0.0                     0.00   \n",
       "110318                1344.0                7420.0                   300.75   \n",
       "110319                   0.0                  11.0                   300.75   \n",
       "\n",
       "        ...  date_region_item_avg_item_cnt_lag_1  \\\n",
       "9940    ...                                  0.0   \n",
       "9941    ...                                  0.0   \n",
       "9942    ...                                  0.0   \n",
       "9943    ...                                230.0   \n",
       "9944    ...                                206.0   \n",
       "...     ...                                  ...   \n",
       "110315  ...                               7224.0   \n",
       "110316  ...                                488.0   \n",
       "110317  ...                                  0.0   \n",
       "110318  ...                               1124.0   \n",
       "110319  ...                                  0.0   \n",
       "\n",
       "        date_region_item_avg_item_cnt_lag_2  \\\n",
       "9940                                    0.0   \n",
       "9941                                    7.0   \n",
       "9942                                    0.0   \n",
       "9943                                   81.0   \n",
       "9944                                    9.0   \n",
       "...                                     ...   \n",
       "110315                              10536.0   \n",
       "110316                               1842.0   \n",
       "110317                                  0.0   \n",
       "110318                               1344.0   \n",
       "110319                                  0.0   \n",
       "\n",
       "        date_region_item_avg_item_cnt_lag_3  \\\n",
       "9940                                    0.0   \n",
       "9941                                    0.0   \n",
       "9942                                    0.0   \n",
       "9943                                  262.0   \n",
       "9944                                  423.0   \n",
       "...                                     ...   \n",
       "110315                               5556.0   \n",
       "110316                               1082.0   \n",
       "110317                                  0.0   \n",
       "110318                               7420.0   \n",
       "110319                                 11.0   \n",
       "\n",
       "        date_region_subtype_avg_item_cnt_lag_1  delta_price_lag  \\\n",
       "9940                                     0.000        -0.057953   \n",
       "9941                                  1211.000         0.032562   \n",
       "9942                                     0.000         0.070618   \n",
       "9943                                   718.500        -0.038300   \n",
       "9944                                  1211.000        -0.035309   \n",
       "...                                        ...              ...   \n",
       "110315                                 433.750         0.091797   \n",
       "110316                                 278.250        -0.006069   \n",
       "110317                                   0.000         0.000000   \n",
       "110318                                 217.375        -0.006493   \n",
       "110319                                 197.125        -0.010872   \n",
       "\n",
       "        delta_revenue_lag_1  month  days  item_subtype_first_sale  \\\n",
       "9940           3.895117e+05      1    28                        4   \n",
       "9941           2.354960e+07      1    28                        3   \n",
       "9942           6.287216e+05      1    28                        4   \n",
       "9943           5.590607e+06      1    28                        4   \n",
       "9944           2.354960e+07      1    28                        4   \n",
       "...                     ...    ...   ...                      ...   \n",
       "110315         3.829482e+06      1    28                       24   \n",
       "110316         2.064940e+06      1    28                        4   \n",
       "110317         0.000000e+00      1    28                        0   \n",
       "110318         3.506226e+06      1    28                        6   \n",
       "110319         6.008424e+06      1    28                       24   \n",
       "\n",
       "        item_first_sale  \n",
       "9940                  4  \n",
       "9941                  3  \n",
       "9942                  4  \n",
       "9943                  4  \n",
       "9944                  4  \n",
       "...                 ...  \n",
       "110315               24  \n",
       "110316                4  \n",
       "110317                0  \n",
       "110318                6  \n",
       "110319               24  \n",
       "\n",
       "[100380 rows x 26 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.fillna(0)  #需要吗"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3771a8b8",
   "metadata": {},
   "source": [
    "# 3.Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "dcf8e035",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "import pickle\n",
    "from xgboost import XGBRegressor\n",
    "# import lightgbm as lgb\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "439ece37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 补充一下编码\n",
    "# itemcode\n",
    "LE2 = LabelEncoder()\n",
    "matrix[\"item_code\"] = LE2.fit_transform(matrix.item_code )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "485758c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = matrix.copy()  # 使用copy，不要直接=\n",
    "del matrix\n",
    "gc.collect() # 释放内存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5e364c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9940       4\n",
       "9941       4\n",
       "9942       4\n",
       "9943       4\n",
       "9944       4\n",
       "          ..\n",
       "110315    40\n",
       "110316    40\n",
       "110317    40\n",
       "110318    40\n",
       "110319    40\n",
       "Name: date_block_num, Length: 100380, dtype: int8"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date_block_num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9f2d7d",
   "metadata": {},
   "source": [
    "Use month 38、39 as validation for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "59b2a958",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid = 38  # 最终这里是38，前期可以是37\n",
    "# 38 我们检查的就是验证集，37检查的就是验证集或者测试集\n",
    "\n",
    "X_train = data[data.date_block_num < valid].drop(['item_cnt_month'], axis=1)\n",
    "Y_train = data[data.date_block_num < valid]['item_cnt_month']\n",
    "X_valid = data[data.date_block_num.isin([valid,valid+1])].drop(['item_cnt_month'], axis=1)\n",
    "Y_valid = data[data.date_block_num.isin([valid,valid+1])]['item_cnt_month']\n",
    "X_test = data[data.date_block_num == valid+2].drop(['item_cnt_month'], axis=1)\n",
    "\n",
    "\n",
    "# 最后是要剔除的\n",
    "Y_test = data[data.date_block_num == valid+2]['item_cnt_month']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9562456a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0845bb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='item_cnt_month'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测一下数据分布，不知道这里是否需要归一化\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.xlim(-100, 3000)\n",
    "flierprops = dict(marker='o', markerfacecolor='purple', markersize=6,\n",
    "                  linestyle='none', markeredgecolor='black')\n",
    "sns.boxplot(x=Y_train, flierprops=flierprops)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "c742c107",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 划分完就删删删\n",
    "del data\n",
    "gc.collect();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "eb88c9ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 超参数调优\n",
    "# import numpy as np\n",
    "# from sklearn.model_selection import RandomizedSearchCV\n",
    "# from xgboost import XGBRegressor\n",
    "# import time\n",
    "\n",
    "# ts = time.time()\n",
    "\n",
    "# model = XGBRegressor(seed=42)\n",
    "\n",
    "# # 定义超参数空间\n",
    "# params = {\n",
    "#     'max_depth': range(3, 30),\n",
    "# #     'min_child_weight': np.arange(0.1, 1.1, 0.1),\n",
    "# #     'subsample': np.arange(0.5, 1.01, 0.1),\n",
    "# #     'colsample_bytree': np.arange(0.5, 1.01, 0.1),\n",
    "# #     'gamma': np.arange(0, 1.1, 0.1),\n",
    "# #     'reg_alpha': np.arange(0, 1.1, 0.1),\n",
    "# #     'reg_lambda': np.arange(0, 1.1, 0.1),\n",
    "#     'learning_rate': np.arange(0.01, 0.2, 0.01),\n",
    "#     'n_estimators': [100, 500, 1000]\n",
    "# }\n",
    "\n",
    "# # 使用RandomizedSearchCV搜索最佳超参数\n",
    "# search = RandomizedSearchCV(\n",
    "#     model, params, n_iter=10, scoring='neg_root_mean_squared_error', \n",
    "#     n_jobs=-1, cv=5, random_state=42, verbose=1)\n",
    "\n",
    "# search.fit(X_train, Y_train)\n",
    "\n",
    "# # 输出最佳超参数和最佳得分\n",
    "# print('Best score:', -search.best_score_)\n",
    "# print('Best params:', search.best_params_)\n",
    "# print('Time elapsed:', time.time()-ts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530ea20d",
   "metadata": {},
   "source": [
    "lightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "b180cfd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41051197052001953"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lightGBM\n",
    "\n",
    "from lightgbm import LGBMRegressor\n",
    "ts = time.time()\n",
    "learning_rate=0.034 \n",
    "max_depth=66 \n",
    "num_leaves=200 \n",
    "model = LGBMRegressor(learning_rate=learning_rate,\n",
    "                      objective = 'tweedie',\n",
    "                  max_depth=max_depth,\n",
    "                  num_leaves=num_leaves,\n",
    "                  n_jobs=-1,\n",
    "                  n_estimators=100)\n",
    " \n",
    "model.fit(X_train,Y_train)\n",
    "\n",
    "time.time() - ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e988cc",
   "metadata": {},
   "source": [
    "XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "09043138",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ts = time.time()\n",
    "\n",
    "# model = XGBRegressor(\n",
    "#     max_depth=10,\n",
    "#     n_estimators=1000,\n",
    "#     min_child_weight=0.5, \n",
    "#     colsample_bytree=0.8, \n",
    "#     subsample=0.8, \n",
    "#     eta=0.1,\n",
    "# #     tree_method='gpu_hist',\n",
    "#     seed=42)\n",
    "\n",
    "\n",
    "# model.fit(\n",
    "#     X_train, \n",
    "#     Y_train, \n",
    "#     eval_metric=\"rmse\", \n",
    "#     eval_set=[(X_train, Y_train), (X_valid, Y_valid)], \n",
    "#     verbose=True, \n",
    "#     early_stopping_rounds = 20)\n",
    "\n",
    "# time.time() - ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "19f91eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_valid_pred = model.predict(X_valid) # 验证集的预测y\n",
    "Y_test_pred = model.predict(X_test) # 预测集的预测y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "10d92fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里取决于我们要检查的是预测集还是验证集的效果\n",
    "\n",
    "# 如果是验证集\n",
    "Y_pred = Y_valid_pred\n",
    "Y_valid = Y_valid\n",
    "\n",
    "# 如果是测试集\n",
    "# X_valid = X_test\n",
    "# Y_pred = Y_test_pred\n",
    "# Y_valid = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4461e7d5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "#     \"ID\": Y_valid.index, \n",
    "    \"date_block_num\":X_valid.date_block_num,\n",
    "    \"sales_region_code\":X_valid.sales_region_code,\n",
    "    \"item_code\":X_valid.item_code,\n",
    "    \"first_cate_code\":X_valid.first_cate_code,\n",
    "    \"second_cate_code\":X_valid.second_cate_code,\n",
    "    \"item_cnt_month_valid\": Y_valid,\n",
    "    \"item_cnt_month_pred\": Y_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "e34250de",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 剔除所有item_cnt_month_valid为0的\n",
    "submission = submission[submission[\"item_cnt_month_valid\"] != 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "12e9fd30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 还原编码\n",
    "submission[\"sales_region_code\"] = LE1.inverse_transform(submission[\"sales_region_code\"])\n",
    "submission[\"first_cate_code\"] = LE3.inverse_transform(submission[\"first_cate_code\"])\n",
    "submission[\"second_cate_code\"] = LE4.inverse_transform(submission[\"second_cate_code\"])\n",
    "submission[\"item_code\"] = LE2.inverse_transform(submission[\"item_code\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "b71e818d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把对数转换回来\n",
    "if is_need_log:\n",
    "    submission[\"item_cnt_month_valid\"] = list(map(lambda x : 2**x-1,submission[\"item_cnt_month_valid\"] ))\n",
    "    submission[\"item_cnt_month_pred\"] = list(map(lambda x : 2**x-1,submission[\"item_cnt_month_pred\"] ))\n",
    "# 填补负值\n",
    "submission[\"item_cnt_month_pred\"] =list(map(lambda x : x if x>0 else 0.1,submission[\"item_cnt_month_pred\"] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "6821b3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('lgbm_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe2e87d1",
   "metadata": {},
   "source": [
    "用tweedie指标评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "be677969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweedie deviance: 4853380333951816.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_tweedie_deviance\n",
    "\n",
    "# 计算模型在两列数据上的Tweedie偏差\n",
    "tweedie_variance_power = 1.5\n",
    "tweedie_dev = mean_tweedie_deviance(submission[\"item_cnt_month_valid\"], submission[\"item_cnt_month_pred\"], power=tweedie_variance_power)\n",
    "    \n",
    "# 打印Tweedie偏差值\n",
    "print(\"Tweedie deviance:\", tweedie_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "a8b2d5e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(91237, 25)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d5336444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1008 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lightgbm import plot_importance\n",
    "\n",
    "def plot_features(booster, figsize):    \n",
    "    fig, ax = plt.subplots(1,1,figsize=figsize)\n",
    "    return plot_importance(booster=booster, ax=ax)\n",
    "\n",
    "plot_features(model, (10,14))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f06b78",
   "metadata": {},
   "source": [
    "最后其实是没有这步的，前期我们检验一下有没有过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4d3085c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_test_pred =  model.predict(X_test)\n",
    "\n",
    "# # 计算模型在两列数据上的Tweedie偏差\n",
    "# tweedie_variance_power = 1\n",
    "# tweedie_dev = mean_tweedie_deviance(Y_test_pred, Y_test, power=tweedie_variance_power)\n",
    "\n",
    "# # 打印Tweedie偏差值\n",
    "# print(\"Tweedie deviance:\", tweedie_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33551347",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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