learn_pytorch_25_pytorch实战项目之多元线性回归交通流量预测1调试版.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 数据集介绍 \n",
    "\n",
    "数据集地址：https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume#   \n",
    "\n",
    "数据集说明：  \n",
    "    MN DoT ATR站每小时94号州际公路交通流量，地址在明尼阿波利斯和明尼苏达州圣保罗之间。   \n",
    "    包括每小时天气特征和节假日和每小时天气特征和节假日对交通量的影响。  \n",
    "    样本数48204个，特征属性8个，目标值1个。\n",
    "    \n",
    "特征属性：  \n",
    "holiday：Categorical US National holidays plus regional holiday, Minnesota State Fair，所有样本值为None。  \n",
    "temp： Numeric Average temp in kelvin，开尔文数值平均温度。  \n",
    "rain_1h：Numeric Amount in mm of rain that occurred in the hour，每小时的降雨数量。  \n",
    "snow_1h：Numeric Amount in mm of snow that occurred in the hour，每小时的降雪数量。  \n",
    "clouds_all：Numeric Percentage of cloud cover，云量的百分比。  \n",
    "weather_main：Categorical Short textual description of the current weather，天气的简单描述。  \n",
    "weather_description：Categorical Longer textual description of the current weather，天气的详细描述。  \n",
    "date_time：DateTime Hour of the data collected in local CST time，时间。  \n",
    "\n",
    "目标值：  \n",
    "traffic_volume：Numeric Hourly I-94 ATR 301 reported westbound traffic volume，每小时的交通流量。  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 实战建模：使用多元线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 导入需要的模块、包等 \n",
    "import torch \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import torch.nn as nn \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  2 数据处理\n",
    "# 2.1 读取文件  \n",
    "data = pd.read_csv(\"./Metro_Interstate_Traffic_Volume.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "      <td>5026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  holiday    temp  rain_1h  snow_1h  clouds_all weather_main  \\\n",
       "0    None  288.28      0.0      0.0          40       Clouds   \n",
       "1    None  289.36      0.0      0.0          75       Clouds   \n",
       "2    None  289.58      0.0      0.0          90       Clouds   \n",
       "3    None  290.13      0.0      0.0          90       Clouds   \n",
       "4    None  291.14      0.0      0.0          75       Clouds   \n",
       "\n",
       "  weather_description            date_time  traffic_volume  \n",
       "0    scattered clouds  2012-10-02 09:00:00            5545  \n",
       "1       broken clouds  2012-10-02 10:00:00            4516  \n",
       "2     overcast clouds  2012-10-02 11:00:00            4767  \n",
       "3     overcast clouds  2012-10-02 12:00:00            5026  \n",
       "4       broken clouds  2012-10-02 13:00:00            4918  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.2 将特征属性和目标值分开 \n",
    "\n",
    "feature = data.iloc[:,0:-1]\n",
    "target = data.iloc[:,-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>291.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>293.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>293.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>294.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>2012-10-02 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>293.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>2012-10-02 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>None</td>\n",
       "      <td>290.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>2012-10-02 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>None</td>\n",
       "      <td>289.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>None</td>\n",
       "      <td>288.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>None</td>\n",
       "      <td>287.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>None</td>\n",
       "      <td>285.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-02 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>None</td>\n",
       "      <td>284.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>None</td>\n",
       "      <td>283.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>None</td>\n",
       "      <td>281.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>None</td>\n",
       "      <td>281.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>None</td>\n",
       "      <td>279.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>None</td>\n",
       "      <td>278.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>None</td>\n",
       "      <td>278.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>None</td>\n",
       "      <td>278.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>None</td>\n",
       "      <td>282.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>None</td>\n",
       "      <td>291.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>None</td>\n",
       "      <td>293.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>None</td>\n",
       "      <td>294.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>None</td>\n",
       "      <td>295.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>None</td>\n",
       "      <td>295.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Clear</td>\n",
       "      <td>sky is clear</td>\n",
       "      <td>2012-10-03 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>None</td>\n",
       "      <td>293.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>2012-10-03 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48174</th>\n",
       "      <td>None</td>\n",
       "      <td>280.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-29 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48175</th>\n",
       "      <td>None</td>\n",
       "      <td>280.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-29 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48176</th>\n",
       "      <td>None</td>\n",
       "      <td>280.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-29 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48177</th>\n",
       "      <td>None</td>\n",
       "      <td>280.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-29 23:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48178</th>\n",
       "      <td>None</td>\n",
       "      <td>280.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48179</th>\n",
       "      <td>None</td>\n",
       "      <td>280.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48180</th>\n",
       "      <td>None</td>\n",
       "      <td>280.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48181</th>\n",
       "      <td>None</td>\n",
       "      <td>280.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48182</th>\n",
       "      <td>None</td>\n",
       "      <td>279.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 04:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48183</th>\n",
       "      <td>None</td>\n",
       "      <td>279.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48184</th>\n",
       "      <td>None</td>\n",
       "      <td>280.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48185</th>\n",
       "      <td>None</td>\n",
       "      <td>280.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48186</th>\n",
       "      <td>None</td>\n",
       "      <td>280.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48187</th>\n",
       "      <td>None</td>\n",
       "      <td>280.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48188</th>\n",
       "      <td>None</td>\n",
       "      <td>281.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48189</th>\n",
       "      <td>None</td>\n",
       "      <td>282.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48190</th>\n",
       "      <td>None</td>\n",
       "      <td>282.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48191</th>\n",
       "      <td>None</td>\n",
       "      <td>283.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Rain</td>\n",
       "      <td>light rain</td>\n",
       "      <td>2018-09-30 13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48192</th>\n",
       "      <td>None</td>\n",
       "      <td>283.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Rain</td>\n",
       "      <td>light rain</td>\n",
       "      <td>2018-09-30 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48193</th>\n",
       "      <td>None</td>\n",
       "      <td>283.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Drizzle</td>\n",
       "      <td>light intensity drizzle</td>\n",
       "      <td>2018-09-30 14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48194</th>\n",
       "      <td>None</td>\n",
       "      <td>283.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Rain</td>\n",
       "      <td>proximity shower rain</td>\n",
       "      <td>2018-09-30 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48195</th>\n",
       "      <td>None</td>\n",
       "      <td>283.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Drizzle</td>\n",
       "      <td>light intensity drizzle</td>\n",
       "      <td>2018-09-30 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48196</th>\n",
       "      <td>None</td>\n",
       "      <td>284.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Rain</td>\n",
       "      <td>light rain</td>\n",
       "      <td>2018-09-30 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48197</th>\n",
       "      <td>None</td>\n",
       "      <td>284.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48198</th>\n",
       "      <td>None</td>\n",
       "      <td>284.20</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Rain</td>\n",
       "      <td>light rain</td>\n",
       "      <td>2018-09-30 18:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48199</th>\n",
       "      <td>None</td>\n",
       "      <td>283.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 19:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>None</td>\n",
       "      <td>282.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 20:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>None</td>\n",
       "      <td>282.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>proximity thunderstorm</td>\n",
       "      <td>2018-09-30 21:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48202</th>\n",
       "      <td>None</td>\n",
       "      <td>282.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 22:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>None</td>\n",
       "      <td>282.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 23:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48204 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      holiday    temp  rain_1h  snow_1h  clouds_all  weather_main  \\\n",
       "0        None  288.28     0.00      0.0          40        Clouds   \n",
       "1        None  289.36     0.00      0.0          75        Clouds   \n",
       "2        None  289.58     0.00      0.0          90        Clouds   \n",
       "3        None  290.13     0.00      0.0          90        Clouds   \n",
       "4        None  291.14     0.00      0.0          75        Clouds   \n",
       "5        None  291.72     0.00      0.0           1         Clear   \n",
       "6        None  293.17     0.00      0.0           1         Clear   \n",
       "7        None  293.86     0.00      0.0           1         Clear   \n",
       "8        None  294.14     0.00      0.0          20        Clouds   \n",
       "9        None  293.10     0.00      0.0          20        Clouds   \n",
       "10       None  290.97     0.00      0.0          20        Clouds   \n",
       "11       None  289.38     0.00      0.0           1         Clear   \n",
       "12       None  288.61     0.00      0.0           1         Clear   \n",
       "13       None  287.16     0.00      0.0           1         Clear   \n",
       "14       None  285.45     0.00      0.0           1         Clear   \n",
       "15       None  284.63     0.00      0.0           1         Clear   \n",
       "16       None  283.47     0.00      0.0           1         Clear   \n",
       "17       None  281.18     0.00      0.0           1         Clear   \n",
       "18       None  281.09     0.00      0.0           1         Clear   \n",
       "19       None  279.53     0.00      0.0           1         Clear   \n",
       "20       None  278.62     0.00      0.0           1         Clear   \n",
       "21       None  278.23     0.00      0.0           1         Clear   \n",
       "22       None  278.12     0.00      0.0           1         Clear   \n",
       "23       None  282.48     0.00      0.0           1         Clear   \n",
       "24       None  291.97     0.00      0.0           1         Clear   \n",
       "25       None  293.23     0.00      0.0           1         Clear   \n",
       "26       None  294.31     0.00      0.0           1         Clear   \n",
       "27       None  295.17     0.00      0.0           1         Clear   \n",
       "28       None  295.13     0.00      0.0           1         Clear   \n",
       "29       None  293.66     0.00      0.0          20        Clouds   \n",
       "...       ...     ...      ...      ...         ...           ...   \n",
       "48174    None  280.55     0.00      0.0          90        Clouds   \n",
       "48175    None  280.40     0.00      0.0          90        Clouds   \n",
       "48176    None  280.54     0.00      0.0          90        Clouds   \n",
       "48177    None  280.32     0.00      0.0          90        Clouds   \n",
       "48178    None  280.30     0.00      0.0          90        Clouds   \n",
       "48179    None  280.19     0.00      0.0          90        Clouds   \n",
       "48180    None  280.07     0.00      0.0          90        Clouds   \n",
       "48181    None  280.08     0.00      0.0          90        Clouds   \n",
       "48182    None  279.88     0.00      0.0          90        Clouds   \n",
       "48183    None  279.96     0.00      0.0          90        Clouds   \n",
       "48184    None  280.17     0.00      0.0          90        Clouds   \n",
       "48185    None  280.16     0.00      0.0          90        Clouds   \n",
       "48186    None  280.28     0.00      0.0          90        Clouds   \n",
       "48187    None  280.62     0.00      0.0          90        Clouds   \n",
       "48188    None  281.38     0.00      0.0          75        Clouds   \n",
       "48189    None  282.18     0.00      0.0          90        Clouds   \n",
       "48190    None  282.69     0.00      0.0          75        Clouds   \n",
       "48191    None  283.03     0.00      0.0          90          Rain   \n",
       "48192    None  283.48     0.00      0.0          90          Rain   \n",
       "48193    None  283.48     0.00      0.0          90       Drizzle   \n",
       "48194    None  283.84     0.00      0.0          75          Rain   \n",
       "48195    None  283.84     0.00      0.0          75       Drizzle   \n",
       "48196    None  284.38     0.00      0.0          75          Rain   \n",
       "48197    None  284.79     0.00      0.0          75        Clouds   \n",
       "48198    None  284.20     0.25      0.0          75          Rain   \n",
       "48199    None  283.45     0.00      0.0          75        Clouds   \n",
       "48200    None  282.76     0.00      0.0          90        Clouds   \n",
       "48201    None  282.73     0.00      0.0          90  Thunderstorm   \n",
       "48202    None  282.09     0.00      0.0          90        Clouds   \n",
       "48203    None  282.12     0.00      0.0          90        Clouds   \n",
       "\n",
       "           weather_description            date_time  \n",
       "0             scattered clouds  2012-10-02 09:00:00  \n",
       "1                broken clouds  2012-10-02 10:00:00  \n",
       "2              overcast clouds  2012-10-02 11:00:00  \n",
       "3              overcast clouds  2012-10-02 12:00:00  \n",
       "4                broken clouds  2012-10-02 13:00:00  \n",
       "5                 sky is clear  2012-10-02 14:00:00  \n",
       "6                 sky is clear  2012-10-02 15:00:00  \n",
       "7                 sky is clear  2012-10-02 16:00:00  \n",
       "8                   few clouds  2012-10-02 17:00:00  \n",
       "9                   few clouds  2012-10-02 18:00:00  \n",
       "10                  few clouds  2012-10-02 19:00:00  \n",
       "11                sky is clear  2012-10-02 20:00:00  \n",
       "12                sky is clear  2012-10-02 21:00:00  \n",
       "13                sky is clear  2012-10-02 22:00:00  \n",
       "14                sky is clear  2012-10-02 23:00:00  \n",
       "15                sky is clear  2012-10-03 00:00:00  \n",
       "16                sky is clear  2012-10-03 01:00:00  \n",
       "17                sky is clear  2012-10-03 02:00:00  \n",
       "18                sky is clear  2012-10-03 03:00:00  \n",
       "19                sky is clear  2012-10-03 04:00:00  \n",
       "20                sky is clear  2012-10-03 05:00:00  \n",
       "21                sky is clear  2012-10-03 06:00:00  \n",
       "22                sky is clear  2012-10-03 08:00:00  \n",
       "23                sky is clear  2012-10-03 09:00:00  \n",
       "24                sky is clear  2012-10-03 12:00:00  \n",
       "25                sky is clear  2012-10-03 13:00:00  \n",
       "26                sky is clear  2012-10-03 14:00:00  \n",
       "27                sky is clear  2012-10-03 15:00:00  \n",
       "28                sky is clear  2012-10-03 16:00:00  \n",
       "29                  few clouds  2012-10-03 18:00:00  \n",
       "...                        ...                  ...  \n",
       "48174          overcast clouds  2018-09-29 20:00:00  \n",
       "48175          overcast clouds  2018-09-29 21:00:00  \n",
       "48176          overcast clouds  2018-09-29 22:00:00  \n",
       "48177          overcast clouds  2018-09-29 23:00:00  \n",
       "48178          overcast clouds  2018-09-30 00:00:00  \n",
       "48179          overcast clouds  2018-09-30 01:00:00  \n",
       "48180          overcast clouds  2018-09-30 02:00:00  \n",
       "48181          overcast clouds  2018-09-30 03:00:00  \n",
       "48182          overcast clouds  2018-09-30 04:00:00  \n",
       "48183          overcast clouds  2018-09-30 05:00:00  \n",
       "48184          overcast clouds  2018-09-30 06:00:00  \n",
       "48185          overcast clouds  2018-09-30 07:00:00  \n",
       "48186          overcast clouds  2018-09-30 08:00:00  \n",
       "48187          overcast clouds  2018-09-30 09:00:00  \n",
       "48188            broken clouds  2018-09-30 10:00:00  \n",
       "48189          overcast clouds  2018-09-30 11:00:00  \n",
       "48190            broken clouds  2018-09-30 12:00:00  \n",
       "48191               light rain  2018-09-30 13:00:00  \n",
       "48192               light rain  2018-09-30 14:00:00  \n",
       "48193  light intensity drizzle  2018-09-30 14:00:00  \n",
       "48194    proximity shower rain  2018-09-30 15:00:00  \n",
       "48195  light intensity drizzle  2018-09-30 15:00:00  \n",
       "48196               light rain  2018-09-30 16:00:00  \n",
       "48197            broken clouds  2018-09-30 17:00:00  \n",
       "48198               light rain  2018-09-30 18:00:00  \n",
       "48199            broken clouds  2018-09-30 19:00:00  \n",
       "48200          overcast clouds  2018-09-30 20:00:00  \n",
       "48201   proximity thunderstorm  2018-09-30 21:00:00  \n",
       "48202          overcast clouds  2018-09-30 22:00:00  \n",
       "48203          overcast clouds  2018-09-30 23:00:00  \n",
       "\n",
       "[48204 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5545\n",
       "1        4516\n",
       "2        4767\n",
       "3        5026\n",
       "4        4918\n",
       "5        5181\n",
       "6        5584\n",
       "7        6015\n",
       "8        5791\n",
       "9        4770\n",
       "10       3539\n",
       "11       2784\n",
       "12       2361\n",
       "13       1529\n",
       "14        963\n",
       "15        506\n",
       "16        321\n",
       "17        273\n",
       "18        367\n",
       "19        814\n",
       "20       2718\n",
       "21       5673\n",
       "22       6511\n",
       "23       5471\n",
       "24       5097\n",
       "25       4887\n",
       "26       5337\n",
       "27       5692\n",
       "28       6137\n",
       "29       4623\n",
       "         ... \n",
       "48174    3338\n",
       "48175    2950\n",
       "48176    2607\n",
       "48177    3856\n",
       "48178    1826\n",
       "48179     920\n",
       "48180     696\n",
       "48181     364\n",
       "48182     335\n",
       "48183     444\n",
       "48184     802\n",
       "48185    1175\n",
       "48186    2002\n",
       "48187    2882\n",
       "48188    3701\n",
       "48189    4049\n",
       "48190    4429\n",
       "48191    4547\n",
       "48192    4380\n",
       "48193    4380\n",
       "48194    4302\n",
       "48195    4302\n",
       "48196    4283\n",
       "48197    4132\n",
       "48198    3947\n",
       "48199    3543\n",
       "48200    2781\n",
       "48201    2159\n",
       "48202    1450\n",
       "48203     954\n",
       "Name: traffic_volume, Length: 48204, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.3 删除无用属性holiday、weather_main、weather_main、weather_description和date_time\n",
    "feature = feature.iloc[:,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "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>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>288.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>289.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>289.58</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>290.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>291.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>291.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>293.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>293.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>294.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>293.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>290.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>289.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>288.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>287.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>285.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>284.63</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>283.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>281.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>281.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>279.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>278.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>278.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>278.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>282.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>291.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>293.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>294.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>295.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>295.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>293.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48174</th>\n",
       "      <td>280.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48175</th>\n",
       "      <td>280.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48176</th>\n",
       "      <td>280.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48177</th>\n",
       "      <td>280.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48178</th>\n",
       "      <td>280.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48179</th>\n",
       "      <td>280.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48180</th>\n",
       "      <td>280.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48181</th>\n",
       "      <td>280.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48182</th>\n",
       "      <td>279.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48183</th>\n",
       "      <td>279.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48184</th>\n",
       "      <td>280.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48185</th>\n",
       "      <td>280.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48186</th>\n",
       "      <td>280.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48187</th>\n",
       "      <td>280.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48188</th>\n",
       "      <td>281.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48189</th>\n",
       "      <td>282.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48190</th>\n",
       "      <td>282.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48191</th>\n",
       "      <td>283.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48192</th>\n",
       "      <td>283.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48193</th>\n",
       "      <td>283.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48194</th>\n",
       "      <td>283.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48195</th>\n",
       "      <td>283.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48196</th>\n",
       "      <td>284.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48197</th>\n",
       "      <td>284.79</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48198</th>\n",
       "      <td>284.20</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48199</th>\n",
       "      <td>283.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>282.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>282.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48202</th>\n",
       "      <td>282.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>282.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48204 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         temp  rain_1h  snow_1h  clouds_all\n",
       "0      288.28     0.00      0.0          40\n",
       "1      289.36     0.00      0.0          75\n",
       "2      289.58     0.00      0.0          90\n",
       "3      290.13     0.00      0.0          90\n",
       "4      291.14     0.00      0.0          75\n",
       "5      291.72     0.00      0.0           1\n",
       "6      293.17     0.00      0.0           1\n",
       "7      293.86     0.00      0.0           1\n",
       "8      294.14     0.00      0.0          20\n",
       "9      293.10     0.00      0.0          20\n",
       "10     290.97     0.00      0.0          20\n",
       "11     289.38     0.00      0.0           1\n",
       "12     288.61     0.00      0.0           1\n",
       "13     287.16     0.00      0.0           1\n",
       "14     285.45     0.00      0.0           1\n",
       "15     284.63     0.00      0.0           1\n",
       "16     283.47     0.00      0.0           1\n",
       "17     281.18     0.00      0.0           1\n",
       "18     281.09     0.00      0.0           1\n",
       "19     279.53     0.00      0.0           1\n",
       "20     278.62     0.00      0.0           1\n",
       "21     278.23     0.00      0.0           1\n",
       "22     278.12     0.00      0.0           1\n",
       "23     282.48     0.00      0.0           1\n",
       "24     291.97     0.00      0.0           1\n",
       "25     293.23     0.00      0.0           1\n",
       "26     294.31     0.00      0.0           1\n",
       "27     295.17     0.00      0.0           1\n",
       "28     295.13     0.00      0.0           1\n",
       "29     293.66     0.00      0.0          20\n",
       "...       ...      ...      ...         ...\n",
       "48174  280.55     0.00      0.0          90\n",
       "48175  280.40     0.00      0.0          90\n",
       "48176  280.54     0.00      0.0          90\n",
       "48177  280.32     0.00      0.0          90\n",
       "48178  280.30     0.00      0.0          90\n",
       "48179  280.19     0.00      0.0          90\n",
       "48180  280.07     0.00      0.0          90\n",
       "48181  280.08     0.00      0.0          90\n",
       "48182  279.88     0.00      0.0          90\n",
       "48183  279.96     0.00      0.0          90\n",
       "48184  280.17     0.00      0.0          90\n",
       "48185  280.16     0.00      0.0          90\n",
       "48186  280.28     0.00      0.0          90\n",
       "48187  280.62     0.00      0.0          90\n",
       "48188  281.38     0.00      0.0          75\n",
       "48189  282.18     0.00      0.0          90\n",
       "48190  282.69     0.00      0.0          75\n",
       "48191  283.03     0.00      0.0          90\n",
       "48192  283.48     0.00      0.0          90\n",
       "48193  283.48     0.00      0.0          90\n",
       "48194  283.84     0.00      0.0          75\n",
       "48195  283.84     0.00      0.0          75\n",
       "48196  284.38     0.00      0.0          75\n",
       "48197  284.79     0.00      0.0          75\n",
       "48198  284.20     0.25      0.0          75\n",
       "48199  283.45     0.00      0.0          75\n",
       "48200  282.76     0.00      0.0          90\n",
       "48201  282.73     0.00      0.0          90\n",
       "48202  282.09     0.00      0.0          90\n",
       "48203  282.12     0.00      0.0          90\n",
       "\n",
       "[48204 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.4 特征数据归一化处理 \n",
    "\n",
    "for i in range(feature.shape[1]):\n",
    "    if i == 0 or i == 3:\n",
    "        min_f, max_f = min(feature.iloc[:, i]), max(feature.iloc[:, i])\n",
    "        diff = max_f - min_f\n",
    "        for j in range(feature.shape[0]):\n",
    "            feature.loc[j, i] = feature.iloc[j, i] / diff\n",
    "            \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = feature.iloc[:,[4,5,1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>3</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.929726</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.933209</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.933918</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.935692</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.938949</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.940820</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.945496</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.947721</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.948625</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.945270</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.938401</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.933273</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.930790</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.926113</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.920599</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.917954</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.914213</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.906827</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.906537</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.901506</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.898571</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.897314</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.896959</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.911020</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.941626</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.945690</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.949173</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.951946</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.951817</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.947076</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48174</th>\n",
       "      <td>0.904796</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48175</th>\n",
       "      <td>0.904312</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48176</th>\n",
       "      <td>0.904763</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48177</th>\n",
       "      <td>0.904054</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48178</th>\n",
       "      <td>0.903989</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48179</th>\n",
       "      <td>0.903635</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48180</th>\n",
       "      <td>0.903248</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48181</th>\n",
       "      <td>0.903280</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48182</th>\n",
       "      <td>0.902635</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48183</th>\n",
       "      <td>0.902893</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48184</th>\n",
       "      <td>0.903570</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48185</th>\n",
       "      <td>0.903538</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48186</th>\n",
       "      <td>0.903925</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48187</th>\n",
       "      <td>0.905021</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48188</th>\n",
       "      <td>0.907473</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48189</th>\n",
       "      <td>0.910053</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48190</th>\n",
       "      <td>0.911697</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48191</th>\n",
       "      <td>0.912794</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48192</th>\n",
       "      <td>0.914245</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48193</th>\n",
       "      <td>0.914245</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48194</th>\n",
       "      <td>0.915406</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48195</th>\n",
       "      <td>0.915406</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48196</th>\n",
       "      <td>0.917148</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48197</th>\n",
       "      <td>0.918470</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48198</th>\n",
       "      <td>0.916567</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48199</th>\n",
       "      <td>0.914148</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>0.911923</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>0.911826</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48202</th>\n",
       "      <td>0.909762</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>0.909859</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48204 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0     3  rain_1h  snow_1h\n",
       "0      0.929726  0.40     0.00      0.0\n",
       "1      0.933209  0.75     0.00      0.0\n",
       "2      0.933918  0.90     0.00      0.0\n",
       "3      0.935692  0.90     0.00      0.0\n",
       "4      0.938949  0.75     0.00      0.0\n",
       "5      0.940820  0.01     0.00      0.0\n",
       "6      0.945496  0.01     0.00      0.0\n",
       "7      0.947721  0.01     0.00      0.0\n",
       "8      0.948625  0.20     0.00      0.0\n",
       "9      0.945270  0.20     0.00      0.0\n",
       "10     0.938401  0.20     0.00      0.0\n",
       "11     0.933273  0.01     0.00      0.0\n",
       "12     0.930790  0.01     0.00      0.0\n",
       "13     0.926113  0.01     0.00      0.0\n",
       "14     0.920599  0.01     0.00      0.0\n",
       "15     0.917954  0.01     0.00      0.0\n",
       "16     0.914213  0.01     0.00      0.0\n",
       "17     0.906827  0.01     0.00      0.0\n",
       "18     0.906537  0.01     0.00      0.0\n",
       "19     0.901506  0.01     0.00      0.0\n",
       "20     0.898571  0.01     0.00      0.0\n",
       "21     0.897314  0.01     0.00      0.0\n",
       "22     0.896959  0.01     0.00      0.0\n",
       "23     0.911020  0.01     0.00      0.0\n",
       "24     0.941626  0.01     0.00      0.0\n",
       "25     0.945690  0.01     0.00      0.0\n",
       "26     0.949173  0.01     0.00      0.0\n",
       "27     0.951946  0.01     0.00      0.0\n",
       "28     0.951817  0.01     0.00      0.0\n",
       "29     0.947076  0.20     0.00      0.0\n",
       "...         ...   ...      ...      ...\n",
       "48174  0.904796  0.90     0.00      0.0\n",
       "48175  0.904312  0.90     0.00      0.0\n",
       "48176  0.904763  0.90     0.00      0.0\n",
       "48177  0.904054  0.90     0.00      0.0\n",
       "48178  0.903989  0.90     0.00      0.0\n",
       "48179  0.903635  0.90     0.00      0.0\n",
       "48180  0.903248  0.90     0.00      0.0\n",
       "48181  0.903280  0.90     0.00      0.0\n",
       "48182  0.902635  0.90     0.00      0.0\n",
       "48183  0.902893  0.90     0.00      0.0\n",
       "48184  0.903570  0.90     0.00      0.0\n",
       "48185  0.903538  0.90     0.00      0.0\n",
       "48186  0.903925  0.90     0.00      0.0\n",
       "48187  0.905021  0.90     0.00      0.0\n",
       "48188  0.907473  0.75     0.00      0.0\n",
       "48189  0.910053  0.90     0.00      0.0\n",
       "48190  0.911697  0.75     0.00      0.0\n",
       "48191  0.912794  0.90     0.00      0.0\n",
       "48192  0.914245  0.90     0.00      0.0\n",
       "48193  0.914245  0.90     0.00      0.0\n",
       "48194  0.915406  0.75     0.00      0.0\n",
       "48195  0.915406  0.75     0.00      0.0\n",
       "48196  0.917148  0.75     0.00      0.0\n",
       "48197  0.918470  0.75     0.00      0.0\n",
       "48198  0.916567  0.75     0.25      0.0\n",
       "48199  0.914148  0.75     0.00      0.0\n",
       "48200  0.911923  0.90     0.00      0.0\n",
       "48201  0.911826  0.90     0.00      0.0\n",
       "48202  0.909762  0.90     0.00      0.0\n",
       "48203  0.909859  0.90     0.00      0.0\n",
       "\n",
       "[48204 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.5 目标数据归一化\n",
    "target_ = (target-min(target))/(max(target)-min(target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.761676\n",
       "1        0.620330\n",
       "2        0.654808\n",
       "3        0.690385\n",
       "4        0.675549\n",
       "5        0.711676\n",
       "6        0.767033\n",
       "7        0.826236\n",
       "8        0.795467\n",
       "9        0.655220\n",
       "10       0.486126\n",
       "11       0.382418\n",
       "12       0.324313\n",
       "13       0.210027\n",
       "14       0.132280\n",
       "15       0.069505\n",
       "16       0.044093\n",
       "17       0.037500\n",
       "18       0.050412\n",
       "19       0.111813\n",
       "20       0.373352\n",
       "21       0.779258\n",
       "22       0.894368\n",
       "23       0.751511\n",
       "24       0.700137\n",
       "25       0.671291\n",
       "26       0.733104\n",
       "27       0.781868\n",
       "28       0.842995\n",
       "29       0.635027\n",
       "           ...   \n",
       "48174    0.458516\n",
       "48175    0.405220\n",
       "48176    0.358104\n",
       "48177    0.529670\n",
       "48178    0.250824\n",
       "48179    0.126374\n",
       "48180    0.095604\n",
       "48181    0.050000\n",
       "48182    0.046016\n",
       "48183    0.060989\n",
       "48184    0.110165\n",
       "48185    0.161401\n",
       "48186    0.275000\n",
       "48187    0.395879\n",
       "48188    0.508379\n",
       "48189    0.556181\n",
       "48190    0.608379\n",
       "48191    0.624588\n",
       "48192    0.601648\n",
       "48193    0.601648\n",
       "48194    0.590934\n",
       "48195    0.590934\n",
       "48196    0.588324\n",
       "48197    0.567582\n",
       "48198    0.542170\n",
       "48199    0.486676\n",
       "48200    0.382005\n",
       "48201    0.296566\n",
       "48202    0.199176\n",
       "48203    0.131044\n",
       "Name: traffic_volume, Length: 48204, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# 2.6 数据类型转换\n",
    "print(type(feature))\n",
    "print(type(target_))\n",
    "\n",
    "print(type(feature.values))\n",
    "print(type(target_.values))\n",
    "\n",
    "x_feature = torch.from_numpy(feature.values).float()\n",
    "y_target = torch.from_numpy(target_.values).float().unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9297, 0.4000, 0.0000, 0.0000],\n",
       "        [0.9332, 0.7500, 0.0000, 0.0000],\n",
       "        [0.9339, 0.9000, 0.0000, 0.0000],\n",
       "        ...,\n",
       "        [0.9118, 0.9000, 0.0000, 0.0000],\n",
       "        [0.9098, 0.9000, 0.0000, 0.0000],\n",
       "        [0.9099, 0.9000, 0.0000, 0.0000]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.7617],\n",
       "        [0.6203],\n",
       "        [0.6548],\n",
       "        ...,\n",
       "        [0.2966],\n",
       "        [0.1992],\n",
       "        [0.1310]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 创建模型 \n",
    "class PolyLinearRegression(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(PolyLinearRegression, self).__init__()\n",
    "        self.ploy = nn.Linear(4, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.ploy(x)\n",
    "        return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 训练测试\n",
    "def train_and_test():\n",
    "    if torch.cuda.is_available():\n",
    "        device = torch.device(\"cuda:0\")\n",
    "        model = PolyLinearRegression()\n",
    "        model.to(device)\n",
    "    else:\n",
    "        device = torch.device(\"cpu\")\n",
    "        model = PolyLinearRegression()\n",
    "        model.to(device)\n",
    "\n",
    "    criterion = nn.MSELoss()\n",
    "    optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)\n",
    "\n",
    "    loss_list = []\n",
    "    for i in range(10000):\n",
    "        i += 1\n",
    "\n",
    "        inputs = x_feature.to(device)\n",
    "        labels = y_target.to(device)\n",
    "\n",
    "        # 1 前向传播\n",
    "        output = model(inputs)\n",
    "        # 计算损失\n",
    "        loss = criterion(output, labels)\n",
    "\n",
    "        # 2 反向传播\n",
    "        # 梯度要清0\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        # 更新权重参数\n",
    "        optimizer.step()\n",
    "\n",
    "        # 打印\n",
    "        if i % 1 == 0:\n",
    "            print(\"迭代次数：%d,  loss值：%.8f\" % (i, loss.item()))\n",
    "            loss_list.append(loss.item())\n",
    "\n",
    "    # 测试模式\n",
    "    model.eval()\n",
    "    \n",
    "    predict = model(x_feature).data.numpy()\n",
    "    print(min(target),max(target))\n",
    "    \n",
    "    plt.plot(y_target[0:1000].numpy()*(max(target)-min(target))+min(target),\"r\")\n",
    "    plt.plot(predict[0:1000]*(max(target)-min(target))+min(target),\"b\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(loss_list)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "迭代次数：1,  loss值：91.71772766\n",
      "迭代次数：2,  loss值：33.04233170\n",
      "迭代次数：3,  loss值：12.00544167\n",
      "迭代次数：4,  loss值：4.46305132\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型和使用模型\n",
    "# torch.save(model.state_dict(),\"ploy_model.pt\")\n",
    "# model.load_state_dict(torch.load(\"model.pkl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}