w1-黄希彤-16750199.ipynb · emu/w1-黄希彤-16750199

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w1-黄希彤-16750199.ipynb 248.40 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capital Bikeshare每天单车共享数量预测\n",
    "要求以2011年的数据训练模型，预测2012年每天的单车共享数量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## step 0 数据探索\n",
    "mnth:月份( 1 to 12)\n",
    "hr:小时 (0 to 23) (只在 hour.csv 有，作业忽略此字段) holiday:是否是节假日\n",
    "weekday:星期中的哪天，取值为 0~6 workingday:是否工作日\n",
    "1=工作日 (非周末和节假日)\n",
    "0=周末 \n",
    "weathersit:天气  1:晴天，多云 2:雾天，阴天 3:小雪，小雨 4:大雨，大雪，大雾\n",
    "temp:气温摄氏度\n",
    "atemp:体感温度\n",
    "hum:湿度\n",
    "windspeed:风速"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.226642</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>247</td>\n",
       "      <td>1867</td>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>644</td>\n",
       "      <td>2451</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>159</td>\n",
       "      <td>1182</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727  2012-12-27       1   1    12        0        4           1   \n",
       "727      728  2012-12-28       1   1    12        0        5           1   \n",
       "728      729  2012-12-29       1   1    12        0        6           0   \n",
       "729      730  2012-12-30       1   1    12        0        0           0   \n",
       "730      731  2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  "
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv(\"/Volumes/TFDisk/download/Bike-Sharing-Dataset/day.csv\")\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把2011和2012年的数据分割\n",
    "data2011 = data[data.yr == 0]\n",
    "data2012 = data[data.yr == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r+RSUVDNmYIrfJUU93ZndCznneHlDEfGxMdwxb7zf5Yh02pUzhhEXY7rMuIco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\nKAWFiIgE9f8Btet+vT+kWX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1148c1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2011年cnt的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data2011.cnt.values, bins=70, kde=True)\n",
    "plt.xlabel('cnt/day 2011', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "曲线看到有两个峰，分别对应1800和4500左右（2012年只有一个峰在7000左右）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lvLKKNTsKWZiTz7b8Yrp0iOXWMwbTMT5GS39LuxAfE80T08fw3Uc+4bq/LeHl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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd01ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data2012.cnt.values, bins=70, kde=True)\n",
    "plt.xlabel('cnt/day 2012', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qtRmA0RAa7eHRLBodsybtebet2saRm484fdRdx1Wmgyz1lepa6OTCNXJee//a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\nGEaH8v8B5zEnFgJmoNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cf65320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data2011.shape[0]), data2011[\"cnt\"].values,color='green')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "个别日子租出去很多，个别日子租出去很少，在1000-2000和4000-5000这两段比较聚集，跟直方图的两个峰的提示一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cf65860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 天气（weathersit）的直方图／分布\n",
    "sns.countplot(data2011.weathersit);\n",
    "plt.xlabel('1:sunny 2:cloudy 3:rain');\n",
    "plt.ylabel('count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b2vPtSHPDQtdGdD3N6Q0O2E5zbpEPAW+kOd3sLppvC0pzwy8WacOJiKQ5j/m9EXEx8NfA\nz2TmUwNHkzrpNEm0NI8yc/XZGi8G7rPMVYFDLtro3kRz3h9p7lno2uh+SDNLjjT3LHRtdH8DnBsR\nW4YOInXlm6La8CLiNpqj9N+kOR3u0cCVwAuZedOQ2aRJeIQuNRMY3AF8hWYuyd3AMs3RuzQ3PEKX\npCI8QpekIix0SSrCQpekIix0SSrCQpekIix0SSrCQpekIix0SSrCQpekIv4PjDQMVstSG8gAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d600b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 温度（temp)直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data2011.temp.values*100, bins=80, kde=False)\n",
    "plt.xlabel('‘C', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=data2011.columns \n",
    "data_corr = data2011.corr().abs()\n",
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m0/tMtFguAPt6dSgy+CPQ6bi7fA235v9ptj6/nw+FP30fQ6Qxz+2lK7i7Yg15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szMihAdHauzt2Lt2kX07z88d77w8yERYZFY2SbfIFlalyQy/P4rcuQeoaHh2Nomt5+NrVWaR92h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\nNiJ+a8aFk7rb9zhWuS8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5bXaX9B8+Jj14nXDiWcRVqwNA1qqF7Hq/r9tRsgylb3yKzJWF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1147b84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "温度和体感温度的相关度强相关，月份和季节强相关，湿度和天气相关，温度、体感温度都和cnt有较强相关。workingday居然和cnt不怎么相关\n",
    "yr、casual、registered需要降维"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# step 1 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/emu/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "# from sklearn.preprocessing import OneHotEncoder \n",
    "#one hot 用不来，自己瞎搞一下\n",
    "\n",
    "#增加新特征\n",
    "\n",
    "\n",
    "#丢弃不需要的特征,增加新特征\n",
    "X = data.drop('dteday', axis = 1)\\\n",
    "    .drop('instant', axis = 1)\\\n",
    "    .drop('atemp', axis = 1)\\\n",
    "    .drop('season', axis = 1)\\\n",
    "    .drop('mnth', axis = 1)\\\n",
    "    .drop('holiday', axis = 1)\\\n",
    "    .drop('hum', axis = 1)\\\n",
    "    .drop('casual', axis = 1)\\\n",
    "    .drop('temp', axis = 1)\\\n",
    "    .drop('weekday', axis = 1)\\\n",
    "    .drop('windspeed',axis = 1)\\\n",
    "    .drop('weathersit',axis = 1)\\\n",
    "    .drop('registered', axis = 1)\n",
    "\n",
    "\n",
    "instant_sqr = np.zeros(len(data))\n",
    "atemp_abs = np.zeros(len(data))\n",
    "\n",
    "hum_abs = np.zeros(len(data))\n",
    "\n",
    "windspeed_sqr = np.zeros(len(data))\n",
    "weathersit_e =np.zeros(len(data))\n",
    "#不会用one_hot，自己实现一下试试\n",
    "season_1 = np.zeros(len(data))\n",
    "season_2 = np.zeros(len(data))\n",
    "season_3 = np.zeros(len(data))\n",
    "season_4 = np.zeros(len(data))\n",
    "weathersit_1 = np.zeros(len(data))\n",
    "weathersit_2 = np.zeros(len(data))\n",
    "weathersit_3 = np.zeros(len(data))\n",
    "weathersit_4 = np.zeros(len(data))\n",
    "\n",
    "for i in range(len(data)):\n",
    "    instant_sqr[i]=data[\"instant\"][i]**1.9\n",
    "    # 市场初期用户数的增长率和用基数接近于正比(随着市场饱和度收敛)，因此用户数增长和时间成接近于平方正比\n",
    "    atemp_abs[i]=math.fabs(data[\"atemp\"][i]-0.23)**0.8\n",
    "    # 23度的天气是最适合骑车的，高了或者低了都会减少骑车人数\n",
    "    hum_abs[i]=math.fabs(data[\"hum\"][i]-0.2)**0.1\n",
    "    # 湿度高了或者低了都会减少骑车人数\n",
    "    windspeed_sqr[i] = data[\"windspeed\"][i]**4\n",
    "    # 风力为骑车造成的阻力是平方正比的，同时还通过影响体感温度和其他方式平方正比的影响了骑车感受\n",
    "    '''\n",
    "    # one_hot搞不定，只好先放弃几个离散值的维度\n",
    "    season_1[i] = 1 if data[\"season\"][i]==1 else 0\n",
    "    season_2[i] = 1 if data[\"season\"][i]==2 else 0\n",
    "    season_3[i] = 1 if data[\"season\"][i]==3 else 0\n",
    "    season_4[i] = 1 if data[\"season\"][i]==4 else 0\n",
    "    weathersit_1[i] = 1 if data[\"weathersit\"][i]==1 else 0\n",
    "    weathersit_2[i] = 1 if data[\"weathersit\"][i]==2 else 0\n",
    "    weathersit_3[i] = 1 if data[\"weathersit\"][i]==3 else 0\n",
    "    weathersit_4[i] = 1 if data[\"weathersit\"][i]==4 else 0\n",
    "    '''\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "X.insert(0,'instant_sqr', instant_sqr)\n",
    "X.insert(0,'atemp_abs', atemp_abs)\n",
    "X.insert(0,'hum_abs', hum_abs)\n",
    "X.insert(0,'windspeed_sqr', windspeed_sqr)\n",
    "'''\n",
    "one_hot不成，放弃\n",
    "X.insert(0,'season_1', season_1)\n",
    "X.insert(0,'season_2', season_2)\n",
    "X.insert(0,'season_3', season_3)\n",
    "X.insert(0,'season_4', season_4)\n",
    "X.insert(0,'weathersit_1', weathersit_1)\n",
    "X.insert(0,'weathersit_2', weathersit_2)\n",
    "X.insert(0,'weathersit_3', weathersit_3)\n",
    "X.insert(0,'weathersit_4', weathersit_4)\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "#X.insert(0,'o_season', o_season)\n",
    "    \n",
    "#把2011和2012年的数据分割\n",
    "X_2011_train = X[X.yr == 0].drop('yr', axis = 1)\n",
    "X_2012_test = X[X.yr == 1].drop('yr', axis = 1)\n",
    "\n",
    "y_2011_train = X_2011_train['cnt'].values\n",
    "y_2012_test = X_2012_test['cnt'].values\n",
    "\n",
    "X_2011_train = X_2011_train.drop('cnt',axis = 1)\n",
    "X_2012_test = X_2012_test.drop('cnt',axis = 1)\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = X_2011_train.columns\n",
    "\n",
    "#print(y_2011_train,'\\n',y_2012_test)\n",
    "#X_2011_train.tail()\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以值进行标准化处理\n",
    "X_2011_train = ss_X.fit_transform(X_2011_train)\n",
    "X_2012_test = ss_X.transform(X_2012_test)\n",
    "\n",
    "# 目标值也需要标准化\n",
    "\n",
    "y_2011_train = ss_y.fit_transform(y_2011_train.reshape(-1, 1))\n",
    "y_2012_test = ss_y.transform(y_2012_test.reshape(-1, 1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step2 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.505732558433\n",
      "The r2 score of RidgeCV on train is 0.674251459505\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "from sklearn.metrics import r2_score  \n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 1e-04,1e-03,1e-02, 1e-01,1,1e+01,1e+02,1e+03]\n",
    "\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_2011_train, y_2011_train)\n",
    "\n",
    "\n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_2012_test)\n",
    "y_train_pred_ridge = ridge.predict(X_2011_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_2012_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_2011_train, y_train_pred_ridge))\n",
    "#print(y_2011_train)\n",
    "#print(y_train_pred_ridge)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GtQCHSpZ7qeIvtFolqR1YA2zPtpLJSaqXtBM4AjwXEanUO6eewS3pK8A7x1n1\nKeA/AXfNbEWTm6zeiHg6Ij4FfErSQ8Am4JEZLbBEuVqTPp+iOMz/i5msbTyV1FvFNE5b1Y4sa5Gk\ny4DPA784ZhRfdSJiGLglmQv8gqSbImLa54fmVFhExA+P1y5pNdAB7JIExcMkL0haFxH/MoMlvslE\n9Y7jCeDLZBgW5WqV9ADwQeCHogrO157Cn2016gVWliy3An0Z1TLrSJpHMSj+IiL+Out6KhURxyR9\nneL80LSHhQ9DARHxrYi4KiLaI6Kd4j/GW7MMinIkrSpZ3AD8c1a1lCNpPfDrwIaIOJ11PbNAF7BK\nUoekRuBeYGvGNc0KKv60+L+BVyLit7OupxxJzaNnF0paCPwwKX0XOCxq16clvSzpJYqHz6r5FL/f\nA5YAzyWn+v5h1gVNRtI9knqB7wG+LOnZrGsqlZwssAl4luIE7OciYne2VU1M0pPAPwHXS+qV9B+y\nrmkSdwA/A/xg8nd1p6R/m3VRk1gBfC35HuiiOGfxTBo78hXcZmZWlkcWZmZWlsPCzMzKcliYmVlZ\nDgszMyvLYWFmZmU5LGzOk3SqfK9Jt/8rSdeU6fP1cnewraTPmP7Nkv6m0v5ml8JhYXYJJL0HqI+I\n/TO974g4ChyWdMdM79vmHoeFWUJFn0kudvyWpI8k7XWSfj95XsAzkrZJ+slks58Gni75jD+Q1D3Z\nswUknZL0PyS9IOnvJDWXrP5w8nyCVyV9f9K/XdI3k/4vSPrekv5fTGowS5XDwuyiD1F8JsB3U7xt\nwmckrUja24HVwM9RvLJ71B3AjpLlT0VEJ3Az8G8k3TzOfhYDL0TErcDf8+Z7ejVExDrgF0vajwB3\nJv0/AvxOSf9u4Pun/ls1m5o5dSNBszK+D3gyuYvndyT9PbA2af8/ETEC/Iukr5VsswI4WrL8U5I2\nUvy3tYLiw4leGrOfEeAvk/d/DpTerG70/Q6KAQXFZxT8nqRbgGHgupL+R4CqfUqizR4OC7OLxrv1\n92TtAGeABQCSOoBfAdZGxICkPx1dV0bpPXfOJb8Oc/Hf5y8B36E44qkDzpb0X5DUYJYqH4Yyu+gb\nwEeSh8k0A+8DcsD/A34imbt4B/D+km1eAb4reX858AZwPOn3IxPspw4YnfO4P/n8ySwFDicjm58B\n6kvWXUcKt6M2G8sjC7OLvkBxPmIXxZ/2fy0i/kXS54Efovil/CrFJ6cdT7b5MsXw+EpE7JL0IrAb\n2A/8wwT7eQN4j6Qdyed8pExdvw98XtKHga8l24/6gaQGs1T5rrNmFZB0WUScknQlxdHGHUmQLKT4\nBX5HMtdRyWediojLpqmubwBr+A+qAAAAXElEQVR3R8TAdHye2UQ8sjCrzDPJQ2YagUdHH4wVEWck\nPULxGdgHZ7Kg5FDZbzsobCZ4ZGFmZmV5gtvMzMpyWJiZWVkOCzMzK8thYWZmZTkszMysLIeFmZmV\n9f8BSS3aLkYcVKEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b9fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 1.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[0.753198054342]</td>\n",
       "      <td>atemp_abs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.230094242153]</td>\n",
       "      <td>instant_sqr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.0068443961188]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-0.182212591758]</td>\n",
       "      <td>hum_abs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-0.18480128216]</td>\n",
       "      <td>windspeed_sqr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           coef_ridge        columns\n",
       "2    [0.753198054342]      atemp_abs\n",
       "3    [0.230094242153]    instant_sqr\n",
       "4  [-0.0068443961188]     workingday\n",
       "1   [-0.182212591758]        hum_abs\n",
       "0    [-0.18480128216]  windspeed_sqr"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns),  \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.505978047813\n",
      "The r2 score of LassoCV on train is 0.674257096738\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/emu/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [ 1e-06,1e-05,1e-04,1e-03,1e-02, 1e-01, 1,1e+1]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)  \n",
    "#lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_2011_train, y_2011_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_2012_test)\n",
    "y_train_pred_lasso = lasso.predict(X_2011_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_2012_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_2011_train, y_train_pred_lasso))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b9fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.0001\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.755217</td>\n",
       "      <td>[0.753198054342]</td>\n",
       "      <td>atemp_abs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.230642</td>\n",
       "      <td>[0.230094242153]</td>\n",
       "      <td>instant_sqr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.006815</td>\n",
       "      <td>[-0.0068443961188]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.183201</td>\n",
       "      <td>[-0.182212591758]</td>\n",
       "      <td>hum_abs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.185264</td>\n",
       "      <td>[-0.18480128216]</td>\n",
       "      <td>windspeed_sqr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coef_lasso          coef_ridge        columns\n",
       "2    0.755217    [0.753198054342]      atemp_abs\n",
       "3    0.230642    [0.230094242153]    instant_sqr\n",
       "4   -0.006815  [-0.0068443961188]     workingday\n",
       "1   -0.183201   [-0.182212591758]        hum_abs\n",
       "0   -0.185264    [-0.18480128216]  windspeed_sqr"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns),  \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lasso'],ascending=False)"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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