Python55-使用神经网络预测日常的自行车出租用户量.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个项目中，我们将会建立第一个神经网络并使用它来预测每天自行车出租的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载和准备数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个重要的步骤就是给神经网络准备正确的数据，不同规模的数据很难使神经网络有效地学习权重，所以我们下面将要着重讲述数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path='files/data/python55-data/hour.csv'\n",
    "rides=pd.read_csv(data_path)\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- instant:记录索引；\n",
    "- dteday:日期；\n",
    "- season：季节（1：春季2：夏季3：秋季4：冬季）\n",
    "- yr：年份（0:2011 1:2012） \n",
    "- mnth：月份（1-12）；hr:hour(0-2)\n",
    "- holiday:是否为假期\n",
    "- weekday:周几\n",
    "- workingday：如果一天既不是假期也不是周末，则为1，否则为0\n",
    "- weathersit：1 晴天 2 多云 3 下雪 4 大雨\n",
    "- temp：温度\n",
    "- atemp：体感温度\n",
    "- hum：湿度\n",
    "- windspeed：风速\n",
    "- casual：普通用户的数量\n",
    "- registed：注册用户的数量\n",
    "- cnt:普通用户和注册用户的数量和"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检查数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集包括了从2011年1月1日到2012年12月31日每一天每一小时的自行车用户数量，用户数量按普通和会员分为两部分。\n",
    "\n",
    "下面画出数据集中的前十天数据中的用户数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1268e955908>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday',y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 虚拟变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dumpy_fields=['season','weathersit','mnth','hr','weekday']\n",
    "for each in dumpy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides=pd.concat([rides,dummies],axis=1)\n",
    "fields_to_drop=['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data=rides.drop(fields_to_drop,axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  缩放目标变量\n",
    "为了使神经网络训练更加简单，我们将标准化每一个连续型变量，使每一个变量减去平均值，然后处于标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "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>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_21</th>\n",
       "      <th>hr_22</th>\n",
       "      <th>hr_23</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.947345</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.930162</td>\n",
       "      <td>-0.956312</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.561326</td>\n",
       "      <td>-0.804632</td>\n",
       "      <td>-0.823998</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.438475</td>\n",
       "      <td>0.895513</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.622172</td>\n",
       "      <td>-0.837666</td>\n",
       "      <td>-0.868103</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.636351</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.662736</td>\n",
       "      <td>-0.949983</td>\n",
       "      <td>-0.972851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.334609</td>\n",
       "      <td>0.636351</td>\n",
       "      <td>-1.553844</td>\n",
       "      <td>-0.723582</td>\n",
       "      <td>-1.009445</td>\n",
       "      <td>-1.039008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr  holiday      temp       hum  windspeed    casual  registered       cnt  \\\n",
       "0   0        0 -1.334609  0.947345  -1.553844 -0.662736   -0.930162 -0.956312   \n",
       "1   0        0 -1.438475  0.895513  -1.553844 -0.561326   -0.804632 -0.823998   \n",
       "2   0        0 -1.438475  0.895513  -1.553844 -0.622172   -0.837666 -0.868103   \n",
       "3   0        0 -1.334609  0.636351  -1.553844 -0.662736   -0.949983 -0.972851   \n",
       "4   0        0 -1.334609  0.636351  -1.553844 -0.723582   -1.009445 -1.039008   \n",
       "\n",
       "   season_1  season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  \\\n",
       "0         1         0    ...          0      0      0          0          0   \n",
       "1         1         0    ...          0      0      0          0          0   \n",
       "2         1         0    ...          0      0      0          0          0   \n",
       "3         1         0    ...          0      0      0          0          0   \n",
       "4         1         0    ...          0      0      0          0          0   \n",
       "\n",
       "   weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          1  \n",
       "1          0          0          0          0          1  \n",
       "2          0          0          0          0          1  \n",
       "3          0          0          0          0          1  \n",
       "4          0          0          0          0          1  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_features=['casual','registered','cnt','temp','hum','windspeed']\n",
    "\n",
    "scaled_features={}\n",
    "for each in quant_features:\n",
    "    mean,std=data[each].mean(),data[each].std()\n",
    "    scaled_features[each]=[mean,std]\n",
    "    data.loc[:,each]=(data[each]-mean)/std\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练集，测试集，验证集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "data = data[:-21*24]\n",
    "\n",
    "# Separate the data into features and targets\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hold out the last 60 days of the remaining data as a validation set\n",
    "train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
    "val_features, val_targets = features[-60*24:], targets[-60*24:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建神经网络\n",
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Initialize weights\n",
    "        self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
    "                                       (self.hidden_nodes, self.input_nodes))\n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, \n",
    "                                       (self.output_nodes, self.hidden_nodes))\n",
    "        self.lr = learning_rate\n",
    "        \n",
    "        #### Set this to your implemented sigmoid function ####\n",
    "        # Activation function is the sigmoid function\n",
    "        self.activation_function = lambda x: 1 / (1 + np.exp(-x))\n",
    "    \n",
    "    def train(self, inputs_list, targets_list):\n",
    "        # Convert inputs list to 2d array\n",
    "        inputs = np.array(inputs_list, ndmin=2).T\n",
    "        targets = np.array(targets_list, ndmin=2).T\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        ### Forward pass ###\n",
    "        # TODO: Hidden layer\n",
    "        hidden_inputs =np.dot(self.weights_input_to_hidden,inputs) # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs)# signals from hidden layer\n",
    "        \n",
    "        # TODO: Output layer\n",
    "        final_inputs = np.dot(self.weights_hidden_to_output,hidden_outputs)# signals into final output layer\n",
    "        final_outputs = final_inputs# signals from final output layer\n",
    "        \n",
    "        #### Implement the backward pass here ####\n",
    "        ### Backward pass ###\n",
    "        \n",
    "        # TODO: Output error\n",
    "        output_errors = targets-final_outputs\n",
    "\n",
    "        hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors) # errors propagated to the hidden layer\n",
    "        hidden_grad = hidden_grad = hidden_outputs * (1.0 - hidden_outputs)# hidden layer gradients\n",
    "        \n",
    "        # TODO: Update the weights\n",
    "        self.weights_hidden_to_output += self.lr*np.dot(output_errors,hidden_outputs.T)# update hidden-to-output weights with gradient descent step\n",
    "        self.weights_input_to_hidden += self.lr * np.dot(hidden_errors * hidden_grad, inputs.T) # update input-to-hidden weights with gradient descent step\n",
    " \n",
    "        \n",
    "    def run(self, inputs_list):\n",
    "        # Run a forward pass through the network\n",
    "        inputs = np.array(inputs_list, ndmin=2).T\n",
    "        \n",
    "        #### Implement the forward pass here ####\n",
    "        hidden_inputs =np.dot(self.weights_input_to_hidden,inputs) # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(hidden_inputs)# signals from hidden layer\n",
    "        \n",
    "        # TODO: Output layer\n",
    "        final_inputs = np.dot(self.weights_hidden_to_output,hidden_outputs)# signals into final output layer\n",
    "        final_outputs = final_inputs# signals from final output layer\n",
    "        \n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练神网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  app.launch_new_instance()\n",
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 100.% ... Training loss: 0.073 ... Validation loss: 0.194"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "### Set the hyperparameters here ###\n",
    "epochs = 500\n",
    "learning_rate = 0.2\n",
    "hidden_nodes = 30\n",
    "output_nodes = 1\n",
    "\n",
    "N_i = train_features.shape[1]\n",
    "network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
    "\n",
    "losses = {'train':[], 'validation':[]}\n",
    "for e in range(epochs):\n",
    "    # Go through a random batch of 128 records from the training data set\n",
    "    batch = np.random.choice(train_features.index, size=128)\n",
    "    for record, target in zip(train_features.ix[batch].values, \n",
    "                              train_targets.ix[batch]['cnt']):\n",
    "        network.train(record, target)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features), train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features), val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: \" + str(100 * e/float(epochs-1))[:4] \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.025988387390207604, 0.5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses['train'], label='Training loss')\n",
    "plt.plot(losses['validation'], label='Validation loss')\n",
    "plt.legend()\n",
    "plt.ylim(ymax=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 检查预测值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "predictions = network.run(test_features)*std + mean\n",
    "ax.plot(predictions[0], label='Prediction')\n",
    "ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
    "ax.set_xlim(right=len(predictions))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}