Predicting_bike_sharing_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your first neural network\n",
    "\n",
    "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare the data\n",
    "\n",
    "A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>0.24</td>\n",
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       "      <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",
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       "      <td>2011-01-01</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.2727</td>\n",
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       "      <th>3</th>\n",
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       "      <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",
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       "  </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking out the data\n",
    "\n",
    "This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
    "\n",
    "Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c9dec45f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 254,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy variables\n",
    "Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 710,
       "width": 1431
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "\n",
    "plt.subplot(2, 3, 1)\n",
    "rides['season'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2, 3, 2)\n",
    "\n",
    "rides['weathersit'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2, 3, 3)\n",
    "\n",
    "rides['mnth'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2, 3, 4)\n",
    "\n",
    "rides['hr'].value_counts().plot.bar()\n",
    "\n",
    "plt.subplot(2, 3, 5)\n",
    "\n",
    "rides['weekday'].value_counts().plot.bar()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why did we drop : \"instant\", \"dteday\", \"atemp\" and \"workingday\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 × 59 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 59 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>count</th>\n",
       "      <td>17379.000000</td>\n",
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       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
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       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>17379.000000</td>\n",
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       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.502561</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "      <td>0.244088</td>\n",
       "      <td>0.253697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041890</td>\n",
       "      <td>0.041890</td>\n",
       "      <td>0.041890</td>\n",
       "      <td>0.143967</td>\n",
       "      <td>0.142643</td>\n",
       "      <td>0.141147</td>\n",
       "      <td>0.142413</td>\n",
       "      <td>0.142183</td>\n",
       "      <td>0.143104</td>\n",
       "      <td>0.144542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500008</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "      <td>0.429557</td>\n",
       "      <td>0.435139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200343</td>\n",
       "      <td>0.200343</td>\n",
       "      <td>0.200343</td>\n",
       "      <td>0.351066</td>\n",
       "      <td>0.349719</td>\n",
       "      <td>0.348184</td>\n",
       "      <td>0.349484</td>\n",
       "      <td>0.349248</td>\n",
       "      <td>0.350189</td>\n",
       "      <td>0.351649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 yr       holiday          temp           hum     windspeed  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.502561      0.028770      0.496987      0.627229      0.190098   \n",
       "std        0.500008      0.167165      0.192556      0.192930      0.122340   \n",
       "min        0.000000      0.000000      0.020000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.340000      0.480000      0.104500   \n",
       "50%        1.000000      0.000000      0.500000      0.630000      0.194000   \n",
       "75%        1.000000      0.000000      0.660000      0.780000      0.253700   \n",
       "max        1.000000      1.000000      1.000000      1.000000      0.850700   \n",
       "\n",
       "             casual    registered           cnt      season_1      season_2  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean      35.676218    153.786869    189.463088      0.244088      0.253697   \n",
       "std       49.305030    151.357286    181.387599      0.429557      0.435139   \n",
       "min        0.000000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%        4.000000     34.000000     40.000000      0.000000      0.000000   \n",
       "50%       17.000000    115.000000    142.000000      0.000000      0.000000   \n",
       "75%       48.000000    220.000000    281.000000      0.000000      1.000000   \n",
       "max      367.000000    886.000000    977.000000      1.000000      1.000000   \n",
       "\n",
       "           ...              hr_21         hr_22         hr_23     weekday_0  \\\n",
       "count      ...       17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       ...           0.041890      0.041890      0.041890      0.143967   \n",
       "std        ...           0.200343      0.200343      0.200343      0.351066   \n",
       "min        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "25%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "50%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "75%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "max        ...           1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          weekday_1     weekday_2     weekday_3     weekday_4     weekday_5  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.142643      0.141147      0.142413      0.142183      0.143104   \n",
       "std        0.349719      0.348184      0.349484      0.349248      0.350189   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          weekday_6  \n",
       "count  17379.000000  \n",
       "mean       0.144542  \n",
       "std        0.351649  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 59 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# My code, to decribe the sada\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling target variables\n",
    "To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
    "\n",
    "The scaling factors are saved so we can go backwards when we use the network for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
    "# Store scalings in a dictionary so we can convert back later\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>count</th>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>1.737900e+04</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.502561</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>6.541621e-17</td>\n",
       "      <td>-2.992792e-16</td>\n",
       "      <td>1.390095e-16</td>\n",
       "      <td>3.270811e-17</td>\n",
       "      <td>-5.805689e-17</td>\n",
       "      <td>-9.321810e-17</td>\n",
       "      <td>0.244088</td>\n",
       "      <td>0.253697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041890</td>\n",
       "      <td>0.041890</td>\n",
       "      <td>0.041890</td>\n",
       "      <td>0.143967</td>\n",
       "      <td>0.142643</td>\n",
       "      <td>0.141147</td>\n",
       "      <td>0.142413</td>\n",
       "      <td>0.142183</td>\n",
       "      <td>0.143104</td>\n",
       "      <td>0.144542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.500008</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.429557</td>\n",
       "      <td>0.435139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200343</td>\n",
       "      <td>0.200343</td>\n",
       "      <td>0.200343</td>\n",
       "      <td>0.351066</td>\n",
       "      <td>0.349719</td>\n",
       "      <td>0.348184</td>\n",
       "      <td>0.349484</td>\n",
       "      <td>0.349248</td>\n",
       "      <td>0.350189</td>\n",
       "      <td>0.351649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.477133e+00</td>\n",
       "      <td>-3.251072e+00</td>\n",
       "      <td>-1.553844e+00</td>\n",
       "      <td>-7.235817e-01</td>\n",
       "      <td>-1.016052e+00</td>\n",
       "      <td>-1.039008e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.152801e-01</td>\n",
       "      <td>-7.631212e-01</td>\n",
       "      <td>-6.996685e-01</td>\n",
       "      <td>-6.424541e-01</td>\n",
       "      <td>-7.914179e-01</td>\n",
       "      <td>-8.239984e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.564651e-02</td>\n",
       "      <td>1.436357e-02</td>\n",
       "      <td>3.189788e-02</td>\n",
       "      <td>-3.787893e-01</td>\n",
       "      <td>-2.562603e-01</td>\n",
       "      <td>-2.616667e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.465731e-01</td>\n",
       "      <td>7.918483e-01</td>\n",
       "      <td>5.198813e-01</td>\n",
       "      <td>2.499498e-01</td>\n",
       "      <td>4.374625e-01</td>\n",
       "      <td>5.046481e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.612292e+00</td>\n",
       "      <td>1.932159e+00</td>\n",
       "      <td>5.399715e+00</td>\n",
       "      <td>6.719878e+00</td>\n",
       "      <td>4.837647e+00</td>\n",
       "      <td>4.341735e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 yr       holiday          temp           hum     windspeed  \\\n",
       "count  17379.000000  17379.000000  1.737900e+04  1.737900e+04  1.737900e+04   \n",
       "mean       0.502561      0.028770  6.541621e-17 -2.992792e-16  1.390095e-16   \n",
       "std        0.500008      0.167165  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min        0.000000      0.000000 -2.477133e+00 -3.251072e+00 -1.553844e+00   \n",
       "25%        0.000000      0.000000 -8.152801e-01 -7.631212e-01 -6.996685e-01   \n",
       "50%        1.000000      0.000000  1.564651e-02  1.436357e-02  3.189788e-02   \n",
       "75%        1.000000      0.000000  8.465731e-01  7.918483e-01  5.198813e-01   \n",
       "max        1.000000      1.000000  2.612292e+00  1.932159e+00  5.399715e+00   \n",
       "\n",
       "             casual    registered           cnt      season_1      season_2  \\\n",
       "count  1.737900e+04  1.737900e+04  1.737900e+04  17379.000000  17379.000000   \n",
       "mean   3.270811e-17 -5.805689e-17 -9.321810e-17      0.244088      0.253697   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00      0.429557      0.435139   \n",
       "min   -7.235817e-01 -1.016052e+00 -1.039008e+00      0.000000      0.000000   \n",
       "25%   -6.424541e-01 -7.914179e-01 -8.239984e-01      0.000000      0.000000   \n",
       "50%   -3.787893e-01 -2.562603e-01 -2.616667e-01      0.000000      0.000000   \n",
       "75%    2.499498e-01  4.374625e-01  5.046481e-01      0.000000      1.000000   \n",
       "max    6.719878e+00  4.837647e+00  4.341735e+00      1.000000      1.000000   \n",
       "\n",
       "           ...              hr_21         hr_22         hr_23     weekday_0  \\\n",
       "count      ...       17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       ...           0.041890      0.041890      0.041890      0.143967   \n",
       "std        ...           0.200343      0.200343      0.200343      0.351066   \n",
       "min        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "25%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "50%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "75%        ...           0.000000      0.000000      0.000000      0.000000   \n",
       "max        ...           1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          weekday_1     weekday_2     weekday_3     weekday_4     weekday_5  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.142643      0.141147      0.142413      0.142183      0.143104   \n",
       "std        0.349719      0.348184      0.349484      0.349248      0.350189   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "          weekday_6  \n",
       "count  17379.000000  \n",
       "mean       0.144542  \n",
       "std        0.351649  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[8 rows x 59 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# My code, to decribe the sada\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data into training, testing, and validation sets\n",
    "\n",
    "We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "\n",
    "# Now remove the test data from the data set \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": "markdown",
   "metadata": {},
   "source": [
    "We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hold out the last 60 days or so 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": "markdown",
   "metadata": {},
   "source": [
    "## Time to build the network\n",
    "\n",
    "Below you'll build your network. We've built out the structure. You'll implement both the forward pass and backwards pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
    "\n",
    "<img src=\"assets/neural_network.png\" width=300px>\n",
    "\n",
    "The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
    "\n",
    "We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
    "\n",
    "> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
    "\n",
    "Below, you have these tasks:\n",
    "1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
    "2. Implement the forward pass in the `train` method.\n",
    "3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
    "4. Implement the forward pass in the `run` method.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############\n",
    "# In the my_answers.py file, fill out the TODO sections as specified\n",
    "#############\n",
    "\n",
    "from my_answers import NeuralNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MSE(y, Y):\n",
    "    return np.mean((y-Y)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unit tests\n",
    "\n",
    "Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.009s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "inputs = np.array([[0.5, -0.2, 0.1]])\n",
    "targets = np.array([[0.4]])\n",
    "test_w_i_h = np.array([[0.1, -0.2],\n",
    "                       [0.4, 0.5],\n",
    "                       [-0.3, 0.2]])\n",
    "test_w_h_o = np.array([[0.3],\n",
    "                       [-0.1]])\n",
    "\n",
    "class TestMethods(unittest.TestCase):\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for data loading\n",
    "    ##########\n",
    "    \n",
    "    def test_data_path(self):\n",
    "        # Test that file path to dataset has been unaltered\n",
    "        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
    "        \n",
    "    def test_data_loaded(self):\n",
    "        # Test that data frame loaded\n",
    "        self.assertTrue(isinstance(rides, pd.DataFrame))\n",
    "    \n",
    "    ##########\n",
    "    # Unit tests for network functionality\n",
    "    ##########\n",
    "\n",
    "    def test_activation(self):\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        # Test that the activation function is a sigmoid\n",
    "        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
    "\n",
    "    def test_train(self):\n",
    "        # Test that weights are updated correctly on training\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "        \n",
    "        network.train(inputs, targets)\n",
    "        self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
    "                                    np.array([[ 0.37275328], \n",
    "                                              [-0.03172939]])))\n",
    "        self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
    "                                    np.array([[ 0.10562014, -0.20185996], \n",
    "                                              [0.39775194, 0.50074398], \n",
    "                                              [-0.29887597, 0.19962801]])))\n",
    "\n",
    "    def test_run(self):\n",
    "        # Test correctness of run method\n",
    "        network = NeuralNetwork(3, 2, 1, 0.5)\n",
    "        network.weights_input_to_hidden = test_w_i_h.copy()\n",
    "        network.weights_hidden_to_output = test_w_h_o.copy()\n",
    "\n",
    "        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the network\n",
    "\n",
    "Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
    "\n",
    "You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
    "\n",
    "### Choose the number of iterations\n",
    "This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, this process can have sharply diminishing returns and can waste computational resources if you use too many iterations.  You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. The ideal number of iterations would be a level that stops shortly after the validation loss is no longer decreasing.\n",
    "\n",
    "### Choose the learning rate\n",
    "This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. Normally a good choice to start at is 0.1; however, if you effectively divide the learning rate by n_records, try starting out with a learning rate of 1. In either case, if the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
    "\n",
    "### Choose the number of hidden nodes\n",
    "In a model where all the weights are optimized, the more hidden nodes you have, the more accurate the predictions of the model will be.  (A fully optimized model could have weights of zero, after all.) However, the more hidden nodes you have, the harder it will be to optimize the weights of the model, and the more likely it will be that suboptimal weights will lead to overfitting. With overfitting, the model will memorize the training data instead of learning the true pattern, and won't generalize well to unseen data.  \n",
    "\n",
    "Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.  You'll generally find that the best number of hidden nodes to use ends up being between the number of input and output nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress: 1.8% ... Training loss: 0.645 ... Validation loss: 1.097"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aims/anaconda3/envs/aims/lib/python3.5/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: 99.9% ... Training loss: 0.081 ... Validation loss: 0.168"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "####################\n",
    "### Set the hyperparameters in you myanswers.py file ###\n",
    "####################\n",
    "\n",
    "from my_answers import iterations, learning_rate, hidden_nodes, output_nodes\n",
    "\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 ii in range(iterations):\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",
    "    X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']\n",
    "                             \n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)\n",
    "    val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)\n",
    "    sys.stdout.write(\"\\rProgress: {:2.1f}\".format(100 * ii/float(iterations)) \\\n",
    "                     + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
    "                     + \" ... Validation loss: \" + str(val_loss)[:5])\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "    losses['train'].append(train_loss)\n",
    "    losses['validation'].append(val_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 364
      },
      "needs_background": "light"
     },
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check out your predictions\n",
    "\n",
    "Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aims/anaconda3/envs/aims/lib/python3.5/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": {
      "image/png": {
       "height": 272,
       "width": 502
      },
      "needs_background": "light"
     },
     "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).T*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": "markdown",
   "metadata": {},
   "source": [
    "## OPTIONAL: Thinking about your results(this question will not be evaluated in the rubric).\n",
    " \n",
    "Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
    "\n",
    "> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
    "\n",
    "#### Your answer below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5045.270413607081"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = predictions[0]\n",
    "B = (test_targets['cnt']*std + mean).values\n",
    "((A - B)**2).mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My model is strugling to predict recent data, and they are less volitile than the older one "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}