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",
    "The data comes from the [UCI Machine Learning Database](https://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset)."
   ]
  },
  {
   "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": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
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       "      <th>mnth</th>\n",
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       "      <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",
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      "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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
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      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides.tail()"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cnt: count of total rental bikes including both casual and registered')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 353,
       "width": 390
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rides[:24*10].plot(x='dteday', y='cnt')\n",
    "plt.xlabel('dteday : date')\n",
    "plt.ylabel('cnt: count of total rental bikes including both casual and registered')"
   ]
  },
  {
   "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": 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": {
      "text/plain": [
       "Index(['yr', 'holiday', 'temp', 'hum', 'windspeed', 'casual', 'registered',\n",
       "       'cnt', 'season_1', 'season_2', 'season_3', 'season_4', 'weathersit_1',\n",
       "       'weathersit_2', 'weathersit_3', 'weathersit_4', 'mnth_1', 'mnth_2',\n",
       "       'mnth_3', 'mnth_4', 'mnth_5', 'mnth_6', 'mnth_7', 'mnth_8', 'mnth_9',\n",
       "       'mnth_10', 'mnth_11', 'mnth_12', 'hr_0', 'hr_1', 'hr_2', 'hr_3', 'hr_4',\n",
       "       'hr_5', 'hr_6', 'hr_7', 'hr_8', 'hr_9', 'hr_10', 'hr_11', 'hr_12',\n",
       "       'hr_13', 'hr_14', 'hr_15', 'hr_16', 'hr_17', 'hr_18', 'hr_19', 'hr_20',\n",
       "       'hr_21', 'hr_22', 'hr_23', 'weekday_0', 'weekday_1', 'weekday_2',\n",
       "       'weekday_3', 'weekday_4', 'weekday_5', 'weekday_6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shows columns attributes right now\n",
    "data.columns"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "{'casual': [35.67621842453536, 49.30503038705308],\n",
       " 'registered': [153.78686920996606, 151.35728591258314],\n",
       " 'cnt': [189.46308763450142, 181.38759909186473],\n",
       " 'temp': [0.4969871684216583, 0.1925561212497219],\n",
       " 'hum': [0.6272288394038783, 0.19292983406291508],\n",
       " 'windspeed': [0.1900976063064618, 0.12234022857279049]}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see the scale factors of selected variable\n",
    "scaled_features"
   ]
  },
  {
   "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": [
    {
     "data": {
      "text/plain": [
       "(17379, 59)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data: (504, 59)\n",
      "rest data: (16875, 59)\n"
     ]
    }
   ],
   "source": [
    "# Save data for approximately the last 21 days \n",
    "test_data = data[-21*24:]\n",
    "print('test data:', test_data.shape)\n",
    "# Now remove the test data from the data set \n",
    "data = data[:-21*24]\n",
    "print('rest data:', data.shape)\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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  15435\n",
      "Validation:  1440\n",
      "sum of train and validating 16875\n"
     ]
    }
   ],
   "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:]\n",
    "print('Train: ', len(train_features))\n",
    "print('Validation: ', len(val_features))\n",
    "print('sum of train and validating', len(data))"
   ]
  },
  {
   "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": 13,
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.005s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 15,
     "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: 0.1% ... Training loss: 57.18 ... Validation loss: 49.90"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python\\python37\\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: 100.0% ... Training loss: 0.068 ... Validation loss: 0.154"
     ]
    }
   ],
   "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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 370
      },
      "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": [
      "c:\\python\\python37\\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": {
      "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\n",
    "\n",
    "The model works great in normal days. However, it does not perform well during Christmas Holiday. In my opinion, due to usually people do not rent bikes a lot during this period. The model mostly trianed from data in normal days, which lead to the situation that the prediction are higher than data from 22th to 31st of December. "
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}