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\n",
    "import requests, zipfile\n",
    "from io import BytesIO\n",
    "import os"
   ]
  },
  {
   "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": [
    "def download_dataset(url, path):\n",
    "    os.makedirs(path, exist_ok = True)\n",
    "    file = requests.get(url)\n",
    "    zip_file = zipfile.ZipFile(BytesIO(file.content))\n",
    "    zip_file.extractall(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "download_dataset('https://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip','Bike-Sharing-Dataset')\n",
    "data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
    "\n",
    "rides = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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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",
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       "      <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": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dteday'>"
      ]
     },
     "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": 261,
       "width": 375
      },
      "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": 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",
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       "    }\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",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <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": "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": 7,
   "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",
    "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\n",
    "    test_data.loc[:, each] = (test_data[each] - mean)/std"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork(object):\n",
    "    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
    "        # Set number of nodes in input, hidden and output layers.\n",
    "        self.input_nodes = input_nodes\n",
    "        self.hidden_nodes = hidden_nodes\n",
    "        self.output_nodes = output_nodes\n",
    "\n",
    "        # Initialize weights\n",
    "        self.weights_input_to_hidden = np.random.normal(\n",
    "            0.0, self.input_nodes ** -0.5, (self.input_nodes, self.hidden_nodes)\n",
    "        )\n",
    "\n",
    "        self.weights_hidden_to_output = np.random.normal(\n",
    "            0.0, self.hidden_nodes ** -0.5, (self.hidden_nodes, self.output_nodes)\n",
    "        )\n",
    "        self.lr = learning_rate\n",
    "\n",
    "        self.activation_function = lambda x: 1 / (\n",
    "            1 + np.exp(-x)\n",
    "        )  \n",
    "\n",
    "    def train(self, features, targets):\n",
    "        \"\"\"Train the network on batch of features and targets.\n",
    "\n",
    "        Arguments\n",
    "        ---------\n",
    "\n",
    "        features: 2D array, each row is one data record, each column is a feature\n",
    "        targets: 1D array of target values\n",
    "\n",
    "        \"\"\"\n",
    "        n_records = features.shape[0]\n",
    "        delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)\n",
    "        delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)\n",
    "        for X, y in zip(features, targets):\n",
    "\n",
    "            final_outputs, hidden_outputs = self.forward_pass_train(\n",
    "                X\n",
    "            )  # Implement the forward pass function below\n",
    "            # Implement the backproagation function below\n",
    "            delta_weights_i_h, delta_weights_h_o = self.backpropagation(\n",
    "                final_outputs,\n",
    "                hidden_outputs,\n",
    "                X,\n",
    "                y,\n",
    "                delta_weights_i_h,\n",
    "                delta_weights_h_o,\n",
    "            )\n",
    "        self.update_weights(delta_weights_i_h, delta_weights_h_o, n_records)\n",
    "\n",
    "    def forward_pass_train(self, X):\n",
    "        \"\"\"Implement forward pass here\n",
    "\n",
    "        Arguments\n",
    "        ---------\n",
    "        X: features batch\n",
    "\n",
    "        \"\"\"\n",
    "        #### Implement the forward pass here ####\n",
    "        ### Forward pass ###\n",
    "        # Hidden layer\n",
    "        hidden_inputs = np.dot(\n",
    "            X, self.weights_input_to_hidden\n",
    "        )  # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(\n",
    "            hidden_inputs\n",
    "        )  # signals from hidden layer\n",
    "\n",
    "        final_inputs = np.dot(\n",
    "            hidden_outputs, self.weights_hidden_to_output\n",
    "        )  # signals into final output layer\n",
    "        final_outputs = final_inputs  # signals from final output layer\n",
    "        return final_outputs, hidden_outputs\n",
    "\n",
    "    def backpropagation(\n",
    "        self, final_outputs, hidden_outputs, X, y, delta_weights_i_h, delta_weights_h_o\n",
    "    ):\n",
    "        \"\"\"Implement backpropagation\n",
    "\n",
    "        Arguments\n",
    "        ---------\n",
    "        final_outputs: output from forward pass\n",
    "        y: target (i.e. label) batch\n",
    "        delta_weights_i_h: change in weights from input to hidden layers\n",
    "        delta_weights_h_o: change in weights from hidden to output layers\n",
    "\n",
    "        \"\"\"\n",
    "        #### Implement the backward pass here ####\n",
    "        ### Backward pass ###\n",
    "\n",
    "        # Output error\n",
    "        error = (\n",
    "            y - final_outputs\n",
    "        )  # Output layer error is the difference between desired target and actual output.\n",
    "\n",
    "        # Backpropagated error terms \n",
    "        output_error_term = error\n",
    "\n",
    "        # Hidden layer's contribution to the error\n",
    "        hidden_error = np.dot(output_error_term, self.weights_hidden_to_output.T)\n",
    "\n",
    "        hidden_error_term = hidden_error * hidden_outputs * (1 - hidden_outputs)\n",
    "\n",
    "        # Add Weight step (input to hidden) and Weight step (hidden to output).\n",
    "        # Weight step (input to hidden)\n",
    "        delta_weights_i_h += hidden_error_term * X[:, None]\n",
    "        # Weight step (hidden to output)\n",
    "        delta_weights_h_o += output_error_term * hidden_outputs[:, None]\n",
    "        return delta_weights_i_h, delta_weights_h_o\n",
    "\n",
    "    def update_weights(self, delta_weights_i_h, delta_weights_h_o, n_records):\n",
    "        \"\"\"Update weights on gradient descent step\n",
    "\n",
    "        Arguments\n",
    "        ---------\n",
    "        delta_weights_i_h: change in weights from input to hidden layers\n",
    "        delta_weights_h_o: change in weights from hidden to output layers\n",
    "        n_records: number of records\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        # Update the weights with gradient descent step\n",
    "        self.weights_hidden_to_output += (\n",
    "            self.lr * delta_weights_h_o / n_records\n",
    "        )  # update hidden-to-output weights with gradient descent step\n",
    "        self.weights_input_to_hidden += (\n",
    "            self.lr * delta_weights_i_h / n_records\n",
    "        )  # update input-to-hidden weights with gradient descent step\n",
    "\n",
    "    def run(self, features):\n",
    "        \"\"\"Run a forward pass through the network with input features\n",
    "\n",
    "        Arguments\n",
    "        ---------\n",
    "        features: 1D array of feature values\n",
    "        \"\"\"\n",
    "\n",
    "        #### Implement the forward pass here ####\n",
    "        # Hidden layer\n",
    "        hidden_inputs = np.dot(\n",
    "            features, self.weights_input_to_hidden\n",
    "        )  # signals into hidden layer\n",
    "        hidden_outputs = self.activation_function(\n",
    "            hidden_inputs\n",
    "        )  # signals from hidden layer\n",
    "\n",
    "        # Output layer\n",
    "        final_inputs = np.dot(\n",
    "            hidden_outputs, self.weights_hidden_to_output\n",
    "        )  # signals into final output layer\n",
    "        final_outputs = final_inputs  # signals from final output layer\n",
    "\n",
    "        return final_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 before you starting trying to train it. These tests must all be successful to pass the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ".....\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 0.003s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
      ]
     },
     "execution_count": 12,
     "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 known 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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       yr  holiday      temp       hum  windspeed  season_1  season_2  \\\n",
      "0       0        0 -1.370528  0.946639  -1.558407         1         0   \n",
      "1       0        0 -1.474766  0.894996  -1.558407         1         0   \n",
      "2       0        0 -1.474766  0.894996  -1.558407         1         0   \n",
      "3       0        0 -1.370528  0.636780  -1.558407         1         0   \n",
      "4       0        0 -1.370528  0.636780  -1.558407         1         0   \n",
      "...    ..      ...       ...       ...        ...       ...       ...   \n",
      "15430   1        0 -0.223907  1.308142  -1.558407         0         0   \n",
      "15431   1        0 -0.223907  1.308142  -0.700194         0         0   \n",
      "15432   1        0 -0.223907  1.308142  -0.822562         0         0   \n",
      "15433   1        0 -0.223907  1.308142  -0.822562         0         0   \n",
      "15434   1        0 -0.223907  0.998282  -0.700194         0         0   \n",
      "\n",
      "       season_3  season_4  weathersit_1  ...  hr_21  hr_22  hr_23  weekday_0  \\\n",
      "0             0         0             1  ...      0      0      0          0   \n",
      "1             0         0             1  ...      0      0      0          0   \n",
      "2             0         0             1  ...      0      0      0          0   \n",
      "3             0         0             1  ...      0      0      0          0   \n",
      "4             0         0             1  ...      0      0      0          0   \n",
      "...         ...       ...           ...  ...    ...    ...    ...        ...   \n",
      "15430         0         1             0  ...      0      0      0          0   \n",
      "15431         0         1             0  ...      0      0      0          0   \n",
      "15432         0         1             0  ...      0      0      0          0   \n",
      "15433         0         1             0  ...      0      0      0          0   \n",
      "15434         0         1             1  ...      0      0      0          0   \n",
      "\n",
      "       weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
      "0              0          0          0          0          0          1  \n",
      "1              0          0          0          0          0          1  \n",
      "2              0          0          0          0          0          1  \n",
      "3              0          0          0          0          0          1  \n",
      "4              0          0          0          0          0          1  \n",
      "...          ...        ...        ...        ...        ...        ...  \n",
      "15430          0          0          1          0          0          0  \n",
      "15431          0          0          1          0          0          0  \n",
      "15432          0          0          1          0          0          0  \n",
      "15433          0          0          1          0          0          0  \n",
      "15434          0          0          1          0          0          0  \n",
      "\n",
      "[15435 rows x 56 columns]\n",
      "Progress: 100.0% ... Training loss: 0.068 ... Validation loss: 0.146"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "# Hyperparameters are set here\n",
    "iterations = 2700\n",
    "learning_rate = 1.0\n",
    "hidden_nodes = 12\n",
    "output_nodes = 1\n",
    "\n",
    "print(train_features)\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.iloc[batch].values, train_targets.iloc[batch]['cnt']\n",
    "                             \n",
    "    network.train(X, y)\n",
    "    \n",
    "    # Printing out the training progress\n",
    "    train_loss = MSE(np.array(network.run(train_features)).T, train_targets['cnt'].values)\n",
    "    val_loss = MSE(np.array(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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 378
      },
      "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 270,
       "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 = np.array(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.iloc[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": "markdown",
   "metadata": {},
   "source": [
    "<!--\n",
    " Copyright 2021 priya\n",
    " \n",
    " Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    " you may not use this file except in compliance with the License.\n",
    " You may obtain a copy of the License at\n",
    " \n",
    "     http://www.apache.org/licenses/LICENSE-2.0\n",
    " \n",
    " Unless required by applicable law or agreed to in writing, software\n",
    " distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    " WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    " See the License for the specific language governing permissions and\n",
    " limitations under the License.\n",
    "-->\n",
    "\n",
    "The predictions are good till the end of december. Maybe the model failed to understand the variations in demand during the holiday seasons as it is only trained with two years of data. If that is the case then the model needs to be trained with more data.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}