%matplotlib inline
%config InlineBackend.figure_format = 'retina'

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

"""
Explanation: My first work review
This project is to build the basic neural network using rides data.
End of explanation
"""


data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)

rides.head()

"""
Explanation: Load and prepare the data
End of explanation
"""


rides[:24*10].plot(x='dteday',y='cnt')

dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
    rides = pd.concat([rides, dummies], axis=1)

fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', 
                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
    

quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
    mean, std = data[each].mean(), data[each].std()
    scaled_features[each] = [mean, std]
    data.loc[:, each] = (data[each] - mean)/std
data.head()

"""
Explanation: Checking out the data
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.
Below is a plot showing the number of bike riders over the first 10 days in the data set. 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.
End of explanation
"""


# Save the last 21 days 
test_data = data[-21*24:]
data = data[:-21*24]

# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]

"""
Explanation: Splitting the data into training, testing, and validation sets
We'll save the last 21 days of the data 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.
End of explanation
"""


# Hold out the last 60 days of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]

"""
Explanation: 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).
End of explanation
"""


def sigmoid(x):
    return 1/(1 + np.exp(-x))
    

class NeuralNetwork(object):
    def __init__(self, input_nodes,hidden_nodes,output_nodes,learning_rate):
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes
        
        # Initialize weidghts
        self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, 
                                       (self.hidden_nodes, self.input_nodes))

        self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, 
                                       (self.output_nodes, self.hidden_nodes))
        self.lr = learning_rate
        
        self.activation_function = sigmoid
        
    def train(self,inputs_list,targets_list):
         # Convert inputs list to 2d array
        inputs = np.array(inputs_list, ndmin=2).T
        targets = np.array(targets_list, ndmin=2).T
         #### Implement the forward pass here ####
        ### Forward pass ###
        hidden_inputs = np.dot(self.weights_input_to_hidden,inputs)
        hidden_outputs = sigmoid(hidden_inputs)
        # TODO: Output layer
        final_inputs =np.dot(self.weights_hidden_to_output, hidden_outputs)
        output = final_inputs
        
         #### Implement the backward pass here ####
        ### Backward pass ###
        
        # TODO: Output error
        o_error = targets - output
#         print('weights_hidden_to_output.shape is %d,%d '%self.weights_hidden_to_output.shape)
#         print('output.shape is %d,%d' %output.shape)
        h_error = np.dot(self.weights_hidden_to_output.T,o_error)
        h_grad = hidden_outputs * (1 - hidden_outputs)
        
        # TODO: Update the weight
#         print('o_error.shape is %d,%d '%o_error.shape)
#         print('h_ouputs.shape is %d,%d' %hidden_outputs.shape)
        
        self.weights_hidden_to_output += self.lr * np.dot(o_error, hidden_outputs.T) * 1
        self.weights_input_to_hidden  += self.lr * np.dot(h_error, inputs.T) * h_grad
        
    def run(self, inputs_list):
        # Run a forward pass through the network
        inputs = np.array(inputs_list, ndmin=2).T
        
        #### Implement the forward pass here ####
        # TODO: Hidden layer
        hidden_inputs = np.dot(self.weights_input_to_hidden, inputs)# signals into hidden layer
        hidden_outputs = sigmoid(hidden_inputs)# signals from hidden layer
        
        # TODO: Output layer
        final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)# signals into final output layer
        final_outputs = final_inputs# signals from final output layer 
        
        return final_outputs
        
        

def MSE(y, Y):
    return np.mean((y-Y)**2)

"""
Explanation: Time to build the network
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
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.
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.

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 $f(x)$.

Below, you have these tasks:
1. Implement the sigmoid function to use as the activation function. Set self.activation_function in __init__ to your sigmoid function.
2. Implement the forward pass in the train method.
3. Implement the backpropagation algorithm in the train method, including calculating the output error.
4. Implement the forward pass in the run method.
End of explanation
"""


import sys

### Set the hyperparameters here ###
epochs = 200
learning_rate = 0.2
hidden_nodes = 10
output_nodes = 1

N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)

losses = {'train':[], 'validation':[]}
for e in range(epochs):
    # Go through a random batch of 128 records from the training data set
    batch = np.random.choice(train_features.index, size=128)
    for record, target in zip(train_features.ix[batch].values, 
                              train_targets.ix[batch]['cnt']):
        network.train(record, target)
    
    # Printing out the training progress
    train_loss = MSE(network.run(train_features), train_targets['cnt'].values)
    val_loss = MSE(network.run(val_features), val_targets['cnt'].values)
    sys.stdout.write("\rProgress: " + str(100 * e/float(epochs))[:4] \
                     + "% ... Training loss: " + str(train_loss)[:5] \
                     + " ... Validation loss: " + str(val_loss)[:5])
    
    losses['train'].append(train_loss)
    losses['validation'].append(val_loss)

plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.ylim(ymax=0.5)

fig, ax = plt.subplots(figsize=(8,4))

mean, std = scaled_features['cnt']
predictions = network.run(test_features)*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()

dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)

import unittest

inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3], 
                       [-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])

class TestMethods(unittest.TestCase):
    
    ##########
    # Unit tests for data loading
    ##########
    
    def test_data_path(self):
        # Test that file path to dataset has been unaltered
        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
        
    def test_data_loaded(self):
        # Test that data frame loaded
        self.assertTrue(isinstance(rides, pd.DataFrame))
    
    ##########
    # Unit tests for network functionality
    ##########

    def test_activation(self):
        network = NeuralNetwork(3, 2, 1, 0.5)
        # Test that the activation function is a sigmoid
        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))

    def test_train(self):
        # Test that weights are updated correctly on training
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()
        
        network.train(inputs, targets)
        self.assertTrue(np.allclose(network.weights_hidden_to_output, 
                                    np.array([[ 0.37275328, -0.03172939]])))
        self.assertTrue(np.allclose(network.weights_input_to_hidden,
                                    np.array([[ 0.10562014,  0.39775194, -0.29887597],
                                              [-0.20185996,  0.50074398,  0.19962801]])))

    def test_run(self):
        # Test correctness of run method
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()

        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))

suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)

"""
Explanation: Training the network
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.
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.
Choose the number of epochs
This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.
Choose the learning rate
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. A good choice to start at is 0.1. 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.
Choose the number of hidden nodes
The more hidden nodes you have, the more accurate predictions the model will make. 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.
End of explanation
"""


