"""
Python 3: line pattern prediction using deep learning


Training data 80% . Test data 20%

Requirements:
        Rename dataset to coinbasedata.csv or restate the variable: dataset in line 23

Results:
		Figure shows a transforming orange line as the algorithm learns to identify the trend patterns in the test data. 
        Learning achieved in 4 epochs.


"""

# Import
import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt

dataset = 'coinbasedata.csv'

data = pd.read_csv(dataset)

# Drop date variable
data = data[['Open','High','Close']]

# Dimensions of dataset
n = data.shape[0]
p = data.shape[1]

# Make data a np.array
data = data.values

# Training and test data
train_start = 0
train_end = int(np.floor(0.8*n))
test_start = train_end + 1
test_end = n
data_train = data[np.arange(train_start, train_end),:]
data_test = data[np.arange(test_start, test_end),:]

# Scale data
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler.fit(data_train)
data_train = scaler.transform(data_train)
data_test = scaler.transform(data_test)

#build x nd y
X_train = data_train[:, 1:]
y_train = data_train[:, 0]
X_test = data_test[:, 1:]
y_test = data_test[:, 0]

#placement holder in training data
n_placement = X_train.shape[1]

# Neurons
n_neurons_1 = n_placement *4
n_neurons_2 = n_neurons_1/2 
n_neurons_3 = n_neurons_2/2
n_neurons_4 = n_neurons_3/2

# Session
net = tf.InteractiveSession()

# Placeholder
X = tf.placeholder(dtype=tf.float32, shape=[None, n_placement])
Y = tf.placeholder(dtype=tf.float32, shape=[None])

# Initializers
sigma = 1
weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=sigma)
bias_initializer = tf.zeros_initializer()

# Hidden weights
W_hidden_1 = tf.Variable(weight_initializer([n_placement, n_neurons_1]))
bias_hidden_1 = tf.Variable(bias_initializer([n_neurons_1]))
W_hidden_2 = tf.Variable(weight_initializer([int(n_neurons_1), int(n_neurons_2)]))
bias_hidden_2 = tf.Variable(bias_initializer([n_neurons_2]))
W_hidden_3 = tf.Variable(weight_initializer([int(n_neurons_2), int(n_neurons_3)]))
bias_hidden_3 = tf.Variable(bias_initializer([int(n_neurons_3)]))
W_hidden_4 = tf.Variable(weight_initializer([int(n_neurons_3), int(n_neurons_4)]))
bias_hidden_4 = tf.Variable(bias_initializer([n_neurons_4]))

# Output weights
W_out = tf.Variable(weight_initializer([int(n_neurons_4), 1]))
bias_out = tf.Variable(bias_initializer([1]))

## Hidden layer - 3 layers for better line prediction
hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1))
hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2))
hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3))
hidden_4 = tf.nn.relu(tf.add(tf.matmul(hidden_3, W_hidden_4), bias_hidden_4))

# Output layer (transpose!)
out = tf.transpose(tf.add(tf.matmul(hidden_4, W_out), bias_out))

# Cost function
mse = tf.reduce_mean(tf.squared_difference(out, Y))

# Optimizer trend detection
opt = tf.train.AdamOptimizer().minimize(mse)

# Init
net.run(tf.global_variables_initializer())

# Setup plot
plt.ion()
fig = plt.figure()
ax1 = fig.add_subplot(111)
line1, = ax1.plot(y_test)
line2, = ax1.plot(y_test * 0.5)
plt.show()

# Fit neural net
batch_size = 10000
mse_train = []
mse_test = []

# Run 
epochs = 5
for e in range(epochs):

    # Shuffle training data
    shuffle_indices = np.random.permutation(np.arange(len(y_train)))
    X_train = X_train[shuffle_indices]
    y_train = y_train[shuffle_indices]

    # Minibatch training
    for i in range(0, len(y_train) // batch_size):
        start = i * batch_size
        batch_x = X_train[start:start + batch_size]
        batch_y = y_train[start:start + batch_size]
        # Run optimizer with batch
        net.run(opt, feed_dict={X: batch_x, Y: batch_y})

        # Show progress
        if np.mod(i, 50) == 0:
            # MSE train and test
            mse_train.append(net.run(mse, feed_dict={X: X_train, Y: y_train}))
            mse_test.append(net.run(mse, feed_dict={X: X_test, Y: y_test}))
            print('MSE Train: ', mse_train[-1])
            print('MSE Test: ', mse_test[-1])
            # Prediction
            pred = net.run(out, feed_dict={X: X_test})
            line2.set_ydata(pred)
            plt.title('Epoch ' + str(e) + ', Batch ' + str(i))
            plt.pause(0.01)
