StockPrediction_mv_Online.py

import tensorflow as tf
import matplotlib as mplt
mplt.use('agg')  # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
import numpy as np
import random
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from math import sqrt


class RNNConfig():
    input_size = 1
    num_steps = 2
    lstm_size = 128
    num_layers = 1
    keep_prob = 0.8
    batch_size = 1
    init_learning_rate = 0.01
    learning_rate_decay = 0.99
    features = 2
    test_ratio = 0.2
    fileName = 'AIG.csv'
    graph = tf.Graph()
    column1_min = 10
    column1_max = 2000
    column2_min = 0
    column2_max = 50000000
    column1 = 'Close'
    column2 = 'Volume'


config = RNNConfig()

def segmentation(data):

    seq = [price for tup in data[['Volume', 'Close']].values for price in tup]

    seq = np.array(seq)

    # split into items of features
    seq = [np.array(seq[i * config.features: (i + 1) * config.features])
           for i in range(len(seq) // config.features)]

    # split into groups of num_steps
    X = np.array([seq[i: i + config.num_steps] for i in range(len(seq) - config.num_steps)])

    y = np.array([seq[i + config.num_steps] for i in range(len(seq) - config.num_steps)])

    # get only close value
    y = [y[i][1] for i in range(len(y))]

    return X, y

def scale(data):

    data[config.column1] = (data[config.column1] - config.column1_min) / (config.column1_max - config.column1_min)

    data[config.column2] = (data[config.column2] - config.column2_min) / (config.column2_max - config.column2_min)

    return data


def pre_process():

    stock_data = pd.read_csv(config.fileName)
    stock_data = stock_data.reindex(index=stock_data.index[::-1])

    # ---for segmenting original data ---------------------------------
    original_data = pd.read_csv(config.fileName)
    original_data = original_data.reindex(index=original_data.index[::-1])

    train_data = stock_data
    original_data = original_data

    # -------------- processing train data---------------------------------------

    scaled_train_data = scale(train_data)
    train_X, train_y = segmentation(scaled_train_data)

    # ----segmenting original test data-----------------------------------------------

    nonescaled_X, nonescaled_y = segmentation(original_data)


    return train_X, train_y, nonescaled_y


def generate_batches(train_X, train_y,nonescaled_y, batch_size):
    num_batches = int(len(train_X)) // batch_size
    if batch_size * num_batches < len(train_X):
        num_batches += 1

    batch_indices = range(num_batches)
    for j in batch_indices:
        batch_X = train_X[j * batch_size: (j + 1) * batch_size]
        batch_y = train_y[j * batch_size: (j + 1) * batch_size]
        nonesclaed_batch_y = nonescaled_y[j * batch_size: (j + 1) * batch_size]
        assert set(map(len, batch_X)) == {config.num_steps}
        yield batch_X, batch_y,nonesclaed_batch_y

def mean_absolute_percentage_error(y_true, y_pred):
    y_true, y_pred = np.array(y_true), np.array(y_pred)
    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100

def plot(RMSE):

    days = range(len(RMSE))
    plt.plot(days, RMSE, label='RMSE')
    plt.legend(loc='upper left', frameon=False)
    plt.xlabel("day")
    plt.ylabel("closing price")
    # plt.ylim((min(test_y), max(test_y)))
    plt.grid(ls='--')
    plt.savefig("Stock price Prediction VS Truth mv online.png", format='png', bbox_inches='tight', transparent=False)
    plt.close()


def train_test():
    train_X, train_y, nonescaled_y= pre_process()

    # Create the graph object
    graph = tf.Graph()
    # Add nodes to the graph
    with graph.as_default():

        tf.set_random_seed(1)

        learning_rate = tf.placeholder(tf.float32, None, name="learning_rate")
        inputs = tf.placeholder(tf.float32, [None, config.num_steps, config.features], name="inputs")
        targets = tf.placeholder(tf.float32, [None], name="targets")
        keep_prob = tf.placeholder(tf.float32, None, name="keep_prob")

        lstm_cell = tf.contrib.rnn.LSTMCell(config.lstm_size, state_is_tuple=True)

        # Add dropout to the cell
        drop = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=keep_prob)

        # Stack up multiple LSTM layers, for deep learning
        cell = tf.contrib.rnn.MultiRNNCell([drop] * config.num_layers)

        val1, _ = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)

        val = tf.transpose(val1, [1, 0, 2])

        last = tf.gather(val, int(val.get_shape()[0]) - 1, name="last_lstm_output")

        weight = tf.Variable(tf.truncated_normal([config.lstm_size, config.input_size]))
        bias = tf.Variable(tf.constant(0.1, shape=[config.input_size]))

        prediction = tf.matmul(last, weight) + bias

        loss = tf.reduce_mean(tf.square(prediction - targets))
        optimizer = tf.train.AdamOptimizer(learning_rate)
        minimize = optimizer.minimize(loss)

    # --------------------training------------------------------------------------------

    with tf.Session(graph=graph) as sess:
        tf.set_random_seed(1)

        tf.global_variables_initializer().run()

        iteration = 1
        RMSE = []


        for batch_X, batch_y, nonescaled_batch_y in generate_batches(train_X, train_y, nonescaled_y, config.batch_size):

            train_data_feed = {
                inputs: batch_X,
                targets: batch_y,
                learning_rate: config.init_learning_rate,
                keep_prob: config.keep_prob
            }

            test_pred = sess.run(prediction, train_data_feed)

            test_pred[0][0] = (test_pred[0][0] * (config.column1_max - config.column1_min)) + config.column1_min



            meanSquaredError = mean_squared_error(nonescaled_batch_y, test_pred[0])
            rootMeanSquaredError = sqrt(meanSquaredError)
            print("RMSE:", rootMeanSquaredError)

            RMSE.append(rootMeanSquaredError)

            train_loss, _, value = sess.run([loss, minimize, val1], train_data_feed)

            print("Iteration: {}".format(iteration),
                  "Train loss: {:.3f}".format(train_loss))

            iteration += 1

        saver = tf.train.Saver()
        saver.save(sess, "checkpoints_stock/stock_pred_online.ckpt")

        plot(RMSE)


if __name__ == '__main__':
    train_test()