#!/usr/bin/env python

# -*- coding: utf-8 -*-

# Import useful packages

from __future__ import absolute_import

from __future__ import print_function

from __future__ import division

# Hide the Configuration and Warnings

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'

import random

import numpy as np

import tensorflow as tf

from Models.DatasetAPI.DataLoader import DatasetLoader

from Models.Network.GRU import GRU

from Models.Loss_Function.Loss import loss

from Models.Evaluation_Metrics.Metrics import evaluation

# Model Name

Model = 'Gated_Recurrent_Unit'

# Clear all the stack and use GPU resources as much as possible

tf.reset_default_graph()

config = tf.ConfigProto()

config.gpu_options.allow_growth = True

sess = tf.Session(config=config)

# Your Dataset Location, for example EEG-Motor-Movement-Imagery-Dataset

# The CSV file should be named as training_set.csv, training_label.csv, test_set.csv, and test_label.csv

DIR = 'DatasetAPI/EEG-Motor-Movement-Imagery-Dataset/'

SAVE = 'Saved_Files/' + Model + '/'

if not os.path.exists(SAVE):  # If the SAVE folder doesn't exist, create one

    os.mkdir(SAVE)

# Load the dataset, here it uses one-hot representation for labels

train_data, train_labels, test_data, test_labels = DatasetLoader(DIR=DIR)

train_labels = tf.one_hot(indices=train_labels, depth=4)

train_labels = tf.squeeze(train_labels).eval(session=sess)

test_labels = tf.one_hot(indices=test_labels, depth=4)

test_labels = tf.squeeze(test_labels).eval(session=sess)

# Model Hyper-parameters

n_input  = 64   # The input size of signals at each time

max_time = 64   # The unfolded time slices of the GRU Model

gru_size = 256  # The number of GRUs inside the GRU Model

n_class   = 4     # The number of classification classes

n_hidden  = 64    # The number of hidden units in the first fully-connected layer

num_epoch = 300   # The number of Epochs that the Model run

keep_rate = 0.75  # Keep rate of the Dropout

lr = tf.constant(1e-4, dtype=tf.float32)  # Learning rate

lr_decay_epoch = 50    # Every (50) epochs, the learning rate decays

lr_decay       = 0.50  # Learning rate Decay by (50%)

batch_size = 1024

n_batch = train_data.shape[0] // batch_size

# Initialize Model Parameters (Network Weights and Biases)

# This Model only uses Two fully-connected layers, and u sure can add extra layers DIY

weights_1 = tf.Variable(tf.truncated_normal([gru_size, n_hidden], stddev=0.01))

biases_1  = tf.Variable(tf.constant(0.01, shape=[n_hidden]))

weights_2 = tf.Variable(tf.truncated_normal([n_hidden, n_class], stddev=0.01))

biases_2  = tf.Variable(tf.constant(0.01, shape=[n_class]))

# Define Placeholders

x = tf.placeholder(tf.float32, [None, 64 * 64])

y = tf.placeholder(tf.float32, [None, 4])

keep_prob = tf.placeholder(tf.float32)

# Load Model Network

prediction, features = GRU(Input=x,

                           max_time=max_time,

                           n_input=n_input,

                           gru_size=gru_size,

                           keep_prob=keep_prob,

                           weights_1=weights_1,

                           biases_1=biases_1,

                           weights_2=weights_2,

                           biases_2=biases_2)

# Load Loss Function

loss = loss(y=y, prediction=prediction, l2_norm=True)

# Load Optimizer

train_step = tf.train.AdamOptimizer(lr).minimize(loss)

# Load Evaluation Metrics

Global_Average_Accuracy = evaluation(y=y, prediction=prediction)

# Merge all the summaries

merged = tf.summary.merge_all()

train_writer = tf.summary.FileWriter(SAVE + '/train_Writer', sess.graph)

test_writer = tf.summary.FileWriter(SAVE + '/test_Writer')

# Initialize all the variables

sess.run(tf.global_variables_initializer())

for epoch in range(num_epoch + 1):

    # U can use learning rate decay or not

    # Here, we set a minimum learning rate

    # If u don't want this, u definitely can modify the following lines

    learning_rate = sess.run(lr)

    if epoch % lr_decay_epoch == 0 and epoch != 0:

        if learning_rate <= 1e-6:

            lr = lr * 1.0

            sess.run(lr)

        else:

            lr = lr * lr_decay

            sess.run(lr)

    # Randomly shuffle the training dataset and train the Model

    for batch_index in range(n_batch):

        random_batch = random.sample(range(train_data.shape[0]), batch_size)

        batch_xs = train_data[random_batch]

        batch_ys = train_labels[random_batch]

        sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: keep_rate})

    # Show Accuracy and Loss on Training and Test Set

    # Here, for training set, we only show the result of first 100 samples

    # If u want to show the result on the entire training set, please modify it.

    train_accuracy, train_loss = sess.run([Global_Average_Accuracy, loss], feed_dict={x: train_data[0:100], y: train_labels[0:100], keep_prob: 1.0})

    Test_summary, test_accuracy, test_loss = sess.run([merged, Global_Average_Accuracy, loss], feed_dict={x: test_data, y: test_labels, keep_prob: 1.0})

    test_writer.add_summary(Test_summary, epoch)

    # Show the Model Capability

    print("Iter " + str(epoch) + ", Testing Accuracy: " + str(test_accuracy) + ", Training Accuracy: " + str(train_accuracy))

    print("Iter " + str(epoch) + ", Testing Loss: " + str(test_loss) + ", Training Loss: " + str(train_loss))

    print("Learning rate is ", learning_rate)

    print('\n')

    # Save the prediction and labels for testing set

    # The "labels_for_test.csv" is the same as the "test_label.csv"

    # We will use the files to draw ROC CCurve and AUC

    if epoch == num_epoch:

        output_prediction = sess.run(prediction, feed_dict={x: test_data, y: test_labels, keep_prob: 1.0})

        np.savetxt(SAVE + "prediction_for_test.csv", output_prediction, delimiter=",")

        np.savetxt(SAVE + "labels_for_test.csv", test_labels, delimiter=",")

    # if you want to extract and save the features from fully-connected layer, use all the dataset and uncomment this.

    # All data is the total data = training data + testing data

    # We use the features from the overall dataset

    # ML models might be used to classify the features further

    # if epoch == num_epoch:

    #     Features = sess.run(features, feed_dict={x: all_data, y: all_labels, keep_prob: 1.0})

    #     np.savetxt(SAVE + "Features.csv", features, delimiter=",")

train_writer.close()

test_writer.close()

sess.close()