navalMine.py

#Import statements
#Libraries used: Matplotlib, tensorflow, numpy, sklearn
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split

#reading the dataset
def read_dataset():
    df = pd.read_csv('/Users/omkarshanbhag/Desktop/kepler-labelled-time-series-data/exoTrain.csv')
    X = df[df.columns[1:]].values
    y = df[df.columns[0]]

    #Encode the dependent variable
    encoder = LabelEncoder()
    encoder.fit(y)
    y = encoder.transform(y)
    Y = one_hot_encode(y)
    print(X.shape)
    return(X, Y)


#Define the encoder fuction
def one_hot_encode(labels):
    n_labels = len(labels)
    n_unique_labels = len(np.unique(labels))
    one_hot_encode = np.zeros((n_labels, n_unique_labels))
    one_hot_encode[np.arange(n_labels), labels] = 1
    return one_hot_encode


#Read the dataset
X, Y = read_dataset()

#Shuffle the dataset to mix up the rows.
X, Y = shuffle(X, Y, random_state=1)

#Convert the dataset into train and test part
train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.20, random_state = 425)

# Inspect the shape of the training and testing
print(train_x.shape)
print(train_y.shape)
print(test_x.shape)

#Define the important parameters and variable to work with the tensors
learning_rate = 0.3
training_epochs = 1000
cost_history = np.empty(shape= [1], dtype = float)
n_dim = X.shape[1]
print("n_dim", n_dim)
n_class = 2
model_path = ("/Users/omkarshanbhag/Desktop/ML")

# Define the number of hidden layers and number of neurons for each layer
n_hidden_1 = 60
n_hidden_2 = 60
n_hidden_3 = 60
n_hidden_4 = 60

x = tf.placeholder(tf.float32, [None, n_dim])
W = tf.Variable(tf.zeros([n_dim, n_class]))
b = tf.Variable(tf.zeros([n_class]))
y_ = tf.placeholder(tf.float32, [None, n_class])

#Define the model
def multilayer_percepteron(x, weights, biases):

    #hidden layer
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.sigmoid(layer_1)

    #hidden layer with sigmoid activation
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.sigmoid(layer_2)

    #hidden layer with sigmoid activation
    layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
    layer_3 = tf.nn.sigmoid(layer_3)

    #hidden layer with RELU activation
    layer_4 = tf.add(tf.matmul(layer_3, weights['h4']), biases['b4'])
    layer_4 = tf.nn.relu(layer_4)

    #output layer with linear activation
    out_layer = tf.matmul(layer_4, weights['out']) + biases['out']
    return out_layer

#Define the weights and the biases for each layer

weights = {
    'h1': tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])),
    'h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),
    'h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3])),
    'h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4])),
    'out': tf.Variable(tf.truncated_normal([n_hidden_4, n_class]))
}

biases = {
    'b1': tf.Variable(tf.truncated_normal([n_hidden_1])),
    'b2': tf.Variable(tf.truncated_normal([n_hidden_2])),
    'b3': tf.Variable(tf.truncated_normal([n_hidden_3])),
    'b4': tf.Variable(tf.truncated_normal([n_hidden_4])),
    'out': tf.Variable(tf.truncated_normal([n_class])),
}

#Initialize all of the variables
init = tf.global_variables_initializer()

#Saver object to save the model
saver = tf.train.Saver()

#Call the model
y = multilayer_percepteron(x, weights, biases)

#Define the cost function optimizer
cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y, labels = y_))
training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)

sess = tf.Session()
sess.run(init)

#Calculate cost and accuracy for each epoch

mse_history = []
accuracy_history = []

for epoch in range(training_epochs):
    sess.run(training_step, feed_dict = {x: train_x, y_: train_y})
    cost = sess.run(cost_function, feed_dict = {x: train_x, y_: train_y})
    cost_history = np.append(cost_history, cost)
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    pred_y = sess.run(y, feed_dict={x: test_x})
    mse = tf.reduce_mean(tf.square(pred_y - test_y))
    mse_ = sess.run(mse)
    mse_history.append(mse_)
    accuracy = (sess.run(accuracy, feed_dict={x:train_x, y_: train_y}))
    accuracy_history.append(accuracy)

    print('epoch : ', epoch, ' - ', 'cost: ', cost, " - MSE: ", mse_, "- Train Accuracy: ", accuracy)

save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)

#Plot mse and accuracy graph

plt.plot(mse_history, 'r')
plt.show()
plt.plot(accuracy_history)
plt.show()

#Print the final accuracy

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Test Accuracy: ", sess.run(accuracy, feed_dict = {x: test_x, y_: test_y}))

#Print the final mean squared error

pred_y = sess.run(y, feed_dict = {x: test_x})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
print("MSE: %.4f" % sess.run(mse))