import tensorflow as tf
import matplotlib.pyplot as plt
import requests
import numpy as np
sess = tf.Session()

birthdata_url = 'https://github.com/nfmcclure/tensorflow_cookbook/raw/master/01_Introduction/07_Working_with_Data_Sources/birthweight_data/birthweight.dat'
birth_file = requests.get(birthdata_url)
birth_data = birth_file.text.split('\r\n')
birth_header = birth_data[0].split('\t')
birth_data = [[float(x) for x in y.split('\t') if len(x)>=1] for y in birth_data[1:] if len(y) >=1]
birth_data = [[float(x) for x in row] for row in birth_data]

y_vals = np.array([x[0] for x in birth_data])
x_vals = np.array([x[2:9] for x in birth_data])
train_indices = np.random.choice(len(x_vals), round(len(x_vals) * 0.8), replace=False)
test_indices=  np.array(list(set(range(len(x_vals))) - set(train_indices)))

x_vals_train = x_vals[train_indices]
x_vals_test = x_vals[test_indices]
y_vals_train = y_vals[train_indices]
y_vals_test = y_vals[test_indices]

def normalize_cols(m):
    col_max = m.max(axis=0)
    col_min = m.min(axis=0)
    return (m-col_min) / (col_max - col_min)

x_vals_train = np.nan_to_num(normalize_cols(x_vals_train))
x_vals_test = np.nan_to_num(normalize_cols(x_vals_test))

batch_size = 90
x_data = tf.placeholder(shape=[None,7],dtype=tf.float32)
y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)

def init_variable(shape):
    return tf.Variable(tf.random_normal(shape=shape))

def logistic(input_layer, multiplication_weight, bias_weight, activation=True):
    linear_layer = tf.add(tf.matmul(input_layer, multiplication_weight), bias_weight)
    if activation:
        return tf.nn.sigmoid(linear_layer)
    else:
        return linear_layer

A1 = init_variable(shape=[7,14])
b1 = init_variable(shape=[14])
logistic_layer1 = logistic(x_data, A1,b1)

A2 = init_variable(shape=[14,5])
b2 = init_variable(shape=[5])
logistic_layer2 = logistic(logistic_layer1, A2, b2)

A3 = init_variable(shape=[5,1])
b3 = init_variable(shape=[1])
final_output = logistic(logistic_layer2, A3,b3,activation=False)

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=final_output, labels=y_target))
my_opt = tf.train.AdamOptimizer(learning_rate=0.002)
train_step = my_opt.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)

prediction = tf.round(tf.nn.sigmoid(final_output))
prediction_correct = tf.cast(tf.equal(prediction, y_target),tf.float32)
accuracy = tf.reduce_mean(prediction_correct)

loss_vec = []
train_acc = []
test_acc = []
for i in range(1500):
    rand_index = np.random.choice(len(x_vals_train),size=batch_size)
    rand_x = x_vals_train[rand_index]
    rand_y = np.transpose([y_vals_train[rand_index]])
    sess.run(train_step, feed_dict={x_data: rand_x, y_target:rand_y})
    temp_loss = sess.run(loss, feed_dict={x_data:rand_x, y_target:rand_y})
    loss_vec.append(temp_loss)
    temp_acc_train = sess.run(accuracy, feed_dict={x_data:x_vals_train,y_target:np.transpose([y_vals_train])})
    train_acc.append(temp_acc_train)
    temp_acc_test = sess.run(accuracy, feed_dict={x_data:x_vals_test, y_target:np.transpose([y_vals_test])})
    test_acc.append(temp_acc_test)
    if (i+1) % 150 ==0:
        print('Loss = ' + str(temp_loss))

plt.plot(loss_vec,'k-')
plt.title('Cross Entropy Loss per Generation')
plt.xlabel('Generation')
plt.ylabel('Cross Entropy Loss')
plt.show()

plt.plot(train_acc, 'k-', label='Train Set Accuracy')
plt.plot(test_acc, 'r--', label = 'Test Set Accuracy')
plt.title('Train and Test Accuracy')
plt.xlabel('Generation')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.show()