import tensorflow as tf

import matplotlib.pyplot as plt

import csv

import numpy as np

import os

import string

import requests

import io

import nltk

from zipfile import ZipFile

from sklearn.feature_extraction.text import TfidfVectorizer

from tensorflow.python.framework import ops

ops.reset_default_graph()

# Start a graph session

sess = tf.Session()

#定义批处理大小和特征向量长度

batch_size = 200

max_features = 1000

# Define tokenizer

def tokenizer(text):

    words = nltk.word_tokenize(text)

    return words

# Create TF-IDF of texts

tfidf = TfidfVectorizer(tokenizer=tokenizer, stop_words='english', max_features=max_features)

sparse_tfidf_texts = tfidf.fit_transform(texts)

# Split up data set into train/test

train_indices = np.random.choice(sparse_tfidf_texts.shape[0], round(0.8*sparse_tfidf_texts.shape[0]), replace=False)

test_indices = np.array(list(set(range(sparse_tfidf_texts.shape[0])) - set(train_indices)))

texts_train = sparse_tfidf_texts[train_indices]

texts_test = sparse_tfidf_texts[test_indices]

target_train = np.array([x for ix, x in enumerate(target) if ix in train_indices])

target_test = np.array([x for ix, x in enumerate(target) if ix in test_indices])

# Create variables for logistic regression设置权重和偏置项

A = tf.Variable(tf.random_normal(shape=[max_features,1]))

b = tf.Variable(tf.random_normal(shape=[1,1]))

# Initialize placeholders设置数据的占位符

x_data = tf.placeholder(shape=[None, max_features], dtype=tf.float32)

y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)

# Declare logistic model (sigmoid in loss function)

model_output = tf.add(tf.matmul(x_data, A), b)

# Declare loss function (Cross Entropy loss)损失函数计算

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(model_output, y_target))

# Actual Prediction 预测结果

prediction = tf.round(tf.sigmoid(model_output))

predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32)

accuracy = tf.reduce_mean(predictions_correct)

# Declare optimizer 用GD优化算法更新权重，最小化损失

my_opt = tf.train.GradientDescentOptimizer(0.0025)

train_step = my_opt.minimize(loss) 

# Intitialize Variables

init = tf.initialize_all_variables()

sess.run(init)

# Start Logistic Regression

train_loss = []

test_loss = []

train_acc = []

test_acc = []

i_data = []

for i in range(10000):

    rand_index = np.random.choice(texts_train.shape[0], size=batch_size)

    rand_x = texts_train[rand_index].todense()

    rand_y = np.transpose([target_train[rand_index]])

    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})

    # Only record loss and accuracy every 100 generations，100回记录，500回输出状态

    if (i+1)%100==0:

        i_data.append(i+1)

        train_loss_temp = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})

        train_loss.append(train_loss_temp)

        test_loss_temp = sess.run(loss, feed_dict={x_data: texts_test.todense(), y_target: np.transpose([target_test])})

        test_loss.append(test_loss_temp)

        train_acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x, y_target: rand_y})

        train_acc.append(train_acc_temp)

        test_acc_temp = sess.run(accuracy, feed_dict={x_data: texts_test.todense(), y_target: np.transpose([target_test])})

        test_acc.append(test_acc_temp)

    if (i+1)%500==0:

        acc_and_loss = [i+1, train_loss_temp, test_loss_temp, train_acc_temp, test_acc_temp]

        acc_and_loss = [np.round(x,2) for x in acc_and_loss]

        print('Generation # {}. Train Loss (Test Loss): {:.2f} ({:.2f}). Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss))

# Plot loss over time

plt.plot(i_data, train_loss, 'k-', label='Train Loss')

plt.plot(i_data, test_loss, 'r--', label='Test Loss', linewidth=4)

plt.title('Cross Entropy Loss per Generation')

plt.xlabel('Generation')

plt.ylabel('Cross Entropy Loss')

plt.legend(loc='upper right')

plt.show()

# Plot train and test accuracy

plt.plot(i_data, train_acc, 'k-', label='Train Set Accuracy')

plt.plot(i_data, test_acc, 'r--', label='Test Set Accuracy', linewidth=4)

plt.title('Train and Test Accuracy')

plt.xlabel('Generation')

plt.ylabel('Accuracy')

plt.legend(loc='lower right')

plt.show()