tensorflow在文本处理中的使用——词袋

import tensorflow as tf
import matplotlib.pyplot as plt
import os
import numpy as np
import csv
import string
import requests
import io
from zipfile import ZipFile
from tensorflow.contrib import learn
from tensorflow.python.framework import ops
ops.reset_default_graph()

# Start a graph session
sess = tf.Session()

# Check if data was downloaded, otherwise download it and save for future use
save_file_name = os.path.join(‘temp‘,‘temp_spam_data.csv‘)
if os.path.isfile(save_file_name):
    text_data = []
    with open(save_file_name, ‘r‘) as temp_output_file:
        reader = csv.reader(temp_output_file)
        for row in reader:
            text_data.append(row)
else:
    zip_url = ‘http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip‘
    r = requests.get(zip_url)
    z = ZipFile(io.BytesIO(r.content))
    file = z.read(‘SMSSpamCollection‘)
    # Format Data
    text_data = file.decode()
    text_data = text_data.encode(‘ascii‘,errors=‘ignore‘)
    text_data = text_data.decode().split(‘\n‘)
    text_data = [x.split(‘\t‘) for x in text_data if len(x)>=1]
    
    # And write to csv
    with open(save_file_name, ‘w‘) as temp_output_file:
        writer = csv.writer(temp_output_file)
        writer.writerows(text_data)

texts = [x[1] for x in text_data]
target = [x[0] for x in text_data]

# Relabel ‘spam‘ as 1, ‘ham‘ as 0
target = [1 if x==‘spam‘ else 0 for x in target]

# Normalize text，为减少无意义的词汇，对文本进行规则化处理
# Lower case
texts = [x.lower() for x in texts]

# Remove punctuation
texts = [‘‘.join(c for c in x if c not in string.punctuation) for x in texts]

# Remove numbers
texts = [‘‘.join(c for c in x if c not in ‘0123456789‘) for x in texts]

# Trim extra whitespace
texts = [‘ ‘.join(x.split()) for x in texts]

# Plot histogram of text lengths文本数据中的单词数的直方图
text_lengths = [len(x.split()) for x in texts]
text_lengths = [x for x in text_lengths if x < 50]
plt.hist(text_lengths, bins=25)
plt.title(‘Histogram of # of Words in Texts‘)

# Choose max text word length at 25，也可以设为30或者40
sentence_size = 25
min_word_freq = 3

# Setup vocabulary processor
vocab_processor = learn.preprocessing.VocabularyProcessor(sentence_size, min_frequency=min_word_freq)

# Have to fit transform to get length of unique words.
vocab_processor.fit_transform(texts)
embedding_size = len(vocab_processor.vocabulary_)

# Split up data set into train/test
train_indices = np.random.choice(len(texts), round(len(texts)*0.8), replace=False)
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))
texts_train = [x for ix, x in enumerate(texts) if ix in train_indices]
texts_test = [x for ix, x in enumerate(texts) if ix in test_indices]
target_train = [x for ix, x in enumerate(target) if ix in train_indices]
target_test = [x for ix, x in enumerate(target) if ix in test_indices]

# Setup Index Matrix for one-hot-encoding，使用该矩阵为每个单词查找稀疏向量
identity_mat = tf.diag(tf.ones(shape=[embedding_size]))

# Create variables for logistic regression
A = tf.Variable(tf.random_normal(shape=[embedding_size,1]))
b = tf.Variable(tf.random_normal(shape=[1,1]))

# Initialize placeholders
x_data = tf.placeholder(shape=[sentence_size], dtype=tf.int32)
y_target = tf.placeholder(shape=[1, 1], dtype=tf.float32)

# Text-Vocab Embedding，使用tf的嵌入查找函数来映射句子中的单词为单位矩阵的one-hot向量，再进行求和
# tf.nn.embedding_lookup(y,x)x为索引，找出y中对应索引的值
x_embed = tf.nn.embedding_lookup(identity_mat, x_data)
x_col_sums = tf.reduce_sum(x_embed, 0)

# Declare model operations
x_col_sums_2D = tf.expand_dims(x_col_sums, 0)

model_output = tf.add(tf.matmul(x_col_sums_2D, A), b)

# Declare loss function (Cross Entropy loss)
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(model_output, y_target))

# Prediction operation
prediction = tf.sigmoid(model_output)

# Declare optimizer
my_opt = tf.train.GradientDescentOptimizer(0.001)
train_step = my_opt.minimize(loss)

# Intitialize Variables
init = tf.initialize_all_variables()
sess.run(init)

# Start Logistic Regression
print(‘Starting Training Over {} Sentences.‘.format(len(texts_train)))
loss_vec = []
train_acc_all = []
train_acc_avg = []
for ix, t in enumerate(vocab_processor.fit_transform(texts_train)):
    y_data = [[target_train[ix]]]
    
    
    sess.run(train_step, feed_dict={x_data: t, y_target: y_data})
    temp_loss = sess.run(loss, feed_dict={x_data: t, y_target: y_data})
    loss_vec.append(temp_loss)
    
    if (ix+1)%10==0:
        print(‘Training Observation #‘ + str(ix+1) + ‘: Loss = ‘ + str(temp_loss))
        
    # Keep trailing average of past 50 observations accuracy
    # Get prediction of single observation
    [[temp_pred]] = sess.run(prediction, feed_dict={x_data:t, y_target:y_data})
    # Get True/False if prediction is accurate
    train_acc_temp = target_train[ix]==np.round(temp_pred)
    train_acc_all.append(train_acc_temp)
    if len(train_acc_all) >= 50:
        train_acc_avg.append(np.mean(train_acc_all[-50:]))

# Get test set accuracy
print(‘Getting Test Set Accuracy For {} Sentences.‘.format(len(texts_test)))
test_acc_all = []
for ix, t in enumerate(vocab_processor.fit_transform(texts_test)):
    y_data = [[target_test[ix]]]
    
    if (ix+1)%50==0:
        print(‘Test Observation #‘ + str(ix+1))    
    
    # Keep trailing average of past 50 observations accuracy
    # Get prediction of single observation
    [[temp_pred]] = sess.run(prediction, feed_dict={x_data:t, y_target:y_data})
    # Get True/False if prediction is accurate
    test_acc_temp = target_test[ix]==np.round(temp_pred)
    test_acc_all.append(test_acc_temp)

print(‘\nOverall Test Accuracy: {}‘.format(np.mean(test_acc_all)))

# Plot training accuracy over time
plt.plot(range(len(train_acc_avg)), train_acc_avg, ‘k-‘, label=‘Train Accuracy‘)
plt.title(‘Avg Training Acc Over Past 50 Generations‘)
plt.xlabel(‘Generation‘)
plt.ylabel(‘Training Accuracy‘)
plt.show()