tensorflow_word2vec.py

#!/usr/bin/python
# -*- coding:utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
import zipfile

import numpy as np
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
# Read the data into a list of strings.
def read_data(filename):
    with open(filename,encoding='utf8') as f:
        data = tf.compat.as_str(f.read()).split()
    return data

# 单词表
root_path = os.getcwd() + os.sep
filename = os.path.join(root_path+"data", "segment.txt")
words = read_data(filename)
# print(words)
# print(collections.Counter(words).most_common(10))
vocabulary_size = 50000
def build_dataset(words, vocabulary_size):
    count = [['UNK', -1]]
    # extend追加一个列表
    # Counter用来统计每个词出现的次数
    # most_common返回一个TopN列表，只留50000个单词包括UNK
    # c = Counter('abracadabra')
    # c.most_common()
    # [('a', 5), ('r', 2), ('b', 2), ('c', 1), ('d', 1)]
    # c.most_common(3)
    # [('a', 5), ('r', 2), ('b', 2)]
    # 前50000个出现次数最多的词
    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
    # 生成 dictionary，词对应编号, word:id(0-49999)
    # 词频越高编号越小
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary)
    # data把数据集的词都编号
    data = list()
    unk_count = 0
    for word in words:
        if word in dictionary:
            index = dictionary[word]
        else:
            index = 0  # dictionary['UNK']
            unk_count += 1
        data.append(index)
    # 记录UNK词的数量
    count[0][1] = unk_count
    # 编号对应词的字典
    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    return data, count, dictionary, reverse_dictionary

# data 数据集，编号形式
# count 前50000个出现次数最多的词
# dictionary 词对应编号
# reverse_dictionary 编号对应词

data, count, dictionary, reverse_dictionary = build_dataset(words, vocabulary_size)
del words  # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0

# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window

    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)

    span = 2 * skip_window + 1  # [ skip_window target skip_window ]
    buffer = collections.deque(maxlen=span)
    # [ skip_window target skip_window ]
    # [ skip_window target skip_window ]
    # [ skip_window target skip_window ]

    #     [0 1 2 3 4 5 6 7 8 9 ...]
    #            t     i
    # 循环3次
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    # 获取batch和labels
    for i in range(batch_size // num_skips):
        target = skip_window  # target label at the center of the buffer
        targets_to_avoid = [skip_window]
        # 循环2次，一个目标单词对应两个上下文单词
        for j in range(num_skips):
            while target in targets_to_avoid:
                # 可能先拿到前面的单词也可能先拿到后面的单词
                target = random.randint(0, span - 1)
            targets_to_avoid.append(target)
            batch[i * num_skips + j] = buffer[skip_window]
            labels[i * num_skips + j, 0] = buffer[target]
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    # Backtrack a little bit to avoid skipping words in the end of a batch
    # 回溯3个词。因为执行完一个batch的操作之后，data_index会往右多偏移span个位置
    data_index = (data_index + len(data) - span) % len(data)
    return batch, labels


# 打印sample data
batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
    print(batch[i], reverse_dictionary[batch[i]],
          '->', labels[i, 0], reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.
batch_size = 128
# 词向量维度
embedding_size = 100  # Dimension of the embedding vector.
skip_window = 1  # How many words to consider left and right.
num_skips = 2  # How many times to reuse an input to generate a label.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16  # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
# 从0-100抽取16个整数，无放回抽样
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
# 负采样样本数
num_sampled = 64  # Number of negative examples to sample.

graph = tf.Graph()
with graph.as_default():
    # Input data.
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # Ops and variables pinned to the CPU because of missing GPU implementation
    #     with tf.device('/cpu:0'):
    # 词向量
    # Look up embeddings for inputs.
    embeddings = tf.Variable(
        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
    # embedding_lookup(params,ids)其实就是按照ids顺序返回params中的第ids行
    # 比如说，ids=[1,7,4],就是返回params中第1,7,4行。返回结果为由params的1,7,4行组成的tensor
    # 提取要训练的词
    embed = tf.nn.embedding_lookup(embeddings, train_inputs)

    # Construct the variables for the noise-contrastive estimation(NCE) loss
    nce_weights = tf.Variable(
        tf.truncated_normal([vocabulary_size, embedding_size],
                            stddev=1.0 / math.sqrt(embedding_size)))
    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

    # Compute the average NCE loss for the batch.
    # tf.nce_loss automatically draws a new sample of the negative labels each
    # time we evaluate the loss.
    loss = tf.reduce_mean(
        tf.nn.nce_loss(weights=nce_weights,
                       biases=nce_biases,
                       labels=train_labels,
                       inputs=embed,
                       num_sampled=num_sampled,
                       num_classes=vocabulary_size))

    # Construct the SGD optimizer using a learning rate of 1.0.
    optimizer = tf.train.GradientDescentOptimizer(1).minimize(loss)

    # Compute the cosine similarity between minibatch examples and all embeddings.
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    # 抽取一些常用词来测试余弦相似度
    valid_embeddings = tf.nn.embedding_lookup(
        normalized_embeddings, valid_dataset)
    # valid_size == 16
    # [16,1] * [1*50000] = [16,50000]
    similarity = tf.matmul(
        valid_embeddings, normalized_embeddings, transpose_b=True)

    # Add variable initializer.
    init = tf.global_variables_initializer()

# Step 5: Begin training.
num_steps = 100001
final_embeddings = []

with tf.Session(graph=graph) as session:
    # We must initialize all variables before we use them.
    init.run()
    print("Initialized")

    average_loss = 0
    for step in xrange(num_steps):
        # 获取一个批次的target，以及对应的labels，都是编号形式的
        batch_inputs, batch_labels = generate_batch(
            batch_size, num_skips, skip_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # We perform one update step by evaluating the optimizer op (including it
        # in the list of returned values for session.run()
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        # 计算训练2000次的平均loss
        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            # The average loss is an estimate of the loss over the last 2000 batches.
            print("Average loss at step ", step, ": ", average_loss)
            average_loss = 0

        # Note that this is expensive (~20% slowdown if computed every 500 steps)
        if step % 20000 == 0:
            sim = similarity.eval()
            # 计算验证集的余弦相似度最高的词
            for i in xrange(valid_size):
                # 根据id拿到对应单词
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8  # number of nearest neighbors
                # 从大到小排序，排除自己本身，取前top_k个值
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = "Nearest to %s:" % valid_word
                for k in xrange(top_k):
                    close_word = reverse_dictionary[nearest[k]]
                    log_str = "%s %s," % (log_str, close_word)
                print(log_str)
    # 训练结束得到的词向量
    final_embeddings = normalized_embeddings.eval()

# 生成模型所需要的格式：优势 -0.012575351 0.04629608 -0.034832083
vecfile = os.path.join(root_path+"data", "vec.txt")
with open(vecfile,'w',encoding='utf8') as vec:
    for index,embedding in enumerate(final_embeddings):
        vec.write(reverse_dictionary[index])
        vec.write(' ')
        for emb in embedding:
            vec.write(str(emb))
            vec.write(' ')
        vec.write('\n')