tensorflow在文本处理中的使用——辅助函数

代码来源于：tensorflow机器学习实战指南（曾益强 译，2017年9月）——第七章：自然语言处理

代码地址：https://github.com/nfmcclure/tensorflow-cookbook

在讲述skip-gram,CBOW,Word2Vec,Doc2Vec模型时需要复用的函数

• 加载数据函数
• 归一化文本函数
• 生成词汇表函数
• 生成单词索引表
• 生成批量数据函数

---

加载数据函数

# Load the movie review data
# Check if data was downloaded, otherwise download it and save for future use
def load_movie_data(data_folder_name):
    pos_file = os.path.join(data_folder_name, 'rt-polarity.pos')
    neg_file = os.path.join(data_folder_name, 'rt-polarity.neg')
 
    # Check if files are already downloaded
    if os.path.isfile(pos_file):
        pos_data = []
        with open(pos_file, 'r') as temp_pos_file:
            for row in temp_pos_file:
                pos_data.append(row)
        neg_data = []
        with open(neg_file, 'r') as temp_neg_file:
            for row in temp_neg_file:
                neg_data.append(row)
    else: # If not downloaded, download and save
        movie_data_url = 'http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz'
        stream_data = urllib.request.urlopen(movie_data_url)
        tmp = io.BytesIO()
        while True:
            s = stream_data.read(16384)
            if not s:
                break
            tmp.write(s)
            stream_data.close()
            tmp.seek(0)
 
        tar_file = tarfile.open(fileobj=tmp, mode="r:gz")
        pos = tar_file.extractfile('rt-polaritydata/rt-polarity.pos')
        neg = tar_file.extractfile('rt-polaritydata/rt-polarity.neg')
        # Save pos/neg reviews
        pos_data = []
        for line in pos:
            pos_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode())
        neg_data = []
        for line in neg:
            neg_data.append(line.decode('ISO-8859-1').encode('ascii',errors='ignore').decode())
        tar_file.close()
        # Write to file
        if not os.path.exists(save_folder_name):
            os.makedirs(save_folder_name)
        # Save files
        with open(pos_file, "w") as pos_file_handler:
            pos_file_handler.write(''.join(pos_data))
        with open(neg_file, "w") as neg_file_handler:
            neg_file_handler.write(''.join(neg_data))
    texts = pos_data + neg_data
    target = [1]*len(pos_data) + [0]*len(neg_data)
    return(texts, target)

归一化文本函数

# Normalize text
def normalize_text(texts, stops):
    # 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 '') for x in texts]
 
    # Remove stopwords
    texts = [' '.join([word for word in x.split() if word not in (stops)]) for x in texts]
 
    # Trim extra whitespace
    texts = [' '.join(x.split()) for x in texts]
 
    return(texts)

生成词汇表函数

# Build dictionary of words构建词汇表（单词和单词数对），词频不够的单词（即标记为unknown的单词）标记为RARE
def build_dictionary(sentences, vocabulary_size):
    # Turn sentences (list of strings) into lists of words
    split_sentences = [s.split() for s in sentences]
    words = [x for sublist in split_sentences for x in sublist]
 
    # Initialize list of [word, word_count] for each word, starting with unknown
    count = [['RARE', -1]]
 
    # Now add most frequent words, limited to the N-most frequent (N=vocabulary size)
    count.extend(collections.Counter(words).most_common(vocabulary_size-1))
 
    # Now create the dictionary
    word_dict = {}
    # For each word, that we want in the dictionary, add it, then make it
    # the value of the prior dictionary length
    for word, word_count in count:
        word_dict[word] = len(word_dict)
 
    return(word_dict)

生成单词索引表

# Turn text data into lists of integers from dictionary
def text_to_numbers(sentences, word_dict):
    # Initialize the returned data
    data = []
    for sentence in sentences:
        sentence_data = []
        # For each word, either use selected index or rare word index
        for word in sentence.split():
            if word in word_dict:
                word_ix = word_dict[word]
            else:
                word_ix = 0
            sentence_data.append(word_ix)
        data.append(sentence_data)
    return(data)

生成批量数据函数

# Generate data randomly (N words behind, target, N words ahead)
def generate_batch_data(sentences, batch_size, window_size, method='skip_gram'):
    # Fill up data batch
    batch_data = []
    label_data = []
    while len(batch_data) < batch_size:
        # select random sentence to start
        rand_sentence_ix = int(np.random.choice(len(sentences), size=1))
        rand_sentence = sentences[rand_sentence_ix]
        # Generate consecutive windows to look at
        window_sequences = [rand_sentence[max((ix-window_size),0):(ix+window_size+1)] for ix, x in enumerate(rand_sentence)]
        # Denote which element of each window is the center word of interest
        label_indices = [ix if ix<window_size else window_size for ix,x in enumerate(window_sequences)]
 
        # Pull out center word of interest for each window and create a tuple for each window
        if method=='skip_gram':
            batch_and_labels = [(x[y], x[:y] + x[(y+1):]) for x,y in zip(window_sequences, label_indices)]
            # Make it in to a big list of tuples (target word, surrounding word)
            tuple_data = [(x, y_) for x,y in batch_and_labels for y_ in y]
            batch, labels = [list(x) for x in zip(*tuple_data)]
        elif method=='cbow':
            batch_and_labels = [(x[:y] + x[(y+1):], x[y]) for x,y in zip(window_sequences, label_indices)]
            # Only keep windows with consistent 2*window_size
            batch_and_labels = [(x,y) for x,y in batch_and_labels if len(x)==2*window_size]
            batch, labels = [list(x) for x in zip(*batch_and_labels)]
        elif method=='doc2vec':
            # For doc2vec we keep LHS window only to predict target word
            batch_and_labels = [(rand_sentence[i:i+window_size], rand_sentence[i+window_size]) for i in range(0, len(rand_sentence)-window_size)]
            batch, labels = [list(x) for x in zip(*batch_and_labels)]
            # Add document index to batch!! Remember that we must extract the last index in batch for the doc-index
            batch = [x + [rand_sentence_ix] for x in batch]
        else:
            raise ValueError('Method {} not implmented yet.'.format(method))
 
        # extract batch and labels
        batch_data.extend(batch[:batch_size])
        label_data.extend(labels[:batch_size])
    # Trim batch and label at the end
    batch_data = batch_data[:batch_size]
    label_data = label_data[:batch_size]
 
    # Convert to numpy array
    batch_data = np.array(batch_data)
    label_data = np.transpose(np.array([label_data]))
 
    return(batch_data, label_data)