Python reuters.fileids方法代碼示例

# 需要導入模塊: from nltk.corpus import reuters [as 別名]
# 或者: from nltk.corpus.reuters import fileids [as 別名]
def get_corpus_text():
    '''
    return raw text of reuters corpus
    '''
    return [" ".join(reuters.words(fid)) for fid in reuters.fileids()] 

# 需要導入模塊: from nltk.corpus import reuters [as 別名]
# 或者: from nltk.corpus.reuters import fileids [as 別名]
def gen_financial_top_words(maxN=40000): # generate corpus based on Reuters news
    if not os.path.isfile('./input/topWords.json'):
        wordCnt = {}
        for field in reuters.fileids():
            for word in reuters.words(field):
                word = unify_word(word)
                if word in nltk.corpus.stopwords.words('english'):
                    continue
                wordCnt[word] = wordCnt.get(word, 0) + 1

        sorted_wordCnt = sorted(wordCnt.items(), key=operator.itemgetter(1), reverse=True)
        wordCnt = {} # reset wordCnt
        for i in sorted_wordCnt[:maxN]: wordCnt[i[0]] = i[1] # convert list to dict
        with open('./input/topWords.json', 'w') as fout: json.dump(wordCnt, fout, indent=4)
    else: return 

# 需要導入模塊: from nltk.corpus import reuters [as 別名]
# 或者: from nltk.corpus.reuters import fileids [as 別名]
def get_reuters_data(n_vocab):
    # return variables
    sentences = []
    word2idx = {'START': 0, 'END': 1}
    idx2word = ['START', 'END']
    current_idx = 2
    word_idx_count = {0: float('inf'), 1: float('inf')}
    tag = 0
    for field in reuters.fileids():
        sentence = reuters.words(field)
        tokens = [unify_word(t) for t in sentence]
        for t in tokens:
            if t not in word2idx:
                word2idx[t] = current_idx
                idx2word.append(t)
                current_idx += 1
            idx = word2idx[t]
            word_idx_count[idx] = word_idx_count.get(idx, 0) + 1
        sentence_by_idx = [word2idx[t] for t in tokens]
        sentences.append(sentence_by_idx)
        tag += 1
        print(tag)

    # restrict vocab size
    sorted_word_idx_count = sorted(word_idx_count.items(), key=operator.itemgetter(1), reverse=True)
    word2idx_small = {}
    new_idx = 0
    idx_new_idx_map = {}
    for idx, count in sorted_word_idx_count[:n_vocab]:
        word = idx2word[idx]
        print word, count
        word2idx_small[word] = new_idx
        idx_new_idx_map[idx] = new_idx
        new_idx += 1
    # let 'unknown' be the last token
    word2idx_small['UNKNOWN'] = new_idx 
    unknown = new_idx

    # map old idx to new idx
    sentences_small = []
    for sentence in sentences:
        if len(sentence) > 1:
            new_sentence = [idx_new_idx_map[idx] if idx in idx_new_idx_map else unknown for idx in sentence]
            sentences_small.append(new_sentence)

    return sentences_small, word2idx_small