tflearn的VocabularyProcessor用法：建立中文词汇表和把文本转为词ID序列

-- coding: utf-8 --

from hanziconv import HanziConv
from jieba import cut
from tflearn.data_utils import VocabularyProcessor

DOCUMENTS = [
'这是一条测试1',
'这是一条测试2',
'这是一条测试3',
'这是其他测试',
]

def chinese_tokenizer(documents):
"""
把中文文本转为词序列
"""

for document in documents:
    # 繁体转简体
    text = HanziConv.toSimplified(document)
    # 英文转小写
    text = text.lower()
    # 分词
    yield list(cut(text))

序列长度填充或截取到100，删除词频<=2的词

vocab = VocabularyProcessor(100, 2, tokenizer_fn=chinese_tokenizer)

创建词汇表，创建后不能更改

vocab.fit(DOCUMENTS)

保存和加载词汇表

vocab.save('vocab.pickle')
vocab = VocabularyProcessor.restore('vocab.pickle')

文本转为词ID序列，未知或填充用的词ID为0

id_documents = list(vocab.transform(DOCUMENTS))
for id_document in id_documents:
print(id_document)

[2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

[2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

[2 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

[2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

词ID序列转为文本

for document in vocab.reverse(id_documents):
print(document)

这是 一条 测试 ...

这是 一条 测试 ...

这是 一条 测试 ...

这是 测试 ...