def build_dictionary(self):
logging.debug('=' * 20)
logging.debug('首先,构建训练库字典,然后将每个词映射到一个索引,再将所有句子映射成索引的列表')
# 构建训练库字典
# 将训练库所有句子切分成列表,构成 2D的训练文档,每个单元是一个token,
# 比如: [['今年','你','多少岁'],['你', '二十四','小时','在线','吗'],...]
train_document = map(lambda x: x.split(),self.__seg_sentence__)
gensim_dict = Dictionary.from_documents(train_document)
# 更新字典,再字典中添加特殊符号,其中
# U表示未知字符,即OOV词汇
gensim_dict.add_documents([[u'UNKOWN']])
logging.debug('更新字典,再字典中添加特殊符号(UNKOWN),之后字典大小为:%d' % (len(gensim_dict.keys())))
# print '更新字典,再字典中添加特殊符号,之后字典大小为:%d' % (len(gensim_dict.keys()))
self.__gensim_dict__ = gensim_dict
self.__vocabulary_size__ = len(gensim_dict.keys())
logging.debug('训练库字典为:%d' % (self.__vocabulary_size__))
print '训练库字典为:%d' % self.__vocabulary_size__
logging.debug(u'字典有:%s' % (','.join(gensim_dict.token2id.keys())))
print u'字典有:%s' % (','.join(gensim_dict.token2id.keys()))
# word2embedding = {}
# unknow_token_index = self.__gensim_dict__.token2id[u'UNKOWN']
embedding_weights = np.zeros((self.__vocabulary_size__ + 1, self.__word_embedding_length__ ))
for key,value in gensim_dict.token2id.items():
embedding_weights[value,:] = self.get_w2vEmbedding(key)
# todo 创建词向量字典
self.__embedding_weights__ = embedding_weights
def build_dictionary(self):
logging.debug('=' * 20)
logging.debug('首先,构建训练库字典,然后将每个词映射到一个索引,再将所有句子映射成索引的列表')
# 构建训练库字典
# 将训练库所有句子切分成列表,构成 2D的训练文档,每个单元是一个token,
# 比如: [['今年','你','多少岁'],['你', '二十四','小时','在线','吗'],...]
train_document = map(lambda x: x.split(),self.__seg_sentence__)
gensim_dict = Dictionary.from_documents(train_document)
logging.debug('训练库字典为:%d' % (len(gensim_dict.keys())))
print '训练库字典为:%d' % (len(gensim_dict.keys()))
# 更新字典,再字典中添加特殊符号,其中
# U表示未知字符,即OOV词汇
gensim_dict.add_documents([[u'UNKOWN']])
logging.debug('更新字典,再字典中添加特殊符号(UNKOWN),之后字典大小为:%d' % (len(gensim_dict.keys())))
print '更新字典,再字典中添加特殊符号,之后字典大小为:%d' % (len(gensim_dict.keys()))
logging.debug(u'字典有:%s' % (','.join(gensim_dict.token2id.keys())))
print u'字典有:%s' % (','.join(gensim_dict.token2id.keys()))
self.__gensim_dict__ = gensim_dict
def build_dictionary(self, train_X=None, test_X=None):
"""
1.对数据进行分词
2.构建训练库字典,插入 一个特殊字符 'UNKOWN'表示未知词
Parameters
----------
train_X : array-like
test_X : array-like
Returns
--------
object:
self
"""
# region -------------- 1.将训练集和测试集合并 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('1.将训练集和测试集合并')
print('1.将训练集和测试集合并')
if self.kwargs.get('vocabulary_including_test_set', True):
X = np.concatenate((train_X, test_X), axis=0)
else:
X = train_X
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
# endregion -------------- 1.将训练集和测试集合并 ---------------
# region -------------- 2.对数据进行分词 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('对数据进行分词')
print('对数据进行分词')
# -------------- code start : 开始 -------------
if self.need_segmented:
segmented_sentences = map(self.segment_sentence, X)
else:
segmented_sentences = X
# -------------- code start : 结束 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
# endregion -------------- 2.对数据进行分词 ---------------
# region -------------- 3. 将句子补齐到等长 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('2. 将句子补齐到等长')
print('2. 将句子补齐到等长')
# -------------- code start : 开始 -------------
# 将不等长的句子都对齐,超出padding_length长度的句子截断,小于的则补 PADDING
padded_sentences = np.asarray(
map(self.sentence_padding, segmented_sentences))
# endregion -------------- 3. 将句子补齐到等长 -------------
# region -------------- region start : 4.构建训练库字典 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('4.构建训练库字典')
print('4.构建训练库字典')
# -------------- code start : 开始 -------------
logging.debug('=' * 20)
logging.debug('首先,构建训练库字典,然后将每个词映射到一个索引,再将所有句子映射成索引的列表')
# 将训练库所有句子切分成列表,构成 2D的训练文档,每个单元是一个token,
# 比如: [['今年','你','多少岁'],['你', '二十四','小时','在线','吗'],...]
# 将分完词句子转成合适的数据格式
train_document = map(lambda x: x.split(), padded_sentences)
# 获取训练库字典
if self.padding_mode != 'none':
# 为了确保padding的索引是0,所以在最前面加入 PADDING
train_document.insert(0, [u'PADDING'])
self.train_data_dict = Dictionary.from_documents(train_document)
# 更新字典,再字典中添加特殊符号,其中
# UNKOWN表示未知字符,即OOV词汇
if self.add_unkown_word:
self.train_data_dict.add_documents([[u'UNKOWN']])
# 获取padding和UNKOWN 的字典索引
self.padding_token_index = self.train_data_dict.token2id.get(
u'PADDING', -1)
self.unknow_token_index = self.train_data_dict.token2id.get(
u'UNKOWN', -1)
self.vocabulary_size = len(self.train_data_dict.keys())
# 按索引从小到大排序
self.vocabulary = [
token
for token, id in sorted(self.train_data_dict.token2id.items(),
key=lambda x: x[1])
]
# print(self.vocabulary_size)
# print((self.train_data_dict.token2id.items()))
# quit()
# -------------- print start : just print info -------------
if self.verbose > 1:
logging.debug('训练库字典为:%d' % (len(self.train_data_dict.keys())))
print('训练库字典为:%d' % (len(self.train_data_dict.keys())))
logging.debug(u'字典有:%s' %
(','.join(self.train_data_dict.token2id.keys())))
print(u'字典有:%s' % (','.join(self.train_data_dict.token2id.keys())))
# -------------- print end : just print info -------------
# -------------- code start : 结束 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
# endregion -------------- 4.构建训练库字典 ---------------
return padded_sentences
def build_dictionary(self, train_X=None, test_X=None):
"""
1.对数据进行分词
2.构建训练库字典,插入 一个特殊字符 'UNKOWN'表示未知词
Parameters
----------
train_X : array-like
test_X : array-like
Returns
--------
object:
self
"""
# region -------------- 1.将训练集和测试集合并 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('1.将训练集和测试集合并')
print('1.将训练集和测试集合并')
if self.kwargs.get('vocabulary_including_test_set', True):
X = np.concatenate((train_X, test_X), axis=0)
else:
X = train_X
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
# endregion -------------- 1.将训练集和测试集合并 ---------------
# region -------------- 2.对数据进行分词 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('对数据进行分词')
print('对数据进行分词')
# -------------- code start : 开始 -------------
if self.need_segmented:
segmented_sentences = map(self.segment_sentence, X)
else:
segmented_sentences = X
# -------------- code start : 结束 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
# endregion -------------- 2.对数据进行分词 ---------------
# region -------------- 3. 将句子补齐到等长 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('2. 将句子补齐到等长')
print('2. 将句子补齐到等长')
# -------------- code start : 开始 -------------
# 将不等长的句子都对齐,超出padding_length长度的句子截断,小于的则补 PADDING
padded_sentences = np.asarray(map(self.sentence_padding, segmented_sentences))
# endregion -------------- 3. 将句子补齐到等长 -------------
# region -------------- region start : 4.构建训练库字典 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('4.构建训练库字典')
print('4.构建训练库字典')
# -------------- code start : 开始 -------------
logging.debug('=' * 20)
logging.debug('首先,构建训练库字典,然后将每个词映射到一个索引,再将所有句子映射成索引的列表')
# 将训练库所有句子切分成列表,构成 2D的训练文档,每个单元是一个token,
# 比如: [['今年','你','多少岁'],['你', '二十四','小时','在线','吗'],...]
# 将分完词句子转成合适的数据格式
train_document = map(lambda x: x.split(), padded_sentences)
# 获取训练库字典
if self.padding_mode != 'none':
# 为了确保padding的索引是0,所以在最前面加入 PADDING
train_document.insert(0, [u'PADDING'])
self.train_data_dict = Dictionary.from_documents(train_document)
# 更新字典,再字典中添加特殊符号,其中
# UNKOWN表示未知字符,即OOV词汇
if self.add_unkown_word:
self.train_data_dict.add_documents([[u'UNKOWN']])
# 获取padding和UNKOWN 的字典索引
self.padding_token_index = self.train_data_dict.token2id.get(u'PADDING', -1)
self.unknow_token_index = self.train_data_dict.token2id.get(u'UNKOWN', -1)
self.vocabulary_size = len(self.train_data_dict.keys())
# 按索引从小到大排序
self.vocabulary = [token for token, id in sorted(self.train_data_dict.token2id.items(), key=lambda x: x[1])]
# print(self.vocabulary_size)
# print((self.train_data_dict.token2id.items()))
# quit()
# -------------- print start : just print info -------------
if self.verbose > 1:
logging.debug('训练库字典为:%d' % (len(self.train_data_dict.keys())))
print('训练库字典为:%d' % (len(self.train_data_dict.keys())))
logging.debug(u'字典有:%s' % (','.join(self.train_data_dict.token2id.keys())))
print(u'字典有:%s' % (','.join(self.train_data_dict.token2id.keys())))
# -------------- print end : just print info -------------
# -------------- code start : 结束 -------------
if self.verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
# endregion -------------- 4.构建训练库字典 ---------------
return padded_sentences