def to_tfidf_vectors2(texts: list, tokenizer: tokenizers.BaseTokenizer(),
count_model, tfidf_model):
"""
:param texts:
:param tokenizer:
:param count_model:
:param tfidf_model:
:return:
"""
texts_tokenized = [' '.join(tokenizer(text)) for text in texts]
return to_tfidf_vectors3(texts_tokenized, count_model, tfidf_model)
def to_tfidf_vectors(texts: list, tokenizer: tokenizers.BaseTokenizer()):
"""
:param texts: list of str
:param tokenizer:
:return:
"""
texts_tokenized = [' '.join(tokenizer(text)) for text in texts]
vectorizer = sklearn_text.CountVectorizer()
freq_word_matrix = vectorizer.fit_transform(texts_tokenized)
transformer = sklearn_text.TfidfTransformer()
tfidf_matrix = transformer.fit_transform(freq_word_matrix)
X = tfidf_matrix.toarray()
return X
def get_trained_count_and_tfidf_model(texts: list,
tokenizer: tokenizers.BaseTokenizer()):
"""
:param texts:
:param tokenizer:
:return:
"""
texts_tokenized = [' '.join(tokenizer(text)) for text in texts]
count_model = sklearn_text.CountVectorizer()
count_model.fit(texts_tokenized)
freq_word_matrix = count_model.transform(texts_tokenized)
tfidf_model = sklearn_text.TfidfTransformer()
tfidf_model.fit(freq_word_matrix)
return count_model, tfidf_model
def to_tfidf_vectors(texts: list, tokenizer: tokenizers.BaseTokenizer()):
"""
:param texts: list of str
:param tokenizer: 分词器
:return: 向量数组，每行一个对应一个text的向量化结果
"""
texts_tokenized = [' '.join(tokenizer(text)) for text in texts]
# 词频矩阵：矩阵元素a[i][j] 表示j词在i类文本下的词频
vectorizer = sklearn_text.CountVectorizer()
freq_word_matrix = vectorizer.fit_transform(texts_tokenized)
# 统计每个词语的tf-idf权值
transformer = sklearn_text.TfidfTransformer()
tfidf_matrix = transformer.fit_transform(freq_word_matrix)
X = tfidf_matrix.toarray()
return X
def get_trained_count_and_tfidf_model(texts: list,
tokenizer: tokenizers.BaseTokenizer()):
"""
:param texts:
:param tokenizer:
:return:
"""
texts_tokenized = [' '.join(tokenizer(text)) for text in texts]
# 词频矩阵：矩阵元素a[i][j] 表示j词在i类文本下的词频
count_model = sklearn_text.CountVectorizer()
count_model.fit(texts_tokenized)
freq_word_matrix = count_model.transform(texts_tokenized)
# 统计每个词语的tf-idf权值
tfidf_model = sklearn_text.TfidfTransformer()
tfidf_model.fit(freq_word_matrix)
return count_model, tfidf_model
X = tfidf_matrix.toarray()
return X
if __name__ == '__main__':
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
corpus = [
'This is the first document.',
'This is the second second document.',
'And the third one.',
'Is this the first document?',
]
vectorizer, transformer = get_trained_count_and_tfidf_model(
corpus, tokenizer=tokenizers.BaseTokenizer())
# vectorizer = CountVectorizer()
# count = vectorizer.fit_transform(corpus)
corpus = [
'This is the first document.',
'This is the second second document.',
]
count = vectorizer.transform(corpus)
print(vectorizer.get_feature_names())
print(vectorizer.vocabulary_)
print(count.toarray())
# transformer = TfidfTransformer()
# transformer.fit(count)
tfidf_matrix = transformer.transform(count)
print(tfidf_matrix.toarray())
