python-tutorial/11scikit-learn/idf.py at master · EronVisualStudio/python-tutorial

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# -*- coding: utf-8 -*-

# Author: XuMing <[email protected]>

# Brief:

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = ["I come to China to travel",

"This is a car polupar in China",

"I love tea and Apple ",

"The work is to write some papers in science"]

vectorizer = TfidfVectorizer()

tfidf = vectorizer.fit_transform(corpus)

print(tfidf)

print('vocab:')

print(vectorizer.vocabulary_)

word = vectorizer.get_feature_names() # 获取词袋模型中的所有词语

weight = tfidf.toarray() # 将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重

for i in range(len(weight)): # 打印每类文本的tf-idf词语权重，第一个for遍历所有文本，第二个for便利某一类文本下的词语权重

print(u"-------这里输出第", i, u"类文本的词语tf-idf权重------")

k_v = dict()

for j in range(len(word)):

print(word[j], weight[i][j])

k_v[word[j]] = weight[i][j]

sorts = sorted(k_v.items(), key=lambda d:d[1],reverse=True)

print(sorts[:5])

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