NLP实践-Task3

1.TF-IDF

TF-IDF参考链接：https://www.cnblogs.com/pinard/p/6693230.html

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"]

# max_features是最大特征数
# min_df是词频低于此值则忽略，数据类型为int或float
# max_df是词频高于此值则忽略，数据类型为Int或float
tfidf_model = TfidfVectorizer(max_features=5, min_df=2, max_df=5).fit_transform(corpus)
print(tfidf_model.todense())

2.互信息

互信息参考链接：https://blog.csdn.net/u013710265/article/details/72848755
特征选择参考链接1：https://www.jianshu.com/p/b3056d10a20f
特征选择参考链接2：https://www.jianshu.com/p/b3056d10a20f
特征选择参考链接3：https://baijiahao.baidu.com/s?id=1604074325918456186&wfr=spider&for=pc

import pandas as pd
from sklearn import datasets
from sklearn import metrics as mr

iris = datasets.load_iris()
x = iris.data
y = iris.target

x0 = x[:, 0]
x1 = x[:, 1]
x2 = x[:, 2]
x3 = x[:, 3]

# 计算x和y的互信息
print(mr.mutual_info_score(x0, y))
print(mr.mutual_info_score(x1, y))
print(mr.mutual_info_score(x2, y))
print(mr.mutual_info_score(x3, y))