您可以使用scikit-learn的
mutual_info_classif
示例
from sklearn.datasets import fetch_20newsgroupsfrom sklearn.feature_selection import mutual_info_classiffrom sklearn.feature_extraction.text import CountVectorizercategories = ['talk.religion.misc', 'comp.graphics', 'sci.space']newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)X, Y = newsgroups_train.data, newsgroups_train.targetcv = CountVectorizer(max_df=0.95, min_df=2, max_features=10000, stop_words='english')X_vec = cv.fit_transform(X)res = dict(zip(cv.get_feature_names(), mutual_info_classif(X_vec, Y, discrete_features=True) ))print(res)
这将输出每个属性的字典，即词汇表中的项作为键，其信息增益作为值
这是输出的样本
{'bible': 0.072327479595571439, 'christ': 0.057293733680219089, 'christian': 0.12862867565281702, 'christians': 0.068511328611810071, 'file': 0.048056478042481157, 'god': 0.12252523919766867, 'gov': 0.053547274485785577, 'graphics': 0.13044709565039875, 'jesus': 0.09245436105573257, 'launch': 0.059882179387444862, 'moon': 0.064977781072557236, 'morality': 0.050235104394123153, 'nasa': 0.11146392824624819, 'orbit': 0.087254803670582998, 'people': 0.068118370234354936, 'prb': 0.049176995204404481, 'religion': 0.067695617096125316, 'shuttle': 0.053440976618359261, 'space': 0.20115901737978983, 'thanks': 0.060202010019767334}
