机器学习-分类算法-精确率和召回率08

from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report


def naviebayes():
    # 准备数据
    news = fetch_20newsgroups(subset="all")
    print(news.data)
    print(news.target)
    # 数据分割
    x_train,x_test,y_train,y_test = train_test_split(news.data,news.target,test_size=0.25)
    # 对数据集进行特征抽取
    tf = TfidfVectorizer()
    # 以训练集当中的词的列表进行每篇文章的重要性统计
    x_train = tf.fit_transform(x_train)
    print(tf.get_feature_names())
    x_test = tf.transform(x_test)

    # 进行朴素贝叶斯算法的预测
    mlt = MultinomialNB(alpha=1.0)
    print(x_train.toarray())
    mlt.fit(x_train,y_train)
    y_predict= mlt.predict(x_test)
    print("预测的文章类别为：",y_predict)
    # 得出准确率
    print("准确率：",mlt.score(x_test,y_test))
    print("每个类别的精确率和召回率:",classification_report(y_test,y_predict,target_names=news.target_names))
    return None


if __name__=="__main__":
    naviebayes()