贝叶斯公式实战

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


def nb_news():
    """
    用贝叶斯算法，对新闻进行分类
    :return:
    """
    # 1)获取数据
    news = fetch_20newsgroups(subset="all")
    # print(news)
    # 2)划分数据集
    x_train, x_test, y_train, y_test = train_test_split(news.data, news.target)

    # 3)特征工程：文本的特征抽取tfidf
    tf = TfidfVectorizer()
    x_train = tf.fit_transform(x_train)
    x_test = tf.transform(x_test)

    # 4）贝叶斯算法预估器流程
    estimator = MultinomialNB()
    estimator.fit(x_train, y_train)

    # 5）模型的评估
    # 方法一：直接比较
    y_predict = estimator.predict(x_test)
    print("预测值和真实值对比：\n", y_test == y_predict)

    # 方法二：计算准确率
    score = estimator.score(x_test, y_test)
    print("准确率为:\n", score)
    return None



# def nbcls():
#     """
#     朴素贝叶斯对新闻数据集进行预测
#     :return:
#     """
#     # 获取新闻的数据，20个类别
#     news = fetch_20newsgroups(subset='all')
#
#     # 进行数据集分割
#     x_train, x_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.3)
#
#     # 对于文本数据，进行特征抽取
#     tf = TfidfVectorizer()
#
#     x_train = tf.fit_transform(x_train)
#     # 这里打印出来的列表是：训练集当中的所有不同词的组成的一个列表
#     print(tf.get_feature_names())
#     # print(x_train.toarray())
#
#     # 不能调用fit_transform
#     x_test = tf.transform(x_test)
#
#     # estimator估计器流程
#     mlb = MultinomialNB(alpha=1.0)
#
#     mlb.fit(x_train, y_train)
#
#     # 进行预测
#     y_predict = mlb.predict(x_test)
#
#     print("预测每篇文章的类别：", y_predict[:100])
#     print("真实类别为：", y_test[:100])
#
#     print("预测准确率为：", mlb.score(x_test, y_test))
#
#     return None


if __name__ == "__main__":
    nb_news()