字典特征抽取

#特征抽取 feature_extraction
#导包
# from sklearn.feature_extraction.text import CountVectorizer
#
# vector = CountVectorizer()
#
# res = vector.fit_transform(["life is short,i like python ", "life is too long ,i dislike python"])
#
# print(vector.get_feature_names())
#
# print(res.toarray())

#导包 字典特征抽取
from sklearn.feature_extraction import DictVectorizer
#字典数据抽取：把字典中一些类别数据，分别进行转换成特征
def dictvec():
    #实例化
    dict = DictVectorizer(sparse=False) #sparse=False 取消稀疏矩阵
    data = dict.fit_transform([{'city': '北京', 'temp': 100}, {'city': '上海', 'temp': 60}, {'city': '江西', 'temp': 30}])
    print(data)#sparse格式 节约内存 便于读取处理
    # [[0.   1.   0. 100.]
    #  [1.   0.   0.  60.]
    #  [0.   0.   1.  30.]]
    print(dict.get_feature_names()) #读取特征值
    # ['city=上海', 'city=北京', 'city=江西', 'temp']
    return None

if __name__ == "__main__":
    dictvec()

运行结果：

 

 

转载于:https://www.cnblogs.com/shixinzei/p/10162370.html