机器学习sklearn

sklearn相关模块导入

 from sklearn.feature_extraction import DictVectorizer
 from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer
 from sklearn.preprocessing import MinMaxScaler,StandardScaler,Imputer
 from sklearn.feature_selection import VarianceThreshold
 from sklearn.decomposition import PCA
 import jieba
 import numpy as np

一、字典数据抽取

 def dictvec():
     """
     字典数据抽取
     :return: None
     """
     dict = DictVectorizer(sparse=False)
     # 调用ift_transform
     data = dict.fit_transform([{"city": "北京", "temperature": 100}])
     print(dict.get_feature_names())
     print(dict.inverse_transform(data))
     print(data)

     return None

二、对文本进行特征值化

　　1、英文

 def countvec():
     """
     对文本进行特征值化
     :return:None
     """
     cv = CountVectorizer()
     data = cv.fit_transform(["life is short i like python", "life is too long, i dislike python"])
     print(cv.get_feature_names())
     print(data.toarray())
     return None

　　2、中文

 def cutword():
     """
     中文特征值化分词
     :return:None
     """
     con1 = jieba.cut("这是一个什么样的时代，这是一个以互联网时代为代表的时代\n")
     con2 = jieba.cut("看到这些我们都想到了什么，什么才能让我们想起不该想起的东西")
     # 转换成列表
     # content1 = list(con1)
     # content2 = list(con2)
     # 转换成字符串
     c1 = " ".join(con1)
     c2 = " ".join(con2)
     print(c1,c2)
     return c1, c2

 def hanzivec():
     """
     中文特征值化
     :return:None
     """
     c1, c2 = cutword()
     # print(c1, c2)
     cv = CountVectorizer()
     data = cv.fit_transform([c1, c2])
     print(cv.get_feature_names())
     print(data.toarray())

     return None

 def tfidfvec():
     """
     中文特征值化
     :return:None
     """
     c1, c2 = cutword()
     # print(c1, c2)
     tf = TfidfVectorizer()
     data = tf.fit_transform([c1, c2])
     # print(data)
     print(tf.get_feature_names())
     print(data.toarray())

     return None

三、归一化计算

 def mm():
     """
     归一化计算
     :return: None
     """
     mm=MinMaxScaler(feature_range=(4,5))
     data=mm.fit_transform([[60,2,40],[90,4,30],[75,6,50]])
     print(data)

四、标准化计算

 def ss():
     """
     标准化计算
     :return: None
     """
     ss=StandardScaler()
     data=ss.fit_transform([[1,-1,4],[2,1,0],[9,2,3]])
     print(data)

五、缺失值处理

 def im():
     """
     缺失值处理
     :return:
     """
     im=Imputer(missing_values="NaN",strategy="mean",axis=0)
     data=im.fit_transform([[1,2],[np.nan,3],[7,6]])
     print(data)

六、特征选择-删除低方差的特征

 def var():
     """
     特征选择-删除低方差的特征
     :return:
     """
     var=VarianceThreshold(threshold=0.0)
     data=var.fit_transform([[0,3,5,4],[0,2,9,4],[0,8,3,4],[0,8,1,4]])
     print(data)

七、数据降维处理

 def pca():
     """
     数据降维处理
     :return:
     """
     pca=PCA(n_components=0.9)
     data=pca.fit_transform([[1,2,3],[4,5,6],[7,8,9],[10,11,12],[7,8,9]])
     print(data)