one-hot encoding 对于一个特征包含多个特征id的一种处理方法

 1 from sklearn.preprocessing import OneHotEncoder
 2 import numpy
 3 onehot = OneHotEncoder()
 4 #建立一个映射，将多个特征共存的情况定义为单个数表示比如有特征{a，b}，a用1,b用2,ab用3----（1）
 5 import pandas
 6 data = pandas.read_csv("userFeature_part_bat.csv")
 7 
 8 #列出所有特征值，遍历,uid是用户唯一标识，不应该算作特征
 9 
10 userfeature = ["age","carrier","consumptionAbility","ct","education","gender","house","interest1","interest2","interest3","interest4","interest5","kw1","kw2","kw3","marriageStatus","os","topic1","topic2","topic3","LBS","appIdAction","appIdInstall"]
11 #for index in data[feature] :
12 userdata = []
13 for index in range(len(data["uid"])):
14     feature_li = []
15     for feature in userfeature:
16         # a = data[feature]
17         # print(a[0],type(a[index]),isinstance(a[0],(numpy.int64)))
18 
19         if isinstance(data[feature][index],numpy.int64):
20             feature_li.append(int(data[feature][index]))
21         elif isinstance(data[feature][index],numpy.float64):
22             feature_li.append(0)#缺失值用0填充，这是不合理的，有待改进
23         elif isinstance(data[feature][index], numpy.float):
24             feature_li.append(0)
25         else :
26             trans = data[feature][index].strip().split(" ")
27             trans = map(int,trans)
28             trans = sorted(trans)
29             #print(trans)
30             s = 0
31             for num in trans :
32                 s += num
33             feature_li.append(s)
34 
35     print(feature_li)
36     userdata.append(feature_li)
37 userdata = numpy.array(userdata)
38 print("--------------------------------------------------------------------")
39 print(userdata)
40 '''
41 第一步要把所有特征值id提取，
42 特征特征值id进行排序，组合成一个特征值，这样就实现了（1）的目标
43 多特征值id时我们把特征值排序再进行onehot编码，带来的缺点：
44 例如 特征a有特征id1,2,3 b有特征id1，2他们显然是相近的，我的方法onehot
45 编码后他们不再相关;我想应该有更好的方法可以解决，比如：word2vec
46 '''