基于单层决策树的AdaBoost算法源码

Mian.py

 1 # -*- coding: utf-8 -*-
 2 # coding: UTF-8
 3
 4 import numpy as np
 5 from AdaBoost import AdaBoost
 6 from sklearn.model_selection import train_test_split
 7 from sklearn.metrics import accuracy_score
 8
 9 def main():
10
11     # load data
12     dataset = np.loadtxt(‘data.txt‘, delimiter=",")
13     x = dataset[:, 0:8]
14     y = dataset[:, 8]
15
16     # prepare train data
17     x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
18
19     # prepare test and train data
20     x_train=x_train.transpose()
21     y_train[y_train==1] = 1
22     y_train[y_train==0] = -1
23
24     x_test=x_test.transpose()
25     y_test[y_test == 1] = 1
26     y_test[y_test == 0] = -1
27
28     # train
29     ada=AdaBoost(x_train, y_train)
30     ada.train(50)
31
32     # predict
33     y_pred = ada.pred(x_test)
34     print("total test", len(y_pred))
35     print("true pred",  len(y_pred[y_pred == y_test]))
36     print("acc", accuracy_score(y_test, y_pred))
37
38 if __name__==‘__main__‘:
39     main()

AdaBoost.py

 1 # -*- coding: utf-8 -*-
 2 """
 3 Created on Wed Mar 29 14:19:42 2017
 4
 5 @author: Jarily
 6 """
 7 # coding: UTF-8
 8
 9 import numpy as np
10 from WeakClassify import DecisionStump
11 from sklearn.metrics import accuracy_score
12
13 class AdaBoost:
14     def __init__(self,X,y,Weaker=DecisionStump):
15         self.X=np.array(X)
16         self.y=np.array(y).flatten(1)
17         self.Weaker=Weaker
18         self.sums=np.zeros(self.y.shape)
19
20         ‘‘‘
21         W为权值，初试情况为均匀分布，即所有样本都为1/n
22         ‘‘‘
23         self.W=np.ones((self.X.shape[1],1)).flatten(1)/self.X.shape[1]
24
25         self.Q=0  #弱分类器的实际个数
26
27        # M 为弱分类器的最大数量，可以在main函数中修改
28
29     def train(self,M=5):
30         self.G={}         # 表示弱分类器的字典
31         self.alpha={}     # 每个弱分类器的参数
32         for i in range(M):
33             self.G.setdefault(i)
34             self.alpha.setdefault(i)
35         for i in range(M):   # self.G[i]为第i个弱分类器
36             self.G[i]=self.Weaker(self.X,self.y)
37             e=self.G[i].train(self.W) #根据当前权值进行该个弱分类器训练
38             self.alpha[i]=1.0/2*np.log((1-e)/e) #计算该分类器的系数
39             res=self.G[i].pred(self.X)  #res表示该分类器得出的输出
40
41             # 计算当前次数训练精确度
42             print("weak classfier acc", accuracy_score(self.y, res), "\n======================================================")
43
44             # Z表示规范化因子
45             Z=self.W*np.exp(-self.alpha[i]*self.y*res.transpose())
46             self.W=(Z/Z.sum()).flatten(1) #更新权值
47             self.Q=i
48             # errorcnt返回分错的点的数量，为0则表示perfect
49             if (self.errorcnt(i)==0):
50                 print("%d个弱分类器可以将错误率降到0"%(i+1))
51                 break
52
53
54     def errorcnt(self,t):   #返回错误分类的点
55         self.sums=self.sums+self.G[t].pred(self.X).flatten(1)*self.alpha[t]
56
57         pre_y=np.zeros(np.array(self.sums).shape)
58         pre_y[self.sums>=0]=1
59         pre_y[self.sums<0]=-1
60
61         t=(pre_y!=self.y).sum()
62         return t
63
64     def pred(self,test_X):  #测试最终的分类器
65         test_X=np.array(test_X)
66         sums=np.zeros(test_X.shape[1])
67         for i in range(self.Q+1):
68             sums=sums+self.G[i].pred(test_X).flatten(1)*self.alpha[i]
69         pre_y=np.zeros(np.array(sums).shape)
70         pre_y[sums>=0]=1
71         pre_y[sums<0]=-1
72         return pre_y

WeakClassifer.py

 1 # -*- coding: utf-8 -*-
 2 """
 3 Created on Wed Mar 29 14:19:42 2017
 4
 5 @author: Jarily
 6 """
 7
 8 import numpy as np
 9
10 ‘‘‘
11 Decision Stump  单层决策树算法   弱分类器
12 ‘‘‘
13
14 class DecisionStump:
15     def __init__(self,X,y):
16         self.X=np.array(X)
17         self.y=np.array(y)
18         self.N=self.X.shape[0]
19
20
21
22     def train(self,W,steps=100):  #返回所有参数中阈值最小的
23         ‘‘‘
24         W长度为N的向量,表示N个样本的权值
25         threshold_value为阈值
26         threshold_pos为第几个参数
27         threshold_tag为1或者-1.大于阈值则分为threshold_tag，小于阈值则相反
28         ‘‘‘
29         min = float("inf")    #将min初始化为无穷大
30         threshold_value=0;
31         threshold_pos=0;
32         threshold_tag=0;
33         self.W=np.array(W)
34         for i in range(self.N):  #  value表示阈值，errcnt表示错误的数量
35             value,errcnt = self.findmin(i,1,steps)
36             if (errcnt < min):
37                 min = errcnt
38                 threshold_value = value
39                 threshold_pos = i
40                 threshold_tag = 1
41         for i in range(self.N):  # -1
42             value,errcnt= self.findmin(i,-1,steps)
43             if (errcnt < min):
44                 min = errcnt
45                 threshold_value = value
46                 threshold_pos = i
47                 threshold_tag = -1
48         #最终更新
49         self.threshold_value=threshold_value
50         self.threshold_pos=threshold_pos
51         self.threshold_res=threshold_tag
52         print(self.threshold_value,self.threshold_pos,self.threshold_res)
53         return min
54
55     def findmin(self,i,tag,steps):  #找出第i个参数的最小的阈值,tag为1或-1
56         t = 0
57         tmp = self.predintrain(self.X,i,t,tag).transpose()
58         errcnt = np.sum((tmp!=self.y)*self.W)
59         #print now
60         buttom=np.min(self.X[i,:])  #该项属性的最小值，下界
61         up=np.max(self.X[i,:])      #该项属性的最大值，上界
62         minerr = float("inf")       #将minerr初始化为无穷大
63         value=0                     #value表示阈值
64         st=(up-buttom)/steps        #间隔
65         for t in np.arange(buttom,up,st):
66             tmp = self.predintrain(self.X,i,t,tag).transpose()
67             errcnt = np.sum((tmp!=self.y)*self.W)
68             if errcnt < minerr:
69                 minerr=errcnt
70                 value=t
71         return value,minerr
72
73     def predintrain(self,test_set,i,t,tag): #训练时按照阈值为t时预测结果
74         test_set=np.array(test_set).reshape(self.N,-1)
75         pre_y = np.ones((np.array(test_set).shape[1],1))
76         pre_y[test_set[i,:]*tag<t*tag]=-1
77         return pre_y
78
79     def pred(self,test_X):  #弱分类器的预测
80         test_X=np.array(test_X).reshape(self.N,-1) #转换为N行X列，-1懒得算
81         pre_y = np.ones((np.array(test_X).shape[1],1))
82         pre_y[test_X[self.threshold_pos,:]*self.threshold_res<self.threshold_value*self.threshold_res]=-1
83         return pre_y
84     

data

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原文地址：https://www.cnblogs.com/kexinxin/p/10380949.html