-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathshizuo_tr.py
165 lines (142 loc) · 6.19 KB
/
shizuo_tr.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
# -*- coding: utf-8 -*-
###以西瓜书76为例决策树
import pandas as pd
import numpy as np
import math
import copy
data = pd.read_excel('watermelon20.xlsx')
#data.isnull().sum()
#根节点
#y = data['好瓜'].value_counts().shape[0]
#计算信息熵
def Information_Entropy(plist):
ent = 0
for p in plist:
ent += p*math.log(p,2)
ent = -ent
return ent
def get_p(feature,data):
idx = data[feature].value_counts().index.tolist()
val = data[feature].value_counts().tolist()
sum_val = sum(val)
plist = []
for i in val:
plist.append(i/sum_val)
return idx,plist
####属性的信息增益
def Decision_Tree(data,root_node,old_features_list,entD,flag=0):
#data:根节点未划分的数据
#root_node:根节点
#feature:当前属性集合（分支节点）
#entD:根节点的信息熵
#example for西瓜数据集2.0:
#对于初始时，data为整个数据集，root_node为好瓜，feature为要考察的属性（分支节点）,entD为好瓜坏瓜信息熵
# total_num = data.shape[0]##根节点D的样本个数
print('--------------------------------')
print('输入根节点:',root_node)
print('取值:',data[root_node].values[0])
flag +=1
if len(old_features_list) == 0:
return
if entD==0:
print('结论：坏瓜!') if data['好瓜'].values[0]=='否' else print('结论：好瓜!')
print('--------------------------------')
return
gain_step1 = []
ent_whole_step1 = []
ent_whole_name = []
#注意：如果直接将features_list输出，那么在这个分支remove了这个属性，对于其他分支也无法使用，将产生错误影响!!!!
#因此输出和输入不是同一个列表!!!!!
features_list = copy.deepcopy(old_features_list)
# features_list = old_features_list
for feature in features_list: #对于不同属性
grouped = data.groupby(feature)
fe = data[feature].value_counts().index.tolist()
# print(fe)
subclass = []
for f in fe:
subclass.append(grouped.get_group(f))#按分支节点划分子类
idx,plist = [],[]
sub_num = []##每个子类的样本个数
for sub in subclass:
i,p = get_p('好瓜',sub) #计算每个子类好坏瓜的概率
idx.append(i)
plist.append(p)
sub_num.append(sub.shape[0])##每个子类的样本个数
##多个分支节点的信息熵
ent_whole = []
for per_plist in plist:
per_ent = Information_Entropy(per_plist)
ent_whole.append(per_ent)
gain_step1.append(get_Gain(sub_num,ent_whole,entD)) #每个属性的信息增益,每个属性一个数
ent_whole_step1.append(ent_whole) #这个属性几个子类的信息熵
ent_whole_name.append(fe) #这个属性几个子类的信息熵对应的名字，如‘清晰’
print('属性:',feature,'增益:',get_Gain(sub_num,ent_whole,entD))
#####找到信息增益最大的，选为划分属性
idmax = gain_step1.index(max(gain_step1))
root_node = features_list[idmax] ##新的根节点“纹理”
entD = ent_whole_step1[idmax]
entD_name = ent_whole_name[idmax]
re1 = save_re(features_list,gain_step1)
re1.to_excel(str(flag)+'.xlsx')
print('产生新的节点:',root_node,'下一阶段可取值:',entD_name)
print('--------------------------------')
features_list.remove(root_node) ##属性中 去除新的根节点
die_data = data.groupby(root_node)
# new_depart = data[root_node].value_counts().index.tolist()
# for i in range(len(new_depart)):
# print('根节点:',root_node,'------------','取值:',new_depart)
# Decision_Tree(die_data.get_group(new_depart[i]),root_node,features_list,entD[i],flag)
return die_data,root_node,features_list,entD,entD_name
# ent_whole_2 = ent_whole_step1[idmax]
def get_Gain(sub_num,ent_whole,entD):##信息增益
add = 0
total_num = sum(sub_num)
for i in range(len(ent_whole)):
add += (sub_num[i]/total_num)*ent_whole[i]
return entD - add
# Gain = get_Gain(sub_num,ent_whole,entD)
##保存每个属性的增益结果
def save_re(features_list,gain_step):
features_listcp,gain_stepcp = features_list.copy(),gain_step.copy()
features_listcp = np.array(features_listcp)
gain_stepcp = np.array(gain_step)
re = np.hstack((features_listcp.reshape((features_listcp.shape[0],1)),gain_stepcp.reshape((features_listcp.shape[0],1))))
return pd.DataFrame(re,columns=['属性','信息增益'])
##以好瓜为根节点，计算各个属性的信息增益
features_list = ['色泽','根蒂','敲声','纹理','脐部','触感']
root_node = '好瓜'
idx,plist = get_p('好瓜',data)
entD = Information_Entropy(plist)
##############
print('第0层')
##根节点为好瓜
die_data,root_node,fl,entD1,entDname1 = Decision_Tree(data,root_node,features_list,entD)
##############
#print('第1层')
##根节点为纹理
node1 = Decision_Tree(die_data.get_group(entDname1[0]),root_node,fl,entD1[0])
node2 = Decision_Tree(die_data.get_group(entDname1[1]),root_node,fl,entD1[1])
node3 = Decision_Tree(die_data.get_group(entDname1[2]),root_node,fl,entD1[2])
#
###############
print('第2层')
#根节点为根蒂
node11 = Decision_Tree(node1[0].get_group(node1[4][0]),node1[1],node1[2],node1[3][0])
node12 = Decision_Tree(node1[0].get_group(node1[4][1]),node1[1],node1[2],node1[3][1])
node13 = Decision_Tree(node1[0].get_group(node1[4][2]),node1[1],node1[2],node1[3][2])
#
#根节点为触感
node21 = Decision_Tree(node2[0].get_group(node2[4][0]),node2[1],node2[2],node2[3][0])
node22 = Decision_Tree(node2[0].get_group(node2[4][1]),node2[1],node2[2],node2[3][1])
#
###############
print('第3层')
node31 = Decision_Tree(node12[0].get_group(node12[4][0]),node12[1],node12[2],node12[3][0])
node32 = Decision_Tree(node12[0].get_group(node12[4][1]),node12[1],node12[2],node12[3][1])
#
###############
print('第4层')
node41 = Decision_Tree(node31[0].get_group(node31[4][0]),node31[1],node31[2],node31[3][0])
node41 = Decision_Tree(node31[0].get_group(node31[4][1]),node31[1],node31[2],node31[3][1])
#