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python实现基于信息增益的决策树归纳

更新时间:2020-06-16 12:48:01 作者:startmvc
本文实例为大家分享了基于信息增益的决策树归纳的Python实现代码,供大家参考,具体内容

本文实例为大家分享了基于信息增益的决策树归纳的Python实现代码,供大家参考,具体内容如下


# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from copy import copy
 
#加载训练数据
#文件格式:属性标号,是否连续【yes|no】,属性说明
attribute_file_dest = 'F:\\bayes_categorize\\attribute.dat'
attribute_file = open(attribute_file_dest)
 
#文件格式:rec_id,attr1_value,attr2_value,...,attrn_value,class_id
trainning_data_file_dest = 'F:\\bayes_categorize\\trainning_data.dat'
trainning_data_file = open(trainning_data_file_dest)
 
#文件格式:class_id,class_desc
class_desc_file_dest = 'F:\\bayes_categorize\\class_desc.dat'
class_desc_file = open(class_desc_file_dest)
 
 
root_attr_dict = {}
for line in attribute_file :
 line = line.strip()
 fld_list = line.split(',')
 root_attr_dict[int(fld_list[0])] = tuple(fld_list[1:])
 
class_dict = {}
for line in class_desc_file :
 line = line.strip()
 fld_list = line.split(',')
 class_dict[int(fld_list[0])] = fld_list[1]
 
trainning_data_dict = {}
class_member_set_dict = {}
for line in trainning_data_file :
 line = line.strip()
 fld_list = line.split(',')
 rec_id = int(fld_list[0])
 a1 = int(fld_list[1])
 a2 = int(fld_list[2])
 a3 = float(fld_list[3])
 c_id = int(fld_list[4])
 
 if c_id not in class_member_set_dict :
 class_member_set_dict[c_id] = set()
 class_member_set_dict[c_id].add(rec_id)
 trainning_data_dict[rec_id] = (a1 , a2 , a3 , c_id)
 
attribute_file.close()
class_desc_file.close()
trainning_data_file.close()
 
class_possibility_dict = {}
for c_id in class_member_set_dict :
 class_possibility_dict[c_id] = (len(class_member_set_dict[c_id]) + 0.0)/len(trainning_data_dict) 
 
#等待分类的数据
data_to_classify_file_dest = 'F:\\bayes_categorize\\trainning_data_new.dat'
data_to_classify_file = open(data_to_classify_file_dest)
data_to_classify_dict = {}
for line in data_to_classify_file :
 line = line.strip()
 fld_list = line.split(',')
 rec_id = int(fld_list[0])
 a1 = int(fld_list[1])
 a2 = int(fld_list[2])
 a3 = float(fld_list[3])
 c_id = int(fld_list[4])
 data_to_classify_dict[rec_id] = (a1 , a2 , a3 , c_id)
data_to_classify_file.close()
 
 
 
 
'''
决策树的表达
结点的需求:
1、指示出是哪一种分区 一共3种 一是离散穷举 二是连续有分裂点 三是离散有判别集合 零是叶子结点
2、保存分类所需信息
3、子结点列表
每个结点用Tuple类型表示
元素一是整形,取值123 分别对应两种分裂类型
元素二是集合类型 对于1保存所有的离散值 对于2保存分裂点 对于3保存判别集合 对于0保存分类结果类标号
元素三是dict key对于1来说是某个的离散值 对于23来说只有12两种 对于2来说1代表小于等于分裂点
对于3来说1代表属于判别集合
'''
 
 
#对于一个成员列表,计算其熵
#公式为 Info_D = - sum(pi * log2 (pi)) pi为一个元素属于Ci的概率,用|Ci|/|D|计算 ,对所有分类求和
def get_entropy( member_list ) :
 #成员总数
 mem_cnt = len(member_list)
 #首先找出member中所包含的分类
 class_dict = {}
 for mem_id in member_list :
 c_id = trainning_data_dict[mem_id][3]
 if c_id not in class_dict :
 class_dict[c_id] = set()
 class_dict[c_id].add(mem_id)
 
 tmp_sum = 0.0
 for c_id in class_dict :
 pi = ( len(class_dict[c_id]) + 0.0 ) / mem_cnt
 tmp_sum += pi * mlab.log2(pi)
 tmp_sum = -tmp_sum
 return tmp_sum
 
 
def attribute_selection_method( member_list , attribute_dict ) :
 #先计算原始的熵
 info_D = get_entropy(member_list)
 
 max_info_Gain = 0.0
 attr_get = 0
 split_point = 0.0
 for attr_id in attribute_dict :
 #对于每一个属性计算划分后的熵
 #信息增益等于原始的熵减去划分后的熵
 info_D_new = 0
 #如果是连续属性
 if attribute_dict[attr_id][0] == 'yes' :
 #先得到memberlist中此属性的取值序列,把序列中每一对相邻项的中值作为划分点计算熵
 #找出其中最小的,作为此连续属性的划分点
 value_list = []
 for mem_id in member_list :
 value_list.append(trainning_data_dict[mem_id][attr_id - 1])
 
 #获取相邻元素的中值序列
 mid_value_list = []
 value_list.sort()
 #print value_list
 last_value = None
 for value in value_list :
 if value == last_value :
 continue
 if last_value is not None :
 mid_value_list.append((last_value+value)/2)
 last_value = value
 #print mid_value_list
 #对于中值序列做循环
 #计算以此值做为划分点的熵
 #总的熵等于两个划分的熵乘以两个划分的比重
 min_info = 1000000000.0
 total_mens = len(member_list) + 0.0
 for mid_value in mid_value_list :
 #小于mid_value的mem
 less_list = []
 #大于
 more_list = []
 for tmp_mem_id in member_list :
 if trainning_data_dict[tmp_mem_id][attr_id - 1] <= mid_value :
 less_list.append(tmp_mem_id)
 else :
 more_list.append(tmp_mem_id)
 sum_info = len(less_list)/total_mens * get_entropy(less_list) \
 + len(more_list)/total_mens * get_entropy(more_list)
 
 if sum_info < min_info :
 min_info = sum_info
 split_point = mid_value
 
 info_D_new = min_info
 #如果是离散属性
 else :
 #计算划分后的熵
 #采用循环累加的方式
 attr_value_member_dict = {} #键为attribute value , 值为memberlist
 for tmp_mem_id in member_list :
 attr_value = trainning_data_dict[tmp_mem_id][attr_id - 1]
 if attr_value not in attr_value_member_dict :
 attr_value_member_dict[attr_value] = []
 attr_value_member_dict[attr_value].append(tmp_mem_id)
 #将每个离散值的熵乘以比重加到这上面
 total_mens = len(member_list) + 0.0
 sum_info = 0.0
 for a_value in attr_value_member_dict :
 sum_info += len(attr_value_member_dict[a_value])/total_mens \
 * get_entropy(attr_value_member_dict[a_value])
 
 info_D_new = sum_info
 
 info_Gain = info_D - info_D_new
 if info_Gain > max_info_Gain :
 max_info_Gain = info_Gain
 attr_get = attr_id
 
 #如果是离散的
 #print 'attr_get ' + str(attr_get)
 if attribute_dict[attr_get][0] == 'no' :
 return (1 , attr_get , split_point)
 else : 
 return (2 , attr_get , split_point)
 #第三类先不考虑
 
def get_decision_tree(father_node , key , member_list , attr_dict ) :
 #最终的结果是新建一个结点,并且添加到father_node的sub_node_dict,对key为键
 #检查memberlist 如果都是同类的,则生成一个叶子结点,set里面保存类标号
 class_set = set()
 for mem_id in member_list :
 class_set.add(trainning_data_dict[mem_id][3])
 if len(class_set) == 1 :
 father_node[2][key] = (0 , (1 , class_set) , {} )
 return
 
 #检查attribute_list,如果为空,产生叶子结点,类标号为memberlist中多数元素的类标号
 #如果几个类的成员等量,则打印提示,并且全部添加到set里面
 if not attr_dict :
 class_cnt_dict = {}
 for mem_id in member_list :
 c_id = trainning_data_dict[mem_id][3]
 if c_id not in class_cnt_dict :
 class_cnt_dict[c_id] = 1
 else :
 class_cnt_dict[c_id] += 1
 
 class_set = set()
 max_cnt = 0
 for c_id in class_cnt_dict :
 if class_cnt_dict[c_id] > max_cnt :
 max_cnt = class_cnt_dict[c_id]
 class_set.clear()
 class_set.add(c_id)
 elif class_cnt_dict[c_id] == max_cnt :
 class_set.add(c_id)
 
 if len(class_set) > 1 :
 print 'more than one class !'
 
 father_node[2][key] = (0 , (1 , class_set ) , {} )
 return
 
 #找出最好的分区方案 , 暂不考虑第三种划分方法
 #比较所有离散属性和所有连续属性的所有中值点划分的信息增益
 split_criterion = attribute_selection_method(member_list , attr_dict)
 #print split_criterion
 selected_plan_id = split_criterion[0]
 selected_attr_id = split_criterion[1]
 
 #如果采用的是离散属性做为分区方案,删除这个属性
 new_attr_dict = copy(attr_dict)
 if attr_dict[selected_attr_id][0] == 'no' :
 del new_attr_dict[selected_attr_id]
 
 #建立一个结点new_node,father_node[2][key] = new_node
 #然后对new node的每一个key , sub_member_list,
 #调用 get_decision_tree(new_node , new_key , sub_member_list , new_attribute_dict)
 #实现递归
 ele2 = ( selected_attr_id , set() )
 #如果是1 , ele2保存所有离散值
 if selected_plan_id == 1 :
 for mem_id in member_list :
 ele2[1].add(trainning_data_dict[mem_id][selected_attr_id - 1])
 #如果是2,ele2保存分裂点
 elif selected_plan_id == 2 :
 ele2[1].add(split_criterion[2])
 #如果是3则保存判别集合,先不管
 else :
 print 'not completed'
 pass
 
 new_node = ( selected_plan_id , ele2 , {} )
 father_node[2][key] = new_node
 
 #生成KEY,并递归调用
 if selected_plan_id == 1 :
 #每个attr_value是一个key
 attr_value_member_dict = {}
 for mem_id in member_list :
 attr_value = trainning_data_dict[mem_id][selected_attr_id - 1 ]
 if attr_value not in attr_value_member_dict :
 attr_value_member_dict[attr_value] = []
 attr_value_member_dict[attr_value].append(mem_id)
 for attr_value in attr_value_member_dict :
 get_decision_tree(new_node , attr_value , attr_value_member_dict[attr_value] , new_attr_dict)
 pass
 elif selected_plan_id == 2 :
 #key 只有12 , 小于等于分裂点的是1 , 大于的是2
 less_list = []
 more_list = []
 for mem_id in member_list :
 attr_value = trainning_data_dict[mem_id][selected_attr_id - 1 ]
 if attr_value <= split_criterion[2] :
 less_list.append(mem_id)
 else :
 more_list.append(mem_id)
 #if len(less_list) != 0 :
 get_decision_tree(new_node , 1 , less_list , new_attr_dict)
 #if len(more_list) != 0 :
 get_decision_tree(new_node , 2 , more_list , new_attr_dict)
 pass
 #如果是3则保存判别集合,先不管
 else :
 print 'not completed'
 pass
 
def get_class_sub(node , tp ) :
 #
 attr_id = node[1][0]
 plan_id = node[0]
 key = 0
 if plan_id == 0 :
 return node[1][1]
 elif plan_id == 1 :
 key = tp[attr_id - 1]
 elif plan_id == 2 :
 split_point = tuple(node[1][1])[0]
 attr_value = tp[attr_id - 1]
 if attr_value <= split_point :
 key = 1
 else :
 key = 2
 else :
 print 'error'
 return set()
 
 return get_class_sub(node[2][key] , tp )
 
def get_class(r_node , tp) :
 #tp为一组属性值
 if r_node[0] != -1 :
 print 'error'
 return set()
 
 if 1 in r_node[2] :
 return get_class_sub(r_node[2][1] , tp)
 else :
 print 'error'
 return set()
 
 
if __name__ == '__main__' :
 root_node = ( -1 , set() , {} )
 mem_list = trainning_data_dict.keys()
 get_decision_tree(root_node , 1 , mem_list , root_attr_dict )
 
 #测试分类器的准确率
 diff_cnt = 0
 for mem_id in data_to_classify_dict :
 c_id = get_class(root_node , data_to_classify_dict[mem_id][0:3])
 if tuple(c_id)[0] != data_to_classify_dict[mem_id][3] :
 print tuple(c_id)[0]
 print data_to_classify_dict[mem_id][3]
 print 'different'
 diff_cnt += 1
 print diff_cnt

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。

python 信息增益 决策树