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编程实现基于信息熵/基尼指数划分选择的决策树算法
手动建立一个csv文件
#csv的内容为
Idx,color,root,knocks,texture,navel,touch,density,sugar_ratio,label
1,dark_green,curl_up,little_heavily,distinct,sinking,hard_smooth,0.697,0.46,1
2,black,curl_up,heavily,distinct,sinking,hard_smooth,0.774,0.376,1
3,black,curl_up,little_heavily,distinct,sinking,hard_smooth,0.634,0.264,1
4,dark_green,curl_up,heavily,distinct,sinking,hard_smooth,0.608,0.318,1
5,light_white,curl_up,little_heavily,distinct,sinking,hard_smooth,0.556,0.215,1
6,dark_green,little_curl_up,little_heavily,distinct,little_sinking,soft_stick,0.403,0.237,1
7,black,little_curl_up,little_heavily,little_blur,little_sinking,soft_stick,0.481,0.149,1
8,black,little_curl_up,little_heavily,distinct,little_sinking,hard_smooth,0.437,0.211,1
9,black,little_curl_up,heavily,little_blur,little_sinking,hard_smooth,0.666,0.091,0
10,dark_green,stiff,clear,distinct,even,soft_stick,0.243,0.267,0
11,light_white,stiff,clear,blur,even,hard_smooth,0.245,0.057,0
12,light_white,curl_up,little_heavily,blur,even,soft_stick,0.343,0.099,0
13,dark_green,little_curl_up,little_heavily,little_blur,sinking,hard_smooth,0.639,0.161,0
14,light_white,little_curl_up,heavily,little_blur,sinking,hard_smooth,0.657,0.198,0
15,black,little_curl_up,little_heavily,distinct,little_sinking,soft_stick,0.36,0.37,0
16,light_white,curl_up,little_heavily,blur,even,hard_smooth,0.593,0.042,0
17,dark_green,curl_up,heavily,little_blur,little_sinking,hard_smooth,0.719,0.103,0代码
import csv
from sklearn.feature_extraction import DictVectorizer
from sklearn import preprocessing
from sklearn import tree
from matplotlib import pyplot as plt
import graphviz
import os
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"os.environ["PATH"] += os.pathsep + 'C:\Program Files\Graphviz\\bin'
def is_number(n):is_number = Truetry:num = float(n)is_number = num == numexcept ValueError:# 不是float类型则返回falseis_number = Falsereturn is_numberdef loadData(filename):data = open(filename,'r',encoding='utf-8')reader = csv.reader(data)headers = next(reader) # 通过调用next方法来一行一行的读取数据featureList =[] # 添加属性数据labelList = [] # 添加结果for row in reader:# 每行每行的来labelList.append(row[len(row)-1])rowDict = {}for i in range(1,len(row)-1):# 跳过序号和结果# 对于每个属性数据，if is_number(row[i]) == True:rowDict[headers[i]] = float(row[i])else:rowDict[headers[i]] = row[i]featureList.append(rowDict)return featureList,labelListdef createDTree_information(featureList,labelList):# 对离散值进行编码处理vec = DictVectorizer()dummyX = vec.fit_transform(featureList).toarray()lb = preprocessing.LabelBinarizer()dummyY = lb.fit_transform(labelList)clf = tree.DecisionTreeClassifier(criterion='entropy')clf = clf.fit(dummyX,dummyY)target_name=['0','1']dot_data = tree.export_graphviz(clf,feature_names=vec.get_feature_names_out(),class_names=target_name,out_file=None,filled=True,rounded=True)graph = graphviz.Source(dot_data)return graphdef createDTree_GiNi(featureList,labelList):# 对离散值进行编码处理vec = DictVectorizer()dummyX = vec.fit_transform(featureList).toarray()lb = preprocessing.LabelBinarizer()dummyY = lb.fit_transform(labelList)clf = tree.DecisionTreeClassifier(criterion='entropy')clf = clf.fit(dummyX,dummyY)target_name=['0','1']dot_data = tree.export_graphviz(clf,feature_names=vec.get_feature_names_out(),class_names=target_name,out_file=None,filled=True,rounded=True)graph = graphviz.Source(dot_data)return graph
featureList,labelList = loadData('watermelo.csv')
graph = createDTree_information(featureList,labelList)
graph_gini = createDTree_GiNi(featureList,labelList)
print('以基尼指数作为划分准则的决策树')
graph_gini
print('以信息熵作为划分准则的决策树')
graph注意，前面导包的时候path的值是Graphviz的bin文件夹路径，这个Graphviz要手动去官网离线下载，然后记住它的安装位置
输出：以基尼指数作为划分准则的决策树
以信息熵作为划分准则的决策树
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