关键代码：
生成决策树：
def createDataSet(): # 创造示例数据
col_type = [str, float, float, float, str, str, str];
dataSet = [] # 数据源列表
with open("pd1_table.csv") as f:
f_csv = csv.reader(f)
# 获得headers
headers = next(f_csv)
# init the row from each row
for row in f_csv:
data = []
row = tuple(convert(value) for convert, value in zip(col_type, row))
#print(row)
if (row[1] <0.21*row[2]):
data.append("0")
else:
data.append("1")
if (row[1] < row[3]):
data.append("0")
else:
data.append("1")
if (row[4] > row[5]):
data.append("0")
else:
data.append("1")
if(row[6]=="1"):
data.append("no")
else:
data.append("yes")
# 0错 1对
dataSet.append(data)
label = ['销税-购税', '销税-票税', '开业日期-开票日期']
return dataSet,label
def calcShannonEnt(dataSet): #计算信息熵
numEntries=len(dataSet) #数据条数
labelCounts={}
for featVec in dataSet:
currentLabel=featVec[-1] #每行数据的最后一个字
if currentLabel not in labelCounts.keys():
labelCounts[currentLabel]=0
labelCounts[currentLabel]+=1 #统计有多少个类以及每个类的数量
shannonEnt=0
for key in labelCounts:
prob=float(labelCounts[key])/numEntries #计算单个类的熵值
shannonEnt-=prob*log(prob,2) #累加每个类的熵值
return shannonEnt
def splitDataSet(dataSet,axis,value): # 按某个特征分类后的数据
retDataSet=[]
for featVec in dataSet:
if featVec[axis]==value:
reducedFeatVec=featVec[:axis]
reducedFeatVec.extend(featVec[axis+1:])
retDataSet.append(reducedFeatVec)
return retDataSet
def chooseBestFeatureToSplit(dataSet): # 选择最优的分类特征
numFeatures = len(dataSet[0])-1
baseEntropy = calcShannonEnt(dataSet) # 原始的熵
bestInfoGain = 0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataSet]
uniqueVals = set(featList)
newEntropy = 0
for value in uniqueVals:
subDataSet = splitDataSet(dataSet,i,value)
prob =len(subDataSet)/float(len(dataSet))
newEntropy +=prob*calcShannonEnt(subDataSet) # 按特征分类后的熵
infoGain = baseEntropy - newEntropy # 原始熵与按特征分类后的熵的差值
if (infoGain>bestInfoGain): # 若按某特征划分后，熵值减少的最大，则次特征为最优分类特征
bestInfoGain=infoGain
bestFeature = i
return bestFeature
def majorityCnt(classList): #按分类后类别数量排序，比如：最后分类为2男1女，则判定为男；
classCount={}
for vote in classList:
if vote not in classCount.keys():
classCount[vote]=0
classCount[vote]+=1
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def createTree(dataSet,labels):
classList=[example[-1] for example in dataSet] # 类别：男或女
if classList.count(classList[0])==len(classList):
return classList[0]
if len(dataSet[0])==1:
return majorityCnt(classList)
bestFeat=chooseBestFeatureToSplit(dataSet) #选择最优特征
bestFeatLabel=labels[bestFeat]
myTree={bestFeatLabel:{}} #分类结果以字典形式保存
del(labels[bestFeat])
featValues=[example[bestFeat] for example in dataSet]
uniqueVals=set(featValues)
for value in uniqueVals:
subLabels=labels[:]
myTree[bestFeatLabel][value]=createTree(splitDataSet\
(dataSet,bestFeat,value),subLabels)
return myTree
def getDataSet(): # 创造测试数据
col_type = [str, float, float, float, str, str, str];
dataSet = [] # 数据源列表
with open("pd_table.csv") as f:
f_csv = csv.reader(f)
# 获得headers
headers = next(f_csv)
# init the row from each row
for row in f_csv:
data = []
row = tuple(convert(value) for convert, value in zip(col_type, row))
#print(row)
if (row[1] <100*row[2]):
data.append('0')
else:
data.append('1')
if (row[1] < row[3]):
data.append('0')
else:
data.append('1')
if (row[4] > row[5]):
data.append('0')
else:
data.append('1')
if(row[6]=="1"):
data.append('no')
# 0错 1对
#dataSet.append(data)
else:
data.append('yes')
# 0错 1对
#dataSet.append(data)
dataSet.append(data)
print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
label = ["销税-购税", "销税-票税", "开业日期-开票日期"]
# list=[]
# for i in dataSet:
# dirc={}
# dirc["销税-购税"]=i[0]
# dirc["销税-票税"]=i[1]
# dirc["开业日期-开票日期"]=i[2]
# list.append(dirc)
return dataSet
def adjust():
num1=0 #yes
num2=0 #no
dataSet, label = createDataSet() # 创造示列数据
tree=createTree(dataSet,label)
list = getDataSet()
for i in list:
key=list(tree.keys())[0]
value=i[key]
while 1:
if value=='yes':
num1+=1
break
if value=='no':
num2+=1
break
if list(value.keys())[0]=='1':
value=value["1"]
对测试集进行分类def classify(Tree, featnames, X):
classLabel=''
root = list(Tree.keys())[0]
firstDict = Tree[root]
featindex = featnames.index(root) #根节点的属性下标
#classLabel='0'
for key in firstDict.keys(): #根属性的取值,取哪个就走往哪颗子树
if X[featindex] == key:
if type(firstDict[key]) == type({}):
classLabel = classify(firstDict[key],featnames,X)
else:
classLabel = firstDict[key]
return classLabel