连续属性的决策树算法实现--基于西瓜3.0数据

#连续属性的最大增益计算
def InfoGainContous(DatSet,Label,k):
DatSetk = DatSet[:,k]
nk = len(DatSetk)
uniqueDatSetk = list(set(DatSetk))#set不能用索引获取值
uniquesortDatSetk = np.sort(uniqueDatSetk)
n = len(uniquesortDatSetk) #对于set用len方法,set无序
selectPoint = []
for index in range(n-1):
#print index
selectPoint.append((uniquesortDatSetk[index] + uniquesortDatSetk[index + 1])/2.0)
#print 'selectPoint: ',selectPoint
maxinfoEnt = 0.0
bestPoint = -1
bestLabel = []
maxGain = 0
#print 'Label: ',Label
for index in range(n-1):
Label0 = []  #用于存放小于划分点的值
Label1 = []  #用于存放大于划分点的值
labelCount = 0
infoEnt = 0.0
for datindex in range(nk):
if DatSetk[datindex] < selectPoint[index]:
labelCount += 1
Label0.append(Label[datindex])
else: Label1.append(Label[datindex])
sumEnt = len(Label0)/(len(Label)*1.0)*InfoEntCalc(Label0) + len(Label1)/(len(Label)*1.0)*InfoEntCalc(Label1)
infoEnt = InfoEntCalc(Label) - sumEnt
if infoEnt > maxinfoEnt:
maxinfoEnt = infoEnt
bestPoint = selectPoint[index] #得到最佳划分点
bestLabel = Label0
return maxinfoEnt,bestPoint

#计算最大增益
def MaxGain(DatSet,Label,Table):
m,n = np.shape(DatSet)  #多了一些重复计算
Gain = 0.0
maxGain = -1
bestFeature = -1
bestPoint = -1
for tab in Table:
featureNum = list(Table).index(tab)
#print "featureNum: ",featureNum
try:
float(tab)
except:
Gain = InfoGain(DatSet,Label,featureNum)
Point = -1
else:
Gain,Point = InfoGainContous(DatSet,Label,featureNum)
if Gain > maxGain:
bestFeature = featureNum
maxGain = Gain
bestPoint = Point
return bestFeature,bestPoint

#完成基本的决策树构建
def TreeGenerate(Dat,DatOri,Table):  #输入位np array格式
DatSet = Dat[:,:-1]  #取出所有的数据集
Label = Dat[:,-1]   #取出样本对应得类别集
Tables = Table[:]
m,n = np.shape(DatSet)
#当所有数据集的分类相同时：
if list(Label).count(Label[0]) == m:
return Label[0]
#属性集已经遍历完成，但是数据中仍然有多个分类类别时
if n == 1:  #n=1表示只剩下了类别
return majorCnt(Label)
bestFeature,bestPoint = MaxGain(DatSet,Label,Table) #bestFeature对应特征的编号
bestFeatureTable = Table[bestFeature]
del(Table[bestFeature])
#print Table
Tree = {bestFeatureTable:{}}
try:
int(bestFeatureTable)#根据选出的属性是否可以转化为int型确定是否为密度和含糖量
except:
for value in set(DatOri[:,bestFeature]):
#print (bestFeatureTable,value)
subDatSetR = Dat[Dat[:,bestFeature] == value] #选出属性bestFeature，值为value的行
subDatSet = np.concatenate((subDatSetR[:,:bestFeature],subDatSetR[:,bestFeature+1:]),axis=1) #数据集将bestFeature属性去掉
subDatOri = np.concatenate((DatOri[:,:bestFeature],DatOri[:,bestFeature+1:]),axis=1) #数据集将bestFeature属性去掉
subTabel = Table[:]
subm,subn = np.shape(subDatSet)
#print subm
#print "Label:", Label
if(subm == 0):  #当子集的数据集为空时，说明没有这样的特征样本，根据其父集中样本最多的类
Tree[bestFeatureTable][value] = majorCnt(Label)#return majorCnt(Label)
else:
Tree[bestFeatureTable][value] = TreeGenerate(subDatSet,subDatOri,subTabel)  #Tree[bestFeature][value]两层深度的树
else:
for value in [-1,1]: #-1表示小于划分点的情况；1表示大于划分点的情况
if value == -1:
subDatSetR = Dat[Dat[:,bestFeature] < bestPoint] #选出属性bestFeature，值为value的行
subDatSet = np.concatenate((subDatSetR[:,:bestFeature],subDatSetR[:,bestFeature+1:]),axis=1) #数据集将bestFeature属性去掉
subDatOri = np.concatenate((DatOri[:,:bestFeature],DatOri[:,bestFeature+1:]),axis=1) #数据集将bestFeature属性去掉
subTabel = Table[:]
subm,subn = np.shape(subDatSet)
strval = '<' + str(bestPoint)
if(subm == 0):  #当子集的数据集为空时，说明没有这样的特征样本，根据其父集中样本最多的类
Tree[bestFeatureTable][strval] = majorCnt(Label)#return majorCnt(Label)
else:
Tree[bestFeatureTable][strval] = TreeGenerate(subDatSet,subDatOri,subTabel)  #Tree[bestFeature][value]两层深度的树
if value == 1:
subDatSetR = Dat[Dat[:,bestFeature] >= bestPoint] #选出属性bestFeature，值为value的行
subDatSet = np.concatenate((subDatSetR[:,:bestFeature],subDatSetR[:,bestFeature+1:]),axis=1) #数据集将bestFeature属性去掉
subDatOri = np.concatenate((DatOri[:,:bestFeature],DatOri[:,bestFeature+1:]),axis=1) #数据集将bestFeature属性去掉
subTabel = Table[:]
subm,subn = np.shape(subDatSet)
strval = '>=' + str(bestPoint)
if(subm == 0):  #当子集的数据集为空时，说明没有这样的特征样本，根据其父集中样本最多的类
Tree[bestFeatureTable][strval] = majorCnt(Label)#return majorCnt(Label)
else:
Tree[bestFeatureTable][strval] = TreeGenerate(subDatSet,subDatOri,subTabel)  #Tree[bestFeature][value]两层深度的树

return Tree