Python實現決策樹演算法ID3

#coding=utf-8

# 構造資料集
def createdataset():  
dataset = [[1,1,'yes'],[1,1,'yes'],[1,0,'no'],[0,1,'no'],[0,1,'no']]  
features = ['no su***cing','flippers']  
return dataset,features  
# 採用字典來遞迴構造決策樹
def treegrowth(dataset,features):  
classlist = [example[-1] for example in dataset]  
if classlist.count(classlist[0])==len(classlist):  
return classlist[0]  
if len(dataset[0])==1:# no more features  
return classify(classlist)  
bestfeat = findbestsplit(dataset)#bestfeat is the index of best feature  
bestfeatlabel = features[bestfeat]  
mytree = }  
featvalues = [example[bestfeat] for example in dataset]  
uniquefeatvalues = set(featvalues)  
del (features[bestfeat])  
for values in uniquefeatvalues:  
subdataset = splitdataset(dataset,bestfeat,values)  
mytree[bestfeatlabel][values] = treegrowth(subdataset,features)  
return mytree  
# 當沒有多餘的feature，但是剩下的樣本不完全是一樣的類別是，採用出現次數多的那個類別
def classify(classlist):  
classcount = {}  
for vote in classlist:  
if vote not in classcount.keys():  
classcount[vote] = 0  
classcount[vote] += 1  
sortedclasscount = sorted(classcount.iteritems(),key = operator.itemgetter(1),reverse = true)  
return sortedclasscount[0][0]  
# 尋找用於**的最佳屬性（遍歷所有屬性，算資訊增益）
def findbestsplit(dataset):  
numfeatures = len(dataset[0])-1  
baseentropy = calcshannonent(dataset)  
bestinfogain = 0.0  
bestfeat = -1  
for i in range(numfeatures):  
featvalues = [example[i] for example in dataset]  
uniquefeatvalues = set(featvalues)  
newentropy = 0.0  
for val in uniquefeatvalues:  
subdataset = splitdataset(dataset,i,val)  
prob = len(subdataset)/float(len(dataset))  
newentropy += prob*calcshannonent(subdataset)  
if(baseentropy - newentropy)>bestinfogain:  
bestinfogain = baseentropy - newentropy  
bestfeat = i  
return bestfeat  
# 選擇完**屬性後，就行資料集的**
def splitdataset(dataset,feat,values):  
retdataset =   
for featvec in dataset:  
if featvec[feat] == values:  
reducedfeatvec = featvec[:feat]  
reducedfeatvec.extend(featvec[feat+1:])  
return retdataset
# 計算資料集的熵
def calcshannonent(dataset):  
numentries = len(dataset)  
labelcounts = {}  
for featvec in dataset:  
currentlabel = featvec[-1]  
if currentlabel not in labelcounts.keys():  
labelcounts[currentlabel] = 0  
labelcounts[currentlabel] += 1  
shannonent = 0.0  
for key in labelcounts:  
prob = float(labelcounts[key])/numentries  
if prob != 0:  
shannonent -= prob*log(prob,2)  
return shannonent  
# 根據構造的決策樹進行資料的分類
def predict(tree,newobject):  
while isinstance(tree,dict):  
key = tree.keys()[0]  
tree = tree[key][newobject[key]]  
return tree  
if __name__ == '__main__':  
dataset,features = createdataset()  
tree = treegrowth(dataset,features)  
print tree  
print predict(tree,)  
print predict(tree,)  
print predict(tree,)  
print predict(tree,)

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