依然是學習《統計學習方法》一書所做的簡單實驗,寫代碼的過程參考了大量其他的博客,本人在此深表感謝。代碼實現的依然是書上的例子:
import numpy as np
import math
import operator
def CreateDataSet():
dataset = [ [1, 0,0,0,'no'],
[1, 0,0,1,'no'],
[1, 1,0,1,'yes'],
[1, 1,1,0,'yes'],
[1, 0,0,0,'no'],
[2, 0,0,0,'no'],
[2, 0,0,1,'no'],
[2, 1,1,1,'yes'],
[2, 0,1,2,'yes'],
[2, 0,1,2,'yes'],
[3, 0,1,2,'yes'],
[3, 0,1,1,'yes'],
[3, 1,0,1,'yes'],
[3, 1,0,2,'yes'],
[3, 0,0,0,'no'] ]
labels = ['age', 'job','building','credit']
return dataset, labels
#計算香農熵
def calcShannonEnt(dataSet):
Ent = 0.0
numEntries = len(dataSet)
labelCounts = {}
for feaVec in dataSet:
currentLabel = feaVec[-1]
if currentLabel not in labelCounts:
labelCounts[currentLabel] = 0
labelCounts[currentLabel] += 1
for key in labelCounts:
prob = float(labelCounts[key])/numEntries
Ent -= prob * math.log(prob, 2)
return Ent
def majorityCnt(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]
def splitDataSet(dataSet,axis,value):
retDataSet=[]
for featVec in dataSet:
if featVec[axis]==value :
reduceFeatVec=featVec[:axis]
reduceFeatVec.extend(featVec[axis+1:])
retDataSet.append(reduceFeatVec)
return retDataSet #返回不含劃分特徵的子集
def choiceBestFea(dataSet):
baseEntropy = calcShannonEnt(dataSet)
numberFeatures = len(dataSet[0]) - 1
bestFeatureId = -1;
bestInfoGain = 0.0
for i in range(numberFeatures):
featList = [example[i] for example in dataSet]
uniqueVals = set(featList)
newEntropy = 0.0
for value in uniqueVals:
subFea = splitDataSet(dataSet,i,value)
prob = len(subFea) / float(len(dataSet))
newEntropy += prob * calcShannonEnt(subFea)
infoGain = baseEntropy - newEntropy
if (infoGain > bestInfoGain):
bestInfoGain = infoGain
bestFeatureId = i
return bestFeatureId
def createDTree(dataSet,labels):
#第一步,判斷數據是不是都是同一類的,如果是同一類的,則只有一個節點即根節點
classList = [example[-1] for example in dataSet]
if classList.count(classList[0]) == len(classList):
return classList[0]
# 第二步,判斷特徵的個數,特徵集爲空,則只有一個節點即根節點,此時,需要通過投票的方式決定根節點的屬性
if len(dataSet[0]) == 1:
return majorityCnt(classList)
# 第三步,通過計算信息增益,選擇出最優的特徵,也就是信息增益最大的特徵
bestFeaId = choiceBestFea(dataSet)
#第四步,選擇出信息增益最大的特徵,並使用該特徵切分數據
bestFeatLabel = labels[bestFeaId]
del (labels[bestFeaId])
featValues = [example[bestFeaId] for example in dataSet]
uniqueVals = set(featValues)
myTree = {bestFeatLabel: {}}
#第五步,遞歸調用createDTree
for value in uniqueVals:
subLabels = labels[:]
myTree[bestFeatLabel][value] = createDTree(splitDataSet(dataSet, bestFeaId, value), subLabels)
return myTree
#輸入兩個變量(決策樹,測試的數據)
def classify(inputTree,testVec):
print(inputTree)
firstStr=list(inputTree.keys())[0] #獲取樹的第一個特徵屬性
secondDict=inputTree[firstStr] #樹的分支,子集合Dict
i=0
classLabel = ""
for key in secondDict.keys():
if testVec[i]==key:
if type(secondDict[key]).__name__=='dict':
classLabel=classify(secondDict[key],testVec)
else:
#表明已經是葉子節點了
classLabel=secondDict[key]
break
i += 1
return classLabel
def storeTree(inputTree,filename):
import pickle
fw=open(filename,'wb') #pickle默認方式是二進制,需要制定'wb'
pickle.dump(inputTree,fw)
fw.close()
def reStoreTree(filename):
import pickle
fr=open(filename,'rb')#需要制定'rb',以byte形式讀取
return pickle.load(fr)
def test():
dataSet,labels = CreateDataSet1()
tree = createDTree(dataSet,labels);
print(tree)
return None
def train():
myDat, labels = CreateDataSet()
tree = createDTree(myDat, labels)
storeTree(tree,"dtree.txt")
return None
def test():
tree = reStoreTree("dtree.txt")
result = classify(tree,[0,0])
return result
result = test()
print(result)
#train()
train()方法用來生成決策樹,生成的決策樹會被保存在dtree.txt文件中
test()方法用來測試決策樹。
從生成的決策樹來看,總共只有兩個節點。第一個節點是有沒有房,第二個節點是有沒有工作。所以,測試的時候只需輸入【0,0】或者【1,0】這樣的長度爲2的向量即可。