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决策树3:基尼指数–Gini index(CART)

开发技术 开发技术 2周前 (04-30) 5次浏览

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

 pi表示在信息熵部分中有介绍,如下图中介绍

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

 决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

 选择最小的那个0.3

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

决策树3:基尼指数--Gini index(CART)

 

 

#整个c4.5决策树的所有算法
import numpy as np
import operator

def creatDataSet():
    """
    outlook-> 0:sunny | 1:overcast | 2:rain
    temperature-> 0:hot | 1:mild | 2:cool
    humidity-> 0:high | 1:normal
    windy-> 0:false | 1:true
    """
    dataSet = np.array([[0, 0, 0, 0, 'N'],
               [0, 0, 0, 1, 'N'],
               [1, 0, 0, 0, 'Y'],
               [2, 1, 0, 0, 'Y'],
               [2, 2, 1, 0, 'Y'],
               [2, 2, 1, 1, 'N'],
               [1, 2, 1, 1, 'Y']])
    labels = np.array(['outlook', 'temperature', 'humidity', 'windy'])
    return dataSet, labels


def createTestSet():
    """
    outlook-> 0:sunny | 1:overcast | 2:rain
    temperature-> 0:hot | 1:mild | 2:cool
    humidity-> 0:high | 1:normal
    windy-> 0:false | 1:true
    """
    testSet = np.array([[0, 1, 0, 0],
               [0, 2, 1, 0],
               [2, 1, 1, 0],
               [0, 1, 1, 1],
               [1, 1, 0, 1],
               [1, 0, 1, 0],
               [2, 1, 0, 1]])
    return testSet

def dataset_entropy(dataset):
    """
    计算数据集的信息熵
    """
    classLabel=dataset[:,-1]
    labelCount={}
    for i in range(classLabel.size):
        label=classLabel[i]
        labelCount[label]=labelCount.get(label,0)+1     #将所有的类别都计算出来了
    #熵值(第一步)
    cnt=0
    for k,v in labelCount.items():
        cnt += -v/classLabel.size*np.log2(v/classLabel.size)
    
    return cnt

    #接下来切分,然后算最优属性
def splitDataSet(dataset,featureIndex,value):
    subdataset=[]
    #迭代所有的样本
    for example in dataset:
        if example[featureIndex]==value:
            subdataset.append(example)
    return np.delete(subdataset,featureIndex,axis=1)

def classLabelPi(dataset):
    #多叉树
    classLabel=dataset[:,-1]
    labelCount={}
    for i in range(classLabel.size):
        label=classLabel[i]
        labelCount[label]=labelCount.get(label,0)+1
    valueList=list(labelCount.values())
    sum=np.sum(valueList)
    pi=0
    for i in valueList:
        pi+=(i/sum)**2
    return pi

def chooseBestFeature(dataset,labels):
    """
    选择最优特征,但是特征是不包括名称的。
    如何选择最优特征:增益率最小
    """
    #特征的个数
    featureNum=labels.size
    baseEntropy=dataset_entropy(dataset)
    #设置最大增益值
    maxRatio,bestFeatureIndex=0,None
    #样本总数
    n=dataset.shape[0] 
    #最小基尼值 
    minGini=1
    for i in range(featureNum):
        #指定特征的条件熵
        featureEntropy=0
        gini=0
        #返回所有子集
        featureList=dataset[:,i]
        featureValues=set(featureList)
        for value in featureValues:
            subDataSet=splitDataSet(dataset,i,value) 
            pi=subDataSet.shape[0]/n 
            gini+=pi*(1-classLabelPi(subDataSet))  
        if minGini > gini:
            minGini=gini
            bestFeatureIndex=i
    return bestFeatureIndex #最佳增益

def mayorClass(classList):
    labelCount={}
    for i in range(classList.size):
        label=classList[i]
        labelCount[label]=labelCount.get(label,0)+1
    sortedLabel=sorted(labelCount.items(),key=operator.itemgetter(1),reverse=True)
    return sortedLabel[0][0]

def createTree(dataset,labels):
    """
    参考hunt算法那张图片
    """
    classList=dataset[:,-1]
    if len(set(dataset[:,-1]))==1:
        return dataset[:,-1][0] #返回类别
    if labels.size==0 or len(dataset[0])==1:  #条件熵最少的一定是类别最多的
        #条件熵算不下去的时候,
        return mayorClass(classList)
    bestFeatureIndex=chooseBestFeature(dataset,labels)
    bestFeature=labels[bestFeatureIndex]
    dtree={bestFeature:{}}  #用代码表示这棵树
    featureList=dataset[:,bestFeatureIndex]
    featureValues=set(featureList)
    for value in featureValues:
        subdataset=splitDataSet(dataset,bestFeatureIndex,value)
        sublabels=np.delete(labels,bestFeatureIndex)
        dtree[bestFeature][value]=createTree(subdataset,sublabels) #将原始的labels干掉一列
    return dtree

def predict(tree,labels,testData):
    #分类,预测
    rootName=list(tree.keys())[0]
    rootValue=tree[rootName]
    featureIndex =list(labels).index(rootName)
    classLabel=None 
    for key in rootValue.keys():
        if testData[featureIndex]==int(key):
            if type(rootValue[key]).__name__=="dict":
                classLabel=predict(rootValue[key],labels,testData)    #递归
            else:
                classLabel=rootValue[key]
    return classLabel

def predictAll(tree,labels,testSet):
    classLabels=[]
    for i in testSet:
        classLabels.append(predict(tree,labels,i))
    return classLabels

if __name__ == "__main__":
    dataset,labels=creatDataSet()
    # print(dataset_entropy(dataset)
    # s=splitDataSet(dataset,0)
    # for item in s:
    #     print(item)
    tree=createTree(dataset,labels)
    testSet=createTestSet()
    print(predictAll(tree,labels,testSet))
····························································
输出:
['N', 'N', 'Y', 'N', 'Y', 'Y', 'N']

 

 

 


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