【风马一族_Python】 决策树

《机器学习实战》第三章 决策树

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#1 trees.py　　计算给定数据集的香农熵

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 from math import log

 # 计算给定数据集的香农熵
 def calcShannonEnt(dataSet):
     numEnres = len(dataSet)
     labelCoounts = {}
     for featVec in dataSet:
         #为所有可能分类创建字典
         currentLabel = featVec[-1]
         if currentLabel not in labelCoounts.keys():
             labelCoounts[currentLabel] = 0
         labelCoounts[currentLabel] += 1
     shannonEnt = 0.0
     for key in labelCoounts:
         prob = float(labelCoounts[key]) / numEnres
         shannonEnt -= prob * log(prob, 2)               #以2为底求对数
     return shannonEnt

 #用来 得到简单鱼类鉴定数据集
 def createDataSet():
     dataSet = [[1, 1, 'yes'],
                [1, 1, 'yes'],
                [1, 0, 'no'],
                [0, 1, 'no'],
                [0, 1, 'no']]
     labels = ['no surfacing', 'flippers']
     return dataSet, labels

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#2 trees.py　　划分数据集 待划分的数据集、划分数据集的待征、需要返回的特征的值

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 # 划分数据集   待划分的数据集、划分数据集的待征、需要返回的特征的值
 def splitDataSet(dataSet, axis, value):
     retDataSet = []
     for featVec in dataSet:
         if featVec[axis] == value:
             reducedFeatVec = featVec[:axis]
             reducedFeatVec.extend(featVec[axis + 1:])
             retDataSet.append(reducedFeatVec)
     return retDataSet

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#3 trees.py　　选择最好的数据集划分方式

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 # 划分数据集   待划分的数据集、划分数据集的待征、需要返回的特征的值
 def splitDataSet(dataSet, axis, value):
     retDataSet = []
     for featVec in dataSet:
         if featVec[axis] == value:
             reducedFeatVec = featVec[:axis]
             reducedFeatVec.extend(featVec[axis + 1:])
             retDataSet.append(reducedFeatVec)
     return retDataSet

 # 选择最好的数据集划分方式
 def chooseBestFeatureToSplit(dataSet):
     numFeatures = len(dataSet[0]) - 1
     baseEntropy = calcShannonEnt(dataSet)
     bestInfoGain = 0.0;
     bestFeature = -1;
     for i in range(numFeatures):
         featList = [example[i] for example in dataSet]
         uniqueVals = set(featList)
         newEntropy = 0.0;

         for value in uniqueVals:
             subDataSet = splitDataSet(dataSet, i, value)
             prob = len(subDataSet) / float(len(dataSet))
             newEntropy += prob * calcShannonEnt(subDataSet)

         infoGain = baseEntropy - newEntropy

         if (infoGain > bestInfoGain):
             bestInfoGain = infoGain
             bestFeature = i

     return bestFeature

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#4 trees.py　　创建树的函数代码   两个参数：数据集、标签列表

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 import operator

 # 创建树的函数代码 两个参数：数据集、标签列表
 def createTree(dataSet, labels):
     classList = [example[-1] for example in dataSet]

     # 类别完全相同则停止继续划分
     if classList.count(classList[0]) == len(classList):
         return classList[0]

     # 遍历完所有特征时返回出现次数最多的
     if len(dataSet[0]) == 1:
         return majorityCnt(classList)

     bestFeat = chooseBestFeatureToSplit(dataSet)
     bestFeatLabel = labels[bestFeat]
     myTree = {bestFeatLabel: {}}
     del (labels[bestFeat])

     # 得到列表包含的所有属性值
     featValues = [example[bestFeat] for example in dataSet]
     uniqueVals = set(featValues)

     # 遍历当前选择特征包含的所有属性值，在每个数据集划分上递归调用函数createTree()
     for value in uniqueVals:
         subLabels = labels[:]
         myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)

     return myTree