决策树

tree.py

from math import log
import operator

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
        shannonEnt -= prob * log(prob, 2)
    return shannonEnt

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


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

def step1():
    myDat, labels = createDataSet()
    print myDat
    shannonRes = calcShannonEnt(myDat)
    print shannonRes


def step2():
    a = [1, 2, 3]
    b = [4, 5, 6]
    a.append(b)
    print a
    a = [1, 2, 3]
    a.extend(b)
    print a


def step3():
    myDat, labels = createDataSet()
    print splitDataSet(myDat, 0, 1)
    print splitDataSet(myDat, 0, 0)

def chooseBestFeatToSplit(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


def step4():
    myDat, labels = createDataSet()
    res = chooseBestFeatToSplit(myDat)
    print res
    print myDat


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 createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]
    if classList.count(classList[0]) == len(classList):
        return classList[0]
    if len(dataSet[0]) == 1:
        return majorityCnt(classList)
    bestFeat = chooseBestFeatToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)
    return myTree

def step5():
    myDat, labels = createDataSet()
    print myDat
    print labels
    myTree = createTree(myDat, labels)
    print myTree


if __name__ == '__main__':
    step5()