tree.py

# coding:utf-8

from math import log
import operator
import treePlotter
from collections import Counter


pre_pruning = True
post_pruning = True


def read_dataset(filename):
    """
    年龄段：0代表青年，1代表中年，2代表老年；
    有工作：0代表否，1代表是；
    有自己的房子：0代表否，1代表是；
    信贷情况：0代表一般，1代表好，2代表非常好；
    类别(是否给贷款)：0代表否，1代表是
    """
    fr = open(filename, 'r')
    all_lines = fr.readlines()  # list形式,每行为1个str
    # print all_lines
    labels = ['年龄段', '有工作', '有自己的房子', '信贷情况']
    # featname=all_lines[0].strip().split(',')  #list形式
    # featname=featname[:-1]
    labelCounts = {}
    dataset = []
    for line in all_lines[0:]:
        line = line.strip().split(',')  # 以逗号为分割符拆分列表
        dataset.append(line)
    return dataset, labels


def read_testset(testfile):
    """
    年龄段：0代表青年，1代表中年，2代表老年；
    有工作：0代表否，1代表是；
    有自己的房子：0代表否，1代表是；
    信贷情况：0代表一般，1代表好，2代表非常好；
    类别(是否给贷款)：0代表否，1代表是
    """
    fr = open(testfile, 'r')
    all_lines = fr.readlines()
    testset = []
    for line in all_lines[0:]:
        line = line.strip().split(',')  # 以逗号为分割符拆分列表
        testset.append(line)
    return testset


# 计算信息熵
def cal_entropy(dataset):
    numEntries = len(dataset)
    labelCounts = {}
    # 给所有可能分类创建字典
    for featVec in dataset:
        currentlabel = featVec[-1]
        if currentlabel not in labelCounts.keys():
            labelCounts[currentlabel] = 0
        labelCounts[currentlabel] += 1
    Ent = 0.0
    for key in labelCounts:
        p = float(labelCounts[key]) / numEntries
        Ent = Ent - p * log(p, 2)  # 以2为底求对数
    return Ent


# 划分数据集
def splitdataset(dataset, axis, value):
    retdataset = []  # 创建返回的数据集列表
    for featVec in dataset:  # 抽取符合划分特征的值
        if featVec[axis] == value:
            reducedfeatVec = featVec[:axis]  # 去掉axis特征
            reducedfeatVec.extend(featVec[axis + 1:])  # 将符合条件的特征添加到返回的数据集列表
            retdataset.append(reducedfeatVec)
    return retdataset


'''
选择最好的数据集划分方式
ID3算法:以信息增益为准则选择划分属性
C4.5算法：使用“增益率”来选择划分属性
'''


# ID3算法
def ID3_chooseBestFeatureToSplit(dataset):
    numFeatures = len(dataset[0]) - 1
    baseEnt = cal_entropy(dataset)
    bestInfoGain = 0.0
    bestFeature = -1
    for i in range(numFeatures):  # 遍历所有特征
        # for example in dataset:
        # featList=example[i]
        featList = [example[i] for example in dataset]
        uniqueVals = set(featList)  # 将特征列表创建成为set集合，元素不可重复。创建唯一的分类标签列表
        newEnt = 0.0
        for value in uniqueVals:  # 计算每种划分方式的信息熵
            subdataset = splitdataset(dataset, i, value)
            p = len(subdataset) / float(len(dataset))
            newEnt += p * cal_entropy(subdataset)
        infoGain = baseEnt - newEnt
        print(u"ID3中第%d个特征的信息增益为：%.3f" % (i, infoGain))
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain  # 计算最好的信息增益
            bestFeature = i
    return bestFeature


# C4.5算法
def C45_chooseBestFeatureToSplit(dataset):
    numFeatures = len(dataset[0]) - 1
    baseEnt = cal_entropy(dataset)
    bestInfoGain_ratio = 0.0
    bestFeature = -1
    for i in range(numFeatures):  # 遍历所有特征
        featList = [example[i] for example in dataset]
        uniqueVals = set(featList)  # 将特征列表创建成为set集合，元素不可重复。创建唯一的分类标签列表
        newEnt = 0.0
        IV = 0.0
        for value in uniqueVals:  # 计算每种划分方式的信息熵
            subdataset = splitdataset(dataset, i, value)
            p = len(subdataset) / float(len(dataset))
            newEnt += p * cal_entropy(subdataset)
            IV = IV - p * log(p, 2)
        infoGain = baseEnt - newEnt
        if (IV == 0):  # fix the overflow bug
            continue
        infoGain_ratio = infoGain / IV  # 这个feature的infoGain_ratio
        print(u"C4.5中第%d个特征的信息增益率为：%.3f" % (i, infoGain_ratio))
        if (infoGain_ratio > bestInfoGain_ratio):  # 选择最大的gain ratio
            bestInfoGain_ratio = infoGain_ratio
            bestFeature = i  # 选择最大的gain ratio对应的feature
    return bestFeature


# CART算法
def CART_chooseBestFeatureToSplit(dataset):
    numFeatures = len(dataset[0]) - 1
    bestGini = 999999.0
    bestFeature = -1
    for i in range(numFeatures):
        featList = [example[i] for example in dataset]
        uniqueVals = set(featList)
        gini = 0.0
        for value in uniqueVals:
            subdataset = splitdataset(dataset, i, value)
            p = len(subdataset) / float(len(dataset))
            subp = len(splitdataset(subdataset, -1, '0')) / float(len(subdataset))
        gini += p * (1.0 - pow(subp, 2) - pow(1 - subp, 2))
        print(u"CART中第%d个特征的基尼值为：%.3f" % (i, gini))
        if (gini < bestGini):
            bestGini = gini
            bestFeature = i
    return bestFeature


def majorityCnt(classList):
    '''
    数据集已经处理了所有属性，但是类标签依然不是唯一的，
    此时我们需要决定如何定义该叶子节点，在这种情况下，我们通常会采用多数表决的方法决定该叶子节点的分类
    '''
    classCont = {}
    for vote in classList:
        if vote not in classCont.keys():
            classCont[vote] = 0
        classCont[vote] += 1
    sortedClassCont = sorted(classCont.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCont[0][0]


# 利用ID3算法创建决策树
def ID3_createTree(dataset, labels, test_dataset):
    classList = [example[-1] for example in dataset]
    if classList.count(classList[0]) == len(classList):
        # 类别完全相同，停止划分
        return classList[0]
    if len(dataset[0]) == 1:
        # 遍历完所有特征时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = ID3_chooseBestFeatureToSplit(dataset)
    bestFeatLabel = labels[bestFeat]
    print(u"此时最优索引为：" + (bestFeatLabel))

    ID3Tree = {bestFeatLabel: {}}
    del (labels[bestFeat])
    # 得到列表包括节点所有的属性值
    featValues = [example[bestFeat] for example in dataset]
    uniqueVals = set(featValues)

    if pre_pruning:
        ans = []
        for index in range(len(test_dataset)):
            ans.append(test_dataset[index][-1])
        result_counter = Counter()
        for vec in dataset:
            result_counter[vec[-1]] += 1
        leaf_output = result_counter.most_common(1)[0][0]
        root_acc = cal_acc(test_output=[leaf_output] * len(test_dataset), label=ans)
        outputs = []
        ans = []
        for value in uniqueVals:
            cut_testset = splitdataset(test_dataset, bestFeat, value)
            cut_dataset = splitdataset(dataset, bestFeat, value)
            for vec in cut_testset:
                ans.append(vec[-1])
            result_counter = Counter()
            for vec in cut_dataset:
                result_counter[vec[-1]] += 1
            leaf_output = result_counter.most_common(1)[0][0]
            outputs += [leaf_output] * len(cut_testset)
        cut_acc = cal_acc(test_output=outputs, label=ans)

        if cut_acc <= root_acc:
            return leaf_output

    for value in uniqueVals:
        subLabels = labels[:]
        ID3Tree[bestFeatLabel][value] = ID3_createTree(
            splitdataset(dataset, bestFeat, value),
            subLabels,
            splitdataset(test_dataset, bestFeat, value))

    if post_pruning:
        tree_output = classifytest(ID3Tree,
                                   featLabels=['年龄段', '有工作', '有自己的房子', '信贷情况'],
                                   testDataSet=test_dataset)
        ans = []
        for vec in test_dataset:
            ans.append(vec[-1])
        root_acc = cal_acc(tree_output, ans)
        result_counter = Counter()
        for vec in dataset:
            result_counter[vec[-1]] += 1
        leaf_output = result_counter.most_common(1)[0][0]
        cut_acc = cal_acc([leaf_output] * len(test_dataset), ans)

        if cut_acc >= root_acc:
            return leaf_output

    return ID3Tree


def C45_createTree(dataset, labels, test_dataset):
    classList = [example[-1] for example in dataset]
    if classList.count(classList[0]) == len(classList):
        # 类别完全相同，停止划分
        return classList[0]
    if len(dataset[0]) == 1:
        # 遍历完所有特征时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = C45_chooseBestFeatureToSplit(dataset)
    bestFeatLabel = labels[bestFeat]
    print(u"此时最优索引为：" + (bestFeatLabel))
    C45Tree = {bestFeatLabel: {}}
    del (labels[bestFeat])
    # 得到列表包括节点所有的属性值
    featValues = [example[bestFeat] for example in dataset]
    uniqueVals = set(featValues)

    if pre_pruning:
        ans = []
        for index in range(len(test_dataset)):
            ans.append(test_dataset[index][-1])
        result_counter = Counter()
        for vec in dataset:
            result_counter[vec[-1]] += 1
        leaf_output = result_counter.most_common(1)[0][0]
        root_acc = cal_acc(test_output=[leaf_output] * len(test_dataset), label=ans)
        outputs = []
        ans = []
        for value in uniqueVals:
            cut_testset = splitdataset(test_dataset, bestFeat, value)
            cut_dataset = splitdataset(dataset, bestFeat, value)
            for vec in cut_testset:
                ans.append(vec[-1])
            result_counter = Counter()
            for vec in cut_dataset:
                result_counter[vec[-1]] += 1
            leaf_output = result_counter.most_common(1)[0][0]
            outputs += [leaf_output] * len(cut_testset)
        cut_acc = cal_acc(test_output=outputs, label=ans)

        if cut_acc <= root_acc:
            return leaf_output

    for value in uniqueVals:
        subLabels = labels[:]
        C45Tree[bestFeatLabel][value] = C45_createTree(
            splitdataset(dataset, bestFeat, value),
            subLabels,
            splitdataset(test_dataset, bestFeat, value))

    if post_pruning:
        tree_output = classifytest(C45Tree,
                                   featLabels=['年龄段', '有工作', '有自己的房子', '信贷情况'],
                                   testDataSet=test_dataset)
        ans = []
        for vec in test_dataset:
            ans.append(vec[-1])
        root_acc = cal_acc(tree_output, ans)
        result_counter = Counter()
        for vec in dataset:
            result_counter[vec[-1]] += 1
        leaf_output = result_counter.most_common(1)[0][0]
        cut_acc = cal_acc([leaf_output] * len(test_dataset), ans)

        if cut_acc >= root_acc:
            return leaf_output

    return C45Tree


def CART_createTree(dataset, labels, test_dataset):
    classList = [example[-1] for example in dataset]
    if classList.count(classList[0]) == len(classList):
        # 类别完全相同，停止划分
        return classList[0]
    if len(dataset[0]) == 1:
        # 遍历完所有特征时返回出现次数最多的
        return majorityCnt(classList)
    bestFeat = CART_chooseBestFeatureToSplit(dataset)
    # print(u"此时最优索引为："+str(bestFeat))
    bestFeatLabel = labels[bestFeat]
    print(u"此时最优索引为：" + (bestFeatLabel))
    CARTTree = {bestFeatLabel: {}}
    del (labels[bestFeat])
    # 得到列表包括节点所有的属性值
    featValues = [example[bestFeat] for example in dataset]
    uniqueVals = set(featValues)

    if pre_pruning:
        ans = []
        for index in range(len(test_dataset)):
            ans.append(test_dataset[index][-1])
        result_counter = Counter()
        for vec in dataset:
            result_counter[vec[-1]] += 1
        leaf_output = result_counter.most_common(1)[0][0]
        root_acc = cal_acc(test_output=[leaf_output] * len(test_dataset), label=ans)
        outputs = []
        ans = []
        for value in uniqueVals:
            cut_testset = splitdataset(test_dataset, bestFeat, value)
            cut_dataset = splitdataset(dataset, bestFeat, value)
            for vec in cut_testset:
                ans.append(vec[-1])
            result_counter = Counter()
            for vec in cut_dataset:
                result_counter[vec[-1]] += 1
            leaf_output = result_counter.most_common(1)[0][0]
            outputs += [leaf_output] * len(cut_testset)
        cut_acc = cal_acc(test_output=outputs, label=ans)

        if cut_acc <= root_acc:
            return leaf_output

    for value in uniqueVals:
        subLabels = labels[:]
        CARTTree[bestFeatLabel][value] = CART_createTree(
            splitdataset(dataset, bestFeat, value),
            subLabels,
            splitdataset(test_dataset, bestFeat, value))

        if post_pruning:
            tree_output = classifytest(CARTTree,
                                       featLabels=['年龄段', '有工作', '有自己的房子', '信贷情况'],
                                       testDataSet=test_dataset)
            ans = []
            for vec in test_dataset:
                ans.append(vec[-1])
            root_acc = cal_acc(tree_output, ans)
            result_counter = Counter()
            for vec in dataset:
                result_counter[vec[-1]] += 1
            leaf_output = result_counter.most_common(1)[0][0]
            cut_acc = cal_acc([leaf_output] * len(test_dataset), ans)

            if cut_acc >= root_acc:
                return leaf_output

    return CARTTree


def classify(inputTree, featLabels, testVec):
    """
    输入：决策树，分类标签，测试数据
    输出：决策结果
    描述：跑决策树
    """
    firstStr = list(inputTree.keys())[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    classLabel = '0'
    for key in secondDict.keys():
        if testVec[featIndex] == key:
            if type(secondDict[key]).__name__ == 'dict':
                classLabel = classify(secondDict[key], featLabels, testVec)
            else:
                classLabel = secondDict[key]
    return classLabel


def classifytest(inputTree, featLabels, testDataSet):
    """
    输入：决策树，分类标签，测试数据集
    输出：决策结果
    描述：跑决策树
    """
    classLabelAll = []
    for testVec in testDataSet:
        classLabelAll.append(classify(inputTree, featLabels, testVec))
    return classLabelAll


def cal_acc(test_output, label):
    """
    :param test_output: the output of testset
    :param label: the answer
    :return: the acc of
    """
    assert len(test_output) == len(label)
    count = 0
    for index in range(len(test_output)):
        if test_output[index] == label[index]:
            count += 1

    return float(count / len(test_output))


if __name__ == '__main__':
    filename = 'dataset.txt'
    testfile = 'testset.txt'
    dataset, labels = read_dataset(filename)
    # dataset,features=createDataSet()
    print('dataset', dataset)
    print("---------------------------------------------")
    print(u"数据集长度", len(dataset))
    print("Ent(D):", cal_entropy(dataset))
    print("---------------------------------------------")

    print(u"以下为首次寻找最优索引:\n")
    print(u"ID3算法的最优特征索引为:" + str(ID3_chooseBestFeatureToSplit(dataset)))
    print("--------------------------------------------------")
    print(u"C4.5算法的最优特征索引为:" + str(C45_chooseBestFeatureToSplit(dataset)))
    print("--------------------------------------------------")
    print(u"CART算法的最优特征索引为:" + str(CART_chooseBestFeatureToSplit(dataset)))
    print(u"首次寻找最优索引结束！")
    print("---------------------------------------------")

    print(u"下面开始创建相应的决策树-------")

    while True:
        dec_tree = '1'
        # ID3决策树
        if dec_tree == '1':
            labels_tmp = labels[:]  # 拷贝，createTree会改变labels
            ID3desicionTree = ID3_createTree(dataset, labels_tmp, test_dataset=read_testset(testfile))
            print('ID3desicionTree:\n', ID3desicionTree)
            # treePlotter.createPlot(ID3desicionTree)
            treePlotter.ID3_Tree(ID3desicionTree)
            testSet = read_testset(testfile)
            print("下面为测试数据集结果：")
            print('ID3_TestSet_classifyResult:\n', classifytest(ID3desicionTree, labels, testSet))
            print("---------------------------------------------")

        # C4.5决策树
        if dec_tree == '2':
            labels_tmp = labels[:]  # 拷贝，createTree会改变labels
            C45desicionTree = C45_createTree(dataset, labels_tmp, test_dataset=read_testset(testfile))
            print('C45desicionTree:\n', C45desicionTree)
            treePlotter.C45_Tree(C45desicionTree)
            testSet = read_testset(testfile)
            print("下面为测试数据集结果：")
            print('C4.5_TestSet_classifyResult:\n', classifytest(C45desicionTree, labels, testSet))
            print("---------------------------------------------")

        # CART决策树
        if dec_tree == '3':
            labels_tmp = labels[:]  # 拷贝，createTree会改变labels
            CARTdesicionTree = CART_createTree(dataset, labels_tmp, test_dataset=read_testset(testfile))
            print('CARTdesicionTree:\n', CARTdesicionTree)
            treePlotter.CART_Tree(CARTdesicionTree)
            testSet = read_testset(testfile)
            print("下面为测试数据集结果：")
            print('CART_TestSet_classifyResult:\n', classifytest(CARTdesicionTree, labels, testSet))

        break