python实现决策树分类（二）

来源：互联网发布：新浪数据nba 编辑：程序博客网时间：2024/05/21 20:26

上一篇博客主要介绍了决策树的原理，这篇主要介绍他的实现，代码环境python 3.4，实现的是ID3算法，首先为了后面matplotlib的绘图方便，我把原来的中文数据集变成了英文

原始数据集：

变化后的数据集在程序代码中体现，这就不截图了

构建决策树的代码如下：

#coding :utf-8'''2017.6.25 author :Erin           function: "decesion tree" ID3          '''import numpy as npimport pandas as pdfrom math import logimport operator  def load_data():        #data=np.array(data)    data=[['teenager' ,'high', 'no' ,'same', 'no'],          ['teenager', 'high', 'no', 'good', 'no'],          ['middle_aged' ,'high', 'no', 'same', 'yes'],          ['old_aged', 'middle', 'no' ,'same', 'yes'],          ['old_aged', 'low', 'yes', 'same' ,'yes'],          ['old_aged', 'low', 'yes', 'good', 'no'],          ['middle_aged', 'low' ,'yes' ,'good', 'yes'],          ['teenager' ,'middle' ,'no', 'same', 'no'],          ['teenager', 'low' ,'yes' ,'same', 'yes'],          ['old_aged' ,'middle', 'yes', 'same', 'yes'],          ['teenager' ,'middle', 'yes', 'good', 'yes'],          ['middle_aged' ,'middle', 'no', 'good', 'yes'],          ['middle_aged', 'high', 'yes', 'same', 'yes'],          ['old_aged', 'middle', 'no' ,'good' ,'no']]    features=['age','input','student','level']    return data,featuresdef cal_entropy(dataSet):    '''    输入data ,表示带最后标签列的数据集    计算给定数据集总的信息熵    {'是': 9, '否': 5}    0.9402859586706309    '''        numEntries = len(dataSet)    labelCounts = {}    for featVec in dataSet:        label = featVec[-1]        if label not in labelCounts.keys():            labelCounts[label] = 0        labelCounts[label] += 1    entropy = 0.0    for key in labelCounts.keys():        p_i = float(labelCounts[key]/numEntries)        entropy -= p_i * log(p_i,2)#log(x,10)表示以10 为底的对数    return entropydef split_data(data,feature_index,value):    '''    划分数据集    feature_index：用于划分特征的列数，例如“年龄”    value:划分后的属性值：例如“青少年”    '''    data_split=[]#划分后的数据集    for feature in data:        if feature[feature_index]==value:            reFeature=feature[:feature_index]            reFeature.extend(feature[feature_index+1:])            data_split.append(reFeature)    return data_splitdef choose_best_to_split(data):        '''    根据每个特征的信息增益，选择最大的划分数据集的索引特征    '''        count_feature=len(data[0])-1#特征个数4    #print(count_feature)#4    entropy=cal_entropy(data)#原数据总的信息熵    #print(entropy)#0.9402859586706309        max_info_gain=0.0#信息增益最大    split_fea_index = -1#信息增益最大，对应的索引号    for i in range(count_feature):                feature_list=[fe_index[i] for fe_index in data]#获取该列所有特征值        #######################################        '''        print('feature_list')        ['青少年', '青少年', '中年', '老年', '老年', '老年', '中年', '青少年', '青少年', '老年',        '青少年', '中年', '中年', '老年']        0.3467680694480959 #对应上篇博客中的公式  =（1）*5/14        0.3467680694480959        0.6935361388961918        '''       # print(feature_list)        unqval=set(feature_list)#去除重复        Pro_entropy=0.0#特征的熵        for value in unqval:#遍历改特征下的所有属性            sub_data=split_data(data,i,value)            pro=len(sub_data)/float(len(data))            Pro_entropy+=pro*cal_entropy(sub_data)            #print(Pro_entropy)                    info_gain=entropy-Pro_entropy        if(info_gain>max_info_gain):            max_info_gain=info_gain            split_fea_index=i    return split_fea_index                ##################################################def most_occur_label(labels):    #sorted_label_count[0][0]  次数最多的类标签    label_count={}    for label in labels:        if label not in label_count.keys():            label_count[label]=0        else:            label_count[label]+=1        sorted_label_count = sorted(label_count.items(),key = operator.itemgetter(1),reverse = True)    return sorted_label_count[0][0]def build_decesion_tree(dataSet,featnames):    '''    字典的键存放节点信息，分支及叶子节点存放值    '''    featname = featnames[:]              ################    classlist = [featvec[-1] for featvec in dataSet]  #此节点的分类情况    if classlist.count(classlist[0]) == len(classlist):  #全部属于一类        return classlist[0]    if len(dataSet[0]) == 1:         #分完了,没有属性了        return Vote(classlist)       #少数服从多数    # 选择一个最优特征进行划分    bestFeat = choose_best_to_split(dataSet)    bestFeatname = featname[bestFeat]    del(featname[bestFeat])     #防止下标不准    DecisionTree = {bestFeatname:{}}    # 创建分支,先找出所有属性值,即分支数    allvalue = [vec[bestFeat] for vec in dataSet]    specvalue = sorted(list(set(allvalue)))  #使有一定顺序    for v in specvalue:        copyfeatname = featname[:]        DecisionTree[bestFeatname][v] =  build_decesion_tree(split_data(dataSet,bestFeat,v),copyfeatname)    return DecisionTree

绘制可视化图的代码如下：

def getNumLeafs(myTree):    '计算决策树的叶子数'        # 叶子数    numLeafs = 0    # 节点信息    sides = list(myTree.keys())      firstStr =sides[0]    # 分支信息    secondDict = myTree[firstStr]        for key in secondDict.keys():   # 遍历所有分支        # 子树分支则递归计算        if type(secondDict[key]).__name__=='dict':            numLeafs += getNumLeafs(secondDict[key])        # 叶子分支则叶子数+1        else:   numLeafs +=1            return numLeafsdef getTreeDepth(myTree):    '计算决策树的深度'        # 最大深度    maxDepth = 0    # 节点信息    sides = list(myTree.keys())       firstStr =sides[0]    # 分支信息    secondDict = myTree[firstStr]        for key in secondDict.keys():   # 遍历所有分支        # 子树分支则递归计算        if type(secondDict[key]).__name__=='dict':            thisDepth = 1 + getTreeDepth(secondDict[key])        # 叶子分支则叶子数+1        else:   thisDepth = 1                # 更新最大深度        if thisDepth > maxDepth: maxDepth = thisDepth            return maxDepthimport matplotlib.pyplot as pltdecisionNode = dict(boxstyle="sawtooth", fc="0.8")leafNode = dict(boxstyle="round4", fc="0.8")arrow_args = dict(arrowstyle="<-")    # ==================================================# 输入：#        nodeTxt:     终端节点显示内容#        centerPt:    终端节点坐标#        parentPt:    起始节点坐标#        nodeType:    终端节点样式# 输出：#        在图形界面中显示输入参数指定样式的线段(终端带节点)# ==================================================def plotNode(nodeTxt, centerPt, parentPt, nodeType):    '画线(末端带一个点)'            createPlot.ax1.annotate(nodeTxt, xy=parentPt,  xycoords='axes fraction', xytext=centerPt, textcoords='axes fraction', va="center", ha="center", bbox=nodeType, arrowprops=arrow_args )# =================================================================# 输入：#        cntrPt:      终端节点坐标#        parentPt:    起始节点坐标#        txtString:   待显示文本内容# 输出：#        在图形界面指定位置(cntrPt和parentPt中间)显示文本内容(txtString)# =================================================================def plotMidText(cntrPt, parentPt, txtString):    '在指定位置添加文本'        # 中间位置坐标    xMid = (parentPt[0]-cntrPt[0])/2.0 + cntrPt[0]    yMid = (parentPt[1]-cntrPt[1])/2.0 + cntrPt[1]        createPlot.ax1.text(xMid, yMid, txtString, va="center", ha="center", rotation=30)# ===================================# 输入：#        myTree:    决策树#        parentPt:  根节点坐标#        nodeTxt:   根节点坐标信息# 输出：#        在图形界面绘制决策树# ===================================def plotTree(myTree, parentPt, nodeTxt):    '绘制决策树'        # 当前树的叶子数    numLeafs = getNumLeafs(myTree)    # 当前树的节点信息    sides = list(myTree.keys())       firstStr =sides[0]        # 定位第一棵子树的位置(这是蛋疼的一部分)    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)        # 绘制当前节点到子树节点(含子树节点)的信息    plotMidText(cntrPt, parentPt, nodeTxt)    plotNode(firstStr, cntrPt, parentPt, decisionNode)        # 获取子树信息    secondDict = myTree[firstStr]    # 开始绘制子树，纵坐标-1。            plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD          for key in secondDict.keys():   # 遍历所有分支        # 子树分支则递归        if type(secondDict[key]).__name__=='dict':            plotTree(secondDict[key],cntrPt,str(key))        # 叶子分支则直接绘制        else:            plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))         # 子树绘制完毕，纵坐标+1。    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD# ==============================# 输入：#        myTree:    决策树# 输出：#        在图形界面显示决策树# ==============================def createPlot(inTree):    '显示决策树'        # 创建新的图像并清空 - 无横纵坐标    fig = plt.figure(1, facecolor='white')    fig.clf()    axprops = dict(xticks=[], yticks=[])    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)        # 树的总宽度 高度    plotTree.totalW = float(getNumLeafs(inTree))    plotTree.totalD = float(getTreeDepth(inTree))        # 当前绘制节点的坐标    plotTree.xOff = -0.5/plotTree.totalW;     plotTree.yOff = 1.0;        # 绘制决策树    plotTree(inTree, (0.5,1.0), '')        plt.show()                        if __name__ == '__main__':    data,features=load_data()    split_fea_index=choose_best_to_split(data)    newtree=build_decesion_tree(data,features)    print(newtree)    createPlot(newtree)    '''    {'age': {'old_aged': {'level': {'same': 'yes', 'good': 'no'}}, 'teenager': {'student': {'no': 'no', 'yes': 'yes'}}, 'middle_aged': 'yes'}}    '''

结果如下：

怎么用决策树分类，将会在下一章介绍python实现决策树分类（三）

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