机器学习与数据挖掘实验四--编程实现基于信息增益进行划分选择的决策树

机器学习与数据挖掘实验四

（编程实现基于信息增益进行划分选择的决策树算法）

实验目的：

掌握构建决策树的基本流程

实验环境：

Anaconda/Jupyter notebook/Pycharm

实验内容：

编码实现基于信息增益进行划分选择的决策树算法，为给定数据生成一棵决策树；

实验步骤：

from math import log
import numpy as np
import operator
import csv
#方式1；
def loaddata ():
    dataSet = [[0, 0,0,0,0,0, 'yes'],
               [1, 0,1,0,0,0,'yes'],
               [1, 0,0,0,0,0,'yes'],
               [0, 0,1,0,0,0,'yes'],
               [2, 0,0,0,0,0,'yes'],
               [0, 1,0,0,1,1,'yes'],
               [1, 1,0,1,1,1,'yes'],
               [1, 1,0,0,1,0, 'yes'],
               [1, 1,1,1,1,0,'no'],
               [0, 2,2,0,2,1,'no'],
               [2, 2,2,2,2,0,'no'],
               [2, 0,0,2,2,1,'no'],
               [0, 1,0,1,0,0, 'no'],
               [2, 1,1,1,0,0,'no'],
               [1, 1,0,0,1,1,'no'],
               [2, 0,0,2,2,0,'no'],
               [0, 0,1,1,1,0,'no']]
    feature_name = ['a1','a2','a3','a4','a5','a6']
    return dataSet, feature_name

def entropy(dataSet):
    #数据集条数
    m = len(dataSet)
#保存所有的类别及属于该类别的样本数
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        if currentLabel not in labelCounts.keys(): 
            labelCounts[currentLabel] = 0
        labelCounts[currentLabel] += 1
    #保存熵值
    e = 0.0 
#补充计算信息熵的代码
    for key in labelCounts:
        prob=float(labelCounts[key])/m
        e-=prob*log(prob,2)
    return e

def splitDataSet(dataSet, axis, value):
#补充按给定特征和特征值划分好的数据集的代码
# axis对应的是特征的索引;
    retDataSet = []
    #遍历数据集
    for featVec in dataSet:
        if featVec[axis] == value:
            reduceFeatVec = featVec[:axis]
            reduceFeatVec.extend(featVec[axis+1:])
            retDataSet.append(reduceFeatVec)
    return retDataSet

def chooseBestFeature(dataSet):
    n = len(dataSet[0]) - 1
    #计数整个数据集的熵
    baseEntropy = entropy(dataSet)
    bestInfoGain = 0.0; bestFeature = -1
    #遍历每个特征
    for i in range(n):  
        #获取当前特征i的所有可能取值
        featList = [example[i] for example in dataSet]
        uniqueVals = set(featList) 
        newEntropy = 0.0
        #遍历特征i的每一个可能的取值
        for value in uniqueVals:
            #按特征i的value值进行数据集的划分
            subDataSet = splitDataSet(dataSet, i, value)
            #补充计算条件熵的代码
            prob=len(subDataSet)/float(len(dataSet))
            newEntropy += prob*entropy(subDataSet)
        #计算信息增益
        infoGain = baseEntropy - newEntropy  
        #保存当前最大的信息增益及对应的特征
        if (infoGain > bestInfoGain):
            bestInfoGain = infoGain
            bestFeature = i
    return bestFeature

def classVote(classList):
    #定义字典，保存每个标签对应的个数 
    classCount={}
    for vote in classList:
        if vote not in classCount.keys(): 
            classCount[vote] = 0
        classCount[vote] += 1
     #排序
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def trainTree(dataSet,feature_name):
    classList = [example[-1] for example in dataSet]
#所有类别都一致
    if classList.count(classList[0]) == len(classList): 
        return classList[0] 
#数据集中没有特征
    if len(dataSet[0]) == 1: 
        return classVote(classList)
#选择最优划分特征
    bestFeat = chooseBestFeature(dataSet)
    bestFeatName = feature_name[bestFeat]
    myTree = {bestFeatName:{}}
    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
#遍历uniqueVals中的每个值，生成相应的分支
    for value in uniqueVals:
        sub_feature_name = feature_name[:]
        # 生成在dataSet中bestFeat取值为value的子集；
        sub_dataset=splitDataSet(dataSet,bestFeat,value)
        # 根据得到的子集，生成决策树
        myTree[bestFeatName][value]=trainTree(sub_dataset,sub_feature_name)
    return myTree

myDat,feature_name = loaddata()
myTree = trainTree(myDat,feature_name)
print(myTree)

def predict(inputTree,featLabels,testVec):
    firstStr = list(inputTree.keys())[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    key = testVec[featIndex]
    valueOfFeat = secondDict[key]
    if isinstance(valueOfFeat, dict): 
        classLabel = predict(valueOfFeat, featLabels, testVec)
    else: classLabel = valueOfFeat
    return classLabel

print(predict(myTree,feature_name,[1,1,0,1,0,0]))