ID3的python实现

import math
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 * math.log(prob, 2)
return shannonEnt


def CreateDataSet():
'''dataset = [[1, 1, 'yes'],
[1, 1, 'yes'],
[1, 0, 'no'],
[0, 1, 'no'],
[0, 1, 'no']]
labels = ['outlook', 'temperature','humidity','false']
return dataset, labels'''


lines_set = open( 'Dataset.txt').readlines()
labelLine = lines_set[ 2];
labels = labelLine.strip().split()
lines_set = lines_set[ 4: 11]
dataSet = [];
for line in lines_set:
data = line.split();
dataSet.append(data);
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 chooseBestFeatureToSplit(dataSet):
numberFeatures = len(dataSet[ 0]) - 1
baseEntropy = calcShannonEnt(dataSet)
bestInfoGain = 0.0;
bestFeature = -1;
for i in range(numberFeatures):
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 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 = chooseBestFeatureToSplit(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


myDat, labels = CreateDataSet()
myTree = createTree(myDat, labels)
print myTree

运行结果如下：

{'outlook': {'overcast': 'Y', 'sunny': 'N', 'rain': {'windy': {'false': 'Y', 'true': 'N'}}}}

训练集和测试集

训练集:

outlook temperature humidity windy
---------------------------------------------------------
sunny hot high false N
sunny hot high true N
overcast hot high false Y
rain mild high false Y
rain cool normal false Y
rain cool normal true N
overcast cool normal true Y

测试集
outlook temperature humidity windy
---------------------------------------------------------
sunny mild high false
sunny cool normal false
rain mild normal false
sunny mild normal true
overcast mild high true
overcast hot normal false
rain mild high true