MachineLearning in Action_trees

ID3 algorithm

trees.py

'''Created on Oct 12, 2010Decision Tree Source Code for Machine Learning in Action Ch. 3@author: Peter Harrington'''from math import logimport operatordef createDataSet():    dataSet = [[1, 1, 'yes'],               [1, 1, 'yes'],               [1, 0, 'no'],               [0, 1, 'no'],               [0, 1, 'no']]    labels = ['no surfacing','flippers']    #change to discrete values    return dataSet, labelsdef calcShannonEnt(dataSet):    numEntries = len(dataSet)    labelCounts = {}    for featVec in dataSet: #the the number of unique elements and their occurance        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) #log base 2    return shannonEnt    def splitDataSet(dataSet, axis, value):    retDataSet = []    for featVec in dataSet:        if featVec[axis] == value:            reducedFeatVec = featVec[:axis]     #chop out axis used for splitting            reducedFeatVec.extend(featVec[axis+1:])            retDataSet.append(reducedFeatVec)    return retDataSet    def chooseBestFeatureToSplit(dataSet):    numFeatures = len(dataSet[0]) - 1      #the last column is used for the labels    baseEntropy = calcShannonEnt(dataSet)    bestInfoGain = 0.0; bestFeature = -1    for i in range(numFeatures):        #iterate over all the features        featList = [example[i] for example in dataSet]#create a list of all the examples of this feature        uniqueVals = set(featList)       #get a set of unique values        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     #calculate the info gain; ie reduction in entropy        if (infoGain > bestInfoGain):       #compare this to the best gain so far            bestInfoGain = infoGain         #if better than current best, set to best            bestFeature = i    return bestFeature                      #returns an integerdef 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]#stop splitting when all of the classes are equal    if len(dataSet[0]) == 1: #stop splitting when there are no more features in dataSet        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[:]       #copy all of labels, so trees don't mess up existing labels        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)    return myTree                                def classify(inputTree,featLabels,testVec):    firstStr = inputTree.keys()[0]    secondDict = inputTree[firstStr]    featIndex = featLabels.index(firstStr)    key = testVec[featIndex]    valueOfFeat = secondDict[key]    if isinstance(valueOfFeat, dict):         classLabel = classify(valueOfFeat, featLabels, testVec)    else: classLabel = valueOfFeat    return classLabeldef storeTree(inputTree,filename):    import pickle    fw = open(filename,'w')    pickle.dump(inputTree,fw)    fw.close()    def grabTree(filename):    import pickle    fr = open(filename)    return pickle.load(fr)    

treePlotter.py

'''Created on Oct 14, 2010@author: Peter Harrington'''import matplotlib.pyplot as pltdecisionNode = dict(boxstyle="sawtooth", fc="0.8")leafNode = dict(boxstyle="round4", fc="0.8")arrow_args = dict(arrowstyle="<-")def getNumLeafs(myTree):    numLeafs = 0    firstStr = myTree.keys()[0]    secondDict = myTree[firstStr]    for key in secondDict.keys():        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes            numLeafs += getNumLeafs(secondDict[key])        else:   numLeafs +=1    return numLeafsdef getTreeDepth(myTree):    maxDepth = 0    firstStr = myTree.keys()[0]    secondDict = myTree[firstStr]    for key in secondDict.keys():        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes            thisDepth = 1 + getTreeDepth(secondDict[key])        else:   thisDepth = 1        if thisDepth > maxDepth: maxDepth = thisDepth    return maxDepthdef plotNode(nodeTxt, centerPt, parentPt, nodeType):    createPlot.ax1.annotate(nodeTxt, xy=parentPt,  xycoords='axes fraction',             xytext=centerPt, textcoords='axes fraction',             va="center", ha="center", bbox=nodeType, arrowprops=arrow_args )    def plotMidText(cntrPt, parentPt, txtString):    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)def plotTree(myTree, parentPt, nodeTxt):#if the first key tells you what feat was split on    numLeafs = getNumLeafs(myTree)  #this determines the x width of this tree    depth = getTreeDepth(myTree)    firstStr = myTree.keys()[0]     #the text label for this node should be this    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW, plotTree.yOff)    plotMidText(cntrPt, parentPt, nodeTxt)    plotNode(firstStr, cntrPt, parentPt, decisionNode)    secondDict = myTree[firstStr]    plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD    for key in secondDict.keys():        if type(secondDict[key]).__name__=='dict':#test to see if the nodes are dictonaires, if not they are leaf nodes               plotTree(secondDict[key],cntrPt,str(key))        #recursion        else:   #it's a leaf node print the leaf node            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))    plotTree.yOff = plotTree.yOff + 1.0/plotTree.totalD#if you do get a dictonary you know it's a tree, and the first element will be another dictdef createPlot(inTree):    fig = plt.figure(1, facecolor='white')    fig.clf()    axprops = dict(xticks=[], yticks=[])    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)    #no ticks    #createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses     plotTree.totalW = float(getNumLeafs(inTree))    plotTree.totalD = float(getTreeDepth(inTree))    plotTree.xOff = -0.5/plotTree.totalW; plotTree.yOff = 1.0;    plotTree(inTree, (0.5,1.0), '')    plt.show()def createPlot2():    fig = plt.figure(1, facecolor='white')    fig.clf()    createPlot.ax1 = plt.subplot(111, frameon=False) #ticks for demo puropses     plotNode('a decision node', (0.5, 0.1), (0.1, 0.5), decisionNode)    plotNode('a leaf node', (0.8, 0.1), (0.3, 0.8), leafNode)    plt.show()def retrieveTree(i):    listOfTrees =[{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},                  {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}                  ]    return listOfTrees[i]#createPlot(thisTree)

lenses.txt

youngmyopenoreducedno lensesyoungmyopenonormalsoftyoungmyopeyesreducedno lensesyoungmyopeyesnormalhardyounghypernoreducedno lensesyounghypernonormalsoftyounghyperyesreducedno lensesyounghyperyesnormalhardpremyopenoreducedno lensespremyopenonormalsoftpremyopeyesreducedno lensespremyopeyesnormalhardprehypernoreducedno lensesprehypernonormalsoftprehyperyesreducedno lensesprehyperyesnormalno lensespresbyopicmyopenoreducedno lensespresbyopicmyopenonormalno lensespresbyopicmyopeyesreducedno lensespresbyopicmyopeyesnormalhardpresbyopichypernoreducedno lensespresbyopichypernonormalsoftpresbyopichyperyesreducedno lensespresbyopichyperyesnormalno lenses