700字范文 > 机器学习实战-决策树预测隐形眼镜类型

机器学习实战-决策树预测隐形眼镜类型

时间：2023-06-20 07:09:53

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机器学习实战-决策树预测隐形眼镜类型

我们将用递归来构建分类器并使用 Matplotlib 绘制。分类器获取隐形眼镜处方的数据，并用尝试预测人们需要什么镜片。

需要用到的库

# -*- coding: utf-8 -*-from math import logimport operatorimport matplotlib.pyplot as plt

主要代码

def calcShannonEnt(dataSet):numEntries = len(dataSet)labelCounts = {}for featVec in dataSet:currentLabel = featVec[-1]if currentLabel not in labelCounts.keys():labelCounts[currentLabel] = 0labelCounts[currentLabel] += 1shannonEnt = 0.0for key in labelCounts:prob = float(labelCounts[key]) / numEntriesshannonEnt -= prob * log(prob, 2)return shannonEntdef 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 retDataSetdef chooseBestFeatureToSplit(dataSet):numFeatures = len(dataSet[0]) - 1baseEntropy = calcShannonEnt(dataSet)bestInfoGain = 0.0bestFeature = -1for i in range(numFeatures):featList = [example[i] for example in dataSet]uniqueVals = set(featList)newEntropy = 0.0for value in uniqueVals:subDataSet = splitDataSet(dataSet, i, value)prob = len(subDataSet) / float(len(dataSet))newEntropy += prob * calcShannonEnt(subDataSet)infoGain = baseEntropy - newEntropyif (infoGain > bestInfoGain):bestInfoGain = infoGainbestFeature = ireturn bestFeaturedef majorityCnt(classList):classCount = {}for vote in classList:if vote not in classCount.keys():classCount[vote] = 0classCount[vote] += 1sortedClassCount = sorted(classCount.items(), 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: {}}# print("0tree", myTree)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 myTreedef getNumLeafs(myTree):numLeafs = 0temp_keys = list(myTree.keys())firstStr = temp_keys[0]secondDict = myTree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':numLeafs += getNumLeafs(secondDict[key])else:numLeafs += 1return numLeafsdef getTreeDepth(myTree):maxDepth = 0firstStr = next(iter(myTree))secondDict = myTree[firstStr]for key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':thisDepth = 1 + getTreeDepth(secondDict[key])else:thisDepth = 1if thisDepth > maxDepth:maxDepth = thisDepthreturn maxDepthdef 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]decisionNode = dict(boxstyle="sawtooth", fc="0.8")leafNode = dict(boxstyle="round4", fc="0.8")arrow_args = dict(arrowstyle="<-")def 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):numLeafs = getNumLeafs(myTree)depth = getTreeDepth(myTree)firstStr = list(myTree.keys())[0]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.totalDfor key in secondDict.keys():if type(secondDict[key]).__name__ == 'dict':plotTree(secondDict[key], cntrPt, str(key))else:plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalWplotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalDdef createPlot(inTree):fig = plt.figure(1, facecolor='white')fig.clf()axprops = dict(xticks=[], yticks=[])createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)plotTree.totalW = float(getNumLeafs(inTree))plotTree.totalD = float(getTreeDepth(inTree))plotTree.xOff = -0.5 / plotTree.totalWplotTree.yOff = 1.0plotTree(inTree, (0.5, 1.0), '')plt.show()def prodict():with open("lenses.txt", "rb") as fr:lenses = [inst.decode().strip().split('\t') for inst in fr.readlines()]lensesLabels = ['age', 'prescript', 'astigmatic', "tearRate"]lensesTree = createTree(lenses, lensesLabels)createPlot(lensesTree)return lensesTreedef classify(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 = classify(valueOfFeat, featLabels, testVec)else:classLabel = valueOfFeatreturn classLabel

主函数如下

if __name__ == "__main__":mytree = prodict()labels = ['age', 'prescript', 'astigmatic', "tearRate"]result = classify(mytree, labels, ["presbyopic", "hyper", "yes", "normal"])if result == 'no lenses':print("视力良好")if result == 'soft':print("轻微近视")if result == 'hard':print("重度近视")

需要用到隐形眼镜数据集lenses.txt文件如下

young myope no reduced no lenses

young myope no normal soft

young myope yes reduced no lenses

young myope yes normal hard

young hyper no reduced no lenses

young hyper no normal soft

young hyper yes reduced no lenses

young hyper yes normal hard

pre myope no reduced no lenses

pre myope no normal soft

pre myope yes reduced no lenses