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248 lines
8.3 KiB
Python
248 lines
8.3 KiB
Python
#!/usr/bin/python
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# coding:utf8
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'''
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Created on Oct 12, 2010
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Update on 2017-02-27
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Decision Tree Source Code for Machine Learning in Action Ch. 3
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@author: Peter Harrington/片刻
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'''
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print(__doc__)
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import operator
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from math import log
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import decisionTreePlot as dtPlot
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def createDataSet():
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"""DateSet 基础数据集
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Args:
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无需传入参数
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Returns:
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返回数据集和对应的label标签
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"""
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dataSet = [[1, 1, 'yes'],
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[1, 1, 'yes'],
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[1, 0, 'no'],
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[0, 1, 'no'],
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[0, 1, 'no']]
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# dataSet = [['yes'],
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# ['yes'],
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# ['no'],
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# ['no'],
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# ['no']]
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labels = ['no surfacing', 'flippers']
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# change to discrete values
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return dataSet, labels
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def calcShannonEnt(dataSet):
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"""calcShannonEnt(calculate Shannon entropy 计算label分类标签的香农熵)
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Args:
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dataSet 数据集
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Returns:
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返回 每一组feature下的某个分类下，香农熵的信息期望
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"""
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# 求list的长度，表示计算参与训练的数据量
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numEntries = len(dataSet)
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# print type(dataSet), 'numEntries: ', numEntries
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# 计算分类标签label出现的次数
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labelCounts = {}
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# the the number of unique elements and their occurance
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for featVec in dataSet:
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currentLabel = featVec[-1]
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if currentLabel not in labelCounts.keys():
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labelCounts[currentLabel] = 0
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labelCounts[currentLabel] += 1
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# print '-----', featVec, labelCounts
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# 对于label标签的占比，求出label标签的香农熵
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shannonEnt = 0.0
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for key in labelCounts:
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prob = float(labelCounts[key])/numEntries
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# log base 2
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shannonEnt -= prob * log(prob, 2)
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# print '---', prob, prob * log(prob, 2), shannonEnt
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return shannonEnt
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def splitDataSet(dataSet, axis, value):
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"""splitDataSet(通过遍历dataSet数据集，求出axis对应的colnum列的值为value的行)
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Args:
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dataSet 数据集
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axis 表示每一行的axis列
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value 表示axis列对应的value值
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Returns:
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axis列为value的数据集【该数据集需要排除axis列】
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"""
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retDataSet = []
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for featVec in dataSet:
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# axis列为value的数据集【该数据集需要排除axis列】
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if featVec[axis] == value:
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# chop out axis used for splitting
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reducedFeatVec = featVec[:axis]
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'''
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请百度查询一下： extend和append的区别
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'''
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reducedFeatVec.extend(featVec[axis+1:])
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# 收集结果值 axis列为value的行【该行需要排除axis列】
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retDataSet.append(reducedFeatVec)
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return retDataSet
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def chooseBestFeatureToSplit(dataSet):
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"""chooseBestFeatureToSplit(选择最好的特征)
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Args:
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dataSet 数据集
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Returns:
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bestFeature 最优的特征列
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"""
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# 求第一行有多少列的 Feature, 最后一列是label列嘛
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numFeatures = len(dataSet[0]) - 1
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# label的信息熵
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baseEntropy = calcShannonEnt(dataSet)
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# 最优的信息增益值, 和最优的Featurn编号
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bestInfoGain, bestFeature = 0.0, -1
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# iterate over all the features
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for i in range(numFeatures):
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# create a list of all the examples of this feature
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# 获取每一个feature的list集合
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featList = [example[i] for example in dataSet]
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# get a set of unique values
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# 获取剔重后的集合
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uniqueVals = set(featList)
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# 创建一个临时的信息熵
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newEntropy = 0.0
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# 遍历某一列的value集合，计算该列的信息熵
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for value in uniqueVals:
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subDataSet = splitDataSet(dataSet, i, value)
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prob = len(subDataSet)/float(len(dataSet))
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newEntropy += prob * calcShannonEnt(subDataSet)
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# gain[信息增益] 值越大，意味着该分类提供的信息量越大，该特征对分类的不确定程度越小
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# 也就说： 列进行group分组后，对应的类别越多，信息量越大，那么香农熵越小，那么信息增益就越大，所以gain越大
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infoGain = baseEntropy - newEntropy
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# print 'infoGain=', infoGain, 'bestFeature=', i
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if (infoGain > bestInfoGain):
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bestInfoGain = infoGain
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bestFeature = i
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return bestFeature
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def majorityCnt(classList):
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"""majorityCnt(选择出线次数最多的一个结果)
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Args:
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classList label列的集合
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Returns:
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bestFeature 最优的特征列
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"""
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classCount = {}
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for vote in classList:
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if vote not in classCount.keys():
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classCount[vote] = 0
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classCount[vote] += 1
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# 倒叙排列classCount得到一个字典集合，然后取出第一个就是结果（yes/no）
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sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
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# print 'sortedClassCount:', sortedClassCount
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return sortedClassCount[0][0]
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def createTree(dataSet, labels):
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classList = [example[-1] for example in dataSet]
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# 如果数据集的最后一列的第一个值出现的次数=整个集合的数量，也就说只有一个类别，就只直接返回结果就行
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if classList.count(classList[0]) == len(classList):
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return classList[0]
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# 如果数据集只有1列，那么最初出现label次数最多的一类，作为结果
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if len(dataSet[0]) == 1:
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return majorityCnt(classList)
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# 选择最优的列，得到最有列对应的label含义
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bestFeat = chooseBestFeatureToSplit(dataSet)
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# 获取label的名称
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bestFeatLabel = labels[bestFeat]
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# 初始化myTree
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myTree = {bestFeatLabel: {}}
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# 注：labels列表是可变对象，在PYTHON函数中作为参数时传址引用，能够被全局修改
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# 所以这行代码导致函数外的同名变量被删除了元素，造成例句无法执行，提示'no surfacing' is not in list
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del(labels[bestFeat])
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# 取出最优列，然后它的branch做分类
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featValues = [example[bestFeat] for example in dataSet]
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uniqueVals = set(featValues)
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for value in uniqueVals:
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# 求出剩余的标签label
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subLabels = labels[:]
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myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)
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# print 'myTree', value, myTree
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return myTree
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def classify(inputTree, featLabels, testVec):
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"""classify(给输入的节点，进行分类)
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Args:
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inputTree 决策树模型
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featLabels label标签对应的名称
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testVec 测试输入的数据
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Returns:
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classLabel 分类的结果值，需要映射label才能知道名称
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"""
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# 获取tree的根节点对于的key值
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firstStr = inputTree.keys()[0]
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# 通过key得到根节点对应的value
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secondDict = inputTree[firstStr]
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# 判断根节点名称获取根节点在label中的先后顺序，这样就知道输入的testVec怎么开始对照树来做分类
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featIndex = featLabels.index(firstStr)
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# 测试数据，找到根节点对应的label位置，也就知道从输入的数据的第几位来开始分类
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key = testVec[featIndex]
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valueOfFeat = secondDict[key]
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print '+++', firstStr, 'xxx', secondDict, '---', key, '>>>', valueOfFeat
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# 判断分枝是否结束: 判断valueOfFeat是否是dict类型
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if isinstance(valueOfFeat, dict):
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classLabel = classify(valueOfFeat, featLabels, testVec)
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else:
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classLabel = valueOfFeat
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return classLabel
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def storeTree(inputTree, filename):
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import pickle
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fw = open(filename, 'w')
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pickle.dump(inputTree, fw)
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fw.close()
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def grabTree(filename):
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import pickle
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fr = open(filename)
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return pickle.load(fr)
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if __name__ == "__main__":
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# 1.创建数据和结果标签
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myDat, labels = createDataSet()
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# print myDat, labels
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# # 计算label分类标签的香农熵
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# calcShannonEnt(myDat)
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# # 求第0列 为 1/0的列的数据集【排除第0列】
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# print '1---', splitDataSet(myDat, 0, 1)
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# print '0---', splitDataSet(myDat, 0, 0)
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# # 计算最好的信息增益的列
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# print chooseBestFeatureToSplit(myDat)
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import copy
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myTree = createTree(myDat, copy.deepcopy(labels))
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print myTree
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# [1, 1]表示要取的分支上的节点位置，对应的结果值
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print classify(myTree, labels, [1, 1])
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# 画图可视化展现
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dtPlot.createPlot(myTree)
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