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125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
#!/usr/bin/python
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# coding: utf8
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from math import log
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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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返回香农熵的计算值
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Raises:
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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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Raises:
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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 getFeatureShannonEnt(dataSet, labels):
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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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Raises:
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"""
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# 求第一行有多少列的 Feature
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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, endEntropy = 0.0, -1, 0.0
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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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# gain[信息增益]=0, 表示与类别相同，无需其他的分类
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# gain[信息增益]=baseEntropy, 表示分类和没分类没有区别
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infoGain = baseEntropy - newEntropy
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# print(infoGain)
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if (infoGain > bestInfoGain):
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endEntropy = newEntropy
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bestInfoGain = infoGain
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bestFeature = i
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else:
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if numFeatures < 0:
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labels[bestFeature] = 'null'
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return labels[bestFeature], baseEntropy, endEntropy, bestInfoGain
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if __name__ == '__main__':
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labels = ['no surfacing', 'flippers']
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dataSet1 = [['yes'], ['yes'], ['no'], ['no'], ['no']]
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dataSet2 = [['a', 1, 'yes'], ['a', 2, 'yes'], ['b', 3, 'no'], ['c', 4, 'no'], ['c', 5, 'no']]
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dataSet3 = [[1, 'yes'], [1, 'yes'], [1, 'no'], [3, 'no'], [3, 'no']]
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infoGain1 = getFeatureShannonEnt(dataSet1, labels)
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infoGain2 = getFeatureShannonEnt(dataSet2, labels)
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infoGain3 = getFeatureShannonEnt(dataSet3, labels)
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print('信息增益: \n\t%s, \n\t%s, \n\t%s' % (infoGain1, infoGain2, infoGain3))
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