基于Python实现的ID3决策树功能示例
2019/7/13 22:27:43
本文主要是介绍基于Python实现的ID3决策树功能示例,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
本文实例讲述了基于Python实现的ID3决策树功能。分享给大家供大家参考,具体如下:
ID3算法是决策树的一种,它是基于奥卡姆剃刀原理的,即用尽量用较少的东西做更多的事。ID3算法,即Iterative Dichotomiser 3,迭代二叉树3代,是Ross Quinlan发明的一种决策树算法,这个算法的基础就是上面提到的奥卡姆剃刀原理,越是小型的决策树越优于大的决策树,尽管如此,也不总是生成最小的树型结构,而是一个启发式算法。
如下示例是一个判断海洋生物数据是否是鱼类而构建的基于ID3思想的决策树
# coding=utf-8 import operator from math import log import time def createDataSet(): dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], [1, 0, 'no'], [0, 1, 'no'], [0, 1, 'no'], [0,0,'maybe']] labels = ['no surfaceing', 'flippers'] return dataSet, labels # 计算香农熵 def calcShannonEnt(dataSet): numEntries = len(dataSet) labelCounts = {} for feaVec in dataSet: currentLabel = feaVec[-1] if currentLabel not in labelCounts: labelCounts[currentLabel] = 0 labelCounts[currentLabel] += 1 shannonEnt = 0.0 for key in labelCounts: prob = float(labelCounts[key]) / numEntries shannonEnt -= prob * log(prob, 2) return shannonEnt 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): numFeatures = len(dataSet[0]) - 1 # 因为数据集的最后一项是标签 baseEntropy = calcShannonEnt(dataSet) bestInfoGain = 0.0 bestFeature = -1 for i in range(numFeatures): 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 # 因为我们递归构建决策树是根据属性的消耗进行计算的,所以可能会存在最后属性用完了,但是分类 # 还是没有算完,这时候就会采用多数表决的方式计算节点分类 def majorityCnt(classList): classCount = {} for vote in classList: if vote not in classCount.keys(): classCount[vote] = 0 classCount[vote] += 1 return max(classCount) def createTree(dataSet, labels): classList = [example[-1] for example in dataSet] if classList.count(classList[0]) == len(classList): # 类别相同则停止划分 return classList[0] if len(dataSet[0]) == 1: # 所有特征已经用完 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[:] # 为了不改变原始列表的内容复制了一下 myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels) return myTree def main(): data, label = createDataSet() t1 = time.clock() myTree = createTree(data, label) t2 = time.clock() print myTree print 'execute for ', t2 - t1 if __name__ == '__main__': main()
运行结果如下:
{'no surfaceing': {0: {'flippers': {0: 'maybe', 1: 'no'}}, 1: {'flippers': {0: 'no', 1: 'yes'}}}} execute for 0.0103958394532
最后我们测试一下这个脚本即可,如果想把这个生成的决策树用图像画出来,也只是在需要在脚本里面定义一个plottree的函数即可。
更多关于Python相关内容感兴趣的读者可查看本站专题:《Python数据结构与算法教程》、《Python加密解密算法与技巧总结》、《Python编码操作技巧总结》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》及《Python入门与进阶经典教程》
希望本文所述对大家Python程序设计有所帮助。
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