基于Python实现的ID3决策树功能示例

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

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。