概述

这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，供大家参考，具体内容如下

# coding=utf-8
import operator
from math import log
import time


def createDataSet():
  dataSet = [[1,1,'yes'],[1,'no'],[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()

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。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持编程小技巧。

总结

以上是编程之家为你收集整理的python基于ID3思想的决策树全部内容，希望文章能够帮你解决python基于ID3思想的决策树所遇到的程序开发问题。

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