Python实现决策树C4.5算法的示例

#coding=utf-8
import operator
from math import log
import time
import os, sys
import string

def createDataSet(trainDataFile):
 print trainDataFile
 dataSet = []
 try:
 fin = open(trainDataFile)
 for line in fin:
  line = line.strip()
  cols = line.split('\t')
  row = [cols[1], cols[2], cols[3], cols[4], cols[5], cols[6], cols[7], cols[8], cols[9], cols[10], cols[0]]
  dataSet.append(row)
  #print row
 except:
 print 'Usage xxx.py trainDataFilePath'
 sys.exit()
 labels = ['cip1', 'cip2', 'cip3', 'cip4', 'sip1', 'sip2', 'sip3', 'sip4', 'sport', 'domain']
 print 'dataSetlen', len(dataSet)
 return dataSet, labels

#calc shannon entropy of label or feature
def calcShannonEntOfFeature(dataSet, feat):
 numEntries = len(dataSet)
 labelCounts = {}
 for feaVec in dataSet:
 currentLabel = feaVec[feat]
 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 #last col is label
 baseEntropy = calcShannonEntOfFeature(dataSet, -1)
 bestInfoGainRate = 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 *calcShannonEntOfFeature(subDataSet, -1) #calc conditional entropy
 infoGain = baseEntropy - newEntropy
 　　 iv = calcShannonEntOfFeature(dataSet, i)
 if(iv == 0): #value of the feature is all same,infoGain and iv all equal 0, skip the feature
 continue
 　　 infoGainRate = infoGain / iv
 if infoGainRate > bestInfoGainRate:
  bestInfoGainRate = infoGainRate
  bestFeature = i
 return bestFeature
  
#feature is exhaustive, reture what you want label
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): #all data is the same label
 return classList[0]
 if len(dataSet[0]) == 1: #all feature is exhaustive
 return majorityCnt(classList)
 bestFeat = chooseBestFeatureToSplit(dataSet)
 bestFeatLabel = labels[bestFeat]
 if(bestFeat == -1): #特征一样，但类别不一样，即类别与特征不相关，随机选第一个类别做分类结果
 return classList[0] 
 myTree = {bestFeatLabel:{}}
 del(labels[bestFeat])
 featValues = [example[bestFeat] for example in dataSet]
 uniqueVals = set(featValues)
 for value in uniqueVals:
 subLabels = labels[:]
 myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value),subLabels)
 return myTree
 
def main():
 if(len(sys.argv) < 3):
 print 'Usage xxx.py trainSet outputTreeFile'
 sys.exit()
 data,label = createDataSet(sys.argv[1])
 t1 = time.clock()
 myTree = createTree(data,label)
 t2 = time.clock()
 fout = open(sys.argv[2], 'w')
 fout.write(str(myTree))
 fout.close()
 print 'execute for ',t2-t1
if __name__=='__main__':
 main()