decision.py

def createDataSet():
  dataSet = [[1,1,'yes'], [1,1,'yes'], [1,0,'no'], [0,1,'no'], [0,1,'no']]
  labels = ['no surfacing', 'flippers']
  return dataSet, labels
  
def majorityCnt(classList):
  classCount={}
  for vote in classList:
    if vote not in classCount.keys():
      classCount[vote] = 0
    classCount[vote] += 1
  sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
  return sortedClassCount[0][0]
  
def calcShannonEnt(dataSet):
  numEntries = len(dataSet)
  labelCounts = {}
  for featVec in dataSet:
    currentLabel = featVec[-1]
    if currentLabel not in labelCounts.keys():
      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+1:1])
      retDataSet.append(reducedFeatVec)
  return retDataSet
  
def chooseBestFeatureToSplit(dataSet):
  numFeatures = len(dataSet[0]) - 1
  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
    
def createTree(dataSet, labels):
  classList = [example[-1] for example in dataSet]
  if classList.count(classList[0]) == len(classList):
    return classList[0]
  if len(dataSet[0]) == 1:
    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[:]
    myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat,value), subLabels)
  return myTree