def chooseBestFeatureToSplit(dataset):
numFeatures = len(dataset[0])
baseEntropy = entropy_shannon.calcShannonEnt(dataset)
print "baseEntropy", baseEntropy
bestInfoGain = 0.0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataset]
uniqueVals = set(featList)
#print "uniqueVals",uniqueVals
newEntorpy = 0.0
for value in uniqueVals:
subDataSet = split_dataset.splitDataSet(dataset, i, value)
prob = len(subDataSet) / float(len(dataset))
newEntorpy += prob * entropy_shannon.calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntorpy
print "infoGain & entropy for " + str(i) + " => " + str(
infoGain) + " " + str(newEntorpy)
if (infoGain > bestInfoGain):
bestInfoGain = infoGain
bestFeature = i
return bestFeature
def chooseBestFeatureToSplit(dataset):
numFeatures = len(dataset[0])
baseEntropy = entropy_shannon.calcShannonEnt(dataset)
print "baseEntropy", baseEntropy
bestInfoGain = 0.0
bestFeature = -1
for i in range(numFeatures):
featList = [example[i] for example in dataset]
uniqueVals = set(featList)
# print "uniqueVals",uniqueVals
newEntorpy = 0.0
for value in uniqueVals:
subDataSet = split_dataset.splitDataSet(dataset, i, value)
prob = len(subDataSet) / float(len(dataset))
newEntorpy += prob * entropy_shannon.calcShannonEnt(subDataSet)
infoGain = baseEntropy - newEntorpy
print "infoGain & entropy for " + str(i) + " => " + str(infoGain) + " " + str(newEntorpy)
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:
majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataset)
bestFeatLabel = labels[bestFeat]
myTree = {}
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(split_dataset.splitDataSet(dataset, bestFeat, value), subLabels)
return myTree
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:
majorityCnt(classList)
bestFeat = chooseBestFeatureToSplit(dataset)
bestFeatLabel = labels[bestFeat]
myTree = {}
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(
split_dataset.splitDataSet(dataset, bestFeat, value), subLabels)
return myTree
