def PreprocessData(Data,option):
IDs = []
if (option['idFlag']): # means that the last attribute is id
attributeremove = AttributeRemove()
attributeremove.setInvertSelection(Boolean(True)) # remove every attribute but the last one which is ID
attributeremove.setAttributeIndices(String(str(Data.numAttributes())))
attributeremove.setInputFormat(Data)
IDs = Filter.useFilter(Data, attributeremove)
attributeremove = AttributeRemove()
attributeremove.setInvertSelection(Boolean(False)) # remove IDs from dataset
attributeremove.setAttributeIndices(String(str(Data.numAttributes())))
attributeremove.setInputFormat(Data)
Data = Filter.useFilter(Data, attributeremove)
# set the class Index - the index of the dependent variable
Data.setClassIndex(Data.numAttributes() - 1)
# remove of the classes
if (option['rmClassFlag']): # means that instances with specified class label must be removed
ClassLabel = option['rmClass']
removewithvalues = RemoveWithValues()
removewithvalues.setAttributeIndex(String('last'))
removewithvalues.setNominalIndices(String(str(ClassLabel)))
removewithvalues.setInputFormat(Data)
newData = Filter.useFilter(Data, removewithvalues)
else:
newData = Data
if (option['weightFlag']): # it means that instances should be weighted according to number of samples
# if there is only two classes, do it as before
if (Data.numClasses()==2):
# weight instances with reciprocal weight with number of samples
numInstancesC1 = 0
numInstancesC2 = 0
# get numerical value of the class attribute for the first class because we don't know it
classLabel = newData.instance(1).classAttribute()
c1 = newData.instance(1).value(classLabel)
# find number of instances per class
for cnt in range(0,newData.numInstances()):
if (newData.instance(cnt).value(classLabel) == c1):
numInstancesC1 = numInstancesC1 + 1
else:
numInstancesC2 = numInstancesC2 + 1
# calculate weights
weightC1 = numInstancesC2 /(numInstancesC2 + numInstancesC1 + 0.0)
weightC2 = numInstancesC1 /(numInstancesC2 + numInstancesC1 + 0.0)
# assign weight to instances of classes
for cnt in range(0,newData.numInstances()):
if (newData.instance(cnt).value(classLabel) == c1):
newData.instance(cnt).setWeight(weightC1)
else:
newData.instance(cnt).setWeight(weightC2)
# if number of class are more than two then ....
elif (Data.numClasses()>2):
numClasses = Data.numClasses()
stats = Data.attributeStats(Data.classIndex())
AttributeStats = stats.nominalCounts
classLabels = Data.instance(1).classAttribute()
# assign weight to instances of classes
cnt = 0
sumWeigths = 0.0
numInstancesPerClass = {}
weightPerClass = {}
mapClassLabels = {}
for e in classLabels.enumerateValues():
numInst = AttributeStats[cnt] + 0.0
w = 1.0 / numInst
mapClassLabels.update({e:cnt})
weightPerClass.update({cnt:w})
numInstancesPerClass.update({cnt:numInst})
sumWeigths = sumWeigths + w
cnt = cnt + 1
# normalize weights
for k in weightPerClass.keys():
weightPerClass[k] = weightPerClass[k]/sumWeigths
for cnt in range(0,newData.numInstances()):
w = weightPerClass[ newData.instance(cnt).value(classLabels) ]
newData.instance(cnt).setWeight(w)
return newData, IDs
usage()
return 1
options = {'idFlag':True, 'weightFlag': False, 'rmClassFlag': False, 'rmClass': 0}
# read the first dataset
fn = inputList[0]
fid = FileReader(fn)
Data = Instances(fid)
Data, IDs = PreprocessData(Data,options)
# remove class label
attributeremove = AttributeRemove()
attributeremove.setInvertSelection(Boolean(False)) # remove class labels from dataset
attributeremove.setAttributeIndices(String(str(Data.numAttributes())))
attributeremove.setInputFormat(Data)
newData = Filter.useFilter(Data, attributeremove)
# loop over input arff file
cnt = Data.numAttributes()
for fnCnt in range(1,len(inputList)):
fn = inputList[fnCnt]
fid = FileReader(fn)
Data = Instances(fid)
Data, IDs = PreprocessData(Data,options)
# remove class label
attributeremove = AttributeRemove()
attributeremove.setInvertSelection(Boolean(True)) # remove every attribute but the last one which is class label
attributeremove.setAttributeIndices(String(str(Data.numAttributes())))
attributeremove.setInputFormat(Data)
labels = Filter.useFilter(Data, attributeremove)
attributeremove = AttributeRemove()
attributeremove.setInvertSelection(Boolean(False)) # remove class labels from dataset
