def readFeature(num_features,type,numtrees):
#filename1=resultFileTest
#filename2=resultFileTest2
filename1=resultFile+'_'+type+'_'+num_features+'_train.arff'
filename2=resultFile+'_'+type+'_'+num_features+'_test.arff'
#print filename1
#loader=CSVLoader()
#loader.setSource(File(filename1))
#data=loader.getDataSet()
#print data.numAttributes()
print "Loading data......"
train_file=FileReader(filename1)
train_data=Instances(train_file)
train_data.setClassIndex(train_data.numAttributes()-1)
rf=RF()
rf.setNumTrees(numtrees)
rf.buildClassifier(train_data)
#print rf
#loader.setSource(File(filename2))
#test_data=Instances(loader.getDataSet())
# test_data.setClassIndex(test_data.numAttributes()-1)
test_file=FileReader(filename2)
test_data=Instances(test_file)
test_data.setClassIndex(test_data.numAttributes()-1)
''' num=test_data.numInstances()
print num
for i in xrange(num):
r1=rf.distributionForInstance(test_data.instance(i))
r2=rf.classifyInstance(test_data.instance(i))
ptrixrint r1
print r2'''
buffer = StringBuffer() # buffer for the predictions
output=PlainText()
output.setHeader(test_data)
output.setBuffer(buffer)
attRange = Range() # attributes to output
outputDistribution = Boolean(True)
evaluator=Evaluation(train_data)
evaluator.evaluateModel(rf,test_data,[output,attRange,outputDistribution])
#print evaluator.evaluateModel(RF(),['-t',filename1,'-T',filename2,'-I',str(numtrees)])
#evaluator1=Evaluation(test_data)
print evaluator.toSummaryString()
print evaluator.toClassDetailsString()
print evaluator.toMatrixString()
return [evaluator.precision(0),evaluator.recall(0),evaluator.fMeasure(0),evaluator.matthewsCorrelationCoefficient(0),evaluator.numTruePositives(0),evaluator.numFalsePositives(0),evaluator.numTrueNegatives(0),evaluator.numFalseNegatives(0),evaluator.areaUnderROC(0)]
def readCross(num,type,select_feature,numtrees):
filename=resultFile+'_'+type+'_'+num+'_'+select_feature+'_all.csv'
loader=CSVLoader()
loader.setSource(File(filename))
data=loader.getDataSet()
#print data.numAttributes()
data.setClassIndex(data.numAttributes()-1)
rf=RF()
rf.setNumTrees(numtrees)
#pred_output = PredictionOutput( classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
buffer = StringBuffer() # buffer for the predictions
output=PlainText()
output.setHeader(data)
output.setBuffer(buffer)
output.setOutputDistribution(True)
attRange = Range() # attributes to output
outputDistributions = Boolean(True)
evaluator=Evaluation(data)
evaluator.crossValidateModel(rf,data,10, Random(1),[output,attRange,outputDistributions])
print evaluator.toSummaryString()
print evaluator.toClassDetailsString()
print evaluator.toMatrixString()
return [evaluator.precision(1),evaluator.recall(1),evaluator.fMeasure(1),evaluator.matthewsCorrelationCoefficient(1),evaluator.numTruePositives(1),evaluator.numFalsePositives(1),evaluator.numTrueNegatives(1),evaluator.numFalseNegatives(1),evaluator.areaUnderROC(1)]
