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)]