def classify(runID, trainingSet, testingSet):
[trainingFile,
testingFile] = arffWriter.writeArffFiles(runID, trainingSet,
testingSet)
# deep learning (AE)
if config.DEEP_LEARNING_METHOD != -1:
#[trainingFile, testingFile] = dA_2.calcAE([trainingFile, testingFile]) # one layer dA
#[trainingFile, testingFile] = dA_2.calcAE([trainingFile, testingFile]) # two layers dA
#[trainingFile, testingFile] = dA_2.calcAE([trainingFile, testingFile])
#SdA_2.calcSdA([trainingFile, testingFile])
if config.DEEP_LEARNING_METHOD == 1:
[trainingFile, testingFile
] = logistic_sgd_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 2:
[trainingFile,
testingFile] = dA_2.runDL([trainingFile, testingFile])
[trainingFile,
testingFile] = dA_2.runDL([trainingFile, testingFile])
#[trainingFile, testingFile] = dA_2.runDL([trainingFile, testingFile])
#[trainingFile, testingFile] = dA_2.runDL([trainingFile, testingFile])
#[trainingFile, testingFile] = dA_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 3:
# DL classifier
return mlp_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 4:
return SdA_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 5:
return mlp_3.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 6:
return SdA_3.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 7:
return LeNetConvPoolLayer_2.runDL([trainingFile, testingFile])
return wekaAPI.execute(trainingFile, testingFile,
"weka.classifiers.bayes.NaiveBayes", ['-K'])
def classify(runID, trainingSet, testingSet):
[trainingFile,
testingFile] = arffWriter.writeArffFiles(runID, trainingSet,
testingSet)
[trainingFileOrig, testingFileOrig] = [trainingFile, testingFile]
if config.NUM_MONITORED_SITES != -1: #no need to classify as this is for generating openworld datasets. See the line above (arffWriter)
[accuracy, debugInfo] = ['NA', []]
return [accuracy, debugInfo]
if config.n_components_PCA != 0:
[trainingFile,
testingFile] = Utils.calcPCA2([trainingFile, testingFile])
if config.n_components_LDA != 0:
[trainingFile,
testingFile] = Utils.calcLDA4([trainingFile, testingFile])
if config.n_components_QDA != 0:
[trainingFile,
testingFile] = Utils.calcQDA([trainingFile, testingFile])
if config.lasso != 0:
#[trainingFile,testingFile] = Utils.calcLasso3([trainingFile,testingFile])
#[trainingFile,testingFile] = Utils.calcLogisticRegression([trainingFile,testingFile])
Utils.calcLogisticRegression([trainingFile, testingFile])
# deep learning
if config.DEEP_LEARNING_METHOD != -1:
#[trainingFile, testingFile] = dA_2.calcAE([trainingFile, testingFile]) # one layer dA
#[trainingFile, testingFile] = dA_2.calcAE([trainingFile, testingFile]) # two layers dA
#[trainingFile, testingFile] = dA_2.calcAE([trainingFile, testingFile])
#SdA_2.calcSdA([trainingFile, testingFile])
if config.DEEP_LEARNING_METHOD == 1:
[trainingFile, testingFile
] = logistic_sgd_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 2:
[trainingFile,
testingFile] = dA_2.runDL([trainingFile, testingFile])
[trainingFile,
testingFile] = dA_2.runDL([trainingFile, testingFile])
#[trainingFile, testingFile] = dA_2.runDL([trainingFile, testingFile])
#[trainingFile, testingFile] = dA_2.runDL([trainingFile, testingFile])
#[trainingFile, testingFile] = dA_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 3:
# DL classifier
return mlp_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 4:
return SdA_2.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 5:
return mlp_3.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 6:
return SdA_3.runDL([trainingFile, testingFile])
elif config.DEEP_LEARNING_METHOD == 7:
return LeNetConvPoolLayer_2.runDL([trainingFile, testingFile])
#Utils.plotDensity([trainingFile,testingFile])
#Utils.plot([trainingFile,testingFile])
if config.OC_SVM == 0: # multi-class svm
if config.CROSS_VALIDATION == 0:
#print 'WARNING: NB classifier with Bi-Di. ###########///////////XXXXXX???????? '
#return wekaAPI.execute(trainingFile, testingFile, "weka.classifiers.bayes.NaiveBayes", ['-K'])
return wekaAPI.execute(
trainingFile,
testingFile,
"weka.Run weka.classifiers.functions.LibSVM",
[
'-K',
'2', # RBF kernel
'-G',
'0.0000019073486328125', # Gamma
##May20 '-Z', # normalization 18 May 2015
'-C',
'131072', # Cost
#'-S','2', # one-class svm
'-B'
]) # confidence
else:
file = Utils.joinTrainingTestingFiles(
trainingFile, testingFile) # join and shuffle
return wekaAPI.executeCrossValidation(
file,
"weka.Run weka.classifiers.functions.LibSVM",
[
'-x',
str(config.CROSS_VALIDATION), # number of folds
'-K',
'2', # RBF kernel
'-G',
'0.0000019073486328125', # Gamma
##May20 '-Z', # normalization 18 May 2015
'-C',
'131072', # Cost
'-B'
]) # confidence
else: # one-class svm
if config.CROSS_VALIDATION == 0:
print str(config.SVM_KERNEL)
print str(config.OC_SVM_Nu)
return wekaAPI.executeOneClassSVM(
trainingFile,
testingFile,
"weka.Run weka.classifiers.functions.LibSVM",
[
'-K',
str(config.SVM_KERNEL),
#'-K','2', # RBF kernel
#'-G','0.0000019073486328125', # Gamma
##May20 '-Z', # normalization 18 May 2015
#'-C','131072', # Cost
#'-N','0.001', # nu
'-N',
str(config.OC_SVM_Nu), # nu
'-S',
'2'
]) #, # one-class svm
#'-B'] ) # confidence
else:
file = Utils.joinTrainingTestingFiles(
trainingFile, testingFile) # join and shuffle
return wekaAPI.executeCrossValidation(
file,
"weka.Run weka.classifiers.functions.LibSVM",
[
'-x',
str(config.CROSS_VALIDATION), # number of folds
'-K',
'2', # RBF kernel
'-G',
'0.0000019073486328125', # Gamma
##May20 '-Z', # normalization 18 May 2015
'-C',
'131072', # Cost
'-B'
]) # confidence