Ejemplo n.º 1
0
def classifier_ssk_modular(fm_train_dna=traindat,
                           fm_test_dna=testdat,
                           label_train_dna=label_traindat,
                           C=1,
                           maxlen=1,
                           decay=1):
    from modshogun import StringCharFeatures, BinaryLabels
    from modshogun import LibSVM, StringSubsequenceKernel, DNA
    from modshogun import ErrorRateMeasure

    feats_train = StringCharFeatures(fm_train_dna, DNA)
    feats_test = StringCharFeatures(fm_test_dna, DNA)
    labels = BinaryLabels(label_train_dna)
    kernel = StringSubsequenceKernel(feats_train, feats_train, maxlen, decay)

    svm = LibSVM(C, kernel, labels)
    svm.train()

    out = svm.apply(feats_train)
    evaluator = ErrorRateMeasure()
    trainerr = evaluator.evaluate(out, labels)
    # print(trainerr)

    kernel.init(feats_train, feats_test)
    predicted_labels = svm.apply(feats_test).get_labels()
    # print predicted_labels

    return predicted_labels
def classifier_libsvm_minimal_modular (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,width=2.1,C=1):
	from modshogun import RealFeatures, BinaryLabels
	from modshogun import LibSVM, GaussianKernel, CSVFile
	from modshogun import ErrorRateMeasure

	feats_train=RealFeatures(CSVFile(train_fname))
	feats_test=RealFeatures(CSVFile(test_fname))
	labels=BinaryLabels(CSVFile(label_fname))
	kernel=GaussianKernel(feats_train, feats_train, width);

	svm=LibSVM(C, kernel, labels);
	svm.train();

	out=svm.apply(feats_train);
	evaluator = ErrorRateMeasure()
	testerr = evaluator.evaluate(out,labels)
Ejemplo n.º 3
0
def classifier_libsvm_minimal_modular(train_fname=traindat,
                                      test_fname=testdat,
                                      label_fname=label_traindat,
                                      width=2.1,
                                      C=1):
    from modshogun import RealFeatures, BinaryLabels
    from modshogun import LibSVM, GaussianKernel, CSVFile
    from modshogun import ErrorRateMeasure

    feats_train = RealFeatures(CSVFile(train_fname))
    feats_test = RealFeatures(CSVFile(test_fname))
    labels = BinaryLabels(CSVFile(label_fname))
    kernel = GaussianKernel(feats_train, feats_train, width)

    svm = LibSVM(C, kernel, labels)
    svm.train()

    out = svm.apply(feats_train)
    evaluator = ErrorRateMeasure()
    testerr = evaluator.evaluate(out, labels)
def classifier_ssk_modular (fm_train_dna=traindat,fm_test_dna=testdat,
		label_train_dna=label_traindat,C=1,maxlen=1,decay=1):
	from modshogun import StringCharFeatures, BinaryLabels
	from modshogun import LibSVM, StringSubsequenceKernel, DNA
	from modshogun import ErrorRateMeasure

	feats_train=StringCharFeatures(fm_train_dna, DNA)
	feats_test=StringCharFeatures(fm_test_dna, DNA)
	labels=BinaryLabels(label_train_dna)
	kernel=StringSubsequenceKernel(feats_train, feats_train, maxlen, decay);

	svm=LibSVM(C, kernel, labels);
	svm.train();

	out=svm.apply(feats_train);
	evaluator = ErrorRateMeasure()
	trainerr = evaluator.evaluate(out,labels)
	# print(trainerr)

	kernel.init(feats_train, feats_test)
	predicted_labels=svm.apply(feats_test).get_labels()
	# print predicted_labels

	return predicted_labels
Ejemplo n.º 5
0
def evaluation_contingencytableevaluation_modular(ground_truth, predicted):
    from modshogun import BinaryLabels
    from modshogun import ContingencyTableEvaluation
    from modshogun import AccuracyMeasure, ErrorRateMeasure, BALMeasure
    from modshogun import WRACCMeasure, F1Measure, CrossCorrelationMeasure
    from modshogun import RecallMeasure, PrecisionMeasure, SpecificityMeasure

    ground_truth_labels = BinaryLabels(ground_truth)
    predicted_labels = BinaryLabels(predicted)

    base_evaluator = ContingencyTableEvaluation()
    base_evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = AccuracyMeasure()
    accuracy = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = ErrorRateMeasure()
    errorrate = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = BALMeasure()
    bal = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = WRACCMeasure()
    wracc = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = F1Measure()
    f1 = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = CrossCorrelationMeasure()
    crosscorrelation = evaluator.evaluate(predicted_labels,
                                          ground_truth_labels)

    evaluator = RecallMeasure()
    recall = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = PrecisionMeasure()
    precision = evaluator.evaluate(predicted_labels, ground_truth_labels)

    evaluator = SpecificityMeasure()
    specificity = evaluator.evaluate(predicted_labels, ground_truth_labels)

    return accuracy, errorrate, bal, wracc, f1, crosscorrelation, recall, precision, specificity