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)
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
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
