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 kernel_ssk_string_modular(fm_train_dna=traindat,
fm_test_dna=testdat,
maxlen=1,
decay=1):
from modshogun import StringSubsequenceKernel
from modshogun import StringCharFeatures, DNA
feats_train = StringCharFeatures(fm_train_dna, DNA)
feats_test = StringCharFeatures(fm_train_dna, DNA)
kernel = StringSubsequenceKernel(feats_train, feats_train, maxlen, decay)
km_train = kernel.get_kernel_matrix()
# print(km_train)
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
# print(km_test)
return km_train, km_test, kernel
