def runShogunSVMDNASubsequenceStringKernel(train_xt, train_lt, test_xt):
"""
run svm with spectrum kernel
"""
##################################################
# set up svm
feats_train = StringCharFeatures(train_xt, DNA)
feats_test = StringCharFeatures(test_xt, DNA)
kernel=SubsequenceStringKernel(feats_train, feats_train, MAXLEN, DECAY)
kernel.io.set_loglevel(MSG_DEBUG)
kernel.init(feats_train, feats_train)
# init kernel
labels = BinaryLabels(train_lt)
# run svm model
print "Ready to train!"
svm=LibSVM(SVMC, kernel, labels)
svm.io.set_loglevel(MSG_DEBUG)
svm.train()
# predictions
print "Making predictions!"
out1DecisionValues = svm.apply(feats_train)
out1=out1DecisionValues.get_labels()
kernel.init(feats_train, feats_test)
out2DecisionValues = svm.apply(feats_test)
out2=out2DecisionValues.get_labels()
return out1,out2,out1DecisionValues,out2DecisionValues
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, SubsequenceStringKernel, 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 = SubsequenceStringKernel(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 SubsequenceStringKernel from modshogun import StringCharFeatures, DNA feats_train=StringCharFeatures(fm_train_dna, DNA) feats_test=StringCharFeatures(fm_test_dna, DNA) kernel=SubsequenceStringKernel(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
def runShogunSVMDNASubsequenceStringKernel(train_xt, train_lt, test_xt):
"""
run svm with spectrum kernel
"""
##################################################
# set up svm
feats_train = StringCharFeatures(train_xt, DNA)
feats_test = StringCharFeatures(test_xt, DNA)
kernel = SubsequenceStringKernel(feats_train, feats_train, MAXLEN, DECAY)
kernel.io.set_loglevel(MSG_DEBUG)
kernel.init(feats_train, feats_train)
# init kernel
labels = BinaryLabels(train_lt)
# run svm model
print "Ready to train!"
svm = LibSVM(SVMC, kernel, labels)
svm.io.set_loglevel(MSG_DEBUG)
svm.train()
# predictions
print "Making predictions!"
out1DecisionValues = svm.apply(feats_train)
out1 = out1DecisionValues.get_labels()
kernel.init(feats_train, feats_test)
out2DecisionValues = svm.apply(feats_test)
out2 = out2DecisionValues.get_labels()
return out1, out2, out1DecisionValues, out2DecisionValues
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, SubsequenceStringKernel, 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=SubsequenceStringKernel(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 SubsequenceStringKernel
from modshogun import StringCharFeatures, DNA
feats_train = StringCharFeatures(fm_train_dna, DNA)
feats_test = StringCharFeatures(fm_test_dna, DNA)
kernel = SubsequenceStringKernel(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
