def kernel_combined_modular (fm_train_real=traindat,fm_test_real=testdat,fm_train_dna=traindna,fm_test_dna=testdna ): from modshogun import CombinedKernel, GaussianKernel, FixedDegreeStringKernel, LocalAlignmentStringKernel from modshogun import RealFeatures, StringCharFeatures, CombinedFeatures, DNA kernel=CombinedKernel() feats_train=CombinedFeatures() feats_test=CombinedFeatures() subkfeats_train=RealFeatures(fm_train_real) subkfeats_test=RealFeatures(fm_test_real) subkernel=GaussianKernel(10, 1.1) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train=StringCharFeatures(fm_train_dna, DNA) subkfeats_test=StringCharFeatures(fm_test_dna, DNA) degree=3 subkernel=FixedDegreeStringKernel(10, degree) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train=StringCharFeatures(fm_train_dna, DNA) subkfeats_test=StringCharFeatures(fm_test_dna, DNA) subkernel=LocalAlignmentStringKernel(10) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_combined_custom_poly_modular(train_fname=traindat,
test_fname=testdat,
train_label_fname=label_traindat):
from modshogun import CombinedFeatures, RealFeatures, BinaryLabels
from modshogun import CombinedKernel, PolyKernel, CustomKernel
from modshogun import LibSVM, CSVFile
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(CSVFile(train_fname))
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(CSVFile(train_fname))
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = BinaryLabels(CSVFile(train_label_fname))
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(CSVFile(test_fname))
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(CSVFile(test_fname))
feats_pred.append_feature_obj(subkfeats_test)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_pred)
svm.set_kernel(kernel)
svm.apply()
km_train = kernel.get_kernel_matrix()
return km_train, kernel
def kernel_combined_custom_poly_modular (train_fname = traindat,test_fname = testdat,train_label_fname=label_traindat):
from modshogun import CombinedFeatures, RealFeatures, BinaryLabels
from modshogun import CombinedKernel, PolyKernel, CustomKernel
from modshogun import LibSVM, CSVFile
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(CSVFile(train_fname))
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(CSVFile(train_fname))
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10,2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = BinaryLabels(CSVFile(train_label_fname))
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(CSVFile(test_fname))
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(CSVFile(test_fname))
feats_pred.append_feature_obj(subkfeats_test)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_pred)
svm.set_kernel(kernel)
svm.apply()
km_train=kernel.get_kernel_matrix()
return km_train,kernel
def make_combined_kernel(feats_train, raw_train, use_sign=True, minseq=3, maxseq=8):
from modshogun import CombinedKernel
from modshogun import CommUlongStringKernel
# init the combined kernel
kernel=CombinedKernel()
# initialize the subkernels
count = 0
for seqlen in range(minseq, maxseq+1):
subkernel=CommUlongStringKernel(raw_train[count], raw_train[count], use_sign)
kernel.append_kernel(subkernel)
count += 1
kernel.init(feats_train, feats_train)
km_train=kernel.get_kernel_matrix()
return kernel
