def get_kernel_mat(fm_train_dna,
fm_test_dna,
N,
M,
pseudo=1e-1,
order=1,
gap=0,
reverse=False):
# train HMM for positive class
print "hmm training"
charfeat = StringCharFeatures(fm_train_dna, DNA)
#charfeat.io.set_loglevel(MSG_DEBUG)
hmm_train = StringWordFeatures(charfeat.get_alphabet())
hmm_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
pos = HMM(hmm_train, N, M, pseudo)
pos.baum_welch_viterbi_train(BW_NORMAL)
neg = HMM(pos)
print "Kernel training data"
charfeat = StringCharFeatures(fm_train_dna, DNA)
wordfeats_train = StringWordFeatures(charfeat.get_alphabet())
wordfeats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
print "Kernel testing data"
charfeat = StringCharFeatures(fm_test_dna, DNA)
wordfeats_test = StringWordFeatures(charfeat.get_alphabet())
wordfeats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)
print "get kernel on training data"
pos.set_observations(wordfeats_train)
neg.set_observations(wordfeats_train)
feats_train = FKFeatures(10, pos, neg)
feats_train.set_opt_a(-1) #estimate prior
print 'getting feature matrix'
v0 = feats_train.get_feature_vector(0)
v1 = feats_train.get_feature_vector(1)
print np.dot(v0, v1)
kernel = LinearKernel(feats_train, feats_train)
#kernel=PolyKernel(feats_train, feats_train, *kargs)
km_train = kernel.get_kernel_matrix()
print km_train.shape, km_train[0, 1]
print "get kernel on testing data"
pos_clone = HMM(pos)
neg_clone = HMM(neg)
pos_clone.set_observations(wordfeats_test)
neg_clone.set_observations(wordfeats_test)
feats_test = FKFeatures(10, pos_clone, neg_clone)
feats_test.set_a(feats_train.get_a()) #use prior from training data
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def kernel_linear_byte_modular (train_fname=traindat,test_fname=testdat): from modshogun import LinearKernel, ByteFeatures, CSVFile feats_train=ByteFeatures(CSVFile(train_fname)) feats_test=ByteFeatures(CSVFile(test_fname)) kernel=LinearKernel(feats_train, feats_train) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return kernel
def kernel_linear_modular (train_fname=traindat,test_fname=testdat,scale=1.2): from modshogun import RealFeatures, LinearKernel, AvgDiagKernelNormalizer, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) kernel=LinearKernel() kernel.set_normalizer(AvgDiagKernelNormalizer(scale)) 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_linear_modular(train_fname=traindat, test_fname=testdat, scale=1.2):
from modshogun import RealFeatures, LinearKernel, AvgDiagKernelNormalizer, CSVFile
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
kernel = LinearKernel()
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
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_sparse_linear_modular (fm_train_real=traindat,fm_test_real=testdat,scale=1.1): from modshogun import SparseRealFeatures from modshogun import LinearKernel, AvgDiagKernelNormalizer feats_train=SparseRealFeatures(fm_train_real) feats_test=SparseRealFeatures(fm_test_real) kernel=LinearKernel() kernel.set_normalizer(AvgDiagKernelNormalizer(scale)) 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 get_kernel_mat(fm_train_dna, fm_test_dna, N, M, pseudo=1e-1,order=1,gap=0,reverse=False): # train HMM for positive class print "hmm training" charfeat=StringCharFeatures(fm_train_dna, DNA) #charfeat.io.set_loglevel(MSG_DEBUG) hmm_train=StringWordFeatures(charfeat.get_alphabet()) hmm_train.obtain_from_char(charfeat, order-1, order, gap, reverse) pos=HMM(hmm_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) neg = HMM(pos) print "Kernel training data" charfeat=StringCharFeatures(fm_train_dna, DNA) wordfeats_train=StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) print "Kernel testing data" charfeat=StringCharFeatures(fm_test_dna, DNA) wordfeats_test=StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) print "get kernel on training data" pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train=FKFeatures(10, pos, neg) feats_train.set_opt_a(-1) #estimate prior print 'getting feature matrix' v0 = feats_train.get_feature_vector(0) v1 = feats_train.get_feature_vector(1) print np.dot(v0, v1) kernel=LinearKernel(feats_train, feats_train) #kernel=PolyKernel(feats_train, feats_train, *kargs) km_train=kernel.get_kernel_matrix() print km_train.shape, km_train[0, 1] print "get kernel on testing data" pos_clone=HMM(pos) neg_clone=HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test=FKFeatures(10, pos_clone, neg_clone) feats_test.set_a(feats_train.get_a()) #use prior from training data kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_linear_word_modular (fm_train_word=traindat,fm_test_word=testdat,scale=1.2): from modshogun import LinearKernel, AvgDiagKernelNormalizer from modshogun import WordFeatures feats_train=WordFeatures(fm_train_word) feats_test=WordFeatures(fm_test_word) kernel=LinearKernel(feats_train, feats_train) kernel.set_normalizer(AvgDiagKernelNormalizer(scale)) 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 kernel
def kernel_sparse_linear_modular(fm_train_real=traindat,
fm_test_real=testdat,
scale=1.1):
from modshogun import SparseRealFeatures
from modshogun import LinearKernel, AvgDiagKernelNormalizer
feats_train = SparseRealFeatures(fm_train_real)
feats_test = SparseRealFeatures(fm_test_real)
kernel = LinearKernel()
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
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_linear_word_modular(fm_train_word=traindat,
fm_test_word=testdat,
scale=1.2):
from modshogun import LinearKernel, AvgDiagKernelNormalizer
from modshogun import WordFeatures
feats_train = WordFeatures(fm_train_word)
feats_test = WordFeatures(fm_test_word)
kernel = LinearKernel(feats_train, feats_train)
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
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 kernel
def kernel_director_linear_modular(fm_train_real=traindat,
fm_test_real=testdat,
scale=1.2):
try:
from modshogun import DirectorKernel
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
class DirectorLinearKernel(DirectorKernel):
def __init__(self):
DirectorKernel.__init__(self, True)
def kernel_function(self, idx_a, idx_b):
seq1 = self.get_lhs().get_feature_vector(idx_a)
seq2 = self.get_rhs().get_feature_vector(idx_b)
return numpy.dot(seq1, seq2)
from modshogun import LinearKernel, AvgDiagKernelNormalizer
from modshogun import Time
feats_train = RealFeatures(fm_train_real)
#feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.parallel.set_num_threads(1)
feats_test = RealFeatures(fm_test_real)
kernel = LinearKernel()
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
kernel.init(feats_train, feats_train)
dkernel = DirectorLinearKernel()
dkernel.set_normalizer(AvgDiagKernelNormalizer(scale))
dkernel.init(feats_train, feats_train)
#print "km_train"
t = Time()
km_train = kernel.get_kernel_matrix()
#t1=t.cur_time_diff(True)
#print "dkm_train"
t = Time()
dkm_train = dkernel.get_kernel_matrix()
#t2=t.cur_time_diff(True)
#print "km_train", km_train
#print "dkm_train", dkm_train
return km_train, dkm_train
