def kernel_fisher_modular(
fm_train_dna=traindat,
fm_test_dna=testdat,
label_train_dna=label_traindat,
N=1,
M=4,
pseudo=1e-1,
order=1,
gap=0,
reverse=False,
kargs=[1, False, True],
):
from shogun.Features import StringCharFeatures, StringWordFeatures, FKFeatures, DNA
from shogun.Kernel import PolyKernel
from shogun.Distribution import HMM, BW_NORMAL # , MSG_DEBUG
# train HMM for positive class
charfeat = StringCharFeatures(fm_hmm_pos, DNA)
# charfeat.io.set_loglevel(MSG_DEBUG)
hmm_pos_train = StringWordFeatures(charfeat.get_alphabet())
hmm_pos_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
pos = HMM(hmm_pos_train, N, M, pseudo)
pos.baum_welch_viterbi_train(BW_NORMAL)
# train HMM for negative class
charfeat = StringCharFeatures(fm_hmm_neg, DNA)
hmm_neg_train = StringWordFeatures(charfeat.get_alphabet())
hmm_neg_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
neg = HMM(hmm_neg_train, N, M, pseudo)
neg.baum_welch_viterbi_train(BW_NORMAL)
# 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)
# 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)
# 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
kernel = PolyKernel(feats_train, feats_train, *kargs)
km_train = kernel.get_kernel_matrix()
# 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_fisher_modular(fm_train_dna=traindat,
fm_test_dna=testdat,
label_train_dna=label_traindat,
N=1,
M=4,
pseudo=1e-1,
order=1,
gap=0,
reverse=False,
kargs=[1, False, True]):
from shogun.Features import StringCharFeatures, StringWordFeatures, FKFeatures, DNA
from shogun.Kernel import PolyKernel
from shogun.Distribution import HMM, BW_NORMAL #, MSG_DEBUG
# train HMM for positive class
charfeat = StringCharFeatures(fm_hmm_pos, DNA)
#charfeat.io.set_loglevel(MSG_DEBUG)
hmm_pos_train = StringWordFeatures(charfeat.get_alphabet())
hmm_pos_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
pos = HMM(hmm_pos_train, N, M, pseudo)
pos.baum_welch_viterbi_train(BW_NORMAL)
# train HMM for negative class
charfeat = StringCharFeatures(fm_hmm_neg, DNA)
hmm_neg_train = StringWordFeatures(charfeat.get_alphabet())
hmm_neg_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
neg = HMM(hmm_neg_train, N, M, pseudo)
neg.baum_welch_viterbi_train(BW_NORMAL)
# 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)
# 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)
# 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
kernel = PolyKernel(feats_train, feats_train, *kargs)
km_train = kernel.get_kernel_matrix()
# 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_top_modular(
fm_train_dna=traindat,
fm_test_dna=testdat,
label_train_dna=label_traindat,
pseudo=1e-1,
order=1,
gap=0,
reverse=False,
kargs=[1, False, True],
):
from shogun.Features import StringCharFeatures, StringWordFeatures, TOPFeatures, DNA
from shogun.Kernel import PolyKernel
from shogun.Distribution import HMM, BW_NORMAL
N = 1 # toy HMM with 1 state
M = 4 # 4 observations -> DNA
# train HMM for positive class
charfeat = StringCharFeatures(fm_hmm_pos, DNA)
hmm_pos_train = StringWordFeatures(charfeat.get_alphabet())
hmm_pos_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
pos = HMM(hmm_pos_train, N, M, pseudo)
pos.baum_welch_viterbi_train(BW_NORMAL)
# train HMM for negative class
charfeat = StringCharFeatures(fm_hmm_neg, DNA)
hmm_neg_train = StringWordFeatures(charfeat.get_alphabet())
hmm_neg_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
neg = HMM(hmm_neg_train, N, M, pseudo)
neg.baum_welch_viterbi_train(BW_NORMAL)
# 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)
# 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)
# get kernel on training data
pos.set_observations(wordfeats_train)
neg.set_observations(wordfeats_train)
feats_train = TOPFeatures(10, pos, neg, False, False)
kernel = PolyKernel(feats_train, feats_train, *kargs)
km_train = kernel.get_kernel_matrix()
# 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 = TOPFeatures(10, pos_clone, neg_clone, False, False)
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def kernel_top_modular(fm_train_dna=traindat,
fm_test_dna=testdat,
label_train_dna=label_traindat,
pseudo=1e-1,
order=1,
gap=0,
reverse=False,
kargs=[1, False, True]):
from shogun.Features import StringCharFeatures, StringWordFeatures, TOPFeatures, DNA
from shogun.Kernel import PolyKernel
from shogun.Distribution import HMM, BW_NORMAL
N = 1 # toy HMM with 1 state
M = 4 # 4 observations -> DNA
# train HMM for positive class
charfeat = StringCharFeatures(fm_hmm_pos, DNA)
hmm_pos_train = StringWordFeatures(charfeat.get_alphabet())
hmm_pos_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
pos = HMM(hmm_pos_train, N, M, pseudo)
pos.baum_welch_viterbi_train(BW_NORMAL)
# train HMM for negative class
charfeat = StringCharFeatures(fm_hmm_neg, DNA)
hmm_neg_train = StringWordFeatures(charfeat.get_alphabet())
hmm_neg_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
neg = HMM(hmm_neg_train, N, M, pseudo)
neg.baum_welch_viterbi_train(BW_NORMAL)
# 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)
# 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)
# get kernel on training data
pos.set_observations(wordfeats_train)
neg.set_observations(wordfeats_train)
feats_train = TOPFeatures(10, pos, neg, False, False)
kernel = PolyKernel(feats_train, feats_train, *kargs)
km_train = kernel.get_kernel_matrix()
# 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 = TOPFeatures(10, pos_clone, neg_clone, False, False)
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def fisher (): print "Fisher Kernel" from shogun.Features import StringCharFeatures, StringWordFeatures, FKFeatures, DNA from shogun.Kernel import PolyKernel from shogun.Distribution import HMM, BW_NORMAL N=1 # toy HMM with 1 state M=4 # 4 observations -> DNA pseudo=1e-1 order=1 gap=0 reverse=False kargs=[1, False, True] # train HMM for positive class charfeat=StringCharFeatures(fm_hmm_pos, DNA) hmm_pos_train=StringWordFeatures(charfeat.get_alphabet()) hmm_pos_train.obtain_from_char(charfeat, order-1, order, gap, reverse) pos=HMM(hmm_pos_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) # train HMM for negative class charfeat=StringCharFeatures(fm_hmm_neg, DNA) hmm_neg_train=StringWordFeatures(charfeat.get_alphabet()) hmm_neg_train.obtain_from_char(charfeat, order-1, order, gap, reverse) neg=HMM(hmm_neg_train, N, M, pseudo) neg.baum_welch_viterbi_train(BW_NORMAL) # 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) # 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) # 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 kernel=PolyKernel(feats_train, feats_train, *kargs) km_train=kernel.get_kernel_matrix() # 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()
def kernel_poly_modular (fm_train_real=traindat,fm_test_real=testdat,degree=4,inhomogene=False, use_normalization=True): from shogun.Features import RealFeatures from shogun.Kernel import PolyKernel feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=PolyKernel( feats_train, feats_train, degree, inhomogene, use_normalization) 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 poly (): print 'Poly' from shogun.Features import RealFeatures from shogun.Kernel import PolyKernel feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) degree=4 inhomogene=False use_normalization=True kernel=PolyKernel( feats_train, feats_train, degree, inhomogene, use_normalization) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix()
def kernel_combined_custom_poly_modular(fm_train_real=traindat,
fm_test_real=testdat,
fm_label_twoclass=label_traindat):
from shogun.Features import CombinedFeatures, RealFeatures, Labels
from shogun.Kernel import CombinedKernel, PolyKernel, CustomKernel
from shogun.Classifier import LibSVM
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(fm_train_real)
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = Labels(fm_label_twoclass)
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(fm_test_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(fm_test_real)
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.classify()
km_train = kernel.get_kernel_matrix()
return km_train, kernel
def kernel_poly_modular(fm_train_real=traindat,
fm_test_real=testdat,
degree=4,
inhomogene=False,
use_normalization=True):
from shogun.Features import RealFeatures
from shogun.Kernel import PolyKernel
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
kernel = PolyKernel(feats_train, feats_train, degree, inhomogene,
use_normalization)
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_poly_modular (fm_train_real=traindat,fm_test_real=testdat, size_cache=10,degree=3,inhomogene=True ): from shogun.Features import SparseRealFeatures from shogun.Kernel import PolyKernel feats_train=SparseRealFeatures(fm_train_real) feats_test=SparseRealFeatures(fm_test_real) kernel=PolyKernel(feats_train, feats_train, size_cache, degree, inhomogene) 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(fm_train_real = traindat,fm_test_real = testdat,fm_label_twoclass=label_traindat):
from shogun.Features import CombinedFeatures, RealFeatures, BinaryLabels
from shogun.Kernel import CombinedKernel, PolyKernel, CustomKernel
from shogun.Classifier import LibSVM
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(fm_train_real)
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10,2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = BinaryLabels(fm_label_twoclass)
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(fm_test_real)
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(fm_test_real)
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 mkl_binclass_modular(fm_train_real=traindat,
fm_test_real=testdat,
fm_label_twoclass=label_traindat):
##################################
# set up and train
# create some poly train/test matrix
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K_train = tkernel.get_kernel_matrix()
pfeats = RealFeatures(fm_test_real)
tkernel.init(tfeats, pfeats)
K_test = tkernel.get_kernel_matrix()
# create combined train features
feats_train = CombinedFeatures()
feats_train.append_feature_obj(RealFeatures(fm_train_real))
# and corresponding combined kernel
kernel = CombinedKernel()
kernel.append_kernel(CustomKernel(K_train))
kernel.append_kernel(PolyKernel(10, 2))
kernel.init(feats_train, feats_train)
# train mkl
labels = BinaryLabels(fm_label_twoclass)
mkl = MKLClassification()
# which norm to use for MKL
mkl.set_mkl_norm(1) #2,3
# set cost (neg, pos)
mkl.set_C(1, 1)
# set kernel and labels
mkl.set_kernel(kernel)
mkl.set_labels(labels)
# train
mkl.train()
#w=kernel.get_subkernel_weights()
#kernel.set_subkernel_weights(w)
##################################
# test
# create combined test features
feats_pred = CombinedFeatures()
feats_pred.append_feature_obj(RealFeatures(fm_test_real))
# and corresponding combined kernel
kernel = CombinedKernel()
kernel.append_kernel(CustomKernel(K_test))
kernel.append_kernel(PolyKernel(10, 2))
kernel.init(feats_train, feats_pred)
# and classify
mkl.set_kernel(kernel)
mkl.apply()
return mkl.apply(), kernel
def mkl_binclass_modular(fm_train_real=traindat, fm_test_real=testdat, fm_label_twoclass=label_traindat):
##################################
# set up and train
# create some poly train/test matrix
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K_train = tkernel.get_kernel_matrix()
pfeats = RealFeatures(fm_test_real)
tkernel.init(tfeats, pfeats)
K_test = tkernel.get_kernel_matrix()
# create combined train features
feats_train = CombinedFeatures()
feats_train.append_feature_obj(RealFeatures(fm_train_real))
# and corresponding combined kernel
kernel = CombinedKernel()
kernel.append_kernel(CustomKernel(K_train))
kernel.append_kernel(PolyKernel(10, 2))
kernel.init(feats_train, feats_train)
# train mkl
labels = BinaryLabels(fm_label_twoclass)
mkl = MKLClassification()
# which norm to use for MKL
mkl.set_mkl_norm(1) # 2,3
# set cost (neg, pos)
mkl.set_C(1, 1)
# set kernel and labels
mkl.set_kernel(kernel)
mkl.set_labels(labels)
# train
mkl.train()
# w=kernel.get_subkernel_weights()
# kernel.set_subkernel_weights(w)
##################################
# test
# create combined test features
feats_pred = CombinedFeatures()
feats_pred.append_feature_obj(RealFeatures(fm_test_real))
# and corresponding combined kernel
kernel = CombinedKernel()
kernel.append_kernel(CustomKernel(K_test))
kernel.append_kernel(PolyKernel(10, 2))
kernel.init(feats_train, feats_pred)
# and classify
mkl.set_kernel(kernel)
mkl.apply()
return mkl.apply(), kernel