def kernel_comm_word_string_modular(
fm_train_dna=traindat, fm_test_dna=testdat, order=3, gap=0, reverse=False, use_sign=False
):
from modshogun import CommWordStringKernel
from modshogun import StringWordFeatures, StringCharFeatures, DNA
from modshogun import SortWordString
charfeat = StringCharFeatures(DNA)
charfeat.set_features(fm_train_dna)
feats_train = StringWordFeatures(charfeat.get_alphabet())
feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
preproc = SortWordString()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
charfeat = StringCharFeatures(DNA)
charfeat.set_features(fm_test_dna)
feats_test = StringWordFeatures(charfeat.get_alphabet())
feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = CommWordStringKernel(feats_train, feats_train, use_sign)
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 preprocessor_sortwordstring_modular(fm_train_dna=traindna,
fm_test_dna=testdna,
order=3,
gap=0,
reverse=False,
use_sign=False):
from modshogun import CommWordStringKernel
from modshogun import StringCharFeatures, StringWordFeatures, DNA
from modshogun import SortWordString
charfeat = StringCharFeatures(fm_train_dna, DNA)
feats_train = StringWordFeatures(charfeat.get_alphabet())
feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
preproc = SortWordString()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
charfeat = StringCharFeatures(fm_test_dna, DNA)
feats_test = StringWordFeatures(charfeat.get_alphabet())
feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = CommWordStringKernel(feats_train, feats_train, use_sign)
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 runShogunOneClassSVMDNASpectrumKernel(train_xt, train_lt, test_xt):
"""
run svm with spectrum kernel
"""
##################################################
# set up svr
charfeat_train = StringCharFeatures(train_xt, DNA)
feats_train = StringWordFeatures(DNA)
feats_train.obtain_from_char(charfeat_train, K-1, K, GAP, False)
preproc=SortWordString()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
charfeat_test = StringCharFeatures(test_xt, DNA)
feats_test=StringWordFeatures(DNA)
feats_test.obtain_from_char(charfeat_test, K-1, K, GAP, False)
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel=CommWordStringKernel(feats_train, feats_train, False)
kernel.io.set_loglevel(MSG_DEBUG)
# init kernel
labels = BinaryLabels(train_lt)
# run svm model
print "Ready to train!"
svm=LibSVMOneClass(SVMC, kernel)
svm.set_epsilon(EPSILON)
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()
# predictions = svm.apply(feats_test)
# return predictions, svm, predictions.get_labels()
return out1,out2,out1DecisionValues,out2DecisionValues
def runShogunSVRSpectrumKernel(train_xt, train_lt, test_xt, svm_c=1):
"""
serialize svr with spectrum kernels
"""
##################################################
# set up svr
charfeat_train = StringCharFeatures(train_xt, DNA)
feats_train = StringWordFeatures(DNA)
feats_train.obtain_from_char(charfeat_train, K-1, K, GAP, False)
preproc=SortWordString()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
charfeat_test = StringCharFeatures(test_xt, DNA)
feats_test=StringWordFeatures(DNA)
feats_test.obtain_from_char(charfeat_test, K-1, K, GAP, False)
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel=CommWordStringKernel(feats_train, feats_train, False)
kernel.io.set_loglevel(MSG_DEBUG)
# init kernel
labels = RegressionLabels(train_lt)
# two svr models: epsilon and nu
print "Ready to train!"
svr_epsilon=LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_EPSILON_SVR)
svr_epsilon.io.set_loglevel(MSG_DEBUG)
svr_epsilon.train()
# predictions
print "Making predictions!"
out1_epsilon=svr_epsilon.apply(feats_train).get_labels()
kernel.init(feats_train, feats_test)
out2_epsilon=svr_epsilon.apply(feats_test).get_labels()
return out1_epsilon,out2_epsilon,kernel
def runShogunSVRSpectrumKernel(train_xt, train_lt, test_xt, svm_c=1):
"""
serialize svr with spectrum kernels
"""
##################################################
# set up svr
charfeat_train = StringCharFeatures(train_xt, DNA)
feats_train = StringWordFeatures(DNA)
feats_train.obtain_from_char(charfeat_train, K - 1, K, GAP, False)
preproc = SortWordString()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
charfeat_test = StringCharFeatures(test_xt, DNA)
feats_test = StringWordFeatures(DNA)
feats_test.obtain_from_char(charfeat_test, K - 1, K, GAP, False)
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = CommWordStringKernel(feats_train, feats_train, False)
kernel.io.set_loglevel(MSG_DEBUG)
# init kernel
labels = RegressionLabels(train_lt)
# two svr models: epsilon and nu
print "Ready to train!"
svr_epsilon = LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_EPSILON_SVR)
svr_epsilon.io.set_loglevel(MSG_DEBUG)
svr_epsilon.train()
# predictions
print "Making predictions!"
out1_epsilon = svr_epsilon.apply(feats_train).get_labels()
kernel.init(feats_train, feats_test)
out2_epsilon = svr_epsilon.apply(feats_test).get_labels()
return out1_epsilon, out2_epsilon, kernel
def tests_check_commwordkernel_memleak_modular (num, order, gap, reverse): import gc from modshogun import Alphabet,StringCharFeatures,StringWordFeatures,DNA from modshogun import SortWordString, MSG_DEBUG from modshogun import CommWordStringKernel, IdentityKernelNormalizer from numpy import mat POS=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT'] NEG=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT'] for i in range(10): alpha=Alphabet(DNA) traindat=StringCharFeatures(alpha) traindat.set_features(POS+NEG) trainudat=StringWordFeatures(traindat.get_alphabet()); trainudat.obtain_from_char(traindat, order-1, order, gap, reverse) #trainudat.io.set_loglevel(MSG_DEBUG) pre = SortWordString() #pre.io.set_loglevel(MSG_DEBUG) pre.init(trainudat) trainudat.add_preprocessor(pre) trainudat.apply_preprocessor() spec = CommWordStringKernel(10, False) spec.set_normalizer(IdentityKernelNormalizer()) spec.init(trainudat, trainudat) K=spec.get_kernel_matrix() del POS del NEG del order del gap del reverse return K
def tests_check_commwordkernel_memleak_modular(num, order, gap, reverse):
import gc
from modshogun import Alphabet, StringCharFeatures, StringWordFeatures, DNA
from modshogun import SortWordString, MSG_DEBUG
from modshogun import CommWordStringKernel, IdentityKernelNormalizer
from numpy import mat
POS = [
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT'
]
NEG = [
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT',
num * 'TTGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT',
num * 'ACGT'
]
for i in range(10):
alpha = Alphabet(DNA)
traindat = StringCharFeatures(alpha)
traindat.set_features(POS + NEG)
trainudat = StringWordFeatures(traindat.get_alphabet())
trainudat.obtain_from_char(traindat, order - 1, order, gap, reverse)
#trainudat.io.set_loglevel(MSG_DEBUG)
pre = SortWordString()
#pre.io.set_loglevel(MSG_DEBUG)
pre.init(trainudat)
trainudat.add_preprocessor(pre)
trainudat.apply_preprocessor()
spec = CommWordStringKernel(10, False)
spec.set_normalizer(IdentityKernelNormalizer())
spec.init(trainudat, trainudat)
K = spec.get_kernel_matrix()
del POS
del NEG
del order
del gap
del reverse
return K
