def distance_hammingword_modular (fm_train_dna=traindna,fm_test_dna=testdna, fm_test_real=testdat,order=3,gap=0,reverse=False,use_sign=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString from modshogun import HammingWordDistance 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() distance=HammingWordDistance(feats_train, feats_train, use_sign) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def distance_canberraword_modular (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString from modshogun import CanberraWordDistance 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() distance=CanberraWordDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def kernel_weighted_comm_word_string_modular (fm_train_dna=traindat,fm_test_dna=testdat,order=3,gap=0,reverse=True ): from modshogun import WeightedCommWordStringKernel from modshogun import StringWordFeatures, StringCharFeatures, 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() use_sign=False kernel=WeightedCommWordStringKernel(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 distance_manhattenword_modular(train_fname=traindna,
test_fname=testdna,
order=3,
gap=0,
reverse=False):
from modshogun import StringCharFeatures, StringWordFeatures, DNA
from modshogun import SortWordString, ManhattanWordDistance, CSVFile
charfeat = StringCharFeatures(CSVFile(train_fname), 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(CSVFile(test_fname), 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()
distance = ManhattanWordDistance(feats_train, feats_train)
dm_train = distance.get_distance_matrix()
distance.init(feats_train, feats_test)
dm_test = distance.get_distance_matrix()
return dm_train, dm_test
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 runShogunSVMDNACombinedSpectrumKernel(train_xt, train_lt, test_xt):
"""
run svm with combined spectrum kernel
"""
##################################################
# set up svm
kernel=CombinedKernel()
feats_train=CombinedFeatures()
feats_test=CombinedFeatures()
for K in KList:
# Iterate through the K's and make a spectrum kernel for each
charfeat_train = StringCharFeatures(train_xt, DNA)
current_feats_train = StringWordFeatures(DNA)
current_feats_train.obtain_from_char(charfeat_train, K-1, K, GAP, False)
preproc=SortWordString()
preproc.init(current_feats_train)
current_feats_train.add_preprocessor(preproc)
current_feats_train.apply_preprocessor()
feats_train.append_feature_obj(current_feats_train)
charfeat_test = StringCharFeatures(test_xt, DNA)
current_feats_test=StringWordFeatures(DNA)
current_feats_test.obtain_from_char(charfeat_test, K-1, K, GAP, False)
current_feats_test.add_preprocessor(preproc)
current_feats_test.apply_preprocessor()
feats_test.append_feature_obj(current_feats_test)
current_kernel=CommWordStringKernel(10, False)
kernel.append_kernel(current_kernel)
kernel.io.set_loglevel(MSG_DEBUG)
# init kernel
labels = BinaryLabels(train_lt)
# run svm model
print "Ready to train!"
kernel.init(feats_train, feats_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 get_spectrum_features(data, order=3, gap=0, reverse=True):
"""
create feature object used by spectrum kernel
"""
charfeat = StringCharFeatures(data, DNA)
feat = StringWordFeatures(charfeat.get_alphabet())
feat.obtain_from_char(charfeat, order-1, order, gap, reverse)
preproc = SortWordString()
preproc.init(feat)
feat.add_preprocessor(preproc)
feat.apply_preprocessor()
return feat
def get_spectrum_features(data, order=3, gap=0, reverse=True):
"""
create feature object used by spectrum kernel
"""
charfeat = StringCharFeatures(data, PROTEIN)
feat = StringWordFeatures(charfeat.get_alphabet())
feat.obtain_from_char(charfeat, order - 1, order, gap, reverse)
preproc = SortWordString()
preproc.init(feat)
feat.add_preprocessor(preproc)
feat.apply_preprocessor()
return feat
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 runShogunSVMProteinPolyMatchSpectrumKernel(train_xt, train_lt, test_xt):
"""
run svm with spectrum kernel
"""
##################################################
# set up svm
charfeat_train = StringCharFeatures(train_xt, PROTEIN)
feats_train = StringWordFeatures(PROTEIN)
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, PROTEIN)
feats_test = StringWordFeatures(PROTEIN)
feats_test.obtain_from_char(charfeat_test, K - 1, K, GAP, False)
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = PolyMatchWordStringKernel(feats_train, feats_train, 2, True)
kernel.io.set_loglevel(MSG_DEBUG)
# 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 runShogunSVMProteinPolyMatchSpectrumKernel(train_xt, train_lt, test_xt):
"""
run svm with spectrum kernel
"""
##################################################
# set up svm
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=PolyMatchWordStringKernel(feats_train, feats_train, DEGREE, True)
kernel.io.set_loglevel(MSG_DEBUG)
# 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 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 runShogunSVMDNAWeightedCommonWordKernel(train_xt, train_lt, test_xt):
"""
run svm with spectrum kernel
"""
##################################################
# set up svm
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 = WeightedCommWordStringKernel(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 = LibSVM(SVMC, kernel, labels)
svm.io.set_loglevel(MSG_DEBUG)
svm.train()
# predictions
print "Making predictions!"
out1 = svm.apply(feats_train).get_labels()
kernel.init(feats_train, feats_test)
out2 = svm.apply(feats_test).get_labels()
return out1, out2
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 distance_manhattenword_modular (train_fname=traindna,test_fname=testdna,order=3,gap=0,reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString, ManhattanWordDistance, CSVFile charfeat=StringCharFeatures(CSVFile(train_fname), 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(CSVFile(test_fname), 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() distance=ManhattanWordDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return dm_train,dm_test
def runShogunSVMMultipleKernels(train_xt, train_lt, test_xt):
"""
Run SVM with Multiple Kernels
"""
##################################################
# Take all examples
idxs = np.random.randint(1, 14000, 14000)
train_xt = np.array(train_xt)[idxs]
train_lt = np.array(train_lt)[idxs]
# Initialize kernel and features
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
labels = BinaryLabels(train_lt)
##################### Multiple Spectrum Kernels #########################
for i in range(K1, K2, -1):
# append training data to combined feature object
charfeat_train = StringCharFeatures(list(train_xt), DNA)
feats_train_k1 = StringWordFeatures(DNA)
feats_train_k1.obtain_from_char(charfeat_train, i - 1, i, GAP, False)
preproc = SortWordString()
preproc.init(feats_train_k1)
feats_train_k1.add_preprocessor(preproc)
feats_train_k1.apply_preprocessor()
# append testing data to combined feature object
charfeat_test = StringCharFeatures(test_xt, DNA)
feats_test_k1 = StringWordFeatures(DNA)
feats_test_k1.obtain_from_char(charfeat_test, i - 1, i, GAP, False)
feats_test_k1.add_preprocessor(preproc)
feats_test_k1.apply_preprocessor()
# append features
feats_train.append_feature_obj(charfeat_train)
feats_test.append_feature_obj(charfeat_test)
# append spectrum kernel
kernel1 = CommWordStringKernel(10, i)
kernel1.io.set_loglevel(MSG_DEBUG)
kernel.append_kernel(kernel1)
'''
Uncomment this for Multiple Weighted degree kernels and comment
the multiple spectrum kernel block above instead
##################### Multiple Weighted Degree Kernel #########################
for i in range(K1,K2,-1):
# append training data to combined feature object
charfeat_train = StringCharFeatures(list(train_xt), DNA)
# append testing data to combined feature object
charfeat_test = StringCharFeatures(test_xt, DNA)
# append features
feats_train.append_feature_obj(charfeat_train);
feats_test.append_feature_obj(charfeat_test);
# setup weighted degree kernel
kernel1=WeightedDegreePositionStringKernel(10,i);
kernel1.io.set_loglevel(MSG_DEBUG);
kernel1.set_shifts(SHIFT*np.ones(len(train_xt[0]), dtype=np.int32))
kernel1.set_position_weights(np.ones(len(train_xt[0]), dtype=np.float64));
kernel.append_kernel(kernel1);
'''
##################### Training #########################
print "Starting MKL training.."
mkl = MKLClassification()
mkl.set_mkl_norm(3) #1,2,3
mkl.set_C(SVMC, SVMC)
mkl.set_kernel(kernel)
mkl.set_labels(labels)
mkl.train(feats_train)
print "Making predictions!"
out1 = mkl.apply(feats_train).get_labels()
out2 = mkl.apply(feats_test).get_labels()
return out1, out2, train_lt
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
def runShogunSVMMultipleKernels(train_xt, train_lt, test_xt):
"""
Run SVM with Multiple Kernels
"""
##################################################
# Take all examples
idxs = np.random.randint(1,14000,14000);
train_xt = np.array(train_xt)[idxs];
train_lt = np.array(train_lt)[idxs];
# Initialize kernel and features
kernel=CombinedKernel()
feats_train=CombinedFeatures()
feats_test=CombinedFeatures()
labels = BinaryLabels(train_lt)
##################### Multiple Spectrum Kernels #########################
for i in range(K1,K2,-1):
# append training data to combined feature object
charfeat_train = StringCharFeatures(list(train_xt), DNA)
feats_train_k1 = StringWordFeatures(DNA)
feats_train_k1.obtain_from_char(charfeat_train, i-1, i, GAP, False)
preproc=SortWordString()
preproc.init(feats_train_k1)
feats_train_k1.add_preprocessor(preproc)
feats_train_k1.apply_preprocessor()
# append testing data to combined feature object
charfeat_test = StringCharFeatures(test_xt, DNA)
feats_test_k1=StringWordFeatures(DNA)
feats_test_k1.obtain_from_char(charfeat_test, i-1, i, GAP, False)
feats_test_k1.add_preprocessor(preproc)
feats_test_k1.apply_preprocessor()
# append features
feats_train.append_feature_obj(charfeat_train);
feats_test.append_feature_obj(charfeat_test);
# append spectrum kernel
kernel1=CommWordStringKernel(10,i);
kernel1.io.set_loglevel(MSG_DEBUG);
kernel.append_kernel(kernel1);
'''
Uncomment this for Multiple Weighted degree kernels and comment
the multiple spectrum kernel block above instead
##################### Multiple Weighted Degree Kernel #########################
for i in range(K1,K2,-1):
# append training data to combined feature object
charfeat_train = StringCharFeatures(list(train_xt), DNA)
# append testing data to combined feature object
charfeat_test = StringCharFeatures(test_xt, DNA)
# append features
feats_train.append_feature_obj(charfeat_train);
feats_test.append_feature_obj(charfeat_test);
# setup weighted degree kernel
kernel1=WeightedDegreePositionStringKernel(10,i);
kernel1.io.set_loglevel(MSG_DEBUG);
kernel1.set_shifts(SHIFT*np.ones(len(train_xt[0]), dtype=np.int32))
kernel1.set_position_weights(np.ones(len(train_xt[0]), dtype=np.float64));
kernel.append_kernel(kernel1);
'''
##################### Training #########################
print "Starting MKL training.."
mkl = MKLClassification();
mkl.set_mkl_norm(3) #1,2,3
mkl.set_C(SVMC, SVMC)
mkl.set_kernel(kernel)
mkl.set_labels(labels)
mkl.train(feats_train)
print "Making predictions!"
out1 = mkl.apply(feats_train).get_labels();
out2 = mkl.apply(feats_test).get_labels();
return out1,out2,train_lt
