def non_redundant_ulong_features(feats, kmerlen):
"""convert the features from Shogun toolbox to non-redundant ulong features
Arguments:
feats -- StringUlongFeatures
kmerlen -- integer, length of k-mer
Return:
StringUlongFeatures after converting reverse complement k-mer ids
"""
for i in xrange(feats.get_num_vectors()):
nf = [get_rcmap(int(kmerid), kmerlen) \
for kmerid in feats.get_feature_vector(i)]
feats.set_feature_vector(numpy.array(nf, numpy.dtype('u8')), i)
preproc = SortUlongString()
preproc.init(feats)
try:
feats.add_preproc(preproc)
feats.apply_preproc()
except AttributeError:
feats.add_preprocessor(preproc)
feats.apply_preprocessor()
return feats
def kernel_comm_ulong_string_modular(fm_train_dna=traindat,
fm_test_dna=testdat,
order=3,
gap=0,
reverse=False):
from shogun.Kernel import CommUlongStringKernel
from shogun.Features import StringUlongFeatures, StringCharFeatures, DNA
from shogun.PreProc import SortUlongString
charfeat = StringCharFeatures(DNA)
charfeat.set_features(fm_train_dna)
feats_train = StringUlongFeatures(charfeat.get_alphabet())
feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
preproc = SortUlongString()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
charfeat = StringCharFeatures(DNA)
charfeat.set_features(fm_test_dna)
feats_test = StringUlongFeatures(charfeat.get_alphabet())
feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse)
feats_test.add_preproc(preproc)
feats_test.apply_preproc()
use_sign = False
kernel = CommUlongStringKernel(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 comm_ulong_string (): print 'CommUlongString' from shogun.Kernel import CommUlongStringKernel from shogun.Features import StringUlongFeatures, StringCharFeatures, DNA from shogun.PreProc import SortUlongString order=3 gap=0 reverse=False charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_train_dna) feats_train=StringUlongFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc=SortUlongString() preproc.init(feats_train) feats_train.add_preproc(preproc) feats_train.apply_preproc() charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringUlongFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) feats_test.add_preproc(preproc) feats_test.apply_preproc() use_sign=False kernel=CommUlongStringKernel(feats_train, feats_train, use_sign) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix()
def non_redundant_ulong_features(feats, kmerlen):
"""convert the features from Shogun toolbox to non-redundant ulong features
Arguments:
feats -- StringUlongFeatures
kmerlen -- integer, length of k-mer
Return:
StringUlongFeatures after converting reverse complement k-mer ids
"""
for i in xrange(feats.get_num_vectors()):
nf = [get_rcmap(int(kmerid), kmerlen) \
for kmerid in feats.get_feature_vector(i)]
feats.set_feature_vector(numpy.array(nf, numpy.dtype('u8')), i)
preproc = SortUlongString()
preproc.init(feats)
try:
feats.add_preproc(preproc)
feats.apply_preproc()
except AttributeError:
feats.add_preprocessor(preproc)
feats.apply_preprocessor()
return feats
def get_kernel_matrix(li):
"""
Get kernel matrix from a list of strings.
"""
order = 6
gap = 2
reverse = False
charfeat = StringCharFeatures(RAWBYTE)
charfeat.set_features(li)
#Get alphabet.
feats_train = StringUlongFeatures(charfeat.get_alphabet())
feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse)
#CommUlongStringKernel needs sorted features.
preproc = SortUlongString()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
use_sign = False
#Compute kernel matrix between train features.
kernel = CommUlongStringKernel(feats_train, feats_train, use_sign)
km_train = kernel.get_kernel_matrix()
return km_train
def preproc_sortulongstring_modular (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False,use_sign=False): from shogun.Kernel import CommUlongStringKernel from shogun.Features import StringCharFeatures, StringUlongFeatures, DNA from shogun.PreProc import SortUlongString charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_train_dna) feats_train=StringUlongFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringUlongFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc=SortUlongString() preproc.init(feats_train) feats_train.add_preproc(preproc) feats_train.apply_preproc() feats_test.add_preproc(preproc) feats_test.apply_preproc() kernel=CommUlongStringKernel(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 get_kernel_matrix(li):
"""
Get kernel matrix from a list of strings.
"""
order = 6
gap = 2
reverse = False
charfeat = StringCharFeatures(RAWBYTE)
charfeat.set_features(li)
#Get alphabet.
feats_train = StringUlongFeatures(charfeat.get_alphabet())
feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse)
#CommUlongStringKernel needs sorted features.
preproc = SortUlongString()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
use_sign = False
#Compute kernel matrix between train features.
kernel = CommUlongStringKernel(feats_train, feats_train, use_sign)
km_train = kernel.get_kernel_matrix()
return km_train
def create_features(kname, examples, kparam, train_mode, preproc, seq_source, nuc_con):
"""Converts numpy arrays or sequences into shogun features"""
if kname == 'gauss' or kname == 'linear' or kname == 'poly':
examples = numpy.array(examples)
feats = RealFeatures(examples)
elif kname == 'wd' or kname == 'localalign' or kname == 'localimprove':
if seq_source == 'dna':
examples = non_atcg_convert(examples, nuc_con)
feats = StringCharFeatures(examples, DNA)
elif seq_source == 'protein':
examples = non_aminoacid_converter(examples, nuc_con)
feats = StringCharFeatures(examples, PROTEIN)
else:
sys.stderr.write("Sequence source -"+seq_source+"- is invalid. select [dna|protein]\n")
sys.exit(-1)
elif kname == 'spec' or kname == 'cumspec':
if seq_source == 'dna':
examples = non_atcg_convert(examples, nuc_con)
feats = StringCharFeatures(examples, DNA)
elif seq_source == 'protein':
examples = non_aminoacid_converter(examples, nuc_con)
feats = StringCharFeatures(examples, PROTEIN)
else:
sys.stderr.write("Sequence source -"+seq_source+"- is invalid. select [dna|protein]\n")
sys.exit(-1)
wf = StringUlongFeatures( feats.get_alphabet() )
wf.obtain_from_char(feats, kparam['degree']-1, kparam['degree'], 0, kname=='cumspec')
del feats
if train_mode:
preproc = SortUlongString()
preproc.init(wf)
wf.add_preproc(preproc)
ret = wf.apply_preproc()
#assert(ret)
feats = wf
elif kname == 'spec2' or kname == 'cumspec2':
# spectrum kernel on two sequences
feats = {}
feats['combined'] = CombinedFeatures()
reversed = kname=='cumspec2'
(ex0,ex1) = zip(*examples)
f0 = StringCharFeatures(list(ex0), DNA)
wf = StringWordFeatures(f0.get_alphabet())
wf.obtain_from_char(f0, kparam['degree']-1, kparam['degree'], 0, reversed)
del f0
if train_mode:
preproc = SortWordString()
preproc.init(wf)
wf.add_preprocessor(preproc)
ret = wf.apply_preprocessors()
assert(ret)
feats['combined'].append_feature_obj(wf)
feats['f0'] = wf
f1 = StringCharFeatures(list(ex1), DNA)
wf = StringWordFeatures( f1.get_alphabet() )
wf.obtain_from_char(f1, kparam['degree']-1, kparam['degree'], 0, reversed)
del f1
if train_mode:
preproc = SortWordString()
preproc.init(wf)
wf.add_preproc(preproc)
ret = wf.apply_preproc()
assert(ret)
feats['combined'].append_feature_obj(wf)
feats['f1'] = wf
else:
print 'Unknown kernel %s' % kname
return (feats,preproc)
def normal_ulong_feature(feats):
preproc = SortUlongString()
preproc.init(feats)
feats.add_preprocessor(preproc)
feats.apply_preprocessor()
return feats
