def training_run(options):
"""Conduct a training run and return a trained SVM kernel"""
settings = MotifFinderSettings(kirmes_ini.MOTIF_LENGTH, options.window_width, options.replace)
positives = MotifFinder(finder_settings=settings)
positives.setFastaFile(options.positives)
positives.setMotifs(options.pgff)
pmotifs, ppositions = positives.getResults()
negatives = MotifFinder(finder_settings=settings)
negatives.setFastaFile(options.negatives)
negatives.setMotifs(options.ngff)
nmotifs, npositions = negatives.getResults()
wds_kparams = kirmes_ini.WDS_KERNEL_PARAMETERS
wds_svm = EasySVM.EasySVM(wds_kparams)
num_positives = len(pmotifs.values()[0])
num_negatives = len(nmotifs.values()[0])
# Creating Kernel Objects
kernel = CombinedKernel()
features = CombinedFeatures()
kernel_array = []
motifs = pmotifs.keys()
motifs.sort()
# Adding Kmer Kernels
for motif in motifs:
all_examples = pmotifs[motif] + nmotifs[motif]
motif_features = wds_svm.createFeatures(all_examples)
wds_kernel = WeightedDegreePositionStringKernel(motif_features, motif_features, wds_kparams["degree"])
wds_kernel.set_shifts(wds_kparams["shift"] * ones(wds_kparams["seqlength"], dtype=int32))
features.append_feature_obj(motif_features)
kernel_array.append(wds_kernel)
kernel.append_kernel(wds_kernel)
rbf_svm = EasySVM.EasySVM(kirmes_ini.RBF_KERNEL_PARAMETERS)
positions = array(ppositions + npositions, dtype=float64).T
position_features = rbf_svm.createFeatures(positions)
features.append_feature_obj(position_features)
motif_labels = append(ones(num_positives), -ones(num_negatives))
complete_labels = Labels(motif_labels)
rbf_kernel = GaussianKernel(position_features, position_features, kirmes_ini.RBF_KERNEL_PARAMETERS["width"])
kernel_array.append(rbf_kernel)
kernel.append_kernel(rbf_kernel)
# Kernel init
kernel.init(features, features)
kernel.set_cache_size(kirmes_ini.K_CACHE_SIZE)
svm = LibSVM(kirmes_ini.K_COMBINED_C, kernel, complete_labels)
svm.parallel.set_num_threads(kirmes_ini.K_NUM_THREADS)
# Training
svm.train()
if not os.path.exists(options.output_path):
os.mkdir(options.output_path)
html = {}
if options.contrib:
html["contrib"] = contrib(svm, kernel, motif_labels, kernel_array, motifs)
if options.logos:
html["poims"] = poims(svm, kernel, kernel_array, motifs, options.output_path)
if options.query:
html["query"] = evaluate(options, svm, kernel, features, motifs)
htmlize(html, options.output_html)
def create_empty_promoter_kernel(param):
"""
creates an uninitialized promoter kernel
@param param:
"""
# centered WDK/WDK-shift
if param["shifts"] == 0:
kernel_center = WeightedDegreeStringKernel(param["degree"])
else:
kernel_center = WeightedDegreePositionStringKernel(10, param["degree"])
shifts_vector = numpy.ones(param["center_offset"] * 2,
dtype=numpy.int32) * param["shifts"]
kernel_center.set_shifts(shifts_vector)
kernel_center.set_cache_size(param["kernel_cache"] / 3)
# border spetrum kernels
size = param["kernel_cache"] / 3
use_sign = False
kernel_left = WeightedCommWordStringKernel(size, use_sign)
kernel_right = WeightedCommWordStringKernel(size, use_sign)
# assemble combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_center)
kernel.append_kernel(kernel_left)
kernel.append_kernel(kernel_right)
return kernel
def init_sensor(self, kernel, svs):
f = StringCharFeatures(svs, DNA)
kname = kernel['name']
if kname == 'spectrum':
wf = StringWordFeatures(f.get_alphabet())
wf.obtain_from_char(f, kernel['order'] - 1, kernel['order'], 0,
False)
pre = SortWordString()
pre.init(wf)
wf.add_preprocessor(pre)
wf.apply_preprocessor()
f = wf
k = CommWordStringKernel(0, False)
k.set_use_dict_diagonal_optimization(kernel['order'] < 8)
self.preproc = pre
elif kname == 'wdshift':
k = WeightedDegreePositionStringKernel(0, kernel['order'])
k.set_normalizer(IdentityKernelNormalizer())
k.set_shifts(
kernel['shift'] *
numpy.ones(f.get_max_vector_length(), dtype=numpy.int32))
k.set_position_weights(
1.0 / f.get_max_vector_length() *
numpy.ones(f.get_max_vector_length(), dtype=numpy.float64))
else:
raise "Currently, only wdshift and spectrum kernels supported"
self.kernel = k
self.train_features = f
return (self.kernel, self.train_features)
def kernel_weighted_degree_position_string_modular(fm_train_dna=traindat,fm_test_dna=testdat,degree=20): from shogun.Features import StringCharFeatures, DNA from shogun.Kernel import WeightedDegreePositionStringKernel, MSG_DEBUG feats_train=StringCharFeatures(fm_train_dna, DNA) #feats_train.io.set_loglevel(MSG_DEBUG) feats_test=StringCharFeatures(fm_test_dna, DNA) kernel=WeightedDegreePositionStringKernel(feats_train, feats_train, degree) from numpy import zeros,ones,float64,int32 kernel.set_shifts(10*ones(len(fm_train_dna[0]), dtype=int32)) kernel.set_position_weights(ones(len(fm_train_dna[0]), dtype=float64)) 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_weighted_degree_position_string_modular(fm_train_dna=traindat,fm_test_dna=testdat,degree=20): from shogun.Features import StringCharFeatures, DNA from shogun.Kernel import WeightedDegreePositionStringKernel, MSG_DEBUG feats_train=StringCharFeatures(fm_train_dna, DNA) #feats_train.io.set_loglevel(MSG_DEBUG) feats_test=StringCharFeatures(fm_test_dna, DNA) kernel=WeightedDegreePositionStringKernel(feats_train, feats_train, degree) from numpy import zeros,ones,float64,int32 #kernel.set_shifts(zeros(len(fm_train_dna[0]), dtype=int32)) #kernel.set_position_weights(ones(len(fm_train_dna[0]), dtype=float64)) 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 train(self, data, labels):
"""
model training
"""
# centered WDK/WDK-shift
if self.param["shifts"] == 0:
kernel_center = WeightedDegreeStringKernel(self.param["degree"])
else:
kernel_center = WeightedDegreePositionStringKernel(
10, self.param["degree"])
shifts_vector = numpy.ones(
self.param["center_offset"] * 2,
dtype=numpy.int32) * self.param["shifts"]
kernel_center.set_shifts(shifts_vector)
kernel_center.set_cache_size(self.param["kernel_cache"] / 3)
# border spetrum kernels
size = self.param["kernel_cache"] / 3
use_sign = False
kernel_left = WeightedCommWordStringKernel(size, use_sign)
kernel_right = WeightedCommWordStringKernel(size, use_sign)
# assemble combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_center)
kernel.append_kernel(kernel_left)
kernel.append_kernel(kernel_right)
## building features
feat = create_features(data, self.param["center_offset"],
self.param["center_pos"])
# init combined kernel
kernel.init(feat, feat)
print "len(labels) = %i" % (len(labels))
lab = BinaryLabels(numpy.double(labels))
self.svm = SVMLight(self.param["cost"], kernel, lab)
# show debugging output
self.svm.io.enable_progress()
self.svm.io.set_loglevel(MSG_DEBUG)
# optimization settings
num_threads = 2
self.svm.parallel.set_num_threads(num_threads)
self.svm.set_epsilon(10e-8)
self.svm.train()
return self
def create_kernel(kname, kparam, feats_train):
"""Call the corresponding constructor for the kernel"""
if kname == 'gauss':
kernel = GaussianKernel(feats_train, feats_train, kparam['width'])
elif kname == 'linear':
kernel = LinearKernel(feats_train, feats_train)
kernel.set_normalizer(AvgDiagKernelNormalizer(kparam['scale']))
elif kname == 'poly':
kernel = PolyKernel(feats_train, feats_train, kparam['degree'],
kparam['inhomogene'], kparam['normal'])
elif kname == 'wd':
kernel = WeightedDegreePositionStringKernel(feats_train, feats_train,
kparam['degree'])
kernel.set_normalizer(
AvgDiagKernelNormalizer(float(kparam['seqlength'])))
kernel.set_shifts(kparam['shift'] *
numpy.ones(kparam['seqlength'], dtype=numpy.int32))
#kernel=WeightedDegreeStringKernel(feats_train, feats_train, kparam['degree'])
elif kname == 'spec':
kernel = CommUlongStringKernel(feats_train, feats_train)
elif kname == 'cumspec':
kernel = WeightedCommWordStringKernel(feats_train, feats_train)
kernel.set_weights(numpy.ones(kparam['degree']))
elif kname == 'spec2':
kernel = CombinedKernel()
k0 = CommWordStringKernel(feats_train['f0'], feats_train['f0'])
k0.io.disable_progress()
kernel.append_kernel(k0)
k1 = CommWordStringKernel(feats_train['f1'], feats_train['f1'])
k1.io.disable_progress()
kernel.append_kernel(k1)
elif kname == 'cumspec2':
kernel = CombinedKernel()
k0 = WeightedCommWordStringKernel(feats_train['f0'], feats_train['f0'])
k0.set_weights(numpy.ones(kparam['degree']))
k0.io.disable_progress()
kernel.append_kernel(k0)
k1 = WeightedCommWordStringKernel(feats_train['f1'], feats_train['f1'])
k1.set_weights(numpy.ones(kparam['degree']))
k1.io.disable_progress()
kernel.append_kernel(k1)
elif kname == 'localalign':
kernel = LocalAlignmentStringKernel(feats_train, feats_train)
elif kname == 'localimprove':
kernel = LocalityImprovedStringKernel(feats_train, feats_train, kparam['length'],\
kparam['indeg'], kparam['outdeg'])
else:
print 'Unknown kernel %s' % kname
kernel.set_cache_size(32)
return kernel
def serialization_string_kernels_modular(n_data, num_shifts, size):
"""
serialize svm with string kernels
"""
##################################################
# set up toy data and svm
train_xt, train_lt = generate_random_data(n_data)
test_xt, test_lt = generate_random_data(n_data)
feats_train = construct_features(train_xt)
feats_test = construct_features(test_xt)
max_len = len(train_xt[0])
kernel_wdk = WeightedDegreePositionStringKernel(size, 5)
shifts_vector = numpy.ones(max_len, dtype=numpy.int32) * num_shifts
kernel_wdk.set_shifts(shifts_vector)
########
# set up spectrum
use_sign = False
kernel_spec_1 = WeightedCommWordStringKernel(size, use_sign)
kernel_spec_2 = WeightedCommWordStringKernel(size, use_sign)
########
# combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_wdk)
kernel.append_kernel(kernel_spec_1)
kernel.append_kernel(kernel_spec_2)
# init kernel
labels = BinaryLabels(train_lt)
svm = SVMLight(1.0, kernel, labels)
#svm.io.set_loglevel(MSG_DEBUG)
svm.train(feats_train)
##################################################
# serialize to file
fn = "serialized_svm.bz2"
#print("serializing SVM to file", fn)
save(fn, svm)
##################################################
# unserialize and sanity check
#print("unserializing SVM")
svm2 = load(fn)
#print("comparing predictions")
out = svm.apply(feats_test).get_labels()
out2 = svm2.apply(feats_test).get_labels()
# assert outputs are close
for i in xrange(len(out)):
assert abs(out[i] - out2[i] < 0.000001)
#print("all checks passed.")
return out, out2
def serialization_string_kernels_modular(n_data, num_shifts, size):
"""
serialize svm with string kernels
"""
##################################################
# set up toy data and svm
train_xt, train_lt = generate_random_data(n_data)
test_xt, test_lt = generate_random_data(n_data)
feats_train = construct_features(train_xt)
feats_test = construct_features(test_xt)
max_len = len(train_xt[0])
kernel_wdk = WeightedDegreePositionStringKernel(size, 5)
shifts_vector = numpy.ones(max_len, dtype=numpy.int32)*num_shifts
kernel_wdk.set_shifts(shifts_vector)
########
# set up spectrum
use_sign = False
kernel_spec_1 = WeightedCommWordStringKernel(size, use_sign)
kernel_spec_2 = WeightedCommWordStringKernel(size, use_sign)
########
# combined kernel
kernel = CombinedKernel()
kernel.append_kernel(kernel_wdk)
kernel.append_kernel(kernel_spec_1)
kernel.append_kernel(kernel_spec_2)
# init kernel
labels = BinaryLabels(train_lt);
svm = SVMLight(1.0, kernel, labels)
#svm.io.set_loglevel(MSG_DEBUG)
svm.train(feats_train)
##################################################
# serialize to file
fn = "serialized_svm.bz2"
#print("serializing SVM to file", fn)
save(fn, svm)
##################################################
# unserialize and sanity check
#print("unserializing SVM")
svm2 = load(fn)
#print("comparing predictions")
out = svm.apply(feats_test).get_labels()
out2 = svm2.apply(feats_test).get_labels()
# assert outputs are close
for i in range(len(out)):
assert abs(out[i] - out2[i] < 0.000001)
#print("all checks passed.")
return out,out2
def training_run(options):
"""Conduct a training run and return a trained SVM kernel"""
settings = MotifFinderSettings(kirmes_ini.MOTIF_LENGTH,
options.window_width, options.replace)
positives = MotifFinder(finder_settings=settings)
positives.setFastaFile(options.positives)
positives.setMotifs(options.pgff)
pmotifs, ppositions = positives.getResults()
negatives = MotifFinder(finder_settings=settings)
negatives.setFastaFile(options.negatives)
negatives.setMotifs(options.ngff)
nmotifs, npositions = negatives.getResults()
wds_kparams = kirmes_ini.WDS_KERNEL_PARAMETERS
wds_svm = EasySVM.EasySVM(wds_kparams)
num_positives = len(pmotifs.values()[0])
num_negatives = len(nmotifs.values()[0])
#Creating Kernel Objects
kernel = CombinedKernel()
features = CombinedFeatures()
kernel_array = []
motifs = pmotifs.keys()
motifs.sort()
#Adding Kmer Kernels
for motif in motifs:
all_examples = pmotifs[motif] + nmotifs[motif]
motif_features = wds_svm.createFeatures(all_examples)
wds_kernel = WeightedDegreePositionStringKernel(motif_features, motif_features, \
wds_kparams['degree'])
wds_kernel.set_shifts(wds_kparams['shift'] *
ones(wds_kparams['seqlength'], dtype=int32))
features.append_feature_obj(motif_features)
kernel_array.append(wds_kernel)
kernel.append_kernel(wds_kernel)
rbf_svm = EasySVM.EasySVM(kirmes_ini.RBF_KERNEL_PARAMETERS)
positions = array(ppositions + npositions, dtype=float64).T
position_features = rbf_svm.createFeatures(positions)
features.append_feature_obj(position_features)
motif_labels = append(ones(num_positives), -ones(num_negatives))
complete_labels = Labels(motif_labels)
rbf_kernel = GaussianKernel(position_features, position_features, \
kirmes_ini.RBF_KERNEL_PARAMETERS['width'])
kernel_array.append(rbf_kernel)
kernel.append_kernel(rbf_kernel)
#Kernel init
kernel.init(features, features)
kernel.set_cache_size(kirmes_ini.K_CACHE_SIZE)
svm = LibSVM(kirmes_ini.K_COMBINED_C, kernel, complete_labels)
svm.parallel.set_num_threads(kirmes_ini.K_NUM_THREADS)
#Training
svm.train()
if not os.path.exists(options.output_path):
os.mkdir(options.output_path)
html = {}
if options.contrib:
html["contrib"] = contrib(svm, kernel, motif_labels, kernel_array,
motifs)
if options.logos:
html["poims"] = poims(svm, kernel, kernel_array, motifs,
options.output_path)
if options.query:
html["query"] = evaluate(options, svm, kernel, features, motifs)
htmlize(html, options.output_html)
