def predict(self, seq, chunk_size = int(10e6)):
"""
predicts on whole contig, splits up sequence in chunks of size chunk_size
"""
seq_len = len(seq)
num_chunks = int(numpy.ceil(float(seq_len) / float(chunk_size)))
assert(num_chunks > 0)
sys.stderr.write("number of chunks for contig: %i\n" % (num_chunks))
start = 0
stop = min(chunk_size, seq_len)
out = []
# iterate over chunks
for chunk_idx in range(num_chunks):
sys.stderr.write("processing chunk #%i\n" % (chunk_idx))
assert (start < stop)
chunk = seq[start:stop]
assert(len(self.sensors) > 0)
tf = CombinedFeatures()
for i in xrange(len(self.sensors)):
f = self.sensors[i].get_test_features(chunk, self.window)
tf.append_feature_obj(f)
sys.stderr.write("initialising kernel...")
self.kernel.init(self.svs, tf)
sys.stderr.write("..done\n")
self.svm.set_kernel(self.kernel)
lab_out = self.svm.apply().get_values()
assert(len(lab_out) > 0)
out.extend(lab_out)
# increment chunk
start = stop
stop = min(stop+chunk_size, seq_len)
l = (-self.window[0]) * [-42]
r = self.window[1] * [-42]
# concatenate
ret = l + out + r
assert(len(ret) == len(seq))
return ret
def mkl_multiclass_1(fm_train_real, fm_test_real, label_train_multiclass, C):
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
for i in range(-10, 11):
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = GaussianKernel(pow(2, i + 1))
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = MulticlassLabels(label_train_multiclass)
mkl = MKLMulticlass(C, kernel, labels)
mkl.set_epsilon(1e-2)
mkl.parallel.set_num_threads(num_threads)
mkl.set_mkl_epsilon(mkl_epsilon)
mkl.set_mkl_norm(1)
mkl.train()
kernel.init(feats_train, feats_test)
out = mkl.apply().get_labels()
return out
def kernel_combined_custom_poly(train_fname=traindat,
test_fname=testdat,
train_label_fname=label_traindat):
from shogun import CombinedFeatures, RealFeatures, BinaryLabels
from shogun import CombinedKernel, PolyKernel, CustomKernel
from shogun import LibSVM, CSVFile
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(CSVFile(train_fname))
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(CSVFile(train_fname))
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = BinaryLabels(CSVFile(train_label_fname))
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(CSVFile(test_fname))
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(CSVFile(test_fname))
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 create_combined_kernel(kname, kparam, examples, train_mode, preproc):
"""A wrapper for creating combined kernels.
kname, kparam and examples are lists.
"""
num_kernels = len(kname)
feats['combined'] = CombinedFeatures()
kernel = CombinedKernel()
for kix in xrange(num_kernels):
cur_kname = '%s%d' % (kname[kix], kix)
(cur_feats, cur_preproc) = create_features(kname[kix], examples[kix],
kparam[kix], train_mode,
preproc)
feats[cur_kname] = cur_feats
cur_kernel = create_kernel(kname[kix], kparam[kix], cur_feats)
kernel.append_kernel(cur_kernel)
return (feats, kernel)
def kernel_combined_custom_poly (train_fname = traindat,test_fname = testdat,train_label_fname=label_traindat):
from shogun import CombinedFeatures, RealFeatures, BinaryLabels
from shogun import CombinedKernel, PolyKernel, CustomKernel
from shogun import LibSVM, CSVFile
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(CSVFile(train_fname))
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(CSVFile(train_fname))
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10,2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = BinaryLabels(CSVFile(train_label_fname))
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(CSVFile(test_fname))
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(CSVFile(test_fname))
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 construct_features(features):
"""
makes a list
"""
feat_all = [inst for inst in features]
feat_lhs = [inst[0:15] for inst in features]
feat_rhs = [inst[15:] for inst in features]
feat_wd = get_wd_features(feat_all)
feat_spec_1 = get_spectrum_features(feat_lhs, order=3)
feat_spec_2 = get_spectrum_features(feat_rhs, order=3)
feat_comb = CombinedFeatures()
feat_comb.append_feature_obj(feat_wd)
feat_comb.append_feature_obj(feat_spec_1)
feat_comb.append_feature_obj(feat_spec_2)
return feat_comb
def mkl_multiclass(fm_train_real, fm_test_real, label_train_multiclass, width,
C, epsilon, num_threads, mkl_epsilon, mkl_norm):
from shogun import CombinedFeatures, RealFeatures, MulticlassLabels
from shogun import CombinedKernel, GaussianKernel, LinearKernel, PolyKernel
from shogun import MKLMulticlass
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = GaussianKernel(10, width)
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = LinearKernel()
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)
subkfeats_train = RealFeatures(fm_train_real)
subkfeats_test = RealFeatures(fm_test_real)
subkernel = PolyKernel(10, 2)
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = MulticlassLabels(label_train_multiclass)
mkl = MKLMulticlass(C, kernel, labels)
mkl.set_epsilon(epsilon)
mkl.parallel.set_num_threads(num_threads)
mkl.set_mkl_epsilon(mkl_epsilon)
mkl.set_mkl_norm(mkl_norm)
mkl.train()
kernel.init(feats_train, feats_test)
out = mkl.apply().get_labels()
return out
def mkl_multiclass (fm_train_real, fm_test_real, label_train_multiclass, width, C, epsilon, num_threads, mkl_epsilon, mkl_norm): from shogun import CombinedFeatures, RealFeatures, MulticlassLabels from shogun import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel from shogun import MKLMulticlass kernel = CombinedKernel() feats_train = CombinedFeatures() feats_test = CombinedFeatures() subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = GaussianKernel(10, width) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = LinearKernel() feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = PolyKernel(10,2) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) labels = MulticlassLabels(label_train_multiclass) mkl = MKLMulticlass(C, kernel, labels) mkl.set_epsilon(epsilon); mkl.parallel.set_num_threads(num_threads) mkl.set_mkl_epsilon(mkl_epsilon) mkl.set_mkl_norm(mkl_norm) mkl.train() kernel.init(feats_train, feats_test) out = mkl.apply().get_labels() return out
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_preprocessor(preproc)
ret = wf.apply_preprocessor()
#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_preprocessor()
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_preprocessor(preproc)
ret = wf.apply_preprocessor()
assert (ret)
feats['combined'].append_feature_obj(wf)
feats['f1'] = wf
else:
print 'Unknown kernel %s' % kname
return (feats, preproc)
def evaluation_cross_validation_mkl_weight_storage(traindat=traindat, label_traindat=label_traindat):
from shogun import CrossValidation, CrossValidationResult
from shogun import ParameterObserverCV
from shogun import ContingencyTableEvaluation, ACCURACY
from shogun import StratifiedCrossValidationSplitting
from shogun import BinaryLabels
from shogun import RealFeatures, CombinedFeatures
from shogun import GaussianKernel, CombinedKernel
from shogun import LibSVM, MKLClassification
# training data, combined features all on same data
features=RealFeatures(traindat)
comb_features=CombinedFeatures()
comb_features.append_feature_obj(features)
comb_features.append_feature_obj(features)
comb_features.append_feature_obj(features)
labels=BinaryLabels(label_traindat)
# kernel, different Gaussians combined
kernel=CombinedKernel()
kernel.append_kernel(GaussianKernel(10, 0.1))
kernel.append_kernel(GaussianKernel(10, 1))
kernel.append_kernel(GaussianKernel(10, 2))
# create mkl using libsvm, due to a mem-bug, interleaved is not possible
svm=MKLClassification(LibSVM());
svm.set_interleaved_optimization_enabled(False);
svm.set_kernel(kernel);
# splitting strategy for 5 fold cross-validation (for classification its better
# to use "StratifiedCrossValidation", but the standard
# "StratifiedCrossValidationSplitting" is also available
splitting_strategy=StratifiedCrossValidationSplitting(labels, 5)
# evaluation method
evaluation_criterium=ContingencyTableEvaluation(ACCURACY)
# cross-validation instance
cross_validation=CrossValidation(svm, comb_features, labels,
splitting_strategy, evaluation_criterium)
cross_validation.set_autolock(False)
# append cross vlaidation output classes
mkl_storage=ParameterObserverCV()
cross_validation.subscribe_to_parameters(mkl_storage)
cross_validation.set_num_runs(3)
# perform cross-validation
result=cross_validation.evaluate()
# print mkl weights
weights = []
for obs_index in range(mkl_storage.get_num_observations()):
obs = mkl_storage.get_observation(obs_index)
for fold_index in range(obs.get_num_folds()):
fold = obs.get_fold(fold_index)
machine = MKLClassification.obtain_from_generic(fold.get_trained_machine())
w = machine.get_kernel().get_subkernel_weights()
weights.append(w)
print("mkl weights during cross--validation")
print(weights)
def evaluation_cross_validation_multiclass_storage (traindat=traindat, label_traindat=label_traindat):
from shogun import CrossValidation, CrossValidationResult
from shogun import ParameterObserverCV
from shogun import MulticlassAccuracy, F1Measure
from shogun import StratifiedCrossValidationSplitting
from shogun import MulticlassLabels
from shogun import RealFeatures, CombinedFeatures
from shogun import GaussianKernel, CombinedKernel
from shogun import MKLMulticlass
from shogun import Statistics, MSG_DEBUG, Math
from shogun import ROCEvaluation
Math.init_random(1)
# training data, combined features all on same data
features=RealFeatures(traindat)
comb_features=CombinedFeatures()
comb_features.append_feature_obj(features)
comb_features.append_feature_obj(features)
comb_features.append_feature_obj(features)
labels=MulticlassLabels(label_traindat)
# kernel, different Gaussians combined
kernel=CombinedKernel()
kernel.append_kernel(GaussianKernel(10, 0.1))
kernel.append_kernel(GaussianKernel(10, 1))
kernel.append_kernel(GaussianKernel(10, 2))
# create mkl using libsvm, due to a mem-bug, interleaved is not possible
svm=MKLMulticlass(1.0,kernel,labels);
svm.set_kernel(kernel);
# splitting strategy for 5 fold cross-validation (for classification its better
# to use "StratifiedCrossValidation", but the standard
# "StratifiedCrossValidationSplitting" is also available
splitting_strategy=StratifiedCrossValidationSplitting(labels, 3)
# evaluation method
evaluation_criterium=MulticlassAccuracy()
# cross-validation instance
cross_validation=CrossValidation(svm, comb_features, labels,
splitting_strategy, evaluation_criterium)
cross_validation.set_autolock(False)
# append cross validation parameter observer
multiclass_storage=ParameterObserverCV()
cross_validation.subscribe_to_parameters(multiclass_storage)
cross_validation.set_num_runs(3)
# perform cross-validation
result=cross_validation.evaluate()
# get first observation and first fold
obs = multiclass_storage.get_observations()[0]
fold = obs.get_folds_results()[0]
# get fold ROC for first class
eval_ROC = ROCEvaluation()
pred_lab_binary = MulticlassLabels.obtain_from_generic(fold.get_test_result()).get_binary_for_class(0)
true_lab_binary = MulticlassLabels.obtain_from_generic(fold.get_test_true_result()).get_binary_for_class(0)
eval_ROC.evaluate(pred_lab_binary, true_lab_binary)
print eval_ROC.get_ROC()
# get fold evaluation result
acc_measure = F1Measure()
print acc_measure.evaluate(pred_lab_binary, true_lab_binary)
def combined_kernel(file_type, data_name, operate_type):
if file_type == '4':
X, y = loadFromMat(data_name)
elif file_type == '5':
X, y = loadFromLibsvm(data_name)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=test_size)
if type(X_train) == scipy.sparse.csr.csr_matrix and type(
X_test) == scipy.sparse.csr.csr_matrix:
X_train = X_train.todense()
X_test = X_test.todense()
X_train = X_train.T
X_test = X_test.T
y_train = y_train.reshape(y_train.size, ).astype('float64')
y_test = y_test.reshape(y_test.size, ).astype('float64')
kernel = CombinedKernel()
feats_train = CombinedFeatures()
feats_test = CombinedFeatures()
subkfeats_train = RealFeatures(X_train)
subkfeats_test = RealFeatures(X_test)
for i in range(-10, 11):
subkernel = GaussianKernel(pow(2, i + 1))
feats_train.append_feature_obj(subkfeats_train)
feats_test.append_feature_obj(subkfeats_test)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
tmp_train_csv = NamedTemporaryFile(suffix=data_name + '_combined.csv')
import time
start = time.time()
if operate_type == 'save':
km_train = kernel.get_kernel_matrix()
f = CSVFile(tmp_train_csv.name, "w")
kernel.save(f)
elif operate_type == 'load':
f = CSVFile(tmp_train_csv.name, "r")
kernel.load(f)
end = time.time()
print 'for saving or loading, use time : ' + str(end - start)
labels = MulticlassLabels(y_train)
mkl = MKLMulticlass(C, kernel, labels)
mkl.set_epsilon(epsilon)
mkl.parallel.set_num_threads(num_threads)
mkl.set_mkl_epsilon(mkl_epsilon)
mkl.set_mkl_norm(mkl_norm)
import time
start = time.time()
mkl.train()
end = time.time()
print 'use time : ' + str(end - start)
kernel.init(feats_train, feats_test)
out = mkl.apply().get_labels()
print out.shape
print sum(out == y_test) / float(len(out))
def __init__(self):
self.sensors = list()
self.kernel = CombinedKernel()
self.svs = CombinedFeatures()
self.svm = None
self.window = (+100000, -1000000)
class SignalSensor(object):
"""
A collection of sensors
"""
def __init__(self):
self.sensors = list()
self.kernel = CombinedKernel()
self.svs = CombinedFeatures()
self.svm = None
self.window = (+100000, -1000000)
def from_file(self, file):
sys.stderr.write('loading model file')
l = file.readline();
if l != '%arts version: 1.0\n':
sys.stderr.write("\nfile not an arts definition file\n")
return None
bias = None
alphas = None
num_kernels = None
while l:
# skip comment or empty line
if not (l.startswith('%') or l.startswith('\n')):
if bias is None: bias = parse_float(l, 'b')
if alphas is None: alphas = parse_vector(l, file, 'alphas')
if num_kernels is None: num_kernels = parse_int(l, 'num_kernels')
if num_kernels and bias and alphas is not None:
for i in xrange(num_kernels):
s = Sensor()
(k, f) = s.from_file(file, i + 1)
k.io.enable_progress()
self.window = (min(self.window[0], s.window[0]),
max(self.window[1], s.window[2]))
self.sensors.append(s)
self.kernel.append_kernel(k)
self.svs.append_feature_obj(f)
self.kernel.init(self.svs, self.svs)
self.svm = KernelMachine(self.kernel, alphas,
numpy.arange(len(alphas), dtype=numpy.int32), bias)
self.svm.io.set_target_to_stderr()
self.svm.io.enable_progress()
self.svm.parallel.set_num_threads(self.svm.parallel.get_num_cpus())
sys.stderr.write('done\n')
return
l = file.readline()
sys.stderr.write('error loading model file\n')
def predict(self, seq, chunk_size = int(10e6)):
"""
predicts on whole contig, splits up sequence in chunks of size chunk_size
"""
seq_len = len(seq)
num_chunks = int(numpy.ceil(float(seq_len) / float(chunk_size)))
assert(num_chunks > 0)
sys.stderr.write("number of chunks for contig: %i\n" % (num_chunks))
start = 0
stop = min(chunk_size, seq_len)
out = []
# iterate over chunks
for chunk_idx in range(num_chunks):
sys.stderr.write("processing chunk #%i\n" % (chunk_idx))
assert (start < stop)
chunk = seq[start:stop]
assert(len(self.sensors) > 0)
tf = CombinedFeatures()
for i in xrange(len(self.sensors)):
f = self.sensors[i].get_test_features(chunk, self.window)
tf.append_feature_obj(f)
sys.stderr.write("initialising kernel...")
self.kernel.init(self.svs, tf)
sys.stderr.write("..done\n")
self.svm.set_kernel(self.kernel)
lab_out = self.svm.apply().get_values()
assert(len(lab_out) > 0)
out.extend(lab_out)
# increment chunk
start = stop
stop = min(stop+chunk_size, seq_len)
l = (-self.window[0]) * [-42]
r = self.window[1] * [-42]
# concatenate
ret = l + out + r
assert(len(ret) == len(seq))
return ret
def mkl_binclass (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 kernel_combined (fm_train_real=traindat,fm_test_real=testdat,fm_train_dna=traindna,fm_test_dna=testdna ): from shogun import CombinedKernel, GaussianKernel, FixedDegreeStringKernel, LocalAlignmentStringKernel from shogun import RealFeatures, StringCharFeatures, CombinedFeatures, DNA kernel=CombinedKernel() feats_train=CombinedFeatures() feats_test=CombinedFeatures() subkfeats_train=RealFeatures(fm_train_real) subkfeats_test=RealFeatures(fm_test_real) subkernel=GaussianKernel(10, 1.1) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train=StringCharFeatures(fm_train_dna, DNA) subkfeats_test=StringCharFeatures(fm_test_dna, DNA) degree=3 subkernel=FixedDegreeStringKernel(10, degree) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train=StringCharFeatures(fm_train_dna, DNA) subkfeats_test=StringCharFeatures(fm_test_dna, DNA) subkernel=LocalAlignmentStringKernel(10) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) 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 evaluation_cross_validation_multiclass_storage(
traindat=traindat, label_traindat=label_traindat):
from shogun import CrossValidation, CrossValidationResult
from shogun import ParameterObserverCV
from shogun import MulticlassAccuracy, F1Measure
from shogun import StratifiedCrossValidationSplitting
from shogun import MulticlassLabels
from shogun import RealFeatures, CombinedFeatures
from shogun import GaussianKernel, CombinedKernel
from shogun import MKLMulticlass
from shogun import Statistics, MSG_DEBUG, Math
from shogun import ROCEvaluation
Math.init_random(1)
# training data, combined features all on same data
features = RealFeatures(traindat)
comb_features = CombinedFeatures()
comb_features.append_feature_obj(features)
comb_features.append_feature_obj(features)
comb_features.append_feature_obj(features)
labels = MulticlassLabels(label_traindat)
# kernel, different Gaussians combined
kernel = CombinedKernel()
kernel.append_kernel(GaussianKernel(10, 0.1))
kernel.append_kernel(GaussianKernel(10, 1))
kernel.append_kernel(GaussianKernel(10, 2))
# create mkl using libsvm, due to a mem-bug, interleaved is not possible
svm = MKLMulticlass(1.0, kernel, labels)
svm.set_kernel(kernel)
# splitting strategy for 5 fold cross-validation (for classification its better
# to use "StratifiedCrossValidation", but the standard
# "StratifiedCrossValidationSplitting" is also available
splitting_strategy = StratifiedCrossValidationSplitting(labels, 3)
# evaluation method
evaluation_criterium = MulticlassAccuracy()
# cross-validation instance
cross_validation = CrossValidation(svm, comb_features, labels,
splitting_strategy,
evaluation_criterium)
cross_validation.set_autolock(False)
# append cross validation parameter observer
multiclass_storage = ParameterObserverCV()
cross_validation.subscribe_to_parameters(multiclass_storage)
cross_validation.set_num_runs(3)
# perform cross-validation
result = cross_validation.evaluate()
# get first observation and first fold
obs = multiclass_storage.get_observations()[0]
fold = obs.get_folds_results()[0]
# get fold ROC for first class
eval_ROC = ROCEvaluation()
pred_lab_binary = MulticlassLabels.obtain_from_generic(
fold.get_test_result()).get_binary_for_class(0)
true_lab_binary = MulticlassLabels.obtain_from_generic(
fold.get_test_true_result()).get_binary_for_class(0)
eval_ROC.evaluate(pred_lab_binary, true_lab_binary)
print eval_ROC.get_ROC()
# get fold evaluation result
acc_measure = F1Measure()
print acc_measure.evaluate(pred_lab_binary, true_lab_binary)
def mkl_binclass(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