def classifier_svmlight_linear_term_modular (fm_train_dna=traindna,fm_test_dna=testdna, \
label_train_dna=label_traindna,degree=3, \
C=10,epsilon=1e-5,num_threads=1):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel
try:
from modshogun import SVMLight
except ImportError:
print("SVMLight is not available")
exit(0)
feats_train=StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels=BinaryLabels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_qpsize(3)
svm.set_linear_term(-numpy.array([1,2,3,4,5,6,7,8,7,6], dtype=numpy.double));
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
out = svm.apply().get_labels()
return out,kernel
def classifier_domainadaptationsvm_modular (fm_train_dna=traindna,fm_test_dna=testdna, \
label_train_dna=label_traindna, \
label_test_dna=label_testdna,fm_train_dna2=traindna2,fm_test_dna2=testdna2, \
label_train_dna2=label_traindna2,label_test_dna2=label_testdna2,C=1,degree=3):
feats_train = StringCharFeatures(fm_train_dna, DNA)
feats_test = StringCharFeatures(fm_test_dna, DNA)
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.train()
#svm.io.set_loglevel(MSG_DEBUG)
#####################################
#print("obtaining DA SVM from previously trained SVM")
feats_train2 = StringCharFeatures(fm_train_dna, DNA)
feats_test2 = StringCharFeatures(fm_test_dna, DNA)
kernel2 = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels2 = BinaryLabels(label_train_dna)
# we regularize against the previously obtained solution
dasvm = DomainAdaptationSVM(C, kernel2, labels2, svm, 1.0)
dasvm.train()
out = dasvm.apply_binary(feats_test2)
return out #,dasvm TODO
def classifier_domainadaptationsvm_modular (fm_train_dna=traindna,fm_test_dna=testdna, \
label_train_dna=label_traindna, \
label_test_dna=label_testdna,fm_train_dna2=traindna2,fm_test_dna2=testdna2, \
label_train_dna2=label_traindna2,label_test_dna2=label_testdna2,C=1,degree=3):
feats_train = StringCharFeatures(fm_train_dna, DNA)
feats_test = StringCharFeatures(fm_test_dna, DNA)
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.train()
#svm.io.set_loglevel(MSG_DEBUG)
#####################################
#print("obtaining DA SVM from previously trained SVM")
feats_train2 = StringCharFeatures(fm_train_dna, DNA)
feats_test2 = StringCharFeatures(fm_test_dna, DNA)
kernel2 = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels2 = BinaryLabels(label_train_dna)
# we regularize against the previously obtained solution
dasvm = DomainAdaptationSVM(C, kernel2, labels2, svm, 1.0)
dasvm.train()
out = dasvm.apply_binary(feats_test2)
return out #,dasvm TODO
def classifier_svmlight_modular(fm_train_dna=traindat,
fm_test_dna=testdat,
label_train_dna=label_traindat,
C=1.2,
epsilon=1e-5,
num_threads=1):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel
try:
from modshogun import SVMLight
except ImportError:
print('No support for SVMLight available.')
return
feats_train = StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test = StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree = 20
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
svm.apply().get_labels()
return kernel
def classifier_svmlight_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,C=1.2,epsilon=1e-5,num_threads=1):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel
try:
from modshogun import SVMLight
except ImportError:
print('No support for SVMLight available.')
return
feats_train=StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test=StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree=20
kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels=BinaryLabels(label_train_dna)
svm=SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
svm.apply().get_labels()
return kernel
def svm_process(args_tuple):
X_train, Y_train, X_test, Y_test, d, c = args_tuple
kernel = WeightedDegreePositionStringKernel(X_train, X_train, d)
kernel.set_shifts(np.ones(SEQ_LEN, dtype=np.int32))
kernel.set_position_weights(np.ones(SEQ_LEN, dtype=np.float64))
kernel.init(X_train, X_train)
model = SVMLight(c, kernel, Y_train)
model.train()
Y_test_pred = model.apply(X_test).get_labels()
Y_test_dist = model.apply(X_test).get_values()
Y_test_proba = np.exp(Y_test_dist) / (1 + np.exp(Y_test_dist))
accuracy = np.where(Y_test_pred - Y_test == 0)[0].size * 1.0 / Y_test.size
return (accuracy, Y_test_proba)
def svm_process(args_tuple):
X_train, Y_train, X_test, Y_test, d, c = args_tuple
kernel = WeightedDegreePositionStringKernel(X_train, X_train, d)
kernel.set_shifts(np.ones(SEQ_LEN, dtype=np.int32))
kernel.set_position_weights(np.ones(SEQ_LEN, dtype=np.float64))
kernel.init(X_train, X_train)
model = SVMLight(c, kernel, Y_train)
model.train()
Y_test_pred = model.apply(X_test).get_labels()
Y_test_dist = model.apply(X_test).get_values()
Y_test_proba = np.exp(Y_test_dist)/(1 + np.exp(Y_test_dist))
accuracy = np.where(Y_test_pred - Y_test == 0)[0].size*1.0/Y_test.size
return (accuracy, Y_test_proba)
def classifier_svmlight_batch_linadd_modular(
fm_train_dna, fm_test_dna, label_train_dna, degree, C, epsilon, num_threads
):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel, MSG_DEBUG
try:
from modshogun import SVMLight
except ImportError:
print("No support for SVMLight available.")
return
feats_train = StringCharFeatures(DNA)
# feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.set_features(fm_train_dna)
feats_test = StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree = 20
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
# print('SVMLight Objective: %f num_sv: %d' % \)
# (svm.get_objective(), svm.get_num_support_vectors())
svm.set_batch_computation_enabled(False)
svm.set_linadd_enabled(False)
svm.apply().get_labels()
svm.set_batch_computation_enabled(True)
labels = svm.apply().get_labels()
return labels, svm
def classifier_svmlight_batch_linadd_modular(fm_train_dna, fm_test_dna,
label_train_dna, degree, C,
epsilon, num_threads):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel, MSG_DEBUG
try:
from modshogun import SVMLight
except ImportError:
print('No support for SVMLight available.')
return
feats_train = StringCharFeatures(DNA)
#feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.set_features(fm_train_dna)
feats_test = StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
degree = 20
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
#print('SVMLight Objective: %f num_sv: %d' % \)
# (svm.get_objective(), svm.get_num_support_vectors())
svm.set_batch_computation_enabled(False)
svm.set_linadd_enabled(False)
svm.apply().get_labels()
svm.set_batch_computation_enabled(True)
labels = svm.apply().get_labels()
return labels, svm
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 serialization_svmlight_modular(num, dist, width, C):
from modshogun import MSG_DEBUG
from modshogun import RealFeatures, BinaryLabels, DNA, Alphabet
from modshogun import WeightedDegreeStringKernel, GaussianKernel
try:
from modshogun import SVMLight
except ImportError:
print("SVMLight not available")
exit(0)
from numpy import concatenate, ones
from numpy.random import randn, seed
import sys
import types
import random
import bz2
import pickle
import inspect
def save(filename, myobj):
"""
save object to file using pickle
@param filename: name of destination file
@type filename: str
@param myobj: object to save (has to be pickleable)
@type myobj: obj
"""
try:
f = bz2.BZ2File(filename, 'wb')
except IOError as details:
sys.stderr.write('File ' + filename + ' cannot be written\n')
sys.stderr.write(details)
return
pickle.dump(myobj, f, protocol=2)
f.close()
def load(filename):
"""
Load from filename using pickle
@param filename: name of file to load from
@type filename: str
"""
try:
f = bz2.BZ2File(filename, 'rb')
except IOError as details:
sys.stderr.write('File ' + filename + ' cannot be read\n')
sys.stderr.write(details)
return
myobj = pickle.load(f)
f.close()
return myobj
##################################################
# set up toy data and svm
traindata_real = concatenate((randn(2, num) - dist, randn(2, num) + dist),
axis=1)
testdata_real = concatenate((randn(2, num) - dist, randn(2, num) + dist),
axis=1)
trainlab = concatenate((-ones(num), ones(num)))
testlab = concatenate((-ones(num), ones(num)))
feats_train = RealFeatures(traindata_real)
feats_test = RealFeatures(testdata_real)
kernel = GaussianKernel(feats_train, feats_train, width)
#kernel.io.set_loglevel(MSG_DEBUG)
labels = BinaryLabels(trainlab)
svm = SVMLight(C, kernel, labels)
svm.train()
#svm.io.set_loglevel(MSG_DEBUG)
##################################################
# 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 objectives")
svm2.train()
#print("objective before serialization:", svm.get_objective())
#print("objective after serialization:", svm2.get_objective())
#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 True
def serialization_svmlight_modular (num, dist, width, C):
from modshogun import MSG_DEBUG
from modshogun import RealFeatures, BinaryLabels, DNA, Alphabet
from modshogun import WeightedDegreeStringKernel, GaussianKernel
from modshogun import SVMLight
from numpy import concatenate, ones
from numpy.random import randn, seed
import sys
import types
import random
import bz2
try:
import cPickle as pickle
except ImportError:
import pickle as pickle
import inspect
def save(filename, myobj):
"""
save object to file using pickle
@param filename: name of destination file
@type filename: str
@param myobj: object to save (has to be pickleable)
@type myobj: obj
"""
try:
f = bz2.BZ2File(filename, 'wb')
except IOError as details:
sys.stderr.write('File ' + filename + ' cannot be written\n')
sys.stderr.write(details)
return
pickle.dump(myobj, f, protocol=2)
f.close()
def load(filename):
"""
Load from filename using pickle
@param filename: name of file to load from
@type filename: str
"""
try:
f = bz2.BZ2File(filename, 'rb')
except IOError as details:
sys.stderr.write('File ' + filename + ' cannot be read\n')
sys.stderr.write(details)
return
myobj = pickle.load(f)
f.close()
return myobj
##################################################
# set up toy data and svm
traindata_real = concatenate((randn(2,num)-dist, randn(2,num)+dist), axis=1)
testdata_real = concatenate((randn(2,num)-dist, randn(2,num)+dist), axis=1);
trainlab = concatenate((-ones(num), ones(num)));
testlab = concatenate((-ones(num), ones(num)));
feats_train = RealFeatures(traindata_real);
feats_test = RealFeatures(testdata_real);
kernel = GaussianKernel(feats_train, feats_train, width);
#kernel.io.set_loglevel(MSG_DEBUG)
labels = BinaryLabels(trainlab);
svm = SVMLight(C, kernel, labels)
svm.train()
#svm.io.set_loglevel(MSG_DEBUG)
##################################################
# 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 objectives")
svm2.train()
#print("objective before serialization:", svm.get_objective())
#print("objective after serialization:", svm2.get_objective())
#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 True
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 classifier_svmlight_linear_term_modular (fm_train_dna=traindna,fm_test_dna=testdna, \
label_train_dna=label_traindna,degree=3, \
C=10,epsilon=1e-5,num_threads=1):
from modshogun import StringCharFeatures, BinaryLabels, DNA
from modshogun import WeightedDegreeStringKernel
try:
from modshogun import SVMLight
except ImportError:
print("SVMLight is not available")
exit(0)
feats_train = StringCharFeatures(DNA)
feats_train.set_features(fm_train_dna)
feats_test = StringCharFeatures(DNA)
feats_test.set_features(fm_test_dna)
kernel = WeightedDegreeStringKernel(feats_train, feats_train, degree)
labels = BinaryLabels(label_train_dna)
svm = SVMLight(C, kernel, labels)
svm.set_qpsize(3)
svm.set_linear_term(
-numpy.array([1, 2, 3, 4, 5, 6, 7, 8, 7, 6], dtype=numpy.double))
svm.set_epsilon(epsilon)
svm.parallel.set_num_threads(num_threads)
svm.train()
kernel.init(feats_train, feats_test)
out = svm.apply().get_labels()
return out, kernel
