def runShogunSVRWDKernel(train_xt, train_lt, test_xt, svm_c=1, svr_param=0.1):
"""
serialize svr with string kernels
"""
##################################################
# set up svr
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 = np.ones(max_len, dtype=np.int32)*NUMSHIFTS
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 = RegressionLabels(train_lt)
# two svr models: epsilon and nu
svr_epsilon=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR)
print "Ready to train!"
svr_epsilon.train(feats_train)
#svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR)
#svr_nu.train(feats_train)
# predictions
print "Making predictions!"
kernel.init(feats_train, feats_test)
out1_epsilon=svr_epsilon.apply().get_labels()
out2_epsilon=svr_epsilon.apply(feats_test).get_labels()
#out1_nu=svr_epsilon.apply().get_labels()
#out2_nu=svr_epsilon.apply(feats_test).get_labels()
##################################################
# serialize to file
fEpsilon = open(FNEPSILON, 'w+')
#fNu = open(FNNU, 'w+')
svr_epsilon.save(fEpsilon)
#svr_nu.save(fNu)
fEpsilon.close()
#fNu.close()
##################################################
#return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel
return out1_epsilon,out2_epsilon,kernel
def runShogunSVRWDKernel(train_xt, train_lt, test_xt, svm_c=1, svr_param=0.1):
"""
serialize svr with string kernels
"""
##################################################
# set up svr
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 = np.ones(max_len, dtype=np.int32) * NUMSHIFTS
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 = RegressionLabels(train_lt)
# two svr models: epsilon and nu
svr_epsilon = LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR)
print "Ready to train!"
svr_epsilon.train(feats_train)
#svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR)
#svr_nu.train(feats_train)
# predictions
print "Making predictions!"
kernel.init(feats_train, feats_test)
out1_epsilon = svr_epsilon.apply().get_labels()
out2_epsilon = svr_epsilon.apply(feats_test).get_labels()
#out1_nu=svr_epsilon.apply().get_labels()
#out2_nu=svr_epsilon.apply(feats_test).get_labels()
##################################################
# serialize to file
fEpsilon = open(FNEPSILON, 'w+')
#fNu = open(FNNU, 'w+')
svr_epsilon.save(fEpsilon)
#svr_nu.save(fNu)
fEpsilon.close()
#fNu.close()
##################################################
#return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel
return out1_epsilon, out2_epsilon, kernel
def regression_libsvr_modular (svm_c=1, svr_param=0.1, n=100,n_test=100, \ x_range=6,x_range_test=10,noise_var=0.5,width=1, seed=1): from modshogun import RegressionLabels, RealFeatures from modshogun import GaussianKernel from modshogun import LibSVR, LIBSVR_NU_SVR, LIBSVR_EPSILON_SVR # reproducable results random.seed(seed) # easy regression data: one dimensional noisy sine wave n=15 n_test=100 x_range_test=10 noise_var=0.5; X=random.rand(1,n)*x_range X_test=array([[float(i)/n_test*x_range_test for i in range(n_test)]]) Y_test=sin(X_test) Y=sin(X)+random.randn(n)*noise_var # shogun representation labels=RegressionLabels(Y[0]) feats_train=RealFeatures(X) feats_test=RealFeatures(X_test) kernel=GaussianKernel(feats_train, feats_train, width) # two svr models: epsilon and nu svr_epsilon=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR) svr_epsilon.train() svr_nu=LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR) svr_nu.train() # predictions kernel.init(feats_train, feats_test) out1_epsilon=svr_epsilon.apply().get_labels() out2_epsilon=svr_epsilon.apply(feats_test).get_labels() out1_nu=svr_epsilon.apply().get_labels() out2_nu=svr_epsilon.apply(feats_test).get_labels() return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel
def regression_libsvr_modular (svm_c=1, svr_param=0.1, n=100,n_test=100, \
x_range=6,x_range_test=10,noise_var=0.5,width=1, seed=1):
from modshogun import RegressionLabels, RealFeatures
from modshogun import GaussianKernel
from modshogun import LibSVR, LIBSVR_NU_SVR, LIBSVR_EPSILON_SVR
# reproducable results
random.seed(seed)
# easy regression data: one dimensional noisy sine wave
n = 15
n_test = 100
x_range_test = 10
noise_var = 0.5
X = random.rand(1, n) * x_range
X_test = array([[float(i) / n_test * x_range_test for i in range(n_test)]])
Y_test = sin(X_test)
Y = sin(X) + random.randn(n) * noise_var
# shogun representation
labels = RegressionLabels(Y[0])
feats_train = RealFeatures(X)
feats_test = RealFeatures(X_test)
kernel = GaussianKernel(feats_train, feats_train, width)
# two svr models: epsilon and nu
svr_epsilon = LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_EPSILON_SVR)
svr_epsilon.train()
svr_nu = LibSVR(svm_c, svr_param, kernel, labels, LIBSVR_NU_SVR)
svr_nu.train()
# predictions
kernel.init(feats_train, feats_test)
out1_epsilon = svr_epsilon.apply().get_labels()
out2_epsilon = svr_epsilon.apply(feats_test).get_labels()
out1_nu = svr_epsilon.apply().get_labels()
out2_nu = svr_epsilon.apply(feats_test).get_labels()
return out1_epsilon, out2_epsilon, out1_nu, out2_nu, kernel
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 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 runShogunSVRWDKernel(train_xt, train_lt, test_xt, svm_c=1):
"""
serialize svr with string kernels
"""
##################################################
# set up svr
feats_train = StringCharFeatures(train_xt, PROTEIN)
feats_test = StringCharFeatures(test_xt, PROTEIN)
kernel = WeightedDegreePositionStringKernel(feats_train, feats_train, DEGREE)
kernel.io.set_loglevel(MSG_DEBUG)
kernel.set_shifts(NUMSHIFTS*ones(len(train_xt[0]), dtype=int32))
kernel.set_position_weights(ones(len(train_xt[0]), dtype=float64))
# 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()
#svr_nu=LibSVR(svm_c, SVRPARAM, kernel, labels, LIBSVR_NU_SVR)
#svr_nu.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()
#out1_nu=svr_epsilon.apply(feats_train).get_labels()
#out2_nu=svr_epsilon.apply(feats_test).get_labels()
#return out1_epsilon,out2_epsilon,out1_nu,out2_nu ,kernel
return out1_epsilon,out2_epsilon,kernel
