def kernel_combined_custom_poly_modular(fm_train_real=traindat,
fm_test_real=testdat,
fm_label_twoclass=label_traindat):
from shogun.Features import CombinedFeatures, RealFeatures, Labels
from shogun.Kernel import CombinedKernel, PolyKernel, CustomKernel
from shogun.Classifier import LibSVM
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(fm_train_real)
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10, 2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = Labels(fm_label_twoclass)
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(fm_test_real)
tkernel = PolyKernel(10, 3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(fm_test_real)
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.classify()
km_train = kernel.get_kernel_matrix()
return km_train, kernel
def kernel_combined_custom_poly_modular(fm_train_real = traindat,fm_test_real = testdat,fm_label_twoclass=label_traindat):
from shogun.Features import CombinedFeatures, RealFeatures, BinaryLabels
from shogun.Kernel import CombinedKernel, PolyKernel, CustomKernel
from shogun.Classifier import LibSVM
kernel = CombinedKernel()
feats_train = CombinedFeatures()
tfeats = RealFeatures(fm_train_real)
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, tfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_train = RealFeatures(fm_train_real)
feats_train.append_feature_obj(subkfeats_train)
subkernel = PolyKernel(10,2)
kernel.append_kernel(subkernel)
kernel.init(feats_train, feats_train)
labels = BinaryLabels(fm_label_twoclass)
svm = LibSVM(1.0, kernel, labels)
svm.train()
kernel = CombinedKernel()
feats_pred = CombinedFeatures()
pfeats = RealFeatures(fm_test_real)
tkernel = PolyKernel(10,3)
tkernel.init(tfeats, pfeats)
K = tkernel.get_kernel_matrix()
kernel.append_kernel(CustomKernel(K))
subkfeats_test = RealFeatures(fm_test_real)
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 loadSVM(pickled_svm_filename, C, labels):
"""Loads a Shogun SVM object which was pickled by saveSVM"""
from cPickle import Unpickler, PickleError
from shogun.Kernel import CombinedKernel
pickle_file = open(pickled_svm_filename, 'rb')
unpck = Unpickler(pickle_file)
(version, num_sv, name, bias, alphas, svs) = unpck.load()
if (version == __version__):
svm = LibSVM(num_sv) # same as .create_new_model(num_sv)
svm.set_bias(bias)
svm.set_alphas(alphas)
svm.set_support_vectors(svs)
kernel = CombinedKernel() #not sure if this is even required
kernel.set_name(name) # maybe not required
svm.set_kernel(kernel)
else:
print "File was pickled by another version of EasySVM.py or is not a kernel:"
print "Received from ", pickled_svm_filename, ": ", version, " expected: ", __version__
raise PickleError
return svm
C = 0.017
epsilon = 1e-5
tube_epsilon = 1e-2
svm = LibSVM()
svm.set_C(C, C)
svm.set_epsilon(epsilon)
svm.set_tube_epsilon(tube_epsilon)
for i in xrange(3):
data_train = random.rand(num_feats, num_vec)
data_test = random.rand(num_feats, num_vec)
feats_train = RealFeatures(data_train)
feats_test = RealFeatures(data_test)
labels = Labels(random.rand(num_vec).round() * 2 - 1)
svm.set_kernel(LinearKernel(size_cache, scale))
svm.set_labels(labels)
kernel = svm.get_kernel()
print "kernel cache size: %s" % (kernel.get_cache_size())
kernel.init(feats_test, feats_test)
svm.train()
kernel.init(feats_train, feats_test)
print svm.apply().get_labels()
#kernel.remove_lhs_and_rhs()
#import pdb
#pdb.set_trace()
C=0.017 epsilon=1e-5 tube_epsilon=1e-2 svm=LibSVM() svm.set_C(C, C) svm.set_epsilon(epsilon) svm.set_tube_epsilon(tube_epsilon) for i in xrange(3): data_train=random.rand(num_feats, num_vec) data_test=random.rand(num_feats, num_vec) feats_train=RealFeatures(data_train) feats_test=RealFeatures(data_test) labels=Labels(random.rand(num_vec).round()*2-1) svm.set_kernel(LinearKernel(size_cache, scale)) svm.set_labels(labels) kernel=svm.get_kernel() print "kernel cache size: %s" % (kernel.get_cache_size()) kernel.init(feats_test, feats_test) svm.train() kernel.init(feats_train, feats_test) print svm.classify().get_labels() #kernel.remove_lhs_and_rhs() #import pdb #pdb.set_trace()
