def svm_light (): print 'SVMLight' from shogun.Features import StringCharFeatures, Labels, DNA from shogun.Kernel import WeightedDegreeStringKernel try: from shogun.Classifier 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) C=1.2 epsilon=1e-5 num_threads=1 labels=Labels(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.classify().get_labels()
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 shogun.Features import StringCharFeatures, Labels, DNA from shogun.Kernel import WeightedDegreeStringKernel try: from shogun.Classifier 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 = Labels(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.classify().get_labels() return kernel
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 shogun.Features import StringCharFeatures, Labels, DNA from shogun.Kernel import WeightedDegreeStringKernel from shogun.Classifier import SVMLight 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=Labels(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.classify().get_labels() return out,kernel
def do_batch_linadd (): print 'SVMlight batch' from shogun.Features import StringCharFeatures, Labels, DNA from shogun.Kernel import WeightedDegreeStringKernel try: from shogun.Classifier 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) C=1 epsilon=1e-5 num_threads=2 labels=Labels(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.classify().get_labels() svm.set_batch_computation_enabled(True) svm.classify().get_labels()
def classifier_svmlight_batch_linadd_modular(fm_train_dna, fm_test_dna, label_train_dna, degree, C, epsilon, num_threads): from shogun.Features import StringCharFeatures, Labels, DNA from shogun.Kernel import WeightedDegreeStringKernel, MSG_DEBUG try: from shogun.Classifier 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=Labels(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.classify().get_labels() svm.set_batch_computation_enabled(True) labels = svm.classify().get_labels() return labels, svm
print 'SVMLight' from shogun.Features import StringCharFeatures, Labels, DNA from shogun.Kernel import WeightedDegreeStringKernel from shogun.Classifier import SVMLight 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) C=10 epsilon=1e-5 num_threads=1 labels=Labels(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.classify().get_labels()
sv_id = int(old_svm.get_support_vectors()[j]) alpha = old_svm.get_alpha(j) inner_sum = inner_sum + alpha * kv.kernel(sv_id, idx) inner.append(inner_sum) #general case p[idx] = B * tmp_lab[idx] * inner_sum - 1.0 ################ #checking inner term presvm.set_bias(0.0) tmp_out = presvm.classify(feat).get_labels() for i in xrange(len(examples)): #print inner[i], tmp_out[i] assert(abs(inner[i]-tmp_out[i])<= 0.001) svm = SVMLight(1.0, wdk, lab) svm.set_linear_term(p) Math_init_random(1) svm.train() ############### #compare to LibSVM
for j in xrange(old_svm.get_num_support_vectors()): sv_id = int(old_svm.get_support_vectors()[j]) alpha = old_svm.get_alpha(j) inner_sum = inner_sum + alpha * kv.kernel(sv_id, idx) inner.append(inner_sum) #general case p[idx] = B * tmp_lab[idx] * inner_sum - 1.0 ################ #checking inner term presvm.set_bias(0.0) tmp_out = presvm.classify(feat).get_labels() for i in xrange(len(examples)): #print inner[i], tmp_out[i] assert (abs(inner[i] - tmp_out[i]) <= 0.001) svm = SVMLight(1.0, wdk, lab) svm.set_linear_term(p) Math_init_random(1) svm.train() ############### #compare to LibSVM svm2 = LibSVM(1.0, wdk, lab)