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.apply().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.apply().get_labels() return out, kernel
def kernel_weighted_degree_string_modular (fm_train_dna=traindat,fm_test_dna=testdat,degree=20): from shogun.Features import StringCharFeatures, DNA from shogun.Kernel import WeightedDegreeStringKernel, MSG_DEBUG feats_train=StringCharFeatures(fm_train_dna, DNA) #feats_train.io.set_loglevel(MSG_DEBUG) feats_test=StringCharFeatures(fm_test_dna, DNA) kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree) from numpy import arange,double weights=arange(1,degree+1,dtype=double)[::-1]/ \ sum(arange(1,degree+1,dtype=double)) kernel.set_wd_weights(weights) #from numpy import ones,float64,int32 #kernel.set_position_weights(ones(len(fm_train_dna[0]), dtype=float64)) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() #this is how to serializate the kernel #import pickle #pickle.dump(kernel, file('kernel_obj.dump','w'), protocol=2) #k=pickle.load(file('kernel_obj.dump','r')) return km_train, km_test, kernel
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_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, BinaryLabels, 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=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 weighted_degree_string (): print 'WeightedDegreeString' from shogun.Features import StringCharFeatures, DNA from shogun.Kernel import WeightedDegreeStringKernel feats_train=StringCharFeatures(fm_train_dna, DNA) feats_test=StringCharFeatures(fm_test_dna, DNA) degree=20 kernel=WeightedDegreeStringKernel(feats_train, feats_train, degree) #weights=arange(1,degree+1,dtype=double)[::-1]/ \ # sum(arange(1,degree+1,dtype=double)) #kernel.set_wd_weights(weights) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix()
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.apply().get_labels() svm.set_batch_computation_enabled(True) labels = svm.apply().get_labels() return labels, svm
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, BinaryLabels, 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=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
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()