def mkl(train_features, train_labels, test_features, test_labels, width=5, C=1.2, epsilon=1e-2, mkl_epsilon=0.001, mkl_norm=2): from modshogun import CombinedKernel, CombinedFeatures from modshogun import GaussianKernel, LinearKernel, PolyKernel from modshogun import MKLMulticlass, MulticlassAccuracy kernel = CombinedKernel() feats_train = CombinedFeatures() feats_test = CombinedFeatures() feats_train.append_feature_obj(train_features) feats_test.append_feature_obj(test_features) subkernel = GaussianKernel(10, width) kernel.append_kernel(subkernel) feats_train.append_feature_obj(train_features) feats_test.append_feature_obj(test_features) subkernel = LinearKernel() kernel.append_kernel(subkernel) feats_train.append_feature_obj(train_features) feats_test.append_feature_obj(test_features) subkernel = PolyKernel(10, 2) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) mkl = MKLMulticlass(C, kernel, train_labels) mkl.set_epsilon(epsilon) mkl.set_mkl_epsilon(mkl_epsilon) mkl.set_mkl_norm(mkl_norm) mkl.train() train_output = mkl.apply() kernel.init(feats_train, feats_test) test_output = mkl.apply() evaluator = MulticlassAccuracy() print 'MKL training error is %.4f' % ( (1 - evaluator.evaluate(train_output, train_labels)) * 100) print 'MKL test error is %.4f' % ( (1 - evaluator.evaluate(test_output, test_labels)) * 100)
def mkl_multiclass_modular (fm_train_real, fm_test_real, label_train_multiclass, width, C, epsilon, num_threads, mkl_epsilon, mkl_norm): from modshogun import CombinedFeatures, RealFeatures, MulticlassLabels from modshogun import CombinedKernel, GaussianKernel, LinearKernel,PolyKernel from modshogun import MKLMulticlass kernel = CombinedKernel() feats_train = CombinedFeatures() feats_test = CombinedFeatures() subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = GaussianKernel(10, width) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = LinearKernel() feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) subkfeats_train = RealFeatures(fm_train_real) subkfeats_test = RealFeatures(fm_test_real) subkernel = PolyKernel(10,2) feats_train.append_feature_obj(subkfeats_train) feats_test.append_feature_obj(subkfeats_test) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) labels = MulticlassLabels(label_train_multiclass) mkl = MKLMulticlass(C, kernel, labels) mkl.set_epsilon(epsilon); mkl.parallel.set_num_threads(num_threads) mkl.set_mkl_epsilon(mkl_epsilon) mkl.set_mkl_norm(mkl_norm) mkl.train() kernel.init(feats_train, feats_test) out = mkl.apply().get_labels() return out
def mkl(train_features, train_labels, test_features, test_labels, width=5, C=1.2, epsilon=1e-2, mkl_epsilon=0.001, mkl_norm=2): from modshogun import CombinedKernel, CombinedFeatures from modshogun import GaussianKernel, LinearKernel, PolyKernel from modshogun import MKLMulticlass, MulticlassAccuracy kernel = CombinedKernel() feats_train = CombinedFeatures() feats_test = CombinedFeatures() feats_train.append_feature_obj(train_features) feats_test.append_feature_obj(test_features) subkernel = GaussianKernel(10,width) kernel.append_kernel(subkernel) feats_train.append_feature_obj(train_features) feats_test.append_feature_obj(test_features) subkernel = LinearKernel() kernel.append_kernel(subkernel) feats_train.append_feature_obj(train_features) feats_test.append_feature_obj(test_features) subkernel = PolyKernel(10,2) kernel.append_kernel(subkernel) kernel.init(feats_train, feats_train) mkl = MKLMulticlass(C, kernel, train_labels) mkl.set_epsilon(epsilon); mkl.set_mkl_epsilon(mkl_epsilon) mkl.set_mkl_norm(mkl_norm) mkl.train() train_output = mkl.apply() kernel.init(feats_train, feats_test) test_output = mkl.apply() evaluator = MulticlassAccuracy() print 'MKL training error is %.4f' % ((1-evaluator.evaluate(train_output, train_labels))*100) print 'MKL test error is %.4f' % ((1-evaluator.evaluate(test_output, test_labels))*100)