def classifier_libsvm_modular(fm_train_real=traindat, fm_test_real=testdat, label_train_twoclass=label_traindat, width=2.1, C=1, epsilon=1e-5): from shogun.Features import RealFeatures, BinaryLabels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) kernel = GaussianKernel(feats_train, feats_train, width) labels = BinaryLabels(label_train_twoclass) svm = LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) labels = svm.apply().get_labels() supportvectors = sv_idx = svm.get_support_vectors() alphas = svm.get_alphas() predictions = svm.apply() print predictions.get_labels() return predictions, svm, predictions.get_labels()
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.apply() 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 classifier_libsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import LibSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train_twoclass) svm=LibSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train() kernel.init(feats_train, feats_test) labels = svm.apply().get_labels() supportvectors = sv_idx=svm.get_support_vectors() alphas=svm.get_alphas() predictions = svm.apply() return predictions, svm, predictions.get_labels()
def classifier_custom_kernel_modular (C=1,dim=7): from shogun.Features import RealFeatures, BinaryLabels from shogun.Kernel import CustomKernel from shogun.Classifier import LibSVM from numpy import diag,ones,sign from numpy.random import rand,seed seed((C,dim)) lab=sign(2*rand(dim) - 1) data=rand(dim, dim) symdata=data*data.T + diag(ones(dim)) kernel=CustomKernel() kernel.set_full_kernel_matrix_from_full(data) labels=BinaryLabels(lab) svm=LibSVM(C, kernel, labels) svm.train() predictions =svm.apply() out=svm.apply().get_labels() return svm,out
def classifier_custom_kernel_modular(C=1, dim=7): from shogun.Features import RealFeatures, Labels from shogun.Kernel import CustomKernel from shogun.Classifier import LibSVM from numpy import diag, ones, sign from numpy.random import rand, seed seed((C, dim)) lab = sign(2 * rand(dim) - 1) data = rand(dim, dim) symdata = data * data.T + diag(ones(dim)) kernel = CustomKernel() kernel.set_full_kernel_matrix_from_full(data) labels = Labels(lab) svm = LibSVM(C, kernel, labels) svm.train() predictions = svm.apply() out = svm.apply().get_labels() return svm, out
def classifier_libsvm_minimal_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,width=2.1,C=1): from shogun.Features import RealFeatures, BinaryLabels from shogun.Classifier import LibSVM from shogun.Kernel import GaussianKernel feats_train=RealFeatures(fm_train_real); feats_test=RealFeatures(fm_test_real); kernel=GaussianKernel(feats_train, feats_train, width); labels=BinaryLabels(label_train_twoclass); svm=LibSVM(C, kernel, labels); svm.train(); kernel.init(feats_train, feats_test); out=svm.apply().get_labels(); testerr=mean(sign(out)!=label_train_twoclass)
def classifier_libsvm_minimal_modular(fm_train_real=traindat, fm_test_real=testdat, label_train_twoclass=label_traindat, width=2.1, C=1): from shogun.Features import RealFeatures, BinaryLabels from shogun.Classifier import LibSVM from shogun.Kernel import GaussianKernel feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) kernel = GaussianKernel(feats_train, feats_train, width) labels = BinaryLabels(label_train_twoclass) svm = LibSVM(C, kernel, labels) svm.train() kernel.init(feats_train, feats_test) out = svm.apply().get_labels() testerr = mean(sign(out) != label_train_twoclass)
svm = LibSVM(1000.0, gk, labels) svm.train() lda = LDA(1, features, labels) lda.train() ## plot points subplot(211) plot(pos[0, :], pos[1, :], "r.") plot(neg[0, :], neg[1, :], "b.") grid(True) title('Data', size=10) # plot ROC for SVM subplot(223) ROC_evaluation = ROCEvaluation() ROC_evaluation.evaluate(svm.apply(), labels) roc = ROC_evaluation.get_ROC() print roc plot(roc[0], roc[1]) fill_between(roc[0], roc[1], 0, alpha=0.1) text( mean(roc[0]) / 2, mean(roc[1]) / 2, 'auROC = %.5f' % ROC_evaluation.get_auROC()) grid(True) xlabel('FPR') ylabel('TPR') title('LibSVM (Gaussian kernel, C=%.3f) ROC curve' % svm.get_C1(), size=10) # plot ROC for LDA subplot(224) ROC_evaluation.evaluate(lda.apply(), labels)
svm = LibSVM(1000.0, gk, labels) svm.train() lda=LDA(1,features,labels) lda.train() ## plot points subplot(211) plot(pos[0,:], pos[1,:], "r.") plot(neg[0,:], neg[1,:], "b.") grid(True) title('Data',size=10) # plot ROC for SVM subplot(223) ROC_evaluation=ROCEvaluation() ROC_evaluation.evaluate(svm.apply(),labels) roc = ROC_evaluation.get_ROC() print roc plot(roc[0], roc[1]) fill_between(roc[0],roc[1],0,alpha=0.1) text(mean(roc[0])/2,mean(roc[1])/2,'auROC = %.5f' % ROC_evaluation.get_auROC()) grid(True) xlabel('FPR') ylabel('TPR') title('LibSVM (Gaussian kernel, C=%.3f) ROC curve' % svm.get_C1(),size=10) # plot ROC for LDA subplot(224) ROC_evaluation.evaluate(lda.apply(),labels) roc = ROC_evaluation.get_ROC() plot(roc[0], roc[1])
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()
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()