def classifier_multiclassliblinear_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, Labels from shogun.Classifier import MulticlassLibLinear feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=Labels(label_train_multiclass) classifier = MulticlassLibLinear(C,feats_train,labels) classifier.train() out = classifier.apply(feats_test).get_labels() return out
def classifier_multiclass_relaxedtree(fm_train_real=traindat, fm_test_real=testdat, label_train_multiclass=label_traindat, label_test_multiclass=label_testdat, lawidth=2.1, C=1, epsilon=1e-5): from shogun.Features import RealFeatures, MulticlassLabels from shogun.Classifier import RelaxedTree, MulticlassLibLinear from shogun.Kernel import GaussianKernel #print('Working on a problem of %d features and %d samples' % fm_train_real.shape) feats_train = RealFeatures(fm_train_real) labels = MulticlassLabels(label_train_multiclass) machine = RelaxedTree() machine.set_machine_for_confusion_matrix(MulticlassLibLinear()) machine.set_kernel(GaussianKernel()) machine.set_labels(labels) machine.train(feats_train) label_pred = machine.apply_multiclass(RealFeatures(fm_test_real)) out = label_pred.get_labels() if label_test_multiclass is not None: from shogun.Evaluation import MulticlassAccuracy labels_test = MulticlassLabels(label_test_multiclass) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test) print('Accuracy = %.4f' % acc) return out
def classifier_multiclassmultipleoutputliblinear_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, MulticlassLabels, MulticlassMultipleOutputLabels from shogun.Classifier import MulticlassLibLinear feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=MulticlassLabels(label_train_multiclass) classifier = MulticlassLibLinear(C,feats_train,labels) classifier.train() label_pred = classifier.apply_multiclass_multiple_output(feats_test,2) out = label_pred.get_labels() print out return out
def classifier_multiclassmultipleoutputliblinear_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, MulticlassLabels, MulticlassMultipleOutputLabels from shogun.Classifier import MulticlassLibLinear feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=MulticlassLabels(label_train_multiclass) classifier = MulticlassLibLinear(C,feats_train,labels) classifier.train() label_pred = classifier.apply_multiclass_multiple_output(feats_test,2) out = label_pred.get_labels() #print out return out
def classifier_multiclassliblinear_modular( fm_train_real=traindat, fm_test_real=testdat, label_train_multiclass=label_traindat, width=2.1, C=1, epsilon=1e-5): from shogun.Features import RealFeatures, MulticlassLabels from shogun.Classifier import MulticlassLibLinear feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = MulticlassLabels(label_train_multiclass) classifier = MulticlassLibLinear(C, feats_train, labels) classifier.train() out = classifier.apply(feats_test).get_labels() return out
def classifier_multiclassliblinear_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,label_test_multiclass=label_testdat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, MulticlassLabels from shogun.Classifier import MulticlassLibLinear feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=MulticlassLabels(label_train_multiclass) classifier = MulticlassLibLinear(C,feats_train,labels) classifier.train() label_pred = classifier.apply(feats_test) out = label_pred.get_labels() if label_test_multiclass is not None: from shogun.Evaluation import MulticlassAccuracy labels_test = MulticlassLabels(label_test_multiclass) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test) print('Accuracy = %.4f' % acc) return out