def evaluation_multiclassovrevaluation_modular(traindat, label_traindat, testdat, label_testdat): from shogun.Features import MulticlassLabels from shogun.Evaluation import MulticlassOVREvaluation, ROCEvaluation from modshogun import MulticlassLibLinear, RealFeatures, ContingencyTableEvaluation, ACCURACY from shogun.Mathematics import Math Math.init_random(1) ground_truth_labels = MulticlassLabels(label_traindat) svm = MulticlassLibLinear(1.0, RealFeatures(traindat), MulticlassLabels(label_traindat)) svm.train() predicted_labels = svm.apply() binary_evaluator = ROCEvaluation() evaluator = MulticlassOVREvaluation(binary_evaluator) mean_roc = evaluator.evaluate(predicted_labels, ground_truth_labels) #print mean_roc binary_evaluator = ContingencyTableEvaluation(ACCURACY) evaluator = MulticlassOVREvaluation(binary_evaluator) mean_accuracy = evaluator.evaluate(predicted_labels, ground_truth_labels) #print mean_accuracy return mean_roc, mean_accuracy
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 modshogun import RealFeatures, MulticlassLabels from modshogun 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 modshogun import MulticlassAccuracy labels_test = MulticlassLabels(label_test_multiclass) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test) print('Accuracy = %.4f' % acc) return out
def evaluation_multiclassovrevaluation_modular (traindat, label_traindat): from shogun.Features import MulticlassLabels from shogun.Evaluation import MulticlassOVREvaluation,ROCEvaluation from modshogun import MulticlassLibLinear,RealFeatures,ContingencyTableEvaluation,ACCURACY from shogun.Mathematics import Math Math.init_random(1) ground_truth_labels = MulticlassLabels(label_traindat) svm = MulticlassLibLinear(1.0,RealFeatures(traindat),MulticlassLabels(label_traindat)) svm.parallel.set_num_threads(1) svm.train() predicted_labels = svm.apply() binary_evaluator = ROCEvaluation() evaluator = MulticlassOVREvaluation(binary_evaluator) mean_roc = evaluator.evaluate(predicted_labels,ground_truth_labels) #print mean_roc binary_evaluator = ContingencyTableEvaluation(ACCURACY) evaluator = MulticlassOVREvaluation(binary_evaluator) mean_accuracy = evaluator.evaluate(predicted_labels,ground_truth_labels) #print mean_accuracy return mean_roc, mean_accuracy, predicted_labels, svm
def classifier_multilabeloutputliblinear_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 modshogun import RealFeatures, MulticlassLabels, MultilabelLabels from modshogun 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_multilabel_output(feats_test,2) out = label_pred.get_labels() #print out return out
def evaluation_multiclassovrevaluation_modular (traindat, label_traindat, testdat, label_testdat): from shogun.Features import MulticlassLabels from shogun.Evaluation import MulticlassOVREvaluation,ROCEvaluation from modshogun import MulticlassLibLinear,RealFeatures,ContingencyTableEvaluation,ACCURACY ground_truth_labels = MulticlassLabels(label_traindat) svm = MulticlassLibLinear(1.0,RealFeatures(traindat),MulticlassLabels(label_traindat)) svm.train() predicted_labels = svm.apply() binary_evaluator = ROCEvaluation() evaluator = MulticlassOVREvaluation(binary_evaluator) mean_roc = evaluator.evaluate(predicted_labels,ground_truth_labels) print mean_roc binary_evaluator = ContingencyTableEvaluation(ACCURACY) evaluator = MulticlassOVREvaluation(binary_evaluator) mean_accuracy = evaluator.evaluate(predicted_labels,ground_truth_labels) print mean_accuracy return mean_roc, mean_accuracy
def classifier_multilabeloutputliblinear_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 modshogun import RealFeatures, MulticlassLabels, MultilabelLabels from modshogun 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_multilabel_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,label_test_multiclass=label_testdat,width=2.1,C=1,epsilon=1e-5): from modshogun import RealFeatures, MulticlassLabels from modshogun 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 modshogun import MulticlassAccuracy labels_test = MulticlassLabels(label_test_multiclass) evaluator = MulticlassAccuracy() acc = evaluator.evaluate(label_pred, labels_test) print('Accuracy = %.4f' % acc) return out