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 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 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