Python MulticlassOVREvaluation Beispiele

Beispiel #1

Datei: evaluation_multiclassovrevaluation_modular.py Projekt: hushell/shogun

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

Beispiel #2

Datei: evaluation_multiclassovrevaluation_modular.py Projekt: AlexBinder/shogun

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

Beispiel #3

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