Fixed multiclass ROC

shogun-toolbox · Aug 17, 2012 · 80685b0 · 80685b0
1 parent 6684bae
commit 80685b0
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Showing 4 changed files with 22 additions and 13 deletions.
diff --git a/examples/undocumented/python_modular/evaluation_multiclassovrevaluation_modular.py b/examples/undocumented/python_modular/evaluation_multiclassovrevaluation_modular.py
@@ -3,23 +3,32 @@
 lm=LoadMatrix()
 
 random.seed(17)
-ground_truth = lm.load_labels('../data/label_train_multiclass.dat')
+import classifier_multiclass_shared
+[traindat, label_traindat, testdat, label_testdat] = classifier_multiclass_shared.prepare_data(False)
 
-parameter_list = [[ground_truth]]
+parameter_list = [[traindat, label_traindat, testdat, label_testdat]]
 
-def evaluation_multiclassovrevaluation_modular(ground_truth):
+def evaluation_multiclassovrevaluation_modular(traindat, label_traindat, testdat, label_testdat):
 	from shogun.Features import MulticlassLabels
-	from shogun.Evaluation import MulticlassAccuracy,ROCEvaluation
+	from shogun.Evaluation import MulticlassOVREvaluation,ROCEvaluation
+	from modshogun import MulticlassLibLinear,RealFeatures,ContingencyTableEvaluation,ACCURACY
 
-	ground_truth_labels = MulticlassLabels(ground_truth)
-	predicted_labels = MulticlassLabels(ground_truth)
+	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 = MulticlassAccuracy(binary_evaluator)
+	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
+	return mean_roc, mean_accuracy
 
 
 if __name__=='__main__':

diff --git a/src/shogun/evaluation/MulticlassOVREvaluation.cpp b/src/shogun/evaluation/MulticlassOVREvaluation.cpp
@@ -47,6 +47,7 @@ float64_t CMulticlassOVREvaluation::evaluate(CLabels* predicted, CLabels* ground
 	CMulticlassLabels* predicted_mc = (CMulticlassLabels*)predicted;
 	CMulticlassLabels* ground_truth_mc = (CMulticlassLabels*)ground_truth;
 	int32_t n_classes = predicted_mc->get_multiclass_confidences(0).size();
+	ASSERT(n_classes>0);
 	m_last_results = SGVector<float64_t>(n_classes);
 
 	SGMatrix<float64_t> all(n_labels,n_classes);

diff --git a/src/shogun/labels/MulticlassLabels.cpp b/src/shogun/labels/MulticlassLabels.cpp
@@ -21,14 +21,13 @@ CMulticlassLabels::CMulticlassLabels(int32_t num_labels) : CDenseLabels(num_labe
 CMulticlassLabels::CMulticlassLabels(const SGVector<float64_t> src) : CDenseLabels()
 {
 	set_labels(src);
-	m_multiclass_confidences = SG_MALLOC(SGVector<float64_t>, src.vlen);
-	m_num_multiclass_confidences = src.vlen;
-	for (int32_t i=0; i<src.vlen; i++)
-		new (&m_multiclass_confidences[i]) SGVector<float64_t>();
+	m_multiclass_confidences = NULL;
+	m_num_multiclass_confidences = 0;
 }
 
 CMulticlassLabels::CMulticlassLabels(CFile* loader) : CDenseLabels(loader)
 {
+	m_multiclass_confidences = NULL;
 	m_num_multiclass_confidences = 0;
 }
 

diff --git a/src/shogun/machine/MulticlassMachine.cpp b/src/shogun/machine/MulticlassMachine.cpp
@@ -124,7 +124,7 @@ CMulticlassLabels* CMulticlassMachine::apply_multiclass(CFeatures* data)
 				output_for_i[j] = outputs[j]->get_confidence(i);
 
 			result->set_label(i, m_multiclass_strategy->decide_label(output_for_i));
-			result->set_multiclass_confidences(i, output_for_i);
+			result->set_multiclass_confidences(i, output_for_i.clone());
 		}
 
 		for (int32_t i=0; i < num_machines; ++i)