Port kernel svm example to using features/labels factory

Includes LibSVM using new CLabels::as_binary()
shogun-toolbox · Mar 19, 2018 · c85fedd · c85fedd
1 parent e789605
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Showing 6 changed files with 24 additions and 28 deletions.
diff --git a/data b/data
diff --git a/doc/cookbook/source/examples/binary/kernel_support_vector_machine.rst b/doc/cookbook/source/examples/binary/kernel_support_vector_machine.rst
@@ -33,7 +33,7 @@ We then create a :sgclass:`CKernelMachine` instance, here :sgclass:`CLibSVM`, an
 
 .. sgexample:: kernel_support_vector_machine.sg:create_instance
 
-Then we train it on training data and apply it to test data, which here gives :sgclass:`CBinaryLabels`.
+Then we train it on training data and apply it to test data. This gives :sgclass:`CLabels`, which we can extract the label vector from.
 
 .. sgexample:: kernel_support_vector_machine.sg:train_and_apply
 

diff --git a/examples/meta/generator/targets/cpp.json b/examples/meta/generator/targets/cpp.json
@@ -11,7 +11,7 @@
     "Comment": "//$comment\n",
     "Init": {
         "Construct": "auto $name = some<C$typeName>($arguments)$kwargs",
-        "Copy": "auto $name = $expr$kwargs",
+        "Copy": "auto $name = wrap($expr)$kwargs",
         "KeywordArguments": {
             "List": ";\n$elements",
             "Element": "$name->put(\"$keyword\", $expr)",

diff --git a/examples/meta/src/binary/kernel_support_vector_machine.sg b/examples/meta/src/binary/kernel_support_vector_machine.sg
@@ -1,13 +1,13 @@
-CSVFile f_feats_train("../../data/classifier_binary_2d_nonlinear_features_train.dat")
-CSVFile f_feats_test("../../data/classifier_binary_2d_nonlinear_features_test.dat")
-CSVFile f_labels_train("../../data/classifier_binary_2d_nonlinear_labels_train.dat")
-CSVFile f_labels_test("../../data/classifier_binary_2d_nonlinear_labels_test.dat")
+File f_feats_train = csv_file("../../data/classifier_binary_2d_nonlinear_features_train.dat")
+File f_feats_test = csv_file("../../data/classifier_binary_2d_nonlinear_features_test.dat")
+File f_labels_train = csv_file("../../data/classifier_binary_2d_nonlinear_labels_train.dat")
+File f_labels_test = csv_file("../../data/classifier_binary_2d_nonlinear_labels_test.dat")
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
-BinaryLabels labels_train(f_labels_train)
-BinaryLabels labels_test(f_labels_test)
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
+Labels labels_train = labels(f_labels_train)
+Labels labels_test = labels(f_labels_test)
 #![create_features]
 
 #![set_parameters]
@@ -20,7 +20,8 @@ Machine svm = machine("LibSVM", C1=1.0, C2=1.0, kernel=k, labels=labels_train, e
 
 #![train_and_apply]
 svm.train(features_train)
-BinaryLabels labels_predict = svm.apply_binary(features_test)
+Labels labels_predict = svm.apply(features_test)
+RealVector labels = labels_predict.get_real_vector("labels")
 #![train_and_apply]
 
 #![extract_weights_bias]
@@ -33,5 +34,4 @@ AccuracyMeasure eval()
 real accuracy = eval.evaluate(labels_predict, labels_test)
 #![evaluate_accuracy]
 
-# additional integration testing variables
-RealVector output = labels_predict.get_real_vector("labels")
+
diff --git a/src/shogun/classifier/svm/LibSVM.cpp b/src/shogun/classifier/svm/LibSVM.cpp
@@ -48,7 +48,7 @@ bool CLibSVM::train_machine(CFeatures* data)
 	struct svm_node* x_space;
 
 	ASSERT(m_labels && m_labels->get_num_labels())
-	ASSERT(m_labels->get_label_type() == LT_BINARY)
+	auto binary_labels = m_labels->as_binary();
 
 	if (data)
 	{
@@ -94,7 +94,7 @@ bool CLibSVM::train_machine(CFeatures* data)
 
 	for (int32_t i=0; i<problem.l; i++)
 	{
-		problem.y[i]=((CBinaryLabels*) m_labels)->get_label(i);
+		problem.y[i]=binary_labels->get_label(i);
 		problem.x[i]=&x_space[2*i];
 		x_space[2*i].index=i;
 		x_space[2*i+1].index=-1;

diff --git a/src/shogun/evaluation/ContingencyTableEvaluation.cpp b/src/shogun/evaluation/ContingencyTableEvaluation.cpp
@@ -12,15 +12,20 @@ using namespace shogun;
 
 float64_t CContingencyTableEvaluation::evaluate(CLabels* predicted, CLabels* ground_truth)
 {
-	ASSERT(predicted->get_label_type()==LT_BINARY)
-	ASSERT(ground_truth->get_label_type()==LT_BINARY)
+	REQUIRE(predicted->get_num_labels() == ground_truth->get_num_labels(),
+			"Number of predicted labels (%d) must be "
+				"equal to number of ground truth labels (%d)!\n", get_name(),
+				predicted->get_num_labels(), ground_truth->get_num_labels());
+
+	auto predicted_binary = predicted->as_binary();
+	auto ground_truth_binary = ground_truth->as_binary();
 
 	/* commented out: what if a machine only returns +1 in apply() ??
 	 * Heiko Strathamn */
 //	predicted->ensure_valid();
 
 	ground_truth->ensure_valid();
-	compute_scores((CBinaryLabels*)predicted,(CBinaryLabels*)ground_truth);
+	compute_scores(predicted_binary.get(),ground_truth_binary.get());
 	switch (m_type)
 	{
 		case ACCURACY:
@@ -82,15 +87,6 @@ EEvaluationDirection CContingencyTableEvaluation::get_evaluation_direction() con
 
 void CContingencyTableEvaluation::compute_scores(CBinaryLabels* predicted, CBinaryLabels* ground_truth)
 {
-	ASSERT(ground_truth->get_label_type() == LT_BINARY)
-	ASSERT(predicted->get_label_type() == LT_BINARY)
-
-	if (predicted->get_num_labels()!=ground_truth->get_num_labels())
-	{
-		SG_ERROR("%s::compute_scores(): Number of predicted labels (%d) is not "
-				"equal to number of ground truth labels (%d)!\n", get_name(),
-				predicted->get_num_labels(), ground_truth->get_num_labels());
-	}
 	m_TP = 0.0;
 	m_FP = 0.0;
 	m_TN = 0.0;