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more refactoring of API examples and required c++ adjustments #4275

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May 8, 2018
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more refactoring of API examples and required c++ adjustments #4275

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2 changes: 1 addition & 1 deletion data
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Submodule data updated 1 files
+1 −2 testsuite/meta/regression/least_angle_regression.dat
8 changes: 4 additions & 4 deletions examples/meta/src/base_api/put_get_add.sg
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Expand Up @@ -26,11 +26,11 @@ matrix[1,1] = 0.4
#RealMatrix matrix2 = feats.get_real_matrix("feature_matrix")
#feats.put("feature_matrix", matrix2)

EuclideanDistance distance()
knn.put("distance", distance)
Distance dist = distance("EuclideanDistance")
knn.put("distance", dist)

SGObject distance2 = knn.get("distance")
knn.put("distance", distance2)
SGObject dist2 = knn.get("distance")
knn.put("distance", dist2)

LibSVM svm()
svm.put("kernel", k)
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4 changes: 2 additions & 2 deletions examples/meta/src/binary/averaged_perceptron.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
BinaryLabels labels_train(f_labels_train)
BinaryLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![set_parameters]
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4 changes: 2 additions & 2 deletions examples/meta/src/binary/linear_support_vector_machine.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
BinaryLabels labels_train(f_labels_train)
BinaryLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![set_parameters]
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4 changes: 2 additions & 2 deletions examples/meta/src/binary/perceptron.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
BinaryLabels labels_train(f_labels_train)
BinaryLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_instance]
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6 changes: 3 additions & 3 deletions examples/meta/src/multiclass/ecoc_random.sg
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Expand Up @@ -8,12 +8,12 @@ CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
MulticlassLabels labels_train(f_labels_train)
MulticlassLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_classifier]
LibLinear classifier()
Machine classifier = machine("LibLinear")
#![create_classifier]

#![choose_strategy]
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6 changes: 3 additions & 3 deletions examples/meta/src/multiclass/linear.sg
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Expand Up @@ -6,12 +6,12 @@ CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
MulticlassLabels labels_train(f_labels_train)
MulticlassLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_classifier]
LibLinear classifier()
Machine classifier = machine("LibLinear")
#![create_classifier]

#![choose_strategy]
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4 changes: 2 additions & 2 deletions examples/meta/src/multiclass/linear_discriminant_analysis.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
MulticlassLabels labels_train(f_labels_train)
MulticlassLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_instance]
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4 changes: 2 additions & 2 deletions examples/meta/src/multiclass/logistic_regression.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
MulticlassLabels labels_train(f_labels_train)
MulticlassLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]


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4 changes: 2 additions & 2 deletions examples/meta/src/multiclass/quadratic_discriminant_analysis.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
MulticlassLabels labels_train(f_labels_train)
MulticlassLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_instance]
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4 changes: 2 additions & 2 deletions examples/meta/src/multiclass/shareboost.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
MulticlassLabels labels_train(f_labels_train)
MulticlassLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![create_instance]
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8 changes: 4 additions & 4 deletions examples/meta/src/regression/least_angle_regression.sg
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Expand Up @@ -6,8 +6,8 @@ CSVFile f_labels_test("../../data/regression_1d_linear_labels_test.dat")
#![create_features]
Features features_train = features(f_feats_train)
Features features_test = features(f_feats_test)
RegressionLabels labels_train(f_labels_train)
RegressionLabels labels_test(f_labels_test)
Labels labels_train = labels(f_labels_train)
Labels labels_test = labels(f_labels_test)
#![create_features]

#![preprocess_features]
Expand All @@ -28,7 +28,7 @@ Machine lars = machine("LeastAngleRegression", labels=labels_train, lasso=False,

#![train_and_apply]
lars.train(features_train)
RegressionLabels labels_predict = lars.apply_regression(features_test)
Labels labels_predict = lars.apply(features_test)

#[!extract_w]
RealVector weights = lars.get_real_vector("w")
Expand All @@ -40,5 +40,5 @@ real mse = eval.evaluate(labels_predict, labels_test)
#![evaluate_error]

# integration testing variables
RealVector output = labels_test.get_labels()
RealVector output = labels_predict.get_real_vector("labels")

3 changes: 1 addition & 2 deletions examples/undocumented/libshogun/classifier_lda.cpp
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Expand Up @@ -44,12 +44,11 @@ void test()
lda->train();

// Classify and display output
CMulticlassLabels* output=CLabelsFactory::to_multiclass(lda->apply());
auto output = multiclass_labels(lda->apply());
SG_REF(output);
SGVector<float64_t>::display_vector(output->get_labels().vector, output->get_num_labels());

// Free memory
SG_UNREF(output);
SG_UNREF(lda);
#endif
}
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2 changes: 1 addition & 1 deletion src/gpl
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Submodule gpl updated 1 files
+6 −7 shogun/multiclass/MulticlassLogisticRegression.cpp
3 changes: 1 addition & 2 deletions src/shogun/classifier/AveragedPerceptron.cpp
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Expand Up @@ -43,7 +43,6 @@ void CAveragedPerceptron::init()
bool CAveragedPerceptron::train_machine(CFeatures* data)
{
ASSERT(m_labels)
ASSERT(m_labels->get_label_type() == LT_BINARY)

if (data)
{
Expand All @@ -54,7 +53,7 @@ bool CAveragedPerceptron::train_machine(CFeatures* data)
ASSERT(features)
bool converged=false;
int32_t iter=0;
SGVector<int32_t> train_labels=((CBinaryLabels*) m_labels)->get_int_labels();
SGVector<int32_t> train_labels = binary_labels(m_labels)->get_int_labels();
int32_t num_feat=features->get_dim_feature_space();
int32_t num_vec=features->get_num_vectors();

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3 changes: 1 addition & 2 deletions src/shogun/classifier/Perceptron.cpp
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Expand Up @@ -47,7 +47,6 @@ CPerceptron::~CPerceptron()
bool CPerceptron::train_machine(CFeatures* data)
{
ASSERT(m_labels)
ASSERT(m_labels->get_label_type() == LT_BINARY)

if (data)
{
Expand All @@ -59,7 +58,7 @@ bool CPerceptron::train_machine(CFeatures* data)
ASSERT(features)
bool converged=false;
int32_t iter=0;
SGVector<int32_t> train_labels=((CBinaryLabels*) m_labels)->get_int_labels();
SGVector<int32_t> train_labels = binary_labels(m_labels)->get_int_labels();
int32_t num_feat=features->get_dim_feature_space();
int32_t num_vec=features->get_num_vectors();

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4 changes: 2 additions & 2 deletions src/shogun/classifier/svm/LibLinear.cpp
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Expand Up @@ -74,7 +74,6 @@ bool CLibLinear::train_machine(CFeatures* data)
{

ASSERT(m_labels)
ASSERT(m_labels->get_label_type() == LT_BINARY)
init_linear_term();

if (data)
Expand Down Expand Up @@ -142,9 +141,10 @@ bool CLibLinear::train_machine(CFeatures* data)
double Cp = get_C1();
double Cn = get_C2();

auto labels = binary_labels(m_labels);
for (int32_t i = 0; i < prob.l; i++)
{
prob.y[i] = ((CBinaryLabels*)m_labels)->get_int_label(i);
prob.y[i] = labels->get_int_label(i);
if (prob.y[i] == +1)
Cs[i] = get_C1();
else if (prob.y[i] == -1)
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13 changes: 7 additions & 6 deletions src/shogun/evaluation/MulticlassAccuracy.cpp
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Expand Up @@ -15,15 +15,16 @@ float64_t CMulticlassAccuracy::evaluate(CLabels* predicted, CLabels* ground_trut
{
ASSERT(predicted && ground_truth)
ASSERT(predicted->get_num_labels() == ground_truth->get_num_labels())
ASSERT(predicted->get_label_type() == LT_MULTICLASS)
ASSERT(ground_truth->get_label_type() == LT_MULTICLASS)
int32_t length = predicted->get_num_labels();
auto predicted_mc = multiclass_labels(predicted);
auto ground_truth_mc = multiclass_labels(ground_truth);
int32_t correct = 0;
if (m_ignore_rejects)
{
for (int32_t i=0; i<length; i++)
{
if (((CMulticlassLabels*) predicted)->get_int_label(i)==((CMulticlassLabels*) ground_truth)->get_int_label(i))
if (predicted_mc->get_int_label(i) ==
ground_truth_mc->get_int_label(i))
correct++;
}
return ((float64_t)correct)/length;
Expand All @@ -33,11 +34,11 @@ float64_t CMulticlassAccuracy::evaluate(CLabels* predicted, CLabels* ground_trut
int32_t total = length;
for (int32_t i=0; i<length; i++)
{
int32_t predicted_label = ((CMulticlassLabels*) predicted)->get_int_label(i);
int32_t predicted_label = predicted_mc->get_int_label(i);

if (predicted_label==((CMulticlassLabels*) predicted)->REJECTION_LABEL)
if (predicted_label == predicted_mc->REJECTION_LABEL)
total--;
else if (predicted_label==((CMulticlassLabels*) ground_truth)->get_int_label(i))
else if (predicted_label == ground_truth_mc->get_int_label(i))
correct++;
}
m_rejects_num = length-total;
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3 changes: 2 additions & 1 deletion src/shogun/machine/MulticlassMachine.cpp
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Expand Up @@ -247,7 +247,8 @@ bool CMulticlassMachine::train_machine(CFeatures* data)
SG_REF(train_labels);
m_machine->set_labels(train_labels);

m_multiclass_strategy->train_start(CLabelsFactory::to_multiclass(m_labels), train_labels);
m_multiclass_strategy->train_start(
multiclass_labels(m_labels), train_labels);
while (m_multiclass_strategy->train_has_more())
{
SGVector<index_t> subset=m_multiclass_strategy->train_prepare_next();
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5 changes: 3 additions & 2 deletions src/shogun/multiclass/ShareBoost.cpp
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Expand Up @@ -139,7 +139,8 @@ void CShareBoost::compute_pred(const float64_t *W)

void CShareBoost::compute_rho()
{
CMulticlassLabels *lab = dynamic_cast<CMulticlassLabels *>(m_labels);
auto lab = multiclass_labels(m_labels);

for (int32_t i=0; i < m_rho.num_rows; ++i)
{ // i loop classes
for (int32_t j=0; j < m_rho.num_cols; ++j)
Expand All @@ -163,6 +164,7 @@ void CShareBoost::compute_rho()
int32_t CShareBoost::choose_feature()
{
SGVector<float64_t> l1norm(m_fea.num_rows);
auto lab = multiclass_labels(m_labels);
for (int32_t j=0; j < m_fea.num_rows; ++j)
{
if (std::find(&m_activeset[0], &m_activeset[m_activeset.vlen], j) !=
Expand All @@ -173,7 +175,6 @@ int32_t CShareBoost::choose_feature()
else
{
l1norm[j] = 0;
CMulticlassLabels *lab = dynamic_cast<CMulticlassLabels *>(m_labels);
for (int32_t k=0; k < m_multiclass_strategy->get_num_classes(); ++k)
{
float64_t abssum = 0;
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2 changes: 1 addition & 1 deletion src/shogun/multiclass/ShareBoostOptimizer.cpp
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Expand Up @@ -51,7 +51,7 @@ float64_t ShareBoostOptimizer::lbfgs_evaluate(void *userdata, const float64_t *W
int32_t k = optimizer->m_sb->m_multiclass_strategy->get_num_classes();

SGMatrix<float64_t> fea = optimizer->m_sb->m_fea;
CMulticlassLabels *lab = dynamic_cast<CMulticlassLabels *>(optimizer->m_sb->m_labels);
auto lab = multiclass_labels(optimizer->m_sb->m_labels);

// compute gradient
for (int32_t i=0; i < m; ++i)
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4 changes: 3 additions & 1 deletion src/shogun/multiclass/tree/C45ClassifierTree.cpp
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Expand Up @@ -530,14 +530,16 @@ void CC45ClassifierTree::prune_tree_from_current_node(CDenseFeatures<float64_t>*
SG_UNREF(right_child);
}

SG_UNREF(children);
SG_UNREF(children)

CMulticlassLabels* predicted_unpruned=apply_multiclass_from_current_node(feats, current);
SG_REF(predicted_unpruned);
SGVector<float64_t> pruned_labels=SGVector<float64_t>(feature_matrix.num_cols);
for (int32_t i=0; i<feature_matrix.num_cols; i++)
pruned_labels[i]=current->data.class_label;

CMulticlassLabels* predicted_pruned=new CMulticlassLabels(pruned_labels);
SG_REF(predicted_pruned);

CMulticlassAccuracy* accuracy=new CMulticlassAccuracy();
float64_t unpruned_accuracy=accuracy->evaluate(predicted_unpruned, gnd_truth);
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2 changes: 2 additions & 0 deletions src/shogun/multiclass/tree/ID3ClassifierTree.cpp
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Expand Up @@ -335,11 +335,13 @@ void CID3ClassifierTree::prune_tree_machine(CDenseFeatures<float64_t>* feats,
SG_UNREF(children);

CMulticlassLabels* predicted_unpruned = apply_multiclass_from_current_node(feats, current);
SG_REF(predicted_unpruned);
SGVector<float64_t> pruned_labels = SGVector<float64_t>(feature_matrix.num_cols);
for (int32_t i=0; i<feature_matrix.num_cols; i++)
pruned_labels[i] = current->data.class_label;

CMulticlassLabels* predicted_pruned = new CMulticlassLabels(pruned_labels);
SG_REF(predicted_pruned);

CMulticlassAccuracy* accuracy = new CMulticlassAccuracy();
float64_t unpruned_accuracy = accuracy->evaluate(predicted_unpruned, gnd_truth);
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5 changes: 1 addition & 4 deletions src/shogun/regression/LeastAngleRegression.cpp
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Expand Up @@ -104,9 +104,6 @@ void CLeastAngleRegression::plane_rot(ST x0, ST x1,

bool CLeastAngleRegression::train_machine(CFeatures* data)
{
REQUIRE(m_labels->get_label_type() == LT_REGRESSION, "Provided labels (%s) are of type (%d) - they should be regression labels (%d) instead.\n"
, m_labels->get_name(), m_labels->get_label_type(), LT_REGRESSION, m_labels->get_label_type())

if (!data)
{
REQUIRE(features, "No features provided.\n")
Expand Down Expand Up @@ -155,7 +152,7 @@ bool CLeastAngleRegression::train_machine_templated(CDenseFeatures<ST> * data)
m_is_active.resize(n_fea);
fill(m_is_active.begin(), m_is_active.end(), false);

SGVector<ST> y = ((CRegressionLabels*) m_labels)->template get_labels_t<ST>();
SGVector<ST> y = regression_labels(m_labels)->template get_labels_t<ST>();
typename SGVector<ST>::EigenVectorXtMap map_y(y.vector, y.size());

// transpose(X) is more convenient to work with since we care
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1 change: 1 addition & 0 deletions tests/unit/multiclass/MulticlassOCAS_unittest.cc
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Expand Up @@ -28,6 +28,7 @@ TEST(MulticlassOCASTest,train)
mocas->train();

CMulticlassLabels* pred = (CMulticlassLabels*)mocas->apply(test_feats);
SG_REF(pred);
CMulticlassAccuracy evaluate = CMulticlassAccuracy();
float64_t result = evaluate.evaluate(pred, ground_truth);
EXPECT_GT(result, 0.99);
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4 changes: 4 additions & 0 deletions tests/unit/multiclass/tree/C45ClassifierTree_unittest.cc
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Expand Up @@ -496,6 +496,8 @@ TEST(C45ClassifierTree, tree_prune_categorical_attributes)
CDenseFeatures<float64_t>* train_features=new CDenseFeatures<float64_t>(data);
CMulticlassLabels* train_lab=new CMulticlassLabels(train_labels);
CMulticlassLabels* validation_lab=new CMulticlassLabels(validation_labels);
SG_REF(train_lab);
SG_REF(validation_lab);

CC45ClassifierTree* c45tree=new CC45ClassifierTree();
c45tree->set_labels(train_lab);
Expand Down Expand Up @@ -581,8 +583,10 @@ TEST(C45ClassifierTree, tree_prune_continuous_attributes)

CDenseFeatures<float64_t>* train_features=new CDenseFeatures<float64_t>(data);
CMulticlassLabels* train_lab=new CMulticlassLabels(train_labels);
SG_REF(train_lab);
CDenseFeatures<float64_t>* validation_features=new CDenseFeatures<float64_t>(validation_data);
CMulticlassLabels* validation_lab=new CMulticlassLabels(validation_labels);
SG_REF(validation_lab);

CC45ClassifierTree* c45tree=new CC45ClassifierTree();
c45tree->set_labels(train_lab);
Expand Down