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tests/unit/evaluation/CrossValidation_multithread_unittest.cc
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| /* | ||
| * Copyright (c) The Shogun Machine Learning Toolbox | ||
| * Written (w) 2016 Saurabh Mahindre | ||
| * All rights reserved. | ||
| * | ||
| * Redistribution and use in source and binary forms, with or without | ||
| * modification, are permitted provided that the following conditions are met: | ||
| * | ||
| * 1. Redistributions of source code must retain the above copyright notice, this | ||
| * list of conditions and the following disclaimer. | ||
| * 2. Redistributions in binary form must reproduce the above copyright notice, | ||
| * this list of conditions and the following disclaimer in the documentation | ||
| * and/or other materials provided with the distribution. | ||
| * | ||
| * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND | ||
| * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED | ||
| * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR | ||
| * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES | ||
| * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; | ||
| * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND | ||
| * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS | ||
| * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| * | ||
| * The views and conclusions contained in the software and documentation are those | ||
| * of the authors and should not be interpreted as representing official policies, | ||
| * either expressed or implied, of the Shogun Development Team. | ||
| * | ||
| */ | ||
|
|
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| #include <shogun/base/init.h> | ||
| #include <shogun/features/DenseFeatures.h> | ||
| #include <shogun/labels/BinaryLabels.h> | ||
| #include <shogun/kernel/GaussianKernel.h> | ||
| #include <shogun/classifier/svm/LibSVM.h> | ||
| #include <shogun/evaluation/CrossValidation.h> | ||
| #include <shogun/evaluation/StratifiedCrossValidationSplitting.h> | ||
| #include <shogun/evaluation/ContingencyTableEvaluation.h> | ||
| #include <gtest/gtest.h> | ||
| #include <shogun/labels/MulticlassLabels.h> | ||
| #include <shogun/multiclass/KNN.h> | ||
| #include <shogun/evaluation/MulticlassAccuracy.h> | ||
| #include <shogun/distance/EuclideanDistance.h> | ||
|
|
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| using namespace shogun; | ||
|
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| void generate_data(SGMatrix<float64_t>& mat, SGVector<float64_t> &lab) | ||
| { | ||
| int32_t num=lab.size(); | ||
|
|
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| for (index_t i=0; i<num; i++) | ||
| { | ||
| mat(0,i)=i<num/2 ? 0+(CMath::randn_double()*4) : 100+(CMath::randn_double()*4) ; | ||
| mat(1,i)=i; | ||
| } | ||
|
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| for (index_t i=0; i<num; ++i) | ||
| lab.vector[i]=i<num/2 ? 0 : 1; | ||
|
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| } | ||
|
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| TEST(CrossValidation_multithread, LibSVM_unlocked) | ||
| { | ||
| int32_t num=100; | ||
| SGMatrix<float64_t> mat(2, num); | ||
|
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| /* training labels +/- 1 for each cluster */ | ||
| SGVector<float64_t> lab(num); | ||
|
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| /*create simple linearly separable data*/ | ||
| generate_data(mat, lab); | ||
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| for (index_t i=0; i<num/2; ++i) | ||
| lab.vector[i]-=1; | ||
| CBinaryLabels* labels=new CBinaryLabels(lab); | ||
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| CDenseFeatures<float64_t>* features= | ||
| new CDenseFeatures<float64_t>(mat); | ||
| SG_REF(features); | ||
|
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| int32_t width=100; | ||
| CGaussianKernel* kernel=new CGaussianKernel(width); | ||
| kernel->init(features, features); | ||
|
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| /* create svm via libsvm */ | ||
| float64_t svm_C=1; | ||
| CLibSVM* svm=new CLibSVM(svm_C, kernel, labels); | ||
|
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| CContingencyTableEvaluation* eval_crit= | ||
| new CContingencyTableEvaluation(ACCURACY); | ||
|
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| index_t n_folds=4; | ||
| CStratifiedCrossValidationSplitting* splitting= | ||
| new CStratifiedCrossValidationSplitting(labels, n_folds); | ||
|
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| CCrossValidation* cross=new CCrossValidation(svm, features, labels, | ||
| splitting, eval_crit); | ||
|
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| cross->set_autolock(false); | ||
| cross->set_num_runs(4); | ||
| cross->parallel->set_num_threads(1); | ||
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| CCrossValidationResult* result1=(CCrossValidationResult*)cross->evaluate(); | ||
| float64_t mean1 = result1->mean; | ||
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| cross->parallel->set_num_threads(3); | ||
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| CCrossValidationResult* result2=(CCrossValidationResult*)cross->evaluate(); | ||
| float64_t mean2 = result2->mean; | ||
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| EXPECT_EQ(mean1, mean2); | ||
|
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| /* clean up */ | ||
| SG_UNREF(result1); | ||
| SG_UNREF(result2); | ||
| SG_UNREF(cross); | ||
| SG_UNREF(features); | ||
| } | ||
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| TEST(CrossValidation_multithread, KNN) | ||
| { | ||
| int32_t num=100; | ||
| SGMatrix<float64_t> mat(2, num); | ||
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| SGVector<float64_t> lab(num); | ||
|
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| /*create simple linearly separable data*/ | ||
| generate_data(mat, lab); | ||
| CMulticlassLabels* labels=new CMulticlassLabels(lab); | ||
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| CDenseFeatures<float64_t>* features= | ||
| new CDenseFeatures<float64_t>(mat); | ||
| SG_REF(features); | ||
|
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| /* create knn */ | ||
| CEuclideanDistance* distance = new CEuclideanDistance(features, features); | ||
| CKNN* knn=new CKNN (4, distance, labels); | ||
| /* evaluation criterion */ | ||
| CMulticlassAccuracy* eval_crit = new CMulticlassAccuracy (); | ||
|
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| /* splitting strategy */ | ||
| index_t n_folds=4; | ||
| CStratifiedCrossValidationSplitting* splitting= | ||
| new CStratifiedCrossValidationSplitting(labels, n_folds); | ||
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| CCrossValidation* cross=new CCrossValidation(knn, features, labels, | ||
| splitting, eval_crit); | ||
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| cross->set_autolock(false); | ||
| cross->set_num_runs(4); | ||
| cross->parallel->set_num_threads(1); | ||
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| CCrossValidationResult* result1=(CCrossValidationResult*)cross->evaluate(); | ||
| float64_t mean1 = result1->mean; | ||
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| cross->parallel->set_num_threads(3); | ||
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| CCrossValidationResult* result2=(CCrossValidationResult*)cross->evaluate(); | ||
| float64_t mean2 = result2->mean; | ||
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| EXPECT_EQ(mean1, mean2); | ||
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| SG_UNREF(result1); | ||
| SG_UNREF(result2); | ||
| SG_UNREF(cross); | ||
| SG_UNREF(features); | ||
| } |