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181
tests/unit/statistical_testing/KernelSelectionMaxMMD_unittest.cc
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/* | ||
* Copyright (c) The Shogun Machine Learning Toolbox | ||
* Written (W) 2012-2013 Heiko Strathmann | ||
* Written (w) 2016 Soumyajit De | ||
* 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. | ||
*/ | ||
|
||
#include <shogun/base/some.h> | ||
#include <shogun/kernel/GaussianKernel.h> | ||
#include <shogun/kernel/CustomKernel.h> | ||
#include <shogun/features/DenseFeatures.h> | ||
#include <shogun/features/streaming/generators/MeanShiftDataGenerator.h> | ||
#include <shogun/mathematics/Statistics.h> | ||
#include <shogun/mathematics/eigen3.h> | ||
#include <shogun/mathematics/Math.h> | ||
#include <shogun/statistical_testing/LinearTimeMMD.h> | ||
#include <gtest/gtest.h> | ||
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||
using namespace shogun; | ||
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TEST(KernelSelectionMaxMMD, perform_test_permutation_biased_full) | ||
{ | ||
const index_t m=20; | ||
const index_t n=30; | ||
const index_t dim=2; | ||
const float64_t difference=0.5; | ||
const index_t num_kernels=10; | ||
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// use fixed seed | ||
sg_rand->set_seed(12345); | ||
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// streaming data generator for mean shift distributions | ||
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0); | ||
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0); | ||
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||
// create MMD instance, convienience constructor | ||
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q); | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
mmd->set_num_samples_p(m); | ||
mmd->set_num_samples_q(n); | ||
mmd->set_num_blocks_per_burst(1000); | ||
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||
for (auto i=0; i<num_kernels; ++i) | ||
{ | ||
// shoguns kernel width is different | ||
float64_t sigma=(i+1)*0.5; | ||
float64_t sq_sigma_twice=sigma*sigma*2; | ||
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice)); | ||
} | ||
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mmd->select_kernel(EKernelSelectionMethod::MAXIMIZE_MMD); | ||
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel()); | ||
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10); | ||
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// perform test with selected kernel | ||
index_t num_null_samples=10; | ||
mmd->set_num_null_samples(num_null_samples); | ||
mmd->set_null_approximation_method(ENullApproximationMethod::PERMUTATION); | ||
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// compute p-value using permutation for null distribution and | ||
// assert against local machine computed result | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
float64_t p_value=mmd->compute_p_value(mmd->compute_statistic()); | ||
// EXPECT_NEAR(p_value, 0.0, 1E-10); | ||
} | ||
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TEST(KernelSelectionMaxMMD, perform_test_permutation_unbiased_full) | ||
{ | ||
const index_t m=20; | ||
const index_t n=30; | ||
const index_t dim=2; | ||
const float64_t difference=0.5; | ||
const index_t num_kernels=10; | ||
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// use fixed seed | ||
sg_rand->set_seed(12345); | ||
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// streaming data generator for mean shift distributions | ||
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0); | ||
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0); | ||
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// create MMD instance, convienience constructor | ||
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q); | ||
mmd->set_statistic_type(EStatisticType::UNBIASED_FULL); | ||
mmd->set_num_samples_p(m); | ||
mmd->set_num_samples_q(n); | ||
mmd->set_num_blocks_per_burst(1000); | ||
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for (auto i=0; i<num_kernels; ++i) | ||
{ | ||
// shoguns kernel width is different | ||
float64_t sigma=(i+1)*0.5; | ||
float64_t sq_sigma_twice=sigma*sigma*2; | ||
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice)); | ||
} | ||
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mmd->select_kernel(EKernelSelectionMethod::MAXIMIZE_MMD); | ||
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel()); | ||
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10); | ||
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// perform test with selected kernel | ||
index_t num_null_samples=10; | ||
mmd->set_num_null_samples(num_null_samples); | ||
mmd->set_null_approximation_method(ENullApproximationMethod::PERMUTATION); | ||
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// compute p-value using permutation for null distribution and | ||
// assert against local machine computed result | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
float64_t p_value=mmd->compute_p_value(mmd->compute_statistic()); | ||
// EXPECT_NEAR(p_value, 0.0, 1E-10); | ||
} | ||
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TEST(KernelSelectionMaxMMD, perform_test_permutation_unbiased_incomplete) | ||
{ | ||
const index_t m=20; | ||
const index_t n=20; | ||
const index_t dim=2; | ||
const float64_t difference=0.5; | ||
const index_t num_kernels=10; | ||
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// use fixed seed | ||
sg_rand->set_seed(12345); | ||
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// streaming data generator for mean shift distributions | ||
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0); | ||
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0); | ||
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// create MMD instance, convienience constructor | ||
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q); | ||
mmd->set_statistic_type(EStatisticType::UNBIASED_INCOMPLETE); | ||
mmd->set_num_samples_p(m); | ||
mmd->set_num_samples_q(n); | ||
mmd->set_num_blocks_per_burst(1000); | ||
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for (auto i=0; i<num_kernels; ++i) | ||
{ | ||
// shoguns kernel width is different | ||
float64_t sigma=(i+1)*0.5; | ||
float64_t sq_sigma_twice=sigma*sigma*2; | ||
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice)); | ||
} | ||
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mmd->select_kernel(EKernelSelectionMethod::MAXIMIZE_MMD); | ||
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel()); | ||
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10); | ||
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// perform test with selected kernel | ||
index_t num_null_samples=10; | ||
mmd->set_num_null_samples(num_null_samples); | ||
mmd->set_null_approximation_method(ENullApproximationMethod::PERMUTATION); | ||
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// compute p-value using permutation for null distribution and | ||
// assert against local machine computed result | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
float64_t p_value=mmd->compute_p_value(mmd->compute_statistic()); | ||
// EXPECT_NEAR(p_value, 0.0, 1E-10); | ||
} |
181 changes: 181 additions & 0 deletions
181
tests/unit/statistical_testing/KernelSelectionOptMMD_unittest.cc
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,181 @@ | ||
/* | ||
* Copyright (c) The Shogun Machine Learning Toolbox | ||
* Written (W) 2012-2013 Heiko Strathmann | ||
* Written (w) 2016 Soumyajit De | ||
* 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/some.h> | ||
#include <shogun/kernel/GaussianKernel.h> | ||
#include <shogun/kernel/CustomKernel.h> | ||
#include <shogun/features/DenseFeatures.h> | ||
#include <shogun/features/streaming/generators/MeanShiftDataGenerator.h> | ||
#include <shogun/mathematics/Statistics.h> | ||
#include <shogun/mathematics/eigen3.h> | ||
#include <shogun/mathematics/Math.h> | ||
#include <shogun/statistical_testing/LinearTimeMMD.h> | ||
#include <gtest/gtest.h> | ||
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using namespace shogun; | ||
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TEST(KernelSelectionOptMMD, perform_test_permutation_biased_full) | ||
{ | ||
const index_t m=20; | ||
const index_t n=30; | ||
const index_t dim=2; | ||
const float64_t difference=0.5; | ||
const index_t num_kernels=10; | ||
|
||
// use fixed seed | ||
sg_rand->set_seed(12345); | ||
|
||
// streaming data generator for mean shift distributions | ||
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0); | ||
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0); | ||
|
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// create MMD instance, convienience constructor | ||
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q); | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
mmd->set_num_samples_p(m); | ||
mmd->set_num_samples_q(n); | ||
mmd->set_num_blocks_per_burst(1000); | ||
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||
for (auto i=0; i<num_kernels; ++i) | ||
{ | ||
// shoguns kernel width is different | ||
float64_t sigma=(i+1)*0.5; | ||
float64_t sq_sigma_twice=sigma*sigma*2; | ||
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice)); | ||
} | ||
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mmd->select_kernel(EKernelSelectionMethod::OPTIMIZE_MMD); | ||
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel()); | ||
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10); | ||
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// perform test with selected kernel | ||
index_t num_null_samples=10; | ||
mmd->set_num_null_samples(num_null_samples); | ||
mmd->set_null_approximation_method(ENullApproximationMethod::PERMUTATION); | ||
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// compute p-value using permutation for null distribution and | ||
// assert against local machine computed result | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
float64_t p_value=mmd->compute_p_value(mmd->compute_statistic()); | ||
// EXPECT_NEAR(p_value, 0.0, 1E-10); | ||
} | ||
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TEST(KernelSelectionOptMMD, perform_test_permutation_unbiased_full) | ||
{ | ||
const index_t m=20; | ||
const index_t n=30; | ||
const index_t dim=2; | ||
const float64_t difference=0.5; | ||
const index_t num_kernels=10; | ||
|
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// use fixed seed | ||
sg_rand->set_seed(12345); | ||
|
||
// streaming data generator for mean shift distributions | ||
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0); | ||
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0); | ||
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// create MMD instance, convienience constructor | ||
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q); | ||
mmd->set_statistic_type(EStatisticType::UNBIASED_FULL); | ||
mmd->set_num_samples_p(m); | ||
mmd->set_num_samples_q(n); | ||
mmd->set_num_blocks_per_burst(1000); | ||
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for (auto i=0; i<num_kernels; ++i) | ||
{ | ||
// shoguns kernel width is different | ||
float64_t sigma=(i+1)*0.5; | ||
float64_t sq_sigma_twice=sigma*sigma*2; | ||
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice)); | ||
} | ||
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mmd->select_kernel(EKernelSelectionMethod::OPTIMIZE_MMD); | ||
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel()); | ||
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10); | ||
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// perform test with selected kernel | ||
index_t num_null_samples=10; | ||
mmd->set_num_null_samples(num_null_samples); | ||
mmd->set_null_approximation_method(ENullApproximationMethod::PERMUTATION); | ||
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// compute p-value using permutation for null distribution and | ||
// assert against local machine computed result | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
float64_t p_value=mmd->compute_p_value(mmd->compute_statistic()); | ||
// EXPECT_NEAR(p_value, 0.0, 1E-10); | ||
} | ||
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TEST(KernelSelectionOptMMD, perform_test_permutation_unbiased_incomplete) | ||
{ | ||
const index_t m=20; | ||
const index_t n=20; | ||
const index_t dim=2; | ||
const float64_t difference=0.5; | ||
const index_t num_kernels=10; | ||
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// use fixed seed | ||
sg_rand->set_seed(12345); | ||
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// streaming data generator for mean shift distributions | ||
auto gen_p=new CMeanShiftDataGenerator(0, dim, 0); | ||
auto gen_q=new CMeanShiftDataGenerator(difference, dim, 0); | ||
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// create MMD instance, convienience constructor | ||
auto mmd=some<CLinearTimeMMD>(gen_p, gen_q); | ||
mmd->set_statistic_type(EStatisticType::UNBIASED_INCOMPLETE); | ||
mmd->set_num_samples_p(m); | ||
mmd->set_num_samples_q(n); | ||
mmd->set_num_blocks_per_burst(1000); | ||
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for (auto i=0; i<num_kernels; ++i) | ||
{ | ||
// shoguns kernel width is different | ||
float64_t sigma=(i+1)*0.5; | ||
float64_t sq_sigma_twice=sigma*sigma*2; | ||
mmd->add_kernel(new CGaussianKernel(10, sq_sigma_twice)); | ||
} | ||
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mmd->select_kernel(EKernelSelectionMethod::OPTIMIZE_MMD); | ||
auto selected_kernel=static_cast<CGaussianKernel*>(mmd->get_kernel()); | ||
EXPECT_NEAR(selected_kernel->get_width(), 0.5, 1E-10); | ||
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// perform test with selected kernel | ||
index_t num_null_samples=10; | ||
mmd->set_num_null_samples(num_null_samples); | ||
mmd->set_null_approximation_method(ENullApproximationMethod::PERMUTATION); | ||
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// compute p-value using permutation for null distribution and | ||
// assert against local machine computed result | ||
mmd->set_statistic_type(EStatisticType::BIASED_FULL); | ||
float64_t p_value=mmd->compute_p_value(mmd->compute_statistic()); | ||
// EXPECT_NEAR(p_value, 0.0, 1E-10); | ||
} |