added kernel selection unit tests

tests/unit/statistical_testing/KernelSelectionMaxMMD_unittest.cc

@@ -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.
+ */
+
+#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>
+
+using namespace shogun;
+
+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;
+
+	// 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);
+
+	// 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);
+
+	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));
+	}
+
+	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);
+
+	// 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);
+
+	// 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);
+}
+
+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;
+
+	// 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);
+
+	// 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);
+
+	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));
+	}
+
+	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);
+
+	// 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);
+
+	// 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);
+}
+
+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;
+
+	// 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);
+
+	// 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);
+
+	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));
+	}
+
+	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);
+
+	// 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);
+
+	// 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);
+}

tests/unit/statistical_testing/KernelSelectionOptMMD_unittest.cc

@@ -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.
+ */
+
+#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>
+
+using namespace shogun;
+
+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);
+
+	// 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);
+
+	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));
+	}
+
+	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);
+
+	// 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);
+
+	// 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);
+}
+
+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;
+
+	// 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);
+
+	// 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);
+
+	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));
+	}
+
+	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);
+
+	// 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);
+
+	// 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);
+}
+
+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;
+
+	// 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);
+
+	// 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);
+
+	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));
+	}
+
+	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);
+
+	// 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);
+
+	// 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);
+}