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added support for cross-validation setting for kernel selection (inco…
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src/shogun/statistical_testing/internals/MaxXValidation.cpp
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| /* | ||
| * Copyright (c) The Shogun Machine Learning Toolbox | ||
| * Written (W) 2013 Heiko Strathmann | ||
| * Written (w) 2014 - 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 <algorithm> | ||
| #include <shogun/lib/SGVector.h> | ||
| #include <shogun/kernel/Kernel.h> | ||
| #include <shogun/statistical_testing/MMD.h> | ||
| #include <shogun/statistical_testing/internals/MaxXValidation.h> | ||
| #include <shogun/statistical_testing/internals/KernelManager.h> | ||
| #include <shogun/statistical_testing/internals/DataManager.h> | ||
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| using namespace shogun; | ||
| using namespace internal; | ||
|
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| MaxXValidation::MaxXValidation(KernelManager& km, CMMD* est, const index_t& M, const float64_t& alp) | ||
| : KernelSelection(km), estimator(est), num_run(M), alpha(alp) | ||
| { | ||
| // TODO write a more meaningful error message | ||
| REQUIRE(estimator!=nullptr, "Estimator is not set!\n"); | ||
| REQUIRE(kernel_mgr.num_kernels()>0, "Number of kernels is %d!\n", kernel_mgr.num_kernels()); | ||
| REQUIRE(num_run>0, "Number of runs is %d!\n", num_run); | ||
| REQUIRE(alpha>=0.0 && alpha<=1.0, "Threshold is %f!\n", alpha); | ||
| } | ||
|
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| MaxXValidation::~MaxXValidation() | ||
| { | ||
| } | ||
|
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| void MaxXValidation::compute_measures(SGVector<float64_t>& measures, SGVector<index_t>& term_counters) | ||
| { | ||
| const size_t num_kernels=kernel_mgr.num_kernels(); | ||
| for (size_t i=0; i<num_kernels; ++i) | ||
| { | ||
| auto kernel=kernel_mgr.kernel_at(i); | ||
| estimator->set_kernel(kernel); | ||
| bool rejected=estimator->compute_p_value(estimator->compute_statistic())<alpha; | ||
| auto delta=measures[i]-rejected; | ||
| measures[i]=delta/term_counters[i]++; | ||
| estimator->cleanup(); | ||
| } | ||
| } | ||
|
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| CKernel* MaxXValidation::select_kernel() | ||
| { | ||
| auto& dm=estimator->get_data_manager(); | ||
| dm.set_xvalidation_mode(true); | ||
| auto existing_kernel=estimator->get_kernel(); | ||
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| const index_t N=dm.get_num_folds(); | ||
| // TODO write a more meaningful error message | ||
| REQUIRE(N!=0, "Number of folds is not set!\n"); | ||
| SG_SINFO("Performing %d fold cross-validattion!\n", N); | ||
| // train mode is already ON by now! set by the caller | ||
| SGVector<float64_t> measures(kernel_mgr.num_kernels()); | ||
| std::fill(measures.data(), measures.data()+measures.size(), 0); | ||
| SGVector<index_t> term_counters(measures.size()); | ||
| std::fill(term_counters.data(), term_counters.data()+term_counters.size(), 1); | ||
| for (auto i=0; i<num_run; ++i) | ||
| { | ||
| for (auto j=0; j<N; ++j) | ||
| { | ||
| dm.use_fold(j); | ||
| compute_measures(measures, term_counters); | ||
| } | ||
| } | ||
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| estimator->set_kernel(existing_kernel); | ||
| dm.set_xvalidation_mode(false); | ||
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| auto min_element=std::min_element(measures.vector, measures.vector+measures.vlen); | ||
| auto min_idx=std::distance(measures.vector, min_element); | ||
| SG_SDEBUG("Selected kernel at %d position!\n", min_idx); | ||
| return kernel_mgr.kernel_at(min_idx); | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,67 @@ | ||
| /* | ||
| * Copyright (c) The Shogun Machine Learning Toolbox | ||
| * Written (W) 2013 Heiko Strathmann | ||
| * Written (w) 2014 - 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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| #ifndef MAX_XVALIDATIN_H__ | ||
| #define MAX_XVALIDATIN_H__ | ||
|
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| #include <shogun/lib/common.h> | ||
| #include <shogun/statistical_testing/internals/KernelSelection.h> | ||
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| namespace shogun | ||
| { | ||
|
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| class CKernel; | ||
| class CMMD; | ||
| template <typename T> class SGVector; | ||
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| namespace internal | ||
| { | ||
|
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| class MaxXValidation : public KernelSelection | ||
| { | ||
| public: | ||
| MaxXValidation(KernelManager&, CMMD*, const index_t&, const float64_t&); | ||
| MaxXValidation(const MaxXValidation& other)=delete; | ||
| ~MaxXValidation(); | ||
| MaxXValidation& operator=(const MaxXValidation& other)=delete; | ||
| virtual CKernel* select_kernel() override; | ||
| protected: | ||
| void compute_measures(SGVector<float64_t>&, SGVector<index_t>&); | ||
| CMMD* estimator; | ||
| const index_t num_run; | ||
| const float64_t alpha; | ||
| }; | ||
|
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| } | ||
|
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| } | ||
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| #endif // MAX_XVALIDATIN_H__ |