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MaxXValidation.cpp
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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.
*/
#include <algorithm>
#include <shogun/lib/SGVector.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/statistical_testing/MMD.h>
#include <shogun/statistical_testing/internals/KernelManager.h>
#include <shogun/statistical_testing/internals/DataManager.h>
#include <shogun/statistical_testing/kernelselection/internals/MaxXValidation.h>
using namespace shogun;
using namespace internal;
MaxXValidation::MaxXValidation(KernelManager& km, CMMD* est, const index_t& M, const float64_t& alp)
: KernelSelection(km, est), num_run(M), alpha(alp)
{
REQUIRE(num_run>0, "Number of runs is %d!\n", num_run);
REQUIRE(alpha>=0.0 && alpha<=1.0, "Threshold is %f!\n", alpha);
}
MaxXValidation::~MaxXValidation()
{
}
SGVector<float64_t> MaxXValidation::get_measure_vector()
{
return measures;
}
SGMatrix<float64_t> MaxXValidation::get_measure_matrix()
{
return rejections;
}
void MaxXValidation::init_measures()
{
const index_t num_kernels=kernel_mgr.num_kernels();
auto& data_mgr=estimator->get_data_mgr();
const index_t N=data_mgr.get_num_folds();
REQUIRE(N!=0, "Number of folds is not set!\n");
if (rejections.num_rows!=N*num_run || rejections.num_cols!=num_kernels)
rejections=SGMatrix<float64_t>(N*num_run, num_kernels);
std::fill(rejections.data(), rejections.data()+rejections.size(), 0);
if (measures.size()!=num_kernels)
measures=SGVector<float64_t>(num_kernels);
std::fill(measures.data(), measures.data()+measures.size(), 0);
}
void MaxXValidation::compute_measures()
{
auto& data_mgr=estimator->get_data_mgr();
data_mgr.set_cross_validation_mode(true);
const index_t N=data_mgr.get_num_folds();
SG_SINFO("Performing %d fold cross-validattion!\n", N);
const size_t num_kernels=kernel_mgr.num_kernels();
auto existing_kernel=estimator->get_kernel();
for (auto i=0; i<num_run; ++i)
{
data_mgr.shuffle_features();
for (auto j=0; j<N; ++j)
{
data_mgr.use_fold(j);
SG_SDEBUG("Running fold %d\n", j);
for (size_t k=0; k<num_kernels; ++k)
{
auto kernel=kernel_mgr.kernel_at(k);
estimator->set_kernel(kernel);
auto statistic=estimator->compute_statistic();
rejections(i*N+j, k)=estimator->compute_p_value(statistic)<alpha;
estimator->cleanup();
}
}
data_mgr.unshuffle_features();
}
data_mgr.set_cross_validation_mode(false);
estimator->set_kernel(existing_kernel);
for (auto j=0; j<rejections.num_cols; ++j)
{
auto begin=rejections.get_column_vector(j);
auto size=rejections.num_rows;
measures[j]=std::accumulate(begin, begin+size, 0.0)/size;
}
}
CKernel* MaxXValidation::select_kernel()
{
init_measures();
compute_measures();
auto max_element=std::max_element(measures.vector, measures.vector+measures.vlen);
auto max_idx=std::distance(measures.vector, max_element);
SG_SDEBUG("Selected kernel at %d position!\n", max_idx);
return kernel_mgr.kernel_at(max_idx);
}