KernelSelectionStrategy.cpp

/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2012 - 2013 Heiko Strathmann
 * Written (w) 2014 - 2016 Soumyajit De
 * All rights reserved.
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#include <shogun/io/SGIO.h>
#include <shogun/lib/SGVector.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/distance/CustomDistance.h>
#include <shogun/statistical_testing/MMD.h>
#include <shogun/statistical_testing/internals/KernelManager.h>
#include <shogun/statistical_testing/kernelselection/KernelSelectionStrategy.h>
#include <shogun/statistical_testing/kernelselection/internals/KernelSelection.h>
#include <shogun/statistical_testing/kernelselection/internals/MaxMeasure.h>
#include <shogun/statistical_testing/kernelselection/internals/MaxTestPower.h>
#include <shogun/statistical_testing/kernelselection/internals/MaxCrossValidation.h>
#include <shogun/statistical_testing/kernelselection/internals/MedianHeuristic.h>
#include <shogun/statistical_testing/kernelselection/internals/WeightedMaxMeasure.h>
#include <shogun/statistical_testing/kernelselection/internals/WeightedMaxTestPower.h>

using namespace shogun;
using namespace internal;

struct CKernelSelectionStrategy::Self
{
	Self();

	KernelManager kernel_mgr;
	std::unique_ptr<KernelSelection> policy;

	EKernelSelectionMethod method;
	bool weighted;
	index_t num_runs;
	index_t num_folds;
	float64_t alpha;

	void init_policy(CMMD* estimator);

	const static EKernelSelectionMethod default_method;
	const static bool default_weighted;
	const static index_t default_num_runs;
	const static index_t default_num_folds;
	const static float64_t default_alpha;
};

const EKernelSelectionMethod CKernelSelectionStrategy::Self::default_method=EKernelSelectionMethod::KSM_AUTO;
const bool CKernelSelectionStrategy::Self::default_weighted=false;
const index_t CKernelSelectionStrategy::Self::default_num_runs=10;
const index_t CKernelSelectionStrategy::Self::default_num_folds=3;
const float64_t CKernelSelectionStrategy::Self::default_alpha=0.05;

CKernelSelectionStrategy::Self::Self() : policy(nullptr), method(default_method),
	weighted(default_weighted), num_runs(default_num_runs), num_folds(default_num_folds), alpha(default_alpha)
{
}

void CKernelSelectionStrategy::Self::init_policy(CMMD* estimator)
{
	switch (method)
	{
	case EKernelSelectionMethod::KSM_MEDIAN_HEURISTIC:
	{
		REQUIRE(!weighted, "Weighted kernel selection is not possible with MEDIAN_HEURISTIC!\n");
		policy=std::unique_ptr<MedianHeuristic>(new MedianHeuristic(kernel_mgr, estimator));
	}
	break;
	case EKernelSelectionMethod::KSM_MAXIMIZE_CROSS_VALIDATION:
	{
		REQUIRE(!weighted, "Weighted kernel selection is not possible with MAXIMIZE_CROSS_VALIDATION!\n");
		policy=std::unique_ptr<MaxCrossValidation>(new MaxCrossValidation(kernel_mgr, estimator,
			num_runs, num_folds, alpha));
	}
	break;
	case EKernelSelectionMethod::KSM_MAXIMIZE_MMD:
	{
		if (weighted)
			policy=std::unique_ptr<WeightedMaxMeasure>(new WeightedMaxMeasure(kernel_mgr, estimator));
		else
			policy=std::unique_ptr<MaxMeasure>(new MaxMeasure(kernel_mgr, estimator));
	}
	break;
	case EKernelSelectionMethod::KSM_MAXIMIZE_POWER:
	{
		if (weighted)
			policy=std::unique_ptr<WeightedMaxTestPower>(new WeightedMaxTestPower(kernel_mgr, estimator));
		else
			policy=std::unique_ptr<MaxTestPower>(new MaxTestPower(kernel_mgr, estimator));
	}
	break;
	default:
	{
		SG_SERROR("Unsupported kernel selection method specified! Accepted strategies are "
			"MAXIMIZE_MMD (single, weighted), "
			"MAXIMIZE_POWER (single, weighted), "
			"MAXIMIZE_CROSS_VALIDATION (single) and "
			"MEDIAN_HEURISTIC (single)!\n");
	}
	break;
	}
}

CKernelSelectionStrategy::CKernelSelectionStrategy()
{
	init();
}

CKernelSelectionStrategy::CKernelSelectionStrategy(EKernelSelectionMethod method)
{
	init();
	self->method=method;
}

CKernelSelectionStrategy::CKernelSelectionStrategy(EKernelSelectionMethod method, bool weighted)
{
	init();
	self->method=method;
	self->weighted=weighted;
}

CKernelSelectionStrategy::CKernelSelectionStrategy(EKernelSelectionMethod method, index_t num_runs,
	index_t num_folds, float64_t alpha)
{
	init();
	self->method=method;
	self->num_runs=num_runs;
	self->num_folds=num_folds;
	self->alpha=alpha;
}

void CKernelSelectionStrategy::init()
{
	self=std::unique_ptr<Self>(new Self());
}

CKernelSelectionStrategy::~CKernelSelectionStrategy()
{
	self->kernel_mgr.clear();
}

CKernelSelectionStrategy& CKernelSelectionStrategy::use_method(EKernelSelectionMethod method)
{
	self->method=method;
	return *this;
}

CKernelSelectionStrategy& CKernelSelectionStrategy::use_num_runs(index_t num_runs)
{
	self->num_runs=num_runs;
	return *this;
}

CKernelSelectionStrategy& CKernelSelectionStrategy::use_num_folds(index_t num_folds)
{
	self->num_folds=num_folds;
	return *this;
}

CKernelSelectionStrategy& CKernelSelectionStrategy::use_alpha(float64_t alpha)
{
	self->alpha=alpha;
	return *this;
}

CKernelSelectionStrategy& CKernelSelectionStrategy::use_weighted(bool weighted)
{
	self->weighted=weighted;
	return *this;
}

void CKernelSelectionStrategy::add_kernel(CKernel* kernel)
{
	self->kernel_mgr.push_back(kernel);
}

CKernel* CKernelSelectionStrategy::select_kernel(CMMD* estimator)
{
	auto num_kernels=self->kernel_mgr.num_kernels();
	REQUIRE(num_kernels>0, "Number of kernels is 0. Please add kernels using add_kernel method!\n");
	SG_DEBUG("Selecting kernels from a total of %d kernels!\n", num_kernels);

	self->init_policy(estimator);
	ASSERT(self->policy!=nullptr);

	return self->policy->select_kernel();
}

// TODO call this method when test train mode is turned off
void CKernelSelectionStrategy::erase_intermediate_results()
{
	self->policy=nullptr;
	self->kernel_mgr.clear();
}

SGMatrix<float64_t> CKernelSelectionStrategy::get_measure_matrix()
{
	REQUIRE(self->policy!=nullptr, "The kernel selection policy is not initialized!\n");
	return self->policy->get_measure_matrix();
}

SGVector<float64_t> CKernelSelectionStrategy::get_measure_vector()
{
	REQUIRE(self->policy!=nullptr, "The kernel selection policy is not initialized!\n");
	return self->policy->get_measure_vector();
}

const char* CKernelSelectionStrategy::get_name() const
{
	return "KernelSelectionStrategy";
}

const KernelManager& CKernelSelectionStrategy::get_kernel_mgr() const
{
	return self->kernel_mgr;
}