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kernel_pca_test.cpp: Add tests

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Add tests for kernel_pca command line.
This commit is contained in:
Saksham Bansal
2018-12-18 17:36:33 +07:00
parent 2fa579f49d
commit 44939e9b2f
3 changed files with 360 additions and 3 deletions
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src/mlpack/methods/kernel_pca/kernel_pca_main.cpp
+3 -3
View File
@@ -135,19 +135,19 @@ void RunKPCA(arma::mat& dataset,
if (sampling == "kmeans")
{
KernelPCA<KernelType, NystroemKernelRule<KernelType,
KMeansSelection<> > >kpca;
KMeansSelection<> > > kpca(kernel, centerTransformedData);
kpca.Apply(dataset, newDim);
}
else if (sampling == "random")
{
KernelPCA<KernelType, NystroemKernelRule<KernelType,
RandomSelection> > kpca;
RandomSelection> > kpca(kernel, centerTransformedData);
kpca.Apply(dataset, newDim);
}
else if (sampling == "ordered")
{
KernelPCA<KernelType, NystroemKernelRule<KernelType,
OrderedSelection> > kpca;
OrderedSelection> > kpca(kernel, centerTransformedData);
kpca.Apply(dataset, newDim);
}
else
src/mlpack/tests/CMakeLists.txt
+1
View File
@@ -143,6 +143,7 @@ add_executable(mlpack_test
main_tests/hmm_generate_test.cpp
main_tests/radical_test.cpp
main_tests/hmm_test_utils.hpp
main_tests/kernel_pca_test.cpp
)
# Link dependencies of test executable.
src/mlpack/tests/main_tests/kernel_pca_test.cpp
+356
View File
@@ -0,0 +1,356 @@
/**
* @file kernel_pca_test.cpp
* @author Saksham Bansal
*
* Test mlpackMain() of kernel_pca_main.cpp.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#include <mlpack/core.hpp>
#define BINDING_TYPE BINDING_TYPE_TEST
static const std::string testName = "KernelPrincipalComponentsAnalysis";
#include <mlpack/core/util/mlpack_main.hpp>
#include "test_helper.hpp"
#include <mlpack/methods/kernel_pca/kernel_pca_main.cpp>
#include <boost/test/unit_test.hpp>
#include "../test_tools.hpp"
using namespace mlpack;
struct KernelPCATestFixture
{
public:
KernelPCATestFixture()
{
// Cache in the options for this program.
CLI::RestoreSettings(testName);
}
~KernelPCATestFixture()
{
// Clear the settings.
bindings::tests::CleanMemory();
CLI::ClearSettings();
}
};
static void ResetSettings()
{
bindings::tests::CleanMemory();
CLI::ClearSettings();
CLI::RestoreSettings(testName);
}
BOOST_FIXTURE_TEST_SUITE(KernelPCAMainTest, KernelPCATestFixture);
/**
* Make sure that all valid kernels return correct output dimension.
*/
BOOST_AUTO_TEST_CASE(KernelPCADimensionTest)
{
std::string kernels[] = {
"linear", "gaussian", "polynomial",
"hyptan", "laplacian", "epanechnikov", "cosine"
};
for (std::string& kernel: kernels)
{
ResetSettings();
arma::mat x = arma::randu<arma::mat>(5, 5);
// Random input, new dimensionality of 3.
SetInputParam("input", std::move(x));
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", kernel);
mlpackMain();
// Now check that the output has 3 dimensions.
BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("output").n_rows, 3);
BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("output").n_cols, 5);
}
}
/**
* Check that error is thrown when no kernel is specified.
*/
BOOST_AUTO_TEST_CASE(KernelPCANoKernelTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", std::move(x));
SetInputParam("new_dimensionality", (int) 3);
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Check that error is thrown when an invalid kernel is specified.
*/
BOOST_AUTO_TEST_CASE(KernelPCAInvalidKernelTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", std::move(x));
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", (std::string) "badName");
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Make sure if 0 dimensions is specified, we get a dataset with same
* dimensionality as input.
*/
BOOST_AUTO_TEST_CASE(KernelPCA0DimensionalityTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", std::move(x));
SetInputParam("new_dimensionality", (int) 0);
SetInputParam("kernel", (std::string) "gaussian");
mlpackMain();
// Now check that the output has same dimensions as input.
BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("output").n_rows, 5);
BOOST_REQUIRE_EQUAL(CLI::GetParam<arma::mat>("output").n_cols, 5);
}
/**
* Make sure that centering the dataset makes a difference.
*/
BOOST_AUTO_TEST_CASE(KernelPCACenterTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
// Get output without centering the dataset.
SetInputParam("input", x);
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", (std::string) "linear");
mlpackMain();
arma::mat output1 = CLI::GetParam<arma::mat>("output");
// Get output after centering the dataset.
SetInputParam("input", std::move(x));
SetInputParam("center", true);
mlpackMain();
arma::mat output2 = CLI::GetParam<arma::mat>("output");
// The resulting matrices should be different.
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output2)));
}
/**
* Check that we can't specify an invalid new dimensionality.
*/
BOOST_AUTO_TEST_CASE(KernelPCATooHighNewDimensionalityTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", std::move(x));
SetInputParam("new_dimensionality", (int) 7); // Invalid.
SetInputParam("kernel", (std::string) "linear");
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Check that error is thrown when no input is specified.
*/
BOOST_AUTO_TEST_CASE(KernelPCANoInputTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("new_dimensionality", (int) 2);
SetInputParam("kernel", (std::string) "linear");
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Check that error is thrown if invalid sampling scheme is specified.
*/
BOOST_AUTO_TEST_CASE(KernelPCABadSamplingTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", std::move(x));
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", (std::string) "linear");
SetInputParam("nystroem_method", true);
SetInputParam("sampling", (std::string) "badName");
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Test that bandwidth effects the result for gaussian, epanechnikov
* and laplacian kernels.
*/
BOOST_AUTO_TEST_CASE(KernelPCABandWidthTest)
{
std::string kernels[] = {
"gaussian", "epanechnikov", "laplacian"
};
for (std::string& kernel: kernels)
{
ResetSettings();
arma::mat x = arma::randu<arma::mat>(5, 5);
// Get output using bandwidth 1.
SetInputParam("input", x);
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", kernel);
SetInputParam("bandwidth", (double) 1);
mlpackMain();
arma::mat output1 = CLI::GetParam<arma::mat>("output");
// Get output using bandwidth 2.
SetInputParam("input", std::move(x));
SetInputParam("bandwidth", (double) 2);
mlpackMain();
arma::mat output2 = CLI::GetParam<arma::mat>("output");
// The resulting matrices should be different.
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output2)));
}
}
/**
* Test that offset effects the result for polynomial and hyptan kernels.
*/
BOOST_AUTO_TEST_CASE(KernelPCAOffsetTest)
{
std::string kernels[] = {
"polynomial", "hyptan"
};
for (std::string& kernel: kernels)
{
ResetSettings();
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", x);
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", kernel);
SetInputParam("offset", (double) 1);
mlpackMain();
arma::mat output1 = CLI::GetParam<arma::mat>("output");
SetInputParam("input", std::move(x));
SetInputParam("offset", (double) 2);
mlpackMain();
arma::mat output2 = CLI::GetParam<arma::mat>("output");
// The resulting matrices should be different.
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output2)));
}
}
/**
* Test that degree effects the result for polynomial kernel.
*/
BOOST_AUTO_TEST_CASE(KernelPCADegreeTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", x);
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", (std::string) "polynomial");
SetInputParam("degree", (double) 2);
mlpackMain();
arma::mat output1 = CLI::GetParam<arma::mat>("output");
SetInputParam("input", std::move(x));
SetInputParam("degree", (double) 3);
mlpackMain();
arma::mat output2 = CLI::GetParam<arma::mat>("output");
// The resulting matrices should be different.
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output2)));
}
/**
* Test that kernel scale effects the result for hyptan kernel.
*/
BOOST_AUTO_TEST_CASE(KernelPCAKernelScaleTest)
{
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", x);
SetInputParam("new_dimensionality", (int) 3);
SetInputParam("kernel", (std::string) "hyptan");
SetInputParam("kernel_scale", (double) 2);
mlpackMain();
arma::mat output1 = CLI::GetParam<arma::mat>("output");
SetInputParam("input", std::move(x));
SetInputParam("kernel_scale", (double) 3);
mlpackMain();
arma::mat output2 = CLI::GetParam<arma::mat>("output");
// The resulting matrices should be different.
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output2)));
}
/**
* Test that using a sampling scheme with nystroem method makes a difference.
*/
BOOST_AUTO_TEST_CASE(KernelPCASamplingSchemeTest)
{
ResetSettings();
arma::mat x = arma::randu<arma::mat>(5, 5);
SetInputParam("input", x);
SetInputParam("new_dimensionality", (int) 1);
SetInputParam("kernel", (std::string) "gaussian");
SetInputParam("nystroem_method", true);
SetInputParam("sampling", (std::string) "kmeans");
mlpackMain();
arma::mat output1 = CLI::GetParam<arma::mat>("output");
SetInputParam("input", x);
SetInputParam("sampling", (std::string) "random");
mlpackMain();
arma::mat output2 = CLI::GetParam<arma::mat>("output");
SetInputParam("input", x);
SetInputParam("sampling", (std::string) "ordered");
mlpackMain();
arma::mat output3 = CLI::GetParam<arma::mat>("output");
// The resulting matrices should be different.
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output2)));
BOOST_REQUIRE(arma::any(arma::vectorise(output2 != output3)));
BOOST_REQUIRE(arma::any(arma::vectorise(output1 != output3)));
}
BOOST_AUTO_TEST_SUITE_END();