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Merge pull request #2644 from Aakash-kaushik/range_search_test

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Migrate range_search_test from boost to catch2
This commit is contained in:
kartikdutt18
2020-10-10 17:12:48 +05:30
committed by GitHub
parent 378d306efa 76133b4f3f
commit 6b824065e5
4 changed files with 492 additions and 482 deletions
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src/mlpack/tests/CMakeLists.txt
+2 -2
View File
@@ -42,7 +42,6 @@ add_executable(mlpack_test
qdafn_test.cpp
radical_test.cpp
random_test.cpp
range_search_test.cpp
rectangle_tree_test.cpp
reward_clipping_test.cpp
rl_components_test.cpp
@@ -85,7 +84,6 @@ add_executable(mlpack_test
main_tests/nmf_test.cpp
main_tests/perceptron_test.cpp
main_tests/radical_test.cpp
main_tests/range_search_test.cpp
main_tests/test_helper.hpp
)
@@ -139,6 +137,7 @@ add_executable(mlpack_catch_test
quic_svd_test.cpp
random_forest_test.cpp
randomized_svd_test.cpp
range_search_test.cpp
rbm_network_test.cpp
recurrent_network_test.cpp
regularized_svd_test.cpp
@@ -179,6 +178,7 @@ add_executable(mlpack_catch_test
main_tests/random_forest_test.cpp
main_tests/softmax_regression_test.cpp
main_tests/sparse_coding_test.cpp
main_tests/range_search_test.cpp
main_tests/test_helper.hpp
)
src/mlpack/tests/main_tests/range_search_test.cpp
+61 -52
View File
@@ -17,7 +17,7 @@ static const std::string testName = "RangeSearchMain";
#include "test_helper.hpp"
#include <mlpack/methods/range_search/range_search_main.cpp>
#include "range_search_utils.hpp"
#include <boost/test/unit_test.hpp>
#include "../catch.hpp"
using namespace mlpack;
@@ -37,34 +37,35 @@ struct RangeSearchTestFixture
}
};
BOOST_FIXTURE_TEST_SUITE(RangeSearchMainTest, RangeSearchTestFixture);
/**
* Check that we have to specify a reference set or input model.
*/
BOOST_AUTO_TEST_CASE(RangeSearchNoReference)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSearchNoReference",
"[RangeSearchMainTest][BindingTests]")
{
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
REQUIRE_THROWS_AS(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Check that we cannot pass an incorrect parameter.
*/
BOOST_AUTO_TEST_CASE(RangeSearchWrongParameter)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSearchWrongParameter",
"[RangeSearchMainTest][BindingTests]")
{
string wrongString = "abc";
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(SetInputParam("RST", wrongString), std::runtime_error);
REQUIRE_THROWS_AS(SetInputParam("RST", wrongString), std::runtime_error);
Log::Fatal.ignoreInput = false;
}
/**
* Check that we have to specify a query if an input model is specified.
*/
BOOST_AUTO_TEST_CASE(RangeSearchInputModelNoQuery)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSearchInputModelNoQuery",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat inputData;
double minVal = 0, maxVal = 3;
@@ -72,7 +73,7 @@ BOOST_AUTO_TEST_CASE(RangeSearchInputModelNoQuery)
string neighborsFile = "neighbors.csv";
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
SetInputParam("reference", move(inputData));
SetInputParam("min", minVal);
@@ -86,7 +87,7 @@ BOOST_AUTO_TEST_CASE(RangeSearchInputModelNoQuery)
SetInputParam("input_model", move(IO::GetParam<RSModel*>("output_model")));
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
REQUIRE_THROWS_AS(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
remove(neighborsFile.c_str());
@@ -96,7 +97,8 @@ BOOST_AUTO_TEST_CASE(RangeSearchInputModelNoQuery)
/**
* Check that we cannot specify a tree type which is not available or wrong.
*/
BOOST_AUTO_TEST_CASE(RangeSearchDifferentTree)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSearchDifferentTree",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat inputData;
double minVal = 0, maxVal = 3;
@@ -104,7 +106,7 @@ BOOST_AUTO_TEST_CASE(RangeSearchDifferentTree)
string neighborsFile = "neighbors.csv";
string wrongTreeType = "RST";
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
SetInputParam("reference", move(inputData));
SetInputParam("min", minVal);
@@ -114,7 +116,7 @@ BOOST_AUTO_TEST_CASE(RangeSearchDifferentTree)
SetInputParam("tree_type", wrongTreeType);
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
REQUIRE_THROWS_AS(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
remove(neighborsFile.c_str());
@@ -124,7 +126,8 @@ BOOST_AUTO_TEST_CASE(RangeSearchDifferentTree)
/**
* Check that we cannot specify both a reference set and input model.
*/
BOOST_AUTO_TEST_CASE(RangeSearchBothReferenceAndModel)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSearchBothReferenceAndModel",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat inputData, queryData;
double minVal = 0, maxVal = 3;
@@ -132,9 +135,9 @@ BOOST_AUTO_TEST_CASE(RangeSearchBothReferenceAndModel)
string neighborsFile = "neighbors.csv";
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
SetInputParam("reference", move(inputData));
SetInputParam("min", minVal);
@@ -149,7 +152,7 @@ BOOST_AUTO_TEST_CASE(RangeSearchBothReferenceAndModel)
SetInputParam("query", move(queryData));
Log::Fatal.ignoreInput = true;
BOOST_REQUIRE_THROW(mlpackMain(), std::runtime_error);
REQUIRE_THROWS_AS(mlpackMain(), std::runtime_error);
Log::Fatal.ignoreInput = false;
remove(neighborsFile.c_str());
@@ -161,7 +164,8 @@ BOOST_AUTO_TEST_CASE(RangeSearchBothReferenceAndModel)
* by comparing with pre-calculated neighbor and distance values, when no query
* set is specified.
*/
BOOST_AUTO_TEST_CASE(RangeSearchTest)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSearchTest",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat x = {{0, 3, 3, 4, 3, 1},
{4, 4, 4, 5, 5, 2},
@@ -208,7 +212,8 @@ BOOST_AUTO_TEST_CASE(RangeSearchTest)
* Check that the correct output is returned for a small synthetic input case,
* when a query set is provided.
*/
BOOST_AUTO_TEST_CASE(RangeSeachTestwithQuery)
TEST_CASE_METHOD(RangeSearchTestFixture, "RangeSeachTestwithQuery",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat queryData = {{5, 3, 1}, {4, 2, 4}, {3, 1, 7}};
arma::mat x = {{0, 3, 3, 4, 3, 1},
@@ -252,7 +257,8 @@ BOOST_AUTO_TEST_CASE(RangeSeachTestwithQuery)
* Train a model using a synthetic dataset and then output the model, and ensure
* it can be used again.
*/
BOOST_AUTO_TEST_CASE(ModelCheck)
TEST_CASE_METHOD(RangeSearchTestFixture, "ModelCheck",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat inputData, queryData;
double minVal = 0, maxVal = 3;
@@ -262,9 +268,9 @@ BOOST_AUTO_TEST_CASE(ModelCheck)
vector<vector<double>> distances, distancetemp;
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
SetInputParam("reference", move(inputData));
SetInputParam("min", minVal);
@@ -292,8 +298,8 @@ BOOST_AUTO_TEST_CASE(ModelCheck)
CheckMatrices(neighbors, neighborsTemp);
CheckMatrices(distances, distancetemp);
BOOST_REQUIRE_EQUAL(ModelToString(outputModel),
ModelToString(IO::GetParam<RSModel*>("output_model")));
REQUIRE(ModelToString(outputModel) ==
ModelToString(IO::GetParam<RSModel*>("output_model")));
remove(neighborsFile.c_str());
remove(distanceFile.c_str());
@@ -303,11 +309,12 @@ BOOST_AUTO_TEST_CASE(ModelCheck)
* Check that the models are different but the results are the same for three
* different leaf size parameters.
*/
BOOST_AUTO_TEST_CASE(LeafValueTesting)
TEST_CASE_METHOD(RangeSearchTestFixture, "LeafValueTesting",
"[RangeSearchMainTest][BindingTests]")
{
arma::mat inputData;
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
string distanceFile = "distances.csv";
string neighborsFile = "neighbors.csv";
@@ -349,8 +356,8 @@ BOOST_AUTO_TEST_CASE(LeafValueTesting)
CheckMatrices(neighbors, neighborsTemp);
CheckMatrices(distances, distancestemp);
BOOST_REQUIRE_NE(ModelToString(outputModel1),
ModelToString(IO::GetParam<RSModel*>("output_model")));
REQUIRE(ModelToString(outputModel1) !=
ModelToString(IO::GetParam<RSModel*>("output_model")));
if (i != leafSizes.size() - 1)
delete IO::GetParam<RSModel*>("output_model");
@@ -367,7 +374,8 @@ BOOST_AUTO_TEST_CASE(LeafValueTesting)
* different tree types. We use the default kd-tree as the base model to
* compare against.
*/
BOOST_AUTO_TEST_CASE(TreeTypeTesting)
TEST_CASE_METHOD(RangeSearchTestFixture, "TreeTypeTesting",
"[RangeSearchMainTest][BindingTests]")
{
string distanceFile = "distances.csv";
string neighborsFile = "neighbors.csv";
@@ -381,9 +389,9 @@ BOOST_AUTO_TEST_CASE(TreeTypeTesting)
"max-rp", "ub", "oct"};
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
// Define base parameters with the kd-tree.
SetInputParam("tree_type", trees[0]);
@@ -403,9 +411,9 @@ BOOST_AUTO_TEST_CASE(TreeTypeTesting)
for (size_t i = 1; i < trees.size(); ++i)
{
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
SetInputParam("min", minVal);
SetInputParam("max", maxVal);
@@ -422,8 +430,8 @@ BOOST_AUTO_TEST_CASE(TreeTypeTesting)
CheckMatrices(neighbors, neighborsTemp);
CheckMatrices(distances, distancestemp);
BOOST_REQUIRE_NE(ModelToString(outputModel1),
ModelToString(IO::GetParam<RSModel*>("output_model")));
REQUIRE(ModelToString(outputModel1) !=
ModelToString(IO::GetParam<RSModel*>("output_model")));
if (i != trees.size() - 1)
delete IO::GetParam<RSModel*>("output_model");
@@ -439,7 +447,8 @@ BOOST_AUTO_TEST_CASE(TreeTypeTesting)
* Project the data onto a random basis and ensure that this gives identical
* results to non-projected data but different models.
*/
BOOST_AUTO_TEST_CASE(RandomBasisTesting)
TEST_CASE_METHOD(RangeSearchTestFixture, "RandomBasisTesting",
"[RangeSearchMainTest][BindingTests]")
{
string distanceFile = "distances.csv";
string neighborsFile = "neighbors.csv";
@@ -447,9 +456,9 @@ BOOST_AUTO_TEST_CASE(RandomBasisTesting)
arma::mat queryData, inputData;
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
SetInputParam("min", minVal);
SetInputParam("max", maxVal);
@@ -470,8 +479,8 @@ BOOST_AUTO_TEST_CASE(RandomBasisTesting)
mlpackMain();
BOOST_REQUIRE_NE(ModelToString(outputModel),
ModelToString(IO::GetParam<RSModel*>("output_model")));
REQUIRE(ModelToString(outputModel) !=
ModelToString(IO::GetParam<RSModel*>("output_model")));
delete outputModel;
@@ -482,7 +491,8 @@ BOOST_AUTO_TEST_CASE(RandomBasisTesting)
/**
* Ensure that naive mode gives the same result, but different models.
*/
BOOST_AUTO_TEST_CASE(NaiveModeTest)
TEST_CASE_METHOD(RangeSearchTestFixture, "NaiveModeTest",
"[RangeSearchMainTest][BindingTests]")
{
string distanceFile = "distances.csv";
string neighborsFile = "neighbors.csv";
@@ -493,9 +503,9 @@ BOOST_AUTO_TEST_CASE(NaiveModeTest)
vector<vector<double>> distances, distancestemp;
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
SetInputParam("min", minVal);
SetInputParam("max", maxVal);
@@ -524,8 +534,8 @@ BOOST_AUTO_TEST_CASE(NaiveModeTest)
CheckMatrices(neighbors, neighborsTemp);
CheckMatrices(distances, distancestemp);
BOOST_REQUIRE_NE(ModelToString(outputModel),
ModelToString(IO::GetParam<RSModel*>("output_model")));
REQUIRE(ModelToString(outputModel) !=
ModelToString(IO::GetParam<RSModel*>("output_model")));
delete outputModel;
@@ -536,7 +546,8 @@ BOOST_AUTO_TEST_CASE(NaiveModeTest)
/**
* Ensure that single-tree mode gives the same result but different models.
*/
BOOST_AUTO_TEST_CASE(SingleModeTest)
TEST_CASE_METHOD(RangeSearchTestFixture, "SingleModeTest",
"[RangeSearchMainTest][BindingTests]")
{
string distanceFile = "distances.csv";
string neighborsFile = "neighbors.csv";
@@ -547,9 +558,9 @@ BOOST_AUTO_TEST_CASE(SingleModeTest)
vector<vector<double>> distances, distancestemp;
if (!data::Load("iris.csv", inputData))
BOOST_FAIL("Unable to load dataset iris.csv!");
FAIL("Unable to load dataset iris.csv!");
if (!data::Load("iris_test.csv", queryData))
BOOST_FAIL("Unable to load dataset iris_test.csv!");
FAIL("Unable to load dataset iris_test.csv!");
SetInputParam("min", minVal);
SetInputParam("max", maxVal);
@@ -577,13 +588,11 @@ BOOST_AUTO_TEST_CASE(SingleModeTest)
CheckMatrices(neighbors, neighborsTemp);
CheckMatrices(distances, distancestemp);
BOOST_REQUIRE_NE(ModelToString(outputModel),
ModelToString(IO::GetParam<RSModel*>("output_model")));
REQUIRE(ModelToString(outputModel) !=
ModelToString(IO::GetParam<RSModel*>("output_model")));
delete outputModel;
remove(neighborsFile.c_str());
remove(distanceFile.c_str());
}
BOOST_AUTO_TEST_SUITE_END();
src/mlpack/tests/main_tests/range_search_utils.hpp
+7 -7
View File
@@ -12,10 +12,10 @@
#ifndef MLPACK_TESTS_MAIN_TESTS_RANGE_SEARCH_TEST_UTILS_HPP
#define MLPACK_TESTS_MAIN_TESTS_RANGE_SEARCH_TEST_UTILS_HPP
#include <boost/test/unit_test.hpp>
#include <mlpack/methods/range_search/rs_model.hpp>
#include <mlpack/core.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include "../catch.hpp"
/**
* Convert a model to a string using the text_oarchive of boost::serialization.
@@ -42,15 +42,15 @@ inline void CheckMatrices(std::vector<std::vector<double>>& vec1,
std::vector<std::vector<double>>& vec2,
const double tolerance = 1e-3)
{
BOOST_REQUIRE_EQUAL(vec1.size() , vec2.size());
REQUIRE(vec1.size() == vec2.size());
for (size_t i = 0; i < vec1.size(); ++i)
{
BOOST_REQUIRE_EQUAL(vec1[i].size(), vec2[i].size());
REQUIRE(vec1[i].size() == vec2[i].size());
std::sort(vec1[i].begin(), vec1[i].end());
std::sort(vec2[i].begin(), vec2[i].end());
for (size_t j = 0 ; j < vec1[i].size(); ++j)
{
BOOST_REQUIRE_CLOSE(vec1[i][j], vec2[i][j], tolerance);
REQUIRE(vec1[i][j] == Approx(vec2[i][j]).epsilon(tolerance));
}
}
}
@@ -64,15 +64,15 @@ inline void CheckMatrices(std::vector<std::vector<double>>& vec1,
inline void CheckMatrices(std::vector<std::vector<size_t>>& vec1,
std::vector<std::vector<size_t>>& vec2)
{
BOOST_REQUIRE_EQUAL(vec1.size() , vec2.size());
REQUIRE(vec1.size() == vec2.size());
for (size_t i = 0; i < vec1.size(); ++i)
{
BOOST_REQUIRE_EQUAL(vec1[i].size(), vec2[i].size());
REQUIRE(vec1[i].size() == vec2[i].size());
std::sort(vec1[i].begin(), vec1[i].end());
std::sort(vec2[i].begin(), vec2[i].end());
for (size_t j = 0; j < vec1[i].size(); ++j)
{
BOOST_REQUIRE_EQUAL(vec1[i][j], vec2[i][j]);
REQUIRE(vec1[i][j] == vec2[i][j]);
}
}
}
src/mlpack/tests/range_search_test.cpp
+422 -421
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