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Begin refactor CMakeLists debug/release paths

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This commit is contained in:
Ricardo Montañana Gómez
2023-10-06 19:32:29 +02:00
parent 17e079edd5
commit 2f58807322
5 changed files with 87 additions and 64 deletions
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15
tests/CMakeLists.txt
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@@ -1,12 +1,17 @@
if(ENABLE_TESTING)
set(TEST_MAIN "unit_tests")
set(TEST_BAYESNET "unit_tests_bayesnet")
set(TEST_PLATFORM "unit_tests_platform")
include_directories(${BayesNet_SOURCE_DIR}/src/BayesNet)
include_directories(${BayesNet_SOURCE_DIR}/src/Platform)
include_directories(${BayesNet_SOURCE_DIR}/lib/Files)
include_directories(${BayesNet_SOURCE_DIR}/lib/mdlp)
include_directories(${BayesNet_SOURCE_DIR}/lib/json/include)
set(TEST_SOURCES TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestFolding.cc TestUtils.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCES})
add_executable(${TEST_MAIN} ${TEST_SOURCES})
target_link_libraries(${TEST_MAIN} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
add_test(NAME ${TEST_MAIN} COMMAND ${TEST_MAIN})
set(TEST_SOURCES_BAYESNET TestBayesModels.cc TestBayesNetwork.cc TestBayesMetrics.cc TestUtils.cc ${BayesNet_SOURCE_DIR}/src/Platform/Folding.cc ${BayesNet_SOURCES})
set(TEST_SOURCES_PLATFORM TestFolding.cc TestUtils.cc ${Platform_SOURCES})
add_executable(${TEST_BAYESNET} ${TEST_SOURCES_BAYESNET})
add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
target_link_libraries(${TEST_BAYESNET} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
add_test(NAME ${TEST_BAYESNET} COMMAND ${TEST_BAYESNET})
add_test(NAME ${TEST_PLATFORM} COMMAND ${TEST_PLATFORM})
endif(ENABLE_TESTING)

72
tests/TestFolding.cc
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@@ -4,14 +4,14 @@
#include "TestUtils.h"
#include "Folding.h"
TEST_CASE("KFold Test", "[KFold]")
TEST_CASE("KFold Test", "[Platform][KFold]")
{
// Initialize a KFold object with k=5 and a seed of 19.
string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
auto raw = RawDatasets(file_name, true);
int nFolds = 5;
platform::KFold kfold(nFolds, raw.nSamples, 19);s
int number = raw.nSamples * (kfold.getNumberOfFolds() - 1) / kfold.getNumberOfFolds();
platform::KFold kfold(nFolds, raw.nSamples, 19);
int number = raw.nSamples * (kfold.getNumberOfFolds() - 1) / kfold.getNumberOfFolds();
SECTION("Number of Folds")
{
@@ -38,61 +38,57 @@ map<int, int> counts(vector<int> y, vector<int> indices)
return result;
}
TEST_CASE("StratifiedKFold Test", "[StratifiedKFold]")
TEST_CASE("StratifiedKFold Test", "[Platform][StratifiedKFold]")
{
int nFolds = 3;
// Initialize a StratifiedKFold object with k=3, using the y vector, and a seed of 17.
string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
int nFolds = GENERATE(3, 5, 10);
auto raw = RawDatasets(file_name, true);
platform::StratifiedKFold stratified_kfoldt(nFolds, raw.yt, 17);
platform::StratifiedKFold stratified_kfoldv(nFolds, raw.yv, 17);
int number = raw.nSamples * (stratified_kfold.getNumberOfFolds() - 1) / stratified_kfold.getNumberOfFolds();
int number = raw.nSamples * (stratified_kfoldt.getNumberOfFolds() - 1) / stratified_kfoldt.getNumberOfFolds();
// SECTION("Number of Folds")
// {
// REQUIRE(stratified_kfold.getNumberOfFolds() == nFolds);
// }
SECTION("Fold Test")
SECTION("Stratified Number of Folds")
{
REQUIRE(stratified_kfoldt.getNumberOfFolds() == nFolds);
}
SECTION("Stratified Fold Test")
{
// Test each fold's size and contents.
auto counts = vector<int>(raw.classNumStates, 0);
auto counts = map<int, vector<int>>();
// Initialize the counts per Fold
for (int i = 0; i < nFolds; ++i) {
auto [train_indicest, test_indicest] = stratified_kfoldt.getFold(i);
auto [train_indicesv, test_indicesv] = stratified_kfoldv.getFold(i);
counts[i] = vector<int>(raw.classNumStates, 0);
}
// Check fold and compute counts of each fold
for (int fold = 0; fold < nFolds; ++fold) {
auto [train_indicest, test_indicest] = stratified_kfoldt.getFold(fold);
auto [train_indicesv, test_indicesv] = stratified_kfoldv.getFold(fold);
REQUIRE(train_indicest == train_indicesv);
REQUIRE(test_indicest == test_indicesv);
bool result = train_indices.size() == number || train_indices.size() == number + 1;
bool result = train_indicest.size() == number || train_indicest.size() == number + 1;
REQUIRE(result);
REQUIRE(train_indices.size() + test_indices.size() == raw.nSamples);
auto train_t = torch::tensor(train_indices);
REQUIRE(train_indicest.size() + test_indicest.size() == raw.nSamples);
auto train_t = torch::tensor(train_indicest);
auto ytrain = raw.yt.index({ train_t });
cout << "dataset=" << file_name << endl;
cout << "nSamples=" << raw.nSamples << endl;;
cout << "number=" << number << endl;
cout << "train_indices.size()=" << train_indices.size() << endl;
cout << "test_indices.size()=" << test_indices.size() << endl;
cout << "train_indices.size()=" << train_indicest.size() << endl;
cout << "test_indices.size()=" << test_indicest.size() << endl;
cout << "Class Name = " << raw.classNamet << endl;
cout << "Features = ";
for (const auto& item : raw.featurest) {
cout << item << ", ";
}
cout << endl;
cout << "Class States: ";
for (const auto& item : raw.statest.at(raw.classNamet)) {
cout << item << ", ";
}
cout << endl;
// Check that the class labels have been equally assign to each fold
for (const auto& idx : train_indices) {
counts[ytrain[idx].item<int>()]++;
for (const auto& idx : train_indicest) {
counts[fold][ytrain[idx].item<int>()]++;
}
}
// Test the fold counting of every class
for (int fold = 0; fold < nFolds; ++fold) {
for (int j = 1; j < nFolds - 1; ++j) {
for (int k = 0; k < raw.classNumStates; ++k) {
REQUIRE(abs(counts.at(fold).at(k) - counts.at(fold).at(j)) <= 1);
}
}
int j = 0;
for (const auto& item : counts) {
cout << "j=" << j++ << item << endl;
}
}
REQUIRE(1 == 1);
}
}