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Refactor hyperparameters classifier management

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This commit is contained in:
Ricardo Montañana Gómez
2024-04-08 00:55:30 +02:00
parent 0d6a081d01
commit 9014649a0d
8 changed files with 43 additions and 15 deletions
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3
tests/CMakeLists.txt
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@@ -8,12 +8,13 @@ if(ENABLE_TESTING)
${CMAKE_BINARY_DIR}/configured_files/include
)
file(GLOB_RECURSE BayesNet_SOURCES "${BayesNet_SOURCE_DIR}/bayesnet/*.cc")
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestUtils.cc ${BayesNet_SOURCES})
add_executable(TestBayesNet TestBayesNetwork.cc TestBayesNode.cc TestBayesClassifier.cc TestBayesModels.cc TestBayesMetrics.cc TestFeatureSelection.cc TestUtils.cc ${BayesNet_SOURCES})
target_link_libraries(TestBayesNet PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain )
add_test(NAME BayesNetworkTest COMMAND TestBayesNet)
add_test(NAME Network COMMAND TestBayesNet "[Network]")
add_test(NAME Node COMMAND TestBayesNet "[Node]")
add_test(NAME Metrics COMMAND TestBayesNet "[Metrics]")
add_test(NAME FeatureSelection COMMAND TestBayesNet "[FeatureSelection]")
add_test(NAME Classifier COMMAND TestBayesNet "[Classifier]")
add_test(NAME Models COMMAND TestBayesNet "[Models]")
endif(ENABLE_TESTING)

23
tests/TestBayesClassifier.cc Normal file
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@@ -0,0 +1,23 @@
#include <catch2/catch_test_macros.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include <string>
#include "TestUtils.h"
#include "bayesnet/classifiers/TAN.h"
TEST_CASE("Test Cannot build dataset with wrong data vector", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
raw.yv.pop_back();
REQUIRE_THROWS_AS(model.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), std::runtime_error);
REQUIRE_THROWS_WITH(model.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
}
TEST_CASE("Test Cannot build dataset with wrong data tensor", "[Classifier]")
{
auto model = bayesnet::TAN();
auto raw = RawDatasets("iris", true);
auto yshort = torch::zeros({ 149 }, torch::kInt32);
REQUIRE_THROWS_AS(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
}