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Add some tests and code quality badge

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This commit is contained in:
Ricardo Montañana Gómez
2024-04-07 02:08:37 +02:00
parent df45fddd45
commit cb26ef2562
7 changed files with 57 additions and 26 deletions
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48
tests/TestBayesModels.cc
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@@ -207,7 +207,7 @@ TEST_CASE("Model predict_proba", "[Models]")
}
TEST_CASE("BoostAODE voting-proba", "[Models]")
{
auto raw = RawDatasets("iris", false);
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostAODE(false);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score_proba = clf.score(raw.Xv, raw.yv);
@@ -224,9 +224,53 @@ TEST_CASE("BoostAODE voting-proba", "[Models]")
clf.dump_cpt();
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("AODE voting-proba", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::AODE(false);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score_proba = clf.score(raw.Xv, raw.yv);
auto pred_proba = clf.predict_proba(raw.Xv);
clf.setHyperparameters({
{"predict_voting",true},
});
auto score_voting = clf.score(raw.Xv, raw.yv);
auto pred_voting = clf.predict_proba(raw.Xv);
REQUIRE(score_proba == Catch::Approx(0.79439f).epsilon(raw.epsilon));
REQUIRE(score_voting == Catch::Approx(0.78972f).epsilon(raw.epsilon));
REQUIRE(pred_voting[67][0] == Catch::Approx(0.888889).epsilon(raw.epsilon));
REQUIRE(pred_proba[67][0] == Catch::Approx(0.702184).epsilon(raw.epsilon));
REQUIRE(clf.topological_order() == std::vector<std::string>());
}
TEST_CASE("SPODELd dataset", "[Models]")
{
auto raw = RawDatasets("iris", false);
auto clf = bayesnet::SPODELd(0);
raw.dataset.to(torch::kFloat32);
clf.fit(raw.dataset, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xt, raw.yt);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.97333f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.97333f).epsilon(raw.epsilon));
}
TEST_CASE("KDB with hyperparameters", "[Models]")
{
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::KDB(2);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto score = clf.score(raw.Xv, raw.yv);
clf.setHyperparameters({
{"k", 3},
{"theta", 0.7},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
auto scoret = clf.score(raw.Xv, raw.yv);
REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
}
TEST_CASE("BoostAODE order asc, desc & random", "[Models]")
{
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{
{"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }