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Remove catch2 as submodule

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Add link to pdf coverage report
This commit is contained in:
Ricardo Montañana Gómez
2024-04-30 11:02:23 +02:00
parent 793b2d3cd5
commit 23ef0cc5f7
43 changed files with 565 additions and 285 deletions
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43
tests/TestBoostAODE.cc
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@@ -140,21 +140,20 @@ TEST_CASE("Oddities", "[BoostAODE]")
TEST_CASE("Bisection Best", "[BoostAODE]")
{
auto clf = bayesnet::BoostAODE();
auto raw = RawDatasets("mfeat-factors", true, 300, true);
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
clf.setHyperparameters({
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"block_update", false},
{"convergence_best", true},
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 434);
REQUIRE(clf.getNumberOfEdges() == 862);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Used features in train: 16 of 216");
REQUIRE(clf.getNotes()[2] == "Number of models: 1");
REQUIRE(clf.getNumberOfNodes() == 75);
REQUIRE(clf.getNumberOfEdges() == 135);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 9 models eliminated");
REQUIRE(clf.getNotes().at(1) == "Number of models: 5");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
@@ -162,11 +161,9 @@ TEST_CASE("Bisection Best", "[BoostAODE]")
}
TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
{
auto raw = RawDatasets("mfeat-factors", true, 500);
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
auto clf = bayesnet::BoostAODE(true);
auto hyperparameters = nlohmann::json{
{"select_features", "IWSS"},
{"threshold", 0.5},
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
@@ -175,13 +172,13 @@ TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
auto score_best = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_best == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(score_best == Catch::Approx(0.993355f).epsilon(raw.epsilon));
// Now we will set the hyperparameter to use the last accuracy
hyperparameters["convergence_best"] = false;
clf.setHyperparameters(hyperparameters);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(score_last == Catch::Approx(0.996678f).epsilon(raw.epsilon));
}
TEST_CASE("Block Update", "[BoostAODE]")
@@ -195,14 +192,22 @@ TEST_CASE("Block Update", "[BoostAODE]")
{"convergence", true},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 217);
REQUIRE(clf.getNumberOfEdges() == 431);
REQUIRE(clf.getNumberOfNodes() == 868);
REQUIRE(clf.getNumberOfEdges() == 1724);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Used features in train: 16 of 216");
REQUIRE(clf.getNotes()[2] == "Number of models: 1");
REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
REQUIRE(clf.getNotes()[2] == "Number of models: 4");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(1.0f).epsilon(raw.epsilon));
REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.99f).epsilon(raw.epsilon));
//
// std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
// std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
// std::cout << "Notes size " << clf.getNotes().size() << std::endl;
// for (auto note : clf.getNotes()) {
// std::cout << note << std::endl;
// }
// std::cout << "Score " << score << std::endl;
}