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Add hyperparameter convergence_best

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move test libraries to test folder
This commit is contained in:
Ricardo Montañana Gómez
2024-04-30 00:52:09 +02:00
parent f014928411
commit ae469b8146
721 changed files with 206095 additions and 2496 deletions
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56
tests/TestBoostAODE.cc
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@@ -17,7 +17,7 @@ TEST_CASE("Feature_select CFS", "[BoostAODE]")
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "CFS"} });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
@@ -29,7 +29,7 @@ TEST_CASE("Feature_select IWSS", "[BoostAODE]")
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
@@ -41,7 +41,7 @@ TEST_CASE("Feature_select FCBF", "[BoostAODE]")
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::BoostAODE();
clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 90);
REQUIRE(clf.getNumberOfEdges() == 153);
REQUIRE(clf.getNotes().size() == 2);
@@ -57,7 +57,7 @@ TEST_CASE("Test used features in train note and score", "[BoostAODE]")
{"convergence", true},
{"select_features","CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 72);
REQUIRE(clf.getNumberOfEdges() == 120);
REQUIRE(clf.getNotes().size() == 2);
@@ -72,7 +72,7 @@ TEST_CASE("Voting vs proba", "[BoostAODE]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::BoostAODE(false);
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
auto score_proba = clf.score(raw.Xv, raw.yv);
auto pred_proba = clf.predict_proba(raw.Xv);
clf.setHyperparameters({
@@ -101,7 +101,7 @@ TEST_CASE("Order asc, desc & random", "[BoostAODE]")
{"maxTolerance", 1},
{"convergence", false},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("BoostAODE order: " + order);
@@ -133,52 +133,76 @@ TEST_CASE("Oddities", "[BoostAODE]")
for (const auto& hyper : bad_hyper_fit.items()) {
INFO("BoostAODE hyper: " + hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv), std::invalid_argument);
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states), std::invalid_argument);
}
}
TEST_CASE("Bisection", "[BoostAODE]")
TEST_CASE("Bisection Best", "[BoostAODE]")
{
auto clf = bayesnet::BoostAODE();
auto raw = RawDatasets("mfeat-factors", true);
auto raw = RawDatasets("mfeat-factors", true, 500);
clf.setHyperparameters({
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"block_update", false},
{"convergence_best", true},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 217);
REQUIRE(clf.getNumberOfEdges() == 431);
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");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
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));
}
TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
{
auto raw = RawDatasets("mfeat-factors", true, 1500);
auto clf = bayesnet::BoostAODE(true);
auto hyperparameters = nlohmann::json{
{"select_features", "IWSS"},
{"threshold", 0.5},
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"convergence_best", true},
};
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));
// 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));
}
TEST_CASE("Block Update", "[BoostAODE]")
{
auto clf = bayesnet::BoostAODE();
auto raw = RawDatasets("mfeat-factors", true);
auto raw = RawDatasets("mfeat-factors", true, 500);
clf.setHyperparameters({
{"bisection", true},
{"block_update", true},
{"maxTolerance", 3},
{"convergence", true},
});
clf.fit(raw.Xv, raw.yv, raw.featuresv, raw.classNamev, raw.statesv);
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 217);
REQUIRE(clf.getNumberOfEdges() == 431);
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");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
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));
}