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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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22
tests/TestBayesModels.cc
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@@ -54,7 +54,7 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
auto clf = models[name];
auto discretize = name.substr(name.length() - 2) != "Ld";
auto raw = RawDatasets(file_name, discretize);
clf->fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
auto score = clf->score(raw.Xt, raw.yt);
INFO("Classifier: " + name + " File: " + file_name);
REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
@@ -81,7 +81,7 @@ TEST_CASE("Models features & Graph", "[Models]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::TAN();
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() == 5);
REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.getNumberOfStates() == 19);
@@ -93,7 +93,7 @@ TEST_CASE("Models features & Graph", "[Models]")
{
auto clf = bayesnet::TANLd();
auto raw = RawDatasets("iris", false);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.getNumberOfStates() == 19);
@@ -106,7 +106,7 @@ TEST_CASE("Get num features & num edges", "[Models]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::KDB(2);
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() == 5);
REQUIRE(clf.getNumberOfEdges() == 8);
}
@@ -166,7 +166,7 @@ TEST_CASE("Model predict_proba", "[Models]")
SECTION("Test " + model + " predict_proba")
{
auto clf = models[model];
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 y_pred_proba = clf->predict_proba(raw.Xv);
auto yt_pred_proba = clf->predict_proba(raw.Xt);
auto y_pred = clf->predict(raw.Xv);
@@ -203,7 +203,7 @@ 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);
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({
@@ -222,9 +222,9 @@ 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);
clf.fit(raw.dataset, raw.features, raw.className, raw.states);
auto score = clf.score(raw.Xt, raw.yt);
clf.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states);
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));
@@ -233,13 +233,13 @@ 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);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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);
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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));
@@ -248,7 +248,7 @@ TEST_CASE("Incorrect type of data for SPODELd", "[Models]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::SPODELd(0);
REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.features, raw.className, raw.states), std::runtime_error);
}
TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
{