rmontanana/BayesNet
1
0
Fork 0
Code Issues 2 Pull Requests 1 Actions Packages Projects Releases 6 Wiki Activity

Complete implementation with tests

Browse Source
This commit is contained in:
Ricardo Montañana Gómez
2025-07-08 11:42:20 +02:00
parent 2c7352ac38
commit ed380b1494
13 changed files with 255 additions and 170 deletions
Download Patch File Download Diff File Expand all files Collapse all files

152
tests/TestBayesModels.cc
View File

@@ -31,9 +31,9 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
{{"diabetes", "SPODE"}, 0.802083},
{{"diabetes", "TAN"}, 0.821615},
{{"diabetes", "AODELd"}, 0.8125f},
{{"diabetes", "KDBLd"}, 0.80208f},
{{"diabetes", "KDBLd"}, 0.804688f},
{{"diabetes", "SPODELd"}, 0.7890625f},
{{"diabetes", "TANLd"}, 0.803385437f},
{{"diabetes", "TANLd"}, 0.8125f},
{{"diabetes", "BoostAODE"}, 0.83984f},
// Ecoli
{{"ecoli", "AODE"}, 0.889881},
@@ -42,9 +42,9 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
{{"ecoli", "SPODE"}, 0.880952},
{{"ecoli", "TAN"}, 0.892857},
{{"ecoli", "AODELd"}, 0.875f},
{{"ecoli", "KDBLd"}, 0.880952358f},
{{"ecoli", "KDBLd"}, 0.872024f},
{{"ecoli", "SPODELd"}, 0.839285731f},
{{"ecoli", "TANLd"}, 0.848214269f},
{{"ecoli", "TANLd"}, 0.869047642f},
{{"ecoli", "BoostAODE"}, 0.89583f},
// Glass
{{"glass", "AODE"}, 0.79439},
@@ -53,9 +53,9 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
{{"glass", "SPODE"}, 0.775701},
{{"glass", "TAN"}, 0.827103},
{{"glass", "AODELd"}, 0.799065411f},
{{"glass", "KDBLd"}, 0.82710278f},
{{"glass", "KDBLd"}, 0.864485979f},
{{"glass", "SPODELd"}, 0.780373812f},
{{"glass", "TANLd"}, 0.869158864f},
{{"glass", "TANLd"}, 0.831775725f},
{{"glass", "BoostAODE"}, 0.84579f},
// Iris
{{"iris", "AODE"}, 0.973333},
@@ -68,29 +68,29 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
{{"iris", "SPODELd"}, 0.96f},
{{"iris", "TANLd"}, 0.97333f},
{{"iris", "BoostAODE"}, 0.98f} };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"AODE", new bayesnet::AODE()},
{"AODELd", new bayesnet::AODELd()},
{"BoostAODE", new bayesnet::BoostAODE()},
{"KDB", new bayesnet::KDB(2)},
{"KDBLd", new bayesnet::KDBLd(2)},
{"XSPODE", new bayesnet::XSpode(1)},
{"SPODE", new bayesnet::SPODE(1)},
{"SPODELd", new bayesnet::SPODELd(1)},
{"TAN", new bayesnet::TAN()},
{"TANLd", new bayesnet::TANLd()} };
std::map<std::string, std::unique_ptr<bayesnet::BaseClassifier>> models;
models["AODE"] = std::make_unique<bayesnet::AODE>();
models["AODELd"] = std::make_unique<bayesnet::AODELd>();
models["BoostAODE"] = std::make_unique<bayesnet::BoostAODE>();
models["KDB"] = std::make_unique<bayesnet::KDB>(2);
models["KDBLd"] = std::make_unique<bayesnet::KDBLd>(2);
models["XSPODE"] = std::make_unique<bayesnet::XSpode>(1);
models["SPODE"] = std::make_unique<bayesnet::SPODE>(1);
models["SPODELd"] = std::make_unique<bayesnet::SPODELd>(1);
models["TAN"] = std::make_unique<bayesnet::TAN>();
models["TANLd"] = std::make_unique<bayesnet::TANLd>();
std::string name = GENERATE("AODE", "AODELd", "KDB", "KDBLd", "SPODE", "XSPODE", "SPODELd", "TAN", "TANLd");
auto clf = models[name];
auto clf = std::move(models[name]);
SECTION("Test " + name + " classifier")
{
for (const std::string& file_name : { "glass", "iris", "ecoli", "diabetes" }) {
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.features, raw.className, raw.states, raw.smoothing);
auto score = clf->score(raw.Xt, raw.yt);
// std::cout << "Classifier: " << name << " File: " << file_name << " Score: " << score << " expected = " <<
// scores[{file_name, name}] << std::endl;
// scores[{file_name, name}] << std::endl;
INFO("Classifier: " << name << " File: " << file_name);
REQUIRE(score == Catch::Approx(scores[{file_name, name}]).epsilon(raw.epsilon));
REQUIRE(clf->getStatus() == bayesnet::NORMAL);
@@ -101,7 +101,6 @@ TEST_CASE("Test Bayesian Classifiers score & version", "[Models]")
INFO("Checking version of " << name << " classifier");
REQUIRE(clf->getVersion() == ACTUAL_VERSION);
}
delete clf;
}
TEST_CASE("Models features & Graph", "[Models]")
{
@@ -133,7 +132,7 @@ TEST_CASE("Models features & Graph", "[Models]")
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 7);
REQUIRE(clf.getNumberOfStates() == 27);
REQUIRE(clf.getNumberOfStates() == 26);
REQUIRE(clf.getClassNumStates() == 3);
REQUIRE(clf.show() == std::vector<std::string>{"class -> sepallength, sepalwidth, petallength, petalwidth, ",
"petallength -> sepallength, ", "petalwidth -> ",
@@ -149,7 +148,6 @@ TEST_CASE("Get num features & num edges", "[Models]")
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 8);
}
TEST_CASE("Model predict_proba", "[Models]")
{
std::string model = GENERATE("TAN", "SPODE", "BoostAODEproba", "BoostAODEvoting", "TANLd", "SPODELd", "KDBLd");
@@ -180,15 +178,15 @@ TEST_CASE("Model predict_proba", "[Models]")
{0.0284828, 0.770524, 0.200993},
{0.0213182, 0.857189, 0.121493},
{0.00868436, 0.949494, 0.0418215} });
auto res_prob_tanld = std::vector<std::vector<double>>({ {0.000544493, 0.995796, 0.00365992 },
{0.000908092, 0.997268, 0.00182429 },
{0.000908092, 0.997268, 0.00182429 },
{0.000908092, 0.997268, 0.00182429 },
{0.00228423, 0.994645, 0.00307078 },
{0.00120539, 0.0666788, 0.932116 },
{0.00361847, 0.979203, 0.017179 },
{0.00483293, 0.985326, 0.00984064 },
{0.000595606, 0.9977, 0.00170441 } });
auto res_prob_tanld = std::vector<std::vector<double>>({ {0.000597557, 0.9957, 0.00370254},
{0.000731377, 0.997914, 0.0013544},
{0.000731377, 0.997914, 0.0013544},
{0.000731377, 0.997914, 0.0013544},
{0.000838614, 0.998122, 0.00103923},
{0.00130852, 0.0659492, 0.932742},
{0.00365946, 0.979412, 0.0169281},
{0.00435035, 0.986248, 0.00940212},
{0.000583815, 0.997746, 0.00167066} });
auto res_prob_spodeld = std::vector<std::vector<double>>({ {0.000908024, 0.993742, 0.00535024 },
{0.00187726, 0.99167, 0.00645308 },
{0.00187726, 0.99167, 0.00645308 },
@@ -216,29 +214,33 @@ TEST_CASE("Model predict_proba", "[Models]")
{"TANLd", res_prob_tanld},
{"SPODELd", res_prob_spodeld},
{"KDBLd", res_prob_kdbld} };
std::map<std::string, bayesnet::BaseClassifier*> models{ {"TAN", new bayesnet::TAN()},
{"SPODE", new bayesnet::SPODE(0)},
{"BoostAODEproba", new bayesnet::BoostAODE(false)},
{"BoostAODEvoting", new bayesnet::BoostAODE(true)},
{"TANLd", new bayesnet::TANLd()},
{"SPODELd", new bayesnet::SPODELd(0)},
{"KDBLd", new bayesnet::KDBLd(2)} };
std::map<std::string, std::unique_ptr<bayesnet::BaseClassifier>> models;
models["TAN"] = std::make_unique<bayesnet::TAN>();
models["SPODE"] = std::make_unique<bayesnet::SPODE>(0);
models["BoostAODEproba"] = std::make_unique<bayesnet::BoostAODE>(false);
models["BoostAODEvoting"] = std::make_unique<bayesnet::BoostAODE>(true);
models["TANLd"] = std::make_unique<bayesnet::TANLd>();
models["SPODELd"] = std::make_unique<bayesnet::SPODELd>(0);
models["KDBLd"] = std::make_unique<bayesnet::KDBLd>(2);
int init_index = 78;
SECTION("Test " + model + " predict_proba")
{
INFO("Testing " << model << " predict_proba");
auto ld_model = model.substr(model.length() - 2) == "Ld";
auto discretize = !ld_model;
auto raw = RawDatasets("iris", discretize);
auto clf = models[model];
clf->fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
auto yt_pred_proba = clf->predict_proba(raw.Xt);
auto yt_pred = clf->predict(raw.Xt);
auto& clf = *models[model];
clf.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing);
auto yt_pred_proba = clf.predict_proba(raw.Xt);
auto yt_pred = clf.predict(raw.Xt);
std::vector<int> y_pred;
std::vector<std::vector<double>> y_pred_proba;
if (!ld_model) {
y_pred = clf->predict(raw.Xv);
y_pred_proba = clf->predict_proba(raw.Xv);
y_pred = clf.predict(raw.Xv);
y_pred_proba = clf.predict_proba(raw.Xv);
REQUIRE(y_pred.size() == y_pred_proba.size());
REQUIRE(y_pred.size() == yt_pred.size(0));
REQUIRE(y_pred.size() == yt_pred_proba.size(0));
@@ -267,18 +269,20 @@ TEST_CASE("Model predict_proba", "[Models]")
} else {
// Check predict_proba values for vectors and tensors
auto predictedClasses = yt_pred_proba.argmax(1);
// std::cout << model << std::endl;
for (int i = 0; i < 9; i++) {
REQUIRE(predictedClasses[i].item<int>() == yt_pred[i].item<int>());
// std::cout << "{";
for (int j = 0; j < 3; j++) {
// std::cout << yt_pred_proba[i + init_index][j].item<double>() << ", ";
REQUIRE(res_prob[model][i][j] ==
Catch::Approx(yt_pred_proba[i + init_index][j].item<double>()).epsilon(raw.epsilon));
}
// std::cout << "\b\b}," << std::endl;
}
}
delete clf;
}
}
TEST_CASE("AODE voting-proba", "[Models]")
{
auto raw = RawDatasets("glass", true);
@@ -324,11 +328,15 @@ TEST_CASE("KDB with hyperparameters", "[Models]")
REQUIRE(score == Catch::Approx(0.827103).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.761682).epsilon(raw.epsilon));
}
TEST_CASE("Incorrect type of data for SPODELd", "[Models]")
TEST_CASE("Incorrect type of data for Ld models", "[Models]")
{
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::SPODELd(0);
REQUIRE_THROWS_AS(clf.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
auto clfs = bayesnet::SPODELd(0);
REQUIRE_THROWS_AS(clfs.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
auto clft = bayesnet::TANLd();
REQUIRE_THROWS_AS(clft.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
auto clfk = bayesnet::KDBLd(0);
REQUIRE_THROWS_AS(clfk.fit(raw.dataset, raw.features, raw.className, raw.states, raw.smoothing), std::runtime_error);
}
TEST_CASE("Predict, predict_proba & score without fitting", "[Models]")
{
@@ -428,3 +436,49 @@ TEST_CASE("Check KDB loop detection", "[Models]")
REQUIRE_NOTHROW(clf.test_add_m_edges(features, 0, S, weights));
REQUIRE_NOTHROW(clf.test_add_m_edges(features, 1, S, weights));
}
TEST_CASE("Local discretization hyperparameters", "[Models]")
{
auto raw = RawDatasets("iris", false);
auto clfs = bayesnet::SPODELd(0);
clfs.setHyperparameters({
{"max_iterations", 7},
{"verbose_convergence", true},
});
REQUIRE_NOTHROW(clfs.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
REQUIRE(clfs.getStatus() == bayesnet::NORMAL);
auto clfk = bayesnet::KDBLd(0);
clfk.setHyperparameters({
{"k", 3},
{"theta", 1e-4},
});
REQUIRE_NOTHROW(clfk.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
REQUIRE(clfk.getStatus() == bayesnet::NORMAL);
auto clfa = bayesnet::AODELd();
clfa.setHyperparameters({
{"ld_proposed_cuts", 9},
{"ld_algorithm", "BINQ"},
});
REQUIRE_NOTHROW(clfa.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
REQUIRE(clfa.getStatus() == bayesnet::NORMAL);
auto clft = bayesnet::TANLd();
clft.setHyperparameters({
{"ld_proposed_cuts", 7},
{"mdlp_max_depth", 5},
{"mdlp_min_length", 3},
{"ld_algorithm", "MDLP"},
});
REQUIRE_NOTHROW(clft.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
REQUIRE(clft.getStatus() == bayesnet::NORMAL);
clft.setHyperparameters({
{"ld_proposed_cuts", 9},
{"ld_algorithm", "BINQ"},
});
REQUIRE_NOTHROW(clft.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
REQUIRE(clft.getStatus() == bayesnet::NORMAL);
clft.setHyperparameters({
{"ld_proposed_cuts", 5},
{"ld_algorithm", "BINU"},
});
REQUIRE_NOTHROW(clft.fit(raw.Xt, raw.yt, raw.features, raw.className, raw.states, raw.smoothing));
REQUIRE(clft.getStatus() == bayesnet::NORMAL);
}