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BayesNet/tests/TestXBA2DE.cc

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// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include <catch2/catch_approx.hpp>
#include <catch2/catch_test_macros.hpp>
#include <catch2/generators/catch_generators.hpp>
#include <catch2/matchers/catch_matchers.hpp>
#include "TestUtils.h"
#include "bayesnet/ensembles/XBA2DE.h"
TEST_CASE("Normal test", "[XBA2DE]") {
auto raw = RawDatasets("iris", true);
auto clf = bayesnet::XBA2DE();
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 5);
REQUIRE(clf.getNumberOfEdges() == 8);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getVersion() == "0.9.7");
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 13 models eliminated");
REQUIRE(clf.getNotes()[1] == "Number of models: 1");
REQUIRE(clf.getNumberOfStates() == 64);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(1.0f));
REQUIRE(clf.graph().size() == 1);
}
TEST_CASE("Feature_select CFS", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({{"select_features", "CFS"}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 220);
REQUIRE(clf.getNumberOfEdges() == 506);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 22");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.720930219));
}
TEST_CASE("Feature_select IWSS", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({{"select_features", "IWSS"}, {"threshold", 0.5}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 220);
REQUIRE(clf.getNumberOfEdges() == 506);
REQUIRE(clf.getNotes().size() == 4);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
REQUIRE(clf.getNotes()[1] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[2] == "Pairs not used in train: 2");
REQUIRE(clf.getNotes()[3] == "Number of models: 22");
REQUIRE(clf.getNumberOfStates() == 5346);
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.72093));
}
TEST_CASE("Feature_select FCBF", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({{"select_features", "FCBF"}, {"threshold", 1e-7}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 290);
REQUIRE(clf.getNumberOfEdges() == 667);
REQUIRE(clf.getNumberOfStates() == 7047);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 2");
REQUIRE(clf.getNotes()[2] == "Number of models: 29");
REQUIRE(clf.score(raw.X_test, raw.y_test) == Catch::Approx(0.744186));
}
TEST_CASE("Test used features in train note and score", "[XBA2DE]") {
auto raw = RawDatasets("diabetes", true);
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({
{"order", "asc"},
{"convergence", true},
{"select_features", "CFS"},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 144);
REQUIRE(clf.getNumberOfEdges() == 320);
REQUIRE(clf.getNumberOfStates() == 5504);
REQUIRE(clf.getNotes().size() == 2);
REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
REQUIRE(clf.getNotes()[1] == "Number of models: 16");
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
REQUIRE(score == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.850260437f).epsilon(raw.epsilon));
}
TEST_CASE("Order asc, desc & random", "[XBA2DE]") {
auto raw = RawDatasets("glass", true);
std::map<std::string, double> scores{{"asc", 0.827103}, {"desc", 0.808411}, {"rand", 0.827103}};
for (const std::string &order : {"asc", "desc", "rand"}) {
auto clf = bayesnet::XBA2DE();
clf.setHyperparameters({
{"order", order},
{"bisection", false},
{"maxTolerance", 1},
{"convergence", true},
});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
auto score = clf.score(raw.Xv, raw.yv);
auto scoret = clf.score(raw.Xt, raw.yt);
INFO("XBA2DE order: " << order);
REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
}
}
TEST_CASE("Oddities", "[XBA2DE]") {
auto clf = bayesnet::XBA2DE();
auto raw = RawDatasets("iris", true);
auto bad_hyper = nlohmann::json{
{{"order", "duck"}},
{{"select_features", "duck"}},
{{"maxTolerance", 0}},
{{"maxTolerance", 7}},
};
for (const auto &hyper : bad_hyper.items()) {
INFO("XBA2DE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
REQUIRE_THROWS_AS(clf.setHyperparameters({{"maxTolerance", 0}}), std::invalid_argument);
auto bad_hyper_fit = nlohmann::json{
{{"select_features", "IWSS"}, {"threshold", -0.01}},
{{"select_features", "IWSS"}, {"threshold", 0.51}},
{{"select_features", "FCBF"}, {"threshold", 1e-8}},
{{"select_features", "FCBF"}, {"threshold", 1.01}},
};
for (const auto &hyper : bad_hyper_fit.items()) {
INFO("XBA2DE hyper: " << hyper.value().dump());
clf.setHyperparameters(hyper.value());
REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing),
std::invalid_argument);
}
auto bad_hyper_fit2 = nlohmann::json{
{{"alpha_block", true}, {"block_update", true}},
{{"bisection", false}, {"block_update", true}},
};
for (const auto &hyper : bad_hyper_fit2.items()) {
INFO("XBA2DE hyper: " << hyper.value().dump());
REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
}
// Check not enough selected features
raw.Xv.pop_back();
raw.Xv.pop_back();
raw.Xv.pop_back();
raw.features.pop_back();
raw.features.pop_back();
raw.features.pop_back();
clf.setHyperparameters({{"select_features", "CFS"}, {"alpha_block", false}, {"block_update", false}});
clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNotes().size() == 1);
REQUIRE(clf.getNotes()[0] == "No features selected in initialization");
}
TEST_CASE("Bisection Best", "[XBA2DE]") {
auto clf = bayesnet::XBA2DE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
clf.setHyperparameters({
{"bisection", true},
{"maxTolerance", 3},
{"convergence", true},
{"convergence_best", false},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 330);
REQUIRE(clf.getNumberOfEdges() == 836);
REQUIRE(clf.getNumberOfStates() == 31108);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes().at(0) == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes().at(1) == "Pairs not used in train: 83");
REQUIRE(clf.getNotes().at(2) == "Number of models: 22");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(0.975).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.975).epsilon(raw.epsilon));
}
TEST_CASE("Bisection Best vs Last", "[XBA2DE]") {
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
auto clf = bayesnet::XBA2DE();
auto hyperparameters = nlohmann::json{
{"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, raw.smoothing);
auto score_best = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_best == Catch::Approx(0.983333).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, raw.smoothing);
auto score_last = clf.score(raw.X_test, raw.y_test);
REQUIRE(score_last == Catch::Approx(0.99).epsilon(raw.epsilon));
}
TEST_CASE("Block Update", "[XBA2DE]") {
auto clf = bayesnet::XBA2DE();
auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
clf.setHyperparameters({
{"bisection", true},
{"block_update", true},
{"maxTolerance", 3},
{"convergence", true},
});
clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
REQUIRE(clf.getNumberOfNodes() == 120);
REQUIRE(clf.getNumberOfEdges() == 304);
REQUIRE(clf.getNotes().size() == 3);
REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 83");
REQUIRE(clf.getNotes()[2] == "Number of models: 8");
auto score = clf.score(raw.X_test, raw.y_test);
auto scoret = clf.score(raw.X_test, raw.y_test);
REQUIRE(score == Catch::Approx(0.963333).epsilon(raw.epsilon));
REQUIRE(scoret == Catch::Approx(0.963333).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;*/
}
TEST_CASE("Alphablock", "[XBA2DE]") {
auto clf_alpha = bayesnet::XBA2DE();
auto clf_no_alpha = bayesnet::XBA2DE();
auto raw = RawDatasets("diabetes", true);
clf_alpha.setHyperparameters({
{"alpha_block", true},
});
clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
REQUIRE(score_alpha == Catch::Approx(0.714286).epsilon(raw.epsilon));
REQUIRE(score_no_alpha == Catch::Approx(0.714286).epsilon(raw.epsilon));
}
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