212 lines
9.5 KiB
C++
212 lines
9.5 KiB
C++
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// ***************************************************************
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// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
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// SPDX-FileType: SOURCE
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include <type_traits>
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/catch_approx.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include "bayesnet/ensembles/BoostA2DE.h"
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#include "TestUtils.h"
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TEST_CASE("Feature_select CFS", "[BoostA2DE]")
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{
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auto raw = RawDatasets("iris", true);
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auto clf = bayesnet::BoostA2DE();
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clf.setHyperparameters({ {"select_features", "CFS"} });
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clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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REQUIRE(clf.getNumberOfNodes() == 0);
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REQUIRE(clf.getNumberOfEdges() == 0);
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// REQUIRE(clf.getNotes().size() == 2);
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// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 9 with CFS");
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// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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}
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// TEST_CASE("Feature_select IWSS", "[BoostAODE]")
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// {
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// auto raw = RawDatasets("glass", true);
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// auto clf = bayesnet::BoostAODE();
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// clf.setHyperparameters({ {"select_features", "IWSS"}, {"threshold", 0.5 } });
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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// REQUIRE(clf.getNumberOfNodes() == 90);
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// REQUIRE(clf.getNumberOfEdges() == 153);
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// REQUIRE(clf.getNotes().size() == 2);
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// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
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// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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// }
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// TEST_CASE("Feature_select FCBF", "[BoostAODE]")
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// {
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// auto raw = RawDatasets("glass", true);
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// auto clf = bayesnet::BoostAODE();
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// clf.setHyperparameters({ {"select_features", "FCBF"}, {"threshold", 1e-7 } });
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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// REQUIRE(clf.getNumberOfNodes() == 90);
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// REQUIRE(clf.getNumberOfEdges() == 153);
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// REQUIRE(clf.getNotes().size() == 2);
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// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
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// REQUIRE(clf.getNotes()[1] == "Number of models: 9");
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// }
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// TEST_CASE("Test used features in train note and score", "[BoostAODE]")
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// {
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// auto raw = RawDatasets("diabetes", true);
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// auto clf = bayesnet::BoostAODE(true);
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// clf.setHyperparameters({
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// {"order", "asc"},
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// {"convergence", true},
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// {"select_features","CFS"},
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// });
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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// REQUIRE(clf.getNumberOfNodes() == 72);
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// REQUIRE(clf.getNumberOfEdges() == 120);
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// REQUIRE(clf.getNotes().size() == 2);
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// REQUIRE(clf.getNotes()[0] == "Used features in initialization: 6 of 8 with CFS");
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// REQUIRE(clf.getNotes()[1] == "Number of models: 8");
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// auto score = clf.score(raw.Xv, raw.yv);
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// auto scoret = clf.score(raw.Xt, raw.yt);
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// REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
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// REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
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// }
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// TEST_CASE("Voting vs proba", "[BoostAODE]")
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// {
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// auto raw = RawDatasets("iris", true);
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// auto clf = bayesnet::BoostAODE(false);
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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// auto score_proba = clf.score(raw.Xv, raw.yv);
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// auto pred_proba = clf.predict_proba(raw.Xv);
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// clf.setHyperparameters({
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// {"predict_voting",true},
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// });
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// auto score_voting = clf.score(raw.Xv, raw.yv);
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// auto pred_voting = clf.predict_proba(raw.Xv);
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// REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
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// REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
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// REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
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// REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
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// REQUIRE(clf.dump_cpt() == "");
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// REQUIRE(clf.topological_order() == std::vector<std::string>());
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// }
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// TEST_CASE("Order asc, desc & random", "[BoostAODE]")
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// {
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// auto raw = RawDatasets("glass", true);
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// std::map<std::string, double> scores{
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// {"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
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// };
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// for (const std::string& order : { "asc", "desc", "rand" }) {
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// auto clf = bayesnet::BoostAODE();
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// clf.setHyperparameters({
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// {"order", order},
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// {"bisection", false},
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// {"maxTolerance", 1},
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// {"convergence", false},
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// });
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states);
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// auto score = clf.score(raw.Xv, raw.yv);
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// auto scoret = clf.score(raw.Xt, raw.yt);
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// INFO("BoostAODE order: " + order);
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// REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
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// REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
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// }
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// }
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// TEST_CASE("Oddities", "[BoostAODE]")
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// {
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// auto clf = bayesnet::BoostAODE();
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// auto raw = RawDatasets("iris", true);
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// auto bad_hyper = nlohmann::json{
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// { { "order", "duck" } },
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// { { "select_features", "duck" } },
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// { { "maxTolerance", 0 } },
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// { { "maxTolerance", 5 } },
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// };
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// for (const auto& hyper : bad_hyper.items()) {
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// INFO("BoostAODE hyper: " + hyper.value().dump());
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()), std::invalid_argument);
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// }
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// REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }), std::invalid_argument);
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// auto bad_hyper_fit = nlohmann::json{
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// { { "select_features","IWSS" }, { "threshold", -0.01 } },
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// { { "select_features","IWSS" }, { "threshold", 0.51 } },
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// { { "select_features","FCBF" }, { "threshold", 1e-8 } },
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// { { "select_features","FCBF" }, { "threshold", 1.01 } },
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// };
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// for (const auto& hyper : bad_hyper_fit.items()) {
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// INFO("BoostAODE hyper: " + hyper.value().dump());
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// clf.setHyperparameters(hyper.value());
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// REQUIRE_THROWS_AS(clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states), std::invalid_argument);
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// }
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// }
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// TEST_CASE("Bisection Best", "[BoostAODE]")
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// {
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// auto clf = bayesnet::BoostAODE();
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// auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
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// clf.setHyperparameters({
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// {"bisection", true},
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// {"maxTolerance", 3},
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// {"convergence", true},
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// {"block_update", false},
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// {"convergence_best", false},
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// });
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
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// REQUIRE(clf.getNumberOfNodes() == 210);
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// REQUIRE(clf.getNumberOfEdges() == 378);
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// REQUIRE(clf.getNotes().size() == 1);
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// REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
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// auto score = clf.score(raw.X_test, raw.y_test);
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// auto scoret = clf.score(raw.X_test, raw.y_test);
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// REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
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// REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
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// }
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// TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
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// {
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// auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
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// auto clf = bayesnet::BoostAODE(true);
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// auto hyperparameters = nlohmann::json{
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// {"bisection", true},
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// {"maxTolerance", 3},
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// {"convergence", true},
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// {"convergence_best", true},
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// };
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// clf.setHyperparameters(hyperparameters);
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
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// auto score_best = clf.score(raw.X_test, raw.y_test);
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// REQUIRE(score_best == Catch::Approx(0.980000019f).epsilon(raw.epsilon));
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// // Now we will set the hyperparameter to use the last accuracy
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// hyperparameters["convergence_best"] = false;
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// clf.setHyperparameters(hyperparameters);
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
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// auto score_last = clf.score(raw.X_test, raw.y_test);
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// REQUIRE(score_last == Catch::Approx(0.976666689f).epsilon(raw.epsilon));
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// }
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// TEST_CASE("Block Update", "[BoostAODE]")
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// {
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// auto clf = bayesnet::BoostAODE();
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// auto raw = RawDatasets("mfeat-factors", true, 500);
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// clf.setHyperparameters({
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// {"bisection", true},
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// {"block_update", true},
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// {"maxTolerance", 3},
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// {"convergence", true},
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// });
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states);
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// REQUIRE(clf.getNumberOfNodes() == 868);
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// REQUIRE(clf.getNumberOfEdges() == 1724);
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// REQUIRE(clf.getNotes().size() == 3);
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// REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
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// REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
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// REQUIRE(clf.getNotes()[2] == "Number of models: 4");
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// auto score = clf.score(raw.X_test, raw.y_test);
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// auto scoret = clf.score(raw.X_test, raw.y_test);
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// REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
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// REQUIRE(scoret == Catch::Approx(0.99f).epsilon(raw.epsilon));
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// //
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// // std::cout << "Number of nodes " << clf.getNumberOfNodes() << std::endl;
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// // std::cout << "Number of edges " << clf.getNumberOfEdges() << std::endl;
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// // std::cout << "Notes size " << clf.getNotes().size() << std::endl;
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// // for (auto note : clf.getNotes()) {
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// // std::cout << note << std::endl;
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// // }
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// // std::cout << "Score " << score << std::endl;
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// }
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