TestXBAODE complete, fix XBAODE error in no convergence & 99% coverage
This commit is contained in:
@@ -4,12 +4,12 @@
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// SPDX-License-Identifier: MIT
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// ***************************************************************
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#include "TestUtils.h"
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#include "bayesnet/ensembles/XBAODE.h"
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#include <catch2/catch_approx.hpp>
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include <catch2/matchers/catch_matchers.hpp>
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#include "TestUtils.h"
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#include "bayesnet/ensembles/XBAODE.h"
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TEST_CASE("Normal test", "[XBAODE]") {
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auto raw = RawDatasets("iris", true);
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@@ -78,171 +78,139 @@ TEST_CASE("Test used features in train note and score", "[XBAODE]") {
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REQUIRE(score == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(0.819010437f).epsilon(raw.epsilon));
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}
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// TEST_CASE("Voting vs proba", "[XBAODE]")
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// {
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// auto raw = RawDatasets("iris", true);
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// auto clf = bayesnet::XBAODE(false);
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// clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states,
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// raw.smoothing); auto score_proba = clf.score(raw.Xv, raw.yv); auto
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// pred_proba = clf.predict_proba(raw.Xv); 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] ==
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// Catch::Approx(0.86121525).epsilon(raw.epsilon)); 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", "[XBAODE]")
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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::XBAODE();
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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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// raw.smoothing); auto score = clf.score(raw.Xv, raw.yv); auto scoret =
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// clf.score(raw.Xt, raw.yt); INFO("XBAODE 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", "[XBAODE]")
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// {
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// auto clf = bayesnet::XBAODE();
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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", 7 } },
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// };
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// for (const auto& hyper : bad_hyper.items()) {
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// INFO("XBAODE hyper: " << hyper.value().dump());
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()),
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// std::invalid_argument);
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// }
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// REQUIRE_THROWS_AS(clf.setHyperparameters({ {"maxTolerance", 0 } }),
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// std::invalid_argument); 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("XBAODE 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,
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// raw.className, raw.states, raw.smoothing), std::invalid_argument);
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// }
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// auto bad_hyper_fit2 = nlohmann::json{
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// { { "alpha_block", true }, { "block_update", true } },
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// { { "bisection", false }, { "block_update", true } },
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// };
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// for (const auto& hyper : bad_hyper_fit2.items()) {
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// INFO("XBAODE hyper: " << hyper.value().dump());
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// REQUIRE_THROWS_AS(clf.setHyperparameters(hyper.value()),
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// std::invalid_argument);
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// }
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// }
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// TEST_CASE("Bisection Best", "[XBAODE]")
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// {
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// auto clf = bayesnet::XBAODE();
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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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// {"convergence_best", false},
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// });
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// clf.fit(raw.X_train, raw.y_train, raw.features, raw.className,
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// raw.states, raw.smoothing); 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", "[XBAODE]")
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// {
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// auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
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// auto clf = bayesnet::XBAODE(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,
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// raw.states, raw.smoothing); auto score_best = clf.score(raw.X_test,
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// raw.y_test); REQUIRE(score_best ==
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// 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,
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// raw.states, raw.smoothing); auto score_last = clf.score(raw.X_test,
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// raw.y_test); REQUIRE(score_last ==
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// Catch::Approx(0.976666689f).epsilon(raw.epsilon));
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// }
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// TEST_CASE("Block Update", "[XBAODE]")
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// {
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// auto clf = bayesnet::XBAODE();
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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,
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// raw.states, raw.smoothing); 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
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// eliminated"); REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of
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// 216"); REQUIRE(clf.getNotes()[2] == "Number of models: 4"); auto score =
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// clf.score(raw.X_test, raw.y_test); auto scoret = clf.score(raw.X_test,
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// raw.y_test); 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() <<
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// std::endl;
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// // std::cout << "Number of edges " << clf.getNumberOfEdges() <<
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// 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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// TEST_CASE("Alphablock", "[XBAODE]")
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// {
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// auto clf_alpha = bayesnet::XBAODE();
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// auto clf_no_alpha = bayesnet::XBAODE();
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// auto raw = RawDatasets("diabetes", true);
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// clf_alpha.setHyperparameters({
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// {"alpha_block", true},
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// });
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// clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className,
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// raw.states, raw.smoothing); clf_no_alpha.fit(raw.X_train, raw.y_train,
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// raw.features, raw.className, raw.states, raw.smoothing); auto score_alpha
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// = clf_alpha.score(raw.X_test, raw.y_test); auto score_no_alpha =
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// clf_no_alpha.score(raw.X_test, raw.y_test); REQUIRE(score_alpha ==
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// Catch::Approx(0.720779f).epsilon(raw.epsilon)); REQUIRE(score_no_alpha ==
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// Catch::Approx(0.733766f).epsilon(raw.epsilon));
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// }
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TEST_CASE("Order asc, desc & random", "[XBAODE]") {
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auto raw = RawDatasets("glass", true);
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std::map<std::string, double> scores{{"asc", 0.83645f}, {"desc", 0.84579f}, {"rand", 0.84112}};
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for (const std::string &order : {"asc", "desc", "rand"}) {
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auto clf = bayesnet::XBAODE();
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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, raw.smoothing);
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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("XBAODE 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", "[XBAODE]") {
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auto clf = bayesnet::XBAODE();
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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", 7}},
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};
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for (const auto &hyper : bad_hyper.items()) {
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INFO("XBAODE 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("XBAODE 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, raw.smoothing),
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std::invalid_argument);
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}
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auto bad_hyper_fit2 = nlohmann::json{
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{{"alpha_block", true}, {"block_update", true}},
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{{"bisection", false}, {"block_update", true}},
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};
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for (const auto &hyper : bad_hyper_fit2.items()) {
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INFO("XBAODE 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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}
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TEST_CASE("Bisection Best", "[XBAODE]") {
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auto clf = bayesnet::XBAODE();
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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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{"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, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 210);
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REQUIRE(clf.getNumberOfEdges() == 406);
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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", "[XBAODE]") {
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auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
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auto clf = bayesnet::XBAODE();
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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, raw.smoothing);
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auto score_best = clf.score(raw.X_test, raw.y_test);
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REQUIRE(score_best == Catch::Approx(0.973333359f).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, raw.smoothing);
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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", "[XBAODE]") {
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auto clf = bayesnet::XBAODE();
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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, raw.smoothing);
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REQUIRE(clf.getNumberOfNodes() == 1085);
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REQUIRE(clf.getNumberOfEdges() == 2165);
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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: 20 of 216");
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REQUIRE(clf.getNotes()[2] == "Number of models: 5");
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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(1.0f).epsilon(raw.epsilon));
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REQUIRE(scoret == Catch::Approx(1.0f).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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TEST_CASE("Alphablock", "[XBAODE]") {
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auto clf_alpha = bayesnet::XBAODE();
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auto clf_no_alpha = bayesnet::XBAODE();
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auto raw = RawDatasets("diabetes", true);
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clf_alpha.setHyperparameters({
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{"alpha_block", true},
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});
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clf_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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clf_no_alpha.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
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auto score_alpha = clf_alpha.score(raw.X_test, raw.y_test);
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auto score_no_alpha = clf_no_alpha.score(raw.X_test, raw.y_test);
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REQUIRE(score_alpha == Catch::Approx(0.720779f).epsilon(raw.epsilon));
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REQUIRE(score_no_alpha == Catch::Approx(0.733766f).epsilon(raw.epsilon));
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}
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