// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2024 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************

#include <type_traits>
#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include "bayesnet/utils/BayesMetrics.h"
#include "bayesnet/ensembles/BoostA2DE.h"
#include "TestUtils.h"


TEST_CASE("Build basic model", "[BoostA2DE]")
{
    auto raw = RawDatasets("diabetes", true);
    auto clf = bayesnet::BoostA2DE();
    clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
    REQUIRE(clf.getNumberOfNodes() == 342);
    REQUIRE(clf.getNumberOfEdges() == 684);
    REQUIRE(clf.getNotes().size() == 3);
    REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
    REQUIRE(clf.getNotes()[1] == "Pairs not used in train: 20");
    REQUIRE(clf.getNotes()[2] == "Number of models: 38");
    auto score = clf.score(raw.Xv, raw.yv);
    REQUIRE(score == Catch::Approx(0.919271).epsilon(raw.epsilon));
}
// TEST_CASE("Feature_select IWSS", "[BoostAODE]")
// {
//     auto raw = RawDatasets("glass", true);
//     auto clf = bayesnet::BoostAODE();
//     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() == 90);
//     REQUIRE(clf.getNumberOfEdges() == 153);
//     REQUIRE(clf.getNotes().size() == 2);
//     REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with IWSS");
//     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
// }
// TEST_CASE("Feature_select FCBF", "[BoostAODE]")
// {
//     auto raw = RawDatasets("glass", true);
//     auto clf = bayesnet::BoostAODE();
//     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() == 90);
//     REQUIRE(clf.getNumberOfEdges() == 153);
//     REQUIRE(clf.getNotes().size() == 2);
//     REQUIRE(clf.getNotes()[0] == "Used features in initialization: 4 of 9 with FCBF");
//     REQUIRE(clf.getNotes()[1] == "Number of models: 9");
// }
// TEST_CASE("Test used features in train note and score", "[BoostAODE]")
// {
//     auto raw = RawDatasets("diabetes", true);
//     auto clf = bayesnet::BoostAODE(true);
//     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() == 72);
//     REQUIRE(clf.getNumberOfEdges() == 120);
//     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: 8");
//     auto score = clf.score(raw.Xv, raw.yv);
//     auto scoret = clf.score(raw.Xt, raw.yt);
//     REQUIRE(score == Catch::Approx(0.809895813).epsilon(raw.epsilon));
//     REQUIRE(scoret == Catch::Approx(0.809895813).epsilon(raw.epsilon));
// }
// TEST_CASE("Voting vs proba", "[BoostAODE]")
// {
//     auto raw = RawDatasets("iris", true);
//     auto clf = bayesnet::BoostAODE(false);
//     clf.fit(raw.Xv, raw.yv, raw.features, raw.className, raw.states, raw.smoothing);
//     auto score_proba = clf.score(raw.Xv, raw.yv);
//     auto pred_proba = clf.predict_proba(raw.Xv);
//     clf.setHyperparameters({
//         {"predict_voting",true},
//         });
//     auto score_voting = clf.score(raw.Xv, raw.yv);
//     auto pred_voting = clf.predict_proba(raw.Xv);
//     REQUIRE(score_proba == Catch::Approx(0.97333).epsilon(raw.epsilon));
//     REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
//     REQUIRE(pred_voting[83][2] == Catch::Approx(1.0).epsilon(raw.epsilon));
//     REQUIRE(pred_proba[83][2] == Catch::Approx(0.86121525).epsilon(raw.epsilon));
//     REQUIRE(clf.dump_cpt() == "");
//     REQUIRE(clf.topological_order() == std::vector<std::string>());
// }
// TEST_CASE("Order asc, desc & random", "[BoostAODE]")
// {
//     auto raw = RawDatasets("glass", true);
//     std::map<std::string, double> scores{
//         {"asc", 0.83645f }, { "desc", 0.84579f }, { "rand", 0.84112 }
//     };
//     for (const std::string& order : { "asc", "desc", "rand" }) {
//         auto clf = bayesnet::BoostAODE();
//         clf.setHyperparameters({
//             {"order", order},
//             {"bisection", false},
//             {"maxTolerance", 1},
//             {"convergence", false},
//             });
//         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("BoostAODE order: " + order);
//         REQUIRE(score == Catch::Approx(scores[order]).epsilon(raw.epsilon));
//         REQUIRE(scoret == Catch::Approx(scores[order]).epsilon(raw.epsilon));
//     }
// }
// TEST_CASE("Oddities", "[BoostAODE]")
// {
//     auto clf = bayesnet::BoostAODE();
//     auto raw = RawDatasets("iris", true);
//     auto bad_hyper = nlohmann::json{
//         { { "order", "duck" } },
//         { { "select_features", "duck" } },
//         { { "maxTolerance", 0 } },
//         { { "maxTolerance", 5 } },
//     };
//     for (const auto& hyper : bad_hyper.items()) {
//         INFO("BoostAODE 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("BoostAODE 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);
//     }
// }

// TEST_CASE("Bisection Best", "[BoostAODE]")
// {
//     auto clf = bayesnet::BoostAODE();
//     auto raw = RawDatasets("kdd_JapaneseVowels", true, 1200, true, false);
//     clf.setHyperparameters({
//         {"bisection", true},
//         {"maxTolerance", 3},
//         {"convergence", true},
//         {"block_update", false},
//         {"convergence_best", false},
//         });
//     clf.fit(raw.X_train, raw.y_train, raw.features, raw.className, raw.states, raw.smoothing);
//     REQUIRE(clf.getNumberOfNodes() == 210);
//     REQUIRE(clf.getNumberOfEdges() == 378);
//     REQUIRE(clf.getNotes().size() == 1);
//     REQUIRE(clf.getNotes().at(0) == "Number of models: 14");
//     auto score = clf.score(raw.X_test, raw.y_test);
//     auto scoret = clf.score(raw.X_test, raw.y_test);
//     REQUIRE(score == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
//     REQUIRE(scoret == Catch::Approx(0.991666675f).epsilon(raw.epsilon));
// }
// TEST_CASE("Bisection Best vs Last", "[BoostAODE]")
// {
//     auto raw = RawDatasets("kdd_JapaneseVowels", true, 1500, true, false);
//     auto clf = bayesnet::BoostAODE(true);
//     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(0.980000019f).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(0.976666689f).epsilon(raw.epsilon));
// }

// TEST_CASE("Block Update", "[BoostAODE]")
// {
//     auto clf = bayesnet::BoostAODE();
//     auto raw = RawDatasets("mfeat-factors", true, 500);
//     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() == 868);
//     REQUIRE(clf.getNumberOfEdges() == 1724);
//     REQUIRE(clf.getNotes().size() == 3);
//     REQUIRE(clf.getNotes()[0] == "Convergence threshold reached & 15 models eliminated");
//     REQUIRE(clf.getNotes()[1] == "Used features in train: 19 of 216");
//     REQUIRE(clf.getNotes()[2] == "Number of models: 4");
//     auto score = clf.score(raw.X_test, raw.y_test);
//     auto scoret = clf.score(raw.X_test, raw.y_test);
//     REQUIRE(score == Catch::Approx(0.99f).epsilon(raw.epsilon));
//     REQUIRE(scoret == Catch::Approx(0.99f).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;
// }