// ***************************************************************
// 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 <catch2/matchers/catch_matchers.hpp>
#include "bayesnet/ensembles/BoostAODE.h"
#include "TestUtils.h"


TEST_CASE("Feature_select CFS", "[BoostAODE]")
{
    auto raw = RawDatasets("glass", true);
    auto clf = bayesnet::BoostAODE();
    clf.setHyperparameters({ {"select_features", "CFS"} });
    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: 6 of 9 with CFS");
    REQUIRE(clf.getNotes()[1] == "Number of models: 9");
}
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;
}