#include <catch2/catch_test_macros.hpp>
#include <catch2/catch_approx.hpp>
#include <catch2/generators/catch_generators.hpp>
#include "BayesMetrics.h"
#include "TestUtils.h"

using namespace std;

TEST_CASE("Metrics Test", "[BayesNet]")
{
    string file_name = GENERATE("glass", "iris", "ecoli", "diabetes");
    map<string, pair<int, vector<int>>> resultsKBest = {
        {"glass", {7, { 0, 1, 7, 6, 3, 5, 2 }}},
        {"iris", {3, { 0, 3, 2 }} },
        {"ecoli", {6, { 2, 4, 1, 0, 6, 5 }}},
        {"diabetes", {2, { 7, 1 }}}
    };
    map<string, double> resultsMI = {
        {"glass", 0.12805398},
        {"iris", 0.3158139948},
        {"ecoli", 0.0089431099},
        {"diabetes", 0.0345470614}
    };
    map<string, vector<pair<int, int>>> resultsMST = {
        {"glass", {{0,6}, {0,5}, {0,3}, {6,2}, {6,7}, {5,1}, {5,8}, {5,4}}},
        {"iris", {{0,1},{0,2},{1,3}}},
        {"ecoli", {{0,1}, {0,2}, {1,5}, {1,3}, {5,6}, {5,4}}},
        {"diabetes", {{0,7}, {0,2}, {0,6}, {2,3}, {3,4}, {3,5}, {4,1}}}
    };
    auto [XDisc, yDisc, featuresDisc, classNameDisc, statesDisc] = loadDataset(file_name, true, true);
    int classNumStates = statesDisc.at(classNameDisc).size();
    auto yresized = torch::transpose(yDisc.view({ yDisc.size(0), 1 }), 0, 1);
    torch::Tensor dataset = torch::cat({ XDisc, yresized }, 0);
    int nSamples = dataset.size(1);
    double epsilon = 1e-5;
    torch::Tensor weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
    bayesnet::Metrics metrics(dataset, featuresDisc, classNameDisc, classNumStates);

    SECTION("Test Constructor")
    {
        REQUIRE(metrics.getScoresKBest().size() == 0);
    }

    SECTION("Test SelectKBestWeighted")
    {
        vector<int> kBest = metrics.SelectKBestWeighted(weights, true, resultsKBest.at(file_name).first);
        REQUIRE(kBest.size() == resultsKBest.at(file_name).first);
        REQUIRE(kBest == resultsKBest.at(file_name).second);
    }

    SECTION("Test Mutual Information")
    {
        auto result = metrics.mutualInformation(dataset.index({ 1, "..." }), dataset.index({ 2, "..." }), weights);
        REQUIRE(result == Catch::Approx(resultsMI.at(file_name)).epsilon(epsilon));
    }

    SECTION("Test Maximum Spanning Tree")
    {
        auto weights_matrix = metrics.conditionalEdge(weights);
        auto result = metrics.maximumSpanningTree(featuresDisc, weights_matrix, 0);
        REQUIRE(result == resultsMST.at(file_name));
    }
}