// ***************************************************************
// SPDX-FileCopyrightText: Copyright 2025 Ricardo Montañana Gómez
// SPDX-FileType: SOURCE
// SPDX-License-Identifier: MIT
// ***************************************************************
#include "XBAODE.h"
#include "bayesnet/classifiers/XSPODE.h"
#include "bayesnet/utils/TensorUtils.h"
#include <limits.h>
#include <random>
#include <tuple>

namespace bayesnet
{
    XBAODE::XBAODE() : Boost(false) {}
    std::vector<int> XBAODE::initializeModels(const Smoothing_t smoothing)
    {
        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
        std::vector<int> featuresSelected = featureSelection(weights_);
        for (const int &feature : featuresSelected) {
            std::unique_ptr<Classifier> model = std::make_unique<XSpode>(feature);
            model->fit(dataset, features, className, states, weights_, smoothing);
            add_model(std::move(model), 1.0);
        }
        notes.push_back("Used features in initialization: " + std::to_string(featuresSelected.size()) + " of " +
                        std::to_string(features.size()) + " with " + select_features_algorithm);
        return featuresSelected;
    }
    void XBAODE::trainModel(const torch::Tensor &weights, const bayesnet::Smoothing_t smoothing)
    {
        X_train_ = TensorUtils::to_matrix(X_train);
        y_train_ = TensorUtils::to_vector<int>(y_train);
        if (convergence) {
            X_test_ = TensorUtils::to_matrix(X_test);
            y_test_ = TensorUtils::to_vector<int>(y_test);
        }
        fitted = true;
        double alpha_t;
        torch::Tensor weights_ = torch::full({m}, 1.0 / m, torch::kFloat64);
        bool finished = false;
        std::vector<int> featuresUsed;
        n_models = 0;
        if (selectFeatures) {
            featuresUsed = initializeModels(smoothing);
            auto ypred = predict(X_train_);
            auto ypred_t = torch::tensor(ypred);
            std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
            // Update significance of the models
            for (const int &feature : featuresUsed) {
                significanceModels.pop_back();
            }
            for (const int &feature : featuresUsed) {
                significanceModels.push_back(alpha_t);
            }
            // VLOG_SCOPE_F(1, "SelectFeatures. alpha_t: %f n_models: %d", alpha_t,
            // n_models);
            if (finished) {
                return;
            }
        }
        int numItemsPack = 0; // The counter of the models inserted in the current pack
        // Variables to control the accuracy finish condition
        double priorAccuracy = 0.0;
        double improvement = 1.0;
        double convergence_threshold = 1e-4;
        int tolerance = 0; // number of times the accuracy is lower than the convergence_threshold
        // Step 0: Set the finish condition
        // epsilon sub t > 0.5 => inverse the weights_ policy
        // validation error is not decreasing
        // run out of features
        bool ascending = order_algorithm == bayesnet::Orders.ASC;
        std::mt19937 g{173};
        while (!finished) {
            // Step 1: Build ranking with mutual information
            auto featureSelection = metrics.SelectKBestWeighted(weights_, ascending, n); // Get all the features sorted
            if (order_algorithm == bayesnet::Orders.RAND) {
                std::shuffle(featureSelection.begin(), featureSelection.end(), g);
            }
            // Remove used features
            featureSelection.erase(remove_if(featureSelection.begin(), featureSelection.end(),
                                             [&](auto x) {
                                                 return std::find(featuresUsed.begin(), featuresUsed.end(), x) !=
                                                        featuresUsed.end();
                                             }),
                                   featureSelection.end());
            int k = bisection ? pow(2, tolerance) : 1;
            int counter = 0; // The model counter of the current pack
            // VLOG_SCOPE_F(1, "counter=%d k=%d featureSelection.size: %zu", counter, k,
            // featureSelection.size());
            while (counter++ < k && featureSelection.size() > 0) {
                auto feature = featureSelection[0];
                featureSelection.erase(featureSelection.begin());
                std::unique_ptr<Classifier> model;
                model = std::make_unique<XSpode>(feature);
                model->fit(dataset, features, className, states, weights_, smoothing);
                /*dynamic_cast<XSpode*>(model.get())->fitx(X_train, y_train, weights_,
                 * smoothing); // using exclusive XSpode fit method*/
                // DEBUG
                /*std::cout << dynamic_cast<XSpode*>(model.get())->to_string() <<
                 * std::endl;*/
                // DEBUG
                std::vector<int> ypred;
                if (alpha_block) {
                    //
                    // Compute the prediction with the current ensemble + model
                    //
                    // Add the model to the ensemble
                    add_model(std::move(model), 1.0);
                    // Compute the prediction
                    ypred = predict(X_train_);
                    model = std::move(models.back());
                    // Remove the model from the ensemble
                    remove_last_model();
                } else {
                    ypred = model->predict(X_train_);
                }
                // Step 3.1: Compute the classifier amout of say
                auto ypred_t = torch::tensor(ypred);
                std::tie(weights_, alpha_t, finished) = update_weights(y_train, ypred_t, weights_);
                // Step 3.4: Store classifier and its accuracy to weigh its future vote
                numItemsPack++;
                featuresUsed.push_back(feature);
                add_model(std::move(model), alpha_t);
                // VLOG_SCOPE_F(2, "finished: %d numItemsPack: %d n_models: %d
                // featuresUsed: %zu", finished, numItemsPack, n_models,
                // featuresUsed.size());
            } // End of the pack
            if (convergence && !finished) {
                auto y_val_predict = predict(X_test);
                double accuracy = (y_val_predict == y_test).sum().item<double>() / (double)y_test.size(0);
                if (priorAccuracy == 0) {
                    priorAccuracy = accuracy;
                } else {
                    improvement = accuracy - priorAccuracy;
                }
                if (improvement < convergence_threshold) {
                    // VLOG_SCOPE_F(3, "  (improvement<threshold) tolerance: %d
                    // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
                    // numItemsPack, improvement, priorAccuracy, accuracy);
                    tolerance++;
                } else {
                    // VLOG_SCOPE_F(3, "* (improvement>=threshold) Reset. tolerance: %d
                    // numItemsPack: %d improvement: %f prior: %f current: %f", tolerance,
                    // numItemsPack, improvement, priorAccuracy, accuracy);
                    tolerance = 0; // Reset the counter if the model performs better
                    numItemsPack = 0;
                }
                if (convergence_best) {
                    // Keep the best accuracy until now as the prior accuracy
                    priorAccuracy = std::max(accuracy, priorAccuracy);
                } else {
                    // Keep the last accuray obtained as the prior accuracy
                    priorAccuracy = accuracy;
                }
            }
            // VLOG_SCOPE_F(1, "tolerance: %d featuresUsed.size: %zu features.size:
            // %zu", tolerance, featuresUsed.size(), features.size());
            finished = finished || tolerance > maxTolerance || featuresUsed.size() == features.size();
        }
        if (tolerance > maxTolerance) {
            if (numItemsPack < n_models) {
                notes.push_back("Convergence threshold reached & " + std::to_string(numItemsPack) + " models eliminated");
                // VLOG_SCOPE_F(4, "Convergence threshold reached & %d models eliminated
                // of %d", numItemsPack, n_models);
                for (int i = featuresUsed.size() - 1; i >= featuresUsed.size() - numItemsPack; --i) {
                    remove_last_model();
                }
                // VLOG_SCOPE_F(4, "*Convergence threshold %d models left & %d features
                // used.", n_models, featuresUsed.size());
            } else {
                notes.push_back("Convergence threshold reached & 0 models eliminated");
                // VLOG_SCOPE_F(4, "Convergence threshold reached & 0 models eliminated
                // n_models=%d numItemsPack=%d", n_models, numItemsPack);
            }
        }
        if (featuresUsed.size() != features.size()) {
            notes.push_back("Used features in train: " + std::to_string(featuresUsed.size()) + " of " +
                            std::to_string(features.size()));
            status = bayesnet::WARNING;
        }
        notes.push_back("Number of models: " + std::to_string(n_models));
        return;
    }
} // namespace bayesnet