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/*
 * Copyright (c) 2010-2025 Haifeng Li. All rights reserved.
 *
 * Smile is free software: you can redistribute it and/or modify it
 * under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Smile is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Smile. If not, see .
 */
package smile.validation;

import java.io.Serial;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import java.util.function.BiFunction;
import smile.data.formula.Formula;
import smile.math.MathEx;
import smile.data.DataFrame;
import smile.regression.DataFrameRegression;
import smile.regression.Regression;
import smile.util.Index;

/**
 * Regression model validation results.
 *
 * @param model The regression model.
 * @param truth The ground true of validation data.
 * @param prediction The model prediction.
 * @param metrics The regression metrics.
 * @param  The regression model type.
 *
 * @author Haifeng Li
 */
public record RegressionValidation(M model, double[] truth, double[] prediction,
                                      RegressionMetrics metrics) implements Serializable {
    @Serial
    private static final long serialVersionUID = 3L;

    @Override
    public String toString() {
        return metrics.toString();
    }

    /**
     * Trains and validates a model on a train/validation split.
     * @param x the training data.
     * @param y the responsible variable of training data.
     * @param testx the validation data.
     * @param testy the responsible variable of validation data.
     * @param trainer the lambda to train the model.
     * @param  the data type of samples.
     * @param  the model type.
     * @return the validation results.
     */
    public static > RegressionValidation of(T[] x, double[] y, T[] testx, double[] testy, BiFunction trainer) {
        long start = System.nanoTime();
        M model = trainer.apply(x, y);
        double fitTime = (System.nanoTime() - start) / 1E6;

        start = System.nanoTime();
        double[] prediction = model.predict(testx);
        double scoreTime = (System.nanoTime() - start) / 1E6;

        RegressionMetrics metrics = RegressionMetrics.of(fitTime, scoreTime, testy, prediction);
        return new RegressionValidation<>(model, testy, prediction, metrics);
    }

    /**
     * Trains and validates a model on multiple train/validation split.
     * @param bags the data splits.
     * @param x the training data.
     * @param y the responsible variable.
     * @param trainer the lambda to train the model.
     * @param  the data type of samples.
     * @param  the model type.
     * @return the validation results.
     */
    public static > RegressionValidations of(Bag[] bags, T[] x, double[] y, BiFunction trainer) {
        List> rounds = new ArrayList<>(bags.length);

        for (Bag bag : bags) {
            T[] trainx = MathEx.slice(x, bag.samples());
            double[] trainy = MathEx.slice(y, bag.samples());
            T[] testx = MathEx.slice(x, bag.oob());
            double[] testy = MathEx.slice(y, bag.oob());

            rounds.add(of(trainx, trainy, testx, testy, trainer));
        }

        return RegressionValidations.of(rounds);
    }

    /**
     * Trains and validates a model on a train/validation split.
     * @param formula the model formula.
     * @param train the training data.
     * @param test the validation data.
     * @param trainer the lambda to train the model.
     * @param  the model type.
     * @return the validation results.
     */
    public static  RegressionValidation of(Formula formula, DataFrame train, DataFrame test, BiFunction trainer) {
        double[] testy = formula.y(test).toDoubleArray();

        long start = System.nanoTime();
        M model = trainer.apply(formula, train);
        double fitTime = (System.nanoTime() - start) / 1E6;

        start = System.nanoTime();
        int n = test.size();
        double[] prediction = new double[n];
        for (int i = 0; i < n; i++) {
            prediction[i] = model.predict(test.get(i));
        }
        double scoreTime = (System.nanoTime() - start) / 1E6;

        RegressionMetrics metrics = RegressionMetrics.of(fitTime, scoreTime, testy, prediction);
        return new RegressionValidation<>(model, testy, prediction, metrics);
    }

    /**
     * Trains and validates a model on multiple train/validation split.
     * @param bags the data splits.
     * @param formula the model formula.
     * @param data the data.
     * @param trainer the lambda to train the model.
     * @param  the model type.
     * @return the validation results.
     */
    public static  RegressionValidations of(Bag[] bags, Formula formula, DataFrame data, BiFunction trainer) {
        List> rounds = new ArrayList<>(bags.length);

        for (Bag bag : bags) {
            rounds.add(of(formula, data.get(Index.of(bag.samples())), data.get(Index.of(bag.oob())), trainer));
        }

        return RegressionValidations.of(rounds);
    }
}