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/*-
 * ========================LICENSE_START=================================
 * jgea-problem
 * %%
 * Copyright (C) 2018 - 2024 Eric Medvet
 * %%
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =========================LICENSE_END==================================
 */

package io.github.ericmedvet.jgea.problem.classification;

import io.github.ericmedvet.jgea.core.order.ParetoDominance;
import io.github.ericmedvet.jgea.core.order.PartialComparator;
import io.github.ericmedvet.jgea.core.problem.ProblemWithValidation;
import io.github.ericmedvet.jgea.problem.DataUtils;
import io.github.ericmedvet.jnb.datastructure.Pair;
import java.util.ArrayList;
import java.util.List;

public class ClassificationProblem>
    implements ProblemWithValidation, List> {

  // TODO fix this: currently, it enforces just one objective/metric
  private static final PartialComparator> COMPARATOR = ParetoDominance.build(Double.class, 1);

  private final ClassificationFitness fitnessFunction;
  private final ClassificationFitness validationFunction;
  private final List> learningData;
  private final List> validationData;

  public ClassificationProblem(
      List> data,
      int folds,
      int i,
      ClassificationFitness.Metric learningMetric,
      ClassificationFitness.Metric validationMetric) {
    validationData = DataUtils.fold(data, i, folds);
    learningData = new ArrayList<>(data);
    learningData.removeAll(validationData);
    fitnessFunction = new ClassificationFitness<>(learningData, learningMetric);
    validationFunction = new ClassificationFitness<>(validationData, validationMetric);
  }

  public List> getLearningData() {
    return learningData;
  }

  public List> getValidationData() {
    return validationData;
  }

  @Override
  public PartialComparator> qualityComparator() {
    return COMPARATOR;
  }

  @Override
  public ClassificationFitness qualityFunction() {
    return fitnessFunction;
  }

  @Override
  public ClassificationFitness validationQualityFunction() {
    return validationFunction;
  }
}