/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.deeplearning4j.earlystopping.scorecalc;

import org.deeplearning4j.earlystopping.scorecalc.base.BaseIEvaluationScoreCalculator;
import org.deeplearning4j.nn.api.Model;
import org.nd4j.evaluation.regression.RegressionEvaluation;
import org.nd4j.evaluation.regression.RegressionEvaluation.Metric;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;

/**
 * Calculate the regression score of the network (MultiLayerNetwork or ComputationGraph) on a test set, using the
 * specified regression metric - {@link Metric}
 *
 * @author Alex Black
 */
public class RegressionScoreCalculator extends BaseIEvaluationScoreCalculator<Model, RegressionEvaluation> {

    protected final Metric metric;

    public RegressionScoreCalculator(Metric metric, DataSetIterator iterator){
        super(iterator);
        this.metric = metric;
    }

    @Override
    protected RegressionEvaluation newEval() {
        return new RegressionEvaluation();
    }

    @Override
    protected double finalScore(RegressionEvaluation eval) {
        return eval.scoreForMetric(metric);
    }

    @Override
    public boolean minimizeScore() {
        return metric.minimize();
    }
}
