com.insightml.evaluation.functions.MeanAbsoluteError.java Source code

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Here is the source code for com.insightml.evaluation.functions.MeanAbsoluteError.java

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/*
 * Copyright (C) 2016 Stefan Hen
 *
 * 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.
 */
package com.insightml.evaluation.functions;

import java.util.List;

import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;

import com.insightml.data.samples.Sample;
import com.insightml.models.Predictions;

public final class MeanAbsoluteError extends AbstractObjectiveFunctionFrame<Number, Number> {

    private static final long serialVersionUID = 3835286001852872579L;

    private final double min;
    private final double max;

    public MeanAbsoluteError(final double min, final double max) {
        this.min = min;
        this.max = max;
    }

    @Override
    public DescriptiveStatistics acrossLabels(
            final List<? extends Predictions<? extends Number, ? extends Number>>[] predictions) {
        final DescriptiveStatistics stats = new DescriptiveStatistics();
        for (final List<? extends Predictions<? extends Number, ? extends Number>> predz : predictions) {
            for (final Predictions<? extends Number, ? extends Number> preds : predz) {
                final Number[] pred = preds.getPredictions();
                final Number[] exp = preds.getExpected();
                for (int i = 0; i < pred.length; ++i) {
                    if (exp[i] != null) {
                        stats.addValue(instance(pred[i], exp[i], preds.getSample(i)));
                    }
                }
            }
        }
        return stats;
    }

    @Override
    public double instance(final Number prediction, final Number label, final Sample sample) {
        final double pred = Math.min(max, Math.max(min, prediction.doubleValue()));
        return Math.abs(label.doubleValue() - pred);
    }

    @Override
    public double normalize(final double score) {
        return -score;
    }

}