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• LossMAPE.java

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org.nd4j.linalg.lossfunctions.impl.LossMAPE Maven / Gradle / Ivy

package org.nd4j.linalg.lossfunctions.impl;

import lombok.EqualsAndHashCode;
import org.apache.commons.math3.util.Pair;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.Abs;
import org.nd4j.linalg.api.ops.impl.transforms.Sign;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;

/**
 * Created by susaneraly on 8/15/16.
 */
@EqualsAndHashCode
public class LossMAPE implements ILossFunction {
    public INDArray scoreArray(INDArray labels, INDArray preOutput, String activationFn, INDArray mask) {
        INDArray scoreArr;
        INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()));
        scoreArr = output.rsubi(labels).divi(labels);
        Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("abs", scoreArr));
        scoreArr.muli(100.0 / labels.size(1));
        if (mask != null) scoreArr.muliColumnVector(mask);
        return scoreArr;
    }

    @Override
    public double computeScore(INDArray labels, INDArray preOutput, String activationFn, INDArray mask, boolean average) {
        INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);

        double score = scoreArr.sumNumber().doubleValue();

        if (average) score /= scoreArr.size(0);

        return score;
    }

    @Override
    public INDArray computeScoreArray(INDArray labels, INDArray preOutput, String activationFn, INDArray mask) {
        INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
        return scoreArr.sum(1);
    }

    @Override
    public INDArray computeGradient(INDArray labels, INDArray preOutput, String activationFn, INDArray mask) {
        INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()));

        INDArray actSubPredicted = labels.sub(output);
        INDArray dlda = Nd4j.getExecutioner().execAndReturn(new Sign(actSubPredicted));
        INDArray absLabels = Nd4j.getExecutioner().execAndReturn(new Abs(labels.dup()));
        dlda.divi(absLabels).muli(-100.0 / labels.size(1));

        INDArray gradient;
        if ("softmax".equals(activationFn)) {
            gradient = LossUtil.dLdZsoftmaxi(dlda, output);
        } else {
            INDArray sigmaPrimeZ = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()).derivative());
            gradient = dlda.muli(sigmaPrimeZ);
        }

        if (mask != null) {
            gradient.muliColumnVector(mask);
        }

        return gradient;
    }

    @Override
    public org.apache.commons.math3.util.Pair computeGradientAndScore(INDArray labels, INDArray preOutput, String activationFn, INDArray mask, boolean average) {
        //TODO: probably a more efficient way to do this...
        //Yes - will implement in round two. Just want to get done now.

        return new Pair<>(
                computeScore(labels, preOutput, activationFn, mask, average),
                computeGradient(labels, preOutput, activationFn, mask));
    }

    @Override
    public String toString() {
        return "LossMAPE()";
    }

}