com.o19s.es.ltr.ranker.linear.LinearRanker Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of elasticsearch-learning-to-rank Show documentation

Learing to Rank Query w/ RankLib Models

There is a newer version: 6.8.0

Show newest version

/*
 * Copyright [2017] Wikimedia Foundation
 *
 * 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.o19s.es.ltr.ranker.linear;

import com.o19s.es.ltr.ranker.DenseFeatureVector;
import com.o19s.es.ltr.ranker.DenseLtrRanker;
import org.apache.lucene.util.Accountable;
import org.apache.lucene.util.RamUsageEstimator;

import java.util.Arrays;
import java.util.Objects;

/**
 * Simple linear ranker that applies a dot product based
 * on the provided weights array.
 */
public class LinearRanker extends DenseLtrRanker implements Accountable {
    private final float[] weights;

    public LinearRanker(float[] weights) {
        this.weights = Objects.requireNonNull(weights);
    }

    @Override
    public String name() {
        return "linear";
    }

    @Override
    protected float score(DenseFeatureVector point) {
        float[] scores = point.scores;
        float score = 0;
        for (int i = 0; i < weights.length; i++) {
            score += weights[i]*scores[i];
        }
        return score;
    }

    @Override
    protected int size() {
        return weights.length;
    }

    @Override
    public boolean equals(Object o) {
        if (this == o) return true;
        if (o == null || getClass() != o.getClass()) return false;

        LinearRanker ranker = (LinearRanker) o;

        return Arrays.equals(weights, ranker.weights);
    }

    @Override
    public int hashCode() {
        return Arrays.hashCode(weights);
    }

    /**
     * Return the memory usage of this object in bytes. Negative values are illegal.
     */
    @Override
    public long ramBytesUsed() {
        return RamUsageEstimator.NUM_BYTES_OBJECT_HEADER + RamUsageEstimator.sizeOf(weights);
    }
}