com.joliciel.talismane.machineLearning.GeometricMeanScoringStrategy.java Source code

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Here is the source code for com.joliciel.talismane.machineLearning.GeometricMeanScoringStrategy.java

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///////////////////////////////////////////////////////////////////////////////
//Copyright (C) 2012 Assaf Urieli
//
//This file is part of Talismane.
//
//Talismane is free software: you can redistribute it and/or modify
//it under the terms of the GNU Affero General Public License as published by
//the Free Software Foundation, either version 3 of the License, or
//(at your option) any later version.
//
//Talismane is distributed in the hope that it will be useful,
//but WITHOUT ANY WARRANTY; without even the implied warranty of
//MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//GNU Affero General Public License for more details.
//
//You should have received a copy of the GNU Affero General Public License
//along with Talismane.  If not, see <http://www.gnu.org/licenses/>.
//////////////////////////////////////////////////////////////////////////////
package com.joliciel.talismane.machineLearning;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;

/**
 * Takes the geometric mean of the individual decision scores, and multiplies it by the scores of underlying solutions.
 * @author Assaf Urieli
 *
 */
public class GeometricMeanScoringStrategy<T extends Outcome> implements ScoringStrategy<ClassificationSolution<T>> {
    private static final Log LOG = LogFactory.getLog(GeometricMeanScoringStrategy.class);

    @Override
    public double calculateScore(ClassificationSolution<T> solution) {
        double score = 0;
        if (solution != null && solution.getDecisions().size() > 0) {
            for (Decision<?> decision : solution.getDecisions())
                score += decision.getProbabilityLog();

            score = score / solution.getDecisions().size();
        }
        score = Math.exp(score);

        if (LOG.isTraceEnabled()) {
            LOG.trace("Score for solution: " + solution.getClass().getSimpleName());
            LOG.trace(solution.toString());
            StringBuilder sb = new StringBuilder();
            for (Decision<?> decision : solution.getDecisions()) {
                sb.append(" * ");
                sb.append(decision.getProbability());
            }
            sb.append(" root ");
            sb.append(solution.getDecisions().size());
            sb.append(" = ");
            sb.append(score);

            LOG.trace(sb.toString());
        }

        for (Solution underlyingSolution : solution.getUnderlyingSolutions()) {
            if (!underlyingSolution.getScoringStrategy().isAdditive())
                score = score * underlyingSolution.getScore();
        }

        if (LOG.isTraceEnabled()) {
            for (Solution underlyingSolution : solution.getUnderlyingSolutions()) {
                if (!underlyingSolution.getScoringStrategy().isAdditive())
                    LOG.trace(" * " + underlyingSolution.getScore() + " ("
                            + underlyingSolution.getClass().getSimpleName() + ")");
            }
            LOG.trace(" = " + score);
        }

        return score;
    }

    @Override
    public boolean isAdditive() {
        return false;
    }

}