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    • org.grouplens.lenskit.eval.traintest.FunctionMultiModelMetric Maven / Gradle / Ivy

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    /*
 * LensKit, an open source recommender systems toolkit.
 * Copyright 2010-2014 LensKit Contributors.  See CONTRIBUTORS.md.
 * Work on LensKit has been funded by the National Science Foundation under
 * grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2.1 of the
 * License, or (at your option) any later version.
 *
 * This program 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 General Public License for more
 * details.
 *
 * You should have received a copy of the GNU General Public License along with
 * this program; if not, write to the Free Software Foundation, Inc., 51
 * Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
 */
package org.grouplens.lenskit.eval.traintest;

import com.google.common.base.Function;
import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
import org.grouplens.lenskit.Recommender;
import org.grouplens.lenskit.eval.Attributed;
import org.grouplens.lenskit.eval.data.traintest.TTDataSet;
import org.grouplens.lenskit.eval.metrics.Metric;
import org.grouplens.lenskit.util.table.TableLayoutBuilder;
import org.grouplens.lenskit.util.table.writer.CSVWriter;
import org.grouplens.lenskit.util.table.writer.TableWriter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.io.File;
import java.io.IOException;
import java.util.Collections;
import java.util.List;

/**
 * Model metric backed by an arbitrary function that returns multiple rows per algorithmInfo.
 *
 * @author GroupLens Research
 * @since 1.1
 */
public class FunctionMultiModelMetric implements Metric {
    private static final Logger logger = LoggerFactory.getLogger(FunctionMultiModelMetric.class);
    private final File outputFile;
    private final List columnHeaders;
    private final Function>> function;
    private TableWriter writer;
    private ExperimentOutputLayout evalLayout;

    public FunctionMultiModelMetric(File file, List columns,
                                    Function>> func,
                                    ExperimentOutputLayout layout) {
        outputFile = file;
        columnHeaders = Lists.newArrayList(columns);
        function = func;
        evalLayout = layout;

        TableLayoutBuilder builder = TableLayoutBuilder.copy(evalLayout.getCommonLayout());
        for (String col: columnHeaders) {
            builder.addColumn(col);
        }
        try {
            writer = CSVWriter.open(outputFile, builder.build());
        } catch (IOException e) {
            throw new RuntimeException("error opening output file", e);
        }
    }

    @Override
    public List getColumnLabels() {
        return Collections.emptyList();
    }

    @Override
    public List getUserColumnLabels() {
        return Collections.emptyList();
    }

    @Nullable
    @Override
    public Void createContext(Attributed algorithm, TTDataSet dataSet, Recommender recommender) {
        Preconditions.checkState(evalLayout != null, "evaluation not in progress");
        logger.info("Measuring algorithm {} on data set {} with metric {}",
                    algorithm, dataSet, function);
        TableWriter w = evalLayout.prefixTable(writer, algorithm, dataSet);
        List> measurement = function.apply(recommender);
        if (measurement == null) {
            logger.warn("Metric {} on algorithm {} for data set {} returned null.",
                        function, algorithm, dataSet);
            return null;
        }
        logger.debug("got {} rows", measurement.size());
        for (List row: measurement) {
            try {
                w.writeRow(row.toArray());
            } catch (IOException e) {
                throw new RuntimeException("error writing row", e);
            }
        }
        return null;
    }

    @Nonnull
    @Override
    public List measureUser(TestUser user, Void context) {
        return Collections.emptyList();
    }

    @Nonnull
    @Override
    public List getResults(Void context) {
        return Collections.emptyList();
    }

    @Override
    public void close() throws IOException {
        writer.close();
    }

    public static class Factory extends MetricFactory {
        private final File outputFile;
        private final List columns;
        private final Function>> function;

        public Factory(File file, List cols, Function>> func) {
            outputFile = file;
            columns = cols;
            function = func;
        }

        @Override
        public Metric createMetric(TrainTestEvalTask task) {
            return new FunctionMultiModelMetric(outputFile, columns, function,
                                                task.getOutputLayout());
        }

        @Override
        public List getColumnLabels() {
            return Collections.emptyList();
        }

        @Override
        public List getUserColumnLabels() {
            return Collections.emptyList();
        }
    }
}
    
    
    
    

    




    
    
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