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Facilities for evaluating recommender algorithms.
There is a newer version: 3.0-T5
/*
 * 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;

import com.google.common.base.Function;
import com.google.common.base.Preconditions;
import org.apache.commons.lang3.time.StopWatch;
import org.grouplens.lenskit.RecommenderBuildException;
import org.grouplens.lenskit.core.LenskitConfiguration;
import org.grouplens.lenskit.core.LenskitRecommender;
import org.grouplens.lenskit.eval.algorithm.AlgorithmInstance;
import org.grouplens.lenskit.eval.data.DataSource;
import org.grouplens.lenskit.util.LogContext;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;

/**
 * Train a recommender algorithmInfo and process it with a function.
 */
public class TrainModelTask extends AbstractTask {
    private static final Logger logger = LoggerFactory.getLogger(TrainModelTask.class);

    private AlgorithmInstance algorithm;
    private File writeFile;
    private DataSource inputData;
    private Function action;

    public TrainModelTask() {
        super("train-model");
    }

    public TrainModelTask(String name) {
        super(name);
    }

    public AlgorithmInstance getAlgorithm() {
        return algorithm;
    }

    public File getWriteFile() {
        return writeFile;
    }

    public DataSource getInputData() {
        return inputData;
    }

    public Function getAction() {
        return action;
    }

    /**
     * Configure the algorithmInfo.
     * @param algo The algorithmInfo to configure.
     * @return The command (for chaining).
     */
    public TrainModelTask setAlgorithm(AlgorithmInstance algo) {
        algorithm = algo;
        return this;
    }

    /**
     * Specify a file to write. The trained recommender algorithmInfo will be written
     * to this file.
     * @param file The file name.
     * @return The command (for chaining).
     */
    public TrainModelTask setWriteFile(File file) {
        writeFile = file;
        return this;
    }

    /**
     * Specify the data source to train on.
     * @param data The input data source.
     * @return The builder (for chaining).
     */
    public TrainModelTask setInput(DataSource data) {
        inputData = data;
        return this;
    }

    /**
     * Set the action to invoke.  The action's return value will be returned
     * from {@link #perform()}.
     * @param act The action to invoke.
     * @return The command (for chaining).
     */
    public TrainModelTask setAction(Function act) {
        action = act;
        return this;
    }

    @Override
    @SuppressWarnings("PMD.AvoidCatchingThrowable")
    public T perform() throws TaskExecutionException {
        Preconditions.checkState(algorithm != null, "no algorithm specified");
        Preconditions.checkState(inputData != null, "no input data specified");
        Preconditions.checkState(action != null, "no action specified");
        LogContext context = new LogContext();
        try {
            context.put("lenskit.eval.command.class", getName());
            context.put("lenskit.eval.command.name", getName());
            context.put("lenskit.eval.algorithm.name", algorithm.getName());

            // TODO Support serializing the recommender
            LenskitRecommender rec;
            StopWatch timer = new StopWatch();
            timer.start();
            try {
                logger.info("{}: building recommender {}", getName(), algorithm.getName());
                LenskitConfiguration config = new LenskitConfiguration();
                inputData.configure(config);
                rec = algorithm.buildRecommender(config);
            } catch (RecommenderBuildException e) {
                throw new TaskExecutionException(getName() + ": error building recommender", e);
            }
            timer.stop();
            logger.info("{}: trained in {}", getName(), timer);
            return action.apply(rec);
        } finally {
            context.finish();
        }
    }
}