org.lenskit.cli.commands.Predict.java Source code

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Here is the source code for org.lenskit.cli.commands.Predict.java

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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.lenskit.cli.commands;

import com.google.auto.service.AutoService;
import com.google.common.base.Stopwatch;
import net.sourceforge.argparse4j.inf.ArgumentParser;
import net.sourceforge.argparse4j.inf.Namespace;
import org.lenskit.api.RecommenderBuildException;
import org.lenskit.data.dao.ItemNameDAO;
import org.lenskit.LenskitRecommender;
import org.lenskit.LenskitRecommenderEngine;
import org.lenskit.api.RatingPredictor;
import org.lenskit.cli.Command;
import org.lenskit.cli.util.InputData;
import org.lenskit.cli.util.RecommenderLoader;
import org.lenskit.cli.util.ScriptEnvironment;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.IOException;
import java.util.List;
import java.util.Map;

/**
 * Predict item ratings for a user.
 *
 * @since 2.1
 * @author <a href="http://www.grouplens.org">GroupLens Research</a>
 */
@AutoService(Command.class)
public class Predict implements Command {
    private final Logger logger = LoggerFactory.getLogger(Predict.class);

    @Override
    public String getName() {
        return "predict";
    }

    @Override
    public String getHelp() {
        return "generate predictions for a user";
    }

    @Override
    @SuppressWarnings({ "rawtypes", "unchecked" })
    public void execute(Namespace opts) throws IOException, RecommenderBuildException {
        Context ctx = new Context(opts);
        LenskitRecommenderEngine engine = ctx.loader.loadEngine();

        long user = ctx.options.getLong("user");
        List<Long> items = ctx.options.get("items");

        try (LenskitRecommender rec = engine.createRecommender()) {
            RatingPredictor pred = rec.getRatingPredictor();
            ItemNameDAO names = rec.get(ItemNameDAO.class);
            if (pred == null) {
                logger.error("recommender has no rating predictor");
                throw new UnsupportedOperationException("no rating predictor");
            }

            logger.info("predicting {} items", items.size());
            Stopwatch timer = Stopwatch.createStarted();
            Map<Long, Double> preds = pred.predict(user, items);
            System.out.format("predictions for user %d:%n", user);
            for (Map.Entry<Long, Double> e : preds.entrySet()) {
                System.out.format("  %d", e.getKey());
                if (names != null) {
                    System.out.format(" (%s)", names.getItemName(e.getKey()));
                }
                System.out.format(": %.3f", e.getValue());
                System.out.println();
            }
            timer.stop();
            logger.info("predicted for {} items in {}", items.size(), timer);
        }
    }

    public void configureArguments(ArgumentParser parser) {
        parser.description("Predicts a user's rating of some items.");
        InputData.configureArguments(parser);
        ScriptEnvironment.configureArguments(parser);
        RecommenderLoader.configureArguments(parser);
        parser.addArgument("user").type(Long.class).metavar("USER").help("predict for USER");
        parser.addArgument("items").type(Long.class).metavar("ITEM").nargs("+").help("predict for ITEMs");
    }

    private static class Context {
        private final Namespace options;
        private final InputData input;
        private final ScriptEnvironment environment;
        private final RecommenderLoader loader;

        public Context(Namespace opts) {
            options = opts;
            environment = new ScriptEnvironment(opts);
            input = new InputData(environment, opts);
            loader = new RecommenderLoader(input, environment, opts);
        }
    }
}