/*
* 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<T> extends AbstractTask<T> {
private static final Logger logger = LoggerFactory.getLogger(TrainModelTask.class);
private AlgorithmInstance algorithm;
private File writeFile;
private DataSource inputData;
private Function<LenskitRecommender, T> 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<LenskitRecommender, T> getAction() {
return action;
}
/**
* Configure the algorithmInfo.
* @param algo The algorithmInfo to configure.
* @return The command (for chaining).
*/
public TrainModelTask<T> 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<T> 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<T> 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<T> setAction(Function<LenskitRecommender,T> 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();
}
}
}