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Facilities for evaluating recommender algorithms.
/*
* 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 org.grouplens.lenskit.core.LenskitConfiguration;
import org.grouplens.lenskit.data.dao.EventDAO;
import org.grouplens.lenskit.data.pref.PreferenceDomain;
import org.grouplens.lenskit.data.source.DataSource;
import org.grouplens.lenskit.data.source.GenericDataSource;
import org.grouplens.lenskit.eval.EvalConfig;
import org.grouplens.lenskit.eval.EvalProject;
import org.grouplens.lenskit.eval.TaskExecutionException;
import org.grouplens.lenskit.eval.algorithm.AlgorithmInstance;
import org.grouplens.lenskit.eval.algorithm.AlgorithmInstanceBuilder;
import org.grouplens.lenskit.eval.data.crossfold.CrossfoldTask;
import org.grouplens.lenskit.eval.data.traintest.GenericTTDataSet;
import org.grouplens.lenskit.eval.data.traintest.TTDataSet;
import org.grouplens.lenskit.eval.metrics.Metric;
import org.grouplens.lenskit.util.table.Table;
import java.io.File;
import java.util.Properties;
import java.util.concurrent.Callable;
public class SimpleEvaluator implements Callable {
private final EvalProject project;
private TrainTestEvalTask result;
/**
* Construct a simple evaluator.
*/
public SimpleEvaluator() {
this(null);
}
/**
* Create a simple evaluator with a custom configuration.
*
* @param props Properties for the eval configuration.
*/
public SimpleEvaluator(Properties props) {
project = new EvalProject(props, null);
result = new TrainTestEvalTask("simple-eval");
result.setProject(project);
result.setOutput((File) null);
}
public EvalConfig getEvalConfig() {
return project.getConfig();
}
/**
* Adds an algorithmInfo to the {@code TrainTestEvalCommand} being built.
*
* If any exception is thrown while the command is called it is rethrown as a runtime error.
* @param algo The algorithmInfo added to the {@code TrainTestEvalCommand}
* @return Itself to allow chaining
*/
public SimpleEvaluator addAlgorithm(AlgorithmInstance algo){
result.addAlgorithm(algo);
return this;
}
/**
* An algorithm instance constructed with a name and Lenskit configuration
* @param name
* @param config Lenskit configuration
*
*/
public SimpleEvaluator addAlgorithm(String name, LenskitConfiguration config) {
result.addAlgorithm(new AlgorithmInstance(name, config));
return this;
}
/**
* Adds a fully configured algorithmInfo command to the {@code TrainTestEvalCommand} being built.
*
* @param algo The algorithmInfo added to the {@code TrainTestEvalCommand}
* @return Itself to allow chaining
*/
public SimpleEvaluator addAlgorithm(AlgorithmInstanceBuilder algo){
result.addAlgorithm(algo.build());
return this;
}
/**
* Calls the {@code CrossfoldCommand} and adds the resulting {@code TTDataSet}s to the {@code TrainTestEvalCommand}.
*
* Any exceptions that are thrown are wrapped as {@code RuntimeExceptions}.
*
* @param cross
* @return Itself to allow for method chaining.
*/
public SimpleEvaluator addDataset(CrossfoldTask cross){
cross.setProject(project);
try {
for (TTDataSet data: cross.perform()) {
result.addDataset(data);
}
}
catch (TaskExecutionException e) {
throw new RuntimeException(e);
}
return this;
}
/**
* Add a new data set to be cross-folded. This method creates a new {@link CrossfoldTask}
* and passes it to {@link #addDataset(CrossfoldTask)}. All crossfold parameters that are not
* taken as arguments by this method are left at their defaults.
*
* @param name The name of the crossfold
* @param source The source for the crossfold
* @param partitions The number of partitions
* @param holdout The holdout fraction
* @return Itself for chaining.
*/
public SimpleEvaluator addDataset(String name, DataSource source, int partitions, double holdout){
CrossfoldTask cross = new CrossfoldTask(name)
.setSource(source)
.setPartitions(partitions)
.setHoldoutFraction(holdout);
addDataset(cross);
return this;
}
/**
* Add a new data set to be cross-folded. This method creates a new {@link CrossfoldTask}
* and passes it to {@link #addDataset(CrossfoldTask)}. All crossfold parameters that are not
* taken as arguments by this method are left at their defaults.
*
* @param source The source for the crossfold
* @param partitions The number of partitions
* @param holdout The holdout fraction
* @return Itself for chaining.
*/
public SimpleEvaluator addDataset(DataSource source, int partitions, double holdout){
return addDataset(source.getName(), source, partitions, holdout);
}
/**
* Add a new data set to be cross-folded. This method creates a new {@link CrossfoldTask}
* and passes it to {@link #addDataset(CrossfoldTask)}. All crossfold parameters that are not
* taken as arguments by this method are left at their defaults.
*
* Note: Prior to LensKit 2.2, this method used a holdout fraction of 0.2. In
* LensKit 2.2, it was changed to use the {@link CrossfoldTask}'s default holdout.
*
*
* @param name The name of the crossfold
* @param source The source for the crossfold
* @param partitions The number of partitions
* @return Itself for chaining.
*/
public SimpleEvaluator addDataset(String name, DataSource source, int partitions){
return addDataset(new CrossfoldTask(name).setSource(source).setPartitions(partitions));
}
/**
* Add a new data set to be cross-folded. This method creates a new {@link CrossfoldTask}
* and passes it to {@link #addDataset(CrossfoldTask)}. All crossfold parameters that are not
* taken as arguments by this method are left at their defaults.
*
* Note: Prior to LensKit 2.2, this method used a holdout fraction of 0.2. In
* LensKit 2.2, it was changed to use the {@link CrossfoldTask}'s default holdout.
*
*
* @param source The source for the crossfold
* @param partitions The number of partitions
* @return Itself for chaining.
*/
public SimpleEvaluator addDataset(DataSource source, int partitions){
return addDataset(source.getName(), source, partitions);
}
/**
* Adds a single {@code TTDataSet} to the {@code TrainTestEvalCommand}.
*
* This acts a wrapper around {@code TrainTestEvalCommand.addDataset}
* @param data The dataset to be added to the command.
* @return Itself to allow for method chaining.
*/
public SimpleEvaluator addDataset(TTDataSet data) {
result.addDataset(data);
return this;
}
/**
* This constructs a new {@code TTDataSet} and passes it to the {@code TrainTestEvalCommand}.
* @param name The name of the new dataset.
* @param train The {@code DAOFactory} with the train data.
* @param test The {@code DAOFactory} with the test data.
* @param dom The {@code PreferenceDomain} to be supplied to the new {@code TTDataSet}
* @return Itself for method chaining.
*/
public SimpleEvaluator addDataset(String name, EventDAO train, EventDAO test, PreferenceDomain dom){
result.addDataset(GenericTTDataSet.newBuilder(name)
.setTrain(new GenericDataSource(name + ".train", train, dom))
.setTest(new GenericDataSource(name + ".test", test, dom))
.build());
return this;
}
/**
* This constructs a new {@code TTDataSet} and passes it to the {@code TrainTestEvalCommand}.
*
* The name for the data source will default to 'generic-data-source'. Because of this,
* be careful of calling this method more than once.
*
* @param train The {@code DAOFactory} with the train data.
* @param test The {@code DAOFactory} with the test data.
* @return Itself for method chaining.
*/
public SimpleEvaluator addDataset(DataSource train, DataSource test){
result.addDataset(GenericTTDataSet.newBuilder("generic-data-source")
.setTrain(train)
.setTest(test)
.build());
return this;
}
/**
* Adds a completed metric to the {@code TrainTestEvalCommand}
* @param metric The metric to be added.
* @return Itself for method chaining.
*/
public SimpleEvaluator addMetric(Metric metric) {
result.addMetric(metric);
return this;
}
/**
* Adds a completed metric to the {@code TrainTestEvalCommand}
* @param metric The metric to be added.
* @return Itself for method chaining.
*/
public SimpleEvaluator addMetric(Class> metric) {
result.addMetric(metric);
return this;
}
/**
* This provides a wrapper around {@code TrainTestEvalCommand.setOutput()}
* @param file The file set as the output of the command
* @return Itself for method chaining
*/
public SimpleEvaluator setOutput(File file){
result.setOutput(file);
return this;
}
/**
* This provides a wrapper around {@code TrainTestEvalCommand.setPredictOutput}
* @param file The file set as the prediction output.
* @return
*/
public SimpleEvaluator setPredictOutput(File file){
result.setPredictOutput(file);
return this;
}
/**
* This provides a wrapper around {@code TrainTestEvalCommand.setUserOutput}
* @param file The file set as the prediction user.
* @return
*/
public SimpleEvaluator setUserOutput(File file){
result.setUserOutput(file);
return this;
}
/**
* Creates a new file with the {@code name} and passes it to
* {@code TrainTestEvalCommand.setOutput()}
* @param path The path to the file to be created
* @return Itself for method chaining
*/
public SimpleEvaluator setOutputPath(String path){
result.setOutput(new File(path));
return this;
}
/**
* Creates a new file with the {@code name} and passes it to
* {@code TrainTestEvalCommand.setPredictOutput()}
* @param path The path to the file to be created
* @return Itself for method chaining
*/
public SimpleEvaluator setPredictOutputPath(String path){
result.setPredictOutput(new File(path));
return this;
}
/**
* Creates a new file with the {@code name} and passes it to
* {@code TrainTestEvalCommand.setUserOutput()}
* @param path The path to the file to be created
* @return Itself for method chaining
*/
public SimpleEvaluator setUserOutputPath(String path){
result.setUserOutput(new File(path));
return this;
}
/**
* Provides raw unrestricted access for the command.
*
* Use this with caution! Calling certain methods on the {@code TrainTestDataSet} can force this command to throw
* an exception farther down the line.
*
* @return The raw partially configured command.
*/
public TrainTestEvalTask getRawCommand(){
return result;
}
/**
* If this is called more than once it will call of these commands again and most likely throw an exception.
*
* @return The table resulting from calling the command.
*/
@Override
public Table call() throws TaskExecutionException {
result.setProject(project);
try {
return result.perform();
} catch (InterruptedException e) {
throw new TaskExecutionException("execution interrupted", e);
}
}
}
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