/*
* 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.Throwables;
import com.google.common.collect.Lists;
import it.unimi.dsi.fastutil.longs.LongIterator;
import it.unimi.dsi.fastutil.longs.LongSortedSet;
import org.apache.commons.lang3.tuple.Pair;
import org.grouplens.lenskit.Recommender;
import org.grouplens.lenskit.collections.LongUtils;
import org.grouplens.lenskit.eval.Attributed;
import org.grouplens.lenskit.eval.data.traintest.TTDataSet;
import org.grouplens.lenskit.eval.metrics.AbstractMetric;
import org.grouplens.lenskit.symbols.Symbol;
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.grouplens.lenskit.vectors.MutableSparseVector;
import org.grouplens.lenskit.vectors.SparseVector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.File;
import java.io.IOException;
import java.util.Collections;
import java.util.List;
/**
* Predict metric that writes predictions to a file.
*
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class OutputPredictMetric extends AbstractMetric<OutputPredictMetric.Context, Void, Void> {
private static final Logger logger = LoggerFactory.getLogger(OutputPredictMetric.class);
private final ExperimentOutputLayout outputLayout;
private final TableWriter tableWriter;
private final List<Pair<Symbol, String>> channels;
public OutputPredictMetric(ExperimentOutputLayout layout, File file,
List<Pair<Symbol, String>> chans) throws IOException {
super(Void.TYPE, Void.TYPE);
outputLayout = layout;
channels = chans;
TableLayoutBuilder lb = TableLayoutBuilder.copy(layout.getCommonLayout())
.addColumn("User")
.addColumn("Item")
.addColumn("Rating")
.addColumn("Prediction");
for (Pair<Symbol, String> chan: channels) {
lb.addColumn(chan.getRight());
}
tableWriter = CSVWriter.open(file, lb.build());
}
@Override
public Context createContext(Attributed algo, TTDataSet ds, Recommender rec) {
return new Context(outputLayout.prefixTable(tableWriter, algo, ds));
}
@Override
public Void doMeasureUser(TestUser user, Context context) {
SparseVector ratings = user.getTestRatings();
SparseVector predictions = user.getPredictions();
if (predictions == null) {
predictions = MutableSparseVector.create();
}
LongSortedSet items = ratings.keySet();
if (!items.containsAll(predictions.keySet())) {
items = LongUtils.setUnion(items, predictions.keySet());
}
logger.debug("outputting {} predictions for user {}", predictions.size(), user.getUserId());
for (LongIterator iter = items.iterator(); iter.hasNext(); /* no increment */) {
long item = iter.nextLong();
Double rating = ratings.containsKey(item) ? ratings.get(item) : null;
Double pred = predictions.containsKey(item) ? predictions.get(item) : null;
try {
List<Object> row = Lists.<Object>newArrayList(user.getUserId(), item, rating, pred);
for (Pair<Symbol, String> chan: channels) {
SparseVector v = predictions.getChannelVector(chan.getLeft());
if (v != null && v.containsKey(item)) {
row.add(v.get(item));
} else {
row.add(null);
}
}
context.writer.writeRow(row);
} catch (IOException e) {
throw Throwables.propagate(e);
}
}
return null;
}
@Override
protected Void getTypedResults(Context context) {
return null;
}
@Override
public void close() throws IOException {
tableWriter.close();
}
public static class Context {
private final TableWriter writer;
Context(TableWriter tw) {
writer = tw;
}
}
public static class Factory extends MetricFactory<Context> {
private final List<Pair<Symbol, String>> predictChannels;
public Factory(List<Pair<Symbol, String>> pchans) {
predictChannels = pchans;
}
@Override
public OutputPredictMetric createMetric(TrainTestEvalTask task) throws IOException {
return new OutputPredictMetric(task.getOutputLayout(), task.getPredictOutput(),
predictChannels);
}
@Override
public List<String> getColumnLabels() {
return Collections.emptyList();
}
@Override
public List<String> getUserColumnLabels() {
return Collections.emptyList();
}
}
}