/*
* 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.Preconditions;
import org.grouplens.grapht.Component;
import org.grouplens.grapht.Dependency;
import org.grouplens.grapht.InjectionException;
import org.grouplens.grapht.graph.DAGNode;
import org.grouplens.lenskit.RecommenderBuildException;
import org.grouplens.lenskit.core.LenskitRecommender;
import org.grouplens.lenskit.data.dao.UserEventDAO;
import org.grouplens.lenskit.data.event.Event;
import org.grouplens.lenskit.data.history.UserHistory;
import org.grouplens.lenskit.eval.algorithm.AlgorithmInstance;
import org.grouplens.lenskit.eval.data.traintest.TTDataSet;
import org.grouplens.lenskit.eval.metrics.Metric;
import org.grouplens.lenskit.inject.GraphtUtils;
import org.grouplens.lenskit.inject.NodeProcessors;
import org.grouplens.lenskit.inject.RecommenderInstantiator;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.LinkedHashSet;
import java.util.Set;
/**
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
class LenskitEvalJob extends TrainTestJob {
private DAGNode<Component, Dependency> recommenderGraph;
@Nullable
private final ComponentCache cache;
private LenskitRecommender recommender;
private UserEventDAO userEvents;
LenskitEvalJob(TrainTestEvalTask task,
@Nonnull AlgorithmInstance algo,
@Nonnull TTDataSet ds,
DAGNode<Component,Dependency> graph,
@Nullable ComponentCache cache) {
super(task, algo, ds);
recommenderGraph = graph;
this.cache = cache;
}
@Override
protected void buildRecommender() throws RecommenderBuildException {
Preconditions.checkState(recommender == null, "recommender already built");
logger.debug("Starting recommender build");
DAGNode<Component, Dependency> graph;
if (cache == null) {
logger.debug("Building directly without a cache");
RecommenderInstantiator ri = RecommenderInstantiator.create(recommenderGraph);
graph = ri.instantiate();
} else {
logger.debug("Instantiating graph with a cache");
try {
Set<DAGNode<Component, Dependency>> nodes = GraphtUtils.getShareableNodes(recommenderGraph);
logger.debug("resolving {} nodes", nodes.size());
graph = NodeProcessors.processNodes(recommenderGraph, nodes, cache);
logger.debug("graph went from {} to {} nodes",
recommenderGraph.getReachableNodes().size(),
graph.getReachableNodes().size());
} catch (InjectionException e) {
logger.error("Error encountered while pre-processing algorithm components for sharing", e);
throw new RecommenderBuildException("Pre-processing of algorithm components for sharing failed.", e);
}
}
recommender = new LenskitRecommender(graph);
// pre-fetch the test DAO
userEvents = dataSet.getTestData().getUserEventDAO();
}
@Override
protected <A> MetricWithAccumulator<A> makeMetricAccumulator(Metric<A> metric) {
return new MetricWithAccumulator<A>(metric, metric.createContext(algorithmInfo, dataSet, recommender));
}
@Override
protected TestUser getUserResults(long uid) {
Preconditions.checkState(recommender != null, "recommender not built");
UserHistory<Event> userData = userEvents.getEventsForUser(uid);
return new LenskitTestUser(recommender, userData);
}
@Override
protected void cleanup() {
recommender = null;
userEvents = null;
// why do we clear the graph?
recommenderGraph = null;
}
}