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/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.codelibs.elasticsearch.taste.eval;
import java.util.Collection;
import java.util.List;
import java.util.Map;
import java.util.Random;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.util.concurrent.TimeUnit;
import org.apache.mahout.common.RandomUtils;
import org.codelibs.elasticsearch.taste.common.FastByIDMap;
import org.codelibs.elasticsearch.taste.common.LongPrimitiveIterator;
import org.codelibs.elasticsearch.taste.exception.NoSuchItemException;
import org.codelibs.elasticsearch.taste.exception.NoSuchUserException;
import org.codelibs.elasticsearch.taste.exception.TasteException;
import org.codelibs.elasticsearch.taste.model.DataModel;
import org.codelibs.elasticsearch.taste.model.GenericDataModel;
import org.codelibs.elasticsearch.taste.model.GenericPreference;
import org.codelibs.elasticsearch.taste.model.GenericUserPreferenceArray;
import org.codelibs.elasticsearch.taste.model.Preference;
import org.codelibs.elasticsearch.taste.model.PreferenceArray;
import org.codelibs.elasticsearch.taste.recommender.Recommender;
import org.codelibs.elasticsearch.taste.writer.ResultWriter;
import org.codelibs.elasticsearch.util.io.IOUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.google.common.base.Preconditions;
import com.google.common.collect.Lists;
/**
* Abstract superclass of a couple implementations, providing shared functionality.
*/
public abstract class AbstractDifferenceEvaluator implements Evaluator {
private static final Logger log = LoggerFactory
.getLogger(AbstractDifferenceEvaluator.class);
protected final Random random;
protected float maxPreference;
protected float minPreference;
protected ResultWriter resultWriter;
protected String id;
private boolean interrupted = false;
protected AbstractDifferenceEvaluator() {
random = RandomUtils.getRandom();
maxPreference = Float.NaN;
minPreference = Float.NaN;
}
@Override
public void setId(final String id) {
this.id = id;
}
@Override
public String getId() {
return id;
}
@Override
public void setResultWriter(final ResultWriter resultWriter) {
this.resultWriter = resultWriter;
}
public final float getMaxPreference() {
return maxPreference;
}
public final void setMaxPreference(final float maxPreference) {
this.maxPreference = maxPreference;
}
public final float getMinPreference() {
return minPreference;
}
public final void setMinPreference(final float minPreference) {
this.minPreference = minPreference;
}
@Override
public Evaluation evaluate(final RecommenderBuilder recommenderBuilder,
final DataModel dataModel, final EvaluationConfig config) {
Preconditions.checkNotNull(recommenderBuilder);
Preconditions.checkNotNull(dataModel);
final double trainingPercentage = config.getTrainingPercentage();
final double evaluationPercentage = config.getEvaluationPercentage();
Preconditions.checkArgument(trainingPercentage >= 0.0
&& trainingPercentage <= 1.0, "Invalid trainingPercentage: "
+ trainingPercentage
+ ". Must be: 0.0 <= trainingPercentage <= 1.0");
Preconditions.checkArgument(evaluationPercentage >= 0.0
&& evaluationPercentage <= 1.0,
"Invalid evaluationPercentage: " + evaluationPercentage
+ ". Must be: 0.0 <= evaluationPercentage <= 1.0");
log.info("Beginning evaluation using {} of {}", trainingPercentage,
dataModel);
final int numUsers = dataModel.getNumUsers();
final FastByIDMap trainingPrefs = new FastByIDMap(
1 + (int) (evaluationPercentage * numUsers));
final FastByIDMap testPrefs = new FastByIDMap(
1 + (int) (evaluationPercentage * numUsers));
final LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
final long userID = it.nextLong();
if (random.nextDouble() < evaluationPercentage) {
splitOneUsersPrefs(trainingPercentage, trainingPrefs,
testPrefs, userID, dataModel);
}
}
final DataModel trainingModel = new GenericDataModel(trainingPrefs);
final Recommender recommender = recommenderBuilder
.buildRecommender(trainingModel);
final Evaluation result = getEvaluation(testPrefs, recommender,
config.getMarginForError());
result.setTraining(trainingPrefs.size());
result.setTest(testPrefs.size());
log.info("Evaluation result: {}", result);
if (resultWriter != null) {
IOUtils.closeQuietly(resultWriter);
}
return result;
}
private void splitOneUsersPrefs(final double trainingPercentage,
final FastByIDMap trainingPrefs,
final FastByIDMap testPrefs, final long userID,
final DataModel dataModel) {
List oneUserTrainingPrefs = null;
List oneUserTestPrefs = null;
final PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
final int size = prefs.length();
for (int i = 0; i < size; i++) {
final Preference newPref = new GenericPreference(userID,
prefs.getItemID(i), prefs.getValue(i));
if (random.nextDouble() < trainingPercentage) {
if (oneUserTrainingPrefs == null) {
oneUserTrainingPrefs = Lists.newArrayListWithCapacity(3);
}
oneUserTrainingPrefs.add(newPref);
} else {
if (oneUserTestPrefs == null) {
oneUserTestPrefs = Lists.newArrayListWithCapacity(3);
}
oneUserTestPrefs.add(newPref);
}
}
if (oneUserTrainingPrefs != null) {
trainingPrefs.put(userID, new GenericUserPreferenceArray(
oneUserTrainingPrefs));
if (oneUserTestPrefs != null) {
testPrefs.put(userID, new GenericUserPreferenceArray(
oneUserTestPrefs));
}
}
}
protected Evaluation getEvaluation(
final FastByIDMap testPrefs,
final Recommender recommender, final float marginForError) {
reset();
final Collection> estimateCallables = Lists
.newArrayList();
for (final Map.Entry entry : testPrefs
.entrySet()) {
estimateCallables.add(new PreferenceEstimateCallable(recommender,
entry.getKey(), entry.getValue(), marginForError));
}
log.info("Beginning evaluation of {} users", estimateCallables.size());
final int numProcessors = Runtime.getRuntime().availableProcessors();
final ExecutorService executor = Executors
.newFixedThreadPool(numProcessors);
log.info("Starting timing of {} tasks in {} threads",
estimateCallables.size(), numProcessors);
EstimateStatsResult finalResult = null;
try {
final List> futures = executor
.invokeAll(estimateCallables);
int count = 0;
// Go look for exceptions here, really
for (final Future future : futures) {
final EstimateStatsResult result = future.get();
if (Thread.currentThread().isInterrupted()) {
throw new TasteException("Interrupted evaluator.");
}
if (finalResult == null) {
finalResult = result;
} else {
finalResult.merge(result);
}
if (count % 1000 == 0) {
final Runtime runtime = Runtime.getRuntime();
final long totalMemory = runtime.totalMemory();
final long memory = totalMemory - runtime.freeMemory();
log.info("Approximate memory used: {}MB / {}MB",
memory / 1000000L, totalMemory / 1000000L);
}
count++;
}
} catch (final InterruptedException ie) {
throw new TasteException(ie);
} catch (final ExecutionException ee) {
throw new TasteException(ee.getCause());
} finally {
executor.shutdown();
try {
executor.awaitTermination(10, TimeUnit.SECONDS);
} catch (final InterruptedException e) {
throw new TasteException(e.getCause());
}
}
final Evaluation evaluation = new Evaluation();
evaluation.setScore(computeFinalEvaluation());
if (finalResult != null) {
evaluation.setTotalProcessingTime(finalResult
.getTotalProcessingTime());
evaluation.setAverageProcessingTime(finalResult
.getAverageProcessingTime());
evaluation.setMaxProcessingTime(finalResult.getMaxProcessingTime());
evaluation.setSuccessful(finalResult.getSuccessful());
evaluation.setFailure(finalResult.getFailure());
evaluation.setNoEstimate(finalResult.getNoEstimate());
evaluation.setTotalPreference(finalResult.getTotalPreference());
}
return evaluation;
}
protected float capEstimatedPreference(final float estimate) {
if (estimate > maxPreference) {
return maxPreference;
}
if (estimate < minPreference) {
return minPreference;
}
return estimate;
}
protected abstract void reset();
protected abstract void processOneEstimate(float estimatedPreference,
Preference realPref);
protected abstract double computeFinalEvaluation();
protected static class EstimateStatsResult {
private int noEstimate = 0;
private int successful = 0;
private int failure = 0;
private int numOfTime = 0;
private long totalTime = 0;
private long maxTime = 0;
public void incrementNoEstimate() {
noEstimate++;
}
public void incrementSuccess() {
successful++;
}
public void incrementFailure() {
failure++;
}
public int getNoEstimate() {
return noEstimate;
}
public int getSuccessful() {
return successful;
}
public int getFailure() {
return failure;
}
public int getTotalPreference() {
return noEstimate + successful + failure;
}
public long getTotalProcessingTime() {
return totalTime;
}
public long getAverageProcessingTime() {
return totalTime / numOfTime;
}
public long getMaxProcessingTime() {
return maxTime;
}
public void addDuration(final long time) {
numOfTime++;
totalTime += time;
if (maxTime < time) {
maxTime = time;
}
}
public void merge(final EstimateStatsResult result) {
noEstimate += result.noEstimate;
successful += result.successful;
failure += result.failure;
numOfTime += result.numOfTime;
totalTime += result.totalTime;
maxTime += result.maxTime;
}
}
protected class PreferenceEstimateCallable implements
Callable {
private final Recommender recommender;
private final long testUserID;
private final PreferenceArray prefs;
private final float marginForError;
public PreferenceEstimateCallable(final Recommender recommender,
final long testUserID, final PreferenceArray prefs,
final float marginForError) {
this.recommender = recommender;
this.testUserID = testUserID;
this.prefs = prefs;
this.marginForError = marginForError;
}
@Override
public EstimateStatsResult call() {
final EstimateStatsResult stats = new EstimateStatsResult();
for (final Preference realPref : prefs) {
if (interrupted) {
break;
}
float estimatedPreference = Float.NaN;
final float actualPreference = realPref.getValue();
final long start = System.currentTimeMillis();
final long time;
try {
estimatedPreference = recommender.estimatePreference(
testUserID, realPref.getItemID());
} catch (final NoSuchUserException nsue) {
// It's possible that an item exists in the test data but not training data in which case
// NSEE will be thrown. Just ignore it and move on.
log.info(
"User exists in test data but not training data: {}",
testUserID);
} catch (final NoSuchItemException nsie) {
log.info(
"Item exists in test data but not training data: {}",
realPref.getItemID());
} finally {
time = System.currentTimeMillis() - start;
stats.addDuration(time);
}
String estimateResultType;
if (Float.isNaN(estimatedPreference)) {
estimateResultType = "no_estimate";
stats.incrementNoEstimate();
} else {
estimatedPreference = capEstimatedPreference(estimatedPreference);
processOneEstimate(estimatedPreference, realPref);
if (Math.abs(estimatedPreference - realPref.getValue()) < marginForError) {
estimateResultType = "success";
stats.incrementSuccess();
} else {
estimateResultType = "failure";
stats.incrementFailure();
}
}
if (resultWriter != null) {
resultWriter.write(id, testUserID, realPref.getItemID(),
estimateResultType, actualPreference,
estimatedPreference, time);
}
}
return stats;
}
}
@Override
public void interrupt() {
interrupted = true;
}
}
