org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator Maven / Gradle / Ivy
/**
* 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.apache.mahout.cf.taste.impl.eval;
import java.util.List;
import java.util.Random;
import org.apache.mahout.cf.taste.common.NoSuchUserException;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.DataModelBuilder;
import org.apache.mahout.cf.taste.eval.IRStatistics;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.eval.RelevantItemsDataSplitter;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.common.FullRunningAverage;
import org.apache.mahout.cf.taste.impl.common.FullRunningAverageAndStdDev;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.apache.mahout.cf.taste.impl.common.RunningAverageAndStdDev;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.common.RandomUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.google.common.base.Preconditions;
/**
*
* For each user, these implementation determine the top {@code n} preferences, then evaluate the IR
* statistics based on a {@link DataModel} that does not have these values. This number {@code n} is the
* "at" value, as in "precision at 5". For example, this would mean precision evaluated by removing the top 5
* preferences for a user and then finding the percentage of those 5 items included in the top 5
* recommendations for that user.
*
*/
public final class GenericRecommenderIRStatsEvaluator implements RecommenderIRStatsEvaluator {
private static final Logger log = LoggerFactory.getLogger(GenericRecommenderIRStatsEvaluator.class);
private static final double LOG2 = Math.log(2.0);
/**
* Pass as "relevanceThreshold" argument to
* {@link #evaluate(RecommenderBuilder, DataModelBuilder, DataModel, IDRescorer, int, double, double)} to
* have it attempt to compute a reasonable threshold. Note that this will impact performance.
*/
public static final double CHOOSE_THRESHOLD = Double.NaN;
private final Random random;
private final RelevantItemsDataSplitter dataSplitter;
public GenericRecommenderIRStatsEvaluator() {
this(new GenericRelevantItemsDataSplitter());
}
public GenericRecommenderIRStatsEvaluator(RelevantItemsDataSplitter dataSplitter) {
Preconditions.checkNotNull(dataSplitter);
random = RandomUtils.getRandom();
this.dataSplitter = dataSplitter;
}
@Override
public IRStatistics evaluate(RecommenderBuilder recommenderBuilder,
DataModelBuilder dataModelBuilder,
DataModel dataModel,
IDRescorer rescorer,
int at,
double relevanceThreshold,
double evaluationPercentage) throws TasteException {
Preconditions.checkArgument(recommenderBuilder != null, "recommenderBuilder is null");
Preconditions.checkArgument(dataModel != null, "dataModel is null");
Preconditions.checkArgument(at >= 1, "at must be at least 1");
Preconditions.checkArgument(evaluationPercentage > 0.0 && evaluationPercentage <= 1.0,
"Invalid evaluationPercentage: " + evaluationPercentage + ". Must be: 0.0 < evaluationPercentage <= 1.0");
int numItems = dataModel.getNumItems();
RunningAverage precision = new FullRunningAverage();
RunningAverage recall = new FullRunningAverage();
RunningAverage fallOut = new FullRunningAverage();
RunningAverage nDCG = new FullRunningAverage();
int numUsersRecommendedFor = 0;
int numUsersWithRecommendations = 0;
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
long userID = it.nextLong();
if (random.nextDouble() >= evaluationPercentage) {
// Skipped
continue;
}
long start = System.currentTimeMillis();
PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
// List some most-preferred items that would count as (most) "relevant" results
double theRelevanceThreshold = Double.isNaN(relevanceThreshold) ? computeThreshold(prefs) : relevanceThreshold;
FastIDSet relevantItemIDs = dataSplitter.getRelevantItemsIDs(userID, at, theRelevanceThreshold, dataModel);
int numRelevantItems = relevantItemIDs.size();
if (numRelevantItems <= 0) {
continue;
}
FastByIDMap trainingUsers = new FastByIDMap<>(dataModel.getNumUsers());
LongPrimitiveIterator it2 = dataModel.getUserIDs();
while (it2.hasNext()) {
dataSplitter.processOtherUser(userID, relevantItemIDs, trainingUsers, it2.nextLong(), dataModel);
}
DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
: dataModelBuilder.buildDataModel(trainingUsers);
try {
trainingModel.getPreferencesFromUser(userID);
} catch (NoSuchUserException nsee) {
continue; // Oops we excluded all prefs for the user -- just move on
}
int size = numRelevantItems + trainingModel.getItemIDsFromUser(userID).size();
if (size < 2 * at) {
// Really not enough prefs to meaningfully evaluate this user
continue;
}
Recommender recommender = recommenderBuilder.buildRecommender(trainingModel);
int intersectionSize = 0;
List recommendedItems = recommender.recommend(userID, at, rescorer);
for (RecommendedItem recommendedItem : recommendedItems) {
if (relevantItemIDs.contains(recommendedItem.getItemID())) {
intersectionSize++;
}
}
int numRecommendedItems = recommendedItems.size();
// Precision
if (numRecommendedItems > 0) {
precision.addDatum((double) intersectionSize / (double) numRecommendedItems);
}
// Recall
recall.addDatum((double) intersectionSize / (double) numRelevantItems);
// Fall-out
if (numRelevantItems < size) {
fallOut.addDatum((double) (numRecommendedItems - intersectionSize)
/ (double) (numItems - numRelevantItems));
}
// nDCG
// In computing, assume relevant IDs have relevance 1 and others 0
double cumulativeGain = 0.0;
double idealizedGain = 0.0;
for (int i = 0; i < numRecommendedItems; i++) {
RecommendedItem item = recommendedItems.get(i);
double discount = 1.0 / log2(i + 2.0); // Classical formulation says log(i+1), but i is 0-based here
if (relevantItemIDs.contains(item.getItemID())) {
cumulativeGain += discount;
}
// otherwise we're multiplying discount by relevance 0 so it doesn't do anything
// Ideally results would be ordered with all relevant ones first, so this theoretical
// ideal list starts with number of relevant items equal to the total number of relevant items
if (i < numRelevantItems) {
idealizedGain += discount;
}
}
if (idealizedGain > 0.0) {
nDCG.addDatum(cumulativeGain / idealizedGain);
}
// Reach
numUsersRecommendedFor++;
if (numRecommendedItems > 0) {
numUsersWithRecommendations++;
}
long end = System.currentTimeMillis();
log.info("Evaluated with user {} in {}ms", userID, end - start);
log.info("Precision/recall/fall-out/nDCG/reach: {} / {} / {} / {} / {}",
precision.getAverage(), recall.getAverage(), fallOut.getAverage(), nDCG.getAverage(),
(double) numUsersWithRecommendations / (double) numUsersRecommendedFor);
}
return new IRStatisticsImpl(
precision.getAverage(),
recall.getAverage(),
fallOut.getAverage(),
nDCG.getAverage(),
(double) numUsersWithRecommendations / (double) numUsersRecommendedFor);
}
private static double computeThreshold(PreferenceArray prefs) {
if (prefs.length() < 2) {
// Not enough data points -- return a threshold that allows everything
return Double.NEGATIVE_INFINITY;
}
RunningAverageAndStdDev stdDev = new FullRunningAverageAndStdDev();
int size = prefs.length();
for (int i = 0; i < size; i++) {
stdDev.addDatum(prefs.getValue(i));
}
return stdDev.getAverage() + stdDev.getStandardDeviation();
}
private static double log2(double value) {
return Math.log(value) / LOG2;
}
}