/**
* 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.recommender.slopeone;
import java.util.Collection;
import java.util.Iterator;
import java.util.Map;
import java.util.concurrent.Callable;
import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;
import org.apache.mahout.cf.taste.common.Refreshable;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.common.Weighting;
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.RefreshHelper;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.slopeone.DiffStorage;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.google.common.base.Preconditions;
/**
* <p>
* An implementation of {@link DiffStorage} that merely stores item-item diffs in memory. It is fast, but can
* consume a great deal of memory.
* </p>
*/
@Deprecated
public final class MemoryDiffStorage implements DiffStorage {
private static final Logger log = LoggerFactory.getLogger(MemoryDiffStorage.class);
private final DataModel dataModel;
private final boolean stdDevWeighted;
private final long maxEntries;
private final FastByIDMap<FastByIDMap<RunningAverage>> averageDiffs;
private final FastByIDMap<RunningAverage> averageItemPref;
private final FastIDSet allRecommendableItemIDs;
private final ReadWriteLock buildAverageDiffsLock;
private final RefreshHelper refreshHelper;
/**
* <p>
* See {@link SlopeOneRecommender} for the meaning of
* {@code stdDevWeighted}. {@code maxEntries} controls the maximum number of
* item-item average preference differences that will be tracked internally. After the limit is reached, if
* a new item-item pair is observed in the data it will be ignored. This is recommended for large datasets.
* The first {@code maxEntries} item-item pairs observed in the data are tracked. Assuming that item
* ratings are reasonably distributed among users, this should only ignore item-item pairs that are very
* infrequently co-rated by a user. The intuition is that data on these infrequently co-rated item-item
* pairs is less reliable and should be the first that is ignored. This parameter can be used to limit the
* memory requirements of {@link SlopeOneRecommender}, which otherwise grow as the square of the number of
* items that exist in the {@link DataModel}. Memory requirements can reach gigabytes with only about 10000
* items, so this may be necessary on larger datasets.
*
* @param stdDevWeighted
* see {@link SlopeOneRecommender}
* @param maxEntries
* maximum number of item-item average preference differences to track internally
* @throws IllegalArgumentException
* if {@code maxEntries} is not positive or {@code dataModel} is null
*/
public MemoryDiffStorage(DataModel dataModel,
Weighting stdDevWeighted,
long maxEntries) throws TasteException {
Preconditions.checkArgument(dataModel != null, "dataModel is null");
Preconditions.checkArgument(dataModel.getNumItems() >= 1, "dataModel has no items");
Preconditions.checkArgument(maxEntries > 0L, "maxEntries must be positive");
this.dataModel = dataModel;
this.stdDevWeighted = stdDevWeighted == Weighting.WEIGHTED;
this.maxEntries = maxEntries;
this.averageDiffs = new FastByIDMap<FastByIDMap<RunningAverage>>();
this.averageItemPref = new FastByIDMap<RunningAverage>();
this.buildAverageDiffsLock = new ReentrantReadWriteLock();
this.allRecommendableItemIDs = new FastIDSet(dataModel.getNumItems());
this.refreshHelper = new RefreshHelper(new Callable<Object>() {
@Override
public Object call() throws TasteException {
buildAverageDiffs();
return null;
}
});
refreshHelper.addDependency(dataModel);
buildAverageDiffs();
}
@Override
public RunningAverage getDiff(long itemID1, long itemID2) {
boolean inverted = false;
if (itemID1 > itemID2) {
inverted = true;
long temp = itemID1;
itemID1 = itemID2;
itemID2 = temp;
}
FastByIDMap<RunningAverage> level2Map;
try {
buildAverageDiffsLock.readLock().lock();
level2Map = averageDiffs.get(itemID1);
} finally {
buildAverageDiffsLock.readLock().unlock();
}
RunningAverage average = null;
if (level2Map != null) {
average = level2Map.get(itemID2);
}
if (inverted) {
return average == null ? null : average.inverse();
} else {
return average;
}
}
@Override
public RunningAverage[] getDiffs(long userID, long itemID, PreferenceArray prefs) {
try {
buildAverageDiffsLock.readLock().lock();
int size = prefs.length();
RunningAverage[] result = new RunningAverage[size];
for (int i = 0; i < size; i++) {
result[i] = getDiff(prefs.getItemID(i), itemID);
}
return result;
} finally {
buildAverageDiffsLock.readLock().unlock();
}
}
@Override
public RunningAverage getAverageItemPref(long itemID) {
return averageItemPref.get(itemID);
}
@Override
public void addItemPref(long userID, long itemIDA, float prefValue) throws TasteException {
PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID);
try {
buildAverageDiffsLock.writeLock().lock();
FastByIDMap<RunningAverage> aMap = averageDiffs.get(itemIDA);
if (aMap == null) {
aMap = new FastByIDMap<RunningAverage>();
averageDiffs.put(itemIDA, aMap);
}
int length = userPreferences.length();
for (int i = 0; i < length; i++) {
long itemIDB = userPreferences.getItemID(i);
float bValue = userPreferences.getValue(i);
if (itemIDA < itemIDB) {
RunningAverage average = aMap.get(itemIDB);
if (average == null) {
average = buildRunningAverage();
aMap.put(itemIDB, average);
}
average.addDatum(bValue - prefValue);
} else {
FastByIDMap<RunningAverage> bMap = averageDiffs.get(itemIDB);
if (bMap == null) {
bMap = new FastByIDMap<RunningAverage>();
averageDiffs.put(itemIDB, bMap);
}
RunningAverage average = bMap.get(itemIDA);
if (average == null) {
average = buildRunningAverage();
bMap.put(itemIDA, average);
}
average.addDatum(prefValue - bValue);
}
}
} finally {
buildAverageDiffsLock.writeLock().unlock();
}
}
@Override
public void updateItemPref(long itemID, float prefDelta) {
if (stdDevWeighted) {
throw new UnsupportedOperationException("Can't update only when stdDevWeighted is set");
}
try {
buildAverageDiffsLock.readLock().lock();
for (Map.Entry<Long,FastByIDMap<RunningAverage>> entry : averageDiffs.entrySet()) {
boolean matchesItemID1 = itemID == entry.getKey();
for (Map.Entry<Long,RunningAverage> entry2 : entry.getValue().entrySet()) {
RunningAverage average = entry2.getValue();
if (matchesItemID1) {
average.changeDatum(-prefDelta);
} else if (itemID == entry2.getKey()) {
average.changeDatum(prefDelta);
}
}
}
RunningAverage itemAverage = averageItemPref.get(itemID);
if (itemAverage != null) {
itemAverage.changeDatum(prefDelta);
}
} finally {
buildAverageDiffsLock.readLock().unlock();
}
}
@Override
public void removeItemPref(long userID, long itemIDA, float prefValue) throws TasteException {
PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID);
try {
buildAverageDiffsLock.writeLock().lock();
FastByIDMap<RunningAverage> aMap = averageDiffs.get(itemIDA);
int length = userPreferences.length();
for (int i = 0; i < length; i++) {
long itemIDB = userPreferences.getItemID(i);
float bValue = userPreferences.getValue(i);
if (itemIDA < itemIDB) {
if (aMap != null) {
RunningAverage average = aMap.get(itemIDB);
if (average != null) {
if (average.getCount() <= 1) {
aMap.remove(itemIDB);
} else {
average.removeDatum(bValue - prefValue);
}
}
}
} else if (itemIDA > itemIDB) {
FastByIDMap<RunningAverage> bMap = averageDiffs.get(itemIDB);
if (bMap != null) {
RunningAverage average = bMap.get(itemIDA);
if (average != null) {
if (average.getCount() <= 1) {
aMap.remove(itemIDA);
} else {
average.removeDatum(prefValue - bValue);
}
}
}
}
}
} finally {
buildAverageDiffsLock.writeLock().unlock();
}
}
@Override
public FastIDSet getRecommendableItemIDs(long userID) throws TasteException {
FastIDSet result;
try {
buildAverageDiffsLock.readLock().lock();
result = allRecommendableItemIDs.clone();
} finally {
buildAverageDiffsLock.readLock().unlock();
}
Iterator<Long> it = result.iterator();
while (it.hasNext()) {
if (dataModel.getPreferenceValue(userID, it.next()) != null) {
it.remove();
}
}
return result;
}
private void buildAverageDiffs() throws TasteException {
log.info("Building average diffs...");
try {
buildAverageDiffsLock.writeLock().lock();
averageDiffs.clear();
long averageCount = 0L;
LongPrimitiveIterator it = dataModel.getUserIDs();
while (it.hasNext()) {
averageCount = processOneUser(averageCount, it.nextLong());
}
pruneInconsequentialDiffs();
updateAllRecommendableItems();
} finally {
buildAverageDiffsLock.writeLock().unlock();
}
}
private void pruneInconsequentialDiffs() {
// Go back and prune inconsequential diffs. "Inconsequential" means, here, only represented by one
// data point, so possibly unreliable
Iterator<Map.Entry<Long,FastByIDMap<RunningAverage>>> it1 = averageDiffs.entrySet().iterator();
while (it1.hasNext()) {
FastByIDMap<RunningAverage> map = it1.next().getValue();
Iterator<Map.Entry<Long,RunningAverage>> it2 = map.entrySet().iterator();
while (it2.hasNext()) {
RunningAverage average = it2.next().getValue();
if (average.getCount() <= 1) {
it2.remove();
}
}
if (map.isEmpty()) {
it1.remove();
} else {
map.rehash();
}
}
averageDiffs.rehash();
}
private void updateAllRecommendableItems() throws TasteException {
FastIDSet ids = new FastIDSet(dataModel.getNumItems());
for (Map.Entry<Long,FastByIDMap<RunningAverage>> entry : averageDiffs.entrySet()) {
ids.add(entry.getKey());
LongPrimitiveIterator it = entry.getValue().keySetIterator();
while (it.hasNext()) {
ids.add(it.next());
}
}
allRecommendableItemIDs.clear();
allRecommendableItemIDs.addAll(ids);
allRecommendableItemIDs.rehash();
}
private long processOneUser(long averageCount, long userID) throws TasteException {
log.debug("Processing prefs for user {}", userID);
// Save off prefs for the life of this loop iteration
PreferenceArray userPreferences = dataModel.getPreferencesFromUser(userID);
int length = userPreferences.length();
for (int i = 0; i < length; i++) { // Loop to length-1, not length-2, not for diffs but average item pref
float prefAValue = userPreferences.getValue(i);
long itemIDA = userPreferences.getItemID(i);
FastByIDMap<RunningAverage> aMap = averageDiffs.get(itemIDA);
if (aMap == null) {
aMap = new FastByIDMap<RunningAverage>();
averageDiffs.put(itemIDA, aMap);
}
for (int j = i + 1; j < length; j++) {
// This is a performance-critical block
long itemIDB = userPreferences.getItemID(j);
RunningAverage average = aMap.get(itemIDB);
if (average == null && averageCount < maxEntries) {
average = buildRunningAverage();
aMap.put(itemIDB, average);
averageCount++;
}
if (average != null) {
average.addDatum(userPreferences.getValue(j) - prefAValue);
}
}
RunningAverage itemAverage = averageItemPref.get(itemIDA);
if (itemAverage == null) {
itemAverage = buildRunningAverage();
averageItemPref.put(itemIDA, itemAverage);
}
itemAverage.addDatum(prefAValue);
}
return averageCount;
}
private RunningAverage buildRunningAverage() {
return stdDevWeighted ? new FullRunningAverageAndStdDev() : new FullRunningAverage();
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
refreshHelper.refresh(alreadyRefreshed);
}
@Override
public String toString() {
return "MemoryDiffStorage";
}
}