org.apache.mahout.cf.taste.impl.recommender.TopItems 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.recommender;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Queue;
import com.google.common.base.Preconditions;
import org.apache.mahout.cf.taste.common.NoSuchItemException;
import org.apache.mahout.cf.taste.common.NoSuchUserException;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity;
import org.apache.mahout.cf.taste.impl.similarity.GenericUserSimilarity;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
/**
*
* A simple class that refactors the "find top N things" logic that is used in several places.
*
*/
public final class TopItems {
private static final long[] NO_IDS = new long[0];
private TopItems() { }
public static List getTopItems(int howMany,
LongPrimitiveIterator possibleItemIDs,
IDRescorer rescorer,
Estimator estimator) throws TasteException {
Preconditions.checkArgument(possibleItemIDs != null, "possibleItemIDs is null");
Preconditions.checkArgument(estimator != null, "estimator is null");
Queue topItems = new PriorityQueue<>(howMany + 1,
Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));
boolean full = false;
double lowestTopValue = Double.NEGATIVE_INFINITY;
while (possibleItemIDs.hasNext()) {
long itemID = possibleItemIDs.next();
if (rescorer == null || !rescorer.isFiltered(itemID)) {
double preference;
try {
preference = estimator.estimate(itemID);
} catch (NoSuchItemException nsie) {
continue;
}
double rescoredPref = rescorer == null ? preference : rescorer.rescore(itemID, preference);
if (!Double.isNaN(rescoredPref) && (!full || rescoredPref > lowestTopValue)) {
topItems.add(new GenericRecommendedItem(itemID, (float) rescoredPref));
if (full) {
topItems.poll();
} else if (topItems.size() > howMany) {
full = true;
topItems.poll();
}
lowestTopValue = topItems.peek().getValue();
}
}
}
int size = topItems.size();
if (size == 0) {
return Collections.emptyList();
}
List result = new ArrayList<>(size);
result.addAll(topItems);
Collections.sort(result, ByValueRecommendedItemComparator.getInstance());
return result;
}
public static long[] getTopUsers(int howMany,
LongPrimitiveIterator allUserIDs,
IDRescorer rescorer,
Estimator estimator) throws TasteException {
Queue topUsers = new PriorityQueue<>(howMany + 1, Collections.reverseOrder());
boolean full = false;
double lowestTopValue = Double.NEGATIVE_INFINITY;
while (allUserIDs.hasNext()) {
long userID = allUserIDs.next();
if (rescorer != null && rescorer.isFiltered(userID)) {
continue;
}
double similarity;
try {
similarity = estimator.estimate(userID);
} catch (NoSuchUserException nsue) {
continue;
}
double rescoredSimilarity = rescorer == null ? similarity : rescorer.rescore(userID, similarity);
if (!Double.isNaN(rescoredSimilarity) && (!full || rescoredSimilarity > lowestTopValue)) {
topUsers.add(new SimilarUser(userID, rescoredSimilarity));
if (full) {
topUsers.poll();
} else if (topUsers.size() > howMany) {
full = true;
topUsers.poll();
}
lowestTopValue = topUsers.peek().getSimilarity();
}
}
int size = topUsers.size();
if (size == 0) {
return NO_IDS;
}
List sorted = new ArrayList<>(size);
sorted.addAll(topUsers);
Collections.sort(sorted);
long[] result = new long[size];
int i = 0;
for (SimilarUser similarUser : sorted) {
result[i++] = similarUser.getUserID();
}
return result;
}
/**
*
* Thanks to tsmorton for suggesting this functionality and writing part of the code.
*
*
* @see GenericItemSimilarity#GenericItemSimilarity(Iterable, int)
* @see GenericItemSimilarity#GenericItemSimilarity(org.apache.mahout.cf.taste.similarity.ItemSimilarity,
* org.apache.mahout.cf.taste.model.DataModel, int)
*/
public static List getTopItemItemSimilarities(
int howMany, Iterator allSimilarities) {
Queue topSimilarities
= new PriorityQueue<>(howMany + 1, Collections.reverseOrder());
boolean full = false;
double lowestTopValue = Double.NEGATIVE_INFINITY;
while (allSimilarities.hasNext()) {
GenericItemSimilarity.ItemItemSimilarity similarity = allSimilarities.next();
double value = similarity.getValue();
if (!Double.isNaN(value) && (!full || value > lowestTopValue)) {
topSimilarities.add(similarity);
if (full) {
topSimilarities.poll();
} else if (topSimilarities.size() > howMany) {
full = true;
topSimilarities.poll();
}
lowestTopValue = topSimilarities.peek().getValue();
}
}
int size = topSimilarities.size();
if (size == 0) {
return Collections.emptyList();
}
List result = new ArrayList<>(size);
result.addAll(topSimilarities);
Collections.sort(result);
return result;
}
public static List getTopUserUserSimilarities(
int howMany, Iterator allSimilarities) {
Queue topSimilarities
= new PriorityQueue<>(howMany + 1, Collections.reverseOrder());
boolean full = false;
double lowestTopValue = Double.NEGATIVE_INFINITY;
while (allSimilarities.hasNext()) {
GenericUserSimilarity.UserUserSimilarity similarity = allSimilarities.next();
double value = similarity.getValue();
if (!Double.isNaN(value) && (!full || value > lowestTopValue)) {
topSimilarities.add(similarity);
if (full) {
topSimilarities.poll();
} else if (topSimilarities.size() > howMany) {
full = true;
topSimilarities.poll();
}
lowestTopValue = topSimilarities.peek().getValue();
}
}
int size = topSimilarities.size();
if (size == 0) {
return Collections.emptyList();
}
List result = new ArrayList<>(size);
result.addAll(topSimilarities);
Collections.sort(result);
return result;
}
public interface Estimator {
double estimate(T thing) throws TasteException;
}
}