/**
* 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.neighborhood;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.common.SamplingLongPrimitiveIterator;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import com.google.common.base.Preconditions;
/**
* <p>
* Computes a neigbhorhood consisting of all users whose similarity to the given user meets or exceeds a
* certain threshold. Similarity is defined by the given {@link UserSimilarity}.
* </p>
*/
public final class ThresholdUserNeighborhood extends AbstractUserNeighborhood {
private final double threshold;
/**
* @param threshold
* similarity threshold
* @param userSimilarity
* similarity metric
* @param dataModel
* data model
* @throws IllegalArgumentException
* if threshold is {@link Double#NaN}, or if samplingRate is not positive and less than or equal
* to 1.0, or if userSimilarity or dataModel are {@code null}
*/
public ThresholdUserNeighborhood(double threshold, UserSimilarity userSimilarity, DataModel dataModel) {
this(threshold, userSimilarity, dataModel, 1.0);
}
/**
* @param threshold
* similarity threshold
* @param userSimilarity
* similarity metric
* @param dataModel
* data model
* @param samplingRate
* percentage of users to consider when building neighborhood -- decrease to trade quality for
* performance
* @throws IllegalArgumentException
* if threshold or samplingRate is {@link Double#NaN}, or if samplingRate is not positive and less
* than or equal to 1.0, or if userSimilarity or dataModel are {@code null}
*/
public ThresholdUserNeighborhood(double threshold,
UserSimilarity userSimilarity,
DataModel dataModel,
double samplingRate) {
super(userSimilarity, dataModel, samplingRate);
Preconditions.checkArgument(!Double.isNaN(threshold), "threshold must not be NaN");
this.threshold = threshold;
}
@Override
public long[] getUserNeighborhood(long userID) throws TasteException {
DataModel dataModel = getDataModel();
FastIDSet neighborhood = new FastIDSet();
LongPrimitiveIterator usersIterable = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel
.getUserIDs(), getSamplingRate());
UserSimilarity userSimilarityImpl = getUserSimilarity();
while (usersIterable.hasNext()) {
long otherUserID = usersIterable.next();
if (userID != otherUserID) {
double theSimilarity = userSimilarityImpl.userSimilarity(userID, otherUserID);
if (!Double.isNaN(theSimilarity) && theSimilarity >= threshold) {
neighborhood.add(otherUserID);
}
}
}
return neighborhood.toArray();
}
@Override
public String toString() {
return "ThresholdUserNeighborhood";
}
}