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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.apache.mahout.math.neighborhood;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import com.google.common.base.Function;
import com.google.common.base.Preconditions;
import com.google.common.collect.HashMultiset;
import com.google.common.collect.Iterators;
import com.google.common.collect.Lists;
import com.google.common.collect.Multiset;
import org.apache.lucene.util.PriorityQueue;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.random.RandomProjector;
import org.apache.mahout.math.random.WeightedThing;
import org.apache.mahout.math.stats.OnlineSummarizer;
/**
* Implements a Searcher that uses locality sensitivity hash as a first pass approximation
* to estimate distance without floating point math. The clever bit about this implementation
* is that it does an adaptive cutoff for the cutoff on the bitwise distance. Making this
* cutoff adaptive means that we only needs to make a single pass through the data.
*/
public class LocalitySensitiveHashSearch extends UpdatableSearcher {
/**
* Number of bits in the locality sensitive hash. 64 bits fix neatly into a long.
*/
private static final int BITS = 64;
/**
* Bit mask for the computed hash. Currently, it's 0xffffffffffff.
*/
private static final long BIT_MASK = -1L;
/**
* The maximum Hamming distance between two hashes that the hash limit can grow back to.
* It starts at BITS and decreases as more points than are needed are added to the candidate priority queue.
* But, after the observed distribution of distances becomes too good (we're seeing less than some percentage of the
* total number of points; using the hash strategy somewhere less than 25%) the limit is increased to compute
* more distances.
* This is because
*/
private static final int MAX_HASH_LIMIT = 32;
/**
* Minimum number of points with a given Hamming from the query that must be observed to consider raising the minimum
* distance for a candidate.
*/
private static final int MIN_DISTRIBUTION_COUNT = 10;
private final Multiset trainingVectors = HashMultiset.create();
/**
* This matrix of BITS random vectors is used to compute the Locality Sensitive Hash
* we compute the dot product with these vectors using a matrix multiplication and then use just
* sign of each result as one bit in the hash
*/
private Matrix projection;
/**
* The search size determines how many top results we retain. We do this because the hash distance
* isn't guaranteed to be entirely monotonic with respect to the real distance. To the extent that
* actual distance is well approximated by hash distance, then the searchSize can be decreased to
* roughly the number of results that you want.
*/
private int searchSize;
/**
* Controls how the hash limit is raised. 0 means use minimum of distribution, 1 means use first quartile.
* Intermediate values indicate an interpolation should be used. Negative values mean to never increase.
*/
private double hashLimitStrategy = 0.9;
/**
* Number of evaluations of the full distance between two points that was required.
*/
private int distanceEvaluations = 0;
/**
* Whether the projection matrix was initialized. This has to be deferred until the size of the vectors is known,
* effectively until the first vector is added.
*/
private boolean initialized = false;
public LocalitySensitiveHashSearch(DistanceMeasure distanceMeasure, int searchSize) {
super(distanceMeasure);
this.searchSize = searchSize;
this.projection = null;
}
private void initialize(int numDimensions) {
if (initialized) {
return;
}
initialized = true;
projection = RandomProjector.generateBasisNormal(BITS, numDimensions);
}
private PriorityQueue> searchInternal(Vector query) {
long queryHash = HashedVector.computeHash64(query, projection);
// We keep an approximation of the closest vectors here.
PriorityQueue> top = Searcher.getCandidateQueue(getSearchSize());
// We scan the vectors using bit counts as an approximation of the dot product so we can do as few
// full distance computations as possible. Our goal is to only do full distance computations for
// vectors with hash distance at most as large as the searchSize biggest hash distance seen so far.
OnlineSummarizer[] distribution = new OnlineSummarizer[BITS + 1];
for (int i = 0; i < BITS + 1; i++) {
distribution[i] = new OnlineSummarizer();
}
distanceEvaluations = 0;
// We keep the counts of the hash distances here. This lets us accurately
// judge what hash distance cutoff we should use.
int[] hashCounts = new int[BITS + 1];
// Maximum number of different bits to still consider a vector a candidate for nearest neighbor.
// Starts at the maximum number of bits, but decreases and can increase.
int hashLimit = BITS;
int limitCount = 0;
double distanceLimit = Double.POSITIVE_INFINITY;
// In this loop, we have the invariants that:
//
// limitCount = sum_{i= searchSize && limitCount - hashCount[hashLimit-1] < searchSize
for (HashedVector vector : trainingVectors) {
// This computes the Hamming Distance between the vector's hash and the query's hash.
// The result is correlated with the angle between the vectors.
int bitDot = vector.hammingDistance(queryHash);
if (bitDot <= hashLimit) {
distanceEvaluations++;
double distance = distanceMeasure.distance(query, vector);
distribution[bitDot].add(distance);
if (distance < distanceLimit) {
top.insertWithOverflow(new WeightedThing(vector, distance));
if (top.size() == searchSize) {
distanceLimit = top.top().getWeight();
}
hashCounts[bitDot]++;
limitCount++;
while (hashLimit > 0 && limitCount - hashCounts[hashLimit - 1] > searchSize) {
hashLimit--;
limitCount -= hashCounts[hashLimit];
}
if (hashLimitStrategy >= 0) {
while (hashLimit < MAX_HASH_LIMIT && distribution[hashLimit].getCount() > MIN_DISTRIBUTION_COUNT
&& ((1 - hashLimitStrategy) * distribution[hashLimit].getQuartile(0)
+ hashLimitStrategy * distribution[hashLimit].getQuartile(1)) < distanceLimit) {
limitCount += hashCounts[hashLimit];
hashLimit++;
}
}
}
}
}
return top;
}
@Override
public List> search(Vector query, int limit) {
PriorityQueue> top = searchInternal(query);
List> results = Lists.newArrayListWithExpectedSize(top.size());
while (top.size() != 0) {
WeightedThing wv = top.pop();
results.add(new WeightedThing<>(((HashedVector) wv.getValue()).getVector(), wv.getWeight()));
}
Collections.reverse(results);
if (limit < results.size()) {
results = results.subList(0, limit);
}
return results;
}
/**
* Returns the closest vector to the query.
* When only one the nearest vector is needed, use this method, NOT search(query, limit) because
* it's faster (less overhead).
* This is nearly the same as search().
*
* @param query the vector to search for
* @param differentThanQuery if true, returns the closest vector different than the query (this
* only matters if the query is among the searched vectors), otherwise,
* returns the closest vector to the query (even the same vector).
* @return the weighted vector closest to the query
*/
@Override
public WeightedThing searchFirst(Vector query, boolean differentThanQuery) {
// We get the top searchSize neighbors.
PriorityQueue> top = searchInternal(query);
// We then cut the number down to just the best 2.
while (top.size() > 2) {
top.pop();
}
// If there are fewer than 2 results, we just return the one we have.
if (top.size() < 2) {
return removeHash(top.pop());
}
// There are exactly 2 results.
WeightedThing secondBest = top.pop();
WeightedThing best = top.pop();
// If the best result is the same as the query, but we don't want to return the query.
if (differentThanQuery && best.getValue().equals(query)) {
best = secondBest;
}
return removeHash(best);
}
protected static WeightedThing removeHash(WeightedThing input) {
return new WeightedThing<>(((HashedVector) input.getValue()).getVector(), input.getWeight());
}
@Override
public void add(Vector vector) {
initialize(vector.size());
trainingVectors.add(new HashedVector(vector, projection, HashedVector.INVALID_INDEX, BIT_MASK));
}
@Override
public int size() {
return trainingVectors.size();
}
public int getSearchSize() {
return searchSize;
}
public void setSearchSize(int size) {
searchSize = size;
}
public void setRaiseHashLimitStrategy(double strategy) {
hashLimitStrategy = strategy;
}
/**
* This is only for testing.
* @return the number of times the actual distance between two vectors was computed.
*/
public int resetEvaluationCount() {
int result = distanceEvaluations;
distanceEvaluations = 0;
return result;
}
@Override
public Iterator iterator() {
return Iterators.transform(trainingVectors.iterator(), new Function() {
@Override
public Vector apply(org.apache.mahout.math.neighborhood.HashedVector input) {
Preconditions.checkNotNull(input);
//noinspection ConstantConditions
return input.getVector();
}
});
}
@Override
public boolean remove(Vector v, double epsilon) {
return trainingVectors.remove(new HashedVector(v, projection, HashedVector.INVALID_INDEX, BIT_MASK));
}
@Override
public void clear() {
trainingVectors.clear();
}
}
