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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 java.util.Set;
import com.google.common.base.Preconditions;
import com.google.common.collect.AbstractIterator;
import com.google.common.collect.Iterables;
import com.google.common.collect.Lists;
import com.google.common.collect.Sets;
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;
/**
* Does approximate nearest neighbor search by projecting the vectors similar to ProjectionSearch.
* The main difference between this class and the ProjectionSearch is the use of sorted arrays
* instead of binary search trees to implement the sets of scalar projections.
*
* Instead of taking log n time to add a vector to each of the vectors, * the pending additions are
* kept separate and are searched using a brute search. When there are "enough" pending additions,
* they're committed into the main pool of vectors.
*/
public class FastProjectionSearch extends UpdatableSearcher {
// The list of vectors that have not yet been projected (that are pending).
private final List pendingAdditions = Lists.newArrayList();
// The list of basis vectors. Populated when the first vector's dimension is know by calling
// initialize once.
private Matrix basisMatrix = null;
// The list of sorted lists of scalar projections. The outer list has one entry for each basis
// vector that all the other vectors will be projected on.
// For each basis vector, the inner list has an entry for each vector that has been projected.
// These entries are WeightedThing where the weight is the value of the scalar
// projection and the value is the vector begin referred to.
private List>> scalarProjections;
// The number of projection used for approximating the distance.
private final int numProjections;
// The number of elements to keep on both sides of the closest estimated distance as possible
// candidates for the best actual distance.
private final int searchSize;
// Initially, the dimension of the vectors searched by this searcher is unknown. After adding
// the first vector, the basis will be initialized. This marks whether initialization has
// happened or not so we only do it once.
private boolean initialized = false;
// Removing vectors from the searcher is done lazily to avoid the linear time cost of removing
// elements from an array. This member keeps track of the number of removed vectors (marked as
// "impossible" values in the array) so they can be removed when updating the structure.
private int numPendingRemovals = 0;
private static final double ADDITION_THRESHOLD = 0.05;
private static final double REMOVAL_THRESHOLD = 0.02;
public FastProjectionSearch(DistanceMeasure distanceMeasure, int numProjections, int searchSize) {
super(distanceMeasure);
Preconditions.checkArgument(numProjections > 0 && numProjections < 100,
"Unreasonable value for number of projections. Must be: 0 < numProjections < 100");
this.numProjections = numProjections;
this.searchSize = searchSize;
scalarProjections = Lists.newArrayListWithCapacity(numProjections);
for (int i = 0; i < numProjections; ++i) {
scalarProjections.add(Lists.>newArrayList());
}
}
private void initialize(int numDimensions) {
if (initialized) {
return;
}
basisMatrix = RandomProjector.generateBasisNormal(numProjections, numDimensions);
initialized = true;
}
/**
* Add a new Vector to the Searcher that will be checked when getting
* the nearest neighbors.
*
* The vector IS NOT CLONED. Do not modify the vector externally otherwise the internal
* Searcher data structures could be invalidated.
*/
@Override
public void add(Vector vector) {
initialize(vector.size());
pendingAdditions.add(vector);
}
/**
* Returns the number of WeightedVectors being searched for nearest neighbors.
*/
@Override
public int size() {
return pendingAdditions.size() + scalarProjections.get(0).size() - numPendingRemovals;
}
/**
* When querying the Searcher for the closest vectors, a list of WeightedThings is
* returned. The value of the WeightedThing is the neighbor and the weight is the
* the distance (calculated by some metric - see a concrete implementation) between the query
* and neighbor.
* The actual type of vector in the pair is the same as the vector added to the Searcher.
*/
@Override
public List> search(Vector query, int limit) {
reindex(false);
Set candidates = Sets.newHashSet();
Vector projection = basisMatrix.times(query);
for (int i = 0; i < basisMatrix.numRows(); ++i) {
List> currProjections = scalarProjections.get(i);
int middle = Collections.binarySearch(currProjections,
new WeightedThing(projection.get(i)));
if (middle < 0) {
middle = -(middle + 1);
}
for (int j = Math.max(0, middle - searchSize);
j < Math.min(currProjections.size(), middle + searchSize + 1); ++j) {
if (currProjections.get(j).getValue() == null) {
continue;
}
candidates.add(currProjections.get(j).getValue());
}
}
List> top =
Lists.newArrayListWithCapacity(candidates.size() + pendingAdditions.size());
for (Vector candidate : Iterables.concat(candidates, pendingAdditions)) {
top.add(new WeightedThing<>(candidate, distanceMeasure.distance(candidate, query)));
}
Collections.sort(top);
return top.subList(0, Math.min(top.size(), limit));
}
/**
* 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).
*
* @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) {
reindex(false);
double bestDistance = Double.POSITIVE_INFINITY;
Vector bestVector = null;
Vector projection = basisMatrix.times(query);
for (int i = 0; i < basisMatrix.numRows(); ++i) {
List> currProjections = scalarProjections.get(i);
int middle = Collections.binarySearch(currProjections,
new WeightedThing(projection.get(i)));
if (middle < 0) {
middle = -(middle + 1);
}
for (int j = Math.max(0, middle - searchSize);
j < Math.min(currProjections.size(), middle + searchSize + 1); ++j) {
if (currProjections.get(j).getValue() == null) {
continue;
}
Vector vector = currProjections.get(j).getValue();
double distance = distanceMeasure.distance(vector, query);
if (distance < bestDistance && (!differentThanQuery || !vector.equals(query))) {
bestDistance = distance;
bestVector = vector;
}
}
}
for (Vector vector : pendingAdditions) {
double distance = distanceMeasure.distance(vector, query);
if (distance < bestDistance && (!differentThanQuery || !vector.equals(query))) {
bestDistance = distance;
bestVector = vector;
}
}
return new WeightedThing<>(bestVector, bestDistance);
}
@Override
public boolean remove(Vector vector, double epsilon) {
WeightedThing closestPair = searchFirst(vector, false);
if (distanceMeasure.distance(closestPair.getValue(), vector) > epsilon) {
return false;
}
boolean isProjected = true;
Vector projection = basisMatrix.times(vector);
for (int i = 0; i < basisMatrix.numRows(); ++i) {
List> currProjections = scalarProjections.get(i);
WeightedThing searchedThing = new WeightedThing<>(projection.get(i));
int middle = Collections.binarySearch(currProjections, searchedThing);
if (middle < 0) {
isProjected = false;
break;
}
// Elements to be removed are kept in the sorted array until the next reindex, but their inner vector
// is set to null.
scalarProjections.get(i).set(middle, searchedThing);
}
if (isProjected) {
++numPendingRemovals;
return true;
}
for (int i = 0; i < pendingAdditions.size(); ++i) {
if (pendingAdditions.get(i).equals(vector)) {
pendingAdditions.remove(i);
break;
}
}
return true;
}
private void reindex(boolean force) {
int numProjected = scalarProjections.get(0).size();
if (force || pendingAdditions.size() > ADDITION_THRESHOLD * numProjected
|| numPendingRemovals > REMOVAL_THRESHOLD * numProjected) {
// We only need to copy the first list because when iterating we use only that list for the Vector
// references.
// see public Iterator iterator()
List>> scalarProjections = Lists.newArrayListWithCapacity(numProjections);
for (int i = 0; i < numProjections; ++i) {
if (i == 0) {
scalarProjections.add(Lists.newArrayList(this.scalarProjections.get(i)));
} else {
scalarProjections.add(this.scalarProjections.get(i));
}
}
// Project every pending vector onto every basis vector.
for (Vector pending : pendingAdditions) {
Vector projection = basisMatrix.times(pending);
for (int i = 0; i < numProjections; ++i) {
scalarProjections.get(i).add(new WeightedThing<>(pending, projection.get(i)));
}
}
pendingAdditions.clear();
// For each basis vector, sort the resulting list (for binary search) and remove the number
// of pending removals (it's the same for every basis vector) at the end (the weights are
// set to Double.POSITIVE_INFINITY when removing).
for (int i = 0; i < numProjections; ++i) {
List> currProjections = scalarProjections.get(i);
for (WeightedThing v : currProjections) {
if (v.getValue() == null) {
v.setWeight(Double.POSITIVE_INFINITY);
}
}
Collections.sort(currProjections);
for (int j = 0; j < numPendingRemovals; ++j) {
currProjections.remove(currProjections.size() - 1);
}
}
numPendingRemovals = 0;
this.scalarProjections = scalarProjections;
}
}
@Override
public void clear() {
pendingAdditions.clear();
for (int i = 0; i < numProjections; ++i) {
scalarProjections.get(i).clear();
}
numPendingRemovals = 0;
}
/**
* This iterates on the snapshot of the contents first instantiated regardless of any future modifications.
* Changes done after the iterator is created will not be visible to the iterator but will be visible
* when searching.
* @return iterator through the vectors in this searcher.
*/
@Override
public Iterator iterator() {
reindex(true);
return new AbstractIterator() {
private final Iterator> data = scalarProjections.get(0).iterator();
@Override
protected Vector computeNext() {
do {
if (!data.hasNext()) {
return endOfData();
}
WeightedThing next = data.next();
if (next.getValue() != null) {
return next.getValue();
}
} while (true);
}
};
}
}
