mayya-sharipova commented on code in PR #13604: URL: https://github.com/apache/lucene/pull/13604#discussion_r1688624775
########## lucene/sandbox/src/java/org/apache/lucene/sandbox/codecs/quantization/KMeans.java: ########## @@ -0,0 +1,344 @@ +/* + * 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.lucene.sandbox.codecs.quantization; + +import static org.apache.lucene.sandbox.codecs.quantization.SampleReader.createSampleReader; + +import java.io.IOException; +import java.util.Arrays; +import java.util.HashSet; +import java.util.Random; +import java.util.Set; +import org.apache.lucene.index.VectorSimilarityFunction; +import org.apache.lucene.util.ArrayUtil; +import org.apache.lucene.util.VectorUtil; +import org.apache.lucene.util.hnsw.RandomAccessVectorValues; + +/** KMeans clustering algorithm for vectors */ +public class KMeans { + public static final int DEFAULT_RESTARTS = 5; + public static final int DEFAULT_ITRS = 10; + public static final int DEFAULT_SAMPLE_SIZE = 100_000; + + private final RandomAccessVectorValues.Floats vectors; + private final int numVectors; + private final int numCentroids; + private final Random random; + private final KmeansInitializationMethod initializationMethod; + private final int restarts; + private final int iters; + + /** + * Cluster vectors into a given number of clusters + * + * @param vectors float vectors + * @param similarityFunction vector similarity function. For COSINE similarity, vectors must be + * normalized. + * @param numClusters number of cluster to cluster vector into + * @return results of clustering: produced centroids and for each vector its centroid + * @throws IOException when if there is an error accessing vectors + */ + public static Results cluster( + RandomAccessVectorValues.Floats vectors, + VectorSimilarityFunction similarityFunction, + int numClusters) + throws IOException { + return cluster( + vectors, + numClusters, + true, + 42L, + KmeansInitializationMethod.PLUS_PLUS, + similarityFunction == VectorSimilarityFunction.COSINE, + DEFAULT_RESTARTS, + DEFAULT_ITRS, + DEFAULT_SAMPLE_SIZE); + } + + /** + * Expert: Cluster vectors into a given number of clusters + * + * @param vectors float vectors + * @param numClusters number of cluster to cluster vector into + * @param assignCentroidsToVectors if {@code true} assign centroids for all vectors. Centroids are + * computed on a sample of vectors. If this parameter is {@code true}, in results also return + * for all vectors what centroids they belong to. + * @param seed random seed + * @param initializationMethod Kmeans initialization method + * @param normalizeCenters for cosine distance, set to true, to use spherical k-means where + * centers are normalized + * @param restarts how many times to run Kmeans algorithm + * @param iters how many iterations to do within a single run + * @param sampleSize sample size to select from all vectors on which to run Kmeans algorithm + * @return results of clustering: produced centroids and if {@code assignCentroidsToVectors == + * true} also for each vector its centroid + * @throws IOException if there is error accessing vectors + */ + public static Results cluster( + RandomAccessVectorValues.Floats vectors, + int numClusters, + boolean assignCentroidsToVectors, + long seed, + KmeansInitializationMethod initializationMethod, + boolean normalizeCenters, + int restarts, + int iters, + int sampleSize) + throws IOException { + if (numClusters < 1 || numClusters > Short.MAX_VALUE) { + throw new IllegalArgumentException( + "[numClusters] must be between [1] and [" + Short.MAX_VALUE + "]"); + } + + Random random = new Random(seed); + float[][] centroids; + if (numClusters > 1) { + RandomAccessVectorValues.Floats sampleVectors = + vectors.size() <= sampleSize ? vectors : createSampleReader(vectors, sampleSize, seed); + KMeans kmeans = + new KMeans(sampleVectors, numClusters, random, initializationMethod, restarts, iters); + centroids = kmeans.computeCentroids(normalizeCenters); + } else { + centroids = new float[1][vectors.dimension()]; + } + + short[] vectorCentroids = null; + // Assign each vector to the nearest centroid and update the centres + if (assignCentroidsToVectors) { + vectorCentroids = new short[vectors.size()]; + // Use kahan summation to get more precise results + KMeans.runKMeansStep(vectors, random, centroids, vectorCentroids, true, normalizeCenters); + } + return new Results(centroids, vectorCentroids); + } + + private KMeans( + RandomAccessVectorValues.Floats vectors, + int numCentroids, + Random random, + KmeansInitializationMethod initializationMethod, + int restarts, + int iters) { + this.vectors = vectors; + this.numVectors = vectors.size(); + this.numCentroids = numCentroids; + this.random = random; + this.initializationMethod = initializationMethod; + this.restarts = restarts; + this.iters = iters; + } + + private float[][] computeCentroids(boolean normalizeCenters) throws IOException { + short[] vectorCentroids = new short[numVectors]; + double minSquaredDist = Double.MAX_VALUE; + double squaredDist = 0; + float[][] bestCentroids = null; + + for (int restart = 0; restart < restarts; restart++) { + float[][] centroids = + switch (initializationMethod) { + case FORGY -> initializeForgy(); + case RESERVOIR_SAMPLING -> initializeReservoirSampling(); + case PLUS_PLUS -> initializePlusPlus(); + }; + + for (int iter = 0; iter < iters; iter++) { + squaredDist = + runKMeansStep(vectors, random, centroids, vectorCentroids, false, normalizeCenters); + } + if (squaredDist < minSquaredDist) { + minSquaredDist = squaredDist; + bestCentroids = centroids; + } + } + return bestCentroids; + } + + /** + * Initialize centroids using Forgy method: randomly select numCentroids vectors for initial + * centroids + */ + private float[][] initializeForgy() throws IOException { + Set<Integer> selection = new HashSet<>(); + while (selection.size() < numCentroids) { + selection.add(random.nextInt(numVectors)); + } + float[][] initialCentroids = new float[numCentroids][]; + int i = 0; + for (Integer selectedIdx : selection) { + float[] vector = vectors.vectorValue(selectedIdx); + initialCentroids[i++] = ArrayUtil.copyOfSubArray(vector, 0, vector.length); + } + return initialCentroids; + } + + /** Initialize centroids using a reservoir sampling method */ + private float[][] initializeReservoirSampling() throws IOException { + float[][] initialCentroids = new float[numCentroids][]; + for (int index = 0; index < numVectors; index++) { + float[] vector = vectors.vectorValue(index); + if (index < numCentroids) { + initialCentroids[index] = ArrayUtil.copyOfSubArray(vector, 0, vector.length); + } else if (random.nextDouble() < numCentroids * (1.0 / index)) { + int c = random.nextInt(numCentroids); + initialCentroids[c] = ArrayUtil.copyOfSubArray(vector, 0, vector.length); + } + } + return initialCentroids; + } + + /** Initialize centroids using Kmeans++ method */ + private float[][] initializePlusPlus() throws IOException { + float[][] initialCentroids = new float[numCentroids][]; + // Choose the first centroid uniformly at random + int firstIndex = random.nextInt(numVectors); + float[] value = vectors.vectorValue(firstIndex); + initialCentroids[0] = ArrayUtil.copyOfSubArray(value, 0, value.length); + + // Store distances of each point to the nearest centroid + float[] minDistances = new float[numVectors]; + Arrays.fill(minDistances, Float.MAX_VALUE); + + // Step 2 and 3: Select remaining centroids + for (int i = 1; i < numCentroids; i++) { + // Update distances with the new centroid + double totalSum = 0; + for (int j = 0; j < numVectors; j++) { + float dist = VectorUtil.squareDistance(vectors.vectorValue(j), initialCentroids[i - 1]); Review Comment: @benwtrent Thanks for comment. How will this work? Would I need to create a separate `RandomVectorScorer` class for each centroid? Also, in case when I need to access `vectorValue()` outside of scoring as in `runKMeansStep` does it still make sense to use vector scorers> -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: issues-unsubscr...@lucene.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@lucene.apache.org For additional commands, e-mail: issues-h...@lucene.apache.org
