| /* |
| * 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.search.similarities; |
| |
| |
| import java.util.ArrayList; |
| import java.util.List; |
| import java.util.Locale; |
| |
| import org.apache.lucene.search.Explanation; |
| |
| /** |
| * Bayesian smoothing using Dirichlet priors. From Chengxiang Zhai and John |
| * Lafferty. 2001. A study of smoothing methods for language models applied to |
| * Ad Hoc information retrieval. In Proceedings of the 24th annual international |
| * ACM SIGIR conference on Research and development in information retrieval |
| * (SIGIR '01). ACM, New York, NY, USA, 334-342. |
| * <p> |
| * The formula as defined the paper assigns a negative score to documents that |
| * contain the term, but with fewer occurrences than predicted by the collection |
| * language model. The Lucene implementation returns {@code 0} for such |
| * documents. |
| * </p> |
| * |
| * @lucene.experimental |
| */ |
| public class LMDirichletSimilarity extends LMSimilarity { |
| /** The μ parameter. */ |
| private final float mu; |
| |
| /** Instantiates the similarity with the provided μ parameter. */ |
| public LMDirichletSimilarity(CollectionModel collectionModel, float mu) { |
| super(collectionModel); |
| if (Float.isFinite(mu) == false || mu < 0) { |
| throw new IllegalArgumentException("illegal mu value: " + mu + ", must be a non-negative finite value"); |
| } |
| this.mu = mu; |
| } |
| |
| /** Instantiates the similarity with the provided μ parameter. */ |
| public LMDirichletSimilarity(float mu) { |
| if (Float.isFinite(mu) == false || mu < 0) { |
| throw new IllegalArgumentException("illegal mu value: " + mu + ", must be a non-negative finite value"); |
| } |
| this.mu = mu; |
| } |
| |
| /** Instantiates the similarity with the default μ value of 2000. */ |
| public LMDirichletSimilarity(CollectionModel collectionModel) { |
| this(collectionModel, 2000); |
| } |
| |
| /** Instantiates the similarity with the default μ value of 2000. */ |
| public LMDirichletSimilarity() { |
| this(2000); |
| } |
| |
| @Override |
| protected double score(BasicStats stats, double freq, double docLen) { |
| double score = stats.getBoost() * (Math.log(1 + freq / |
| (mu * ((LMStats)stats).getCollectionProbability())) + |
| Math.log(mu / (docLen + mu))); |
| return score > 0.0d ? score : 0.0d; |
| } |
| |
| @Override |
| protected void explain(List<Explanation> subs, BasicStats stats, |
| double freq, double docLen) { |
| if (stats.getBoost() != 1.0d) { |
| subs.add(Explanation.match((float) stats.getBoost(), "query boost")); |
| } |
| double p = ((LMStats)stats).getCollectionProbability(); |
| Explanation explP = Explanation.match((float) p, |
| "P, probability that the current term is generated by the collection"); |
| Explanation explFreq = Explanation.match((float) freq, |
| "freq, number of occurrences of term in the document"); |
| |
| subs.add(Explanation.match(mu, "mu")); |
| Explanation weightExpl = Explanation.match( |
| (float)Math.log(1 + freq / |
| (mu * ((LMStats)stats).getCollectionProbability())), |
| "term weight, computed as log(1 + freq /(mu * P)) from:", |
| explFreq, |
| explP); |
| subs.add(weightExpl); |
| subs.add(Explanation.match( |
| (float)Math.log(mu / (docLen + mu)), |
| "document norm, computed as log(mu / (dl + mu))")); |
| subs.add(Explanation.match((float) docLen,"dl, length of field")); |
| super.explain(subs, stats, freq, docLen); |
| } |
| |
| @Override |
| protected Explanation explain( |
| BasicStats stats, Explanation freq, double docLen) { |
| List<Explanation> subs = new ArrayList<>(); |
| explain(subs, stats, freq.getValue().doubleValue(), docLen); |
| |
| return Explanation.match( |
| (float) score(stats, freq.getValue().doubleValue(), docLen), |
| "score(" + getClass().getSimpleName() + ", freq=" + |
| freq.getValue() +"), computed as boost * " + |
| "(term weight + document norm) from:", |
| subs); |
| } |
| |
| /** Returns the μ parameter. */ |
| public float getMu() { |
| return mu; |
| } |
| |
| @Override |
| public String getName() { |
| return String.format(Locale.ROOT, "Dirichlet(%f)", getMu()); |
| } |
| } |