/*
 * 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 org.apache.lucene.search.Explanation;
import org.apache.lucene.search.similarities.Normalization.NoNormalization;

/**
 * Provides a framework for the family of information-based models, as described in St&eacute;phane
 * Clinchant and Eric Gaussier. 2010. Information-based models for ad hoc IR. In Proceeding of the
 * 33rd international ACM SIGIR conference on Research and development in information retrieval
 * (SIGIR '10). ACM, New York, NY, USA, 234-241.
 *
 * <p>The retrieval function is of the form <em>RSV(q, d) = &sum; -x<sup>q</sup><sub>w</sub> log
 * Prob(X<sub>w</sub> &ge; t<sup>d</sup><sub>w</sub> | &lambda;<sub>w</sub>)</em>, where
 *
 * <ul>
 *   <li><em>x<sup>q</sup><sub>w</sub></em> is the query boost;
 *   <li><em>X<sub>w</sub></em> is a random variable that counts the occurrences of word <em>w</em>;
 *   <li><em>t<sup>d</sup><sub>w</sub></em> is the normalized term frequency;
 *   <li><em>&lambda;<sub>w</sub></em> is a parameter.
 * </ul>
 *
 * <p>The framework described in the paper has many similarities to the DFR framework (see {@link
 * DFRSimilarity}). It is possible that the two Similarities will be merged at one point.
 *
 * <p>To construct an IBSimilarity, you must specify the implementations for all three components of
 * the Information-Based model.
 *
 * <ol>
 *   <li>{@link Distribution}: Probabilistic distribution used to model term occurrence
 *       <ul>
 *         <li>{@link DistributionLL}: Log-logistic
 *         <li>{@link DistributionLL}: Smoothed power-law
 *       </ul>
 *   <li>{@link Lambda}: &lambda;<sub>w</sub> parameter of the probability distribution
 *       <ul>
 *         <li>{@link LambdaDF}: <code>N<sub>w</sub>/N</code> or average number of documents where w
 *             occurs
 *         <li>{@link LambdaTTF}: <code>F<sub>w</sub>/N</code> or average number of occurrences of w
 *             in the collection
 *       </ul>
 *   <li>{@link Normalization}: Term frequency normalization
 *       <blockquote>
 *       Any supported DFR normalization (listed in {@link DFRSimilarity})
 *       </blockquote>
 * </ol>
 *
 * @see DFRSimilarity
 * @lucene.experimental
 */
public class IBSimilarity extends SimilarityBase {
  /** The probabilistic distribution used to model term occurrence. */
  protected final Distribution distribution;
  /** The <em>lambda (&lambda;<sub>w</sub>)</em> parameter. */
  protected final Lambda lambda;
  /** The term frequency normalization. */
  protected final Normalization normalization;

  /**
   * Creates IBSimilarity from the three components.
   *
   * <p>Note that <code>null</code> values are not allowed: if you want no normalization, instead
   * pass {@link NoNormalization}.
   *
   * @param distribution probabilistic distribution modeling term occurrence
   * @param lambda distribution's &lambda;<sub>w</sub> parameter
   * @param normalization term frequency normalization
   */
  public IBSimilarity(Distribution distribution, Lambda lambda, Normalization normalization) {
    this.distribution = distribution;
    this.lambda = lambda;
    this.normalization = normalization;
  }

  @Override
  protected double score(BasicStats stats, double freq, double docLen) {
    return stats.getBoost()
        * distribution.score(stats, normalization.tfn(stats, freq, docLen), lambda.lambda(stats));
  }

  @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(), "boost, query boost"));
    }
    Explanation normExpl = normalization.explain(stats, freq, docLen);
    Explanation lambdaExpl = lambda.explain(stats);
    subs.add(normExpl);
    subs.add(lambdaExpl);
    subs.add(
        distribution.explain(
            stats, normExpl.getValue().floatValue(), lambdaExpl.getValue().floatValue()));
  }

  @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 * "
            + "distribution.score(stats, normalization.tfn(stats, freq,"
            + " docLen), lambda.lambda(stats)) from:",
        subs);
  }

  /**
   * The name of IB methods follow the pattern {@code IB <distribution> <lambda><normalization>}.
   * The name of the distribution is the same as in the original paper; for the names of lambda
   * parameters, refer to the javadoc of the {@link Lambda} classes.
   */
  @Override
  public String toString() {
    return "IB " + distribution.toString() + "-" + lambda.toString() + normalization.toString();
  }

  /** Returns the distribution */
  public Distribution getDistribution() {
    return distribution;
  }

  /** Returns the distribution's lambda parameter */
  public Lambda getLambda() {
    return lambda;
  }

  /** Returns the term frequency normalization */
  public Normalization getNormalization() {
    return normalization;
  }
}
