path: root/elki/src/main/java/de/lmu/ifi/dbs/elki/evaluation/clustering/internal/EvaluateVarianceRatioCriteria.java

package de.lmu.ifi.dbs.elki.evaluation.clustering.internal;

/*
 This file is part of ELKI:
 Environment for Developing KDD-Applications Supported by Index-Structures

 Copyright (C) 2015
 Ludwig-Maximilians-Universität München
 Lehr- und Forschungseinheit für Datenbanksysteme
 ELKI Development Team

 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
 (at your option) any later version.

 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 GNU Affero General Public License for more details.

 You should have received a copy of the GNU Affero General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

import java.util.Iterator;
import java.util.List;

import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.evaluation.Evaluator;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.statistics.DoubleStatistic;
import de.lmu.ifi.dbs.elki.logging.statistics.LongStatistic;
import de.lmu.ifi.dbs.elki.logging.statistics.StringStatistic;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Centroid;
import de.lmu.ifi.dbs.elki.result.EvaluationResult;
import de.lmu.ifi.dbs.elki.result.EvaluationResult.MeasurementGroup;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultHierarchy;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.EnumParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;

/**
 * Compute the Variance Ratio Criteria of a data set.
 *
 * Reference:
 * <p>
 * R. B. Calinski and J. Harabasz<br />
 * A dendrite method for cluster analysis<br />
 * Communications in Statistics-theory and Methods, 3(1)
 * </p>
 *
 * @author Stephan Baier
 * @author Erich Schubert
 *
 * @apiviz.composedOf NoiseHandling
 */
@Reference(authors = "R. B. Calinski and J. Harabasz", //
title = "A dendrite method for cluster analysis",//
booktitle = "Communications in Statistics-theory and Methods, 3(1)",//
url = "http://dx.doi.org/10.1080/03610927408827101")
public class EvaluateVarianceRatioCriteria<O> implements Evaluator {
  /**
   * Logger for debug output.
   */
  private static final Logging LOG = Logging.getLogger(EvaluateVarianceRatioCriteria.class);

  /**
   * Option for noise handling.
   */
  private NoiseHandling noiseOption;

  /**
   * Penalize noise, if {@link NoiseHandling#IGNORE_NOISE} is set.
   */
  private boolean penalize = true;

  /**
   * Key for logging statistics.
   */
  private String key = EvaluateVarianceRatioCriteria.class.getName();

  /**
   * Constructor.
   *
   * @param noiseOption Flag to control noise handling
   * @param penalize noise, if {@link NoiseHandling#IGNORE_NOISE} is set.
   */
  public EvaluateVarianceRatioCriteria(NoiseHandling noiseOption, boolean penalize) {
    super();
    this.noiseOption = noiseOption;
    this.penalize = penalize;
  }

  /**
   * Evaluate a single clustering.
   *
   * @param db Database
   * @param rel Data relation
   * @param c Clustering
   * @return Variance Ratio Criteria
   */
  public double evaluateClustering(Database db, Relation<? extends NumberVector> rel, Clustering<?> c) {
    List<? extends Cluster<?>> clusters = c.getAllClusters();
    NumberVector[] centroids = new NumberVector[clusters.size()];
    int ignorednoise = EvaluateSimplifiedSilhouette.centroids(rel, clusters, centroids, noiseOption);

    // Build global centroid and cluster count:
    final int dim = RelationUtil.dimensionality(rel);
    Centroid overallCentroid = new Centroid(dim);
    int clustercount = globalCentroid(overallCentroid, rel, clusters, centroids, noiseOption);

    // a: Distance to own centroid
    // b: Distance to overall centroid
    double a = 0, b = 0;
    Iterator<? extends Cluster<?>> ci = clusters.iterator();
    for(int i = 0; ci.hasNext(); i++) {
      Cluster<?> cluster = ci.next();
      if(cluster.size() <= 1 || cluster.isNoise()) {
        switch(noiseOption){
        case IGNORE_NOISE:
          continue; // Ignored
        case TREAT_NOISE_AS_SINGLETONS:
          // Singletons: a = 0 by definition.
          for(DBIDIter it = cluster.getIDs().iter(); it.valid(); it.advance()) {
            b += SquaredEuclideanDistanceFunction.STATIC.distance(overallCentroid, rel.get(it));
          }
          continue; // with NEXT cluster.
        case MERGE_NOISE:
          break; // Treat like a cluster below:
        }
      }
      for(DBIDIter it = cluster.getIDs().iter(); it.valid(); it.advance()) {
        NumberVector vec = rel.get(it);
        a += SquaredEuclideanDistanceFunction.STATIC.distance(centroids[i], vec);
        b += SquaredEuclideanDistanceFunction.STATIC.distance(overallCentroid, vec);
      }
    }

    double vrc = ((b - a) / a) * ((rel.size() - clustercount) / (clustercount - 1.));
    // Only if {@link NoiseHandling#IGNORE_NOISE}:
    if(penalize && ignorednoise > 0) {
      vrc *= (rel.size() - ignorednoise) / (double) rel.size();
    }
    if(LOG.isStatistics()) {
      LOG.statistics(new StringStatistic(key + ".vrc.noise-handling", noiseOption.toString()));
      if(ignorednoise > 0) {
        LOG.statistics(new LongStatistic(key + ".vrc.ignored", ignorednoise));
      }
      LOG.statistics(new DoubleStatistic(key + ".vrc", vrc));
    }

    EvaluationResult ev = EvaluationResult.findOrCreate(db.getHierarchy(), c, "Internal Clustering Evaluation", "internal evaluation");
    MeasurementGroup g = ev.findOrCreateGroup("Distance-based Evaluation");
    g.addMeasure("Variance Ratio Criteria", vrc, 0., 1., 0., false);
    return vrc;
  }

  /**
   * Update the global centroid.
   *
   * @param overallCentroid Centroid to udpate
   * @param rel Data relation
   * @param clusters Clusters
   * @param centroids Cluster centroids
   * @return Number of clusters
   */
  public static int globalCentroid(Centroid overallCentroid, Relation<? extends NumberVector> rel, List<? extends Cluster<?>> clusters, NumberVector[] centroids, NoiseHandling noiseOption) {
    int clustercount = 0;
    Iterator<? extends Cluster<?>> ci = clusters.iterator();
    for(int i = 0; ci.hasNext(); i++) {
      Cluster<?> cluster = ci.next();
      if(cluster.size() <= 1 || cluster.isNoise()) {
        switch(noiseOption){
        case IGNORE_NOISE:
          continue; // Ignore completely
        case TREAT_NOISE_AS_SINGLETONS:
          clustercount += cluster.size();
          // Update global centroid:
          for(DBIDIter it = cluster.getIDs().iter(); it.valid(); it.advance()) {
            overallCentroid.put(rel.get(it));
          }
          continue; // With NEXT cluster.
        case MERGE_NOISE:
          break; // Treat as cluster below:
        }
      }
      // Update centroid:
      assert (centroids[i] != null);
      overallCentroid.put(centroids[i], cluster.size());
      ++clustercount;
    }
    return clustercount;
  }

  @Override
  public void processNewResult(ResultHierarchy hier, Result result) {
    List<Clustering<?>> crs = ResultUtil.getClusteringResults(result);
    if(crs.size() < 1) {
      return;
    }
    Database db = ResultUtil.findDatabase(hier);
    Relation<? extends NumberVector> rel = db.getRelation(EuclideanDistanceFunction.STATIC.getInputTypeRestriction());

    for(Clustering<?> c : crs) {
      evaluateClustering(db, (Relation<? extends NumberVector>) rel, c);
    }
  }

  /**
   * Parameterization class.
   *
   * @author Stephan Baier
   *
   * @apiviz.exclude
   */
  public static class Parameterizer<O> extends AbstractParameterizer {
    /**
     * Parameter for the option, how noise should be treated.
     */
    public static final OptionID NOISE_ID = new OptionID("vrc.noisehandling", "Control how noise should be treated.");

    /**
     * Do not penalize ignored noise.
     */
    public static final OptionID NO_PENALIZE_ID = new OptionID("silhouette.no-penalize-noise", "Do not penalize ignored noise.");

    /**
     * Option, how noise should be treated.
     */
    private NoiseHandling noiseOption;

    /**
     * Penalize noise, if {@link NoiseHandling#IGNORE_NOISE} is set.
     */
    private boolean penalize = true;

    @Override
    protected void makeOptions(Parameterization config) {
      super.makeOptions(config);

      EnumParameter<NoiseHandling> noiseP = new EnumParameter<NoiseHandling>(NOISE_ID, NoiseHandling.class, NoiseHandling.TREAT_NOISE_AS_SINGLETONS);
      if(config.grab(noiseP)) {
        noiseOption = noiseP.getValue();
      }

      if(noiseOption == NoiseHandling.IGNORE_NOISE) {
        Flag penalizeP = new Flag(NO_PENALIZE_ID);
        if(config.grab(penalizeP)) {
          penalize = penalizeP.isFalse();
        }
      }
    }

    @Override
    protected EvaluateVarianceRatioCriteria<? extends NumberVector> makeInstance() {
      return new EvaluateVarianceRatioCriteria<>(noiseOption, penalize);
    }
  }
}