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package de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
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.ArrayList;
import java.util.List;
import org.apache.batik.util.SVGConstants;
import org.w3c.dom.Element;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.evaluation.clustering.BCubed;
import de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable;
import de.lmu.ifi.dbs.elki.evaluation.clustering.EditDistance;
import de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy;
import de.lmu.ifi.dbs.elki.evaluation.clustering.EvaluateClustering;
import de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting;
import de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.result.HierarchicalResult;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.utilities.FormatUtil;
import de.lmu.ifi.dbs.elki.visualization.VisualizationTask;
import de.lmu.ifi.dbs.elki.visualization.style.StyleLibrary;
import de.lmu.ifi.dbs.elki.visualization.svg.SVGPlot;
import de.lmu.ifi.dbs.elki.visualization.svg.SVGScoreBar;
import de.lmu.ifi.dbs.elki.visualization.svg.SVGUtil;
import de.lmu.ifi.dbs.elki.visualization.visualizers.AbstractVisFactory;
import de.lmu.ifi.dbs.elki.visualization.visualizers.StaticVisualization;
import de.lmu.ifi.dbs.elki.visualization.visualizers.Visualization;
/**
* Pseudo-Visualizer, that lists the cluster evaluation results found.
*
* @author Erich Schubert
* @author Sascha Goldhofer
*
* @apiviz.stereotype factory
* @apiviz.uses StaticVisualization oneway - - «create»
* @apiviz.has de.lmu.ifi.dbs.elki.evaluation.clustering.EvaluateClustering.ScoreResult oneway - - visualizes
*/
public class ClusterEvaluationVisFactory extends AbstractVisFactory {
/**
* Name for this visualizer.
*/
private static final String NAME = "Cluster Evaluation";
/**
* Constant: width of score bars
*/
private static final double BARLENGTH = 5;
/**
* Constant: height of score bars
*/
private static final double BARHEIGHT = 0.7;
/**
* Constructor.
*/
public ClusterEvaluationVisFactory() {
super();
}
@Override
public void processNewResult(HierarchicalResult baseResult, Result newResult) {
final ArrayList<EvaluateClustering.ScoreResult> srs = ResultUtil.filterResults(newResult, EvaluateClustering.ScoreResult.class);
for(EvaluateClustering.ScoreResult sr : srs) {
final VisualizationTask task = new VisualizationTask(NAME, sr, null, this);
task.width = .5;
task.height = 2.0;
task.put(VisualizationTask.META_LEVEL, VisualizationTask.LEVEL_STATIC);
baseResult.getHierarchy().add(sr, task);
}
}
private double addBarChart(SVGPlot svgp, Element parent, double ypos, String label, double maxValue, double value) {
SVGScoreBar barchart = new SVGScoreBar();
barchart.setFill(value, maxValue);
barchart.showValues(FormatUtil.NF4);
barchart.addLabel(label);
parent.appendChild(barchart.build(svgp, 0.0, ypos, BARLENGTH, BARHEIGHT));
ypos += 1;
return ypos;
}
private double addHeader(SVGPlot svgp, Element parent, double ypos, String text) {
ypos += .5;
Element object = svgp.svgText(0, ypos + BARHEIGHT * 0.5, text);
object.setAttribute(SVGConstants.SVG_STYLE_ATTRIBUTE, "font-size: 0.6; font-weight: bold");
parent.appendChild(object);
ypos += 1;
return ypos;
}
@Override
public Visualization makeVisualization(VisualizationTask task) {
// TODO: make a utility class to wrap SVGPlot + parent layer + ypos.
double ypos = -.5; // Skip space before first header
SVGPlot svgp = task.getPlot();
Element parent = svgp.svgElement(SVGConstants.SVG_G_TAG);
EvaluateClustering.ScoreResult sr = task.getResult();
ClusterContingencyTable cont = sr.getContingencyTable();
List<Result> parents = task.getContext().getHierarchy().getParents(sr);
for(Result r : parents) {
if(r instanceof Clustering) {
ypos = addHeader(svgp, parent, ypos, r.getLongName());
}
}
// TODO: use CSSClass and StyleLibrary
ypos = addHeader(svgp, parent, ypos, "Pair counting measures");
PairCounting paircount = cont.getPaircount();
ypos = addBarChart(svgp, parent, ypos, "Jaccard", 1, paircount.jaccard());
ypos = addBarChart(svgp, parent, ypos, "F1-Measure", 1, paircount.f1Measure());
ypos = addBarChart(svgp, parent, ypos, "Precision", 1, paircount.precision());
ypos = addBarChart(svgp, parent, ypos, "Recall", 1, paircount.recall());
ypos = addBarChart(svgp, parent, ypos, "Rand", 1, paircount.randIndex());
ypos = addBarChart(svgp, parent, ypos, "ARI", 1, paircount.adjustedRandIndex());
ypos = addBarChart(svgp, parent, ypos, "FowlkesMallows", 1, paircount.fowlkesMallows());
ypos = addHeader(svgp, parent, ypos, "Entropy based measures");
Entropy entropy = cont.getEntropy();
ypos = addBarChart(svgp, parent, ypos, "NMI Joint", 1, entropy.entropyNMIJoint());
ypos = addBarChart(svgp, parent, ypos, "NMI Sqrt", 1, entropy.entropyNMISqrt());
ypos = addHeader(svgp, parent, ypos, "BCubed-based measures");
BCubed bcubed = cont.getBCubed();
ypos = addBarChart(svgp, parent, ypos, "F1-Measure", 1, bcubed.f1Measure());
ypos = addBarChart(svgp, parent, ypos, "Recall", 1, bcubed.recall());
ypos = addBarChart(svgp, parent, ypos, "Precision", 1, bcubed.precision());
ypos = addHeader(svgp, parent, ypos, "Set-Matching-based measures");
SetMatchingPurity setm = cont.getSetMatching();
ypos = addBarChart(svgp, parent, ypos, "F1-Measure", 1, setm.f1Measure());
ypos = addBarChart(svgp, parent, ypos, "Purity", 1, setm.purity());
ypos = addBarChart(svgp, parent, ypos, "Inverse Purity", 1, setm.inversePurity());
ypos = addHeader(svgp, parent, ypos, "Editing-distance measures");
EditDistance edit = cont.getEdit();
ypos = addBarChart(svgp, parent, ypos, "F1-Measure", 1, edit.f1Measure());
ypos = addBarChart(svgp, parent, ypos, "Precision", 1, edit.editDistanceFirst());
ypos = addBarChart(svgp, parent, ypos, "Recall", 1, edit.editDistanceSecond());
ypos = addHeader(svgp, parent, ypos, "Gini measures");
final MeanVariance gini = cont.averageSymmetricGini();
ypos = addBarChart(svgp, parent, ypos, "Mean +-" + FormatUtil.format(gini.getSampleStddev(), FormatUtil.NF4), 1, gini.getMean());
// scale vis
double cols = 10; // Math.max(10, (int) (i * task.getHeight() /
// task.getWidth()));
double rows = ypos;
final double margin = task.getContext().getStyleLibrary().getSize(StyleLibrary.MARGIN);
final String transform = SVGUtil.makeMarginTransform(task.getWidth(), task.getHeight(), cols, rows, margin / StyleLibrary.SCALE);
SVGUtil.setAtt(parent, SVGConstants.SVG_TRANSFORM_ATTRIBUTE, transform);
return new StaticVisualization(task, parent);
}
@Override
public boolean allowThumbnails(VisualizationTask task) {
// Don't use thumbnails
return false;
}
}
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