Package de.lmu.ifi.dbs.elki.evaluation.clustering

Examples of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable

75
76
77
78
79
80
81
82
83
84
85
86
    Clustering<?> clustering = opticsxi.run(db);

    // Test F-Measure   
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<Model> rbl = bylabel.run(db);
    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
    ct.process(clustering, rbl);
    double score = ct.getPaircount().f1Measure();
    // We cannot test exactly - due to Hashing, DeLiClu sequence is not
    // identical each time, the results will vary slightly.
    org.junit.Assert.assertTrue(this.getClass().getSimpleName() + ": Score does not match: " + score, score > 0.85);
  }
View Full Code Here
98
99
100
101
102
103
104
105
106
    assertEquals(0.5 /* 0.3834296724470135 */, computeFMeasure(rai, rbl, false), Double.MIN_VALUE);
  }

  private double computeFMeasure(Clustering<?> c1, Clustering<?> c2, boolean noise) {
    ClusterContingencyTable ct = new ClusterContingencyTable(true, noise);
    ct.process(c1, c2);
    return ct.getPaircount().f1Measure();
  }
View Full Code Here
152
153
154
155
156
157
158
159
160
161
162
163
164
  protected <O> void testFMeasure(Database database, Clustering<?> clustering, double expected) {
    // Run by-label as reference
    ByLabelClustering bylabel = new ByLabelClustering();
    Clustering<Model> rbl = bylabel.run(database);

    ClusterContingencyTable ct = new ClusterContingencyTable(true, false);
    ct.process(clustering, rbl);
    double score = ct.getPaircount().f1Measure();
    if(logger.isVerbose()) {
      logger.verbose(this.getClass().getSimpleName() + " score: " + score + " expect: " + expected);
    }
    org.junit.Assert.assertEquals(this.getClass().getSimpleName() + ": Score does not match.", expected, score, 0.0001);
  }
View Full Code Here
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
   
    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()));
View Full Code Here
TOP

Related Classes of de.lmu.ifi.dbs.elki.evaluation.clustering.ClusterContingencyTable

  • de.lmu.ifi.dbs.elki.algorithm.AbstractSimpleAlgorithmTest
  • de.lmu.ifi.dbs.elki.algorithm.clustering.TestDeLiCluResults
  • de.lmu.ifi.dbs.elki.database.ids.DBIDs
  • de.lmu.ifi.dbs.elki.evaluation.paircounting.TestClusterContingencyTable
  • de.lmu.ifi.dbs.elki.math.MeanVariance
  • de.lmu.ifi.dbs.elki.visualization.visualizers.visunproj.ClusterEvaluationVisFactory
  • java.util.BitSet
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.