@Test
public void testIris() throws ScoreException {
double scoreBetter = subject.score(FakeClustering.iris());
double scoreWorser = subject.score(FakeClustering.irisWrong4());
// should recognize better clustering
assertEquals(true, subject.isBetter(scoreBetter, scoreWorser));
}
/** Test of score method, of class NMI. */
@Test
public void testScore_Clustering_Dataset() throws ScoreException {
measure(FakeClustering.iris(), 1.0);
double score = measure(irisWrong, 0.6545182434318744);
double score2 = measure(FakeClustering.irisWrong(), irisCorrect, 0.06793702240876041);
assertTrue(score2 < score);
}
/** Test of score method, of class NMI. */
@Test
public void testScore_Clustering_Clustering() throws ScoreException {
// this is fixed clustering which correspods to true classes in dataset
measure(FakeClustering.iris(), FakeClustering.iris(), 1.0);
double score = measure(irisWrong, irisCorrect, 0.6496820278112178);
double score2 = measure(FakeClustering.irisWrong(), irisCorrect, 0.06793702240876041);
assertTrue(score2 < score);
}
/** Test of score method, of class F-measure. */
@Test
public void testScore_Clustering_Dataset() throws ScoreException {
double score;
// each cluster should have this scores:
// Cabernet = 0.7200
// Syrah = 0.5555
// Pinot = 0.7272
score =
measure(FakeClustering.wineClustering(), FakeClustering.wineCorrect(), 0.48132780082987553);
// when using class labels result should be the same
measure(FakeClustering.wineClustering(), score);
}
public ExternalTest() {
ext100p2 = FakeClustering.ext100p2();
ext100p3 = FakeClustering.ext100p3();
}
public NMIsqrtTest() throws FileNotFoundException, IOException {
irisCorrect = FakeClustering.iris();
irisWrong = FakeClustering.irisWrong2();
subject = new NMIsqrt();
}
@Test
public void testMostlyWrong() throws ScoreException {
double score = subject.score(FakeClustering.irisMostlyWrong());
System.out.println("f-measure (mw) = " + score);
assertEquals(true, score < 0.5);
}
/**
* Check against definition (and tests in R package clusterCrit)
* https://cran.r-project.org/web/packages/clusterCrit/index.html
*
* <p>NOTE: There's a small problem with precision of floating point operations. First 7 decimal
* digits seems to match.
*/
@Test
public void testClusterCrit() throws ScoreException {
double score = subject.score(FakeClustering.int100p4());
// clustCrit: 105.942129943902
assertEquals(105.942129943902, score, delta);
}
