def test_init(self):
data = Orange.data.Table(np.arange(100).reshape((100, 1)))
res = ClusteringResults(data=data, nmethods=2, nrows=100)
res.actual[:50] = 0
res.actual[50:] = 1
res.predicted = np.vstack((res.actual, res.actual))
expected = [1.0, 1.0]
np.testing.assert_almost_equal(AdjustedMutualInfoScore(res), expected)
def test_kmeans(self):
table = Orange.data.Table('iris')
cr = ClusteringEvaluation(table, learners=[KMeans(n_clusters=2),
KMeans(n_clusters=3),
KMeans(n_clusters=5)], k=3)
expected = [0.68081362, 0.55259194, 0.48851755]
np.testing.assert_almost_equal(Silhouette(cr), expected, decimal=2)
expected = [0.51936073, 0.74837231, 0.59178896]
np.testing.assert_almost_equal(AdjustedMutualInfoScore(cr),
expected, decimal=2)
def test_kmeans(self):
table = Orange.data.Table('iris')
cr = ClusteringEvaluation(k=3)(table, learners=[KMeans(n_clusters=2),
KMeans(n_clusters=3),
KMeans(n_clusters=5)])
expected = [0.68081362, 0.55259194, 0.48851755]
np.testing.assert_almost_equal(Silhouette(cr), expected, decimal=2)
expected = [0.65383807, 0.75511917, 0.68721092]
np.testing.assert_almost_equal(AdjustedMutualInfoScore(cr),
expected, decimal=2)
self.assertIsNone(cr.models)
cr = ClusteringEvaluation(k=3, store_models=True)(
table, learners=[KMeans(n_clusters=2)])
self.assertEqual(cr.models.shape, (3, 1))
self.assertTrue(all(isinstance(m, KMeansModel)
for m in cr.models.flatten()))