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_deprecated_silhouette(self): with warnings.catch_warnings(record=True) as w: KMeans(compute_silhouette_score=True) assert len(w) == 1 assert issubclass(w[-1].category, DeprecationWarning) with warnings.catch_warnings(record=True) as w: KMeans(compute_silhouette_score=False) assert len(w) == 1 assert issubclass(w[-1].category, DeprecationWarning)
def test_kmeans(self): kmeans = KMeans(n_clusters=2) c = kmeans(self.iris) X = self.iris.X[:20] p = c(X) # First 20 iris belong to one cluster assert len(set(p.ravel())) == 1
def test_kmeans_parameters(self): kmeans = KMeans(n_clusters=10, max_iter=10, random_state=42, tol=0.001, init='random') c = kmeans(self.iris)
def test_silhouette_sparse(self): """Test if silhouette gets calculated for sparse data""" kmeans = KMeans(compute_silhouette_score=True) sparse_iris = self.iris.copy() sparse_iris.X = csc_matrix(sparse_iris.X) c = kmeans(sparse_iris) self.assertFalse(np.isnan(c.silhouette))
def test_kmeans_parameters(self): kmeans = KMeans(n_clusters=10, max_iter=10, random_state=42, tol=0.001, init='random', compute_silhouette_score=True) c = kmeans(self.iris)
def test_kmeans(self): table = Orange.data.Table('iris') kmeans = KMeans(n_clusters=2) c = kmeans(table) X = table.X[:20] p = c(X) # First 20 iris belong to one cluster assert len(set(p.ravel())) == 1
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()))
def test_kmeans_parameters(self): table = Orange.data.Table('iris') kmeans = KMeans(n_clusters=10, max_iter=10, random_state=42, tol=0.001, init='random') c = kmeans(table)
def test_kmeans_parameters(self): kmeans = KMeans(n_clusters=10, max_iter=10, random_state=42, tol=0.001, init='random') c = kmeans(self.iris) self.assertEqual(np.ndarray, type(c)) self.assertEqual(len(self.iris), len(c))
def test_predict_numpy(self): kmeans = KMeans() c = kmeans(self.iris) X = self.iris.X[::20] p = c(X)
def test_predict_numpy(self): table = Orange.data.Table('iris') kmeans = KMeans() c = kmeans(table) X = table.X[::20] p = c(X)
def test_predict_table(self): table = Orange.data.Table('iris') kmeans = KMeans() c = kmeans(table) table = table[:20] p = c(table)
def test_predict_single_instance(self): table = Orange.data.Table('iris') kmeans = KMeans() c = kmeans(table) inst = table[0] p = c(inst)
def setUp(self): self.kmeans = KMeans(n_clusters=2) self.iris = Orange.data.Table('iris')
def test_predict_sparse(self): kmeans = KMeans() c = kmeans(self.iris) X = csc_matrix(self.iris.X[::20]) p = c(X)
def test_predict_single_instance(self): kmeans = KMeans() c = kmeans(self.iris) inst = self.iris[0] p = c(inst)
def test_predict_table(self): kmeans = KMeans() c = kmeans(self.iris) table = self.iris[:20] p = c(table)