def test_dbscan_parameters(self):
dbscan = DBSCAN(eps=0.1,
min_samples=7,
metric='euclidean',
algorithm='auto',
leaf_size=12,
p=None)
c = dbscan(self.iris)
def test_dbscan_parameters(self):
dbscan = DBSCAN(eps=0.1,
min_samples=7,
metric='euclidean',
algorithm='auto',
leaf_size=12,
p=None)
c = dbscan(self.iris)
self.assertEqual(np.ndarray, type(c))
self.assertEqual(len(self.iris), len(c))
def test_dbscan_parameters(self):
table = Orange.data.Table('iris')
dbscan = DBSCAN(eps=0.1,
min_samples=7,
metric='euclidean',
algorithm='auto',
leaf_size=12,
p=None,
random_state=42)
c = dbscan(table)
def test_dbscan_parameters(self):
dbscan = DBSCAN(
eps=0.1,
min_samples=7,
metric="euclidean",
algorithm="auto",
leaf_size=12,
p=None,
)
c = dbscan(self.iris)
def test_values(self):
dbscan = DBSCAN(eps=1) # it clusters data in two classes
c = dbscan(self.iris)
table = self.iris
p = c(table)
self.assertEqual(2, len(p.domain[0].values))
self.assertSetEqual({"0", "1"}, set(p.domain[0].values))
table.X[0] = [100, 100, 100, 100] # we add a big outlier
p = c(table)
self.assertEqual(3, len(p.domain[0].values))
self.assertSetEqual({"-1", "0", "1"}, set(p.domain[0].values))
def test_predict_numpy(self):
dbscan = DBSCAN()
c = dbscan(self.iris)
X = self.iris.X[::20]
p = c(X)
def test_predict_table(self):
dbscan = DBSCAN()
c = dbscan(self.iris)
table = self.iris[:20]
p = c(table)
def setUp(self):
self.iris = Table('iris')
self.dbscan = DBSCAN()
class TestDBSCAN(unittest.TestCase):
def setUp(self):
self.iris = Table('iris')
self.dbscan = DBSCAN()
def test_dbscan(self):
c = self.dbscan(self.iris)
# First 20 iris belong to one cluster
self.assertEqual(np.ndarray, type(c))
self.assertEqual(len(self.iris), len(c))
self.assertEqual(1, len(set(c[:20].ravel())))
def test_dbscan_parameters(self):
dbscan = DBSCAN(eps=0.1,
min_samples=7,
metric='euclidean',
algorithm='auto',
leaf_size=12,
p=None)
c = dbscan(self.iris)
self.assertEqual(np.ndarray, type(c))
self.assertEqual(len(self.iris), len(c))
def test_predict_table(self):
pred = self.dbscan(self.iris)
self.assertEqual(np.ndarray, type(pred))
self.assertEqual(len(self.iris), len(pred))
def test_predict_numpy(self):
model = self.dbscan.fit(self.iris.X)
self.assertEqual(ClusteringModel, type(model))
self.assertEqual(np.ndarray, type(model.labels))
self.assertEqual(len(self.iris), len(model.labels))
def test_predict_sparse_csc(self):
self.iris.X = csc_matrix(self.iris.X[::20])
c = self.dbscan(self.iris)
self.assertEqual(np.ndarray, type(c))
self.assertEqual(len(self.iris), len(c))
def test_predict_spares_csr(self):
self.iris.X = csr_matrix(self.iris.X[::20])
c = self.dbscan(self.iris)
self.assertEqual(np.ndarray, type(c))
self.assertEqual(len(self.iris), len(c))
def test_model(self):
c = self.dbscan.get_model(self.iris)
self.assertEqual(ClusteringModel, type(c))
self.assertEqual(len(self.iris), len(c.labels))
self.assertRaises(NotImplementedError, c, self.iris)
def test_model_np(self):
"""
Test with numpy array as an input in model.
"""
c = self.dbscan.get_model(self.iris)
self.assertRaises(NotImplementedError, c, self.iris.X)
def test_model_sparse(self):
"""
Test with sparse array as an input in model.
"""
c = self.dbscan.get_model(self.iris)
self.assertRaises(NotImplementedError, c, csr_matrix(self.iris.X))
def test_model_instance(self):
"""
Test with instance as an input in model.
"""
c = self.dbscan.get_model(self.iris)
self.assertRaises(NotImplementedError, c, self.iris[0])
def test_model_list(self):
"""
Test with list as an input in model.
"""
c = self.dbscan.get_model(self.iris)
self.assertRaises(NotImplementedError, c, self.iris.X.tolist())
def test_model_bad_datatype(self):
"""
Check model with data-type that is not supported.
"""
c = self.dbscan.get_model(self.iris)
self.assertRaises(TypeError, c, 10)
def test_predict_numpy(self):
table = Orange.data.Table('iris')
dbscan = DBSCAN()
c = dbscan(table)
X = table.X[::20]
p = c(X)
def test_predict_table(self):
table = Orange.data.Table('iris')
dbscan = DBSCAN()
c = dbscan(table)
table = table[:20]
p = c(table)