def test_self_self_pair(self):
error_msg = ("A duplicate or a self-self pair was observed.")
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity((lambda x, y: x + y),
self.table1,
self.sids1,
id_pairs=[('A', 'B'), ('A', 'A')])
def test_unusable_metric(self):
id_pairs = [('A', 'B'), ('B', 'F'), ('D', 'E')]
error_msg = "partial_beta_diversity is only compatible"
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity('hamming',
self.table2,
self.sids2,
id_pairs=id_pairs)
def test_duplicate_transpose_pairs(self):
# confirm that partial pairwise execution fails if a transpose
# duplicate is observed
error_msg = ("A duplicate or a self-self pair was observed.")
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity((lambda x, y: x + y), self.table1,
self.sids1, id_pairs=[('A', 'B'),
('A', 'B')])
def test_duplicate_transpose_pairs(self):
# confirm that partial pairwise execution fails if a transpose
# duplicate is observed
error_msg = (r"A duplicate or a self-self pair was observed.")
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity((lambda x, y: x + y), self.table1,
self.sids1, id_pairs=[('A', 'B'),
('A', 'B')])
def test_pairs_not_subset(self):
# confirm raise when pairs are not a subset of IDs
error_msg = ("`id_pairs` are not a subset of `ids`")
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity((lambda x, y: x + y),
self.table1,
self.sids1,
id_pairs=[
('x', 'b'),
])
def test_weighted_unifrac_partial_full(self):
# TODO: update npt.assert_almost_equal calls to use DistanceMatrix
# near-equality testing when that support is available
# expected values calculated by hand
dm1 = partial_beta_diversity('weighted_unifrac',
self.table1,
self.sids1,
otu_ids=self.oids1,
tree=self.tree1,
id_pairs=[('A', 'B'), ('A', 'C'),
('B', 'C')])
dm2 = beta_diversity('weighted_unifrac',
self.table1,
self.sids1,
otu_ids=self.oids1,
tree=self.tree1)
self.assertEqual(dm1.shape, (3, 3))
self.assertEqual(dm1, dm2)
expected_data = [[0.0, 0.1750000, 0.12499999],
[0.1750000, 0.0, 0.3000000],
[0.12499999, 0.3000000, 0.0]]
expected_dm = DistanceMatrix(expected_data, ids=self.sids1)
for id1 in self.sids1:
for id2 in self.sids1:
npt.assert_almost_equal(dm1[id1, id2], expected_dm[id1, id2],
6)
def test_id_pairs_as_iterable(self):
id_pairs = iter([('B', 'C'), ])
dm = partial_beta_diversity('unweighted_unifrac', self.table1,
self.sids1, otu_ids=self.oids1,
tree=self.tree1, id_pairs=id_pairs)
self.assertEqual(dm.shape, (3, 3))
expected_data = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.25],
[0.0, 0.25, 0.0]]
expected_dm = DistanceMatrix(expected_data, ids=self.sids1)
for id1 in self.sids1:
for id2 in self.sids1:
npt.assert_almost_equal(dm[id1, id2],
expected_dm[id1, id2], 6)
def test_id_pairs_as_iterable(self):
id_pairs = iter([('B', 'C'), ])
dm = partial_beta_diversity('unweighted_unifrac', self.table1,
self.sids1, otu_ids=self.oids1,
tree=self.tree1, id_pairs=id_pairs)
self.assertEqual(dm.shape, (3, 3))
expected_data = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.25],
[0.0, 0.25, 0.0]]
expected_dm = DistanceMatrix(expected_data, ids=self.sids1)
for id1 in self.sids1:
for id2 in self.sids1:
npt.assert_almost_equal(dm[id1, id2],
expected_dm[id1, id2], 6)
def test_unweighted_unifrac_partial(self):
# TODO: update npt.assert_almost_equal calls to use DistanceMatrix
# near-equality testing when that support is available
# expected values calculated by hand
dm = partial_beta_diversity('unweighted_unifrac', self.table1,
self.sids1, otu_ids=self.oids1,
tree=self.tree1, id_pairs=[('B', 'C'), ])
self.assertEqual(dm.shape, (3, 3))
expected_data = [[0.0, 0.0, 0.0],
[0.0, 0.0, 0.25],
[0.0, 0.25, 0.0]]
expected_dm = DistanceMatrix(expected_data, ids=self.sids1)
for id1 in self.sids1:
for id2 in self.sids1:
npt.assert_almost_equal(dm[id1, id2],
expected_dm[id1, id2], 6)
def test_euclidean(self):
# confirm that pw execution through partial is identical
def euclidean(u, v, **kwargs):
return np.sqrt(((u - v)**2).sum())
id_pairs = [('A', 'B'), ('B', 'F'), ('D', 'E')]
actual_dm = partial_beta_diversity(euclidean, self.table2, self.sids2,
id_pairs=id_pairs)
actual_dm = DistanceMatrix(actual_dm, self.sids2)
expected_data = [
[0., 80.8455317, 0., 0., 0., 0.],
[80.8455317, 0., 0., 0., 0., 14.422205],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 78.7908624, 0.],
[0., 0., 0., 78.7908624, 0., 0.],
[0., 14.422205, 0., 0., 0., 0.]]
expected_dm = DistanceMatrix(expected_data, self.sids2)
for id1 in self.sids2:
for id2 in self.sids2:
npt.assert_almost_equal(actual_dm[id1, id2],
expected_dm[id1, id2], 6)
def test_euclidean(self):
# confirm that pw execution through partial is identical
def euclidean(u, v, **kwargs):
return np.sqrt(((u - v)**2).sum())
id_pairs = [('A', 'B'), ('B', 'F'), ('D', 'E')]
actual_dm = partial_beta_diversity(euclidean, self.table2, self.sids2,
id_pairs=id_pairs)
actual_dm = DistanceMatrix(actual_dm, self.sids2)
expected_data = [
[0., 80.8455317, 0., 0., 0., 0.],
[80.8455317, 0., 0., 0., 0., 14.422205],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 78.7908624, 0.],
[0., 0., 0., 78.7908624, 0., 0.],
[0., 14.422205, 0., 0., 0., 0.]]
expected_dm = DistanceMatrix(expected_data, self.sids2)
for id1 in self.sids2:
for id2 in self.sids2:
npt.assert_almost_equal(actual_dm[id1, id2],
expected_dm[id1, id2], 6)
def test_unusable_metric(self):
id_pairs = [('A', 'B'), ('B', 'F'), ('D', 'E')]
error_msg = r"partial_beta_diversity is only compatible"
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity('hamming', self.table2, self.sids2,
id_pairs=id_pairs)
def test_pairs_not_subset(self):
# confirm raise when pairs are not a subset of IDs
error_msg = (r"`id_pairs` are not a subset of `ids`")
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity((lambda x, y: x + y), self.table1,
self.sids1, id_pairs=[('x', 'b'), ])
def test_self_self_pair(self):
error_msg = (r"A duplicate or a self-self pair was observed.")
with self.assertRaisesRegex(ValueError, error_msg):
partial_beta_diversity((lambda x, y: x + y), self.table1,
self.sids1, id_pairs=[('A', 'B'),
('A', 'A')])