def test_pw_distances_euclidean(self):
actual_dm = pw_distances(self.t1, self.ids1, 'euclidean')
self.assertEqual(actual_dm.shape, (3, 3))
npt.assert_almost_equal(actual_dm['A', 'A'], 0.0)
npt.assert_almost_equal(actual_dm['B', 'B'], 0.0)
npt.assert_almost_equal(actual_dm['C', 'C'], 0.0)
npt.assert_almost_equal(actual_dm['A', 'B'], 2.23606798)
npt.assert_almost_equal(actual_dm['B', 'A'], 2.23606798)
npt.assert_almost_equal(actual_dm['A', 'C'], 4.12310563)
npt.assert_almost_equal(actual_dm['C', 'A'], 4.12310563)
npt.assert_almost_equal(actual_dm['B', 'C'], 2.82842712)
npt.assert_almost_equal(actual_dm['C', 'B'], 2.82842712)
actual_dm = pw_distances(self.t2, self.ids2, 'euclidean')
expected_data = [
[0., 80.8455317, 84.0297566, 36.3042697, 86.0116271, 78.9176786],
[80.8455317, 0., 71.0844568, 74.4714710, 69.3397433, 14.422205],
[84.0297566, 71.0844568, 0., 77.2851861, 8.3066238, 60.7536007],
[36.3042697, 74.4714710, 77.2851861, 0., 78.7908624, 70.7389567],
[86.0116271, 69.3397433, 8.3066238, 78.7908624, 0., 58.4807660],
[78.9176786, 14.422205, 60.7536007, 70.7389567, 58.4807660, 0.]]
expected_dm = DistanceMatrix(expected_data, self.ids2)
for id1 in self.ids2:
for id2 in self.ids2:
npt.assert_almost_equal(actual_dm[id1, id2],
expected_dm[id1, id2], 6)
def test_pw_distances_braycurtis(self):
actual_dm = pw_distances(self.t1, self.ids1, 'braycurtis')
self.assertEqual(actual_dm.shape, (3, 3))
npt.assert_almost_equal(actual_dm['A', 'A'], 0.0)
npt.assert_almost_equal(actual_dm['B', 'B'], 0.0)
npt.assert_almost_equal(actual_dm['C', 'C'], 0.0)
npt.assert_almost_equal(actual_dm['A', 'B'], 0.27272727)
npt.assert_almost_equal(actual_dm['B', 'A'], 0.27272727)
npt.assert_almost_equal(actual_dm['A', 'C'], 0.71428571)
npt.assert_almost_equal(actual_dm['C', 'A'], 0.71428571)
npt.assert_almost_equal(actual_dm['B', 'C'], 0.66666667)
npt.assert_almost_equal(actual_dm['C', 'B'], 0.66666667)
actual_dm = pw_distances(self.t2, self.ids2, 'braycurtis')
expected_data = [
[0., 0.78787879, 0.86666667, 0.30927835, 0.85714286, 0.81521739],
[0.78787879, 0., 0.78142077, 0.86813187, 0.75, 0.1627907],
[0.86666667, 0.78142077, 0., 0.87709497, 0.09392265, 0.71597633],
[0.30927835, 0.86813187, 0.87709497, 0., 0.87777778, 0.89285714],
[0.85714286, 0.75, 0.09392265, 0.87777778, 0., 0.68235294],
[0.81521739, 0.1627907, 0.71597633, 0.89285714, 0.68235294, 0.]]
expected_dm = DistanceMatrix(expected_data, self.ids2)
for id1 in self.ids2:
for id2 in self.ids2:
npt.assert_almost_equal(actual_dm[id1, id2],
expected_dm[id1, id2], 6)
def test_pw_distances_from_table_braycurtis(self):
# results are equal when passed as Table or matrix
m_dm = pw_distances(self.t1, self.ids1, 'braycurtis')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
t_dm = pw_distances_from_table(self.table1, 'braycurtis')
for id1 in self.ids1:
for id2 in self.ids1:
npt.assert_almost_equal(m_dm[id1, id2], t_dm[id1, id2])
m_dm = pw_distances(self.t2, self.ids2, 'braycurtis')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
t_dm = pw_distances_from_table(self.table2, 'braycurtis')
for id1 in self.ids2:
for id2 in self.ids2:
npt.assert_almost_equal(m_dm[id1, id2], t_dm[id1, id2])
