def test_overlapping_names(self):
# here the 3rd level is different, but the 4th level is the same
# across the three assignments. this can happen in practice if
# three different genera are assigned, and under each there is
# an unnamed species
# (e.g., f__x;g__A;s__, f__x;g__B;s__, f__x;g__B;s__)
# in this case, the assignment should be f__x.
in_ = [['Ab', 'Bc', 'De', 'Jk'],
['Ab', 'Bc', 'Fg', 'Jk'],
['Ab', 'Bc', 'Hi', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc'], 1.)
self.assertEqual(actual, expected)
# here the third level is the same in 4/5 of the
# assignments, but one of them (z, y, c) refers to a
# different taxa since the higher levels are different.
# the consensus value should be 3/5, not 4/5, to
# reflect that.
in_ = [['a', 'b', 'c'],
['a', 'd', 'e'],
['a', 'b', 'c'],
['a', 'b', 'c'],
['z', 'y', 'c']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['a', 'b', 'c'], 0.6)
self.assertEqual(actual, expected)
def test_single_annotation(self):
in_ = [['Ab', 'Bc', 'De']]
actual = _compute_consensus_annotation(in_, 1.0, "Unassigned")
expected = (['Ab', 'Bc', 'De'], 1.0)
self.assertEqual(actual, expected)
actual = _compute_consensus_annotation(in_, 0.501, "Unassigned")
expected = (['Ab', 'Bc', 'De'], 1.0)
self.assertEqual(actual, expected)
def test_single_annotation(self):
in_ = [['Ab', 'Bc', 'De']]
actual = _compute_consensus_annotation(in_, 1.0, "Unassigned")
expected = (['Ab', 'Bc', 'De'], 1.0)
self.assertEqual(actual, expected)
actual = _compute_consensus_annotation(in_, 0.501, "Unassigned")
expected = (['Ab', 'Bc', 'De'], 1.0)
self.assertEqual(actual, expected)
def test_no_consensus(self):
in_ = [['Ab', 'Bc', 'De'], ['Cd', 'Bc', 'Fg', 'Hi'],
['Ef', 'Bc', 'Fg', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Unassigned'], 1.)
self.assertEqual(actual, expected)
actual = _compute_consensus_annotation(
in_, 0.51, unassignable_label="Hello world!")
expected = (['Hello world!'], 1.)
self.assertEqual(actual, expected)
def test_no_consensus(self):
in_ = [['Ab', 'Bc', 'De'],
['Cd', 'Bc', 'Fg', 'Hi'],
['Ef', 'Bc', 'Fg', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Unassigned'], 1.)
self.assertEqual(actual, expected)
actual = _compute_consensus_annotation(
in_, 0.51, unassignable_label="Hello world!")
expected = (['Hello world!'], 1.)
self.assertEqual(actual, expected)
def test_varied_min_fraction(self):
in_ = [['Ab', 'Bc', 'De'], ['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc', 'Fg'], 2. / 3.)
self.assertEqual(actual, expected)
# increased min_consensus_fraction yields decreased specificity
in_ = [['Ab', 'Bc', 'De'], ['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.99, "Unassigned")
expected = (['Ab', 'Bc'], 1.0)
self.assertEqual(actual, expected)
def test_adjusts_resolution(self):
# max result depth is that of shallowest assignment
in_ = [['Ab', 'Bc', 'Fg'], ['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Hi'], ['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc', 'Fg'], 1.0)
self.assertEqual(actual, expected)
in_ = [['Ab', 'Bc', 'Fg'], ['Ab', 'Bc', 'Fg', 'Hi', 'Jk'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk'], ['Ab', 'Bc', 'Fg', 'Hi', 'Jk'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc', 'Fg'], 1.0)
self.assertEqual(actual, expected)
def test_varied_min_fraction(self):
in_ = [['Ab', 'Bc', 'De'],
['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc', 'Fg'], 2. / 3.)
self.assertEqual(actual, expected)
# increased min_consensus_fraction yields decreased specificity
in_ = [['Ab', 'Bc', 'De'],
['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.99, "Unassigned")
expected = (['Ab', 'Bc'], 1.0)
self.assertEqual(actual, expected)
def test_adjusts_resolution(self):
# max result depth is that of shallowest assignment
in_ = [['Ab', 'Bc', 'Fg'],
['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Hi'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc', 'Fg'], 1.0)
self.assertEqual(actual, expected)
in_ = [['Ab', 'Bc', 'Fg'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk'],
['Ab', 'Bc', 'Fg', 'Hi', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc', 'Fg'], 1.0)
self.assertEqual(actual, expected)
def test_overlapping_names(self):
# here the 3rd level is different, but the 4th level is the same
# across the three assignments. this can happen in practice if
# three different genera are assigned, and under each there is
# an unnamed species
# (e.g., f__x;g__A;s__, f__x;g__B;s__, f__x;g__B;s__)
# in this case, the assignment should be f__x.
in_ = [['Ab', 'Bc', 'De', 'Jk'], ['Ab', 'Bc', 'Fg', 'Jk'],
['Ab', 'Bc', 'Hi', 'Jk']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['Ab', 'Bc'], 1.)
self.assertEqual(actual, expected)
# here the third level is the same in 4/5 of the
# assignments, but one of them (z, y, c) refers to a
# different taxa since the higher levels are different.
# the consensus value should be 3/5, not 4/5, to
# reflect that.
in_ = [['a', 'b', 'c'], ['a', 'd', 'e'], ['a', 'b', 'c'],
['a', 'b', 'c'], ['z', 'y', 'c']]
actual = _compute_consensus_annotation(in_, 0.51, "Unassigned")
expected = (['a', 'b', 'c'], 0.6)
self.assertEqual(actual, expected)
