def test_from_iterable_with_key_and_keys(self):
iterable = (x for x in range(4))
with self.assertRaises(ValueError):
DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a),
key=str,
keys=['1', '2', '3', '4'])
def test_filter_asymmetric(self):
# 2x2
ids = ['b', 'a']
exp = DissimilarityMatrix([[0, -2], [1, 0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
# 3x3
dm = DissimilarityMatrix([[0, 10, 53], [42, 0, 22.5], [53, 1, 0]],
('bro', 'brah', 'breh'))
ids = ['breh', 'brah']
exp = DissimilarityMatrix([[0, 1], [22.5, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_from_iterable_asymmetric_data(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[-1, 0, 1, 2],
[-2, -1, 0, 1],
[-3, -2, -1, 0]])
res = DissimilarityMatrix.from_iterable(iterable, lambda a, b: b - a)
self.assertEqual(res, exp)
def test_from_iterable_non_hollow_data(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]])
res = DissimilarityMatrix.from_iterable(iterable, lambda a, b: 1)
self.assertEqual(res, exp)
def test_filter_subset(self):
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
ids = ('b', 'a')
exp = DissimilarityMatrix([[0, 0.01], [0.01, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
# 4x4
dm = DissimilarityMatrix([[0, 1, 55, 7], [1, 0, 16, 1],
[55, 16, 0, 23], [7, 1, 23, 0]])
ids = np.asarray(['3', '0', '1'])
exp = DissimilarityMatrix([[0, 7, 1], [7, 0, 1], [1, 1, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def setUp(self):
super(DissimilarityMatrixTests, self).setUp()
self.dm_1x1 = DissimilarityMatrix(self.dm_1x1_data, ['a'])
self.dm_2x2 = DissimilarityMatrix(self.dm_2x2_data, ['a', 'b'])
self.dm_2x2_asym = DissimilarityMatrix(self.dm_2x2_asym_data,
['a', 'b'])
self.dm_3x3 = DissimilarityMatrix(self.dm_3x3_data, ['a', 'b', 'c'])
self.dms = [self.dm_1x1, self.dm_2x2, self.dm_2x2_asym, self.dm_3x3]
self.dm_shapes = [(1, 1), (2, 2), (2, 2), (3, 3)]
self.dm_sizes = [1, 4, 4, 9]
self.dm_transposes = [
self.dm_1x1, self.dm_2x2,
DissimilarityMatrix([[0, -2], [1, 0]], ['a', 'b']), self.dm_3x3]
self.dm_redundant_forms = [np.array(self.dm_1x1_data),
np.array(self.dm_2x2_data),
np.array(self.dm_2x2_asym_data),
np.array(self.dm_3x3_data)]
def test_io(self):
# Very basic check that read/write public API is present and appears to
# be functioning. Roundtrip from memory -> disk -> memory and ensure
# results match.
fh = StringIO()
self.dm_3x3.write(fh)
fh.seek(0)
deserialized = DissimilarityMatrix.read(fh)
self.assertEqual(deserialized, self.dm_3x3)
self.assertTrue(type(deserialized) == DissimilarityMatrix)
def test_from_iterable_no_key(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[1, 0, 1, 2],
[2, 1, 0, 1],
[3, 2, 1, 0]])
res = DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a))
self.assertEqual(res, exp)
def test_plot_no_default(self):
ids = ['0', 'one', '2', 'three', '4.000']
data = ([0, 1, 2, 3, 4], [1, 0, 1, 2, 3], [2, 1, 0, 1, 2],
[3, 2, 1, 0, 1], [4, 3, 2, 1, 0])
dm = DissimilarityMatrix(data, ids)
fig = dm.plot(cmap='Reds', title='Testplot')
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), 'Testplot')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['0', 'one', '2', 'three', '4.000'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['0', 'one', '2', 'three', '4.000'])
def test_from_iterable_with_key(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[1, 0, 1, 2],
[2, 1, 0, 1],
[3, 2, 1, 0]], ['0', '1', '4', '9'])
res = DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a),
key=lambda x: str(x ** 2))
self.assertEqual(res, exp)
def test_init_from_dm(self):
ids = ['foo', 'bar', 'baz']
# DissimilarityMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(self.dm_3x3, ids)
self.assertEqual(obs, exp)
# Test that copy of data is not made.
self.assertTrue(obs.data is self.dm_3x3.data)
obs.data[0, 1] = 424242
self.assertTrue(np.array_equal(obs.data, self.dm_3x3.data))
# DistanceMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(
DistanceMatrix(self.dm_3x3_data, ('a', 'b', 'c')), ids)
self.assertEqual(obs, exp)
# DissimilarityMatrix -> DistanceMatrix
with self.assertRaises(DistanceMatrixError):
DistanceMatrix(self.dm_2x2_asym, ['foo', 'bar'])
def test_from_iterable_skbio_hamming_metric_with_metadata(self):
# test for #1254
seqs = [
Sequence('ACGT'),
Sequence('ACGA', metadata={'id': 'seq1'}),
Sequence('AAAA', metadata={'id': 'seq2'}),
Sequence('AAAA', positional_metadata={'qual': range(4)})
]
exp = DissimilarityMatrix([
[0, 0.25, 0.75, 0.75],
[0.25, 0.0, 0.5, 0.5],
[0.75, 0.5, 0.0, 0.0],
[0.75, 0.5, 0.0, 0.0]], ['a', 'b', 'c', 'd'])
dm = DissimilarityMatrix.from_iterable(
seqs,
metric=skbio.sequence.distance.hamming,
keys=['a', 'b', 'c', 'd'])
self.assertEqual(dm, exp)
def test_to_data_frame_default_ids(self):
df = DissimilarityMatrix(self.dm_2x2_data).to_data_frame()
exp = pd.DataFrame([[0.0, 0.123], [0.123, 0.0]],
index=['0', '1'],
columns=['0', '1'])
assert_data_frame_almost_equal(df, exp)
def test_from_iterable_empty(self):
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix.from_iterable([], lambda x: x)
def test_from_iterable_single(self):
exp = DissimilarityMatrix([[100]])
res = DissimilarityMatrix.from_iterable(["boo"], lambda a, b: 100)
self.assertEqual(res, exp)
#!/usr/bin/env python
from __future__ import print_function
from skbio.stats.distance import DissimilarityMatrix
from os import path
import sys
for distfile in sys.argv[1:]:
prefix = path.splitext(distfile)[0]
print(prefix)
dist = DissimilarityMatrix.read(distfile)
fig = dist.plot(title=prefix)
fig.tight_layout()
fig.savefig("{prefix}.pdf".format(prefix=prefix))
def test_init_no_ids(self):
exp = DissimilarityMatrix(self.dm_3x3_data, ('0', '1', '2'))
obs = DissimilarityMatrix(self.dm_3x3_data)
self.assertEqual(obs, exp)
self.assertEqual(obs['1', '2'], 12.0)
def test_constructor(self):
exp = DissimilarityMatrix(np.asarray([[0.0]]), ['1'])
obs = randdm(1, constructor=DissimilarityMatrix)
self.assertEqual(obs, exp)
self.assertEqual(type(obs), DissimilarityMatrix)
def test_eq(self):
# Compare DistanceMatrix to DissimilarityMatrix, where both have the
# same data and IDs.
eq_dm = DissimilarityMatrix(self.dm_3x3_data, ['a', 'b', 'c'])
self.assertTrue(self.dm_3x3 == eq_dm)
self.assertTrue(eq_dm == self.dm_3x3)
class DissimilarityMatrixTests(DissimilarityMatrixTestData):
def setUp(self):
super(DissimilarityMatrixTests, self).setUp()
self.dm_1x1 = DissimilarityMatrix(self.dm_1x1_data, ['a'])
self.dm_2x2 = DissimilarityMatrix(self.dm_2x2_data, ['a', 'b'])
self.dm_2x2_asym = DissimilarityMatrix(self.dm_2x2_asym_data,
['a', 'b'])
self.dm_3x3 = DissimilarityMatrix(self.dm_3x3_data, ['a', 'b', 'c'])
self.dms = [self.dm_1x1, self.dm_2x2, self.dm_2x2_asym, self.dm_3x3]
self.dm_shapes = [(1, 1), (2, 2), (2, 2), (3, 3)]
self.dm_sizes = [1, 4, 4, 9]
self.dm_transposes = [
self.dm_1x1, self.dm_2x2,
DissimilarityMatrix([[0, -2], [1, 0]], ['a', 'b']), self.dm_3x3
]
self.dm_redundant_forms = [
np.array(self.dm_1x1_data),
np.array(self.dm_2x2_data),
np.array(self.dm_2x2_asym_data),
np.array(self.dm_3x3_data)
]
def test_init_from_dm(self):
ids = ['foo', 'bar', 'baz']
# DissimilarityMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(self.dm_3x3, ids)
self.assertEqual(obs, exp)
# Test that copy of data is not made.
self.assertTrue(obs.data is self.dm_3x3.data)
obs.data[0, 1] = 424242
self.assertTrue(np.array_equal(obs.data, self.dm_3x3.data))
# DistanceMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(
DistanceMatrix(self.dm_3x3_data, ('a', 'b', 'c')), ids)
self.assertEqual(obs, exp)
# DissimilarityMatrix -> DistanceMatrix
with self.assertRaises(DistanceMatrixError):
DistanceMatrix(self.dm_2x2_asym, ['foo', 'bar'])
def test_init_no_ids(self):
exp = DissimilarityMatrix(self.dm_3x3_data, ('0', '1', '2'))
obs = DissimilarityMatrix(self.dm_3x3_data)
self.assertEqual(obs, exp)
self.assertEqual(obs['1', '2'], 12.0)
def test_init_invalid_input(self):
# Empty data.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([], [])
# Another type of empty data.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(np.empty((0, 0)), [])
# Invalid number of dimensions.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([1, 2, 3], ['a'])
# Dimensions don't match.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([[1, 2, 3]], ['a'])
data = [[0, 1], [1, 0]]
# Duplicate IDs.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'a'])
# Number of IDs don't match dimensions.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'b', 'c'])
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, [])
# Non-hollow.
data = [[0.0, 1.0], [1.0, 0.01]]
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'b'])
def test_data(self):
for dm, exp in zip(self.dms, self.dm_redundant_forms):
obs = dm.data
self.assertTrue(np.array_equal(obs, exp))
with self.assertRaises(AttributeError):
self.dm_3x3.data = 'foo'
def test_ids(self):
obs = self.dm_3x3.ids
self.assertEqual(obs, ('a', 'b', 'c'))
# Test that we overwrite the existing IDs and that the ID index is
# correctly rebuilt.
new_ids = ['foo', 'bar', 'baz']
self.dm_3x3.ids = new_ids
obs = self.dm_3x3.ids
self.assertEqual(obs, tuple(new_ids))
self.assertTrue(
np.array_equal(self.dm_3x3['bar'], np.array([0.01, 0.0, 12.0])))
with self.assertRaises(MissingIDError):
self.dm_3x3['b']
def test_ids_invalid_input(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.ids = ['foo', 'bar']
# Make sure that we can still use the dissimilarity matrix after trying
# to be evil.
obs = self.dm_3x3.ids
self.assertEqual(obs, ('a', 'b', 'c'))
def test_dtype(self):
for dm in self.dms:
self.assertEqual(dm.dtype, np.float64)
def test_shape(self):
for dm, shape in zip(self.dms, self.dm_shapes):
self.assertEqual(dm.shape, shape)
def test_size(self):
for dm, size in zip(self.dms, self.dm_sizes):
self.assertEqual(dm.size, size)
def test_transpose(self):
for dm, transpose in zip(self.dms, self.dm_transposes):
self.assertEqual(dm.T, transpose)
self.assertEqual(dm.transpose(), transpose)
# We should get a reference to a different object back, even if the
# transpose is the same as the original.
self.assertTrue(dm.transpose() is not dm)
def test_index(self):
self.assertEqual(self.dm_3x3.index('a'), 0)
self.assertEqual(self.dm_3x3.index('b'), 1)
self.assertEqual(self.dm_3x3.index('c'), 2)
with self.assertRaises(MissingIDError):
self.dm_3x3.index('d')
with self.assertRaises(MissingIDError):
self.dm_3x3.index(1)
def test_redundant_form(self):
for dm, redundant in zip(self.dms, self.dm_redundant_forms):
obs = dm.redundant_form()
self.assertTrue(np.array_equal(obs, redundant))
def test_copy(self):
copy = self.dm_2x2.copy()
self.assertEqual(copy, self.dm_2x2)
self.assertFalse(copy.data is self.dm_2x2.data)
# deepcopy doesn't actually create a copy of the IDs because it is a
# tuple of strings, which is fully immutable.
self.assertTrue(copy.ids is self.dm_2x2.ids)
new_ids = ['hello', 'world']
copy.ids = new_ids
self.assertNotEqual(copy.ids, self.dm_2x2.ids)
copy = self.dm_2x2.copy()
copy.data[0, 1] = 0.0001
self.assertFalse(np.array_equal(copy.data, self.dm_2x2.data))
def test_filter_no_filtering(self):
# Don't actually filter anything -- ensure we get back a different
# object.
obs = self.dm_3x3.filter(['a', 'b', 'c'])
self.assertEqual(obs, self.dm_3x3)
self.assertFalse(obs is self.dm_3x3)
def test_filter_reorder(self):
# Don't filter anything, but reorder the distance matrix.
order = ['c', 'a', 'b']
exp = DissimilarityMatrix(
[[0, 4.2, 12], [4.2, 0, 0.01], [12, 0.01, 0]], order)
obs = self.dm_3x3.filter(order)
self.assertEqual(obs, exp)
def test_filter_single_id(self):
ids = ['b']
exp = DissimilarityMatrix([[0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
def test_filter_asymmetric(self):
# 2x2
ids = ['b', 'a']
exp = DissimilarityMatrix([[0, -2], [1, 0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
# 3x3
dm = DissimilarityMatrix([[0, 10, 53], [42, 0, 22.5], [53, 1, 0]],
('bro', 'brah', 'breh'))
ids = ['breh', 'brah']
exp = DissimilarityMatrix([[0, 1], [22.5, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_filter_subset(self):
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
ids = ('b', 'a')
exp = DissimilarityMatrix([[0, 0.01], [0.01, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
# 4x4
dm = DissimilarityMatrix([[0, 1, 55, 7], [1, 0, 16, 1],
[55, 16, 0, 23], [7, 1, 23, 0]])
ids = np.asarray(['3', '0', '1'])
exp = DissimilarityMatrix([[0, 7, 1], [7, 0, 1], [1, 1, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_filter_duplicate_ids(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.filter(['c', 'a', 'c'])
def test_filter_missing_ids(self):
with self.assertRaises(MissingIDError):
self.dm_3x3.filter(['c', 'bro'])
def test_filter_missing_ids_strict_false(self):
# no exception should be raised
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(['c', 'a', 'not found'], strict=False)
self.assertEqual(obs, exp)
def test_filter_empty_ids(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.filter([])
def test_plot_default(self):
fig = self.dm_1x1.plot()
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), '')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['a'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['a'])
def test_plot_no_default(self):
ids = ['0', 'one', '2', 'three', '4.000']
data = ([0, 1, 2, 3, 4], [1, 0, 1, 2, 3], [2, 1, 0, 1,
2], [3, 2, 1, 0,
1], [4, 3, 2, 1, 0])
dm = DissimilarityMatrix(data, ids)
fig = dm.plot(cmap='Reds', title='Testplot')
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), 'Testplot')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['0', 'one', '2', 'three', '4.000'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['0', 'one', '2', 'three', '4.000'])
def test_repr_png(self):
dm = self.dm_1x1
obs = dm._repr_png_()
self.assertIsInstance(obs, binary_type)
self.assertTrue(len(obs) > 0)
def test_repr_svg(self):
obs = self.dm_1x1._repr_svg_()
# print_figure(format='svg') can return text or bytes depending on the
# version of IPython
self.assertTrue(
isinstance(obs, text_type) or isinstance(obs, binary_type))
self.assertTrue(len(obs) > 0)
def test_png(self):
dm = self.dm_1x1
self.assertIsInstance(dm.png, Image)
def test_svg(self):
dm = self.dm_1x1
self.assertIsInstance(dm.svg, SVG)
def test_str(self):
for dm in self.dms:
obs = str(dm)
# Do some very light testing here to make sure we're getting a
# non-empty string back. We don't want to test the exact
# formatting.
self.assertTrue(obs)
def test_eq(self):
for dm in self.dms:
copy = dm.copy()
self.assertTrue(dm == dm)
self.assertTrue(copy == copy)
self.assertTrue(dm == copy)
self.assertTrue(copy == dm)
self.assertFalse(self.dm_1x1 == self.dm_3x3)
def test_ne(self):
# Wrong class.
self.assertTrue(self.dm_3x3 != 'foo')
# Wrong shape.
self.assertTrue(self.dm_3x3 != self.dm_1x1)
# Wrong IDs.
other = self.dm_3x3.copy()
other.ids = ['foo', 'bar', 'baz']
self.assertTrue(self.dm_3x3 != other)
# Wrong data.
other = self.dm_3x3.copy()
other.data[1, 0] = 42.42
self.assertTrue(self.dm_3x3 != other)
self.assertFalse(self.dm_2x2 != self.dm_2x2)
def test_contains(self):
self.assertTrue('a' in self.dm_3x3)
self.assertTrue('b' in self.dm_3x3)
self.assertTrue('c' in self.dm_3x3)
self.assertFalse('d' in self.dm_3x3)
def test_getslice(self):
# Slice of first dimension only. Test that __getslice__ defers to
# __getitem__.
obs = self.dm_2x2[1:]
self.assertTrue(np.array_equal(obs, np.array([[0.123, 0.0]])))
self.assertEqual(type(obs), np.ndarray)
def test_getitem_by_id(self):
obs = self.dm_1x1['a']
self.assertTrue(np.array_equal(obs, np.array([0.0])))
obs = self.dm_2x2_asym['b']
self.assertTrue(np.array_equal(obs, np.array([-2.0, 0.0])))
obs = self.dm_3x3['c']
self.assertTrue(np.array_equal(obs, np.array([4.2, 12.0, 0.0])))
with self.assertRaises(MissingIDError):
self.dm_2x2['c']
def test_getitem_by_id_pair(self):
# Same object.
self.assertEqual(self.dm_1x1['a', 'a'], 0.0)
# Different objects (symmetric).
self.assertEqual(self.dm_3x3['b', 'c'], 12.0)
self.assertEqual(self.dm_3x3['c', 'b'], 12.0)
# Different objects (asymmetric).
self.assertEqual(self.dm_2x2_asym['a', 'b'], 1.0)
self.assertEqual(self.dm_2x2_asym['b', 'a'], -2.0)
with self.assertRaises(MissingIDError):
self.dm_2x2['a', 'c']
def test_getitem_ndarray_indexing(self):
# Single element access.
obs = self.dm_3x3[0, 1]
self.assertEqual(obs, 0.01)
# Single element access (via two __getitem__ calls).
obs = self.dm_3x3[0][1]
self.assertEqual(obs, 0.01)
# Row access.
obs = self.dm_3x3[1]
self.assertTrue(np.array_equal(obs, np.array([0.01, 0.0, 12.0])))
self.assertEqual(type(obs), np.ndarray)
# Grab all data.
obs = self.dm_3x3[:, :]
self.assertTrue(np.array_equal(obs, self.dm_3x3.data))
self.assertEqual(type(obs), np.ndarray)
with self.assertRaises(IndexError):
self.dm_3x3[:, 3]
def test_validate_invalid_dtype(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3._validate(np.array([[0, 42], [42, 0]]), ['a', 'b'])
def test_filter_missing_ids_strict_false(self):
# no exception should be raised
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(['c', 'a', 'not found'], strict=False)
self.assertEqual(obs, exp)
def setUp(self):
super(DissimilarityMatrixTests, self).setUp()
self.dm_1x1 = DissimilarityMatrix(self.dm_1x1_data, ['a'])
self.dm_2x2 = DissimilarityMatrix(self.dm_2x2_data, ['a', 'b'])
self.dm_2x2_asym = DissimilarityMatrix(self.dm_2x2_asym_data,
['a', 'b'])
self.dm_3x3 = DissimilarityMatrix(self.dm_3x3_data, ['a', 'b', 'c'])
self.dm_5x5 = DissimilarityMatrix(self.dm_5x5_data, list('abcde'))
self.dms = [self.dm_1x1, self.dm_2x2, self.dm_2x2_asym, self.dm_3x3]
self.dm_shapes = [(1, 1), (2, 2), (2, 2), (3, 3)]
self.dm_sizes = [1, 4, 4, 9]
self.dm_transposes = [
self.dm_1x1, self.dm_2x2,
DissimilarityMatrix([[0, -2], [1, 0]], ['a', 'b']), self.dm_3x3]
self.dm_redundant_forms = [np.array(self.dm_1x1_data),
np.array(self.dm_2x2_data),
np.array(self.dm_2x2_asym_data),
np.array(self.dm_3x3_data)]
def test_from_iterable_empty(self):
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix.from_iterable([], lambda x: x)
def test_from_iterable_single(self):
exp = DissimilarityMatrix([[100]])
res = DissimilarityMatrix.from_iterable(["boo"], lambda a, b: 100)
self.assertEqual(res, exp)
class DissimilarityMatrixTests(DissimilarityMatrixTestData):
def setUp(self):
super(DissimilarityMatrixTests, self).setUp()
self.dm_1x1 = DissimilarityMatrix(self.dm_1x1_data, ['a'])
self.dm_2x2 = DissimilarityMatrix(self.dm_2x2_data, ['a', 'b'])
self.dm_2x2_asym = DissimilarityMatrix(self.dm_2x2_asym_data,
['a', 'b'])
self.dm_3x3 = DissimilarityMatrix(self.dm_3x3_data, ['a', 'b', 'c'])
self.dms = [self.dm_1x1, self.dm_2x2, self.dm_2x2_asym, self.dm_3x3]
self.dm_shapes = [(1, 1), (2, 2), (2, 2), (3, 3)]
self.dm_sizes = [1, 4, 4, 9]
self.dm_transposes = [
self.dm_1x1, self.dm_2x2,
DissimilarityMatrix([[0, -2], [1, 0]], ['a', 'b']), self.dm_3x3]
self.dm_redundant_forms = [np.array(self.dm_1x1_data),
np.array(self.dm_2x2_data),
np.array(self.dm_2x2_asym_data),
np.array(self.dm_3x3_data)]
def test_deprecated_io(self):
fh = StringIO()
npt.assert_warns(UserWarning, self.dm_3x3.to_file, fh)
fh.seek(0)
deserialized = npt.assert_warns(UserWarning,
DissimilarityMatrix.from_file, fh)
self.assertEqual(deserialized, self.dm_3x3)
self.assertTrue(type(deserialized) == DissimilarityMatrix)
def test_init_from_dm(self):
ids = ['foo', 'bar', 'baz']
# DissimilarityMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(self.dm_3x3, ids)
self.assertEqual(obs, exp)
# Test that copy of data is not made.
self.assertTrue(obs.data is self.dm_3x3.data)
obs.data[0, 1] = 424242
self.assertTrue(np.array_equal(obs.data, self.dm_3x3.data))
# DistanceMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(
DistanceMatrix(self.dm_3x3_data, ('a', 'b', 'c')), ids)
self.assertEqual(obs, exp)
# DissimilarityMatrix -> DistanceMatrix
with self.assertRaises(DistanceMatrixError):
DistanceMatrix(self.dm_2x2_asym, ['foo', 'bar'])
def test_init_no_ids(self):
exp = DissimilarityMatrix(self.dm_3x3_data, ('0', '1', '2'))
obs = DissimilarityMatrix(self.dm_3x3_data)
self.assertEqual(obs, exp)
self.assertEqual(obs['1', '2'], 12.0)
def test_init_invalid_input(self):
# Empty data.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([], [])
# Another type of empty data.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(np.empty((0, 0)), [])
# Invalid number of dimensions.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([1, 2, 3], ['a'])
# Dimensions don't match.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([[1, 2, 3]], ['a'])
data = [[0, 1], [1, 0]]
# Duplicate IDs.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'a'])
# Number of IDs don't match dimensions.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'b', 'c'])
# Non-hollow.
data = [[0.0, 1.0], [1.0, 0.01]]
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'b'])
def test_data(self):
for dm, exp in zip(self.dms, self.dm_redundant_forms):
obs = dm.data
self.assertTrue(np.array_equal(obs, exp))
with self.assertRaises(AttributeError):
self.dm_3x3.data = 'foo'
def test_ids(self):
obs = self.dm_3x3.ids
self.assertEqual(obs, ('a', 'b', 'c'))
# Test that we overwrite the existing IDs and that the ID index is
# correctly rebuilt.
new_ids = ['foo', 'bar', 'baz']
self.dm_3x3.ids = new_ids
obs = self.dm_3x3.ids
self.assertEqual(obs, tuple(new_ids))
self.assertTrue(np.array_equal(self.dm_3x3['bar'],
np.array([0.01, 0.0, 12.0])))
with self.assertRaises(MissingIDError):
self.dm_3x3['b']
def test_ids_invalid_input(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.ids = ['foo', 'bar']
# Make sure that we can still use the dissimilarity matrix after trying
# to be evil.
obs = self.dm_3x3.ids
self.assertEqual(obs, ('a', 'b', 'c'))
def test_dtype(self):
for dm in self.dms:
self.assertEqual(dm.dtype, np.float64)
def test_shape(self):
for dm, shape in zip(self.dms, self.dm_shapes):
self.assertEqual(dm.shape, shape)
def test_size(self):
for dm, size in zip(self.dms, self.dm_sizes):
self.assertEqual(dm.size, size)
def test_transpose(self):
for dm, transpose in zip(self.dms, self.dm_transposes):
self.assertEqual(dm.T, transpose)
self.assertEqual(dm.transpose(), transpose)
# We should get a reference to a different object back, even if the
# transpose is the same as the original.
self.assertTrue(dm.transpose() is not dm)
def test_index(self):
self.assertEqual(self.dm_3x3.index('a'), 0)
self.assertEqual(self.dm_3x3.index('b'), 1)
self.assertEqual(self.dm_3x3.index('c'), 2)
with self.assertRaises(MissingIDError):
self.dm_3x3.index('d')
with self.assertRaises(MissingIDError):
self.dm_3x3.index(1)
def test_redundant_form(self):
for dm, redundant in zip(self.dms, self.dm_redundant_forms):
obs = dm.redundant_form()
self.assertTrue(np.array_equal(obs, redundant))
def test_copy(self):
copy = self.dm_2x2.copy()
self.assertEqual(copy, self.dm_2x2)
self.assertFalse(copy.data is self.dm_2x2.data)
# deepcopy doesn't actually create a copy of the IDs because it is a
# tuple of strings, which is fully immutable.
self.assertTrue(copy.ids is self.dm_2x2.ids)
new_ids = ['hello', 'world']
copy.ids = new_ids
self.assertNotEqual(copy.ids, self.dm_2x2.ids)
copy = self.dm_2x2.copy()
copy.data[0, 1] = 0.0001
self.assertFalse(np.array_equal(copy.data, self.dm_2x2.data))
def test_filter_no_filtering(self):
# Don't actually filter anything -- ensure we get back a different
# object.
obs = self.dm_3x3.filter(['a', 'b', 'c'])
self.assertEqual(obs, self.dm_3x3)
self.assertFalse(obs is self.dm_3x3)
def test_filter_reorder(self):
# Don't filter anything, but reorder the distance matrix.
order = ['c', 'a', 'b']
exp = DissimilarityMatrix(
[[0, 4.2, 12], [4.2, 0, 0.01], [12, 0.01, 0]], order)
obs = self.dm_3x3.filter(order)
self.assertEqual(obs, exp)
def test_filter_single_id(self):
ids = ['b']
exp = DissimilarityMatrix([[0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
def test_filter_asymmetric(self):
# 2x2
ids = ['b', 'a']
exp = DissimilarityMatrix([[0, -2], [1, 0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
# 3x3
dm = DissimilarityMatrix([[0, 10, 53], [42, 0, 22.5], [53, 1, 0]],
('bro', 'brah', 'breh'))
ids = ['breh', 'brah']
exp = DissimilarityMatrix([[0, 1], [22.5, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_filter_subset(self):
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
ids = ('b', 'a')
exp = DissimilarityMatrix([[0, 0.01], [0.01, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
# 4x4
dm = DissimilarityMatrix([[0, 1, 55, 7], [1, 0, 16, 1],
[55, 16, 0, 23], [7, 1, 23, 0]])
ids = np.asarray(['3', '0', '1'])
exp = DissimilarityMatrix([[0, 7, 1], [7, 0, 1], [1, 1, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_filter_duplicate_ids(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.filter(['c', 'a', 'c'])
def test_filter_missing_ids(self):
with self.assertRaises(MissingIDError):
self.dm_3x3.filter(['c', 'bro'])
def test_filter_missing_ids_strict_false(self):
# no exception should be raised
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(['c', 'a', 'not found'], strict=False)
self.assertEqual(obs, exp)
def test_filter_empty_ids(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.filter([])
def test_plot_default(self):
fig = self.dm_1x1.plot()
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), '')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['a'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['a'])
def test_plot_no_default(self):
ids = ['0', 'one', '2', 'three', '4.000']
data = ([0, 1, 2, 3, 4], [1, 0, 1, 2, 3], [2, 1, 0, 1, 2],
[3, 2, 1, 0, 1], [4, 3, 2, 1, 0])
dm = DissimilarityMatrix(data, ids)
fig = dm.plot(cmap='Reds', title='Testplot')
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), 'Testplot')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['0', 'one', '2', 'three', '4.000'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['0', 'one', '2', 'three', '4.000'])
def test_repr_png(self):
dm = self.dm_1x1
obs = dm._repr_png_()
self.assertIsInstance(obs, binary_type)
self.assertTrue(len(obs) > 0)
def test_repr_svg(self):
dm = self.dm_1x1
obs = dm._repr_svg_()
self.assertIsInstance(obs, text_type)
self.assertTrue(len(obs) > 0)
def test_png(self):
dm = self.dm_1x1
self.assertIsInstance(dm.png, Image)
def test_svg(self):
dm = self.dm_1x1
self.assertIsInstance(dm.svg, SVG)
def test_str(self):
for dm in self.dms:
obs = str(dm)
# Do some very light testing here to make sure we're getting a
# non-empty string back. We don't want to test the exact
# formatting.
self.assertTrue(obs)
def test_eq(self):
for dm in self.dms:
copy = dm.copy()
self.assertTrue(dm == dm)
self.assertTrue(copy == copy)
self.assertTrue(dm == copy)
self.assertTrue(copy == dm)
self.assertFalse(self.dm_1x1 == self.dm_3x3)
def test_ne(self):
# Wrong class.
self.assertTrue(self.dm_3x3 != 'foo')
# Wrong shape.
self.assertTrue(self.dm_3x3 != self.dm_1x1)
# Wrong IDs.
other = self.dm_3x3.copy()
other.ids = ['foo', 'bar', 'baz']
self.assertTrue(self.dm_3x3 != other)
# Wrong data.
other = self.dm_3x3.copy()
other.data[1, 0] = 42.42
self.assertTrue(self.dm_3x3 != other)
self.assertFalse(self.dm_2x2 != self.dm_2x2)
def test_contains(self):
self.assertTrue('a' in self.dm_3x3)
self.assertTrue('b' in self.dm_3x3)
self.assertTrue('c' in self.dm_3x3)
self.assertFalse('d' in self.dm_3x3)
def test_getslice(self):
# Slice of first dimension only. Test that __getslice__ defers to
# __getitem__.
obs = self.dm_2x2[1:]
self.assertTrue(np.array_equal(obs, np.array([[0.123, 0.0]])))
self.assertEqual(type(obs), np.ndarray)
def test_getitem_by_id(self):
obs = self.dm_1x1['a']
self.assertTrue(np.array_equal(obs, np.array([0.0])))
obs = self.dm_2x2_asym['b']
self.assertTrue(np.array_equal(obs, np.array([-2.0, 0.0])))
obs = self.dm_3x3['c']
self.assertTrue(np.array_equal(obs, np.array([4.2, 12.0, 0.0])))
with self.assertRaises(MissingIDError):
self.dm_2x2['c']
def test_getitem_by_id_pair(self):
# Same object.
self.assertEqual(self.dm_1x1['a', 'a'], 0.0)
# Different objects (symmetric).
self.assertEqual(self.dm_3x3['b', 'c'], 12.0)
self.assertEqual(self.dm_3x3['c', 'b'], 12.0)
# Different objects (asymmetric).
self.assertEqual(self.dm_2x2_asym['a', 'b'], 1.0)
self.assertEqual(self.dm_2x2_asym['b', 'a'], -2.0)
with self.assertRaises(MissingIDError):
self.dm_2x2['a', 'c']
def test_getitem_ndarray_indexing(self):
# Single element access.
obs = self.dm_3x3[0, 1]
self.assertEqual(obs, 0.01)
# Single element access (via two __getitem__ calls).
obs = self.dm_3x3[0][1]
self.assertEqual(obs, 0.01)
# Row access.
obs = self.dm_3x3[1]
self.assertTrue(np.array_equal(obs, np.array([0.01, 0.0, 12.0])))
self.assertEqual(type(obs), np.ndarray)
# Grab all data.
obs = self.dm_3x3[:, :]
self.assertTrue(np.array_equal(obs, self.dm_3x3.data))
self.assertEqual(type(obs), np.ndarray)
with self.assertRaises(IndexError):
self.dm_3x3[:, 3]
def test_validate_invalid_dtype(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3._validate(np.array([[0, 42], [42, 0]]), ['a', 'b'])
def test_pprint_ids(self):
# No truncation.
exp = 'a, b, c'
obs = self.dm_3x3._pprint_ids()
self.assertEqual(obs, exp)
# Truncation.
exp = 'a, b, ...'
obs = self.dm_3x3._pprint_ids(max_chars=5)
self.assertEqual(obs, exp)
def test_init_non_hollow_dm(self):
data = [[1, 1], [1, 1]]
obs = DissimilarityMatrix(data, ['a', 'b'])
self.assertTrue(np.array_equal(obs.data, data))
def test_filter_single_id(self):
ids = ['b']
exp = DissimilarityMatrix([[0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
class DissimilarityMatrixTests(DissimilarityMatrixTestData):
def setUp(self):
super(DissimilarityMatrixTests, self).setUp()
self.dm_1x1 = DissimilarityMatrix(self.dm_1x1_data, ['a'])
self.dm_2x2 = DissimilarityMatrix(self.dm_2x2_data, ['a', 'b'])
self.dm_2x2_asym = DissimilarityMatrix(self.dm_2x2_asym_data,
['a', 'b'])
self.dm_3x3 = DissimilarityMatrix(self.dm_3x3_data, ['a', 'b', 'c'])
self.dm_5x5 = DissimilarityMatrix(self.dm_5x5_data, list('abcde'))
self.dms = [self.dm_1x1, self.dm_2x2, self.dm_2x2_asym, self.dm_3x3]
self.dm_shapes = [(1, 1), (2, 2), (2, 2), (3, 3)]
self.dm_sizes = [1, 4, 4, 9]
self.dm_transposes = [
self.dm_1x1, self.dm_2x2,
DissimilarityMatrix([[0, -2], [1, 0]], ['a', 'b']), self.dm_3x3]
self.dm_redundant_forms = [np.array(self.dm_1x1_data),
np.array(self.dm_2x2_data),
np.array(self.dm_2x2_asym_data),
np.array(self.dm_3x3_data)]
def test_avoid_copy_on_construction(self):
# ((data, expect_copy))
tests = (([[0, 1], [1, 0]], True),
([(0, 1), (1, 0)], True),
(((0, 1), (1, 0)), True),
(np.array([[0, 1], [1, 0]], dtype='int'), True),
(np.array([[0, 1], [1, 0]], dtype='float'), False),
(np.array([[0, 1], [1, 0]], dtype=np.float32), False),
(np.array([[0, 1], [1, 0]], dtype=np.float64), False),
(np.array([[0, 1], [1, 0]], dtype='double'), False))
for data, expect in tests:
obj = DissimilarityMatrix(data)
self.assertEqual(id(obj.data) != id(data), expect)
def test_within(self):
exp = pd.DataFrame([['a', 'a', 0.0],
['a', 'c', 4.2],
['c', 'a', 4.2],
['c', 'c', 0.0]],
columns=['i', 'j', 'value'])
obs = self.dm_3x3.within(['a', 'c'])
pdt.assert_frame_equal(obs, exp)
def test_within_order_stability(self):
exp = pd.DataFrame([['a', 'a', 0.0],
['a', 'c', 4.2],
['c', 'a', 4.2],
['c', 'c', 0.0]],
columns=['i', 'j', 'value'])
# NOTE: order was changed from ['a', 'c'] to ['c', 'a']
# but the output order in exp is consistent with
# test_within
obs = self.dm_3x3.within(['c', 'a'])
pdt.assert_frame_equal(obs, exp)
obs = self.dm_3x3.within(['a', 'c'])
pdt.assert_frame_equal(obs, exp)
def test_within_missing_id(self):
with self.assertRaisesRegex(MissingIDError, "not found."):
self.dm_3x3.within(['x', 'a'])
def test_between(self):
exp = pd.DataFrame([['b', 'a', 5.],
['b', 'c', 6.],
['b', 'e', 8.],
['d', 'a', 4.],
['d', 'c', 6.],
['d', 'e', 7.]],
columns=['i', 'j', 'value'])
obs = self.dm_5x5.between(['b', 'd'], ['a', 'c', 'e'])
pdt.assert_frame_equal(obs, exp)
def test_between_order_stability(self):
exp = pd.DataFrame([['b', 'a', 5.],
['b', 'c', 6.],
['b', 'e', 8.],
['d', 'a', 4.],
['d', 'c', 6.],
['d', 'e', 7.]],
columns=['i', 'j', 'value'])
# varying the order of the "i" values, result remains consistent
# with the test_between result
obs = self.dm_5x5.between(['d', 'b'], ['a', 'c', 'e'])
pdt.assert_frame_equal(obs, exp)
# varying the order of the "j" values, result remains consistent
# with the test_between result
obs = self.dm_5x5.between(['b', 'd'], ['a', 'e', 'c'])
pdt.assert_frame_equal(obs, exp)
# varying the order of the "i" and "j" values, result remains
# consistent with the test_between result
obs = self.dm_5x5.between(['d', 'b'], ['a', 'e', 'c'])
pdt.assert_frame_equal(obs, exp)
def test_between_overlap(self):
exp = pd.DataFrame([['b', 'a', 5.],
['b', 'd', 7.],
['b', 'e', 8.],
['d', 'a', 4.],
['d', 'd', 0.],
['d', 'e', 7.]],
columns=['i', 'j', 'value'])
# 'd' in i and j overlap
with self.assertRaisesRegex(KeyError, ("This constraint can "
"removed with "
"allow_overlap=True.")):
self.dm_5x5.between(['b', 'd'], ['a', 'd', 'e'])
obs = self.dm_5x5.between(['b', 'd'], ['a', 'd', 'e'],
allow_overlap=True)
pdt.assert_frame_equal(obs, exp)
def test_between_missing_id(self):
with self.assertRaisesRegex(MissingIDError, "not found."):
self.dm_3x3.between(['x', 'a'], ['a', 'b', 'c'])
with self.assertRaisesRegex(MissingIDError, "not found."):
self.dm_3x3.between(['a', 'b'], ['a', 'x', 'c'])
with self.assertRaisesRegex(MissingIDError, "not found."):
self.dm_3x3.between(['a', 'y'], ['a', 'x', 'c'])
def test_stable_order(self):
exp = np.array([1, 3, 4], dtype=int)
obs = self.dm_5x5._stable_order(['d', 'e', 'b'])
npt.assert_equal(obs, exp)
def test_subset_to_dataframe(self):
exp = pd.DataFrame([['b', 'a', 5.],
['b', 'd', 7.],
['b', 'e', 8.],
['d', 'a', 4.],
['d', 'd', 0.],
['d', 'e', 7.]],
columns=['i', 'j', 'value'])
obs = self.dm_5x5._subset_to_dataframe(['b', 'd'], ['a', 'd', 'e'])
pdt.assert_frame_equal(obs, exp)
# and the empty edge cases
exp = pd.DataFrame([],
columns=['i', 'j', 'value'],
index=pd.RangeIndex(start=0, stop=0))
obs = self.dm_5x5._subset_to_dataframe([], ['a', 'd', 'e'])
pdt.assert_frame_equal(obs, exp, check_dtype=False)
obs = self.dm_5x5._subset_to_dataframe(['b', 'd'], [])
pdt.assert_frame_equal(obs, exp, check_dtype=False)
obs = self.dm_5x5._subset_to_dataframe([], [])
pdt.assert_frame_equal(obs, exp, check_dtype=False)
def test_init_from_dm(self):
ids = ['foo', 'bar', 'baz']
# DissimilarityMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(self.dm_3x3, ids)
self.assertEqual(obs, exp)
# Test that copy of data is not made.
self.assertTrue(obs.data is self.dm_3x3.data)
obs.data[0, 1] = 424242
self.assertTrue(np.array_equal(obs.data, self.dm_3x3.data))
# DistanceMatrix -> DissimilarityMatrix
exp = DissimilarityMatrix(self.dm_3x3_data, ids)
obs = DissimilarityMatrix(
DistanceMatrix(self.dm_3x3_data, ('a', 'b', 'c')), ids)
self.assertEqual(obs, exp)
# DissimilarityMatrix -> DistanceMatrix
with self.assertRaises(DistanceMatrixError):
DistanceMatrix(self.dm_2x2_asym, ['foo', 'bar'])
def test_init_non_hollow_dm(self):
data = [[1, 1], [1, 1]]
obs = DissimilarityMatrix(data, ['a', 'b'])
self.assertTrue(np.array_equal(obs.data, data))
def test_init_no_ids(self):
exp = DissimilarityMatrix(self.dm_3x3_data, ('0', '1', '2'))
obs = DissimilarityMatrix(self.dm_3x3_data)
self.assertEqual(obs, exp)
self.assertEqual(obs['1', '2'], 12.0)
def test_init_invalid_input(self):
# Empty data.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([], [])
# Another type of empty data.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(np.empty((0, 0)), [])
# Invalid number of dimensions.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([1, 2, 3], ['a'])
# Dimensions don't match.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix([[1, 2, 3]], ['a'])
data = [[0, 1], [1, 0]]
# Duplicate IDs.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'a'])
# Number of IDs don't match dimensions.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, ['a', 'b', 'c'])
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix(data, [])
def test_from_iterable_non_hollow_data(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]])
res = DissimilarityMatrix.from_iterable(iterable, lambda a, b: 1)
self.assertEqual(res, exp)
def test_from_iterable_asymmetric_data(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[-1, 0, 1, 2],
[-2, -1, 0, 1],
[-3, -2, -1, 0]])
res = DissimilarityMatrix.from_iterable(iterable, lambda a, b: b - a)
self.assertEqual(res, exp)
def test_from_iterable_no_key(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[1, 0, 1, 2],
[2, 1, 0, 1],
[3, 2, 1, 0]])
res = DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a))
self.assertEqual(res, exp)
def test_from_iterable_with_key(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[1, 0, 1, 2],
[2, 1, 0, 1],
[3, 2, 1, 0]], ['0', '1', '4', '9'])
res = DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a),
key=lambda x: str(x ** 2))
self.assertEqual(res, exp)
def test_from_iterable_empty(self):
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix.from_iterable([], lambda x: x)
def test_from_iterable_single(self):
exp = DissimilarityMatrix([[100]])
res = DissimilarityMatrix.from_iterable(["boo"], lambda a, b: 100)
self.assertEqual(res, exp)
def test_from_iterable_with_keys(self):
iterable = (x for x in range(4))
exp = DissimilarityMatrix([[0, 1, 2, 3],
[1, 0, 1, 2],
[2, 1, 0, 1],
[3, 2, 1, 0]], ['0', '1', '4', '9'])
res = DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a),
keys=iter(['0', '1', '4', '9'])
)
self.assertEqual(res, exp)
def test_from_iterable_with_key_and_keys(self):
iterable = (x for x in range(4))
with self.assertRaises(ValueError):
DissimilarityMatrix.from_iterable(iterable,
lambda a, b: abs(b - a),
key=str,
keys=['1', '2', '3', '4'])
def test_from_iterable_scipy_hamming_metric_with_metadata(self):
# test for #1254
seqs = [
Sequence('ACGT'),
Sequence('ACGA', metadata={'id': 'seq1'}),
Sequence('AAAA', metadata={'id': 'seq2'}),
Sequence('AAAA', positional_metadata={'qual': range(4)})
]
exp = DissimilarityMatrix([
[0, 0.25, 0.75, 0.75],
[0.25, 0.0, 0.5, 0.5],
[0.75, 0.5, 0.0, 0.0],
[0.75, 0.5, 0.0, 0.0]], ['a', 'b', 'c', 'd'])
dm = DissimilarityMatrix.from_iterable(
seqs,
metric=scipy.spatial.distance.hamming,
keys=['a', 'b', 'c', 'd'])
self.assertEqual(dm, exp)
def test_from_iterable_skbio_hamming_metric_with_metadata(self):
# test for #1254
seqs = [
Sequence('ACGT'),
Sequence('ACGA', metadata={'id': 'seq1'}),
Sequence('AAAA', metadata={'id': 'seq2'}),
Sequence('AAAA', positional_metadata={'qual': range(4)})
]
exp = DissimilarityMatrix([
[0, 0.25, 0.75, 0.75],
[0.25, 0.0, 0.5, 0.5],
[0.75, 0.5, 0.0, 0.0],
[0.75, 0.5, 0.0, 0.0]], ['a', 'b', 'c', 'd'])
dm = DissimilarityMatrix.from_iterable(
seqs,
metric=skbio.sequence.distance.hamming,
keys=['a', 'b', 'c', 'd'])
self.assertEqual(dm, exp)
def test_data(self):
for dm, exp in zip(self.dms, self.dm_redundant_forms):
obs = dm.data
self.assertTrue(np.array_equal(obs, exp))
with self.assertRaises(AttributeError):
self.dm_3x3.data = 'foo'
def test_ids(self):
obs = self.dm_3x3.ids
self.assertEqual(obs, ('a', 'b', 'c'))
# Test that we overwrite the existing IDs and that the ID index is
# correctly rebuilt.
new_ids = ['foo', 'bar', 'baz']
self.dm_3x3.ids = new_ids
obs = self.dm_3x3.ids
self.assertEqual(obs, tuple(new_ids))
self.assertTrue(np.array_equal(self.dm_3x3['bar'],
np.array([0.01, 0.0, 12.0])))
with self.assertRaises(MissingIDError):
self.dm_3x3['b']
def test_ids_invalid_input(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.ids = ['foo', 'bar']
# Make sure that we can still use the dissimilarity matrix after trying
# to be evil.
obs = self.dm_3x3.ids
self.assertEqual(obs, ('a', 'b', 'c'))
def test_dtype(self):
for dm in self.dms:
self.assertEqual(dm.dtype, np.float64)
def test_shape(self):
for dm, shape in zip(self.dms, self.dm_shapes):
self.assertEqual(dm.shape, shape)
def test_size(self):
for dm, size in zip(self.dms, self.dm_sizes):
self.assertEqual(dm.size, size)
def test_transpose(self):
for dm, transpose in zip(self.dms, self.dm_transposes):
self.assertEqual(dm.T, transpose)
self.assertEqual(dm.transpose(), transpose)
# We should get a reference to a different object back, even if the
# transpose is the same as the original.
self.assertTrue(dm.transpose() is not dm)
def test_index(self):
self.assertEqual(self.dm_3x3.index('a'), 0)
self.assertEqual(self.dm_3x3.index('b'), 1)
self.assertEqual(self.dm_3x3.index('c'), 2)
with self.assertRaises(MissingIDError):
self.dm_3x3.index('d')
with self.assertRaises(MissingIDError):
self.dm_3x3.index(1)
def test_redundant_form(self):
for dm, redundant in zip(self.dms, self.dm_redundant_forms):
obs = dm.redundant_form()
self.assertTrue(np.array_equal(obs, redundant))
def test_copy(self):
copy = self.dm_2x2.copy()
self.assertEqual(copy, self.dm_2x2)
self.assertFalse(copy.data is self.dm_2x2.data)
# deepcopy doesn't actually create a copy of the IDs because it is a
# tuple of strings, which is fully immutable.
self.assertTrue(copy.ids is self.dm_2x2.ids)
new_ids = ['hello', 'world']
copy.ids = new_ids
self.assertNotEqual(copy.ids, self.dm_2x2.ids)
copy = self.dm_2x2.copy()
copy.data[0, 1] = 0.0001
self.assertFalse(np.array_equal(copy.data, self.dm_2x2.data))
def test_filter_no_filtering(self):
# Don't actually filter anything -- ensure we get back a different
# object.
obs = self.dm_3x3.filter(['a', 'b', 'c'])
self.assertEqual(obs, self.dm_3x3)
self.assertFalse(obs is self.dm_3x3)
def test_filter_reorder(self):
# Don't filter anything, but reorder the distance matrix.
order = ['c', 'a', 'b']
exp = DissimilarityMatrix(
[[0, 4.2, 12], [4.2, 0, 0.01], [12, 0.01, 0]], order)
obs = self.dm_3x3.filter(order)
self.assertEqual(obs, exp)
def test_filter_single_id(self):
ids = ['b']
exp = DissimilarityMatrix([[0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
def test_filter_asymmetric(self):
# 2x2
ids = ['b', 'a']
exp = DissimilarityMatrix([[0, -2], [1, 0]], ids)
obs = self.dm_2x2_asym.filter(ids)
self.assertEqual(obs, exp)
# 3x3
dm = DissimilarityMatrix([[0, 10, 53], [42, 0, 22.5], [53, 1, 0]],
('bro', 'brah', 'breh'))
ids = ['breh', 'brah']
exp = DissimilarityMatrix([[0, 1], [22.5, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_filter_subset(self):
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
ids = ('b', 'a')
exp = DissimilarityMatrix([[0, 0.01], [0.01, 0]], ids)
obs = self.dm_3x3.filter(ids)
self.assertEqual(obs, exp)
# 4x4
dm = DissimilarityMatrix([[0, 1, 55, 7], [1, 0, 16, 1],
[55, 16, 0, 23], [7, 1, 23, 0]])
ids = np.asarray(['3', '0', '1'])
exp = DissimilarityMatrix([[0, 7, 1], [7, 0, 1], [1, 1, 0]], ids)
obs = dm.filter(ids)
self.assertEqual(obs, exp)
def test_filter_duplicate_ids(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.filter(['c', 'a', 'c'])
def test_filter_missing_ids(self):
with self.assertRaises(MissingIDError):
self.dm_3x3.filter(['c', 'bro'])
def test_filter_missing_ids_strict_false(self):
# no exception should be raised
ids = ('c', 'a')
exp = DissimilarityMatrix([[0, 4.2], [4.2, 0]], ids)
obs = self.dm_3x3.filter(['c', 'a', 'not found'], strict=False)
self.assertEqual(obs, exp)
def test_filter_empty_ids(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3.filter([])
def test_plot_default(self):
fig = self.dm_1x1.plot()
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), '')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['a'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['a'])
def test_plot_no_default(self):
ids = ['0', 'one', '2', 'three', '4.000']
data = ([0, 1, 2, 3, 4], [1, 0, 1, 2, 3], [2, 1, 0, 1, 2],
[3, 2, 1, 0, 1], [4, 3, 2, 1, 0])
dm = DissimilarityMatrix(data, ids)
fig = dm.plot(cmap='Reds', title='Testplot')
self.assertIsInstance(fig, mpl.figure.Figure)
axes = fig.get_axes()
self.assertEqual(len(axes), 2)
ax = axes[0]
self.assertEqual(ax.get_title(), 'Testplot')
xticks = []
for tick in ax.get_xticklabels():
xticks.append(tick.get_text())
self.assertEqual(xticks, ['0', 'one', '2', 'three', '4.000'])
yticks = []
for tick in ax.get_yticklabels():
yticks.append(tick.get_text())
self.assertEqual(yticks, ['0', 'one', '2', 'three', '4.000'])
def test_repr_png(self):
dm = self.dm_1x1
obs = dm._repr_png_()
self.assertIsInstance(obs, bytes)
self.assertTrue(len(obs) > 0)
def test_repr_svg(self):
obs = self.dm_1x1._repr_svg_()
self.assertIsInstance(obs, str)
self.assertTrue(len(obs) > 0)
def test_png(self):
dm = self.dm_1x1
self.assertIsInstance(dm.png, Image)
def test_svg(self):
dm = self.dm_1x1
self.assertIsInstance(dm.svg, SVG)
def test_to_data_frame_1x1(self):
df = self.dm_1x1.to_data_frame()
exp = pd.DataFrame([[0.0]], index=['a'], columns=['a'])
assert_data_frame_almost_equal(df, exp)
def test_to_data_frame_3x3(self):
df = self.dm_3x3.to_data_frame()
exp = pd.DataFrame([[0.0, 0.01, 4.2],
[0.01, 0.0, 12.0],
[4.2, 12.0, 0.0]],
index=['a', 'b', 'c'], columns=['a', 'b', 'c'])
assert_data_frame_almost_equal(df, exp)
def test_to_data_frame_default_ids(self):
df = DissimilarityMatrix(self.dm_2x2_data).to_data_frame()
exp = pd.DataFrame([[0.0, 0.123],
[0.123, 0.0]],
index=['0', '1'], columns=['0', '1'])
assert_data_frame_almost_equal(df, exp)
def test_str(self):
for dm in self.dms:
obs = str(dm)
# Do some very light testing here to make sure we're getting a
# non-empty string back. We don't want to test the exact
# formatting.
self.assertTrue(obs)
def test_eq(self):
for dm in self.dms:
copy = dm.copy()
self.assertTrue(dm == dm)
self.assertTrue(copy == copy)
self.assertTrue(dm == copy)
self.assertTrue(copy == dm)
self.assertFalse(self.dm_1x1 == self.dm_3x3)
def test_ne(self):
# Wrong class.
self.assertTrue(self.dm_3x3 != 'foo')
# Wrong shape.
self.assertTrue(self.dm_3x3 != self.dm_1x1)
# Wrong IDs.
other = self.dm_3x3.copy()
other.ids = ['foo', 'bar', 'baz']
self.assertTrue(self.dm_3x3 != other)
# Wrong data.
other = self.dm_3x3.copy()
other.data[1, 0] = 42.42
self.assertTrue(self.dm_3x3 != other)
self.assertFalse(self.dm_2x2 != self.dm_2x2)
def test_contains(self):
self.assertTrue('a' in self.dm_3x3)
self.assertTrue('b' in self.dm_3x3)
self.assertTrue('c' in self.dm_3x3)
self.assertFalse('d' in self.dm_3x3)
def test_getslice(self):
# Slice of first dimension only. Test that __getslice__ defers to
# __getitem__.
obs = self.dm_2x2[1:]
self.assertTrue(np.array_equal(obs, np.array([[0.123, 0.0]])))
self.assertEqual(type(obs), np.ndarray)
def test_getitem_by_id(self):
obs = self.dm_1x1['a']
self.assertTrue(np.array_equal(obs, np.array([0.0])))
obs = self.dm_2x2_asym['b']
self.assertTrue(np.array_equal(obs, np.array([-2.0, 0.0])))
obs = self.dm_3x3['c']
self.assertTrue(np.array_equal(obs, np.array([4.2, 12.0, 0.0])))
with self.assertRaises(MissingIDError):
self.dm_2x2['c']
def test_getitem_by_id_pair(self):
# Same object.
self.assertEqual(self.dm_1x1['a', 'a'], 0.0)
# Different objects (symmetric).
self.assertEqual(self.dm_3x3['b', 'c'], 12.0)
self.assertEqual(self.dm_3x3['c', 'b'], 12.0)
# Different objects (asymmetric).
self.assertEqual(self.dm_2x2_asym['a', 'b'], 1.0)
self.assertEqual(self.dm_2x2_asym['b', 'a'], -2.0)
with self.assertRaises(MissingIDError):
self.dm_2x2['a', 'c']
def test_getitem_ndarray_indexing(self):
# Single element access.
obs = self.dm_3x3[0, 1]
self.assertEqual(obs, 0.01)
# Single element access (via two __getitem__ calls).
obs = self.dm_3x3[0][1]
self.assertEqual(obs, 0.01)
# Row access.
obs = self.dm_3x3[1]
self.assertTrue(np.array_equal(obs, np.array([0.01, 0.0, 12.0])))
self.assertEqual(type(obs), np.ndarray)
# Grab all data.
obs = self.dm_3x3[:, :]
self.assertTrue(np.array_equal(obs, self.dm_3x3.data))
self.assertEqual(type(obs), np.ndarray)
with self.assertRaises(IndexError):
self.dm_3x3[:, 3]
def test_validate_invalid_dtype(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3._validate(np.array([[0, 42], [42, 0]]), ['a', 'b'])
