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_f_lines = [
DM_1x1_F, DM_2x2_F, self.dm_2x2_asym_lines, self.dm_3x3_lines
]
self.dm_fs = [
self.dm_1x1_f, self.dm_2x2_f, self.dm_2x2_asym_f, self.dm_3x3_f
]
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_round_trip_read_write(self):
"""Test reading, writing, and reading again works as expected."""
for dm_f in self.dm_fs:
# Read.
dm1 = DissimilarityMatrix.from_file(dm_f)
# Write.
out_f = StringIO()
dm1.to_file(out_f)
out_f.seek(0)
# Read.
dm2 = DissimilarityMatrix.from_file(out_f)
self.assertEqual(dm1, dm2)
def test_from_file(self):
"""Should parse and return a valid DissimilarityMatrix given a file."""
for dm_f, dm in zip(self.dm_fs, self.dms):
obs = DissimilarityMatrix.from_file(dm_f)
self.assertEqual(obs, dm)
def test_from_file_with_file_path(self):
"""Should identify the filepath correctly and parse from it."""
# should fail with the expected exception
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file(self.bad_dm_fp)
obs = DissimilarityMatrix.from_file(self.dm_2x2_asym_fp)
self.assertEqual(self.dm_2x2_asym, obs)
self.assertTrue(isinstance(obs, DissimilarityMatrix))
obs = DissimilarityMatrix.from_file(self.dm_3x3_fp)
self.assertEqual(self.dm_3x3, obs)
self.assertTrue(isinstance(obs, DissimilarityMatrix))
def test_from_file_extra_junk(self):
"""Should correctly parse a file with extra whitespace and comments."""
obs = DissimilarityMatrix.from_file(self.dm_3x3_whitespace_f)
self.assertEqual(obs, self.dm_3x3)
def test_from_file_list_of_strings(self):
"""Should correctly parse a list of strings."""
obs = DissimilarityMatrix.from_file(DM_3x3_WHITESPACE_F)
self.assertEqual(obs, self.dm_3x3)
def test_from_file_real_file(self):
"""Should correctly parse a real on-disk file."""
with tempfile.TemporaryFile(mode='r+',
prefix='skbio.tests.test_distance',
suffix='.txt') as fh:
fh.write('\n'.join(DM_3x3_WHITESPACE_F))
fh.seek(0)
obs = DissimilarityMatrix.from_file(fh)
self.assertEqual(obs, self.dm_3x3)
def test_from_file_invalid_input(self):
"""Raises error on ill-formatted dissimilarity matrix file."""
# Empty dm.
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file([])
# Number of values don't match number of IDs.
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file(self.bad_dm_f1)
# Mismatched IDs.
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file(self.bad_dm_f2)
# Extra data at end.
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file(self.bad_dm_f3)
# Missing data.
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file(self.bad_dm_f4)
# Header, but no data.
with self.assertRaises(DissimilarityMatrixFormatError):
DissimilarityMatrix.from_file(self.bad_dm_f5)
# Non-hollow.
with self.assertRaises(DissimilarityMatrixError):
DissimilarityMatrix.from_file(self.bad_dm_f6)
def test_to_file(self):
"""Should serialize a DissimilarityMatrix to file."""
for dm_f_line, dm in zip(self.dm_f_lines, self.dms):
for file_type in ('file like', 'file name'):
if file_type == 'file like':
obs_f = StringIO()
dm.to_file(obs_f)
obs = obs_f.getvalue()
obs_f.close()
elif file_type == 'file name':
with tempfile.NamedTemporaryFile('r+') as temp_file:
dm.to_file(temp_file.name)
temp_file.flush()
temp_file.seek(0)
obs = temp_file.read()
self.assertEqual(obs, dm_f_line)
def test_init_from_dm(self):
"""Constructs a dm from a dm."""
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):
"""Raises error on invalid dissimilarity matrix data / IDs."""
# 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):
"""Test retrieving/setting data matrix."""
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):
"""Test retrieving/setting IDs."""
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):
"""Test setting invalid IDs raises an error."""
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):
"""Test retrieving dtype of data matrix."""
for dm in self.dms:
self.assertEqual(dm.dtype, np.float64)
def test_shape(self):
"""Test retrieving shape of data matrix."""
for dm, shape in zip(self.dms, self.dm_shapes):
self.assertEqual(dm.shape, shape)
def test_size(self):
"""Test retrieving size of data matrix."""
for dm, size in zip(self.dms, self.dm_sizes):
self.assertEqual(dm.size, size)
def test_transpose(self):
"""Test retrieving transpose of dissimilarity matrix."""
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):
"""Test retrieving the data matrix in redundant form."""
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):
"""Test correct copying of a DissimilarityMatrix."""
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_str(self):
"""Test retrieving string representation of a DissimilarityMatrix."""
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):
"""DissimilarityMatrix equality test functions correctly."""
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):
"""Test unequal dms are identified as such."""
# 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):
"""Test that __getslice__ defers to __getitem__."""
# Slice of first dimension only.
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):
"""Test retrieving row vectors by ID."""
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):
"""Test retrieving elements by ID pair."""
# 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):
"""Test __getitem__ delegates to underlying ndarray."""
# 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_parse_ids(self):
"""Empty stub: DissimilarityMatrix._parse_ids tested elsewhere."""
pass
def test_validate_invalid_dtype(self):
with self.assertRaises(DissimilarityMatrixError):
self.dm_3x3._validate(np.array([[0, 42], [42, 0]]), ['a', 'b'])
def test_index_list(self):
"""Empty stub: DissimilarityMatrix._index_list tested elsewhere."""
pass
def test_is_id_pair(self):
"""Empty stub: DissimilarityMatrix._is_id_pair tested elsewhere."""
pass
def test_format_ids(self):
"""Empty stub: DissimilarityMatrix._format_ids tested elsewhere."""
pass
def test_pprint_ids(self):
"""Test pretty-print formatting of IDs."""
# 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)
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)
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_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)