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_eq(self):
"""Test data equality between different matrix types."""
# 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)
def test_init_invalid_input(self):
# Requires a DistanceMatrix.
with self.assertRaises(TypeError):
CategoricalStats(DissimilarityMatrix([[0, 2], [3, 0]], ['a', 'b']),
[1, 2])
# Requires column if DataFrame.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, self.df)
# Cannot provide column if not data frame.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, self.grouping, column='Group')
# Column must exist in data frame.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, self.df, column='foo')
# All distance matrix IDs must be in data frame.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, self.df_missing_id, column='Group')
# Grouping vector length must match number of objects in dm.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, [1, 2])
# Grouping vector cannot have only unique values.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, [1, 2, 3])
# Grouping vector cannot have only a single group.
with self.assertRaises(ValueError):
CategoricalStats(self.dm, [1, 1, 1])
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_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_invalid_input(self):
# Requires a DistanceMatrix.
with self.assertRaises(TypeError):
_ = CategoricalStats(
DissimilarityMatrix([[0, 2], [3, 0]], ['a', 'b']), [1, 2])
# Grouping vector length must match number of objects in dm.
with self.assertRaises(ValueError):
_ = CategoricalStats(self.dm, [1, 2])
# Grouping vector cannot have only unique values.
with self.assertRaises(ValueError):
_ = CategoricalStats(self.dm, [1, 2, 3])
# Grouping vector cannot have only a single group.
with self.assertRaises(ValueError):
_ = CategoricalStats(self.dm, [1, 1, 1])
def test_constructor(self):
"""Test generating random dist mats with a specific constructor."""
exp = DissimilarityMatrix(np.asarray([[0.0]]), ['1'])
obs = randdm(1, constructor=DissimilarityMatrix)
self.assertEqual(obs, exp)
self.assertEqual(type(obs), DissimilarityMatrix)
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)
