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# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# ----------------------------------------------------------------------------
from unittest import TestCase, main
import numpy as np
import numpy.testing as npt
from skbio import TreeNode, DistanceMatrix
from skbio.diversity import beta_diversity, block_beta_diversity
from skbio.diversity._block import (_block_party, _generate_id_blocks,
_pairs_to_compute, _block_compute,
_block_kwargs, _map, _reduce)
class ParallelBetaDiversity(TestCase):
def setUp(self):
self.table1 = [[1, 5],
[2, 3],
[0, 1]]
self.sids1 = list('ABC')
self.tree1 = TreeNode.read([
'((O1:0.25, O2:0.50):0.25, O3:0.75)root;'])
self.oids1 = ['O1', 'O2']
def test_block_kwargs(self):
kws = {'ids': [1, 2, 3, 4, 5], 'foo': 'bar', 'k': 2}
exp = [{'row_ids': np.array((0, 1)),
'col_ids': np.array((0, 1)),
'id_pairs': [(0, 1)],
'ids': [1, 2, 3, 4, 5]},
{'row_ids': np.array((0, 1)),
'col_ids': np.array((2, 3)),
'id_pairs': [(0, 2), (0, 3), (1, 2), (1, 3)],
'ids': [1, 2, 3, 4, 5]},
{'row_ids': np.array((0, 1)),
'col_ids': np.array((4,)),
'id_pairs': [(0, 4), (1, 4)],
'ids': [1, 2, 3, 4, 5]},
{'row_ids': np.array((2, 3)),
'col_ids': np.array((2, 3)),
'id_pairs': [(2, 3), ],
'ids': [1, 2, 3, 4, 5]},
{'row_ids': np.array((2, 3)),
'col_ids': np.array((4,)),
'id_pairs': [(2, 4), (3, 4)],
'ids': [1, 2, 3, 4, 5]}]
obs = list(_block_kwargs(**kws))
npt.assert_equal(obs, exp)
def test_block_compute(self):
def mock_metric(u, v):
return (u + v).sum()
counts = np.array([[0, 1, 2, 3, 4, 5],
[1, 2, 3, 4, 5, 0],
[2, 3, 4, 5, 0, 1],
[10, 2, 3, 6, 8, 2],
[9, 9, 2, 2, 3, 4]])
kwargs = {'metric': mock_metric,
'counts': counts,
'row_ids': np.array((2, 3)),
'col_ids': np.array((4, )),
'id_pairs': [(2, 4), (3, 4)],
'ids': [1, 2, 3, 4, 5]}
exp = DistanceMatrix(np.array([[0, 0, 44],
[0, 0, 60],
[44, 60, 0]]), (2, 3, 4))
obs = _block_compute(**kwargs)
npt.assert_equal(obs.data, exp.data)
self.assertEqual(obs.ids, exp.ids)
def test_map(self):
def func(a, b, c=5):
return a + b + c
kwargs = [{'a': 0, 'b': 1, 'c': 0},
{'a': 2, 'b': 3}]
exp = [1, 10]
obs = list(_map(func, kwargs))
self.assertEqual(obs, exp)
def test_reduce(self):
dm1 = DistanceMatrix(np.array([[0, 0, 44],
[0, 0, 60],
[44, 60, 0]]), (2, 3, 4))
dm2 = DistanceMatrix(np.array([[0, 123],
[123, 0]]), (1, 5))
dm3 = DistanceMatrix(np.array([[0, 1, 2, 3],
[1, 0, 4, 5],
[2, 4, 0, 6],
[3, 5, 6, 0]]), (0, 3, 4, 5))
exp = DistanceMatrix(np.array([[0, 0, 0, 1, 2, 3],
[0, 0, 0, 0, 0, 123],
[0, 0, 0, 0, 44, 0],
[1, 0, 0, 0, 64, 5],
[2, 0, 44, 64, 0, 6],
[3, 123, 0, 5, 6, 0]]), list(range(6)))
obs = _reduce([dm1, dm2, dm3])
npt.assert_equal(obs.data, exp.data)
self.assertEqual(obs.ids, exp.ids)
def test_block_beta_diversity(self):
exp = beta_diversity('unweighted_unifrac', self.table1, self.sids1,
tree=self.tree1, taxa=self.oids1)
obs = block_beta_diversity('unweighted_unifrac', self.table1,
self.sids1, taxa=self.oids1,
tree=self.tree1, k=2)
npt.assert_equal(obs.data, exp.data)
self.assertEqual(obs.ids, exp.ids)
def test_generate_id_blocks(self):
ids = [1, 2, 3, 4, 5]
exp = [(np.array((0, 1)), np.array((0, 1))),
(np.array((0, 1)), np.array((2, 3))),
(np.array((0, 1)), np.array((4,))),
(np.array((2, 3)), np.array((2, 3))),
(np.array((2, 3)), np.array((4,))),
(np.array((4,)), np.array((4,)))]
obs = list(_generate_id_blocks(ids, 2))
npt.assert_equal(obs, exp)
def test_block_party_notree(self):
counts = np.arange(15).reshape(5, 3)
exp = [{'counts': np.array([[0, 1, 2], [3, 4, 5]]),
'ids': np.array([0, 1])},
{'counts': np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8],
[9, 10, 11]]),
'ids': np.array([0, 1, 2, 3])},
{'counts': np.array([[0, 1, 2], [3, 4, 5], [12, 13, 14]]),
'ids': np.array([0, 1, 4])},
{'counts': np.array([[6, 7, 8], [9, 10, 11]]),
'ids': np.array([2, 3])},
{'counts': np.array([[6, 7, 8], [9, 10, 11], [12, 13, 14]]),
'ids': np.array([2, 3, 4])},
{'counts': np.array([[12, 13, 14]]), 'ids': np.array([4])}]
obs = [_block_party(counts, rids, cids) for rids, cids in
_generate_id_blocks(list(range(5)), 2)]
npt.assert_equal(obs, exp)
def test_block_party_tree(self):
counts = np.array([[1, 1, 1],
[1, 0, 1],
[1, 0, 1],
[0, 0, 1],
[0, 1, 1]])
tree = TreeNode.read(['(a:1,b:2,c:3);'])
taxa = ['a', 'b', 'c']
kw = {'tree': tree, 'taxa': taxa}
kw_no_a = {'tree': tree.shear(['b', 'c']), 'taxa': ['b', 'c']}
kw_no_b = {'tree': tree.shear(['a', 'c']), 'taxa': ['a', 'c']}
# python >= 3.5 supports {foo: bar, **baz}
exp = [dict(counts=np.array([[1, 1, 1], [1, 0, 1]]), **kw),
dict(counts=np.array([[1, 1, 1], [1, 0, 1], [1, 0, 1],
[0, 0, 1]]), **kw),
dict(counts=np.array([[1, 1, 1], [1, 0, 1], [0, 1, 1]]), **kw),
dict(counts=np.array([[1, 1], [0, 1]]), **kw_no_b),
dict(counts=np.array([[1, 0, 1], [0, 0, 1], [0, 1, 1]]), **kw),
dict(counts=np.array([[1, 1]]), **kw_no_a)]
obs = [_block_party(counts, rids, cids, **kw) for rids, cids in
_generate_id_blocks(list(range(5)), 2)]
for okw, ekw in zip(obs, exp):
npt.assert_equal(okw['counts'], ekw['counts'])
npt.assert_equal(okw['taxa'], ekw['taxa'])
self.assertEqual(str(okw['tree']), str(ekw['tree']))
def test_pairs_to_compute_rids_are_cids(self):
rids = np.array([0, 1, 2, 10])
cids = rids
exp = [(0, 1), (0, 2), (0, 10), (1, 2), (1, 10), (2, 10)]
self.assertEqual(_pairs_to_compute(rids, cids), exp)
def test_pairs_to_compute_rids_are_not_cids(self):
rids = np.array([0, 1, 2])
cids = np.array([3, 4, 5])
exp = [(0, 3), (0, 4), (0, 5), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4),
(2, 5)]
self.assertEqual(_pairs_to_compute(rids, cids), exp)
def test_pairs_to_compute_rids_overlap_cids(self):
rids = np.array([0, 1, 2])
cids = np.array([0, 10, 20])
with self.assertRaises(ValueError):
_pairs_to_compute(rids, cids)
if __name__ == "__main__":
main()
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