# ---------------------------------------------------------------------------- # 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. # ---------------------------------------------------------------------------- import io from unittest import TestCase, main import numpy.testing as npt from skbio import DistanceMatrix, TreeNode from skbio.tree._nj import nj, _tree_from_linkmat class NjTests(TestCase): def setUp(self): data1 = [[0, 5, 9, 9, 8], [5, 0, 10, 10, 9], [9, 10, 0, 8, 7], [9, 10, 8, 0, 3], [8, 9, 7, 3, 0]] ids1 = list('abcde') self.dm1 = DistanceMatrix(data1, ids1) # this newick string was confirmed against http://www.trex.uqam.ca/ # which generated the following (isomorphic) newick string: # (d:2.0000,e:1.0000,(c:4.0000,(a:2.0000,b:3.0000):3.0000):2.0000); self.expected1_str = ("(d:2.000000, (c:4.000000, (b:3.000000," " a:2.000000):3.000000):2.000000, e:1.000000);") self.expected1_TreeNode = TreeNode.read( io.StringIO(self.expected1_str)) # this example was pulled from the Phylip manual # https://phylipweb.github.io/phylip/doc/neighbor.html data2 = [[0.0000, 1.6866, 1.7198, 1.6606, 1.5243, 1.6043, 1.5905], [1.6866, 0.0000, 1.5232, 1.4841, 1.4465, 1.4389, 1.4629], [1.7198, 1.5232, 0.0000, 0.7115, 0.5958, 0.6179, 0.5583], [1.6606, 1.4841, 0.7115, 0.0000, 0.4631, 0.5061, 0.4710], [1.5243, 1.4465, 0.5958, 0.4631, 0.0000, 0.3484, 0.3083], [1.6043, 1.4389, 0.6179, 0.5061, 0.3484, 0.0000, 0.2692], [1.5905, 1.4629, 0.5583, 0.4710, 0.3083, 0.2692, 0.0000]] ids2 = ["Bovine", "Mouse", "Gibbon", "Orang", "Gorilla", "Chimp", "Human"] self.dm2 = DistanceMatrix(data2, ids2) self.expected2_str = ("(Mouse:0.76891, (Gibbon:0.35793, (Orang:0.28469" ", (Gorilla:0.15393, (Chimp:0.15167, Human:0.117" "53):0.03982):0.02696):0.04648):0.42027, Bovine:" "0.91769);") self.expected2_TreeNode = TreeNode.read( io.StringIO(self.expected2_str)) data3 = [[0, 5, 4, 7, 6, 8], [5, 0, 7, 10, 9, 11], [4, 7, 0, 7, 6, 8], [7, 10, 7, 0, 5, 8], [6, 9, 6, 5, 0, 8], [8, 11, 8, 8, 8, 0]] ids3 = map(str, range(6)) self.dm3 = DistanceMatrix(data3, ids3) self.expected3_str = ("((((0:1.000000,1:4.000000):1.000000,2:2.000000" "):1.250000,5:4.750000):0.750000,3:2.750000,4:2." "250000);") self.expected3_TreeNode = TreeNode.read( io.StringIO(self.expected3_str)) # this dm can yield negative branch lengths for nj data4 = [[0, 5, 9, 9, 800], [5, 0, 10, 10, 9], [9, 10, 0, 8, 7], [9, 10, 8, 0, 3], [800, 9, 7, 3, 0]] ids4 = list('abcde') self.dm4 = DistanceMatrix(data4, ids4) def test_nj_dm1(self): actual_TreeNode = nj(self.dm1) self.assertAlmostEqual(actual_TreeNode.compare_cophenet( self.expected1_TreeNode), 0.0) def test_nj_dm2(self): actual_TreeNode = nj(self.dm2) self.assertAlmostEqual(actual_TreeNode.compare_cophenet( self.expected2_TreeNode), 0.0) def test_nj_dm3(self): actual_TreeNode = nj(self.dm3) self.assertAlmostEqual(actual_TreeNode.compare_cophenet( self.expected3_TreeNode), 0.0) def test_nj_inplace(self): dm = self.dm3.copy() obs = nj(dm) exp = self.expected3_TreeNode self.assertAlmostEqual(obs.compare_cophenet(exp), 0.0) npt.assert_almost_equal(dm.data, self.dm3.data) obs = nj(dm, inplace=True) self.assertAlmostEqual(obs.compare_cophenet(exp), 0.0) with self.assertRaises(AssertionError): npt.assert_almost_equal(dm.data, self.dm3.data) def test_nj_zero_branch_length(self): # no nodes have negative branch length when we disallow negative # branch length. self is excluded as branch length is None tree = nj(self.dm4) for n in tree.postorder(include_self=False): self.assertTrue(n.length >= 0) # only tips associated with the large distance in the input # have positive branch lengths when we allow negative branch # length tree = nj(self.dm4, neg_as_zero=False) self.assertTrue(tree.find('a').length > 0) self.assertTrue(tree.find('b').length < 0) self.assertTrue(tree.find('c').length < 0) self.assertTrue(tree.find('d').length < 0) self.assertTrue(tree.find('e').length > 0) # deprecated functionality t2 = nj(self.dm4, disallow_negative_branch_length=False) self.assertAlmostEqual(tree.compare_cophenet(t2), 0.0) def test_nj_trivial(self): data = [[0, 3, 2], [3, 0, 3], [2, 3, 0]] dm = DistanceMatrix(data, list('abc')) exp = TreeNode.read(["(b:2.000000, a:1.000000, c:1.000000);"]) self.assertAlmostEqual(nj(dm).compare_cophenet(exp), 0.0) # deprecated functionality self.assertEqual(nj(dm, result_constructor=str), "(a:1.0,b:2.0,c:1.0);\n") def test_nj_error(self): data = [[0, 3], [3, 0]] dm = DistanceMatrix(data, list('ab')) self.assertRaises(ValueError, nj, dm) def test_tree_from_linkmat(self): taxa = ['human', 'chimp', 'monkey', 'pig', 'mouse', 'rat', 'chicken'] lm = [ [5, 4, 0.065041, 0.060976], # rat, mouse [6, 7, 0.457317, 0.229675], # chicken, rat-mouse [3, 8, 0.161924, 0.050474], # pig, chicken-rat-mouse [2, 9, 0.018293, 0.162602], # monkey, pig-...-mouse [10, 1, 0.006098, 0.001016], # chimp, monkey-...-mouse [0, 11, 0.003049, 0.0], # human, chimp-...-mouse ] obs = str(_tree_from_linkmat(lm, taxa, rooted=True)) exp = ("(human:0.003049,((monkey:0.018293,(pig:0.161924,(chicken:0.457317," "(rat:0.065041,mouse:0.060976):0.229675):0.050474):0.162602):0.006098," "chimp:0.001016):0.0);\n") self.assertEqual(str(obs), exp) obs = str(_tree_from_linkmat(lm, taxa, rooted=False)) exp = ("(human:0.003049,(monkey:0.018293,(pig:0.161924,(chicken:0.457317," "(rat:0.065041,mouse:0.060976):0.229675):0.050474):0.162602):0.006098," "chimp:0.001016);\n") self.assertEqual(str(obs), exp) if __name__ == "__main__": main()