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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 COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
import io
from unittest import TestCase, main
from skbio import DistanceMatrix, TreeNode, nj
from skbio.tree._nj import (
_compute_q, _compute_collapsed_dm, _lowest_index, _otu_to_new_node,
_pair_members_to_new_node)
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
# http://evolution.genetics.washington.edu/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
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):
self.assertEqual(nj(self.dm1, result_constructor=str),
self.expected1_str)
# what is the correct way to compare TreeNode objects for equality?
actual_TreeNode = nj(self.dm1)
self.assertEqual(actual_TreeNode.compare_tip_distances(
self.expected1_TreeNode), 0.0)
def test_nj_dm2(self):
actual_TreeNode = nj(self.dm2)
self.assertAlmostEqual(actual_TreeNode.compare_tip_distances(
self.expected2_TreeNode), 0.0)
def test_nj_dm3(self):
actual_TreeNode = nj(self.dm3)
self.assertAlmostEqual(actual_TreeNode.compare_tip_distances(
self.expected3_TreeNode), 0.0)
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, 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)
def test_nj_trivial(self):
data = [[0, 3, 2],
[3, 0, 3],
[2, 3, 0]]
dm = DistanceMatrix(data, list('abc'))
expected_str = "(b:2.000000, a:1.000000, c:1.000000);"
self.assertEqual(nj(dm, result_constructor=str), expected_str)
def test_nj_error(self):
data = [[0, 3],
[3, 0]]
dm = DistanceMatrix(data, list('ab'))
self.assertRaises(ValueError, nj, dm)
def test_compute_q(self):
expected_data = [[0, -50, -38, -34, -34],
[-50, 0, -38, -34, -34],
[-38, -38, 0, -40, -40],
[-34, -34, -40, 0, -48],
[-34, -34, -40, -48, 0]]
expected_ids = list('abcde')
expected = DistanceMatrix(expected_data, expected_ids)
self.assertEqual(_compute_q(self.dm1), expected)
data = [[0, 3, 2],
[3, 0, 3],
[2, 3, 0]]
dm = DistanceMatrix(data, list('abc'))
# computed this manually
expected_data = [[0, -8, -8],
[-8, 0, -8],
[-8, -8, 0]]
expected = DistanceMatrix(expected_data, list('abc'))
self.assertEqual(_compute_q(dm), expected)
def test_compute_collapsed_dm(self):
expected_data = [[0, 7, 7, 6],
[7, 0, 8, 7],
[7, 8, 0, 3],
[6, 7, 3, 0]]
expected_ids = ['x', 'c', 'd', 'e']
expected1 = DistanceMatrix(expected_data, expected_ids)
self.assertEqual(_compute_collapsed_dm(self.dm1, 'a', 'b', True, 'x'),
expected1)
# computed manually
expected_data = [[0, 4, 3],
[4, 0, 3],
[3, 3, 0]]
expected_ids = ['yy', 'd', 'e']
expected2 = DistanceMatrix(expected_data, expected_ids)
self.assertEqual(
_compute_collapsed_dm(expected1, 'x', 'c', True, 'yy'), expected2)
def test_lowest_index(self):
self.assertEqual(_lowest_index(self.dm1), (4, 3))
self.assertEqual(_lowest_index(_compute_q(self.dm1)), (1, 0))
def test_otu_to_new_node(self):
self.assertEqual(_otu_to_new_node(self.dm1, 'a', 'b', 'c', True), 7)
self.assertEqual(_otu_to_new_node(self.dm1, 'a', 'b', 'd', True), 7)
self.assertEqual(_otu_to_new_node(self.dm1, 'a', 'b', 'e', True), 6)
def test_otu_to_new_node_zero_branch_length(self):
data = [[0, 40, 3],
[40, 0, 3],
[3, 3, 0]]
ids = ['a', 'b', 'c']
dm = DistanceMatrix(data, ids)
self.assertEqual(_otu_to_new_node(dm, 'a', 'b', 'c', True), 0)
self.assertEqual(_otu_to_new_node(dm, 'a', 'b', 'c', False), -17)
def test_pair_members_to_new_node(self):
self.assertEqual(_pair_members_to_new_node(self.dm1, 'a', 'b', True),
(2, 3))
self.assertEqual(_pair_members_to_new_node(self.dm1, 'a', 'c', True),
(4, 5))
self.assertEqual(_pair_members_to_new_node(self.dm1, 'd', 'e', True),
(2, 1))
def test_pair_members_to_new_node_zero_branch_length(self):
# the values in this example don't really make sense
# (I'm not sure how you end up with these distances between
# three sequences), but that doesn't really matter for the sake
# of this test
data = [[0, 4, 2],
[4, 0, 38],
[2, 38, 0]]
ids = ['a', 'b', 'c']
dm = DistanceMatrix(data, ids)
self.assertEqual(_pair_members_to_new_node(dm, 'a', 'b', True), (0, 4))
# this makes it clear why negative branch lengths don't make sense...
self.assertEqual(
_pair_members_to_new_node(dm, 'a', 'b', False), (-16, 20))
if __name__ == "__main__":
main()
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