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#! /usr/bin/env python
import unittest, os
from cogent.phylo.distance import *
from cogent.phylo.nj import nj, gnj
from cogent.phylo.least_squares import wls
from cogent import LoadSeqs, LoadTree
from cogent.evolve.models import JC69, HKY85, F81
from cogent.phylo.consensus import majorityRule, weightedMajorityRule
__author__ = "Peter Maxwell"
__copyright__ = "Copyright 2007-2009, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley", "Matthew Wakefield",\
"Daniel McDonald"]
__license__ = "GPL"
__version__ = "1.4.1"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Production"
def Tree(t):
return LoadTree(treestring=t)
class ConsensusTests(unittest.TestCase):
def setUp(self):
self.trees = [
(1, Tree("((a,b),(c,d));")),
(1, Tree("((a,b),(c,d));")),
(1, Tree("((a,c),(b,d));")),
(1, Tree("((a,b),c,d);")),]
def test_majorityRule(self):
"""Tests for majority rule consensus trees"""
trees = [t for (w,t) in self.trees]
outtrees = majorityRule(trees, strict=False)
self.assertEqual(len(outtrees), 1)
self.assert_(outtrees[0].sameTopology(Tree("((c,d),(a,b));")))
outtrees = majorityRule(trees, strict=True)
self.assertEqual(len(outtrees), 1)
self.assert_(outtrees[0].sameTopology(Tree("(c,d,(a,b));")))
class TreeReconstructionTests(unittest.TestCase):
def setUp(self):
self.tree = LoadTree(treestring='((a:3,b:4):2,(c:6,d:7):30,e:5)')
self.dists = self.tree.getDistances()
def assertTreeDistancesEqual(self, t1, t2):
d1 = t1.getDistances()
d2 = t2.getDistances()
self.assertEqual(len(d1), len(d2))
for key in d2:
self.assertAlmostEqual(d1[key], d2[key])
def test_nj(self):
"""testing nj"""
reconstructed = nj(self.dists)
self.assertTreeDistancesEqual(self.tree, reconstructed)
def test_gnj(self):
"""testing gnj"""
results = gnj(self.dists, keep=1)
(length, reconstructed) = results[0]
self.assertTreeDistancesEqual(self.tree, reconstructed)
results = gnj(self.dists, keep=10)
(length, reconstructed) = results[0]
self.assertTreeDistancesEqual(self.tree, reconstructed)
# Results should be a TreeCollection
len(results)
results.getConsensusTree()
# From GNJ paper. Pearson, Robins, Zhang 1999.
tied_dists = {
('a', 'b'):3, ('a', 'c'):3, ('a', 'd'):4, ('a', 'e'):3,
('b', 'c'):3, ('b', 'd'):3, ('b', 'e'):4,
('c', 'd'):3, ('c', 'e'):3,
('d', 'e'):3}
results = gnj(tied_dists, keep=3)
scores = [score for (score, tree) in results]
self.assertEqual(scores[:2], [7.75, 7.75])
self.assertNotEqual(scores[2], 7.75)
def test_wls(self):
"""testing wls"""
reconstructed = wls(self.dists)
self.assertTreeDistancesEqual(self.tree, reconstructed)
def test_truncated_wls(self):
"""testing wls with order option"""
order = ['e', 'b', 'c', 'd']
reconstructed = wls(self.dists, order=order)
self.assertEqual(set(reconstructed.getTipNames()), set(order))
def test_limited_wls(self):
"""testing (well, exercising at least), wls with constrained start"""
init = LoadTree(treestring='((a,c),b,d)')
reconstructed = wls(self.dists, start=init)
self.assertEqual(len(reconstructed.getTipNames()), 5)
init2 = LoadTree(treestring='((a,d),b,c)')
reconstructed = wls(self.dists, start=[init, init2])
self.assertEqual(len(reconstructed.getTipNames()), 5)
init3 = LoadTree(treestring='((a,d),b,e)')
self.assertRaises(Exception, wls, self.dists, start=[init, init3])
# if start tree has all seq names, should raise an error
self.assertRaises(Exception, wls, self.dists,
start=[LoadTree(treestring='((a,c),b,(d,e))')])
class DistancesTests(unittest.TestCase):
def setUp(self):
self.al = LoadSeqs(data = {'a':'GTACGTACGATC',
'b':'GTACGTACGTAC',
'c':'GTACGTACGTTC',
'e':'GTACGTACTGGT'})
self.collection = LoadSeqs(data = {'a':'GTACGTACGATC',
'b':'GTACGTACGTAC',
'c':'GTACGTACGTTC',
'e':'GTACGTACTGGT'}, aligned=False)
def assertDistsAlmostEqual(self, expected, observed, precision=4):
observed = dict([(frozenset(k),v) for (k,v) in observed.items()])
expected = dict([(frozenset(k),v) for (k,v) in expected.items()])
for key in expected:
self.assertAlmostEqual(expected[key], observed[key], precision)
def test_EstimateDistances(self):
"""testing (well, exercising at least), EstimateDistances"""
d = EstimateDistances(self.al, JC69())
d.run()
canned_result = {('b', 'e'): 0.440840,
('c', 'e'): 0.440840,
('a', 'c'): 0.088337,
('a', 'b'): 0.188486,
('a', 'e'): 0.440840,
('b', 'c'): 0.0883373}
result = d.getPairwiseDistances()
self.assertDistsAlmostEqual(canned_result, result)
# excercise writing to file
d.writeToFile('junk.txt')
try:
os.remove('junk.txt')
except OSError:
pass # probably parallel
def test_EstimateDistancesWithMotifProbs(self):
"""EstimateDistances with supplied motif probs"""
motif_probs= {'A':0.1,'C':0.2,'G':0.2,'T':0.5}
d = EstimateDistances(self.al, HKY85(), motif_probs=motif_probs)
d.run()
canned_result = {('a', 'c'): 0.07537,
('b', 'c'): 0.07537,
('a', 'e'): 0.39921,
('a', 'b'): 0.15096,
('b', 'e'): 0.39921,
('c', 'e'): 0.37243}
result = d.getPairwiseDistances()
self.assertDistsAlmostEqual(canned_result, result)
def test_EstimateDistances_fromThreeway(self):
"""testing (well, exercising at least), EsimateDistances fromThreeway"""
d = EstimateDistances(self.al, JC69(), threeway=True)
d.run()
canned_result = {('b', 'e'): 0.495312,
('c', 'e'): 0.479380,
('a', 'c'): 0.089934,
('a', 'b'): 0.190021,
('a', 'e'): 0.495305,
('b', 'c'): 0.0899339}
result = d.getPairwiseDistances(summary_function="mean")
self.assertDistsAlmostEqual(canned_result, result)
def test_EstimateDistances_fromUnaligned(self):
"""Excercising estimate distances from unaligned sequences"""
d = EstimateDistances(self.collection, JC69(), do_pair_align=True,
rigorous_align=True)
d.run()
canned_result = {('b', 'e'): 0.440840,
('c', 'e'): 0.440840,
('a', 'c'): 0.088337,
('a', 'b'): 0.188486,
('a', 'e'): 0.440840,
('b', 'c'): 0.0883373}
result = d.getPairwiseDistances()
self.assertDistsAlmostEqual(canned_result, result)
d = EstimateDistances(self.collection, JC69(), do_pair_align=True,
rigorous_align=False)
d.run()
canned_result = {('b', 'e'): 0.440840,
('c', 'e'): 0.440840,
('a', 'c'): 0.088337,
('a', 'b'): 0.188486,
('a', 'e'): 0.440840,
('b', 'c'): 0.0883373}
result = d.getPairwiseDistances()
self.assertDistsAlmostEqual(canned_result, result)
def test_EstimateDistances_other_model_params(self):
"""test getting other model params from EstimateDistances"""
d = EstimateDistances(self.al, HKY85(), est_params=['kappa'])
d.run()
# this will be a Number object with Mean, Median etc ..
kappa = d.getParamValues('kappa')
self.assertAlmostEqual(kappa.Mean, 0.8939, 4)
# this will be a dict with pairwise instances, it's called by the above
# method, so the correctness of it's values is already checked
kappa = d.getPairwiseParam('kappa')
def test_EstimateDistances_modify_lf(self):
"""tests modifying the lf"""
def constrain_fit(lf):
lf.setParamRule('kappa', is_const=True)
lf.optimise(local=True, show_progress=False)
return lf
d = EstimateDistances(self.al, HKY85(), modify_lf=constrain_fit)
d.run()
result = d.getPairwiseDistances()
d = EstimateDistances(self.al, F81())
d.run()
expect = d.getPairwiseDistances()
self.assertDistsAlmostEqual(expect, result)
if __name__ == '__main__':
unittest.main()
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