def test_weighted_consensus_from_scored_trees_collection_ii(self):
"""root positions in input tree collection should not effect result"""
sct = LogLikelihoodScoredTreeCollection(self.trees_randomly_rooted)
ctrr = sct.getConsensusTree()
sct = LogLikelihoodScoredTreeCollection(self.trees_rooted_at_A)
ctra = sct.getConsensusTree()
self.assertTrue(ctrr.sameTopology(ctra))
def test_weighted_trees_satisyfing_cutoff(self):
"""build consensus tree from those satisfying cutoff"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
cts = sct.getWeightedTrees(cutoff=0.8)
for weight, tree in cts:
self.assertTrue(tree.sameTopology(Tree('((a,b),c,d);')))
ct = cts.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((a,b),c,d);")))
def test_weighted_trees_satisyfing_cutoff(self):
"""build consensus tree from those satisfying cutoff"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
cts = sct.getWeightedTrees(cutoff=0.8)
for weight, tree in cts:
self.assertTrue(tree.sameTopology(Tree('((a,b),c,d);')))
ct = cts.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((a,b),c,d);")))
def test_weighted_trees_satisyfing_cutoff(self):
"""build consensus tree from those satisfying cutoff"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
cts = sct.getWeightedTrees(cutoff=0.8)
expected_trees = [Tree(t) for t in "((a,b),(c,d));", "((a,b),(c,d));",
"((a,b),c,d);"]
for i in range(len(cts)):
cts[i][1].sameTopology(expected_trees[i])
ct = cts.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((a,b),(c,d));")))
def test_weighted_trees_satisyfing_cutoff(self):
"""build consensus tree from those satisfying cutoff"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
cts = sct.getWeightedTrees(cutoff=0.8)
expected_trees = [Tree(t) for t in "((a,b),(c,d));", "((a,b),(c,d));",
"((a,b),c,d);"]
for i in range(len(cts)):
cts[i][1].sameTopology(expected_trees[i])
ct = cts.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((a,b),(c,d));")))
def test_weighted_consensus_from_scored_trees_collection_ii(self):
"""root positions in input tree collection should not effect result"""
sct = LogLikelihoodScoredTreeCollection(self.trees_randomly_rooted)
ctrr = sct.getConsensusTree()
sct = LogLikelihoodScoredTreeCollection(self.trees_rooted_at_A)
ctra = sct.getConsensusTree()
self.assertTrue(ctrr.sameTopology(ctra))
def test_tree_collection_read_write_file(self):
"""should correctly read / write a collection from a file"""
def eval_klass(coll):
coll.writeToFile('sample.trees')
read = LoadTrees('sample.trees')
self.assertTrue(type(read) == type(coll))
eval_klass(LogLikelihoodScoredTreeCollection(self.scored_trees))
# convert lnL into p
eval_klass(WeightedTreeCollection([(exp(s), t)
for s,t in self.scored_trees]))
remove_files(['sample.trees'], error_on_missing=False)
def test_weighted_consensus_from_scored_trees_collection(self):
"""weighted consensus from a tree collection should be different"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
ct = sct.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((a,b),c,d);")))
def test_consensus_from_scored_trees_collection(self):
"""tree collection should get same consensus as direct approach"""
tree_list = [(1, t) for t in self.trees]
sct = LogLikelihoodScoredTreeCollection(tree_list)
ct = sct.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((c,d),a,b);")))
def test_weighted_consensus_from_scored_trees_collection(self):
"""weighted consensus from a tree collection should be different"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
ct = sct.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((a,b),(c,d));")))
def test_consensus_from_scored_trees_collection(self):
"""tree collection should get same consensus as direct approach"""
sct = LogLikelihoodScoredTreeCollection([(1, t) for t in self.trees])
ct = sct.getConsensusTree()
self.assertTrue(ct.sameTopology(Tree("((c,d),(a,b));")))
