def test_weighted_trees_satisyfing_cutoff(self):
"""build consensus tree from those satisfying cutoff"""
sct = LogLikelihoodScoredTreeCollection(self.scored_trees)
cts = sct.get_weighted_trees(cutoff=0.8)
for weight, tree in cts:
self.assertTrue(tree.same_topology(Tree("((a,b),c,d);")))
ct = cts.get_consensus_tree()
self.assertTrue(ct.same_topology(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.get_consensus_tree()
sct = LogLikelihoodScoredTreeCollection(self.trees_rooted_at_A)
ctra = sct.get_consensus_tree()
self.assertTrue(ctrr.same_topology(ctra))
def test_tree_collection_read_write_file(self):
"""should correctly read / write a collection from a file"""
def eval_klass(coll):
coll.write("sample.trees")
read = make_trees("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.get_consensus_tree()
self.assertTrue(ct.same_topology(Tree("((a,b),c,d);")))
def test_consensus_from_scored_trees_collection(self):
"""tree collection should get same consensus as direct approach"""
tree_list = [(i * -1, t) for i, t in enumerate(self.trees)]
sct = LogLikelihoodScoredTreeCollection(tree_list)
ct = sct.get_consensus_tree()
self.assertTrue(ct.same_topology(Tree("((c,d),a,b);")))
