def find_missing_bipartitions(reference_tree,
comparison_tree,
is_bipartitions_updated=False):
"""
Returns a list of bipartitions that are in ``reference_tree``, but
not in ``comparison_tree``.
Trees need to share the same |TaxonNamespace| reference. The
bipartition bitmasks of the trees must be correct for the current tree
structures (by calling :meth:`Tree.encode_bipartitions()` method) or the
``is_bipartitions_updated`` argument must be |False| to force recalculation of
bipartitions.
Parameters
----------
reference_tree : |Tree| object
The first tree of the two trees being compared. This must share the
same |TaxonNamespace| reference as ``tree2`` and must have
bipartitions encoded.
comparison_tree : |Tree| object
The second tree of the two trees being compared. This must share the
same |TaxonNamespace| reference as ``tree1`` and must have
bipartitions encoded.
is_bipartitions_updated : bool
If |True|, then the bipartitions on *both* trees will be updated
before comparison. If |False| (default) then the bipartitions
will only be calculated for a |Tree| object if they have not been
calculated before, either explicitly or implicitly.
Returns
-------
s : list[|Bipartition|]
A list of bipartitions that are in the first tree but not in the second.
"""
missing = []
if reference_tree.taxon_namespace is not comparison_tree.taxon_namespace:
raise error.TaxonNamespaceIdentityError(reference_tree,
comparison_tree)
if not is_bipartitions_updated:
reference_tree.encode_bipartitions()
comparison_tree.encode_bipartitions()
else:
if reference_tree.bipartition_encoding is None:
reference_tree.encode_bipartitions()
if comparison_tree.bipartition_encoding is None:
comparison_tree.encode_bipartitions()
for bipartition in reference_tree.bipartition_encoding:
if bipartition in comparison_tree.bipartition_encoding:
pass
else:
missing.append(bipartition)
return missing
def mason_gamer_kellogg_score(tree1, tree2, is_bipartitions_updated=False):
"""
Mason-Gamer and Kellogg. Testing for phylogenetic conflict among molecular
data sets in the tribe Triticeae (Gramineae). Systematic Biology (1996)
vol. 45 (4) pp. 524
"""
if tree1.taxon_namespace is not tree2.taxon_namespace:
raise error.TaxonNamespaceIdentityError(tree1, tree2)
if not is_bipartitions_updated:
tree1.encode_bipartitions()
tree2.encode_bipartitions()
else:
if tree1.bipartition_encoding is None:
tree1.encode_bipartitions()
if tree2.bipartition_encoding is None:
tree2.encode_bipartitions()
se1 = tree1.bipartition_encoding
se2 = tree2.bipartition_encoding
bipartitions = sorted(list(set(se1.keys() + se2.keys())))
def _get_length_diffs(tree1,
tree2,
edge_weight_attr="length",
value_type=float,
is_bipartitions_updated=False,
bipartition_length_diff_map=False):
"""
Returns a list of tuples, with the first element of each tuple representing
the length of the branch subtending a particular bipartition on ``tree1``, and
the second element the length of the same branch on ``tree2``. If a
particular bipartition is found on one tree but not in the other, a value of zero
is used for the missing bipartition.
"""
length_diffs = []
bipartition_length_diffs = {}
if tree1.taxon_namespace is not tree2.taxon_namespace:
raise error.TaxonNamespaceIdentityError(tree1, tree2)
if not is_bipartitions_updated:
tree1.encode_bipartitions()
tree2.encode_bipartitions()
else:
if tree1.bipartition_encoding is None:
tree1.encode_bipartitions()
if tree2.bipartition_encoding is None:
tree2.encode_bipartitions()
tree1_bipartition_edge_map = dict(
tree2.bipartition_edge_map) # O(n*(2*bind + dict_item_cost))
tree2_bipartition_edge_map = tree1.bipartition_edge_map
for bipartition in tree2_bipartition_edge_map: # O n : 2*bind
edge = tree2_bipartition_edge_map[bipartition]
elen1 = getattr(edge, edge_weight_attr) # attr + bind
if elen1 is None:
elen1 = 0 # worst-case: bind
value1 = value_type(elen1) # ctor + bind
try:
e2 = tree1_bipartition_edge_map.pop(
bipartition) # attr + dict_lookup + bind
elen2 = getattr(e2, edge_weight_attr) # attr + bind
if elen2 is None:
# allow root edge to have bipartition with no value: raise error if not root edge
if e2.tail_node is None:
elen2 = 0.0
else:
raise ValueError(
"Edge length attribute is 'None': Tree: %s ('%s'), Split: %s"
% (id(tree2), tree2.label,
bipartition.leafset_as_newick_string(
tree2.taxon_namespace)))
except KeyError: # excep
elen2 = 0.0
value2 = value_type(elen2) # ctor + bind # best case
# if abs(value2-value1) > 1e-5:
# print("{}: {}, {}".format(bipartition.leafset_as_newick_string(tree1.taxon_namespace), value2, value1))
length_diffs.append((value1, value2)) # ctor + listappend
bipartition_length_diffs[bipartition] = length_diffs[-1]
for bipartition in tree1_bipartition_edge_map: # best-case not executed, worst case O(n) : 2*bind
edge = tree1_bipartition_edge_map[bipartition]
elen2 = getattr(edge, edge_weight_attr) # attr + bind
if elen2 is None:
elen2 = 0
value2 = value_type(elen2) # ctor + bind
e1 = tree2_bipartition_edge_map.get(
bipartition) # attr + dict_lookup + bind
if e1 is None:
elen1 = 0.0
else:
elen1 = getattr(e1, edge_weight_attr) # attr + bind
if elen1 is None:
# allow root edge to have bipartition with no value: raise error if not root edge
if e1.tail_node is None:
elen1 = 0.0
else:
raise ValueError(
"Edge length attribute is 'None': Tree: %s ('%s'), Split: %s"
% (id(tree1), tree1.label, bipartition))
#elen1 = 0
value1 = value_type(elen1)
length_diffs.append((value1, value2)) # ctor + listappend
bipartition_length_diffs[bipartition] = length_diffs[-1]
# the numbers below do not reflect additions to the code to protect against
# edges with length None
# loops
# best-case:
# O(n * (dict_lookup + 3*attr + 3*ctor + 7*bind + listappend))
# worst-case:
# separated: O(n * (2*dict_lookup + 4*attr + 3*ctor + 8*bind + listappend + excep) + n*(2*dict_lookup + 4*attr + 3*ctor + 8*bind + listappend))
# or:
# O(2n*(2*dict_lookup + 4*attr + 3*ctor + 8*bind + listappend + 0.5*excep))
# total
# best-case:
# O(n * (dict_lookup + 3*attr + 3*ctor + 8*bind + listappend + dict_item_cost))
# worst-case:
# O(2n*(2*dict_lookup + 4*attr + 3*ctor + 9*bind + listappend + 0.5*(dict_item_cost + excep))
if bipartition_length_diff_map:
return length_diffs, bipartition_length_diffs
else:
return length_diffs
def false_positives_and_negatives(reference_tree,
comparison_tree,
is_bipartitions_updated=False):
"""
Counts and returns number of false positive bipar (bipartitions found in
``comparison_tree`` but not in ``reference_tree``) and false negative
bipartitions (bipartitions found in ``reference_tree`` but not in
``comparison_tree``).
Trees need to share the same |TaxonNamespace| reference. The
bipartition bitmasks of the trees must be correct for the current tree
structures (by calling :meth:`Tree.encode_bipartitions()` method) or the
``is_bipartitions_updated`` argument must be |False| to force recalculation of
bipartitions.
Parameters
----------
reference_tree : |Tree| object
The first tree of the two trees being compared. This must share the
same |TaxonNamespace| reference as ``tree2`` and must have
bipartitions encoded.
comparison_tree : |Tree| object
The second tree of the two trees being compared. This must share the
same |TaxonNamespace| reference as ``tree1`` and must have
bipartitions encoded.
is_bipartitions_updated : bool
If |True|, then the bipartitions on *both* trees will be updated
before comparison. If |False| (default) then the bipartitions
will only be calculated for a |Tree| object if they have not been
calculated before, either explicitly or implicitly.
Returns
-------
t : tuple(int)
A pair of integers, with first integer being the number of false
positives and the second being the number of false negatives.
Examples
--------
::
import dendropy
from dendropy.calculate import treecompare
tns = dendropy.TaxonNamespace()
tree1 = tree.get_from_path(
"t1.nex",
"nexus",
taxon_namespace=tns)
tree2 = tree.get_from_path(
"t2.nex",
"nexus",
taxon_namespace=tns)
tree1.encode_bipartitions()
tree2.encode_bipartitions()
print(treecompare.false_positives_and_negatives(tree1, tree2))
"""
if reference_tree.taxon_namespace is not comparison_tree.taxon_namespace:
raise error.TaxonNamespaceIdentityError(reference_tree,
comparison_tree)
if not is_bipartitions_updated:
reference_tree.encode_bipartitions()
comparison_tree.encode_bipartitions()
else:
if reference_tree.bipartition_encoding is None:
reference_tree.encode_bipartitions()
if comparison_tree.bipartition_encoding is None:
comparison_tree.encode_bipartitions()
ref_bipartitions = set(reference_tree.bipartition_encoding)
comparison_bipartitions = set(comparison_tree.bipartition_encoding)
false_positives = ref_bipartitions.difference(comparison_bipartitions)
false_negatives = comparison_bipartitions.difference(ref_bipartitions)
return len(false_positives), len(false_negatives)