def edges(self, nbunch=None, data=False, default=None):
# Force edges to always be returned in upper triangular form
edges = super(GraphHelperMixin, self).edges(nbunch, data, default)
if data:
return (e_(u, v) + (d, ) for u, v, d in edges)
else:
return (e_(u, v) for u, v in edges)
def find_pos_redun_candidate_edges(infr, k=None, verbose=False):
r"""
Searches for augmenting edges that would make PCCs k-positive redundant
Doctest:
>>> from ibeis.algo.graph.mixin_matching import * # NOQA
>>> from ibeis.algo.graph import demo
>>> infr = demo.demodata_infr(ccs=[(1, 2, 3, 4, 5), (7, 8, 9, 10)])
>>> infr.add_feedback((2, 5), 'match')
>>> infr.add_feedback((1, 5), 'notcomp')
>>> infr.params['redun.pos'] = 2
>>> candidate_edges = list(infr.find_pos_redun_candidate_edges())
>>> result = ('candidate_edges = ' + ut.repr2(candidate_edges))
>>> print(result)
candidate_edges = [(1, 3), (7, 10)]
"""
# Add random edges between exisiting non-redundant PCCs
if k is None:
k = infr.params['redun.pos']
# infr.find_non_pos_redundant_pccs(k=k, relax=True)
pcc_gen = list(infr.positive_components())
prog = ut.ProgIter(pcc_gen, enabled=verbose, freq=1, adjust=False)
for pcc in prog:
if not infr.is_pos_redundant(
pcc, k=k, relax=True, assume_connected=True):
for edge in infr.find_pos_augment_edges(pcc, k=k):
yield nxu.e_(*edge)
def _get_cm_edge_data(infr, edges, cm_list=None):
symmetric = True
if cm_list is None:
cm_list = infr.cm_list
# Find scores for the edges that exist in the graph
edge_to_data = ut.ddict(dict)
aid_to_cm = {cm.qaid: cm for cm in cm_list}
for u, v in edges:
if symmetric:
u, v = e_(u, v)
cm1 = aid_to_cm.get(u, None)
cm2 = aid_to_cm.get(v, None)
scores = []
ranks = []
for cm in ut.filter_Nones([cm1, cm2]):
for aid in [u, v]:
idx = cm.daid2_idx.get(aid, None)
if idx is None:
continue
score = cm.annot_score_list[idx]
rank = cm.get_annot_ranks([aid])[0]
scores.append(score)
ranks.append(rank)
if len(scores) == 0:
score = None
rank = None
else:
# Choose whichever one gave the best score
idx = vt.safe_argmax(scores, nans=False)
score = scores[idx]
rank = ranks[idx]
edge_to_data[(u, v)]['score'] = score
edge_to_data[(u, v)]['rank'] = rank
return edge_to_data
def exec_vsone_subset(infr, edges, prog_hook=None):
r"""
Args:
prog_hook (None): (default = None)
CommandLine:
python -m ibeis.algo.graph.core exec_vsone_subset
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.graph.core import * # NOQA
>>> infr = testdata_infr('testdb1')
>>> infr.ensure_full()
>>> edges = [(1, 2), (2, 3)]
>>> result = infr.exec_vsone_subset(edges)
>>> print(result)
"""
match_list = infr._make_matches_from(edges, prog_hook)
# TODO: is this code necessary anymore?
vsone_matches = {
e_(u, v): match
for (u, v), match in zip(edges, match_list)
}
infr.vsone_matches.update(vsone_matches)
edge_to_score = {
e: match.fs.sum()
for e, match in vsone_matches.items()
}
infr.graph.add_edges_from(edge_to_score.keys())
infr.set_edge_attrs('score', edge_to_score)
return match_list
def find_neg_redun_candidate_edges(infr, k=None):
"""
Get pairs of PCCs that are not complete.
Finds edges that might complete them.
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.graph.mixin_matching import * # NOQA
>>> from ibeis.algo.graph import demo
>>> infr = demo.demodata_infr(ccs=[(1,), (2,), (3,)], ignore_pair=True)
>>> edges = list(infr.find_neg_redun_candidate_edges())
>>> assert len(edges) == 3, 'all should be needed here'
>>> infr.add_feedback_from(edges, evidence_decision=NEGTV)
>>> assert len(list(infr.find_neg_redun_candidate_edges())) == 0
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.graph import demo
>>> infr = demo.demodata_infr(pcc_sizes=[3] * 20, ignore_pair=True)
>>> ccs = list(infr.positive_components())
>>> gen = infr.find_neg_redun_candidate_edges(k=2)
>>> for edge in gen:
>>> # What happens when we make ccs positive
>>> print(infr.node_labels(edge))
>>> infr.add_feedback(edge, evidence_decision=POSTV)
>>> import ubelt as ub
>>> infr = demo.demodata_infr(pcc_sizes=[1] * 30, ignore_pair=True)
>>> ccs = list(infr.positive_components())
>>> gen = infr.find_neg_redun_candidate_edges(k=3)
>>> for chunk in ub.chunks(gen, 2):
>>> for edge in chunk:
>>> # What happens when we make ccs positive
>>> print(infr.node_labels(edge))
>>> infr.add_feedback(edge, evidence_decision=POSTV)
list(gen)
"""
if k is None:
k = infr.params['redun.neg']
# Loop through all pairs
for cc1, cc2 in infr.find_non_neg_redun_pccs(k=k):
if len(cc1.intersection(cc2)) > 0:
# If there is modification of the underlying graph while we
# iterate, then two ccs may not be disjoint. Skip these cases.
continue
for u, v in infr.find_neg_augment_edges(cc1, cc2, k):
edge = e_(u, v)
infr.assert_edge(edge)
yield edge
def edges(infr, data=False):
if data:
return ((e_(u, v), d) for u, v, d in infr.graph.edges(data=True))
else:
return (e_(u, v) for u, v in infr.graph.edges())
def e_(u, v):
return e_(u, v)