def smallestDistance(n1, n2):
"""this is inefficient but the hope is that it will be amortized by the
cache in relationMap"""
if n1 == n2:
return 0
if n2 not in common.relationMap(n1):
return None
relation = common.relationMap(n1)[n2]
return min(abs(h1 + h2) for h1, h2 in relation.keys())
def smallestDistance(n1, n2):
"""this is inefficient but the hope is that it will be amortized by the
cache in relationMap"""
if n1 == n2:
return 0
if n2 not in common.relationMap(n1):
return None
relation = common.relationMap(n1)[n2]
return min(abs(h1 + h2) for h1, h2 in relation.keys())
def makePopulation(size=100000, generations=10, public_prob=0.002, public_gens=4):
tree = mate.makeTree(size, generations)
for node in tree[:-public_gens] | pFlatten:
node.ispublic = False
node.isalive = False
for node in tree[-public_gens:] | pFlatten:
node.ispublic = random.random() < public_prob
node.isalive = True
publicnodes = tree[-public_gens:] | pFlatten | Filter(lambda n: n.ispublic) | pSet
for node in status.wrap(publicnodes): #warm up cache
common.relationMap(node)
return tree, publicnodes
def makePopulation(size=100000,
generations=10,
public_prob=0.002,
public_gens=4):
tree = mate.makeTree(size, generations)
for node in tree[:-public_gens] | pFlatten:
node.ispublic = False
node.isalive = False
for node in tree[-public_gens:] | pFlatten:
node.ispublic = random.random() < public_prob
node.isalive = True
publicnodes = tree[-public_gens:] | pFlatten | Filter(
lambda n: n.ispublic) | pSet
for node in status.wrap(publicnodes): #warm up cache
common.relationMap(node)
return tree, publicnodes
def analyzeScores(node, scores, ibdsamples, publicnodes):
result = utils.JStruct()
highscores = dict(
(k, s) for k, s in scores.items() if sum(s) >= sum(scores[node]))
result.num_cands = len(highscores)
siblinggroups = set(
frozenset(k.dad.children & k.mom.children) for k in highscores)
groupreps = utils.permute(
[node if node in group else list(group)[0] for group in siblinggroups])
groupreps = filter(
lambda r: nonmatchScore(node, r, publicnodes, scores, ibdsamples) >
0.1, groupreps)
result.num_groups_unfiltered = len(siblinggroups)
result.num_groups = len(groupreps)
#FIXME: inefficient
#result.distances = [smallestDistance(node, rep) for rep in groupreps]
try:
result.generations = map(lambda n: n.generation, groupreps)
except AttributeError:
pass
result.num_relatives = sum(1 for r in common.relationMap(node)
if r.ispublic)
result.num_matching_relatives = len(scores[node])
result.cand_num_rel_hist = map(lambda n: len(scores[n]),
groupreps) | pHist()
return result
def aggregateScores(node):
relmap = common.relationMap(node)
scores = {}
totallen = sum(1 for n in relmap if n.ispublic)
ibdsamples = {}
for relative, relation in relmap.iteritems():
if not relative.ispublic:
continue
h1, h2 = relation.keys()[0]
if min(map(sum, relation.keys())) < 2:
raise SiblingError
ibdsample = ibdsamples[relative] = \
sampleFromPdfVector(cached.convolvedDensity(relation))
if ibdsample == 0:
continue
for k, s in individualScores(node, relative, relation, ibdsample).iteritems():
# total score is L-p norm
scores.setdefault(k, []).append(s ** 0.5)
return scores, ibdsamples
def individualScores(victim, relative, relation, sample):
"""sample is specified as an array index"""
global _node, _rel
pair = relation.keys()[0]
nodeheight = pair[0]-pair[1]
relmap = common.relationMap(relative, min(8, 9-nodeheight))
if victim not in relmap:
_node = victim
_rel = relative
assert False
scores = {}
for possiblevictim, relation in relmap.iteritems():
h1, h2 = relation.keys()[0]
if abs(h1 - h2) > 3 or min(map(sum, relation.keys())) < 2:
continue
pdfvector = cached.convolvedDensity(relation)
scores[possiblevictim] = pdfvector[sample] if sample < len(pdfvector) else 0
meanscore = utils.mean(scores.itervalues())
maxscore = max(scores.itervalues())
return dict(((k, val / maxscore) for k, val in scores.iteritems()))
def analyzeScores(node, scores, ibdsamples, publicnodes):
result = utils.JStruct()
highscores = dict((k, s) for k, s in scores.items() if sum(s) >= sum(scores[node]))
result.num_cands = len(highscores)
siblinggroups = set(frozenset(k.dad.children & k.mom.children) for k in highscores)
groupreps = utils.permute(
[node if node in group else list(group)[0] for group in siblinggroups])
groupreps = filter(lambda r: nonmatchScore(node, r, publicnodes, scores, ibdsamples) > 0.1, groupreps)
result.num_groups_unfiltered = len(siblinggroups)
result.num_groups = len(groupreps)
#FIXME: inefficient
#result.distances = [smallestDistance(node, rep) for rep in groupreps]
try:
result.generations = map(lambda n:n.generation, groupreps)
except AttributeError:
pass
result.num_relatives = sum(1 for r in common.relationMap(node) if r.ispublic)
result.num_matching_relatives = len(scores[node])
result.cand_num_rel_hist = map(lambda n: len(scores[n]), groupreps) | pHist()
return result
def aggregateScores(node):
relmap = common.relationMap(node)
scores = {}
totallen = sum(1 for n in relmap if n.ispublic)
ibdsamples = {}
for relative, relation in relmap.iteritems():
if not relative.ispublic:
continue
h1, h2 = relation.keys()[0]
if min(map(sum, relation.keys())) < 2:
raise SiblingError
ibdsample = ibdsamples[relative] = \
sampleFromPdfVector(cached.convolvedDensity(relation))
if ibdsample == 0:
continue
for k, s in individualScores(node, relative, relation,
ibdsample).iteritems():
# total score is L-p norm
scores.setdefault(k, []).append(s**0.5)
return scores, ibdsamples
def individualScores(victim, relative, relation, sample):
"""sample is specified as an array index"""
global _node, _rel
pair = relation.keys()[0]
nodeheight = pair[0] - pair[1]
relmap = common.relationMap(relative, min(8, 9 - nodeheight))
if victim not in relmap:
_node = victim
_rel = relative
assert False
scores = {}
for possiblevictim, relation in relmap.iteritems():
h1, h2 = relation.keys()[0]
if abs(h1 - h2) > 3 or min(map(sum, relation.keys())) < 2:
continue
pdfvector = cached.convolvedDensity(relation)
scores[possiblevictim] = pdfvector[sample] if sample < len(
pdfvector) else 0
meanscore = utils.mean(scores.itervalues())
maxscore = max(scores.itervalues())
return dict(((k, val / maxscore) for k, val in scores.iteritems()))
def zeroProb(node):
"""prob that i.b.d. of node and cand is zero"""
relation = common.relationMap(node).get(cand)
return cached.convolvedDensity(relation)[0] if relation else 1.0
def zeroProb(node):
"""prob that i.b.d. of node and cand is zero"""
relation = common.relationMap(node).get(cand)
return cached.convolvedDensity(relation)[0] if relation else 1.0
