def estOE(d):
gt = d['gt']['graph']
gt = bfu.undersample(gt, 1)
e = gk.OCE(d['estimate'], gt)
N = np.double(len(gk.edgelist(gt))) +\
np.double(len(gk.bedgelist(gt)))
return (e['directed'][0] + e['bidirected'][0]) / N
def BackwardBi():
edges = gk.bedgelist(g)
CandidateSet = []
for e in edges:
if e[0] < e[1]:
score = ComparescoreBiEdges('backward', g, int(e[0]),
int(e[1]))
if score < 0:
CandidateSet.append((score, (int(e[0]), int(e[1]))))
while CandidateSet:
CandidateSet.sort(reverse=True)
edgeToDel = CandidateSet.pop()[1]
fr[edgeToDel[1]][edgeToDel[0]] = 0
fr[edgeToDel[0]][edgeToDel[1]] = 0
g[str(edgeToDel[0])][str(edgeToDel[1])].remove((2, 0))
g[str(edgeToDel[1])][str(edgeToDel[0])].remove((2, 0))
reeval_node = edgeToDel
newCandidateSet = []
for a, e in CandidateSet:
if e[1] == reeval_node[1] or e[0] == reeval_node[0]:
CandidateSet.remove((a, e))
score = ComparescoreBiEdges('backward', g, e[0], e[1])
if score < 0:
newCandidateSet.append((score, (e[0], e[1])))
CandidateSet = newCandidateSet + CandidateSet
return g, fr
def estCOE(d):
gt = d['gt']['graph']
gt = bfu.undersample(gt, 1)
e = gk.OCE(d['estimate'], gt)
n = len(gt)
N = np.double(n ** 2 + (n - 1) ** 2 / 2.0
- len(gk.edgelist(gt))
- len(gk.bedgelist(gt)))
return (e['directed'][1] + e['bidirected'][1]) / N
def checkvedge(v, g2):
""" Nodes to check to merge the virtual nodes of v ( b<-a->c )
"""
l = gk.bedgelist(g2)
if (v[1], v[2]) in l:
l = single_nodes(v, g2) + checkbedges(v, l, g2)
for n in v:
if n in g2[n]:
l.append((n, n))
else:
l = checkbedges(v, l, g2)
return list(set(l))
def vedgelist(g, pathtoo=False):
""" Return a list of tuples for edges of g and forks
a superugly organically grown function that badly needs refactoring
"""
l = []
el = gk.edgelist(g)
bl = gk.bedgelist(g)
if pathtoo:
l.extend(make_longpaths(g, el))
l2, r = make_allforks_and_rest(g, el, bl, dofullforks=True)
l.extend(l2)
A, singles = makechains(r)
if singles:
B, singles = makesinks(singles)
else:
B, singles = [], []
l = longpaths_pick(l) + threedges_pick(l) + A + B + singles
return l
def udensity(g):
return (len(gk.edgelist(g)) + len(gk.bedgelist(g)) / 2.) / np.double(len(g) ** 2 + len(g) * (len(g) - 1) / 2.)
