def by_f1_score(self, map):
"""
@return: a sorted list of {rtag:f1score}, for related tags.
"""
return list(sort_v(((k, f1_score(len(self.docs), tot, ix)) for k, (ix, tot) in map.iteritems()), reverse=True))
def by_intersect(self, map):
"""
@return: a sorted list of {rtag:intersection}, for related tags.
"""
return list(sort_v(((k, ix) for k, (ix, tot) in map.iteritems()), reverse=True))
def by_precision(self, map):
"""
@return: a sorted list of {rtag:(precision,rtotal)} for related tags,
where precision = intersect/rtag.total
"""
return list(sort_v(((k, (float(ix)/tot, tot)) for k, (ix, tot) in map.iteritems()), reverse=True))
def by_recall(self, map):
"""
@return: a sorted list of {rtag:(recall,rtotal)}, for related tags,
where recall = intersect/tag.total
"""
return list(sort_v(((k, (float(ix)/len(self.docs), tot)) for k, (ix, tot) in map.iteritems()), reverse=True))
def evaluateScheme(self, scheme):
"""
Evaluate the given address scheme against the perfect address scheme
from the complete data of the world.
"""
prune = scheme.copy()
prune.delete_vertices(v.index for v in prune.vs.select(lambda vx: vx[NAA] is None))
ss = NodeSample()
for tag in prune.vs[NID]:
ss.add_node(self.getTagInfo(tag).build_node())
MAX = float("inf")
def dist(arc, graph):
return -log(arc[AAT]*graph.vs[arc.target][NAT]/graph.vs[arc.source][NAT])
def nattr(dist):
return exp(-dist)
# build address scheme of input tags
local = ss.build(complete=False)
assert None not in local.vs[NAT]
local.es[AAT_AD] = [dist(arc, local) for arc in local.es]
path = local.shortest_paths(0, weights=AAT_AD)[0]
graph_prune_arcs(local, [k for k,v in sort_v(enumerate(path))])
local.vs[NAA] = [nattr(d) for d in path]
# build address scheme of n tags from world data, where n = len(input tags)
# OPT LOW this rebuilds the entire graph each time, not optimal, but means
# we can just use already-existing implementation of dijkstra from igraph
sw = NodeSample()
tinfo = self.getTagInfo(prune.vs[0][NID])
sw.add_node(tinfo.build_node())
visit = set([0]) # visited nodes, sw vids
trail = [(0, 0.0)] # trail of visited nodes, ss vids
for i in xrange(0, len(prune.vs)-1): # n-1 because root already added
for rtag in tinfo.rtag.iterkeys():
if rtag not in sw:
sw.add_node(self.getTagInfo(rtag).build_node())
world = sw.build(complete=False)
world.es[AAT_AD] = [dist(arc, world) for arc in world.es]
path = world.shortest_paths(0, weights=AAT_AD)[0]
# get next tag in world addr scheme
npath = [MAX if i in visit else v for i, v in enumerate(path)]
d = min(npath)
index = npath.index(d)
tinfo = self.getTagInfo(world.vs[index][NID])
visit.add(index)
if tinfo.tag not in ss:
trail.append((len(ss), d))
ss.add_node(tinfo.build_node())
else:
trail.append((prune.vs[NID].index(tinfo.tag),d)) # OPT LOW
world = ss.build()
assert len(trail) == len(local.vs)
graph_prune_arcs(world, [vid for vid, dist in trail])
trail = dict(trail)
world.vs[NAA] = [nattr(trail[i]) if i in trail else None for i in xrange(0, len(world.vs))]
return AddrSchemeEval(prune, local, world)
