def __init__(self, best_match_num, test_triple_num, gold_triple_num):
num_matches, num_only_guessed, num_only_ref = (best_match_num,
test_triple_num - best_match_num,
gold_triple_num - best_match_num)
super().__init__({PRIMARY: evaluation.SummaryStatistics(num_matches, num_only_guessed, num_only_ref)},
default={PRIMARY.name: PRIMARY})
self.p, self.r, self.f1 = smatch.compute_f(best_match_num, test_triple_num, gold_triple_num)
def get_scores(s1, s2, eval_type, verbose):
converter = ConlluConverter()
edges = [[
e for nodes, _ in converter.build_nodes(s) for n in nodes
for e in n.outgoing
] for s in (s1, s2)]
for es in edges:
for e in es:
e.rel = None if eval_type == evaluation.UNLABELED else e.rel.partition(
":")[0]
g, r = map(set, edges)
res = evaluation.EvaluatorResults(
{
PRIMARY:
evaluation.SummaryStatistics(len(g & r), len(g - r), len(r - g))
},
default={PRIMARY.name: PRIMARY})
if verbose:
print("Evaluation type: (" + eval_type + ")")
res.print()
return res
def get_scores(s1, s2, eval_type, verbose):
converter = SdpConverter()
edges = [[
e for nodes, _ in converter.build_nodes(s) for n in nodes
for e in n.outgoing +
([create_top_edge(converter)] if n.is_top else [])
] for s in (s1, s2)]
if eval_type == evaluation.UNLABELED:
for es in edges:
for e in es:
e.rel = None
g, r = map(set, edges)
res = evaluation.EvaluatorResults(
{
PRIMARY:
evaluation.SummaryStatistics(len(g & r), len(g - r), len(r - g))
},
default={PRIMARY.name: PRIMARY})
if verbose:
print("Evaluation type: (" + eval_type + ")")
res.print()
return res
def get_scores(s1, s2, eval_type, verbose):
converter = SdpConverter()
edges = [[
e for g in converter.generate_graphs(s) for n in g.nodes
for e in n.outgoing +
([SdpConverter.Edge(rel=SdpConverter.TOP, remote=False, head=g.root
)] if n.is_top else [])
] for s in (s1, s2)]
if eval_type == evaluation.UNLABELED:
for es in edges:
for e in es:
e.rel = None
g, r = map(set, edges)
res = evaluation.EvaluatorResults(
{
PRIMARY:
evaluation.SummaryStatistics(len(g & r), len(g - r), len(r - g))
},
default={PRIMARY.name: PRIMARY})
if verbose:
print("Evaluation type: (" + eval_type + ")")
res.print()
return res
def get_scores(s1, s2, eval_type, verbose):
converter = SdpConverter()
edges = [{
e
for nodes, _ in converter.build_nodes(s) for n in nodes
for e in n.outgoing
} for s in (s1, s2)]
if eval_type == evaluation.UNLABELED:
for es in edges:
for e in es:
e.rel = None
res = evaluation.EvaluatorResults(
{
PRIMARY:
evaluation.SummaryStatistics(len(edges[0] & edges[1]),
len(edges[0] - edges[1]),
len(edges[1] - edges[0]))
},
default={PRIMARY.name: PRIMARY})
if verbose:
print("Evaluation type: (" + eval_type + ")")
res.print()
return res
def get_scores(s1, s2, eval_type, verbose=False, units=False):
converter = ConlluConverter()
n1, n2 = list(map(list, list(map(converter.build_nodes, (s1, s2)))))
t1, t2 = list(map(join_tokens, (n1, n2)))
assert t1 == t2, "Tokens do not match: '%s' != '%s'" % diff(t1, t2)
edges = [[e for nodes, _ in dep_nodes for n in nodes for e in n.outgoing] for dep_nodes in (n1, n2)]
for es in edges:
for e in es:
e.rel = None if eval_type == evaluation.UNLABELED else e.rel.partition(":")[0]
g, r = map(set, edges)
res = evaluation.EvaluatorResults({PRIMARY: evaluation.SummaryStatistics(len(g & r), len(g - r), len(r - g))},
default={PRIMARY.name: PRIMARY})
if verbose:
print()
print("Evaluation type: (" + eval_type + ")")
if units:
print("==> Mutual Units:")
print(g & r)
print("==> Only in guessed:")
print(g - r)
print("==> Only in reference:")
print(r - g)
res.print()
return res