def get_closest_hyp(hyps : List[str], goal : str, max_length : int):
if len(hyps) == 0:
return ":"
result = max(hyps, key=lambda hyp:
score_hyp_type(limitNumTokens(goal, max_length),
limitNumTokens(serapi_instance.get_hyp_type(hyp), max_length),
max_length))
return result
def get_closest_hyps(hyps : List[str], goal : str, num_hyps : int, max_length : int)\
-> List[Tuple[str, float]]:
if len(hyps) == 0:
return [Prediction(":", 0)] * num_hyps
else:
return list(sorted([(hyp, score_hyp_type(limitNumTokens(goal, max_length),
limitNumTokens(serapi_instance.get_hyp_type(hyp), max_length),
max_length))
for hyp in hyps],
reverse=True,
key=lambda hyp_and_score: hyp_and_score[0]))
def __call__(self, context: TacticContext) -> int:
if len(context.hypotheses) == 0:
return 0
hyp_types = [
limitNumTokens(serapi_instance.get_hyp_type(hyp), self.max_length)
for hyp in context.hypotheses
]
goal = limitNumTokens(context.goal, self.max_length)
closest_hyp_type = max(hyp_types,
key=lambda x: SequenceMatcher(None, goal, x).
ratio() * len(get_symbols(x)))
headToken = get_symbols(closest_hyp_type)[0]
if headToken in self.headKeywords:
return self.headKeywords.index(headToken) + 1
else:
return 0