def predictKTactics(self, in_data : TacticContext, k : int) -> List[Prediction]: if len(in_data.hypotheses) == 0: return [Prediction("eauto", 0)] k = min(k, len(in_data.hypotheses)) best_hyps = sorted(in_data.hypotheses, key=len, reverse=True)[:k] return [Prediction("apply " + serapi_instance.get_first_var_in_hyp(hyp) + ".", .5 ** idx) for idx, hyp in enumerate(best_hyps)]
def add_arg(self, tactic_stem : str, goal : str, hyps : List[str], max_length : int): if serapi_instance.tacticTakesHypArgs(tactic_stem): return tactic_stem + " " + \ serapi_instance.get_first_var_in_hyp(get_closest_hyp(hyps, goal, max_length)) \ + "." else: return tactic_stem + "."
def predictKTactics(self, in_data : TacticContext, k : int) -> List[Prediction]: if len(in_data.hypotheses) == 0: return [Prediction("eauto", 0)] k = min(k, len(in_data.hypotheses)) best_hyps = \ sorted(in_data.hypotheses, reverse=True, key=lambda hyp: SequenceMatcher(None, serapi_instance.get_hyp_type(hyp), in_data.goal).ratio() )[:k] return [Prediction("apply " + serapi_instance.get_first_var_in_hyp(hyp) + ".", .5 ** idx) for idx, hyp in enumerate(best_hyps)]
def add_args(self, stem_predictions : List[Prediction], goal : str, hyps : List[str], max_length : int): possibilities : List[Prediction] = [] for stem, stem_score in stem_predictions: if serapi_instance.tacticTakesHypArgs(stem): for hyp, hyp_score in get_closest_hyps(hyps, goal, len(stem_predictions), max_length): possibilities.append( Prediction(stem + " " + serapi_instance.get_first_var_in_hyp(hyp) + ".", hyp_score * stem_score)) else: possibilities.append(Prediction(stem + ".", stem_score * 0.5)) return list(sorted(possibilities, key=lambda pred: pred.certainty, reverse=True)[:len(stem_predictions)])
def _predictDistribution(self, in_data : TacticContext) -> \ Tuple[torch.FloatTensor, str]: if len(in_data.hypotheses) > 0: relevant_hyp, relevance = \ max([(hyp, term_relevance(in_data.goal, serapi_instance.get_hyp_type(hyp))) for hyp in in_data.hypotheses], key=lambda x: x[1]) else: relevant_hyp = ":" relevance = 0 encoded_hyp = self._encode_term(serapi_instance.get_hyp_type(relevant_hyp)) encoded_relevance = [relevance] encoded_goal = self._encode_term(in_data.goal) stem_distribution = self._run_model(encoded_hyp, encoded_relevance, encoded_goal) return FloatTensor(stem_distribution), \ serapi_instance.get_first_var_in_hyp(relevant_hyp)
def predictKTactics(self, in_data: TacticContext, k: int) -> List[Prediction]: if len(in_data.hypotheses) == 0: return [Prediction("eauto", 0)] with self._lock: distribution = self._predictDistribution(in_data) if k > len(in_data.hypotheses): k = len(in_data.hypotheses) probs, indices = distribution.squeeze().topk(k) if k == 1: probs = FloatTensor([probs]) indices = LongTensor([indices]) return [ Prediction( "apply " + serapi_instance.get_first_var_in_hyp( in_data.hypotheses[idx.item()]) + ".", math.exp(certainty.item())) for certainty, idx in zip(probs, indices) ]
def decode_tactic_structure(stem_embedding : SimpleEmbedding, struct : TacticStructure, hyps : List[str]) -> str: stem_idx, arg_hyp_idxs = struct return " ".join([stem_embedding.decode_token(stem_idx)] + [serapi_instance.get_first_var_in_hyp(hyps[hyp_idx-TOKEN_START]) for hyp_idx in arg_hyp_idxs])
def get_var(idx: int) -> str: if idx == 0: return "UNKNOWN" else: return serapi_instance.get_first_var_in_hyp(hyps[idx - 1])