# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from logging import getLogger
from typing import Tuple, List, Any, Optional
from deeppavlov.core.common.registry import register
from deeppavlov.core.models.component import Component
from deeppavlov.core.models.serializable import Serializable
from deeppavlov.core.common.file import load_pickle
from deeppavlov.models.ranking.rel_ranker import RelRanker
from deeppavlov.models.kbqa.wiki_parser import WikiParser
log = getLogger(__name__)
[docs]@register('rel_ranking_bert_infer')
class RelRankerBertInfer(Component, Serializable):
"""Class for ranking of paths in subgraph"""
[docs] def __init__(self, load_path: str,
rel_q2name_filename: str,
ranker: RelRanker,
wiki_parser: Optional[WikiParser] = None,
batch_size: int = 32,
rels_to_leave: int = 40,
return_confidences: bool = False, **kwargs):
"""
Args:
load_path: path to folder with wikidata files
rel_q2name_filename: name of file which maps relation id to name
wiki_parser: component deeppavlov.models.wiki_parser
ranker: component deeppavlov.models.ranking.rel_ranker
batch_size: infering batch size
rels_to_leave: how many relations to leave after relation ranking
return_confidences: whether to return confidences of candidate answers
**kwargs:
"""
super().__init__(save_path=None, load_path=load_path)
self.rel_q2name_filename = rel_q2name_filename
self.ranker = ranker
self.wiki_parser = wiki_parser
self.batch_size = batch_size
self.rels_to_leave = rels_to_leave
self.return_confidences = return_confidences
self.load()
def load(self) -> None:
self.rel_q2name = load_pickle(self.load_path / self.rel_q2name_filename)
def save(self) -> None:
pass
[docs] def __call__(self, questions_list: List[str], candidate_answers_list: List[List[Tuple[str]]]) -> List[str]:
answers = []
confidence = 0.0
for question, candidate_answers in zip(questions_list, candidate_answers_list):
answers_with_scores = []
answer = "Not Found"
n_batches = len(candidate_answers) // self.batch_size + int(len(candidate_answers) % self.batch_size > 0)
for i in range(n_batches):
questions_batch = []
rels_labels_batch = []
answers_batch = []
confidences_batch = []
for candidate_ans_and_rels in candidate_answers[i * self.batch_size: (i + 1) * self.batch_size]:
candidate_rels = candidate_ans_and_rels[:-2]
candidate_rels = [candidate_rel.split('/')[-1] for candidate_rel in candidate_rels]
candidate_answer = candidate_ans_and_rels[-2]
candidate_confidence = candidate_ans_and_rels[-1]
candidate_rels = " # ".join([self.rel_q2name[candidate_rel] \
for candidate_rel in candidate_rels if
candidate_rel in self.rel_q2name])
if candidate_rels:
questions_batch.append(question)
rels_labels_batch.append(candidate_rels)
answers_batch.append(candidate_answer)
confidences_batch.append(candidate_confidence)
probas = self.ranker(questions_batch, rels_labels_batch)
probas = [proba[1] for proba in probas]
for j, (answer, confidence, rels_labels) in \
enumerate(zip(answers_batch, confidences_batch, rels_labels_batch)):
answers_with_scores.append((answer, rels_labels, max(probas[j], confidence)))
answers_with_scores = sorted(answers_with_scores, key=lambda x: x[-1], reverse=True)
if answers_with_scores:
log.debug(f"answers: {answers_with_scores[0]}")
answer = self.wiki_parser.find_label(answers_with_scores[0][0], question)
confidence = answers_with_scores[0][2]
if self.return_confidences:
answers.append((answer, confidence))
else:
answers.append(answer)
return answers
def rank_rels(self, question: str, candidate_rels: List[str]) -> List[Tuple[str, Any]]:
rels_with_scores = []
n_batches = len(candidate_rels) // self.batch_size + int(len(candidate_rels) % self.batch_size > 0)
for i in range(n_batches):
questions_batch = []
rels_labels_batch = []
rels_batch = []
for candidate_rel in candidate_rels[i * self.batch_size: (i + 1) * self.batch_size]:
if candidate_rel in self.rel_q2name:
questions_batch.append(question)
rels_batch.append(candidate_rel)
rels_labels_batch.append(self.rel_q2name[candidate_rel])
if questions_batch:
probas = self.ranker(questions_batch, rels_labels_batch)
probas = [proba[1] for proba in probas]
for j, rel in enumerate(rels_batch):
rels_with_scores.append((rel, probas[j]))
rels_with_scores = sorted(rels_with_scores, key=lambda x: x[1], reverse=True)
return rels_with_scores[:self.rels_to_leave]