Source code for deeppavlov.models.ranking.siamese_predictor

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
from typing import List, Iterable, Callable, Union

from deeppavlov.core.common.log import get_logger
from deeppavlov.core.models.component import Component
from deeppavlov.models.ranking.keras_siamese_model import SiameseModel
from import SimpleVocabulary
from deeppavlov.core.common.registry import register

log = get_logger(__name__)

[docs]@register('siamese_predictor') class SiamesePredictor(Component): """The class for ranking or paraphrase identification using the trained siamese network in the ``interact`` mode. Args: batch_size: A size of a batch. num_context_turns: A number of ``context`` turns in data samples. ranking: Whether to perform ranking. If it is set to ``False`` paraphrase identification will be performed. attention: Whether any attention mechanism is used in the siamese network. If ``False`` then calculated in advance vectors of ``responses`` will be used to obtain similarity score for the input ``context``; Otherwise the whole siamese architecture will be used to obtain similarity score for the input ``context`` and each particular ``response``. The parameter will be used if the ``ranking`` is set to ``True``. responses: A instance of :class:`` with all possible ``responses`` to perform ranking. Will be used if the ``ranking`` is set to ``True``. preproc_func: A ``__call__`` function of the :class:`~deeppavlov.models.preprocessors.siamese_preprocessor.SiamesePreprocessor`. interact_pred_num: The number of the most relevant ``responses`` which will be returned. Will be used if the ``ranking`` is set to ``True``. **kwargs: Other parameters. """ def __init__(self, model: SiameseModel, batch_size: int, num_context_turns: int = 1, ranking: bool = True, attention: bool = False, responses: SimpleVocabulary = None, preproc_func: Callable = None, interact_pred_num: int = 3, *args, **kwargs) -> None: super().__init__() self.batch_size = batch_size self.num_context_turns = num_context_turns self.ranking = ranking self.attention = attention self.preproc_responses = [] self.response_embeddings = None self.preproc_func = preproc_func self.interact_pred_num = interact_pred_num self.model = model if self.ranking: self.responses = {el[1]: el[0] for el in responses.items()} self._build_preproc_responses() if not self.attention: self._build_response_embeddings() def __call__(self, batch: Iterable[List[np.ndarray]]) -> List[Union[List[str],str]]: context = next(batch) try: next(batch) log.error("It is not intended to use the `%s` with the batch size greater then 1." % self.__class__) except StopIteration: pass if self.ranking: if len(context) == self.num_context_turns: scores = [] if self.attention: for i in range(len(self.preproc_responses) // self.batch_size + 1): responses = self.preproc_responses[i*self.batch_size: (i+1)*self.batch_size] b = [context + el for el in responses] b = self.model._make_batch(b) sc = self.model._predict_on_batch(b) scores += list(sc) else: b = self.model._make_batch([context]) context_emb = self.model._predict_context_on_batch(b) context_emb = np.squeeze(context_emb, axis=0) scores = context_emb @ self.response_embeddings.T ids = np.flip(np.argsort(scores), -1) return [[self.responses[el] for el in ids[:self.interact_pred_num]]] else: return ["Please, provide contexts separated by '&' in the number equal to that used while training."] else: if len(context) == 2: b = self.model._make_batch([context]) sc = self.model._predict_on_batch(b)[0] if sc > 0.5: return ["This is a paraphrase."] else: return ["This is not a paraphrase."] else: return ["Please, provide two sentences separated by '&'."] def reset(self) -> None: pass def process_event(self) -> None: pass def _build_response_embeddings(self) -> None: resp_vecs = [] for i in range(len(self.preproc_responses) // self.batch_size + 1): resp_preproc = self.preproc_responses[i*self.batch_size: (i+1)*self.batch_size] resp_preproc = self.model._make_batch(resp_preproc) resp_preproc = resp_preproc resp_vecs.append(self.model._predict_response_on_batch(resp_preproc)) self.response_embeddings = np.vstack(resp_vecs) def _build_preproc_responses(self) -> None: responses = list(self.responses.values()) for i in range(len(responses) // self.batch_size + 1): el = self.preproc_func(responses[i*self.batch_size: (i+1)*self.batch_size]) self.preproc_responses += list(el)