# Copyright The PyTorch Lightning team. # # 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. # referenced from # Library Name: torchtext # Authors: torchtext authors and @sluks # Date: 2021-11-25 # Link: import itertools from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union import torch from torch import Tensor, tensor from torchmetrics import Metric from torchmetrics.functional.text.chrf import _chrf_score_compute, _chrf_score_update, _prepare_n_grams_dicts _N_GRAM_LEVELS = ("char", "word") _TEXT_LEVELS = ("preds", "target", "matching") _DICT_STATES_NAMES = ( "total_preds_char_n_grams", "total_preds_word_n_grams", "total_target_char_n_grams", "total_target_word_n_grams", "total_matching_char_n_grams", "total_matching_word_n_grams", ) _DICT_STATES_TYPES = Tuple[ Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor], Dict[int, Tensor] ] class CHRFScore(Metric): """Calculate `chrf score`_ of machine translated text with one or more references. This implementation supports both ChrF score computation introduced in `chrF score`_ and `chrF++ score`_ introduced in `chrF++ score`_. This implementation follows the implmenetaions from https://github.com/m-popovic/chrF and https://github.com/mjpost/sacrebleu/blob/master/sacrebleu/metrics/chrf.py. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~Sequence`): An iterable of hypothesis corpus - ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus As output of ``forward`` and ``compute`` the metric returns the following output: - ``chrf`` (:class:`~torch.Tensor`): If `return_sentence_level_score=True` return a list of sentence-level chrF/chrF++ scores, else return a corpus-level chrF/chrF++ score Args: n_char_order: A character n-gram order. If ``n_char_order=6``, the metrics refers to the official chrF/chrF++. n_word_order: A word n-gram order. If ``n_word_order=2``, the metric refers to the official chrF++. If ``n_word_order=0``, the metric is equivalent to the original ChrF. beta: parameter determining an importance of recall w.r.t. precision. If ``beta=1``, their importance is equal. lowercase: An indication whether to enable case-insesitivity. whitespace: An indication whether keep whitespaces during n-gram extraction. return_sentence_level_score: An indication whether a sentence-level chrF/chrF++ score to be returned. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``n_char_order`` is not an integer greater than or equal to 1. ValueError: If ``n_word_order`` is not an integer greater than or equal to 0. ValueError: If ``beta`` is smaller than 0. Example: >>> from torchmetrics import CHRFScore >>> preds = ['the cat is on the mat'] >>> target = [['there is a cat on the mat', 'a cat is on the mat']] >>> chrf = CHRFScore() >>> chrf(preds, target) tensor(0.8640) """ is_differentiable: bool = False higher_is_better: bool = True full_state_update: bool = True sentence_chrf_score: Optional[List[Tensor]] = None def __init__( self, n_char_order: int = 6, n_word_order: int = 2, beta: float = 2.0, lowercase: bool = False, whitespace: bool = False, return_sentence_level_score: bool = False, **kwargs: Any, ): super().__init__(**kwargs) if not isinstance(n_char_order, int) or n_char_order < 1: raise ValueError("Expected argument `n_char_order` to be an integer greater than or equal to 1.") self.n_char_order = n_char_order if not isinstance(n_word_order, int) or n_word_order < 0: raise ValueError("Expected argument `n_word_order` to be an integer greater than or equal to 0.") self.n_word_order = n_word_order if beta < 0: raise ValueError("Expected argument `beta` to be greater than 0.") self.beta = beta self.lowercase = lowercase self.whitespace = whitespace self.return_sentence_level_score = return_sentence_level_score self.n_order = float(n_char_order + n_word_order) # Adding state dynamically for (n_gram_level, n_gram_order), text in self._get_text_n_gram_iterator(): for n in range(1, n_gram_order + 1): state_name = self._get_state_name(text, n_gram_level, n) self.add_state(state_name, tensor(0.0), dist_reduce_fx="sum") if self.return_sentence_level_score: self.add_state("sentence_chrf_score", [], dist_reduce_fx="cat") def update(self, preds: Sequence[str], target: Sequence[Sequence[str]]) -> None: """Update state with predictions and targets.""" n_grams_dicts_tuple = _chrf_score_update( preds, target, *self._convert_states_to_dicts(), self.n_char_order, self.n_word_order, self.n_order, self.beta, self.lowercase, self.whitespace, self.sentence_chrf_score if self.return_sentence_level_score else None, ) self._update_states_from_dicts(n_grams_dicts_tuple[:-1]) if self.sentence_chrf_score is not None: self.sentence_chrf_score = n_grams_dicts_tuple[-1] def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]: """Calculate chrF/chrF++ score.""" if self.sentence_chrf_score is not None: return ( _chrf_score_compute(*self._convert_states_to_dicts(), self.n_order, self.beta), torch.cat(self.sentence_chrf_score), ) return _chrf_score_compute(*self._convert_states_to_dicts(), self.n_order, self.beta) def _convert_states_to_dicts(self) -> _DICT_STATES_TYPES: """Convert global metric states to the n-gram dictionaries to be passed in ``_chrf_score_update``.""" n_grams_dicts: Dict[str, Dict[int, Tensor]] = { name: n_gram_dict for name, n_gram_dict in zip( _DICT_STATES_NAMES, _prepare_n_grams_dicts(self.n_char_order, self.n_word_order) ) } for (n_gram_level, n_gram_order), text in self._get_text_n_gram_iterator(): for n in range(1, n_gram_order + 1): dict_name = self._get_dict_name(text, n_gram_level) state_name = self._get_state_name(text, n_gram_level, n) n_grams_dicts[dict_name][n] = getattr(self, state_name) return tuple(n_grams_dicts.values()) # type: ignore def _update_states_from_dicts(self, n_grams_dicts_tuple: _DICT_STATES_TYPES) -> None: """Update global metric states based on the n-gram dictionaries calculated on the current batch.""" n_grams_dicts = {name: n_gram_dict for name, n_gram_dict, in zip(_DICT_STATES_NAMES, n_grams_dicts_tuple)} for (n_gram_level, n_gram_order), text in self._get_text_n_gram_iterator(): for n in range(1, n_gram_order + 1): dict_name = self._get_dict_name(text, n_gram_level) state_name = self._get_state_name(text, n_gram_level, n) setattr(self, state_name, n_grams_dicts[dict_name][n]) @staticmethod def _get_dict_name(text: str, n_gram_level: str) -> str: """Return a dictionary name w.r.t input args.""" return f"total_{text}_{n_gram_level}_n_grams" @staticmethod def _get_state_name(text: str, n_gram_level: str, n: int) -> str: """Return a metric state name w.r.t input args.""" return f"total_{text}_{n_gram_level}_{n}_grams" def _get_text_n_gram_iterator(self) -> Iterator[Tuple[Tuple[str, int], str]]: """Get iterator over char/word and reference/hypothesis/matching n-gram level.""" return itertools.product(zip(_N_GRAM_LEVELS, [self.n_char_order, self.n_word_order]), _TEXT_LEVELS)