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sacre_bleu.py
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sacre_bleu.py
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# Copyright The 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: 2020-07-18
# Link: https://pytorch.org/text/_modules/torchtext/data/metrics.html#bleu_score
from typing import Any, Optional, Sequence, Union
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.text.bleu import _bleu_score_update
from torchmetrics.functional.text.sacre_bleu import _SacreBLEUTokenizer
from torchmetrics.text.bleu import BLEUScore
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _REGEX_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["SacreBLEUScore.plot"]
AVAILABLE_TOKENIZERS = ("none", "13a", "zh", "intl", "char")
class SacreBLEUScore(BLEUScore):
"""Calculate `BLEU score`_ of machine translated text with one or more references.
This implementation follows the behaviour of `SacreBLEU`_. The SacreBLEU implementation differs from the NLTK BLEU
implementation in tokenization techniques.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~Sequence`): An iterable of machine translated corpus
- ``target`` (:class:`~Sequence`): An iterable of iterables of reference corpus
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``sacre_bleu`` (:class:`~torch.Tensor`): A tensor with the SacreBLEU Score
Args:
n_gram: Gram value ranged from 1 to 4
smooth: Whether to apply smoothing, see `SacreBLEU`_
tokenize: Tokenization technique to be used.
Supported tokenization: ``['none', '13a', 'zh', 'intl', 'char']``
lowercase: If ``True``, BLEU score over lowercased text is calculated.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
weights:
Weights used for unigrams, bigrams, etc. to calculate BLEU score.
If not provided, uniform weights are used.
Raises:
ValueError:
If ``tokenize`` not one of 'none', '13a', 'zh', 'intl' or 'char'
ValueError:
If ``tokenize`` is set to 'intl' and `regex` is not installed
ValueError:
If a length of a list of weights is not ``None`` and not equal to ``n_gram``.
Example:
>>> from torchmetrics.text import SacreBLEUScore
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> sacre_bleu = SacreBLEUScore()
>>> sacre_bleu(preds, target)
tensor(0.7598)
Additional References:
- Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence
and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och `Machine Translation Evolution`_
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = True
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
def __init__(
self,
n_gram: int = 4,
smooth: bool = False,
tokenize: Literal["none", "13a", "zh", "intl", "char"] = "13a",
lowercase: bool = False,
weights: Optional[Sequence[float]] = None,
**kwargs: Any,
) -> None:
super().__init__(n_gram=n_gram, smooth=smooth, weights=weights, **kwargs)
if tokenize not in AVAILABLE_TOKENIZERS:
raise ValueError(f"Argument `tokenize` expected to be one of {AVAILABLE_TOKENIZERS} but got {tokenize}.")
if tokenize == "intl" and not _REGEX_AVAILABLE:
raise ModuleNotFoundError(
"`'intl'` tokenization requires that `regex` is installed."
" Use `pip install regex` or `pip install torchmetrics[text]`."
)
self.tokenizer = _SacreBLEUTokenizer(tokenize, lowercase)
def update(self, preds: Sequence[str], target: Sequence[Sequence[str]]) -> None:
"""Update state with predictions and targets."""
self.preds_len, self.target_len = _bleu_score_update(
preds,
target,
self.numerator,
self.denominator,
self.preds_len,
self.target_len,
self.n_gram,
self.tokenizer,
)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> from torchmetrics.text import SacreBLEUScore
>>> metric = SacreBLEUScore()
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torchmetrics.text import SacreBLEUScore
>>> metric = SacreBLEUScore()
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)