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bleu.py
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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
import torch
from torch import Tensor, tensor
from torchmetrics import Metric
from torchmetrics.functional.text.bleu import _bleu_score_compute, _bleu_score_update, _tokenize_fn
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["BLEUScore.plot"]
class BLEUScore(Metric):
"""Calculate `BLEU score`_ of machine translated text with one or more references.
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 ``update`` the metric returns the following output:
- ``bleu`` (:class:`~torch.Tensor`): A tensor with the BLEU Score
Args:
n_gram: Gram value ranged from 1 to 4
smooth: Whether or not to apply smoothing, see `Machine Translation Evolution`_
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 a length of a list of weights is not ``None`` and not equal to ``n_gram``.
Example:
>>> from torchmetrics.text import BLEUScore
>>> preds = ['the cat is on the mat']
>>> target = [['there is a cat on the mat', 'a cat is on the mat']]
>>> bleu = BLEUScore()
>>> bleu(preds, target)
tensor(0.7598)
"""
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
preds_len: Tensor
target_len: Tensor
numerator: Tensor
denominator: Tensor
def __init__(
self,
n_gram: int = 4,
smooth: bool = False,
weights: Optional[Sequence[float]] = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.n_gram = n_gram
self.smooth = smooth
if weights is not None and len(weights) != n_gram:
raise ValueError(f"List of weights has different weights than `n_gram`: {len(weights)} != {n_gram}")
self.weights = weights if weights is not None else [1.0 / n_gram] * n_gram
self.add_state("preds_len", tensor(0.0), dist_reduce_fx="sum")
self.add_state("target_len", tensor(0.0), dist_reduce_fx="sum")
self.add_state("numerator", torch.zeros(self.n_gram), dist_reduce_fx="sum")
self.add_state("denominator", torch.zeros(self.n_gram), dist_reduce_fx="sum")
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,
_tokenize_fn,
)
def compute(self) -> Tensor:
"""Calculate BLEU score."""
return _bleu_score_compute(
self.preds_len, self.target_len, self.numerator, self.denominator, self.n_gram, self.weights, self.smooth
)
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 BLEUScore
>>> metric = BLEUScore()
>>> 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 BLEUScore
>>> metric = BLEUScore()
>>> 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)