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eed.py
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eed.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.
from typing import Any, List, Optional, Sequence, Tuple, Union
from torch import Tensor, stack
from typing_extensions import Literal
from torchmetrics.functional.text.eed import _eed_compute, _eed_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["ExtendedEditDistance.plot"]
class ExtendedEditDistance(Metric):
"""Compute extended edit distance score (`ExtendedEditDistance`_) for strings or list of strings.
The metric utilises the Levenshtein distance and extends it by adding a jump operation.
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:
- ``eed`` (:class:`~torch.Tensor`): A tensor with the extended edit distance score
Args:
language: Language used in sentences. Only supports English (en) and Japanese (ja) for now.
return_sentence_level_score: An indication of whether sentence-level EED score is to be returned
alpha: optimal jump penalty, penalty for jumps between characters
rho: coverage cost, penalty for repetition of characters
deletion: penalty for deletion of character
insertion: penalty for insertion or substitution of character
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics.text import ExtendedEditDistance
>>> preds = ["this is the prediction", "here is an other sample"]
>>> target = ["this is the reference", "here is another one"]
>>> eed = ExtendedEditDistance()
>>> eed(preds=preds, target=target)
tensor(0.3078)
"""
higher_is_better: bool = False
is_differentiable: bool = False
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
sentence_eed: List[Tensor]
def __init__(
self,
language: Literal["en", "ja"] = "en",
return_sentence_level_score: bool = False,
alpha: float = 2.0,
rho: float = 0.3,
deletion: float = 0.2,
insertion: float = 1.0,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if language not in ("en", "ja"):
raise ValueError(f"Expected argument `language` to either be `en` or `ja` but got {language}")
self.language: Literal["en", "ja"] = language
self.return_sentence_level_score = return_sentence_level_score
# input validation for parameters
for param_name, param in zip(["alpha", "rho", "deletion", "insertion"], [alpha, rho, deletion, insertion]):
if not isinstance(param, float) or isinstance(param, float) and param < 0:
raise ValueError(f"Parameter `{param_name}` is expected to be a non-negative float.")
self.alpha = alpha
self.rho = rho
self.deletion = deletion
self.insertion = insertion
self.add_state("sentence_eed", [], dist_reduce_fx="cat")
def update(
self,
preds: Union[str, Sequence[str]],
target: Sequence[Union[str, Sequence[str]]],
) -> None:
"""Update state with predictions and targets."""
self.sentence_eed = _eed_update(
preds,
target,
self.language,
self.alpha,
self.rho,
self.deletion,
self.insertion,
self.sentence_eed,
)
def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:
"""Calculate extended edit distance score."""
average = _eed_compute(self.sentence_eed)
if self.return_sentence_level_score:
return average, stack(self.sentence_eed)
return average
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 ExtendedEditDistance
>>> metric = ExtendedEditDistance()
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torchmetrics.text import ExtendedEditDistance
>>> metric = ExtendedEditDistance()
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)