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