Source code for ignite.metrics.average_precision

from typing import Callable, Union

import torch

from ignite.metrics.epoch_metric import EpochMetric

def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:
    from sklearn.metrics import average_precision_score

    y_true = y_targets.cpu().numpy()
    y_pred = y_preds.cpu().numpy()
    return average_precision_score(y_true, y_pred)

[docs]class AveragePrecision(EpochMetric): """Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.average_precision_score < sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score>`_ . Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. check_compute_fn: Default False. If True, `average_precision_score < #sklearn.metrics.average_precision_score>`_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. device: optional device specification for internal storage. Note: AveragePrecision expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below: .. code-block:: python def activated_output_transform(output): y_pred, y = output y_pred = torch.softmax(y_pred, dim=1) return y_pred, y avg_precision = AveragePrecision(activated_output_transform) Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: y_pred = torch.tensor([[0.79, 0.21], [0.30, 0.70], [0.46, 0.54], [0.16, 0.84]]) y_true = torch.tensor([[1, 1], [1, 1], [0, 1], [0, 1]]) avg_precision = AveragePrecision() avg_precision.attach(default_evaluator, 'average_precision') state =[[y_pred, y_true]]) print(state.metrics['average_precision']) .. testoutput:: 0.9166... """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): try: from sklearn.metrics import average_precision_score # noqa: F401 except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") super(AveragePrecision, self).__init__( average_precision_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, )

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