Source code for ignite.contrib.metrics.average_precision

from typing import Callable

import torch

from ignite.metrics import EpochMetric

def average_precision_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:
        from sklearn.metrics import average_precision_score
    except ImportError:
        raise RuntimeError("This contrib module requires sklearn to be installed.")

    y_true = y_targets.numpy()
    y_pred = y_preds.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. 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) """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(AveragePrecision, self).__init__( average_precision_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn )

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