# Source code for ignite.contrib.metrics.precision_recall_curve

from typing import Any, Callable, Tuple

import torch

from ignite.metrics import EpochMetric

def precision_recall_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> Tuple[Any, Any, Any]:
try:
from sklearn.metrics import precision_recall_curve
except ImportError:
raise RuntimeError("This contrib module requires sklearn to be installed.")

y_true = y_targets.numpy()
y_pred = y_preds.numpy()
return precision_recall_curve(y_true, y_pred)

[docs]class PrecisionRecallCurve(EpochMetric): """Compute precision-recall pairs for different probability thresholds for binary classification task by accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.precision_recall_curve <https://scikit-learn.org/stable/modules/generated/ sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve>_ . 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, precision_recall_curve <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html #sklearn.metrics.precision_recall_curve>_ 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. PrecisionRecallCurve 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.sigmoid(y_pred) return y_pred, y roc_auc = PrecisionRecallCurve(activated_output_transform) """ def __init__(self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False) -> None: super(PrecisionRecallCurve, self).__init__( precision_recall_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn )