Source code for ignite.contrib.metrics.precision_recall_curve

from typing import Any, Callable, cast, Tuple, Union

import torch

import ignite.distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics import EpochMetric

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

    y_true = y_targets.cpu().numpy()
    y_pred = y_preds.cpu().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 < 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 < #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. Note: 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 sigmoid_output_transform(output): y_pred, y = output y_pred = torch.sigmoid(y_pred) return y_pred, y avg_precision = PrecisionRecallCurve(sigmoid_output_transform) Examples: .. include:: defaults.rst :start-after: :orphan: .. testcode:: y_pred = torch.tensor([0.0474, 0.5987, 0.7109, 0.9997]) y_true = torch.tensor([0, 0, 1, 1]) prec_recall_curve = PrecisionRecallCurve() prec_recall_curve.attach(default_evaluator, 'prec_recall_curve') state =[[y_pred, y_true]]) print("Precision", [round(i, 4) for i in state.metrics['prec_recall_curve'][0].tolist()]) print("Recall", [round(i, 4) for i in state.metrics['prec_recall_curve'][1].tolist()]) print("Thresholds", [round(i, 4) for i in state.metrics['prec_recall_curve'][2].tolist()]) .. testoutput:: Precision [1.0, 1.0, 1.0] Recall [1.0, 0.5, 0.0] Thresholds [0.7109, 0.9997] """ def __init__( self, output_transform: Callable = lambda x: x, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ) -> None: super(PrecisionRecallCurve, self).__init__( precision_recall_curve_compute_fn, output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, )
[docs] def compute(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: if len(self._predictions) < 1 or len(self._targets) < 1: raise NotComputableError("PrecisionRecallCurve must have at least one example before it can be computed.") _prediction_tensor =, dim=0) _target_tensor =, dim=0) ws = idist.get_world_size() if ws > 1 and not self._is_reduced: # All gather across all processes _prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor)) _target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor)) self._is_reduced = True if idist.get_rank() == 0: # Run compute_fn on zero rank only precision, recall, thresholds = self.compute_fn(_prediction_tensor, _target_tensor) precision = torch.tensor(precision) recall = torch.tensor(recall) # thresholds can have negative strides, not compatible with torch tensors # thresholds = torch.tensor(thresholds.copy()) else: precision, recall, thresholds = None, None, None if ws > 1: # broadcast result to all processes precision = idist.broadcast(precision, src=0, safe_mode=True) recall = idist.broadcast(recall, src=0, safe_mode=True) thresholds = idist.broadcast(thresholds, src=0, safe_mode=True) return precision, recall, thresholds

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