Source code for monai.handlers.roc_auc

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from typing import Any, Callable, Union

import torch

from monai.handlers.utils import evenly_divisible_all_gather
from monai.metrics import compute_roc_auc
from monai.utils import Average, exact_version, optional_import

idist, _ = optional_import("ignite", "0.4.4", exact_version, "distributed")
EpochMetric, _ = optional_import("ignite.metrics", "0.4.4", exact_version, "EpochMetric")

[docs]class ROCAUC(EpochMetric): # type: ignore[valid-type, misc] # due to optional_import """ Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). accumulating predictions and the ground-truth during an epoch and applying `compute_roc_auc`. Args: average: {``"macro"``, ``"weighted"``, ``"micro"``, ``"none"``} Type of averaging performed if not binary classification. Defaults to ``"macro"``. - ``"macro"``: calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. - ``"weighted"``: calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). - ``"micro"``: calculate metrics globally by considering each element of the label indicator matrix as a label. - ``"none"``: the scores for each class are returned. output_transform: a callable that is used to transform the :class:`~ignite.engine.Engine` `process_function` 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. device: device specification in case of distributed computation usage. Note: ROCAUC expects y to be comprised of 0's and 1's. y_pred must either be probability estimates or confidence values. """ def __init__( self, average: Union[Average, str] = Average.MACRO, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = "cpu", ) -> None: def _compute_fn(pred, label): return compute_roc_auc( y_pred=pred, y=label, average=Average(average), ) self._is_reduced: bool = False super().__init__( compute_fn=_compute_fn, output_transform=output_transform, check_compute_fn=False, device=device, )
[docs] def compute(self) -> Any: _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 = evenly_divisible_all_gather(_prediction_tensor) _target_tensor = evenly_divisible_all_gather(_target_tensor) self._is_reduced = True result: torch.Tensor = torch.zeros(1) if idist.get_rank() == 0: # Run compute_fn on zero rank only result = self.compute_fn(_prediction_tensor, _target_tensor) if ws > 1: # broadcast result to all processes result = idist.broadcast(result, src=0) return result.item() if torch.is_tensor(result) else result