Source code for monai.handlers.roc_auc

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from typing import Callable, Optional, Union

import torch

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

EpochMetric, _ = optional_import("ignite.metrics", "0.4.2", 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: to_onehot_y: whether to convert `y` into the one-hot format. Defaults to False. softmax: whether to add softmax function to `y_pred` before computation. Defaults to False. other_act: callable function to replace `softmax` as activation layer if needed, Defaults to ``None``. for example: `other_act = lambda x: torch.log_softmax(x)`. 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, to_onehot_y: bool = False, softmax: bool = False, other_act: Optional[Callable] = None, average: Union[Average, str] = Average.MACRO, output_transform: Callable = lambda x: x, device: Optional[torch.device] = None, ) -> None: def _compute_fn(pred, label): return compute_roc_auc( y_pred=pred, y=label, to_onehot_y=to_onehot_y, softmax=softmax, other_act=other_act, average=Average(average), ) super().__init__( compute_fn=_compute_fn, output_transform=output_transform, check_compute_fn=False, device=device, )