Source code for monai.handlers.roc_auc

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from __future__ import annotations

from import Callable

from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import ROCAUCMetric
from monai.utils import Average

[docs] class ROCAUC(IgniteMetricHandler): """ 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: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`. `engine.state` and `output_transform` inherit from the ignite concept:, explanation and usage example are in the tutorial: 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: Average | str = Average.MACRO, output_transform: Callable = lambda x: x) -> None: metric_fn = ROCAUCMetric(average=Average(average)) super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=False)