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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from collections.abc import Callable
from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import ROCAUCMetric
from monai.utils import Average
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:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
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)
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