# Source code for monai.handlers.roc_auc

# Copyright 2020 - 2021 MONAI Consortium
# you may not use this file except in compliance with the License.
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# See the License for the specific language governing permissions and

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,
)