# Source code for monai.handlers.roc_auc

```
# Copyright 2020 - 2021 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 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 = torch.cat(self._predictions, dim=0)
_target_tensor = torch.cat(self._targets, 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
```