# Copyright 2020 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 Callable, Optional, Sequence, Union, List
import torch
from monai.metrics import compute_roc_auc
from monai.utils import exact_version, optional_import, Average
Metric, _ = optional_import("ignite.metrics", "0.3.0", exact_version, "Metric")
[docs]class ROCAUC(Metric):
"""
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.
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,
average: Union[Average, str] = Average.MACRO,
output_transform: Callable = lambda x: x,
device: Optional[Union[str, torch.device]] = None,
):
super().__init__(output_transform, device=device)
self.to_onehot_y = to_onehot_y
self.softmax = softmax
self.average: Average = Average(average)
[docs] def reset(self) -> None:
self._predictions: List[float] = []
self._targets: List[float] = []
[docs] def update(self, output: Sequence[torch.Tensor]):
y_pred, y = output
if y_pred.ndimension() not in (1, 2):
raise ValueError("predictions should be of shape (batch_size, n_classes) or (batch_size, ).")
if y.ndimension() not in (1, 2):
raise ValueError("targets should be of shape (batch_size, n_classes) or (batch_size, ).")
self._predictions.append(y_pred.clone())
self._targets.append(y.clone())
[docs] def compute(self):
_prediction_tensor = torch.cat(self._predictions, dim=0)
_target_tensor = torch.cat(self._targets, dim=0)
return compute_roc_auc(_prediction_tensor, _target_tensor, self.to_onehot_y, self.softmax, self.average)