# Source code for pytorch_lightning.metrics.classification.auroc

```
# Copyright The PyTorch Lightning team.
#
# 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 distutils.version import LooseVersion
from typing import Any, Callable, Optional
import torch
from pytorch_lightning.metrics.functional.auroc import _auroc_compute, _auroc_update
from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.utilities import rank_zero_warn
[docs]class AUROC(Metric):
r"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Further_interpretations>`_.
Works for both binary, multilabel and multiclass problems. In the case of
multiclass, the values will be calculated based on a one-vs-the-rest approach.
Forward accepts
- ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass) tensor
with probabilities, where C is the number of classes.
- ``target`` (long tensor): ``(N, ...)`` or ``(N, C, ...)`` with integer labels
For non-binary input, if the ``preds`` and ``target`` tensor have the same
size the input will be interpretated as multilabel and if ``preds`` have one
dimension more than the ``target`` tensor the input will be interpretated as
multiclass.
Args:
num_classes: integer with number of classes. Not nessesary to provide
for binary problems.
pos_label: integer determining the positive class. Default is ``None``
which for binary problem is translate to 1. For multiclass problems
this argument should not be set as we iteratively change it in the
range [0,num_classes-1]
average:
- ``'macro'`` computes metric for each class and uniformly averages them
- ``'weighted'`` computes metric for each class and does a weighted-average,
where each class is weighted by their support (accounts for class imbalance)
- ``None`` computes and returns the metric per class
max_fpr:
If not ``None``, calculates standardized partial AUC over the
range [0, max_fpr]. Should be a float between 0 and 1.
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather
Example (binary case):
>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
>>> target = torch.tensor([0, 0, 1, 1, 1])
>>> auroc = AUROC(pos_label=1)
>>> auroc(preds, target)
tensor(0.5000)
Example (multiclass case):
>>> preds = torch.tensor([[0.90, 0.05, 0.05],
... [0.05, 0.90, 0.05],
... [0.05, 0.05, 0.90],
... [0.85, 0.05, 0.10],
... [0.10, 0.10, 0.80]])
>>> target = torch.tensor([0, 1, 1, 2, 2])
>>> auroc = AUROC(num_classes=3)
>>> auroc(preds, target)
tensor(0.7778)
"""
def __init__(
self,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
average: Optional[str] = 'macro',
max_fpr: Optional[float] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
):
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.num_classes = num_classes
self.pos_label = pos_label
self.average = average
self.max_fpr = max_fpr
allowed_average = (None, 'macro', 'weighted')
if self.average not in allowed_average:
raise ValueError(
f'Argument `average` expected to be one of the following: {allowed_average} but got {average}'
)
if self.max_fpr is not None:
if (not isinstance(max_fpr, float) and 0 < max_fpr <= 1):
raise ValueError(f"`max_fpr` should be a float in range (0, 1], got: {max_fpr}")
if LooseVersion(torch.__version__) < LooseVersion('1.6.0'):
raise RuntimeError(
'`max_fpr` argument requires `torch.bucketize` which is not available below PyTorch version 1.6'
)
self.mode = None
self.add_state("preds", default=[], dist_reduce_fx=None)
self.add_state("target", default=[], dist_reduce_fx=None)
rank_zero_warn(
'Metric `AUROC` will save all targets and predictions in buffer.'
' For large datasets this may lead to large memory footprint.'
)
[docs] def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets.
Args:
preds: Predictions from model (probabilities, or labels)
target: Ground truth labels
"""
preds, target, mode = _auroc_update(preds, target)
self.preds.append(preds)
self.target.append(target)
if self.mode is not None and self.mode != mode:
raise ValueError(
'The mode of data (binary, multi-label, multi-class) should be constant, but changed'
f' between batches from {self.mode} to {mode}'
)
self.mode = mode
[docs] def compute(self) -> torch.Tensor:
"""
Computes AUROC based on inputs passed in to ``update`` previously.
"""
preds = torch.cat(self.preds, dim=0)
target = torch.cat(self.target, dim=0)
return _auroc_compute(
preds,
target,
self.mode,
self.num_classes,
self.pos_label,
self.average,
self.max_fpr,
)
```