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auc.py
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auc.py
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# 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 typing import Any, Dict, List, Optional
from torch import Tensor
from torchmetrics.functional.classification.auc import _auc_compute, _auc_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
class AUC(Metric):
r"""
Computes Area Under the Curve (AUC) using the trapezoidal rule
Forward accepts two input tensors that should be 1D and have the same number
of elements
Args:
reorder: AUC expects its first input to be sorted. If this is not the case,
setting this argument to ``True`` will use a stable sorting algorithm to
sort the input in descending order
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
"""
is_differentiable = False
x: List[Tensor]
y: List[Tensor]
def __init__(
self,
reorder: bool = False,
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(compute_on_step=compute_on_step, **kwargs)
self.reorder = reorder
self.add_state("x", default=[], dist_reduce_fx="cat")
self.add_state("y", default=[], dist_reduce_fx="cat")
rank_zero_warn(
"Metric `AUC` will save all targets and predictions in buffer."
" For large datasets this may lead to large memory footprint."
)
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model (probabilities, or labels)
target: Ground truth labels
"""
x, y = _auc_update(preds, target)
self.x.append(x)
self.y.append(y)
def compute(self) -> Tensor:
"""Computes AUC based on inputs passed in to ``update`` previously."""
x = dim_zero_cat(self.x)
y = dim_zero_cat(self.y)
return _auc_compute(x, y, reorder=self.reorder)