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roc.py
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roc.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, List, Optional, Tuple, Union
import torch
from torch import Tensor
from torchmetrics.functional.classification.roc import _roc_compute, _roc_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
class ROC(Metric):
"""Computes the Receiver Operating Characteristic (ROC). Works for both binary, multiclass and multilabel
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/multilabel) tensor
with probabilities, where C is the number of classes/labels.
- ``target`` (long tensor): ``(N, ...)`` or ``(N, C, ...)`` with integer labels
.. note::
If either the positive class or negative class is completly missing in the target tensor,
the roc values are not well-defined in this case and a tensor of zeros will be returned (either fpr
or tpr depending on what class is missing) together with a warning.
Args:
num_classes: integer with number of classes for multi-label and multiclass problems.
Should be set to ``None`` for binary problems
pos_label: integer determining the positive class. Default is ``None`` which for binary problem is translated
to 1. For multiclass problems this argument should not be set as we iteratively change it in the range
``[0,num_classes-1]``
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (binary case):
>>> from torchmetrics import ROC
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> roc = ROC(pos_label=1)
>>> fpr, tpr, thresholds = roc(pred, target)
>>> fpr
tensor([0., 0., 0., 0., 1.])
>>> tpr
tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000])
>>> thresholds
tensor([4, 3, 2, 1, 0])
Example (multiclass case):
>>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05],
... [0.05, 0.75, 0.05, 0.05],
... [0.05, 0.05, 0.75, 0.05],
... [0.05, 0.05, 0.05, 0.75]])
>>> target = torch.tensor([0, 1, 3, 2])
>>> roc = ROC(num_classes=4)
>>> fpr, tpr, thresholds = roc(pred, target)
>>> fpr
[tensor([0., 0., 1.]), tensor([0., 0., 1.]), tensor([0.0000, 0.3333, 1.0000]), tensor([0.0000, 0.3333, 1.0000])]
>>> tpr
[tensor([0., 1., 1.]), tensor([0., 1., 1.]), tensor([0., 0., 1.]), tensor([0., 0., 1.])]
>>> thresholds
[tensor([1.7500, 0.7500, 0.0500]),
tensor([1.7500, 0.7500, 0.0500]),
tensor([1.7500, 0.7500, 0.0500]),
tensor([1.7500, 0.7500, 0.0500])]
Example (multilabel case):
>>> pred = torch.tensor([[0.8191, 0.3680, 0.1138],
... [0.3584, 0.7576, 0.1183],
... [0.2286, 0.3468, 0.1338],
... [0.8603, 0.0745, 0.1837]])
>>> target = torch.tensor([[1, 1, 0], [0, 1, 0], [0, 0, 0], [0, 1, 1]])
>>> roc = ROC(num_classes=3, pos_label=1)
>>> fpr, tpr, thresholds = roc(pred, target)
>>> fpr
[tensor([0.0000, 0.3333, 0.3333, 0.6667, 1.0000]),
tensor([0., 0., 0., 1., 1.]),
tensor([0.0000, 0.0000, 0.3333, 0.6667, 1.0000])]
>>> tpr
[tensor([0., 0., 1., 1., 1.]),
tensor([0.0000, 0.3333, 0.6667, 0.6667, 1.0000]),
tensor([0., 1., 1., 1., 1.])]
>>> thresholds
[tensor([1.8603, 0.8603, 0.8191, 0.3584, 0.2286]),
tensor([1.7576, 0.7576, 0.3680, 0.3468, 0.0745]),
tensor([1.1837, 0.1837, 0.1338, 0.1183, 0.1138])]
"""
is_differentiable: bool = False
higher_is_better: Optional[bool] = None
full_state_update: bool = False
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.num_classes = num_classes
self.pos_label = pos_label
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
rank_zero_warn(
"Metric `ROC` 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
target: Ground truth values
"""
preds, target, num_classes, pos_label = _roc_update(preds, target, self.num_classes, self.pos_label)
self.preds.append(preds)
self.target.append(target)
self.num_classes = num_classes
self.pos_label = pos_label
def compute(self) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
"""Compute the receiver operating characteristic.
Returns:
3-element tuple containing
fpr: tensor with false positive rates.
If multiclass, this is a list of such tensors, one for each class.
tpr: tensor with true positive rates.
If multiclass, this is a list of such tensors, one for each class.
thresholds:
thresholds used for computing false- and true-positive rates
"""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
if not self.num_classes:
raise ValueError(f"`num_classes` bas to be positive number, but got {self.num_classes}")
return _roc_compute(preds, target, self.num_classes, self.pos_label)