# 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 Optional, Sequence, Tuple, List, Union
import torch
from pytorch_lightning.metrics.functional.precision_recall_curve import (
_precision_recall_curve_update,
_binary_clf_curve
)
def _roc_update(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor, int, int]:
return _precision_recall_curve_update(preds, target, num_classes, pos_label)
def _roc_compute(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: int,
pos_label: int,
sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]]:
if num_classes == 1:
fps, tps, thresholds = _binary_clf_curve(
preds=preds,
target=target,
sample_weights=sample_weights,
pos_label=pos_label
)
# Add an extra threshold position
# to make sure that the curve starts at (0, 0)
tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps])
fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps])
thresholds = torch.cat([thresholds[0][None] + 1, thresholds])
if fps[-1] <= 0:
raise ValueError("No negative samples in targets, false positive value should be meaningless")
fpr = fps / fps[-1]
if tps[-1] <= 0:
raise ValueError("No positive samples in targets, true positive value should be meaningless")
tpr = tps / tps[-1]
return fpr, tpr, thresholds
# Recursively call per class
fpr, tpr, thresholds = [], [], []
for c in range(num_classes):
preds_c = preds[:, c]
res = roc(
preds=preds_c,
target=target,
num_classes=1,
pos_label=c,
sample_weights=sample_weights,
)
fpr.append(res[0])
tpr.append(res[1])
thresholds.append(res[2])
return fpr, tpr, thresholds
[docs]def roc(
preds: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
Tuple[List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]]:
"""
Computes the Receiver Operating Characteristic (ROC).
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]
sample_weight: sample weights for each data point
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 postive rates
Example (binary case):
>>> pred = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> fpr, tpr, thresholds = roc(pred, target, pos_label=1)
>>> 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])
>>> fpr, tpr, thresholds = roc(pred, target, num_classes=4)
>>> 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 # doctest: +NORMALIZE_WHITESPACE
[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])]
"""
preds, target, num_classes, pos_label = _roc_update(preds, target, num_classes, pos_label)
return _roc_compute(preds, target, num_classes, pos_label, sample_weights)
