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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 List, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor
from torchmetrics.functional.classification.precision_recall_curve import (
_binary_clf_curve,
_precision_recall_curve_update,
)
from torchmetrics.utilities import rank_zero_warn
def _roc_update(
preds: Tensor,
target: Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
) -> Tuple[Tensor, Tensor, int, Optional[int]]:
"""Updates and returns variables required to compute the Receiver Operating Characteristic.
Args:
preds: Predicted tensor
target: Ground truth tensor
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 translate to 1. For multiclass problems
this argument should not be set as we iteratively change it in the
range [0,num_classes-1]
"""
return _precision_recall_curve_update(preds, target, num_classes, pos_label)
def _roc_compute_single_class(
preds: Tensor,
target: Tensor,
pos_label: int,
sample_weights: Optional[Sequence] = None,
) -> Tuple[Tensor, Tensor, Tensor]:
"""Computes Receiver Operating Characteristic for single class inputs. Returns tensor with false positive
rates, tensor with true positive rates, tensor with thresholds used for computing false- and true postive
rates.
Args:
preds: Predicted tensor
target: Ground truth tensor
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_weights: sample weights for each data point
"""
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:
rank_zero_warn(
"No negative samples in targets, false positive value should be meaningless."
" Returning zero tensor in false positive score",
UserWarning,
)
fpr = torch.zeros_like(thresholds)
else:
fpr = fps / fps[-1]
if tps[-1] <= 0:
rank_zero_warn(
"No positive samples in targets, true positive value should be meaningless."
" Returning zero tensor in true positive score",
UserWarning,
)
tpr = torch.zeros_like(thresholds)
else:
tpr = tps / tps[-1]
return fpr, tpr, thresholds
def _roc_compute_multi_class(
preds: Tensor,
target: Tensor,
num_classes: int,
sample_weights: Optional[Sequence] = None,
) -> Tuple[List[Tensor], List[Tensor], List[Tensor]]:
"""Computes Receiver Operating Characteristic for multi class inputs. Returns tensor with false positive rates,
tensor with true positive rates, tensor with thresholds used for computing false- and true postive rates.
Args:
preds: Predicted tensor
target: Ground truth tensor
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_weights: sample weights for each data point
"""
fpr, tpr, thresholds = [], [], []
for cls in range(num_classes):
if preds.shape == target.shape:
target_cls = target[:, cls]
pos_label = 1
else:
target_cls = target
pos_label = cls
res = roc(
preds=preds[:, cls],
target=target_cls,
num_classes=1,
pos_label=pos_label,
sample_weights=sample_weights,
)
fpr.append(res[0])
tpr.append(res[1])
thresholds.append(res[2])
return fpr, tpr, thresholds
def _roc_compute(
preds: Tensor,
target: Tensor,
num_classes: int,
pos_label: Optional[int] = None,
sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
"""Computes Receiver Operating Characteristic based on the number of classes.
Args:
preds: Predicted tensor
target: Ground truth tensor
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 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_weights: sample weights for each data point
Example:
>>> # binary case
>>> preds = torch.tensor([0, 1, 2, 3])
>>> target = torch.tensor([0, 1, 1, 1])
>>> pos_label = 1
>>> preds, target, num_classes, pos_label = _roc_update(preds, target, pos_label=pos_label)
>>> fpr, tpr, thresholds = _roc_compute(preds, target, num_classes, pos_label)
>>> 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])
>>> # multiclass case
>>> preds = 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])
>>> num_classes = 4
>>> preds, target, num_classes, pos_label = _roc_update(preds, target, num_classes)
>>> fpr, tpr, thresholds = _roc_compute(preds, target, num_classes)
>>> 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])]
"""
with torch.no_grad():
if num_classes == 1 and preds.ndim == 1: # binary
if pos_label is None:
pos_label = 1
return _roc_compute_single_class(preds, target, pos_label, sample_weights)
return _roc_compute_multi_class(preds, target, num_classes, sample_weights)
def roc(
preds: Tensor,
target: Tensor,
num_classes: Optional[int] = None,
pos_label: Optional[int] = None,
sample_weights: Optional[Sequence] = None,
) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
"""Computes the Receiver Operating Characteristic (ROC). Works with both binary, multiclass and multilabel
input.
.. 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 an warning.
Args:
preds: predictions from model (logits or probabilities)
target: ground truth values
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 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_weights: sample weights for each data point
Returns:
3-element tuple containing
fpr:
tensor with false positive rates.
If multiclass or multilabel, this is a list of such tensors, one for each class/label.
tpr:
tensor with true positive rates.
If multiclass or multilabel, this is a list of such tensors, one for each class/label.
thresholds:
tensor with thresholds used for computing false- and true postive rates
If multiclass or multilabel, this is a list of such tensors, one for each class/label.
Example (binary case):
>>> from torchmetrics.functional import roc
>>> 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):
>>> from torchmetrics.functional import roc
>>> 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
[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):
>>> from torchmetrics.functional import roc
>>> 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]])
>>> fpr, tpr, thresholds = roc(pred, target, num_classes=3, pos_label=1)
>>> 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])]
"""
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)