-
Notifications
You must be signed in to change notification settings - Fork 388
/
roc.py
145 lines (123 loc) · 5.42 KB
/
roc.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
# 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 torchmetrics.functional.classification.precision_recall_curve import (
_binary_clf_curve,
_precision_recall_curve_update,
)
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
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:
preds: predictions from model (logits or probabilities)
target: ground truth values
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_weights: 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)