-
Notifications
You must be signed in to change notification settings - Fork 388
/
r2.py
152 lines (124 loc) · 5.74 KB
/
r2.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
146
147
148
149
150
151
152
# 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, Callable, Optional
import torch
from torch import Tensor, tensor
from torchmetrics.functional.regression.r2 import _r2_score_compute, _r2_score_update
from torchmetrics.metric import Metric
class R2Score(Metric):
r"""
Computes r2 score also known as `coefficient of determination`_:
.. math:: R^2 = 1 - \frac{SS_{res}}{SS_{tot}}
where :math:`SS_{res}=\sum_i (y_i - f(x_i))^2` is the sum of residual squares, and
:math:`SS_{tot}=\sum_i (y_i - \bar{y})^2` is total sum of squares. Can also calculate
adjusted r2 score given by
.. math:: R^2_{adj} = 1 - \frac{(1-R^2)(n-1)}{n-k-1}
where the parameter :math:`k` (the number of independent regressors) should
be provided as the `adjusted` argument.
Forward accepts
- ``preds`` (float tensor): ``(N,)`` or ``(N, M)`` (multioutput)
- ``target`` (float tensor): ``(N,)`` or ``(N, M)`` (multioutput)
In the case of multioutput, as default the variances will be uniformly
averaged over the additional dimensions. Please see argument `multioutput`
for changing this behavior.
Args:
num_outputs:
Number of outputs in multioutput setting (default is 1)
adjusted:
number of independent regressors for calculating adjusted r2 score.
Default 0 (standard r2 score).
multioutput:
Defines aggregation in the case of multiple output scores. Can be one
of the following strings (default is ``'uniform_average'``.):
* ``'raw_values'`` returns full set of scores
* ``'uniform_average'`` scores are uniformly averaged
* ``'variance_weighted'`` scores are weighted by their individual variances
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step. default: False
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
Raises:
ValueError:
If ``adjusted`` parameter is not an integer larger or equal to 0.
ValueError:
If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``.
Example:
>>> from torchmetrics import R2Score
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> r2score = R2Score()
>>> r2score(preds, target)
tensor(0.9486)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> r2score = R2Score(num_outputs=2, multioutput='raw_values')
>>> r2score(preds, target)
tensor([0.9654, 0.9082])
"""
sum_squared_error: Tensor
sum_error: Tensor
residual: Tensor
total: Tensor
def __init__(
self,
num_outputs: int = 1,
adjusted: int = 0,
multioutput: str = "uniform_average",
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.num_outputs = num_outputs
if adjusted < 0 or not isinstance(adjusted, int):
raise ValueError("`adjusted` parameter should be an integer larger or equal to 0.")
self.adjusted = adjusted
allowed_multioutput = ("raw_values", "uniform_average", "variance_weighted")
if multioutput not in allowed_multioutput:
raise ValueError(
f"Invalid input to argument `multioutput`. Choose one of the following: {allowed_multioutput}"
)
self.multioutput = multioutput
self.add_state("sum_squared_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
self.add_state("sum_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
self.add_state("residual", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
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
"""
sum_squared_error, sum_error, residual, total = _r2_score_update(preds, target)
self.sum_squared_error += sum_squared_error
self.sum_error += sum_error
self.residual += residual
self.total += total
def compute(self) -> Tensor:
"""Computes r2 score over the metric states."""
return _r2_score_compute(
self.sum_squared_error, self.sum_error, self.residual, self.total, self.adjusted, self.multioutput
)
@property
def is_differentiable(self) -> bool:
return True