-
Notifications
You must be signed in to change notification settings - Fork 388
/
symmetric_mape.py
79 lines (62 loc) · 2.78 KB
/
symmetric_mape.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
# 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
from torch import Tensor, tensor
from torchmetrics.functional.regression.symmetric_mape import (
_symmetric_mean_absolute_percentage_error_compute,
_symmetric_mean_absolute_percentage_error_update,
)
from torchmetrics.metric import Metric
class SymmetricMeanAbsolutePercentageError(Metric):
r"""Computes symmetric mean absolute percentage error (`SMAPE`_).
.. math:: \text{SMAPE} = \frac{2}{n}\sum_1^n max(\frac{| y_i - \hat{y_i} |}{| y_i | + | \hat{y_i} |, \epsilon})
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
Args:
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Note:
The epsilon value is taken from `scikit-learn's implementation of SMAPE`_.
Note:
SMAPE output is a non-negative floating point between 0 and 1. Best result is 0.0 .
Example:
>>> from torchmetrics import SymmetricMeanAbsolutePercentageError
>>> target = tensor([1, 10, 1e6])
>>> preds = tensor([0.9, 15, 1.2e6])
>>> smape = SymmetricMeanAbsolutePercentageError()
>>> smape(preds, target)
tensor(0.2290)
"""
is_differentiable: bool = True
higher_is_better: bool = False
full_state_update: bool = False
sum_abs_per_error: Tensor
total: Tensor
def __init__(
self,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.add_state("sum_abs_per_error", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0.0), 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_abs_per_error, num_obs = _symmetric_mean_absolute_percentage_error_update(preds, target)
self.sum_abs_per_error += sum_abs_per_error
self.total += num_obs
def compute(self) -> Tensor:
"""Computes mean absolute percentage error over state."""
return _symmetric_mean_absolute_percentage_error_compute(self.sum_abs_per_error, self.total)