File size: 5,307 Bytes
9c6594c |
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 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 collections.abc import Sequence
from typing import Any, Optional, Union
import torch
from torch import Tensor, tensor
from torchmetrics.functional.regression.mae import _mean_absolute_error_compute, _mean_absolute_error_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["MeanAbsoluteError.plot"]
class MeanAbsoluteError(Metric):
r"""`Compute Mean Absolute Error`_ (MAE).
.. math:: \text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} |
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Predictions from model
- ``target`` (:class:`~torch.Tensor`): Ground truth values
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``mean_absolute_error`` (:class:`~torch.Tensor`): A tensor with the mean absolute error over the state
Args:
num_outputs: Number of outputs in multioutput setting
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torch import tensor
>>> from torchmetrics.regression import MeanAbsoluteError
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> mean_absolute_error = MeanAbsoluteError()
>>> mean_absolute_error(preds, target)
tensor(0.5000)
Example::
Multioutput mse computation:
>>> from torch import tensor
>>> from torchmetrics.regression import MeanAbsoluteError
>>> target = tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
>>> preds = tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
>>> mean_absolute_error = MeanAbsoluteError(num_outputs=3)
>>> mean_absolute_error(preds, target)
tensor([1., 2., 3.])
"""
is_differentiable: bool = True
higher_is_better: bool = False
full_state_update: bool = False
plot_lower_bound: float = 0.0
sum_abs_error: Tensor
total: Tensor
def __init__(
self,
num_outputs: int = 1,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not (isinstance(num_outputs, int) and num_outputs > 0):
raise ValueError(f"Expected num_outputs to be a positive integer but got {num_outputs}")
self.num_outputs = num_outputs
self.add_state("sum_abs_error", default=torch.zeros(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:
"""Update state with predictions and targets."""
sum_abs_error, num_obs = _mean_absolute_error_update(preds, target, num_outputs=self.num_outputs)
self.sum_abs_error += sum_abs_error
self.total += num_obs
def compute(self) -> Tensor:
"""Compute mean absolute error over state."""
return _mean_absolute_error_compute(self.sum_abs_error, self.total)
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import MeanAbsoluteError
>>> metric = MeanAbsoluteError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import MeanAbsoluteError
>>> metric = MeanAbsoluteError()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)
|