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mean_absolute_error.py
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mean_absolute_error.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 Any, Callable, Optional
import torch
from torch import Tensor, tensor
from torchmetrics.functional.regression.mean_absolute_error import (
_mean_absolute_error_compute,
_mean_absolute_error_update,
)
from torchmetrics.metric import Metric
class MeanAbsoluteError(Metric):
r"""
`Computes 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.
Args:
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)
Example:
>>> from torchmetrics import MeanAbsoluteError
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> mean_absolute_error = MeanAbsoluteError()
>>> mean_absolute_error(preds, target)
tensor(0.5000)
"""
is_differentiable = True
higher_is_better = False
sum_abs_error: Tensor
total: Tensor
def __init__(
self,
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.add_state("sum_abs_error", default=tensor(0.0), 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_abs_error, n_obs = _mean_absolute_error_update(preds, target)
self.sum_abs_error += sum_abs_error
self.total += n_obs
def compute(self) -> Tensor:
"""Computes mean absolute error over state."""
return _mean_absolute_error_compute(self.sum_abs_error, self.total)