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log_cosh.py
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log_cosh.py
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# 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 typing import Any, Optional, Sequence, Union
import torch
from torch import Tensor
from torchmetrics.functional.regression.log_cosh import _log_cosh_error_compute, _log_cosh_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__ = ["LogCoshError.plot"]
class LogCoshError(Metric):
r"""Compute the `LogCosh Error`_.
.. math:: \text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)
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`): Estimated labels with shape ``(batch_size,)``
or ``(batch_size, num_outputs)``
- ``target`` (:class:`~torch.Tensor`): Ground truth labels with shape ``(batch_size,)``
or ``(batch_size, num_outputs)``
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``log_cosh_error`` (:class:`~torch.Tensor`): A tensor with the log cosh error
Args:
num_outputs: Number of outputs in multioutput setting
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (single output regression)::
>>> from torchmetrics.regression import LogCoshError
>>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
>>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
>>> log_cosh_error = LogCoshError()
>>> log_cosh_error(preds, target)
tensor(0.3523)
Example (multi output regression)::
>>> from torchmetrics.regression import LogCoshError
>>> preds = torch.tensor([[3.0, 5.0, 1.2], [-2.1, 2.5, 7.0]])
>>> target = torch.tensor([[2.5, 5.0, 1.3], [0.3, 4.0, 8.0]])
>>> log_cosh_error = LogCoshError(num_outputs=3)
>>> log_cosh_error(preds, target)
tensor([0.9176, 0.4277, 0.2194])
"""
is_differentiable = True
higher_is_better = False
full_state_update = False
plot_lower_bound: float = 0.0
sum_log_cosh_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 < 1:
raise ValueError(f"Expected argument `num_outputs` to be an int larger than 0, but got {num_outputs}")
self.num_outputs = num_outputs
self.add_state("sum_log_cosh_error", default=torch.zeros(num_outputs), dist_reduce_fx="sum")
self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets.
Raises:
ValueError:
If ``preds`` or ``target`` has multiple outputs when ``num_outputs=1``
"""
sum_log_cosh_error, n_obs = _log_cosh_error_update(preds, target, self.num_outputs)
self.sum_log_cosh_error += sum_log_cosh_error
self.total += n_obs
def compute(self) -> Tensor:
"""Compute LogCosh error over state."""
return _log_cosh_error_compute(self.sum_log_cosh_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 LogCoshError
>>> metric = LogCoshError()
>>> 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 LogCoshError
>>> metric = LogCoshError()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)