/
log_mse.py
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/
log_mse.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
from torch import Tensor, tensor
from torchmetrics.functional.regression.log_mse import _mean_squared_log_error_compute, _mean_squared_log_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__ = ["MeanSquaredLogError.plot"]
class MeanSquaredLogError(Metric):
r"""Compute `mean squared logarithmic error`_ (MSLE).
.. math:: \text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2
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_squared_log_error`` (:class:`~torch.Tensor`): A tensor with the mean squared log error
Args:
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torch import tensor
>>> from torchmetrics.regression import MeanSquaredLogError
>>> target = tensor([2.5, 5, 4, 8])
>>> preds = tensor([3, 5, 2.5, 7])
>>> mean_squared_log_error = MeanSquaredLogError()
>>> mean_squared_log_error(preds, target)
tensor(0.0397)
.. note::
Half precision is only support on GPU for this metric
"""
is_differentiable: bool = True
higher_is_better: bool = False
full_state_update: bool = False
plot_lower_bound: float = 0.0
sum_squared_log_error: Tensor
total: Tensor
def __init__(
self,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.add_state("sum_squared_log_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:
"""Update state with predictions and targets."""
sum_squared_log_error, num_obs = _mean_squared_log_error_update(preds, target)
self.sum_squared_log_error += sum_squared_log_error
self.total += num_obs
def compute(self) -> Tensor:
"""Compute mean squared logarithmic error over state."""
return _mean_squared_log_error_compute(self.sum_squared_log_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 MeanSquaredLogError
>>> metric = MeanSquaredLogError()
>>> 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 MeanSquaredLogError
>>> metric = MeanSquaredLogError()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)