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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 collections.abc import Sequence
from typing import Any, Optional, Union
import torch
from torch import Tensor, tensor
from torchmetrics.functional.regression.r2 import _r2_score_update
from torchmetrics.functional.regression.rse import _relative_squared_error_compute
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__ = ["RelativeSquaredError.plot"]
class RelativeSquaredError(Metric):
r"""Computes the relative squared error (RSE).
.. math:: \text{RSE} = \frac{\sum_i^N(y_i - \hat{y_i})^2}{\sum_i^N(y_i - \overline{y})^2}
Where :math:`y` is a tensor of target values with mean :math:`\overline{y}`, and
:math:`\hat{y}` is a tensor of predictions.
If num_outputs > 1, the returned value is averaged over all the outputs.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Predictions from model in float tensor with shape ``(N,)``
or ``(N, M)`` (multioutput)
- ``target`` (:class:`~torch.Tensor`): Ground truth values in float tensor with shape ``(N,)``
or ``(N, M)`` (multioutput)
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``rse`` (:class:`~torch.Tensor`): A tensor with the RSE score(s)
Args:
num_outputs: Number of outputs in multioutput setting
squared: If True returns RSE value, if False returns RRSE value.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics.regression import RelativeSquaredError
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> relative_squared_error = RelativeSquaredError()
>>> relative_squared_error(preds, target)
tensor(0.0514)
"""
is_differentiable = True
higher_is_better = False
full_state_update = False
sum_squared_error: Tensor
sum_error: Tensor
residual: Tensor
total: Tensor
def __init__(
self,
num_outputs: int = 1,
squared: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.num_outputs = num_outputs
self.add_state("sum_squared_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
self.add_state("sum_error", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
self.add_state("residual", default=torch.zeros(self.num_outputs), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
self.squared = squared
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
sum_squared_error, sum_error, residual, total = _r2_score_update(preds, target)
self.sum_squared_error += sum_squared_error
self.sum_error += sum_error
self.residual += residual
self.total += total
def compute(self) -> Tensor:
"""Computes relative squared error over state."""
return _relative_squared_error_compute(
self.sum_squared_error, self.sum_error, self.residual, self.total, squared=self.squared
)
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 RelativeSquaredError
>>> metric = RelativeSquaredError()
>>> 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 RelativeSquaredError
>>> metric = RelativeSquaredError()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)
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