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r2.py
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r2.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, tensor
from torchmetrics.functional.regression.r2 import _r2_score_compute, _r2_score_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__ = ["R2Score.plot"]
class R2Score(Metric):
r"""Compute r2 score also known as `R2 Score_Coefficient Determination`_.
.. math:: R^2 = 1 - \frac{SS_{res}}{SS_{tot}}
where :math:`SS_{res}=\sum_i (y_i - f(x_i))^2` is the sum of residual squares, and
:math:`SS_{tot}=\sum_i (y_i - \bar{y})^2` is total sum of squares. Can also calculate
adjusted r2 score given by
.. math:: R^2_{adj} = 1 - \frac{(1-R^2)(n-1)}{n-k-1}
where the parameter :math:`k` (the number of independent regressors) should be provided as the `adjusted` argument.
The score is only proper defined when :math:`SS_{tot}\neq 0`, which can happen for near constant targets. In this
case a score of 0 is returned. By definition the score is bounded between 0 and 1, where 1 corresponds to the
predictions exactly matching the targets.
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:
- ``r2score`` (:class:`~torch.Tensor`): A tensor with the r2 score(s)
In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions.
Please see argument ``multioutput`` for changing this behavior.
Args:
num_outputs: Number of outputs in multioutput setting
adjusted: number of independent regressors for calculating adjusted r2 score.
multioutput: Defines aggregation in the case of multiple output scores. Can be one of the following strings:
* ``'raw_values'`` returns full set of scores
* ``'uniform_average'`` scores are uniformly averaged
* ``'variance_weighted'`` scores are weighted by their individual variances
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If ``adjusted`` parameter is not an integer larger or equal to 0.
ValueError:
If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``.
Example:
>>> from torchmetrics.regression import R2Score
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> r2score = R2Score()
>>> r2score(preds, target)
tensor(0.9486)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> r2score = R2Score(num_outputs=2, multioutput='raw_values')
>>> r2score(preds, target)
tensor([0.9654, 0.9082])
"""
is_differentiable: bool = True
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
sum_squared_error: Tensor
sum_error: Tensor
residual: Tensor
total: Tensor
def __init__(
self,
num_outputs: int = 1,
adjusted: int = 0,
multioutput: str = "uniform_average",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.num_outputs = num_outputs
if adjusted < 0 or not isinstance(adjusted, int):
raise ValueError("`adjusted` parameter should be an integer larger or equal to 0.")
self.adjusted = adjusted
allowed_multioutput = ("raw_values", "uniform_average", "variance_weighted")
if multioutput not in allowed_multioutput:
raise ValueError(
f"Invalid input to argument `multioutput`. Choose one of the following: {allowed_multioutput}"
)
self.multioutput = multioutput
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")
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:
"""Compute r2 score over the metric states."""
return _r2_score_compute(
self.sum_squared_error, self.sum_error, self.residual, self.total, self.adjusted, self.multioutput
)
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 R2Score
>>> metric = R2Score()
>>> 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 R2Score
>>> metric = R2Score()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)