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concordance.py
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concordance.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 Optional, Sequence, Union
from torch import Tensor
from torchmetrics.functional.regression.concordance import _concordance_corrcoef_compute
from torchmetrics.regression.pearson import PearsonCorrCoef, _final_aggregation
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["ConcordanceCorrCoef.plot"]
class ConcordanceCorrCoef(PearsonCorrCoef):
r"""Compute concordance correlation coefficient that measures the agreement between two variables.
.. math::
\rho_c = \frac{2 \rho \sigma_x \sigma_y}{\sigma_x^2 + \sigma_y^2 + (\mu_x - \mu_y)^2}
where :math:`\mu_x, \mu_y` is the means for the two variables, :math:`\sigma_x^2, \sigma_y^2` are the corresponding
variances and \rho is the pearson correlation coefficient between the two variables.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput
float tensor of shape ``(N,d)``
- ``target`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput
float tensor of shape ``(N,d)``
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``concordance`` (:class:`~torch.Tensor`): A scalar float tensor with the concordance coefficient(s) for
non-multioutput input or a float tensor with shape ``(d,)`` for multioutput input
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 ConcordanceCorrCoef
>>> from torch import tensor
>>> target = tensor([3, -0.5, 2, 7])
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> concordance = ConcordanceCorrCoef()
>>> concordance(preds, target)
tensor(0.9777)
Example (multi output regression):
>>> from torchmetrics.regression import ConcordanceCorrCoef
>>> target = tensor([[3, -0.5], [2, 7]])
>>> preds = tensor([[2.5, 0.0], [2, 8]])
>>> concordance = ConcordanceCorrCoef(num_outputs=2)
>>> concordance(preds, target)
tensor([0.7273, 0.9887])
"""
is_differentiable: bool = True
higher_is_better: bool = True
full_state_update: bool = True
plot_lower_bound: float = -1.0
plot_upper_bound: float = 1.0
def compute(self) -> Tensor:
"""Compute final concordance correlation coefficient over metric states."""
if (self.num_outputs == 1 and self.mean_x.numel() > 1) or (self.num_outputs > 1 and self.mean_x.ndim > 1):
mean_x, mean_y, var_x, var_y, corr_xy, n_total = _final_aggregation(
self.mean_x, self.mean_y, self.var_x, self.var_y, self.corr_xy, self.n_total
)
else:
mean_x = self.mean_x
mean_y = self.mean_y
var_x = self.var_x
var_y = self.var_y
corr_xy = self.corr_xy
n_total = self.n_total
return _concordance_corrcoef_compute(mean_x, mean_y, var_x, var_y, corr_xy, n_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 ConcordanceCorrCoef
>>> metric = ConcordanceCorrCoef()
>>> 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 ConcordanceCorrCoef
>>> metric = ConcordanceCorrCoef()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)