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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, List, Optional, Union
from torch import Tensor
from torchmetrics.functional.regression.spearman import _spearman_corrcoef_compute, _spearman_corrcoef_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["SpearmanCorrCoef.plot"]
class SpearmanCorrCoef(Metric):
r"""Compute `spearmans rank correlation coefficient`_.
.. math:
r_s = = \frac{cov(rg_x, rg_y)}{\sigma_{rg_x} * \sigma_{rg_y}}
where :math:`rg_x` and :math:`rg_y` are the rank associated to the variables :math:`x` and :math:`y`.
Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated
on the rank variables.
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,d)``
- ``target`` (:class:`~torch.Tensor`): Ground truth values in float tensor with shape ``(N,d)``
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``spearman`` (:class:`~torch.Tensor`): A tensor with the spearman correlation(s)
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 torch import tensor
>>> from torchmetrics.regression import SpearmanCorrCoef
>>> target = tensor([3, -0.5, 2, 7])
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> spearman = SpearmanCorrCoef()
>>> spearman(preds, target)
tensor(1.0000)
Example (multi output regression):
>>> from torchmetrics.regression import SpearmanCorrCoef
>>> target = tensor([[3, -0.5], [2, 7]])
>>> preds = tensor([[2.5, 0.0], [2, 8]])
>>> spearman = SpearmanCorrCoef(num_outputs=2)
>>> spearman(preds, target)
tensor([1.0000, 1.0000])
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = -1.0
plot_upper_bound: float = 1.0
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
num_outputs: int = 1,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
rank_zero_warn(
"Metric `SpearmanCorrcoef` will save all targets and predictions in the buffer."
" For large datasets, this may lead to large memory footprint."
)
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("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
preds, target = _spearman_corrcoef_update(preds, target, num_outputs=self.num_outputs)
self.preds.append(preds.to(self.dtype))
self.target.append(target.to(self.dtype))
def compute(self) -> Tensor:
"""Compute Spearman's correlation coefficient."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _spearman_corrcoef_compute(preds, target)
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 SpearmanCorrCoef
>>> metric = SpearmanCorrCoef()
>>> 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 SpearmanCorrCoef
>>> metric = SpearmanCorrCoef()
>>> 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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