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spearman.py
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spearman.py
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# Copyright The PyTorch 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, Dict, List, Optional
import torch
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
class SpearmanCorrCoef(Metric):
r"""Computes `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.
Args:
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False.
.. deprecated:: v0.8
Argument has no use anymore and will be removed v0.9.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics import SpearmanCorrCoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> spearman = SpearmanCorrCoef()
>>> spearman(preds, target)
tensor(1.0000)
"""
is_differentiable = False
higher_is_better = True
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
compute_on_step: Optional[bool] = None,
**kwargs: Dict[str, Any],
) -> None:
super().__init__(compute_on_step=compute_on_step, **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."
)
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: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
preds, target = _spearman_corrcoef_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Computes Spearman's correlation coefficient."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _spearman_corrcoef_compute(preds, target)