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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 Tuple
import torch
from torch import Tensor
from torchmetrics.utilities.checks import _check_same_shape
def _find_repeats(data: Tensor):
""" find and return values which have repeats i.e. the same value are more than once in the tensor """
temp = data.detach().clone()
temp = temp.sort()[0]
change = torch.cat([torch.tensor([True], device=temp.device), temp[1:] != temp[:-1]])
unique = temp[change]
change_idx = torch.cat([torch.nonzero(change), torch.tensor([[temp.numel()]], device=temp.device)]).flatten()
freq = change_idx[1:] - change_idx[:-1]
atleast2 = freq > 1
return unique[atleast2]
def _rank_data(data: Tensor):
""" Calculate the rank for each element of a tensor. The rank refers to the indices of an element in the
corresponding sorted tensor (starting from 1). Duplicates of the same value will be assigned the mean of
their rank
Adopted from:
https://github.com/scipy/scipy/blob/v1.6.2/scipy/stats/stats.py#L4140-L4303
"""
n = data.numel()
rank = torch.empty_like(data)
idx = data.argsort()
rank[idx[:n]] = torch.arange(1, n + 1, dtype=data.dtype, device=data.device)
repeats = _find_repeats(data)
for r in repeats:
condition = rank == r
rank[condition] = rank[condition].mean()
return rank
def _spearman_corrcoef_update(preds: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
if preds.dtype != target.dtype:
raise TypeError(
"Expected `preds` and `target` to have the same data type."
f" Got preds: {preds.dtype} and target: {target.dtype}."
)
_check_same_shape(preds, target)
preds = preds.squeeze()
target = target.squeeze()
if preds.ndim > 1 or target.ndim > 1:
raise ValueError('Expected both predictions and target to be 1 dimensional tensors.')
return preds, target
def _spearman_corrcoef_compute(preds: Tensor, target: Tensor, eps: float = 1e-6) -> Tensor:
preds = _rank_data(preds)
target = _rank_data(target)
preds_diff = preds - preds.mean()
target_diff = target - target.mean()
cov = (preds_diff * target_diff).mean()
preds_std = torch.sqrt((preds_diff * preds_diff).mean())
target_std = torch.sqrt((target_diff * target_diff).mean())
corrcoef = cov / (preds_std * target_std + eps)
return torch.clamp(corrcoef, -1.0, 1.0)
def spearman_corrcoef(preds: Tensor, target: Tensor) -> Tensor:
r"""
Computes `spearmans rank correlation coefficient
<https://en.wikipedia.org/wiki/Spearman%27s_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 x and y. Spearmans correlations
coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables.
Args:
preds: estimated scores
target: ground truth scores
Example:
>>> from torchmetrics.functional import spearman_corrcoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> spearman_corrcoef(preds, target)
tensor(1.0000)
"""
preds, target = _spearman_corrcoef_update(preds, target)
return _spearman_corrcoef_compute(preds, target)