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pearson.py
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pearson.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, List, Optional, Tuple
import torch
from deprecate import deprecated, void
from torch import Tensor
from torchmetrics.functional.regression.pearson import _pearson_corrcoef_compute, _pearson_corrcoef_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import _future_warning
def _final_aggregation(
means_x: Tensor,
means_y: Tensor,
vars_x: Tensor,
vars_y: Tensor,
corrs_xy: Tensor,
nbs: Tensor,
) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
"""Aggregate the statistics from multiple devices.
Formula taken from here: `Aggregate the statistics from multiple devices`_
"""
# assert len(means_x) > 1 and len(means_y) > 1 and len(vars_x) > 1 and len(vars_y) > 1 and len(corrs_xy) > 1
mx1, my1, vx1, vy1, cxy1, n1 = means_x[0], means_y[0], vars_x[0], vars_y[0], corrs_xy[0], nbs[0]
for i in range(1, len(means_x)):
mx2, my2, vx2, vy2, cxy2, n2 = means_x[i], means_y[i], vars_x[i], vars_y[i], corrs_xy[i], nbs[i]
nb = n1 + n2
mean_x = (n1 * mx1 + n2 * mx2) / nb
mean_y = (n1 * my1 + n2 * my2) / nb
var_x = 1 / (n1 + n2 - 1) * ((n1 - 1) * vx1 + (n2 - 1) * vx2 + ((n1 * n2) / (n1 + n2)) * (mx1 - mx2) ** 2)
var_y = 1 / (n1 + n2 - 1) * ((n1 - 1) * vy1 + (n2 - 1) * vy2 + ((n1 * n2) / (n1 + n2)) * (my1 - my2) ** 2)
corr1 = n1 * cxy1 + n1 * (mx1 - mean_x) * (my1 - mean_y)
corr2 = n2 * cxy2 + n2 * (mx2 - mean_x) * (my2 - mean_y)
corr_xy = (corr1 + corr2) / (n1 + n2)
mx1, my1, vx1, vy1, cxy1, n1 = mean_x, mean_y, var_x, var_y, corr_xy, nb
return var_x, var_y, corr_xy, nb
class PearsonCorrCoef(Metric):
r"""
Computes `Pearson Correlation Coefficient`_:
.. math::
P_{corr}(x,y) = \frac{cov(x,y)}{\sigma_x \sigma_y}
Where :math:`y` is a tensor of target values, and :math:`x` is a
tensor of predictions.
Forward accepts
- ``preds`` (float tensor): ``(N,)``
- ``target``(float tensor): ``(N,)``
Args:
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False.
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called.
Example:
>>> from torchmetrics import PearsonCorrCoef
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> pearson = PearsonCorrCoef()
>>> pearson(preds, target)
tensor(0.9849)
"""
is_differentiable = True
higher_is_better = None # both -1 and 1 are optimal
preds: List[Tensor]
target: List[Tensor]
mean_x: Tensor
mean_y: Tensor
var_x: Tensor
var_y: Tensor
corr_xy: Tensor
n_total: Tensor
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
)
self.add_state("mean_x", default=torch.tensor(0.0), dist_reduce_fx=None)
self.add_state("mean_y", default=torch.tensor(0.0), dist_reduce_fx=None)
self.add_state("var_x", default=torch.tensor(0.0), dist_reduce_fx=None)
self.add_state("var_y", default=torch.tensor(0.0), dist_reduce_fx=None)
self.add_state("corr_xy", default=torch.tensor(0.0), dist_reduce_fx=None)
self.add_state("n_total", default=torch.tensor(0.0), dist_reduce_fx=None)
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
"""
self.mean_x, self.mean_y, self.var_x, self.var_y, self.corr_xy, self.n_total = _pearson_corrcoef_update(
preds, target, self.mean_x, self.mean_y, self.var_x, self.var_y, self.corr_xy, self.n_total
)
def compute(self) -> Tensor:
"""Computes pearson correlation coefficient over state."""
if self.mean_x.numel() > 1: # multiple devices, need further reduction
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:
var_x = self.var_x
var_y = self.var_y
corr_xy = self.corr_xy
n_total = self.n_total
return _pearson_corrcoef_compute(var_x, var_y, corr_xy, n_total)
class PearsonCorrcoef(PearsonCorrCoef):
r"""
Computes `Pearson Correlation Coefficient`_:
Example:
>>> pearson = PearsonCorrcoef()
>>> pearson(torch.tensor([2.5, 0.0, 2, 8]), torch.tensor([3, -0.5, 2, 7]))
tensor(0.9849)
.. deprecated:: v0.7
Renamed in favor of :class:`torchmetrics.PearsonCorrCoef`. Will be removed in v0.8.
"""
@deprecated(target=PearsonCorrCoef, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
) -> None:
void(compute_on_step, dist_sync_on_step, process_group)