File size: 8,315 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
# 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
import torch
from torch import Tensor
from torchmetrics.functional.regression.pearson import _pearson_corrcoef_compute, _pearson_corrcoef_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["PearsonCorrCoef.plot"]
def _final_aggregation(
means_x: torch.Tensor,
means_y: torch.Tensor,
vars_x: torch.Tensor,
vars_y: torch.Tensor,
corrs_xy: torch.Tensor,
nbs: torch.Tensor,
eps: float = 1e-10,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Aggregate the statistics from multiple devices.
Formula taken from here: `Parallel algorithm for calculating variance
<https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm>`_
We use `eps` to avoid division by zero when `n1` and `n2` are both zero. Generally, the value of `eps` should not
matter, as if `n1` and `n2` are both zero, all the states will also be zero.
"""
if len(means_x) == 1:
return means_x[0], means_y[0], vars_x[0], vars_y[0], corrs_xy[0], nbs[0]
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]
# count
nb = torch.where(torch.logical_or(n1, n2), n1 + n2, eps)
# mean_x
mean_x = (n1 * mx1 + n2 * mx2) / nb
# mean_y
mean_y = (n1 * my1 + n2 * my2) / nb
# intermediates for running variances
n12_b = n1 * n2 / nb
delta_x = mx2 - mx1
delta_y = my2 - my1
# var_x
var_x = vx1 + vx2 + n12_b * delta_x**2
# var_y
var_y = vy1 + vy2 + n12_b * delta_y**2
# corr_xy
corr_xy = cxy1 + cxy2 + n12_b * delta_x * delta_y
mx1, my1, vx1, vy1, cxy1, n1 = mean_x, mean_y, var_x, var_y, corr_xy, nb
return mean_x, mean_y, var_x, var_y, corr_xy, nb
class PearsonCorrCoef(Metric):
r"""Compute `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.
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 tensor with shape ``(N,)``
or multioutput tensor of shape ``(N,d)``
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``pearson`` (:class:`~torch.Tensor`): A tensor with the Pearson Correlation Coefficient
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 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)
Example (multi output regression):
>>> from torchmetrics.regression import PearsonCorrCoef
>>> target = torch.tensor([[3, -0.5], [2, 7]])
>>> preds = torch.tensor([[2.5, 0.0], [2, 8]])
>>> pearson = PearsonCorrCoef(num_outputs=2)
>>> pearson(preds, target)
tensor([1., 1.])
"""
is_differentiable: bool = True
higher_is_better: Optional[bool] = None # both -1 and 1 are optimal
full_state_update: bool = True
plot_lower_bound: float = -1.0
plot_upper_bound: float = 1.0
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,
num_outputs: int = 1,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not isinstance(num_outputs, int) and num_outputs < 1:
raise ValueError("Expected argument `num_outputs` to be an int larger than 0, but got {num_outputs}")
self.num_outputs = num_outputs
self.add_state("mean_x", default=torch.zeros(self.num_outputs), dist_reduce_fx=None)
self.add_state("mean_y", default=torch.zeros(self.num_outputs), dist_reduce_fx=None)
self.add_state("var_x", default=torch.zeros(self.num_outputs), dist_reduce_fx=None)
self.add_state("var_y", default=torch.zeros(self.num_outputs), dist_reduce_fx=None)
self.add_state("corr_xy", default=torch.zeros(self.num_outputs), dist_reduce_fx=None)
self.add_state("n_total", default=torch.zeros(self.num_outputs), dist_reduce_fx=None)
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
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,
self.num_outputs,
)
def compute(self) -> Tensor:
"""Compute pearson correlation coefficient over state."""
if (self.num_outputs == 1 and self.mean_x.numel() > 1) or (self.num_outputs > 1 and self.mean_x.ndim > 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)
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 PearsonCorrCoef
>>> metric = PearsonCorrCoef()
>>> 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 PearsonCorrCoef
>>> metric = PearsonCorrCoef()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)
|