File size: 8,278 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 |
# 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 typing_extensions import Literal
from torchmetrics.functional.regression.kendall import (
_kendall_corrcoef_compute,
_kendall_corrcoef_update,
_MetricVariant,
_TestAlternative,
)
from torchmetrics.metric import Metric
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__ = ["KendallRankCorrCoef.plot"]
class KendallRankCorrCoef(Metric):
r"""Compute `Kendall Rank Correlation Coefficient`_.
.. math::
tau_a = \frac{C - D}{C + D}
where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs.
.. math::
tau_b = \frac{C - D}{\sqrt{(C + D + T_{preds}) * (C + D + T_{target})}}
where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs and :math:`T` represents
a total number of ties.
.. math::
tau_c = 2 * \frac{C - D}{n^2 * \frac{m - 1}{m}}
where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs, :math:`n` is a total number
of observations and :math:`m` is a ``min`` of unique values in ``preds`` and ``target`` sequence.
Definitions according to Definition according to `The Treatment of Ties in Ranking Problems`_.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Sequence of data in float tensor of either shape ``(N,)`` or ``(N,d)``
- ``target`` (:class:`~torch.Tensor`): Sequence of data in float tensor of either shape ``(N,)`` or ``(N,d)``
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``kendall`` (:class:`~torch.Tensor`): A tensor with the correlation tau statistic,
and if it is not None, the p-value of corresponding statistical test.
Args:
variant: Indication of which variant of Kendall's tau to be used
t_test: Indication whether to run t-test
alternative: Alternative hypothesis for t-test. Possible values:
- 'two-sided': the rank correlation is nonzero
- 'less': the rank correlation is negative (less than zero)
- 'greater': the rank correlation is positive (greater than zero)
num_outputs: Number of outputs in multioutput setting
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError: If ``t_test`` is not of a type bool
ValueError: If ``t_test=True`` and ``alternative=None``
Example (single output regression):
>>> from torch import tensor
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> target = tensor([3, -0.5, 2, 1])
>>> kendall = KendallRankCorrCoef()
>>> kendall(preds, target)
tensor(0.3333)
Example (multi output regression):
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([[2.5, 0.0], [2, 8]])
>>> target = tensor([[3, -0.5], [2, 1]])
>>> kendall = KendallRankCorrCoef(num_outputs=2)
>>> kendall(preds, target)
tensor([1., 1.])
Example (single output regression with t-test):
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([2.5, 0.0, 2, 8])
>>> target = tensor([3, -0.5, 2, 1])
>>> kendall = KendallRankCorrCoef(t_test=True, alternative='two-sided')
>>> kendall(preds, target)
(tensor(0.3333), tensor(0.4969))
Example (multi output regression with t-test):
>>> from torchmetrics.regression import KendallRankCorrCoef
>>> preds = tensor([[2.5, 0.0], [2, 8]])
>>> target = tensor([[3, -0.5], [2, 1]])
>>> kendall = KendallRankCorrCoef(t_test=True, alternative='two-sided', num_outputs=2)
>>> kendall(preds, target)
(tensor([1., 1.]), tensor([nan, nan]))
"""
is_differentiable = False
higher_is_better = None
full_state_update = True
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
variant: Literal["a", "b", "c"] = "b",
t_test: bool = False,
alternative: Optional[Literal["two-sided", "less", "greater"]] = "two-sided",
num_outputs: int = 1,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not isinstance(t_test, bool):
raise ValueError(f"Argument `t_test` is expected to be of a type `bool`, but got {type(t_test)}.")
if t_test and alternative is None:
raise ValueError("Argument `alternative` is required if `t_test=True` but got `None`.")
self.variant = _MetricVariant.from_str(str(variant))
self.alternative = _TestAlternative.from_str(str(alternative)) if t_test else None
self.num_outputs = num_outputs
self.add_state("preds", [], dist_reduce_fx="cat")
self.add_state("target", [], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update variables required to compute Kendall rank correlation coefficient."""
self.preds, self.target = _kendall_corrcoef_update(
preds,
target,
self.preds,
self.target,
num_outputs=self.num_outputs,
)
def compute(self) -> Union[Tensor, tuple[Tensor, Tensor]]:
"""Compute Kendall rank correlation coefficient, and optionally p-value of corresponding statistical test."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
tau, p_value = _kendall_corrcoef_compute(
preds,
target,
self.variant, # type: ignore[arg-type] # todo
self.alternative, # type: ignore[arg-type] # todo
)
if p_value is not None:
return tau, p_value
return tau
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 KendallRankCorrCoef
>>> metric = KendallRankCorrCoef()
>>> 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 KendallRankCorrCoef
>>> metric = KendallRankCorrCoef()
>>> values = []
>>> for _ in range(10):
... values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)
"""
return self._plot(val, ax)
|