File size: 6,272 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 |
# 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, Optional, Union
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.nominal.cramers import _cramers_v_compute, _cramers_v_update
from torchmetrics.functional.nominal.utils import _nominal_input_validation
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__ = ["CramersV.plot"]
class CramersV(Metric):
r"""Compute `Cramer's V`_ statistic measuring the association between two categorical (nominal) data series.
.. math::
V = \sqrt{\frac{\chi^2 / n}{\min(r - 1, k - 1)}}
where
.. math::
\chi^2 = \sum_{i,j} \ frac{\left(n_{ij} - \frac{n_{i.} n_{.j}}{n}\right)^2}{\frac{n_{i.} n_{.j}}{n}}
where :math:`n_{ij}` denotes the number of times the values :math:`(A_i, B_j)` are observed with :math:`A_i, B_j`
represent frequencies of values in ``preds`` and ``target``, respectively. Cramer's V is a symmetric coefficient,
i.e. :math:`V(preds, target) = V(target, preds)`, so order of input arguments does not matter. The output values
lies in [0, 1] with 1 meaning the perfect association.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the first data
series with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively.
- ``target`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the second data
series with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively.
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``cramers_v`` (:class:`~torch.Tensor`): Scalar tensor containing the Cramer's V statistic.
Args:
num_classes: Integer specifying the number of classes
bias_correction: Indication of whether to use bias correction.
nan_strategy: Indication of whether to replace or drop ``NaN`` values
nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'``
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ValueError:
If `nan_strategy` is not one of `'replace'` and `'drop'`
ValueError:
If `nan_strategy` is equal to `'replace'` and `nan_replace_value` is not an `int` or `float`
Example::
>>> from torch import randint, randn
>>> from torchmetrics.nominal import CramersV
>>> preds = randint(0, 4, (100,))
>>> target = (preds + randn(100)).round().clamp(0, 4)
>>> cramers_v = CramersV(num_classes=5)
>>> cramers_v(preds, target)
tensor(0.5284)
"""
full_state_update: bool = False
is_differentiable: bool = False
higher_is_better: bool = True
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
confmat: Tensor
def __init__(
self,
num_classes: int,
bias_correction: bool = True,
nan_strategy: Literal["replace", "drop"] = "replace",
nan_replace_value: Optional[float] = 0.0,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.num_classes = num_classes
self.bias_correction = bias_correction
_nominal_input_validation(nan_strategy, nan_replace_value)
self.nan_strategy = nan_strategy
self.nan_replace_value = nan_replace_value
self.add_state("confmat", torch.zeros(num_classes, num_classes), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
confmat = _cramers_v_update(preds, target, self.num_classes, self.nan_strategy, self.nan_replace_value)
self.confmat += confmat
def compute(self) -> Tensor:
"""Compute Cramer's V statistic."""
return _cramers_v_compute(self.confmat, self.bias_correction)
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = 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
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.nominal import CramersV
>>> metric = CramersV(num_classes=5)
>>> metric.update(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.nominal import CramersV
>>> metric = CramersV(num_classes=5)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
|