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# 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.pearson import (
_pearsons_contingency_coefficient_compute,
_pearsons_contingency_coefficient_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__ = ["PearsonsContingencyCoefficient.plot"]
class PearsonsContingencyCoefficient(Metric):
r"""Compute `Pearson's Contingency Coefficient`_ statistic.
This metric measures the association between two categorical (nominal) data series.
.. math::
Pearson = \sqrt{\frac{\chi^2 / n}{1 + \chi^2 / n}}
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. Pearson's Contingency Coefficient is a
symmetric coefficient, i.e. :math:`Pearson(preds, target) = Pearson(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:
- ``pearsons_cc`` (:class:`~torch.Tensor`): Scalar tensor containing the Pearsons Contingency Coefficient statistic.
Args:
num_classes: Integer specifying the number of classes
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 PearsonsContingencyCoefficient
>>> preds = randint(0, 4, (100,))
>>> target = (preds + randn(100)).round().clamp(0, 4)
>>> pearsons_contingency_coefficient = PearsonsContingencyCoefficient(num_classes=5)
>>> pearsons_contingency_coefficient(preds, target)
tensor(0.6948)
"""
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,
nan_strategy: Literal["replace", "drop"] = "replace",
nan_replace_value: Optional[float] = 0.0,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.num_classes = num_classes
_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 = _pearsons_contingency_coefficient_update(
preds, target, self.num_classes, self.nan_strategy, self.nan_replace_value
)
self.confmat += confmat
def compute(self) -> Tensor:
"""Compute Pearson's Contingency Coefficient statistic."""
return _pearsons_contingency_coefficient_compute(self.confmat)
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 PearsonsContingencyCoefficient
>>> metric = PearsonsContingencyCoefficient(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 PearsonsContingencyCoefficient
>>> metric = PearsonsContingencyCoefficient(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)
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