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fleiss_kappa.py
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fleiss_kappa.py
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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 typing import Any, List, Optional, Sequence, Union
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.nominal.fleiss_kappa import _fleiss_kappa_compute, _fleiss_kappa_update
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__ = ["FleissKappa.plot"]
class FleissKappa(Metric):
r"""Calculatees `Fleiss kappa`_ a statistical measure for inter agreement between raters.
.. math::
\kappa = \frac{\bar{p} - \bar{p_e}}{1 - \bar{p_e}}
where :math:`\bar{p}` is the mean of the agreement probability over all raters and :math:`\bar{p_e}` is the mean
agreement probability over all raters if they were randomly assigned. If the raters are in complete agreement then
the score 1 is returned, if there is no agreement among the raters (other than what would be expected by chance)
then a score smaller than 0 is returned.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``ratings`` (:class:`~torch.Tensor`): Ratings of shape ``[n_samples, n_categories]`` or
``[n_samples, n_categories, n_raters]`` depedenent on ``mode``. If ``mode`` is ``counts``, ``ratings`` must be
integer and contain the number of raters that chose each category. If ``mode`` is ``probs``, ``ratings`` must be
floating point and contain the probability/logits that each rater chose each category.
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``fleiss_k`` (:class:`~torch.Tensor`): A float scalar tensor with the calculated Fleiss' kappa score.
Args:
mode: Whether `ratings` will be provided as counts or probabilities.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> # Ratings are provided as counts
>>> import torch
>>> from torchmetrics.nominal import FleissKappa
>>> _ = torch.manual_seed(42)
>>> ratings = torch.randint(0, 10, size=(100, 5)).long() # 100 samples, 5 categories, 10 raters
>>> metric = FleissKappa(mode='counts')
>>> metric(ratings)
tensor(0.0089)
Example:
>>> # Ratings are provided as probabilities
>>> import torch
>>> from torchmetrics.nominal import FleissKappa
>>> _ = torch.manual_seed(42)
>>> ratings = torch.randn(100, 5, 10).softmax(dim=1) # 100 samples, 5 categories, 10 raters
>>> metric = FleissKappa(mode='probs')
>>> metric(ratings)
tensor(-0.0105)
"""
full_state_update: bool = False
is_differentiable: bool = False
higher_is_better: bool = True
plot_upper_bound: float = 1.0
counts: List[Tensor]
def __init__(self, mode: Literal["counts", "probs"] = "counts", **kwargs: Any) -> None:
super().__init__(**kwargs)
if mode not in ["counts", "probs"]:
raise ValueError("Argument ``mode`` must be one of 'counts' or 'probs'.")
self.mode = mode
self.add_state("counts", default=[], dist_reduce_fx="cat")
def update(self, ratings: Tensor) -> None:
"""Updates the counts for fleiss kappa metric."""
counts = _fleiss_kappa_update(ratings, self.mode)
self.counts.append(counts)
def compute(self) -> Tensor:
"""Computes Fleiss' kappa."""
counts = dim_zero_cat(self.counts)
return _fleiss_kappa_compute(counts)
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 FleissKappa
>>> metric = FleissKappa(mode="probs")
>>> metric.update(torch.randn(100, 5, 10).softmax(dim=1))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.nominal import FleissKappa
>>> metric = FleissKappa(mode="probs")
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.randn(100, 5, 10).softmax(dim=1)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)