File size: 5,664 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 |
# 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.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
>>> from torch import randint
>>> from torchmetrics.nominal import FleissKappa
>>> ratings = 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
>>> from torch import randn
>>> from torchmetrics.nominal import FleissKappa
>>> ratings = randn(100, 5, 10).softmax(dim=1) # 100 samples, 5 categories, 10 raters
>>> metric = FleissKappa(mode='probs')
>>> metric(ratings)
tensor(-0.0075)
"""
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)
|