rand_score.py

# 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 torchmetrics.functional.clustering.rand_score import rand_score
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__ = ["RandScore.plot"]


class RandScore(Metric):
    r"""Compute `Rand Score`_ (alternatively known as Rand Index).

    .. math::
        RS(U, V) = \text{number of agreeing pairs} / \text{number of pairs}

    The number of agreeing pairs is every :math:`(i, j)` pair of samples where :math:`i \in U` and :math:`j \in V`
    (the predicted and true clusterings, respectively) that are in the same cluster for both clusterings. The metric is
    symmetric, therefore swapping :math:`U` and :math:`V` yields the same rand score.

    This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not
    be available in practice since clustering in generally is used for unsupervised learning.

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
    - ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels

    As output of ``forward`` and ``compute`` the metric returns the following output:

    - ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score

    Args:
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example::
        >>> import torch
        >>> from torchmetrics.clustering import RandScore
        >>> preds = torch.tensor([2, 1, 0, 1, 0])
        >>> target = torch.tensor([0, 2, 1, 1, 0])
        >>> metric = RandScore()
        >>> metric(preds, target)
        tensor(0.6000)

    """

    is_differentiable = True
    higher_is_better = None
    full_state_update: bool = True
    plot_lower_bound: float = 0.0
    preds: List[Tensor]
    target: List[Tensor]
    contingency: Tensor

    def __init__(self, **kwargs: Any) -> None:
        super().__init__(**kwargs)

        self.add_state("preds", default=[], dist_reduce_fx="cat")
        self.add_state("target", default=[], dist_reduce_fx="cat")

    def update(self, preds: Tensor, target: Tensor) -> None:
        """Update state with predictions and targets."""
        self.preds.append(preds)
        self.target.append(target)

    def compute(self) -> Tensor:
        """Compute rand score over state."""
        return rand_score(dim_zero_cat(self.preds), dim_zero_cat(self.target))

    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.clustering import RandScore
            >>> metric = RandScore()
            >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))
            >>> fig_, ax_ = metric.plot(metric.compute())

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.clustering import RandScore
            >>> metric = RandScore()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)