fowlkes_mallows_index.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 import fowlkes_mallows_index
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__ = ["FowlkesMallowsIndex.plot"]


class FowlkesMallowsIndex(Metric):
    r"""Compute `Fowlkes-Mallows Index`_.

    .. math::
        FMI(U,V) = \frac{TP}{\sqrt{(TP + FP) * (TP + FN)}}

    Where :math:`TP` is the number of true positives, :math:`FP` is the number of false positives, and :math:`FN` is
    the number of false negatives.

    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:

    - ``fmi`` (:class:`~torch.Tensor`): A tensor with the Fowlkes-Mallows index.

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

    Example::
        >>> import torch
        >>> from torchmetrics.clustering import FowlkesMallowsIndex
        >>> preds = torch.tensor([2, 2, 0, 1, 0])
        >>> target = torch.tensor([2, 2, 1, 1, 0])
        >>> fmi = FowlkesMallowsIndex()
        >>> fmi(preds, target)
        tensor(0.5000)

    """

    is_differentiable: bool = True
    higher_is_better: Optional[bool] = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 1.0
    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 Fowlkes-Mallows index over state."""
        return fowlkes_mallows_index(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 FowlkesMallowsIndex
            >>> metric = FowlkesMallowsIndex()
            >>> 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 FowlkesMallowsIndex
            >>> metric = FowlkesMallowsIndex()
            >>> 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)