tweedie_deviance.py

# Copyright The PyTorch 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

import torch
from torch import Tensor

from torchmetrics.functional.regression.tweedie_deviance import (
    _tweedie_deviance_score_compute,
    _tweedie_deviance_score_update,
)
from torchmetrics.metric import Metric


class TweedieDevianceScore(Metric):
    r"""Computes the `Tweedie Deviance Score`_ between targets and predictions:

    .. math::
        deviance\_score(\hat{y},y) =
        \begin{cases}
        (\hat{y} - y)^2, & \text{for }p=0\\
        2 * (y * log(\frac{y}{\hat{y}}) + \hat{y} - y),  & \text{for }p=1\\
        2 * (log(\frac{\hat{y}}{y}) + \frac{y}{\hat{y}} - 1),  & \text{for }p=2\\
        2 * (\frac{(max(y,0))^{2 - p}}{(1 - p)(2 - p)} - \frac{y(\hat{y})^{1 - p}}{1 - p} + \frac{(
            \hat{y})^{2 - p}}{2 - p}), & \text{otherwise}
        \end{cases}

    where :math:`y` is a tensor of targets values, :math:`\hat{y}` is a tensor of predictions, and
    :math:`p` is the `power`.

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

    - ``preds`` (:class:`~torch.Tensor`): Predicted float tensor with shape ``(N,...)``
    - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,...)``

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

    - ``deviance_score`` (:class:`~torch.Tensor`): A tensor with the deviance score

    Args:
        power:

            - power < 0 : Extreme stable distribution. (Requires: preds > 0.)
            - power = 0 : Normal distribution. (Requires: targets and preds can be any real numbers.)
            - power = 1 : Poisson distribution. (Requires: targets >= 0 and y_pred > 0.)
            - 1 < p < 2 : Compound Poisson distribution. (Requires: targets >= 0 and preds > 0.)
            - power = 2 : Gamma distribution. (Requires: targets > 0 and preds > 0.)
            - power = 3 : Inverse Gaussian distribution. (Requires: targets > 0 and preds > 0.)
            - otherwise : Positive stable distribution. (Requires: targets > 0 and preds > 0.)

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

    Example:
        >>> from torchmetrics import TweedieDevianceScore
        >>> targets = torch.tensor([1.0, 2.0, 3.0, 4.0])
        >>> preds = torch.tensor([4.0, 3.0, 2.0, 1.0])
        >>> deviance_score = TweedieDevianceScore(power=2)
        >>> deviance_score(preds, targets)
        tensor(1.2083)
    """
    is_differentiable: bool = True
    higher_is_better = None  # TODO: both -1 and 1 are optimal
    full_state_update: bool = False
    sum_deviance_score: Tensor
    num_observations: Tensor

    def __init__(
        self,
        power: float = 0.0,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        if 0 < power < 1:
            raise ValueError(f"Deviance Score is not defined for power={power}.")

        self.power: float = power

        self.add_state("sum_deviance_score", torch.tensor(0.0), dist_reduce_fx="sum")
        self.add_state("num_observations", torch.tensor(0), dist_reduce_fx="sum")

    def update(self, preds: Tensor, targets: Tensor) -> None:
        """Update metric states with predictions and targets."""
        sum_deviance_score, num_observations = _tweedie_deviance_score_update(preds, targets, self.power)

        self.sum_deviance_score += sum_deviance_score
        self.num_observations += num_observations

    def compute(self) -> Tensor:
        return _tweedie_deviance_score_compute(self.sum_deviance_score, self.num_observations)