symmetric_mean_absolute_percentage_error.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, Callable, Optional

import torch
from torch import Tensor, tensor

from torchmetrics.functional.regression.symmetric_mean_absolute_percentage_error import (
    _symmetric_mean_absolute_percentage_error_compute,
    _symmetric_mean_absolute_percentage_error_update,
)
from torchmetrics.metric import Metric


class SymmetricMeanAbsolutePercentageError(Metric):
    r"""
    Computes symmetric mean absolute percentage error (`SMAPE`_).

    .. math:: \text{SMAPE} = \frac{2}{n}\sum_1^n\frac{max(|   y_i - \hat{y_i} |}{| y_i | + | \hat{y_i} |, \epsilon)}

    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.

    Args:
        compute_on_step:
            Forward only calls ``update()`` and return None if this is set to False.
        dist_sync_on_step:
            Synchronize metric state across processes at each ``forward()`` before returning the value at the step.
        process_group:
            Specify the process group on which synchronization is called. default: None (which selects the entire world)

    Note:
        The epsilon value is taken from `scikit-learn's implementation of SMAPE`_.

    Note:
        SMAPE output is a non-negative floating point between 0 and 1. Best result is 0.0 .


    Example:
        >>> from torchmetrics import SymmetricMeanAbsolutePercentageError
        >>> target = torch.tensor([1, 10, 1e6])
        >>> preds = torch.tensor([0.9, 15, 1.2e6])
        >>> smape = SymmetricMeanAbsolutePercentageError()
        >>> smape(preds, target)
        tensor(0.2290)
    """
    sum_abs_per_error: Tensor
    total: Tensor

    def __init__(
        self,
        compute_on_step: bool = True,
        dist_sync_on_step: bool = False,
        process_group: Optional[Any] = None,
        dist_sync_fn: Callable = None,
    ) -> None:
        super().__init__(
            compute_on_step=compute_on_step,
            dist_sync_on_step=dist_sync_on_step,
            process_group=process_group,
            dist_sync_fn=dist_sync_fn,
        )

        self.add_state("sum_abs_per_error", default=tensor(0.0), dist_reduce_fx="sum")
        self.add_state("total", default=tensor(0.0), dist_reduce_fx="sum")

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

        Args:
            preds: Predictions from model
            target: Ground truth values
        """
        sum_abs_per_error, num_obs = _symmetric_mean_absolute_percentage_error_update(preds, target)

        self.sum_abs_per_error += sum_abs_per_error
        self.total += num_obs

    def compute(self) -> Tensor:
        """Computes mean absolute percentage error over state."""
        return _symmetric_mean_absolute_percentage_error_compute(self.sum_abs_per_error, self.total)

    @property
    def is_differentiable(self) -> bool:
        return True