# 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, Optional, Union import torch from torch import Tensor from torchmetrics.functional.regression.wmape import ( _weighted_mean_absolute_percentage_error_compute, _weighted_mean_absolute_percentage_error_update, ) from torchmetrics.metric import Metric from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE if not _MATPLOTLIB_AVAILABLE: __doctest_skip__ = ["WeightedMeanAbsolutePercentageError.plot"] class WeightedMeanAbsolutePercentageError(Metric): r"""Compute weighted mean absolute percentage error (`WMAPE`_). The output of WMAPE metric is a non-negative floating point, where the optimal value is 0. It is computes as: .. math:: \text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| } Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predictions from model - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``wmape`` (:class:`~torch.Tensor`): A tensor with non-negative floating point wmape value between 0 and 1 Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import randn >>> preds = randn(20,) >>> target = randn(20,) >>> wmape = WeightedMeanAbsolutePercentageError() >>> wmape(preds, target) tensor(1.3967) """ is_differentiable: bool = True higher_is_better: bool = False full_state_update: bool = False plot_lower_bound: float = 0.0 sum_abs_error: Tensor sum_scale: Tensor def __init__(self, **kwargs: Any) -> None: super().__init__(**kwargs) self.add_state("sum_abs_error", default=torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("sum_scale", default=torch.tensor(0.0), dist_reduce_fx="sum") def update(self, preds: Tensor, target: Tensor) -> None: """Update state with predictions and targets.""" sum_abs_error, sum_scale = _weighted_mean_absolute_percentage_error_update(preds, target) self.sum_abs_error += sum_abs_error self.sum_scale += sum_scale def compute(self) -> Tensor: """Compute weighted mean absolute percentage error over state.""" return _weighted_mean_absolute_percentage_error_compute(self.sum_abs_error, self.sum_scale) def plot( self, val: Optional[Union[Tensor, Sequence[Tensor]]] = 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 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import WeightedMeanAbsolutePercentageError >>> metric = WeightedMeanAbsolutePercentageError() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import WeightedMeanAbsolutePercentageError >>> metric = WeightedMeanAbsolutePercentageError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) """ return self._plot(val, ax)