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# 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)
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