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# 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 collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor, tensor
from torchmetrics.functional.regression.minkowski import _minkowski_distance_compute, _minkowski_distance_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.exceptions import TorchMetricsUserError
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["MinkowskiDistance.plot"]
class MinkowskiDistance(Metric):
r"""Compute `Minkowski Distance`_.
.. math::
d_{\text{Minkowski}} = \sum_{i}^N (| y_i - \hat{y_i} |^p)^\frac{1}{p}
where
:math: `y` is a tensor of target values,
:math: `\hat{y}` is a tensor of predictions,
:math: `\p` is a non-negative integer or floating-point number
This metric can be seen as generalized version of the standard euclidean distance which corresponds to minkowski
distance with p=2.
Args:
p: int or float larger than 1, exponent to which the difference between preds and target is to be raised
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> from torchmetrics.regression import MinkowskiDistance
>>> target = tensor([1.0, 2.8, 3.5, 4.5])
>>> preds = tensor([6.1, 2.11, 3.1, 5.6])
>>> minkowski_distance = MinkowskiDistance(3)
>>> minkowski_distance(preds, target)
tensor(5.1220)
"""
is_differentiable: Optional[bool] = True
higher_is_better: Optional[bool] = False
full_state_update: Optional[bool] = False
plot_lower_bound: float = 0.0
minkowski_dist_sum: Tensor
def __init__(self, p: float, **kwargs: Any) -> None:
super().__init__(**kwargs)
if not (isinstance(p, (float, int)) and p >= 1):
raise TorchMetricsUserError(f"Argument ``p`` must be a float or int greater than 1, but got {p}")
self.p = p
self.add_state("minkowski_dist_sum", default=tensor(0.0), dist_reduce_fx="sum")
def update(self, preds: Tensor, targets: Tensor) -> None:
"""Update state with predictions and targets."""
minkowski_dist_sum = _minkowski_distance_update(preds, targets, self.p)
self.minkowski_dist_sum += minkowski_dist_sum
def compute(self) -> Tensor:
"""Compute metric."""
return _minkowski_distance_compute(self.minkowski_dist_sum, self.p)
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 MinkowskiDistance
>>> metric = MinkowskiDistance(p=3)
>>> 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 MinkowskiDistance
>>> metric = MinkowskiDistance(p=3)
>>> 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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