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explained_variance.py
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explained_variance.py
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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 typing import Any, Callable, Optional
import torch
from torchmetrics.functional.regression.explained_variance import (
_explained_variance_compute,
_explained_variance_update,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
class ExplainedVariance(Metric):
r"""
Computes `explained variance
<https://en.wikipedia.org/wiki/Explained_variation>`_:
.. math:: \text{ExplainedVariance} = 1 - \frac{\text{Var}(y - \hat{y})}{\text{Var}(y)}
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
tensor of predictions.
Forward accepts
- ``preds`` (float tensor): ``(N,)`` or ``(N, ...)`` (multioutput)
- ``target`` (long tensor): ``(N,)`` or ``(N, ...)`` (multioutput)
In the case of multioutput, as default the variances will be uniformly
averaged over the additional dimensions. Please see argument `multioutput`
for changing this behavior.
Args:
multioutput:
Defines aggregation in the case of multiple output scores. Can be one
of the following strings (default is `'uniform_average'`.):
* `'raw_values'` returns full set of scores
* `'uniform_average'` scores are uniformly averaged
* `'variance_weighted'` scores are weighted by their individual variances
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step. default: False
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
Example:
>>> from torchmetrics import ExplainedVariance
>>> target = torch.tensor([3, -0.5, 2, 7])
>>> preds = torch.tensor([2.5, 0.0, 2, 8])
>>> explained_variance = ExplainedVariance()
>>> explained_variance(preds, target)
tensor(0.9572)
>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
>>> explained_variance = ExplainedVariance(multioutput='raw_values')
>>> explained_variance(preds, target)
tensor([0.9677, 1.0000])
"""
def __init__(
self,
multioutput: str = 'uniform_average',
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = 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,
)
allowed_multioutput = ('raw_values', 'uniform_average', 'variance_weighted')
if multioutput not in allowed_multioutput:
raise ValueError(
f'Invalid input to argument `multioutput`. Choose one of the following: {allowed_multioutput}'
)
self.multioutput = multioutput
self.add_state("y", default=[], dist_reduce_fx=None)
self.add_state("y_pred", default=[], dist_reduce_fx=None)
rank_zero_warn(
'Metric `ExplainedVariance` will save all targets and'
' predictions in buffer. For large datasets this may lead'
' to large memory footprint.'
)
def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
preds, target = _explained_variance_update(preds, target)
self.y_pred.append(preds)
self.y.append(target)
def compute(self):
"""
Computes explained variance over state.
"""
preds = torch.cat(self.y_pred, dim=0)
target = torch.cat(self.y, dim=0)
return _explained_variance_compute(preds, target, self.multioutput)