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Source code for pytorch_lightning.metrics.regression.explained_variance

# 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.
import torch
from typing import Any, Callable, Optional

from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.metrics.functional.explained_variance import (
    _explained_variance_update,
    _explained_variance_compute,
)


[docs]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 pytorch_lightning.metrics 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.' )
[docs] 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)
[docs] 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)

© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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