125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from typing import Any, Callable, Optional
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from pytorch_lightning.metrics.metric import Metric
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from pytorch_lightning.utilities import rank_zero_warn
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from pytorch_lightning.metrics.functional.explained_variance import (
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_explained_variance_update,
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_explained_variance_compute,
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)
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class ExplainedVariance(Metric):
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r"""
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Computes `explained variance
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<https://en.wikipedia.org/wiki/Explained_variation>`_:
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.. math:: \text{ExplainedVariance} = 1 - \frac{\text{Var}(y - \hat{y})}{\text{Var}(y)}
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Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a
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tensor of predictions.
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Forward accepts
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- ``preds`` (float tensor): ``(N,)`` or ``(N, ...)`` (multioutput)
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- ``target`` (long tensor): ``(N,)`` or ``(N, ...)`` (multioutput)
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In the case of multioutput, as default the variances will be uniformly
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averaged over the additional dimensions. Please see argument `multioutput`
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for changing this behavior.
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Args:
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multioutput:
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Defines aggregation in the case of multiple output scores. Can be one
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of the following strings (default is `'uniform_average'`.):
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* `'raw_values'` returns full set of scores
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* `'uniform_average'` scores are uniformly averaged
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* `'variance_weighted'` scores are weighted by their individual variances
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compute_on_step:
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Forward only calls ``update()`` and return None if this is set to False. default: True
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dist_sync_on_step:
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Synchronize metric state across processes at each ``forward()``
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before returning the value at the step. default: False
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process_group:
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Specify the process group on which synchronization is called. default: None (which selects the entire world)
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Example:
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>>> from pytorch_lightning.metrics import ExplainedVariance
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>>> target = torch.tensor([3, -0.5, 2, 7])
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>>> preds = torch.tensor([2.5, 0.0, 2, 8])
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>>> explained_variance = ExplainedVariance()
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>>> explained_variance(preds, target)
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tensor(0.9572)
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>>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]])
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>>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]])
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>>> explained_variance = ExplainedVariance(multioutput='raw_values')
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>>> explained_variance(preds, target)
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tensor([0.9677, 1.0000])
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"""
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def __init__(
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self,
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multioutput: str = 'uniform_average',
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compute_on_step: bool = True,
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dist_sync_on_step: bool = False,
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process_group: Optional[Any] = None,
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dist_sync_fn: Callable = None,
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):
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super().__init__(
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compute_on_step=compute_on_step,
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dist_sync_on_step=dist_sync_on_step,
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process_group=process_group,
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dist_sync_fn=dist_sync_fn,
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)
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allowed_multioutput = ('raw_values', 'uniform_average', 'variance_weighted')
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if multioutput not in allowed_multioutput:
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raise ValueError(
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f'Invalid input to argument `multioutput`. Choose one of the following: {allowed_multioutput}'
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)
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self.multioutput = multioutput
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self.add_state("y", default=[], dist_reduce_fx=None)
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self.add_state("y_pred", default=[], dist_reduce_fx=None)
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rank_zero_warn(
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'Metric `ExplainedVariance` will save all targets and'
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' predictions in buffer. For large datasets this may lead'
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' to large memory footprint.'
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)
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def update(self, preds: torch.Tensor, target: torch.Tensor):
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"""
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Update state with predictions and targets.
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Args:
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preds: Predictions from model
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target: Ground truth values
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"""
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preds, target = _explained_variance_update(preds, target)
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self.y_pred.append(preds)
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self.y.append(target)
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def compute(self):
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"""
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Computes explained variance over state.
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"""
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preds = torch.cat(self.y_pred, dim=0)
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target = torch.cat(self.y, dim=0)
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return _explained_variance_compute(preds, target, self.multioutput)
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