174 lines
6.8 KiB
Python
174 lines
6.8 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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from distutils.version import LooseVersion
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from typing import Any, Callable, Optional
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import torch
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from pytorch_lightning.metrics.functional.auroc import _auroc_compute, _auroc_update
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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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class AUROC(Metric):
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r"""Compute `Area Under the Receiver Operating Characteristic Curve (ROC AUC)
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<https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Further_interpretations>`_.
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Works for both binary, multilabel and multiclass problems. In the case of
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multiclass, the values will be calculated based on a one-vs-the-rest approach.
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Forward accepts
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- ``preds`` (float tensor): ``(N, ...)`` (binary) or ``(N, C, ...)`` (multiclass) tensor
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with probabilities, where C is the number of classes.
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- ``target`` (long tensor): ``(N, ...)`` or ``(N, C, ...)`` with integer labels
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For non-binary input, if the ``preds`` and ``target`` tensor have the same
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size the input will be interpretated as multilabel and if ``preds`` have one
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dimension more than the ``target`` tensor the input will be interpretated as
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multiclass.
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Args:
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num_classes: integer with number of classes. Not nessesary to provide
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for binary problems.
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pos_label: integer determining the positive class. Default is ``None``
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which for binary problem is translate to 1. For multiclass problems
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this argument should not be set as we iteratively change it in the
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range [0,num_classes-1]
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average:
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- ``'macro'`` computes metric for each class and uniformly averages them
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- ``'weighted'`` computes metric for each class and does a weighted-average,
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where each class is weighted by their support (accounts for class imbalance)
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- ``None`` computes and returns the metric per class
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max_fpr:
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If not ``None``, calculates standardized partial AUC over the
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range [0, max_fpr]. Should be a float between 0 and 1.
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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.
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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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dist_sync_fn:
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Callback that performs the allgather operation on the metric state. When ``None``, DDP
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will be used to perform the allgather
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Example (binary case):
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>>> from pytorch_lightning.metrics import AUROC
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>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34])
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>>> target = torch.tensor([0, 0, 1, 1, 1])
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>>> auroc = AUROC(pos_label=1)
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>>> auroc(preds, target)
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tensor(0.5000)
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Example (multiclass case):
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>>> from pytorch_lightning.metrics import AUROC
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>>> preds = torch.tensor([[0.90, 0.05, 0.05],
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... [0.05, 0.90, 0.05],
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... [0.05, 0.05, 0.90],
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... [0.85, 0.05, 0.10],
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... [0.10, 0.10, 0.80]])
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>>> target = torch.tensor([0, 1, 1, 2, 2])
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>>> auroc = AUROC(num_classes=3)
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>>> auroc(preds, target)
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tensor(0.7778)
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"""
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def __init__(
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self,
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num_classes: Optional[int] = None,
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pos_label: Optional[int] = None,
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average: Optional[str] = 'macro',
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max_fpr: Optional[float] = None,
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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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self.num_classes = num_classes
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self.pos_label = pos_label
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self.average = average
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self.max_fpr = max_fpr
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allowed_average = (None, 'macro', 'weighted')
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if self.average not in allowed_average:
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raise ValueError(
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f'Argument `average` expected to be one of the following: {allowed_average} but got {average}'
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)
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if self.max_fpr is not None:
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if (not isinstance(max_fpr, float) and 0 < max_fpr <= 1):
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raise ValueError(f"`max_fpr` should be a float in range (0, 1], got: {max_fpr}")
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if LooseVersion(torch.__version__) < LooseVersion('1.6.0'):
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raise RuntimeError(
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'`max_fpr` argument requires `torch.bucketize` which is not available below PyTorch version 1.6'
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)
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self.mode = None
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self.add_state("preds", default=[], dist_reduce_fx=None)
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self.add_state("target", default=[], dist_reduce_fx=None)
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rank_zero_warn(
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'Metric `AUROC` will save all targets and predictions in buffer.'
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' For large datasets this may lead 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 (probabilities, or labels)
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target: Ground truth labels
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"""
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preds, target, mode = _auroc_update(preds, target)
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self.preds.append(preds)
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self.target.append(target)
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if self.mode is not None and self.mode != mode:
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raise ValueError(
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'The mode of data (binary, multi-label, multi-class) should be constant, but changed'
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f' between batches from {self.mode} to {mode}'
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)
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self.mode = mode
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def compute(self) -> torch.Tensor:
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"""
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Computes AUROC based on inputs passed in to ``update`` previously.
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"""
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preds = torch.cat(self.preds, dim=0)
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target = torch.cat(self.target, dim=0)
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return _auroc_compute(
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preds,
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target,
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self.mode,
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self.num_classes,
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self.pos_label,
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self.average,
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self.max_fpr,
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)
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