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"""Implementation of AUROC metric based on TorchMetrics.""" |
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import torch |
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from matplotlib.figure import Figure |
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from torchmetrics.classification.roc import BinaryROC |
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from torchmetrics.utilities.compute import auc |
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from .plotting_utils import plot_figure |
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class AUROC(BinaryROC): |
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"""Area under the ROC curve. |
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Examples: |
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>>> import torch |
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>>> from anomalib.metrics import AUROC |
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... |
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>>> preds = torch.tensor([0.13, 0.26, 0.08, 0.92, 0.03]) |
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>>> target = torch.tensor([0, 0, 1, 1, 0]) |
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... |
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>>> auroc = AUROC() |
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>>> auroc(preds, target) |
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tensor(0.6667) |
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It is possible to update the metric state incrementally: |
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>>> auroc.update(preds[:2], target[:2]) |
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>>> auroc.update(preds[2:], target[2:]) |
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>>> auroc.compute() |
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tensor(0.6667) |
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To plot the ROC curve, use the ``generate_figure`` method: |
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>>> fig, title = auroc.generate_figure() |
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""" |
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def compute(self) -> torch.Tensor: |
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"""First compute ROC curve, then compute area under the curve. |
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Returns: |
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Tensor: Value of the AUROC metric |
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""" |
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tpr: torch.Tensor |
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fpr: torch.Tensor |
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fpr, tpr = self._compute() |
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return auc(fpr, tpr, reorder=True) |
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def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: |
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"""Update state with new values. |
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Need to flatten new values as ROC expects them in this format for binary classification. |
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Args: |
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preds (torch.Tensor): predictions of the model |
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target (torch.Tensor): ground truth targets |
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""" |
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super().update(preds.flatten(), target.flatten()) |
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def _compute(self) -> tuple[torch.Tensor, torch.Tensor]: |
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"""Compute fpr/tpr value pairs. |
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Returns: |
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Tuple containing Tensors for fpr and tpr |
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""" |
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tpr: torch.Tensor |
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fpr: torch.Tensor |
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fpr, tpr, _thresholds = super().compute() |
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return (fpr, tpr) |
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def generate_figure(self) -> tuple[Figure, str]: |
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"""Generate a figure containing the ROC curve, the baseline and the AUROC. |
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Returns: |
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tuple[Figure, str]: Tuple containing both the figure and the figure title to be used for logging |
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""" |
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fpr, tpr = self._compute() |
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auroc = self.compute() |
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xlim = (0.0, 1.0) |
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ylim = (0.0, 1.0) |
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xlabel = "False Positive Rate" |
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ylabel = "True Positive Rate" |
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loc = "lower right" |
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title = "ROC" |
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fig, axis = plot_figure(fpr, tpr, auroc, xlim, ylim, xlabel, ylabel, loc, title) |
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axis.plot( |
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[0, 1], |
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[0, 1], |
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color="navy", |
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lw=2, |
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linestyle="--", |
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figure=fig, |
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) |
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return fig, title |
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