Source code for anomalib.utils.metrics.auroc

"""Implementation of AUROC metric based on TorchMetrics."""
from typing import Tuple

import torch
from matplotlib.figure import Figure
from torch import Tensor
from torchmetrics import ROC
from torchmetrics.functional import auc

from .plotting_utils import plot_figure


[docs]class AUROC(ROC):
    """Area under the ROC curve."""

[docs]    def compute(self) -> Tensor:
        """First compute ROC curve, then compute area under the curve.

        Returns:
            Tensor: Value of the AUROC metric
        """
        tpr: Tensor
        fpr: Tensor

        fpr, tpr = self._compute()
        # TODO: use stable sort after upgrading to pytorch 1.9.x (https://github.com/openvinotoolkit/anomalib/issues/92)
        if not (torch.all(fpr.diff() <= 0) or torch.all(fpr.diff() >= 0)):
            return auc(fpr, tpr, reorder=True)  # only reorder if fpr is not increasing or decreasing
        return auc(fpr, tpr)

[docs]    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
        """Update state with new values.

        Need to flatten new values as ROC expects them in this format for binary classification.

        Args:
            preds (Tensor): predictions of the model
            target (Tensor): ground truth targets
        """
        super().update(preds.flatten(), target.flatten())

[docs]    def _compute(self) -> Tuple[Tensor, Tensor]:
        """Compute fpr/tpr value pairs.

        Returns:
            Tuple containing Tensors for fpr and tpr
        """
        tpr: Tensor
        fpr: Tensor
        fpr, tpr, _thresholds = super().compute()
        return (fpr, tpr)

[docs]    def generate_figure(self) -> Tuple[Figure, str]:
        """Generate a figure containing the ROC curve, the baseline and the AUROC.

        Returns:
            Tuple[Figure, str]: Tuple containing both the figure and the figure title to be used for logging
        """
        fpr, tpr = self._compute()
        auroc = self.compute()

        xlim = (0.0, 1.0)
        ylim = (0.0, 1.0)
        xlabel = "False Positive Rate"
        ylabel = "True Positive Rate"
        loc = "lower right"
        title = "ROC"

        fig, axis = plot_figure(fpr, tpr, auroc, xlim, ylim, xlabel, ylabel, loc, title)

        axis.plot(
            [0, 1],
            [0, 1],
            color="navy",
            lw=2,
            linestyle="--",
            figure=fig,
        )

        return fig, title