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roc

ROC Plotting API

This module provides utility functions to plot ROC curves.

Functions:

Functions

roc_auc_plot 

roc_auc_plot(y_true: npt.NDArray, y_prob: npt.NDArray, labels: list[str], save_path: os.PathLike | None = None, **kwargs) -> tuple[plt.Figure, plt.Axes] | None

Generate ROC plot via matplotlib/seaborn

Parameters:

  • y_true (NDArray) –

    True y labels

  • y_prob (NDArray) –

    Predicted y labels

  • labels (list[str]) –

    Label names

  • save_path (str | None, default: None ) –

    Path to save plot. Defaults to None.

Returns:

  • tuple[Figure, Axes] | None –

    tuple[plt.Figure, plt.Axes] | None: Figure and axes

Source code in neuralspot_edge/plotting/roc.py 
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def roc_auc_plot(
    y_true: npt.NDArray,
    y_prob: npt.NDArray,
    labels: list[str],
    save_path: os.PathLike | None = None,
    **kwargs,
) -> tuple[plt.Figure, plt.Axes] | None:
    """Generate ROC plot via matplotlib/seaborn

    Args:
        y_true (npt.NDArray): True y labels
        y_prob (npt.NDArray): Predicted y labels
        labels (list[str]): Label names
        save_path (str | None): Path to save plot. Defaults to None.

    Returns:
        tuple[plt.Figure, plt.Axes] | None: Figure and axes
    """

    fpr, tpr, _ = roc_curve(y_true, y_prob)
    roc_auc = auc(fpr, tpr)
    fig, ax = plt.subplots(figsize=kwargs.get("figsize", (10, 8)))
    label = f"ROC curve (area = {roc_auc:0.2f})"
    ax.plot(fpr, tpr, lw=2, color="darkorange", label=label)
    ax.plot([0, 1], [0, 1], color="navy", lw=2, linestyle="--")
    ax.set_xlim([0.0, 1.0])
    ax.set_ylim([0.0, 1.05])
    ax.set_xlabel("False Positive Rate")
    ax.set_ylabel("True Positive Rate")
    ax.set_title("ROC-AUC")
    fig.legend(loc="lower right")
    if save_path:
        fig.savefig(save_path)
        plt.close(fig)
        return None
    return fig, ax