plots ¶

def PR_contour(ax:[matplotlib.axes.Axes|None]=None):
    """Generate a nice-looking contour plot for Precision vs. Recall

    For an example, see the [Tutorial](getting-started/02-tutorial/#optional-plotting-utilities)

    REQUIRES optional `contingency[plot]` dependencies! See [Installation](getting-started/01-installation).
    """
    if not _has_plot:
        raise ImportError("Optional contingiency[plot] dependencies required.")

    if ax is None: 
        ax = plt.gca()
    thres = np.linspace(0.2, 0.8, num=4)
    lines, labels = [], []
    for t in thres:
        recall_f1 = np.linspace(t/(2-t), 1.)
        recall_fm = np.linspace(t**2,1.)
        prec_f1 = t * recall_f1 / (2 * recall_f1 - t)
        prec_fm = t**2/recall_fm

        (l,) = ax.plot(recall_f1, prec_f1, color="0.8")
        (l,) = ax.plot(recall_fm, prec_fm, color="0.95")
        ax.annotate(
            f"{t:0.1f}",
            xy=(t-.02, t-0.02),
            color='0.8',
            bbox=dict(facecolor='white', linewidth=0, alpha=0.5)
        )

    ax.annotate(r"$F_1$", xy=(1.01, 0.2/(2-0.2)-0.01), color='0.8')
    ax.annotate(r"F-M", xy=(1.01, 0.2**2-0.01), color='0.9')
    ax.set(
        ylim=(0,1.1),
        xlim=(0,1.1),
        ylabel='Precision',
        xlabel='Recall'
    )