roc_vs_pr_curve.png

def fig_roc_vs_pr_curve(y_test, proba) -> None:
    from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score

    fpr, tpr, _ = roc_curve(y_test, proba)
    roc_auc = roc_auc_score(y_test, proba)

    precision, recall, _ = precision_recall_curve(y_test, proba)
    pr_auc = average_precision_score(y_test, proba)

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4.5))

    # ROC Curve
    ax1.plot(fpr, tpr, color=PRIMARY, lw=2.2, label=f"ROC-Kurve (AUC = {roc_auc:.3f})")
    ax1.plot([0, 1], [0, 1], color=SECONDARY, lw=1.2, ls="--", label="Zufall (AUC = 0.500)")
    ax1.fill_between(fpr, tpr, alpha=0.10, color=PRIMARY)
    ax1.set_xlabel("1 - Spezifität (FPR)")
    ax1.set_ylabel("Sensitivität (TPR)")
    ax1.set_title("ROC-Kurve: Gute Gesamtleistung")
    ax1.legend(loc="lower right")
    ax1.grid(True, linestyle=":", alpha=0.6)

    # PR Curve
    ax2.plot(recall, precision, color=EVENT, lw=2.2, label=f"PR-Kurve (AUC/AP = {pr_auc:.3f})")
    base_rate = float(np.mean(y_test))
    ax2.axhline(base_rate, color=SECONDARY, lw=1.2, ls="--", label=f"Zufall / Prävalenz (AP = {base_rate:.3f})")
    ax2.fill_between(recall, precision, alpha=0.10, color=EVENT)
    ax2.set_xlabel("Sensitivität (Recall)")
    ax2.set_ylabel("Präzision (PPV)")
    ax2.set_title("Precision-Recall-Kurve: Demaskiert schlechte PPV bei seltenem Event")
    ax2.legend(loc="lower left")
    ax2.grid(True, linestyle=":", alpha=0.6)

    plt.tight_layout()
    save(fig, ASSETS / "roc_vs_pr_curve.png")