drift_monitoring.png

def fig_drift_monitoring(pipe: Pipeline, X_test: pd.Series, y_test: pd.Series) -> None:
    """Line plot of AUC vs. simulated concept drift level over 5 monitoring periods."""
    rng = np.random.default_rng(SEED)
    aucs = []
    for flip_rate in DRIFT_FLIP_RATES:
        y_drifted = simulate_concept_drift(y_test, flip_rate, rng)
        proba = pipe.predict_proba(X_test)[:, 1]
        aucs.append(roc_auc_score(y_drifted, proba))

    labels = [f"T{i+1}\n({fr:.0%} Drift)" for i, fr in enumerate(DRIFT_FLIP_RATES)]

    fig, ax = plt.subplots(figsize=(6.5, 4))
    ax.plot(range(len(DRIFT_FLIP_RATES)), aucs, marker="o", color=PRIMARY, lw=2, label="AUC")
    ax.axhline(aucs[0], color=SECONDARY, lw=0.9, ls="--",
               label=f"Baseline T1 ({aucs[0]:.2f})")
    ax.axhline(0.70, color=EVENT, lw=0.9, ls=":",
               label="Akzeptanzgrenze (0.70)")
    ax.fill_between(range(len(DRIFT_FLIP_RATES)), aucs, aucs[0],
                    where=[a < aucs[0] for a in aucs],
                    alpha=0.15, color=EVENT)
    ax.set_xticks(range(len(DRIFT_FLIP_RATES)))
    ax.set_xticklabels(labels)
    ax.set_ylim(0.4, 1.0)
    ax.set_xlabel("Monitoring-Zeitpunkt (Anteil Konzept-Drift)")
    ax.set_ylabel("ROC-AUC")
    ax.set_title("AUC-Verlauf unter simuliertem Konzept-Drift")
    ax.legend(loc="lower left")
    save(fig, ASSETS / "drift_monitoring.png")