print("HMC Checker Report") print("=" * 40) print(f"Overall status: {'✅ PASS' if report['passed'] else '❌ FAIL'}") if report["warnings"]: print("\n⚠️ Warnings:") for w in report["warnings"]: print(f" - {w}") if report["failures"]: print("\n❌ Failures:") for f in report["failures"]: print(f" - {f}") You could also wrap it as:
# 6. Energy plot check (text summary) if hasattr(inference_data, "sample_stats") and hasattr(inference_data.sample_stats, "energy"): energy = inference_data.sample_stats.energy.values # simple check: coefficient of variation across chains chain_means = energy.mean(axis=1) cv = np.std(chain_means) / np.mean(chain_means) if cv > 0.1: results["warnings"].append(f"Energy means vary across chains (CV={cv:.3f})") hmc checker
# 4. Tree depth if hasattr(inference_data, "sample_stats") and hasattr(inference_data.sample_stats, "tree_depth"): depths = inference_data.sample_stats.tree_depth.values max_depth = np.max(depths) # depends on sampler # typical max depth is 10 at_max = (depths == max_depth).mean() if at_max > max_tree_depth_fraction: results["warnings"].append(f"Frequent max tree depth ({at_max:.2f})") Divergent transitions if hasattr(inference_data
# 3. Divergent transitions if hasattr(inference_data, "sample_stats"): diverging = inference_data.sample_stats.diverging.values div_frac = np.mean(diverging) if div_frac > max_divergent_fraction: results["failures"].append(f"Divergent fraction = {div_frac:.3f} > {max_divergent_fraction}") results["passed"] = False elif div_frac > 0: results["warnings"].append(f"Some divergent transitions ({div_frac:.3f})") "sample_stats") and hasattr(inference_data.sample_stats
# 5. BFMI if hasattr(inference_data, "sample_stats") and hasattr(inference_data.sample_stats, "bfmi"): bfmi = inference_data.sample_stats.bfmi.values.mean() if bfmi < bfmi_threshold: results["failures"].append(f"BFMI = {bfmi:.2f} < {bfmi_threshold}") results["passed"] = False