Hmc Checker 〈Linux HIGH-QUALITY〉

# 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})")

# For demo, create dummy data import pymc as pm with pm.Model(): x = pm.Normal("x") trace = pm.sample(1000, chains=2, return_inferencedata=True) hmc checker

# 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

# 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})") # 4. Tree depth if hasattr(inference_data