"""ε sensitivity: is the per-episode ranking stable, or an artifact of over-wide state clusters?"""
import sys, json
from pathlib import Path
import numpy as np

sys.path.insert(0, "/workspace/teleop_std_poc")
import os
os.environ.setdefault("TSP_ROOT", "/workspace/std_mm_teleop")
from action_variance import load, score

eps_list = [0.5, 0.3, 0.2, 0.1]
episodes = load("isr")
S = np.concatenate([p for _, p, _ in episodes])
mu, sd = S.mean(0), S.std(0) + 1e-9
Z = (S - mu) / sd
res = {}
rank0 = None
out = {}
for e in eps_list:
    per, ds, _ = score(episodes, e, 2)
    names = list(per)
    vals = np.array([per[n]["score"] if per[n]["score"] is not None else np.nan for n in names])
    cov = np.mean([per[n]["scored_points"] / per[n]["total_points"] for n in names])
    # mean cluster size, sampled (full N^2 already done inside score, recompute cheaply on a sample)
    idx = np.random.default_rng(0).choice(len(Z), size=min(2000, len(Z)), replace=False)
    sizes = [(np.linalg.norm(Z - Z[i], axis=1) <= e).sum() for i in idx]
    r = np.argsort(np.argsort(np.nan_to_num(vals, nan=-1)))
    if rank0 is None:
        rank0 = r
        corr = 1.0
    else:
        corr = float(np.corrcoef(rank0, r)[0, 1])
    out[str(e)] = {"dataset_av": None if ds is None else round(ds, 4),
                   "coverage": round(100 * cov, 1),
                   "mean_cluster_size": round(float(np.mean(sizes)), 1),
                   "median_cluster_size": int(np.median(sizes)),
                   "cluster_pct_of_dataset": round(100 * float(np.mean(sizes)) / len(Z), 2),
                   "rank_corr_vs_eps0.5": round(corr, 3)}
    print(e, out[str(e)])
Path("/workspace/std_mm_teleop/out/eps_sensitivity.json").write_text(json.dumps(
    {"note": "per-episode ActionVariance ranking vs cluster radius on ISR-resampled data",
     "n_points": int(len(Z)), "eps": out}, indent=2))
print("wrote out/eps_sensitivity.json")