| """Write results/exploration_report.md and results/exploration_tau_table.json: every ROM condition tried on |
| goal set A versus Chrono (exploration, Sim2Sim).""" |
| import json |
|
|
| import numpy as np |
|
|
| import rankstats as rs |
| import variants as V |
| from analyze import load_chrono, load_rom |
| from common import RESULTS |
|
|
| B = 2000 |
| goals = json.loads((RESULTS / 'rom_A_explore/goals.json').read_text()) |
| gids = [g['goal_id'] for g in goals] |
| gr = np.array([g['goal_range'] for g in goals]) |
| pols, _, _, _ = load_rom('A', 'explore', 'nominal') |
| Tc, Sc = load_chrono('A', 'explore', pols, gids) |
| boot = rs.boot_indices(gr, B, 20261104) |
| sub_boot = {s: rs.boot_indices(gr[gr == s], B, 20261104 + 7 + i) for i, s in enumerate(['id', 'ood'])} |
| pools = {'all16': list(range(16)), 'new12': [i for i, p in enumerate(pols) if p not in ('rel1', 'rel2', 'rel3', 'rel4')]} |
| names = list(V.EXPLORE) |
| data = {n: load_rom('A', 'explore', n) for n in names} |
|
|
|
|
| def spec_text(n): |
| K, spec = V.EXPLORE[n] |
| if not spec: |
| return 'none (training ROM)' |
| parts = [] |
| for k, d in spec.items(): |
| if d[0] == 'const': |
| parts.append(f'{k}={d[1]:g}') |
| elif d[0] == 'normal': |
| parts.append(f'{k}~N({d[1]:g},{d[2]:g})') |
| elif d[0] == 'choice': |
| parts.append(f'{k} in {{{",".join(f"{x:g}" for x in d[1])}}}') |
| else: |
| parts.append(f'{k}~{"LogU" if d[0] == "loguniform" else "U"}({d[1]:.3g},{d[2]:.3g})') |
| return '; '.join(parts) |
|
|
|
|
| res = {} |
| for pname, idx in pools.items(): |
| Rc = rs.per_goal_ranks(Tc[idx]) |
| R = {n: rs.per_goal_ranks(data[n][2][idx]) for n in names} |
| out = {} |
| for sub, gi in {'all': np.arange(len(gids)), 'id': np.flatnonzero(gr == 'id'), 'ood': np.flatnonzero(gr == 'ood')}.items(): |
| b = boot if sub == 'all' else sub_boot[sub] |
| r, taus = rs.compare_to_truth({n: R[n][:, gi] for n in names}, Rc[:, gi], b) |
| for n in names: |
| o = out.setdefault(n, {}) |
| o[sub] = r[n]['kendall_tau_b'] |
| if sub == 'all': |
| o['ci'] = r[n]['kendall_ci95'] |
| o['d'] = r[n]['kendall_tau_b'] - r['nominal']['kendall_tau_b'] |
| o['dci'] = rs.ci(taus[n] - taus['nominal']) |
| o['sr'] = float(data[n][3][idx].mean()) |
| res[pname] = out |
|
|
| L = ['# Case 2 exploration on goal set A (Sim2Sim)', '', |
| 'Generated by `report_explore.py` from `results/rom_A_explore/*.npz` and `results/chrono_A_explore/episodes.jsonl`.', |
| 'Goal set A: 50 ID (10-20 m) + 50 OOD (20-40 m) goals. Pools: all 16 (released 1-4 + 12 new) and the 12 new policies.', |
| 'Kendall tau-b of the ROM mean-rank order with the Chrono mean-rank order; 95 % goal-bootstrap ranges (2000 replicates,', |
| 'stratified by goal range, paired). Delta = tau(condition) - tau(nominal ROM). These numbers were used to choose', |
| 'the TR design that was then fixed for the held-out evaluation on goal set B. They are optimistic for the chosen', |
| 'condition (selection among all rows); the held-out evaluation tests the choice on new policies and goals.', |
| "Condition 'paper_tr' is the noise-only TR (action and powertrain noise).", '', |
| 'Parameter units: sig_* = sd of additive noise on normalized throttle/steering per 0.1 s step; steer_rate = max change', |
| 'of normalized steering per 0.1 s step; delay and hold in 0.1 s steps; wheelbase in m; vmax in m/s; tau0 in N m;', |
| 'omega0 in rad/s; yaw0, g_rot in rad; obs_pos_sig in m; gx_scale = goal-radius factor. K = trials per goal.', '', |
| '| Condition | Perturbation (per trial) | K | ROM success, 16 | tau, 16 (95 % range) | Delta, 16 (95 % range) | tau ID, 16 | tau OOD, 16 | tau, new 12 (95 % range) | Delta, new 12 (95 % range) |', |
| '|---|---|---:|---:|---|---|---:|---:|---|---|'] |
| f = lambda x: f'{x:.2f}' |
| for n in sorted(names, key=lambda n: -res['all16'][n]['all']): |
| a, b = res['all16'][n], res['new12'][n] |
| L.append(f"| {n} | {spec_text(n)} | {V.EXPLORE[n][0]} | {a['sr']:.2f} | {f(a['all'])} ({f(a['ci'][0])} to {f(a['ci'][1])}) | " |
| f"{a['d']:+.2f} ({a['dci'][0]:+.2f} to {a['dci'][1]:+.2f}) | {f(a['id'])} | {f(a['ood'])} | " |
| f"{f(b['all'])} ({f(b['ci'][0])} to {f(b['ci'][1])}) | {b['d']:+.2f} ({b['dci'][0]:+.2f} to {b['dci'][1]:+.2f}) |") |
| |
| an = json.loads((RESULTS / 'analysis_A_explore.json').read_text()) |
| ch = an['chrono'] |
| L += ['', '## Per policy, goal set A (exploration)', '', |
| 'Mean rank among 16 policies (1 = best). Success = share of goals (ROM TR: share of goal-trials).', '', |
| '| Policy | Chrono success ID | Chrono success OOD | Chrono mean rank | Nominal ROM success | Nominal ROM mean rank | Rate-limit TR (rate_rand_paper) success | Rate-limit TR mean rank | Noise-only TR (paper_tr) mean rank |', |
| '|---|---:|---:|---:|---:|---:|---:|---:|---:|'] |
| for p in sorted(pols, key=lambda p: ch['mean_rank'][p]): |
| c = an['conditions'] |
| L.append(f"| {p} | {ch['success_rate']['id'][p]:.2f} | {ch['success_rate']['ood'][p]:.2f} | {ch['mean_rank'][p]:.2f} | " |
| f"{c['nominal']['rom']['success_rate']['all'][p]:.2f} | {c['nominal']['rom']['mean_rank'][p]:.2f} | " |
| f"{c['rate_rand_paper']['rom']['success_rate']['all'][p]:.2f} | {c['rate_rand_paper']['rom']['mean_rank'][p]:.2f} | " |
| f"{c['paper_tr']['rom']['mean_rank'][p]:.2f} |") |
| (RESULTS / 'exploration_report.md').write_text('\n'.join(L) + '\n') |
| json.dump(res, open(RESULTS / 'exploration_tau_table.json', 'w'), indent=1) |
| print('\n'.join(L[:60])) |
|
|
