File size: 7,926 Bytes
0c3dd1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | """Write results/heldout_report.md from results/confirm_analysis.json (output of analyze_confirm.py). Sim2Sim."""
import json
import sys
import numpy as np
import fixed_design as design
from common import BASE, RESULTS
# optional: analysis json, behavior json, output md (defaults = goal-set-B held-out files)
args = sys.argv[1:] + [None] * 3
A = json.loads(open(args[0] or RESULTS / 'confirm_analysis.json').read())
beh = json.loads(open(args[1] or RESULTS / 'behavior_B_confirm16.json').read())
OUT_MD = args[2] or RESULTS / 'heldout_report.md'
reg = json.loads((BASE / 'policies/registry.json').read_text())
f2 = lambda x: f'{x:.2f}'
rng = lambda c: 'n/a' if c[0] is None else f'{c[0]:.2f} to {c[1]:.2f}'
srng = lambda c: f'{c[0]:+.2f} to {c[1]:+.2f}'
def spec_text(spec):
if not spec:
return 'none (training ROM)'
out = []
for k, d in spec.items():
if d[0] == 'const':
out.append(f'{k} = {d[1]:g}')
elif d[0] == 'normal':
out.append(f'{k} ~ N({d[1]:g}, {d[2]:g})')
elif d[0] == 'choice':
out.append(f'{k} in {{{", ".join(f"{x:g}" for x in d[1])}}}')
else:
out.append(f'{k} ~ {"LogU" if d[0] == "loguniform" else "U"}({d[1]:g}, {d[2]:g})')
return '; '.join(out)
L = ['# Case 2 held-out evaluation on goal set B (Sim2Sim)', '',
'Generated by `report_confirm.py` from `results/confirm_analysis.json` (output of `analyze_confirm.py`).',
f"Goal set B: {A['goals_by_range']['id']} ID (10-20 m) + {A['goals_by_range']['ood']} OOD (20-40 m) goals, never used before the TR design was fixed.",
'Primary pool: 12 held-out PPO policies (seeds 2001-2004). Secondary pool: the 12 + released checkpoints 1-4.',
"Condition 'paper_tr' is the noise-only TR (action and powertrain noise).",
f"Bootstrap: {A['bootstrap']['replicates']} goal resamples, stratified by range, paired across methods. Ranges are 95 % percentile ranges.", '',
f"**Primary test: delta tau = {A['primary_test']['delta_tau']:+.3f} (95 % range {srng(A['primary_test']['ci95'])}); met = {A['primary_test']['supported']}.**", '']
for pname in A['pools']:
P = A['pools'][pname]
L += [f"## Agreement with Chrono, {pname} pool ({len(P['policies'])} policies)", '',
'| Condition | tau, all goals (95 % range) | Delta tau vs nominal (95 % range) | tau ID | tau OOD | Spearman | AUC for Chrono success < 0.5 (95 % range) | Resolved pairs | tau with primary TR order |',
'|---|---|---|---:|---:|---:|---|---:|---:|']
for n, c in P['conditions'].items():
v = c['vs_chrono']
au = P['hidden_failure']['auc'][n]
L.append(f"| {n}{' (primary)' if n == design.PRIMARY_TR else ''} | {f2(v['all']['kendall_tau_b'])} ({rng(v['all']['kendall_ci95'])}) | "
f"{v['all']['delta_tau_vs_nominal']:+.2f} ({srng(v['all']['delta_tau_ci95'])}) | {f2(v['id']['kendall_tau_b'])} | {f2(v['ood']['kendall_tau_b'])} | "
f"{f2(v['all']['spearman'])} | {f2(au['value'])} ({rng(au['ci95'])}) | {c['rom']['resolved_pairs']}/{len(c['rom']['pairs'])} | {f2(c['tau_vs_primary_tr']['value'])} |")
ch = P['chrono']
L += ['', f"Chrono resolved pairs: {ch['resolved_pairs']}/{len(ch['pairs'])}. Chrono success < 0.5: {', '.join(P['hidden_failure']['policies']) or 'none'}.", '']
pa = P['conditions'][design.PRIMARY_TR]['pair_agreement_vs_nominal']
L += [f"Pairs ordered as Chrono orders them: primary TR {pa['method_correct']}/{pa['pairs']}, nominal ROM {pa['base_correct']}/{pa['pairs']}. "
f"On the {pa['discordant']} pairs where the two disagree, Chrono supports the TR on {pa['discordant_method_correct']} and nominal ROM on {pa['discordant_base_correct']}.", '']
# per-policy table
nom = P['conditions'][design.BASELINE]['rom']
tr = P['conditions'][design.PRIMARY_TR]['rom']
L += [f'### Per policy, {pname} pool (mean rank: 1 = best of {len(P["policies"])})', '',
'| Policy | Training config | Seed | Mean step change of steering (nominal ROM) | Chrono success ID | Chrono success OOD | Chrono mean rank | Nominal ROM success ID / OOD | Nominal ROM mean rank | TR success ID / OOD | TR mean rank |',
'|---|---|---:|---:|---:|---:|---:|---|---:|---|---:|']
for p in sorted(P['policies'], key=lambda p: ch['mean_rank'][p]):
cfg = reg[p]['config'] if p in reg else 'released (one PPO run)'
seed = reg[p]['seed'] if p in reg else '-'
L.append(f"| {p} | {cfg} | {seed} | {beh[p]['mean_abs_steer_change']:.3f} | {f2(ch['success_rate']['id'][p])} | {f2(ch['success_rate']['ood'][p])} | "
f"{ch['mean_rank'][p]:.2f} | {f2(nom['success_rate']['id'][p])} / {f2(nom['success_rate']['ood'][p])} | {nom['mean_rank'][p]:.2f} | "
f"{f2(tr['success_rate']['id'][p])} / {f2(tr['success_rate']['ood'][p])} | {tr['mean_rank'][p]:.2f} |")
L += ['', 'Order by mean rank:', '',
f"- Chrono: {' > '.join(ch['order'])}",
f"- Nominal ROM: {' > '.join(nom['order'])}",
f"- Primary TR: {' > '.join(tr['order'])}", '']
rat = P['conditions'][design.BASELINE]['chrono_over_rom_time_ratio']
if rat:
vals = np.array(list(rat.values()))
L += [f"Gap magnitude: Chrono time / nominal ROM time on goals both reach = {vals.min():.2f} to {vals.max():.2f} (median {np.median(vals):.2f}) across policies.", '']
L += ['## Conditions and perturbation magnitudes', '',
'Units: action noise sd in normalized units (throttle range 0.2-0.5, steering range -1 to 1), per 0.1 s step; tau0 in N m; omega0 in rad/s;',
'steer_rate = largest change of normalized steering per 0.1 s step (Chrono loop: 0.1); delay and hold in 0.1 s steps; wheelbase in m.', '',
'| Condition | Role | Perturbation (one draw per trial; noise each step) | Trials per goal |', '|---|---|---|---:|']
roles = {'nominal': 'baseline', design.PRIMARY_TR: 'primary TR', 'paper_tr': 'secondary (noise-only TR)'}
for n, c in A['conditions_spec'].items():
L.append(f"| {n} | {roles.get(n, 'secondary')} | {spec_text(c['spec'])} | {c['trials']} |")
PH = RESULTS / 'posthoc_confirm.json'
if args[0] is None and PH.exists():
ph = json.loads(PH.read_text())
L += ['', '## Post hoc summaries (not part of the fixed primary test, no new simulation; `posthoc_confirm.py`)', '']
for pname, o in ph['pools'].items():
L += [f'### {pname} pool', '', '| Condition | tau(primary TR) - tau(condition) (95 % range) | Chrono-resolved pairs: same direction | opposite direction | unresolved |',
'|---|---|---:|---:|---:|']
for n, v in o['pair_resolution_vs_chrono'].items():
d = o['delta_tau_primary_tr_minus'].get(n)
dt = '-' if d is None else f"{d['value']:+.2f} ({srng(d['ci95'])})"
L.append(f"| {n} | {dt} | {v['same_direction']}/{v['chrono_resolved']} | {v['opposite_direction']} | {v['unresolved']} |")
c = o['chatter_vs_chrono_success']
L += ['', f"Steering chatter (mean step change in nominal ROM) versus Chrono success, Spearman across policies: "
f"ID {c['id']['spearman']:+.2f} (p = {c['id']['p_value']:.3f}), OOD {c['ood']['spearman']:+.2f} (p = {c['ood']['p_value']:.3f}), "
f"all {c['all']['spearman']:+.2f} (p = {c['all']['p_value']:.3f}).", '']
sec = A['pools'][list(A['pools'])[-1]]
csr = sec['chrono']['success_rate']['all']
low = ', '.join(f'{p} at {csr[p]:.2f}' for p in sorted(sec['policies'], key=lambda p: csr[p])[:2])
L += ['## Notes', '',
'- Hidden-failure screening (Chrono success < 0.5) is not evaluable on set B: no policy had Chrono success below 0.5 '
f'(lowest: {low}).',
'- `report_confirm.py` and `posthoc_confirm.py` only format or summarize the outputs of `analyze_confirm.py`; they run no simulation.']
open(OUT_MD, 'w').write('\n'.join(L) + '\n')
print('\n'.join(L))
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