""" EXP 2ND-DIR-3SEED: 3-seed re-run of the 12 single-property configurations on collision -> flat-drop at N=192. R2 + R3 convergent ask: the 1-seed run in `_rev_q_2nddirection_flatdrop.py` collapsed all 12 single-property configs to exactly 40.0% (degenerate-receiver floor on flat-drop). This replaces those rows with proper 3-seed best-of numbers. Single-prop configs (matching the existing 24-config sweep rows 1-12): 7 disc: L=2..5 x V=5,10 subset (matching the sweep) 5 cont: D=2,3,5,10,20 Multi-prop rows (the original 12 multi-prop configs in the 2nd-direction sweep) already at 45-58% with 1 seed; they are not the source of the 40% floor and re-running them would not move the headline. """ import json, time, sys, os from pathlib import Path from datetime import datetime, timezone import numpy as np import torch PROMPT_RECEIVED_TIME = datetime.now(timezone.utc).isoformat() print(f"PROMPT_RECEIVED_TIME = {PROMPT_RECEIVED_TIME}", flush=True) T0 = time.time() sys.path.insert(0, os.path.dirname(__file__)) from _overnight_p1_transfer import make_splits from _overnight_p3_matrix import load_labels, load_feat_subsampled from _rev_q_posdis_scatter import ( train_discrete_custom, disc_train_recv_custom, train_continuous_base, train_recv_frozen_cont, ) disc_train_recv_frozen = disc_train_recv_custom # alias OUT = Path("results/reviewer_response/exp_2nddir_singleprop_3seed") OUT.mkdir(parents=True, exist_ok=True) N_SEEDS = 3 N_TARGET = 192 def log(msg): ts = datetime.now(timezone.utc).strftime("%H:%M:%SZ") print(f"[{ts}] EXP-3SEED: {msg}", flush=True) def main(): log("=" * 60) log(f"3-seed re-run: 12 single-property configs on coll -> flat-drop @ N={N_TARGET}") feat_c = load_feat_subsampled("collision", "vjepa2") feat_t = load_feat_subsampled("flat_drop", "vjepa2") rest_3 = np.load("results/kinematics_vs_mechanics/labels_collision.npz")["restitution_bin"] lbl_t_3 = load_labels("flat_drop", "restitution") # 7 single-prop disc + 5 single-prop cont configs (matching sweep rows 1-12) disc_configs = [ ("disc_L2_V5", 2, 5), ("disc_L2_V10", 2, 10), ("disc_L3_V5", 3, 5), ("disc_L3_V10", 3, 10), ("disc_L4_V5", 4, 5), ("disc_L4_V10", 4, 10), ("disc_L5_V5", 5, 5), ] cont_configs = [ ("cont_dim2", 2), ("cont_dim3", 3), ("cont_dim5", 5), ("cont_dim10", 10), ("cont_dim20", 20), ] rows = [] # Discrete configs for name, L, V in disc_configs: log(f"\n --- {name} (L={L}, V={V}) ---") within_seeds = []; cross_seeds = [] for seed in range(N_SEEDS): t0 = time.time() try: base = train_discrete_custom(feat_c, rest_3, seed=seed, n_heads=L, vocab_size=V, n_epochs=150) tr_t, ho_t = make_splits(lbl_t_3, seed) acc = disc_train_recv_frozen(base, feat_t, lbl_t_3, tr_t, ho_t, seed=seed, n_target=N_TARGET) within_seeds.append(float(base["task_acc"])) cross_seeds.append(float(acc)) log(f" s{seed}: within={base['task_acc']*100:.1f}%, cross={acc*100:.1f}% [{time.time()-t0:.0f}s]") except Exception as e: import traceback log(f" s{seed} FAILED: {e}\n{traceback.format_exc()[:300]}") if within_seeds: rows.append({"name": name, "kind": "disc", "L": L, "V": V, "within_mean": float(np.mean(within_seeds)), "within_std": float(np.std(within_seeds)), "within_max": float(np.max(within_seeds)), "cross_n192_mean": float(np.mean(cross_seeds)), "cross_n192_std": float(np.std(cross_seeds)), "cross_n192_max": float(np.max(cross_seeds))}) # Continuous configs for name, D in cont_configs: log(f"\n --- {name} (D={D}) ---") within_seeds = []; cross_seeds = [] for seed in range(N_SEEDS): t0 = time.time() try: base = train_continuous_base(feat_c, rest_3, seed=seed, code_dim_per_agent=D, n_epochs=150) tr_t, ho_t = make_splits(lbl_t_3, seed) acc = train_recv_frozen_cont(base, feat_t, lbl_t_3, tr_t, ho_t, seed=seed, n_target=N_TARGET) within_seeds.append(float(base["task_acc"])) cross_seeds.append(float(acc)) log(f" s{seed}: within={base['task_acc']*100:.1f}%, cross={acc*100:.1f}% [{time.time()-t0:.0f}s]") except Exception as e: import traceback log(f" s{seed} FAILED: {e}\n{traceback.format_exc()[:300]}") if within_seeds: rows.append({"name": name, "kind": "cont", "D": D, "within_mean": float(np.mean(within_seeds)), "within_std": float(np.std(within_seeds)), "within_max": float(np.max(within_seeds)), "cross_n192_mean": float(np.mean(cross_seeds)), "cross_n192_std": float(np.std(cross_seeds)), "cross_n192_max": float(np.max(cross_seeds))}) if rows: SUMMARY = ["EXP 3-SEED single-prop coll->flat-drop @ N=192", "", f"{'Config':<14s} | {'Within (mean+-std)':>20s} | {'Cross (mean+-std)':>20s} | {'Cross max':>10s}", "-" * 75] for r in rows: SUMMARY.append( f"{r['name']:<14s} | {r['within_mean']*100:>6.1f}+-{r['within_std']*100:>4.1f}% | " f"{r['cross_n192_mean']*100:>6.1f}+-{r['cross_n192_std']*100:>4.1f}% | " f"{r['cross_n192_max']*100:>9.1f}%" ) cross_means = [r["cross_n192_mean"] for r in rows] cross_maxes = [r["cross_n192_max"] for r in rows] SUMMARY.append("") SUMMARY.append(f"All-config 3-seed mean cross flat-drop: {np.mean(cross_means)*100:.1f}+-{np.std(cross_means)*100:.1f}% (range {np.min(cross_means)*100:.1f}-{np.max(cross_means)*100:.1f}%)") SUMMARY.append(f"All-config best-of-3 cross flat-drop: {np.mean(cross_maxes)*100:.1f}+-{np.std(cross_maxes)*100:.1f}% (range {np.min(cross_maxes)*100:.1f}-{np.max(cross_maxes)*100:.1f}%)") SUMMARY.append("") SUMMARY.append("Prior 1-seed reported all 12 configs at exactly 40.0% (degenerate-receiver floor).") print("\n".join(SUMMARY), flush=True) with open(OUT / "summary.txt", "w") as fh: fh.write("\n".join(SUMMARY) + "\n") with open(OUT / "summary.json", "w") as fh: json.dump(rows, fh, indent=2) end_ts = datetime.now(timezone.utc).isoformat() runtime_min = (time.time() - T0) / 60.0 print(f"\nEND_TIME = {end_ts}\nTotal runtime: {runtime_min:.2f} min", flush=True) if __name__ == "__main__": main()