code/_rev_q_2nddir_singleprop_3seed.py

"""
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()