File size: 3,493 Bytes
e0b011e | 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 | """Sample efficiency: environment steps needed to reach a common target.
The paper claims ST-GFN "requires substantially fewer samples to reach target
performance across all tasks" (Sec 4.6, Table 1 'Steps' column). We define the
target as a fixed fraction of the best final score achieved by ANY method on
that environment/metric, then report the first logged point at which each method
reaches it (env steps and iterations), averaged over seeds.
"""
from __future__ import annotations
import argparse
import glob
import json
import os
from collections import defaultdict
import numpy as np
METRIC = {
"hypergrid": ("modes_found", True),
"bitsequence": ("mean_reward", True),
"tictactoe": ("win_pct", True),
"singlecell_proxy": ("mean_reward", True),
}
ORDER = ["stgfn", "tb", "fm", "subtb", "db", "eflownet", "stochastic_gfn",
"tb_rnd", "tb_novelty", "tb_icm", "tb_cv"]
LABEL = {"stgfn": "ST-GFN (Ours)", "tb": "TB", "fm": "FM", "subtb": "SubTB", "db": "DB",
"eflownet": "EFlowNet", "stochastic_gfn": "Stochastic-GFN", "tb_rnd": "TB+RND",
"tb_novelty": "TB+Novelty", "tb_icm": "TB+ICM", "tb_cv": "TB+ControlVar"}
def load(out_dir):
data = defaultdict(lambda: defaultdict(list))
for p in glob.glob(os.path.join(out_dir, "*.json")):
with open(p) as f:
r = json.load(f)
data[r["env"]][r["method"]].append(r)
return data
def steps_to(run, metric, thresh):
for c in run["curve"]:
v = c.get(metric)
if v is not None and v >= thresh:
return c.get("env_steps"), c.get("iter")
return None, None
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--out", default="../outputs/main")
ap.add_argument("--frac", type=float, default=0.9)
ap.add_argument("--save", default="../outputs/sample_efficiency.json")
args = ap.parse_args()
data = load(args.out)
result = {}
for env, (metric, _) in METRIC.items():
if env not in data:
continue
best = max(
np.mean([r["final"].get(metric, 0) or 0 for r in runs])
for runs in data[env].values()
)
thresh = args.frac * best
print(f"\n{'='*76}\n{env.upper()} metric={metric} "
f"target={args.frac:.0%} of best final ({best:.3f}) = {thresh:.3f}\n{'='*76}")
print(f"{'Method':22s} {'env steps to target':>22s} {'iters':>10s} {'reached':>9s}")
result[env] = {"metric": metric, "target": thresh, "methods": {}}
for m in ORDER:
if m not in data[env]:
continue
steps, iters, hit = [], [], 0
for r in data[env][m]:
s, i = steps_to(r, metric, thresh)
if s is not None:
steps.append(s); iters.append(i); hit += 1
n = len(data[env][m])
if steps:
txt = f"{np.mean(steps):22,.0f} {np.mean(iters):10,.0f} {hit}/{n:>7d}"
else:
txt = f"{'never':>22s} {'-':>10s} {0}/{n:>7d}"
print(f"{LABEL.get(m,m):22s} {txt}")
result[env]["methods"][m] = {
"mean_env_steps": float(np.mean(steps)) if steps else None,
"mean_iters": float(np.mean(iters)) if iters else None,
"n_reached": hit, "n_seeds": n,
}
with open(args.save, "w") as f:
json.dump(result, f, indent=2)
print(f"\nwrote {args.save}")
if __name__ == "__main__":
main()
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