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dbc6675 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | """Build candidate-plan validity datasets and frozen-model feature caches."""
from __future__ import annotations
import argparse
import json
import random
from pathlib import Path
import numpy as np
from code.downstream.families import resolve_source_families
from code.downstream.features import FrozenTransitionFeatureExtractor, save_feature_matrix
from code.downstream.plan_utils import (
discover_val_path,
generate_labeled_candidates_for_problem,
iter_problem_names,
label_plan_internal,
load_problem_context,
read_jsonl,
recover_gold_plan,
summarize_labels,
write_jsonl,
)
from code.experiments.config import DOMAINS, SPLITS_EVAL
DEFAULT_SPLITS = ["train", *SPLITS_EVAL]
DEFAULT_SEEDS = [13, 23, 37]
def build_candidates(args) -> dict[str, dict]:
"""Recover positives, generate corruptions, and label all candidates."""
rng = random.Random(args.candidate_seed)
summaries: dict[str, dict] = {}
candidates_dir = Path(args.output_dir) / "candidates"
candidates_dir.mkdir(parents=True, exist_ok=True)
for split in args.splits:
split_rows: list[dict] = []
for domain in args.domains:
problem_names = iter_problem_names(
data_dir=args.source_data_dir,
domain=domain,
split=split,
max_problems=args.max_problems,
)
for problem in problem_names:
context = load_problem_context(
data_dir=args.source_data_dir,
domain=domain,
split=split,
problem=problem,
)
gold_plan = recover_gold_plan(
data_dir=args.source_data_dir,
domain=domain,
split=split,
problem=problem,
)
validator = None
if args.labeler == "internal":
validator = lambda plan, ctx=context: label_plan_internal(ctx, plan)
rows = generate_labeled_candidates_for_problem(
context=context,
gold_plan=gold_plan,
val_path=args.val_path,
negative_ratio=args.negative_ratio,
rng=rng,
validator=validator,
max_attempts_per_negative=args.max_attempts_per_negative,
)
split_rows.extend(rows)
path = candidates_dir / f"{split}.jsonl"
write_jsonl(path, split_rows)
summaries[split] = summarize_labels(split_rows)
print(f"Wrote {path} | {summaries[split]}")
return summaries
def build_features(args, families) -> dict[str, dict]:
"""Extract frozen transition-model features for all selected families."""
feature_root = Path(args.output_dir) / "features"
summaries: dict[str, dict] = {}
for family in families:
for seed in args.seeds:
extractor = FrozenTransitionFeatureExtractor(
run_root=args.run_root,
source_data_dir=args.source_data_dir,
family=family,
seed=seed,
device=args.device,
xgb_n_jobs=args.xgb_n_jobs,
)
family_summary: dict[str, dict] = {}
for split in args.splits:
candidate_path = Path(args.output_dir) / "candidates" / f"{split}.jsonl"
if not candidate_path.exists():
raise FileNotFoundError(
f"Missing candidate file {candidate_path}. Build candidates first."
)
candidates = read_jsonl(candidate_path)
out_path = feature_root / family.family_id / f"seed_{seed}" / f"{split}.npz"
if out_path.exists() and not args.overwrite_features:
print(f"Skipping existing features: {out_path}")
family_summary[split] = {"skipped_existing": True}
continue
rows = []
for idx, candidate in enumerate(candidates, start=1):
rows.append(extractor.extract(candidate))
if args.progress_every and idx % args.progress_every == 0:
print(
f" {family.family_id}/seed_{seed}/{split}: "
f"{idx}/{len(candidates)}"
)
X = np.vstack(rows).astype(np.float32) if rows else np.zeros((0, 0), dtype=np.float32)
save_feature_matrix(
path=out_path,
candidates=candidates,
features=X,
feature_names=extractor.feature_names,
)
family_summary[split] = {
"path": str(out_path),
"num_rows": int(X.shape[0]),
"num_features": int(X.shape[1]) if X.ndim == 2 else 0,
}
print(f"Wrote {out_path} | {family_summary[split]}")
summaries[f"{family.family_id}/seed_{seed}"] = family_summary
return summaries
def main() -> None:
parser = argparse.ArgumentParser(
description="Build frozen-transition-model plan-validity data."
)
parser.add_argument("--run_root", default="outputs/neurips_tokenizer_full")
parser.add_argument("--source_data_dir", default="data")
parser.add_argument(
"--output_dir",
default="outputs/downstream_validity/frozen_transition_validity",
)
parser.add_argument("--domains", nargs="+", default=DOMAINS)
parser.add_argument("--splits", nargs="+", default=DEFAULT_SPLITS)
parser.add_argument("--seeds", nargs="+", type=int, default=DEFAULT_SEEDS)
parser.add_argument(
"--source_families",
nargs="+",
default=["weighted_best"],
help="Family ids or 'weighted_best'.",
)
parser.add_argument("--negative_ratio", type=int, default=4)
parser.add_argument("--candidate_seed", type=int, default=2026)
parser.add_argument("--max_attempts_per_negative", type=int, default=25)
parser.add_argument("--max_problems", type=int, default=None)
parser.add_argument("--val_path", default=None)
parser.add_argument(
"--labeler",
choices=["val", "internal"],
default="val",
help="'internal' is for local smoke tests when VAL is unavailable.",
)
parser.add_argument("--device", choices=["auto", "cuda", "mps", "cpu"], default="cpu")
parser.add_argument("--xgb_n_jobs", type=int, default=1)
parser.add_argument("--progress_every", type=int, default=100)
parser.add_argument("--skip_candidates", action="store_true")
parser.add_argument("--skip_features", action="store_true")
parser.add_argument("--overwrite_features", action="store_true")
args = parser.parse_args()
repo_root = Path(__file__).resolve().parents[2]
args.run_root = str(Path(args.run_root).resolve())
args.source_data_dir = str(Path(args.source_data_dir).resolve())
args.output_dir = str(Path(args.output_dir).resolve())
args.val_path = discover_val_path(repo_root, args.val_path)
if args.labeler == "val" and args.val_path is None and not args.skip_candidates:
raise RuntimeError("VAL binary was not found. Provide --val_path.")
families = resolve_source_families(args.source_families)
manifest = {
"run_root": args.run_root,
"source_data_dir": args.source_data_dir,
"output_dir": args.output_dir,
"domains": args.domains,
"splits": args.splits,
"seeds": args.seeds,
"source_families": [family.family_id for family in families],
"negative_ratio": args.negative_ratio,
"candidate_seed": args.candidate_seed,
"val_path": args.val_path,
"labeler": args.labeler,
}
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
summaries: dict[str, dict] = {}
if not args.skip_candidates:
summaries["candidates"] = build_candidates(args)
if not args.skip_features:
summaries["features"] = build_features(args, families)
manifest["summaries"] = summaries
manifest_path = Path(args.output_dir) / "build_manifest.json"
with open(manifest_path, "w", encoding="utf-8") as f:
json.dump(manifest, f, indent=2)
print(f"Wrote {manifest_path}")
if __name__ == "__main__":
main()
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