Datasets:

INSAIT-Institute
/

ProofRank-outputs

import argparse
import json
import os
from collections import defaultdict
from pathlib import Path

import pandas as pd
from datasets import load_dataset, load_from_disk

DEFAULT_JSON_COLUMNS = ["extra_metadata"]

DERIVED_CONFIGS = {"correctness_labels"}
PAIRWISE_CONFIG = "pairwise_judgments"
RAW_JUDGE_CONFIGS = {
    "pairwise_raw",
    "correctness_raw",
    "diversity_baseline_raw",
    "anti_prompt_raw",
    "anti_prompt_pairwise_raw",
    "single_prompt_raw",
    "validation_raw",
}
CLUSTERING_RAW_CONFIG = "clustering_raw"
JSON_RECORD_CONFIGS = {
    "diversity_samples": Path("data/postprocess/matharena_proofs/diversity_samples.json"),
    "human_solutions": Path("data/postprocess/matharena_proofs/human_sols_ok.json"),
    "all_solutions": Path("data/postprocess/matharena_proofs/all_sols_ok.json"),
    "raw_problems": Path("data/raw/matharena_proofs/sample.json"),
    "human_solutions_sample": Path("data/postprocess/matharena_proofs/human_sols_sample.json"),
    "technique_verifier_samples": Path(
        "data/postprocess/technique_adaptivity/verifier_agreement_samples.json"
    ),
}
OUTPUTS_DIR = Path("outputs")

# eval_package.py reads data/results/*.pkl, but the Sec. 4 and validation
# scripts (single_prompt_diversity_eval.py, technique_verifier_agreement.py,
# diversity_eval.py, ...) read the outputs/ trees directly. The caches carry
# every field those trees need, so rebuild the trees from them rather than
# shipping the same judgments twice.
JUDGE_ORG_DEFAULT = "openai"

# Settings that ship as complete raw trees. Their cache is `is_valid`-filtered,
# so rebuilding a tree from it would under-count solutions; never let the
# rebuild clobber the shipped tree.
TREE_SHIPPED_SETTINGS = {
    "summary_diversity_clustering_main",
    "answer_checker_diversity",
    "completeness_checker_diversity",
}


def load_config(args, name: str):
    if args.from_hub:
        return load_dataset(args.repo_id, name, split="train", token=os.environ.get("HF_API_KEY"))
    return load_from_disk(str(Path(args.source) / name))


def list_configs(args) -> list[str]:
    if args.from_hub:
        from datasets import get_dataset_config_names

        return get_dataset_config_names(args.repo_id, token=os.environ.get("HF_API_KEY"))
    return sorted(p.name for p in Path(args.source).iterdir() if (p / "dataset_info.json").exists())


def restore_setting(name: str, ds, results_dir: Path, json_columns: list[str], overwrite: bool,
                    rebuild_trees: bool = True) -> None:
    out_path = results_dir / f"{name}.pkl"
    if out_path.exists() and not overwrite:
        print(f"  [skip] {out_path} already exists (use --overwrite to replace)")
        return
    df = ds.to_pandas()
    for col in json_columns:
        if col in df.columns:
            df[col] = df[col].apply(lambda v: json.loads(v) if isinstance(v, str) else v)
    for col in ("outputs", "judgements"):
        if col not in df.columns:
            continue
        flag_col = f"_{col}_was_scalar"
        if flag_col in df.columns:
            df[col] = [
                (list(v)[0] if len(v) else None) if scalar else (list(v) if v is not None else [])
                for v, scalar in zip(df[col], df[flag_col])
            ]
            df = df.drop(columns=[flag_col])
        else:
            df[col] = df[col].apply(lambda v: list(v) if v is not None else [])
    df.to_pickle(out_path)
    print(f"  restored {name}: {len(df)} rows -> {out_path}")
    if rebuild_trees:
        rebuild_tree_from_cache(name, df, overwrite)


def rebuild_tree_from_cache(name: str, df: pd.DataFrame, overwrite: bool) -> None:
    """Materialise outputs/<setting>/<judge>/<solver>/<problem>.json from a cache."""
    if name in TREE_SHIPPED_SETTINGS:
        print(f"  [skip] {name} tree ships complete in diversity_baseline_raw")
        return
    required = {"judge", "solver_id", "problem_id", "outputs"}
    if not required.issubset(df.columns):
        return
    written = skipped = 0
    for _, row in df.iterrows():
        judge = str(row["judge"])
        if "/" not in judge:
            judge = f"{JUDGE_ORG_DEFAULT}/{judge}"
        out_path = (
            OUTPUTS_DIR / name / judge / str(row["solver_id"])
            / f"{str(row['problem_id']).replace('/', '_')}.json"
        )
        if out_path.exists() and not overwrite:
            skipped += 1
            continue
        outputs = row["outputs"]
        if not isinstance(outputs, list):
            outputs = [] if outputs is None else [outputs]
        record = {
            "model_id": row["solver_id"],
            "problem_id": row["problem_id"],
            "problem": row.get("problem"),
            "solution": row.get("solution"),
            "outputs": list(outputs),
            # Per-sample verdicts are re-derived from `outputs` by the readers'
            # documented fallback, so they are not duplicated here.
            "judgements": None,
            "majority_vote": row.get("true_grade"),
        }
        out_path.parent.mkdir(parents=True, exist_ok=True)
        with open(out_path, "w", encoding="utf-8") as f:
            json.dump(record, f)
        written += 1
    print(f"  rebuilt {name} tree: {written} files written, {skipped} already existed")


def restore_pairwise(ds, results_dir: Path, overwrite: bool) -> None:
    groups: dict[tuple[str, str, str], list[dict]] = defaultdict(list)
    for row in ds:
        key = (row["metric"], row["setting"], row["judge"])
        groups[key].append(
            {"model_a": row["model_a"], "model_b": row["model_b"], "winner": row["winner"]}
        )
    for (metric, setting, judge), entries in sorted(groups.items()):
        out_path = results_dir / f"pairwise_{metric}_{setting}_{judge.replace('/', '_')}.json"
        if out_path.exists() and not overwrite:
            print(f"  [skip] {out_path.name} already exists (use --overwrite to replace)")
            continue
        with open(out_path, "w") as f:
            json.dump(entries, f)
        print(f"  restored {out_path.name}: {len(entries)} judgments")


def restore_raw_judge(ds, overwrite: bool) -> None:
    written = skipped = 0
    for row in ds:
        out_path = OUTPUTS_DIR / row["project"] / row["judge"] / row["relpath"]
        if out_path.exists() and not overwrite:
            skipped += 1
            continue
        out_path.parent.mkdir(parents=True, exist_ok=True)
        record = {
            "model_id": row["model_id"],
            "problem_id": row["problem_id"],
            "majority_vote": row["majority_vote"],
            "judgements": json.loads(row["judgements"]) if row["judgements"] else None,
            "outputs": json.loads(row["outputs"]) if row["outputs"] else None,
        }
        for field in ("problem", "solution"):
            if field in row:
                record[field] = json.loads(row[field]) if row[field] else None
        with open(out_path, "w") as f:
            json.dump(record, f)
        written += 1
    print(f"  restored raw judge files: {written} written, {skipped} already existed")


def restore_clustering_raw(ds, overwrite: bool) -> None:
    written = skipped = 0
    for row in ds:
        out_path = OUTPUTS_DIR / row["project"] / row["judge"] / row["relpath"]
        if out_path.exists() and not overwrite:
            skipped += 1
            continue
        out_path.parent.mkdir(parents=True, exist_ok=True)
        record = {
            "problem_id": row["problem_id"],
            "model_id": row["model_id"],
            "outputs": json.loads(row["outputs"]) if row["outputs"] else None,
        }
        with open(out_path, "w") as f:
            json.dump(record, f)
        written += 1
    print(f"  restored raw clustering files: {written} written, {skipped} already existed")


def restore_json_records(name: str, ds, overwrite: bool) -> None:
    out_path = JSON_RECORD_CONFIGS[name]
    if out_path.exists() and not overwrite:
        print(f"  [skip] {out_path} already exists (use --overwrite to replace)")
        return
    out_path.parent.mkdir(parents=True, exist_ok=True)
    samples = [json.loads(row["record"]) for row in ds]
    with open(out_path, "w", encoding="utf-8") as f:
        json.dump(samples, f)
    print(f"  restored {out_path}: {len(samples)} entries")


def main():
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("--source", default="data/hf_export", help="local export directory (default mode)")
    ap.add_argument("--from-hub", action="store_true", help="download from the Hub instead of a local directory")
    ap.add_argument("--repo-id", default=None, help="HF repo id, required with --from-hub")
    ap.add_argument("--results-dir", default="data/results", help="destination directory")
    ap.add_argument("--configs", nargs="*", default=None, help="subset of configs to restore (default: all)")
    ap.add_argument("--json-columns", nargs="*", default=DEFAULT_JSON_COLUMNS,
                    help="string columns to decode back into objects")
    ap.add_argument("--overwrite", action="store_true", help="replace existing files in --results-dir")
    ap.add_argument("--no-rebuild-trees", action="store_true",
                    help="skip rebuilding the outputs/ judge trees from the restored caches "
                         "(they are what the Sec. 4 and validation scripts read)")
    args = ap.parse_args()

    if args.from_hub and not args.repo_id:
        ap.error("--from-hub requires --repo-id")

    results_dir = Path(args.results_dir)
    results_dir.mkdir(parents=True, exist_ok=True)

    configs = args.configs if args.configs else list_configs(args)
    for name in configs:
        if name in DERIVED_CONFIGS:
            print(f"Skipping derived config {name} (re-derived by eval_package.py)")
            continue
        print(f"Loading {name}...")
        ds = load_config(args, name)
        if name == PAIRWISE_CONFIG:
            restore_pairwise(ds, results_dir, args.overwrite)
        elif name in RAW_JUDGE_CONFIGS:
            restore_raw_judge(ds, args.overwrite)
        elif name == CLUSTERING_RAW_CONFIG:
            restore_clustering_raw(ds, args.overwrite)
        elif name in JSON_RECORD_CONFIGS:
            restore_json_records(name, ds, args.overwrite)
        else:
            restore_setting(name, ds, results_dir, args.json_columns, args.overwrite,
                            rebuild_trees=not args.no_rebuild_trees)

    print("\nDone. eval_package.py will pick these up from --results-dir on its next run.")


if __name__ == "__main__":
    main()


	import argparse
	import json
	import os
	from collections import defaultdict
	from pathlib import Path

	import pandas as pd
	from datasets import load_dataset, load_from_disk

	DEFAULT_JSON_COLUMNS = ["extra_metadata"]

	DERIVED_CONFIGS = {"correctness_labels"}
	PAIRWISE_CONFIG = "pairwise_judgments"
	RAW_JUDGE_CONFIGS = {
	"pairwise_raw",
	"correctness_raw",
	"diversity_baseline_raw",
	"anti_prompt_raw",
	"anti_prompt_pairwise_raw",
	"single_prompt_raw",
	"validation_raw",
	}
	CLUSTERING_RAW_CONFIG = "clustering_raw"
	JSON_RECORD_CONFIGS = {
	"diversity_samples": Path("data/postprocess/matharena_proofs/diversity_samples.json"),
	"human_solutions": Path("data/postprocess/matharena_proofs/human_sols_ok.json"),
	"all_solutions": Path("data/postprocess/matharena_proofs/all_sols_ok.json"),
	"raw_problems": Path("data/raw/matharena_proofs/sample.json"),
	"human_solutions_sample": Path("data/postprocess/matharena_proofs/human_sols_sample.json"),
	"technique_verifier_samples": Path(
	"data/postprocess/technique_adaptivity/verifier_agreement_samples.json"
	),
	}
	OUTPUTS_DIR = Path("outputs")

	# eval_package.py reads data/results/*.pkl, but the Sec. 4 and validation
	# scripts (single_prompt_diversity_eval.py, technique_verifier_agreement.py,
	# diversity_eval.py, ...) read the outputs/ trees directly. The caches carry
	# every field those trees need, so rebuild the trees from them rather than
	# shipping the same judgments twice.
	JUDGE_ORG_DEFAULT = "openai"

	# Settings that ship as complete raw trees. Their cache is `is_valid`-filtered,
	# so rebuilding a tree from it would under-count solutions; never let the
	# rebuild clobber the shipped tree.
	TREE_SHIPPED_SETTINGS = {
	"summary_diversity_clustering_main",
	"answer_checker_diversity",
	"completeness_checker_diversity",
	}


	def load_config(args, name: str):
	if args.from_hub:
	return load_dataset(args.repo_id, name, split="train", token=os.environ.get("HF_API_KEY"))
	return load_from_disk(str(Path(args.source) / name))


	def list_configs(args) -> list[str]:
	if args.from_hub:
	from datasets import get_dataset_config_names

	return get_dataset_config_names(args.repo_id, token=os.environ.get("HF_API_KEY"))
	return sorted(p.name for p in Path(args.source).iterdir() if (p / "dataset_info.json").exists())


	def restore_setting(name: str, ds, results_dir: Path, json_columns: list[str], overwrite: bool,
	rebuild_trees: bool = True) -> None:
	out_path = results_dir / f"{name}.pkl"
	if out_path.exists() and not overwrite:
	print(f" [skip] {out_path} already exists (use --overwrite to replace)")
	return
	df = ds.to_pandas()
	for col in json_columns:
	if col in df.columns:
	df[col] = df[col].apply(lambda v: json.loads(v) if isinstance(v, str) else v)
	for col in ("outputs", "judgements"):
	if col not in df.columns:
	continue
	flag_col = f"_{col}_was_scalar"
	if flag_col in df.columns:
	df[col] = [
	(list(v)[0] if len(v) else None) if scalar else (list(v) if v is not None else [])
	for v, scalar in zip(df[col], df[flag_col])
	]
	df = df.drop(columns=[flag_col])
	else:
	df[col] = df[col].apply(lambda v: list(v) if v is not None else [])
	df.to_pickle(out_path)
	print(f" restored {name}: {len(df)} rows -> {out_path}")
	if rebuild_trees:
	rebuild_tree_from_cache(name, df, overwrite)


	def rebuild_tree_from_cache(name: str, df: pd.DataFrame, overwrite: bool) -> None:
	"""Materialise outputs/<setting>/<judge>/<solver>/<problem>.json from a cache."""
	if name in TREE_SHIPPED_SETTINGS:
	print(f" [skip] {name} tree ships complete in diversity_baseline_raw")
	return
	required = {"judge", "solver_id", "problem_id", "outputs"}
	if not required.issubset(df.columns):
	return
	written = skipped = 0
	for _, row in df.iterrows():
	judge = str(row["judge"])
	if "/" not in judge:
	judge = f"{JUDGE_ORG_DEFAULT}/{judge}"
	out_path = (
	OUTPUTS_DIR / name / judge / str(row["solver_id"])
	/ f"{str(row['problem_id']).replace('/', '_')}.json"
	)
	if out_path.exists() and not overwrite:
	skipped += 1
	continue
	outputs = row["outputs"]
	if not isinstance(outputs, list):
	outputs = [] if outputs is None else [outputs]
	record = {
	"model_id": row["solver_id"],
	"problem_id": row["problem_id"],
	"problem": row.get("problem"),
	"solution": row.get("solution"),
	"outputs": list(outputs),
	# Per-sample verdicts are re-derived from `outputs` by the readers'
	# documented fallback, so they are not duplicated here.
	"judgements": None,
	"majority_vote": row.get("true_grade"),
	}
	out_path.parent.mkdir(parents=True, exist_ok=True)
	with open(out_path, "w", encoding="utf-8") as f:
	json.dump(record, f)
	written += 1
	print(f" rebuilt {name} tree: {written} files written, {skipped} already existed")


	def restore_pairwise(ds, results_dir: Path, overwrite: bool) -> None:
	groups: dict[tuple[str, str, str], list[dict]] = defaultdict(list)
	for row in ds:
	key = (row["metric"], row["setting"], row["judge"])
	groups[key].append(
	{"model_a": row["model_a"], "model_b": row["model_b"], "winner": row["winner"]}
	)
	for (metric, setting, judge), entries in sorted(groups.items()):
	out_path = results_dir / f"pairwise_{metric}_{setting}_{judge.replace('/', '_')}.json"
	if out_path.exists() and not overwrite:
	print(f" [skip] {out_path.name} already exists (use --overwrite to replace)")
	continue
	with open(out_path, "w") as f:
	json.dump(entries, f)
	print(f" restored {out_path.name}: {len(entries)} judgments")


	def restore_raw_judge(ds, overwrite: bool) -> None:
	written = skipped = 0
	for row in ds:
	out_path = OUTPUTS_DIR / row["project"] / row["judge"] / row["relpath"]
	if out_path.exists() and not overwrite:
	skipped += 1
	continue
	out_path.parent.mkdir(parents=True, exist_ok=True)
	record = {
	"model_id": row["model_id"],
	"problem_id": row["problem_id"],
	"majority_vote": row["majority_vote"],
	"judgements": json.loads(row["judgements"]) if row["judgements"] else None,
	"outputs": json.loads(row["outputs"]) if row["outputs"] else None,
	}
	for field in ("problem", "solution"):
	if field in row:
	record[field] = json.loads(row[field]) if row[field] else None
	with open(out_path, "w") as f:
	json.dump(record, f)
	written += 1
	print(f" restored raw judge files: {written} written, {skipped} already existed")


	def restore_clustering_raw(ds, overwrite: bool) -> None:
	written = skipped = 0
	for row in ds:
	out_path = OUTPUTS_DIR / row["project"] / row["judge"] / row["relpath"]
	if out_path.exists() and not overwrite:
	skipped += 1
	continue
	out_path.parent.mkdir(parents=True, exist_ok=True)
	record = {
	"problem_id": row["problem_id"],
	"model_id": row["model_id"],
	"outputs": json.loads(row["outputs"]) if row["outputs"] else None,
	}
	with open(out_path, "w") as f:
	json.dump(record, f)
	written += 1
	print(f" restored raw clustering files: {written} written, {skipped} already existed")


	def restore_json_records(name: str, ds, overwrite: bool) -> None:
	out_path = JSON_RECORD_CONFIGS[name]
	if out_path.exists() and not overwrite:
	print(f" [skip] {out_path} already exists (use --overwrite to replace)")
	return
	out_path.parent.mkdir(parents=True, exist_ok=True)
	samples = [json.loads(row["record"]) for row in ds]
	with open(out_path, "w", encoding="utf-8") as f:
	json.dump(samples, f)
	print(f" restored {out_path}: {len(samples)} entries")


	def main():
	ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
	ap.add_argument("--source", default="data/hf_export", help="local export directory (default mode)")
	ap.add_argument("--from-hub", action="store_true", help="download from the Hub instead of a local directory")
	ap.add_argument("--repo-id", default=None, help="HF repo id, required with --from-hub")
	ap.add_argument("--results-dir", default="data/results", help="destination directory")
	ap.add_argument("--configs", nargs="*", default=None, help="subset of configs to restore (default: all)")
	ap.add_argument("--json-columns", nargs="*", default=DEFAULT_JSON_COLUMNS,
	help="string columns to decode back into objects")
	ap.add_argument("--overwrite", action="store_true", help="replace existing files in --results-dir")
	ap.add_argument("--no-rebuild-trees", action="store_true",
	help="skip rebuilding the outputs/ judge trees from the restored caches "
	"(they are what the Sec. 4 and validation scripts read)")
	args = ap.parse_args()

	if args.from_hub and not args.repo_id:
	ap.error("--from-hub requires --repo-id")

	results_dir = Path(args.results_dir)
	results_dir.mkdir(parents=True, exist_ok=True)

	configs = args.configs if args.configs else list_configs(args)
	for name in configs:
	if name in DERIVED_CONFIGS:
	print(f"Skipping derived config {name} (re-derived by eval_package.py)")
	continue
	print(f"Loading {name}...")
	ds = load_config(args, name)
	if name == PAIRWISE_CONFIG:
	restore_pairwise(ds, results_dir, args.overwrite)
	elif name in RAW_JUDGE_CONFIGS:
	restore_raw_judge(ds, args.overwrite)
	elif name == CLUSTERING_RAW_CONFIG:
	restore_clustering_raw(ds, args.overwrite)
	elif name in JSON_RECORD_CONFIGS:
	restore_json_records(name, ds, args.overwrite)
	else:
	restore_setting(name, ds, results_dir, args.json_columns, args.overwrite,
	rebuild_trees=not args.no_rebuild_trees)

	print("\nDone. eval_package.py will pick these up from --results-dir on its next run.")


	if __name__ == "__main__":
	main()