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"""Build viewer-friendly sample/index Parquet splits for LiteFold/GOA."""
from __future__ import annotations
import argparse
import gzip
import hashlib
import json
import os
import shutil
from pathlib import Path
from typing import Any
import pandas as pd
import requests
from huggingface_hub import HfApi, hf_hub_url
ANNOTATION_COLUMNS = [
"annotation_id",
"source_file",
"source_format",
"source_row_number",
"db",
"db_object_id",
"db_object_symbol",
"qualifier",
"qualifiers",
"go_id",
"db_references",
"evidence_code",
"with_from",
"aspect",
"db_object_name",
"db_object_synonyms",
"db_object_type",
"taxon_ids",
"interacting_taxon_id",
"date",
"assigned_by",
"annotation_extension",
"gene_product_form_id",
"split_bucket",
]
def load_token() -> str | None:
for key in ("HF_TOKEN", "HUGGINGFACE_HUB_TOKEN"):
value = os.environ.get(key)
if value:
return value
env_path = Path(".env")
if env_path.exists():
for line in env_path.read_text().splitlines():
stripped = line.strip()
if not stripped or stripped.startswith("#") or "=" not in stripped:
continue
key, value = stripped.split("=", 1)
if key.strip() in {"HF_TOKEN", "HUGGINGFACE_HUB_TOKEN"}:
value = value.strip().strip('"').strip("'")
if value:
return value
return None
def stable_bucket(value: str, buckets: int = 10) -> int:
digest = hashlib.sha256(value.encode("utf-8")).hexdigest()[:16]
return int(digest, 16) % buckets
def split_pipe(value: str | None) -> list[str]:
if not value:
return []
return [part for part in value.split("|") if part]
def make_annotation_id(parts: list[str], source_file: str, row_number: int) -> str:
seed = "|".join([source_file, str(row_number), *parts])
return hashlib.sha256(seed.encode("utf-8")).hexdigest()
def parse_gaf(parts: list[str], source_file: str, row_number: int) -> dict[str, Any] | None:
if len(parts) < 17:
return None
annotation_id = make_annotation_id(parts, source_file, row_number)
return {
"annotation_id": annotation_id,
"source_file": source_file,
"source_format": "GAF",
"source_row_number": row_number,
"db": parts[0],
"db_object_id": parts[1],
"db_object_symbol": parts[2],
"qualifier": parts[3] or None,
"qualifiers": split_pipe(parts[3]),
"go_id": parts[4],
"db_references": split_pipe(parts[5]),
"evidence_code": parts[6],
"with_from": split_pipe(parts[7]),
"aspect": parts[8] or None,
"db_object_name": parts[9] or None,
"db_object_synonyms": split_pipe(parts[10]),
"db_object_type": parts[11] or None,
"taxon_ids": split_pipe(parts[12]),
"interacting_taxon_id": None,
"date": parts[13] or None,
"assigned_by": parts[14] or None,
"annotation_extension": parts[15] or None,
"gene_product_form_id": parts[16] or None,
"split_bucket": stable_bucket(annotation_id),
}
def parse_gpa(parts: list[str], source_file: str, row_number: int) -> dict[str, Any] | None:
if len(parts) < 12:
return None
annotation_id = make_annotation_id(parts, source_file, row_number)
return {
"annotation_id": annotation_id,
"source_file": source_file,
"source_format": "GPA",
"source_row_number": row_number,
"db": parts[0],
"db_object_id": parts[1],
"db_object_symbol": None,
"qualifier": parts[2] or None,
"qualifiers": split_pipe(parts[2]),
"go_id": parts[3],
"db_references": split_pipe(parts[4]),
"evidence_code": parts[5],
"with_from": split_pipe(parts[6]),
"aspect": None,
"db_object_name": None,
"db_object_synonyms": [],
"db_object_type": None,
"taxon_ids": [],
"interacting_taxon_id": parts[7] or None,
"date": parts[8] or None,
"assigned_by": parts[9] or None,
"annotation_extension": parts[10] or None,
"gene_product_form_id": parts[11] or None,
"split_bucket": stable_bucket(annotation_id),
}
def stream_rows(repo_id: str, filename: str, token: str | None, limit: int) -> tuple[list[dict[str, Any]], dict[str, str]]:
url = hf_hub_url(repo_id=repo_id, filename=filename, repo_type="dataset")
headers = {"Authorization": f"Bearer {token}"} if token else {}
rows: list[dict[str, Any]] = []
metadata: dict[str, str] = {}
row_number = 0
parser = parse_gaf if filename.endswith(".gaf.gz") else parse_gpa
with requests.get(url, headers=headers, stream=True, timeout=60) as response:
response.raise_for_status()
with gzip.GzipFile(fileobj=response.raw) as handle:
for raw in handle:
line = raw.decode("utf-8", errors="replace").rstrip("\n")
if not line:
continue
if line.startswith("!"):
if ":" in line:
key, value = line.lstrip("!").split(":", 1)
metadata[key.strip()] = value.strip()
continue
if line.startswith("gpa-version:"):
metadata["gpa-version"] = line.split(":", 1)[1].strip()
continue
row_number += 1
parsed = parser(line.split("\t"), filename, row_number)
if parsed is not None:
rows.append(parsed)
if len(rows) >= limit:
break
return rows, metadata
def build_dataset(repo_id: str, out_dir: Path, sample_rows_per_file: int) -> dict[str, Any]:
token = load_token()
api = HfApi(token=token)
info = api.dataset_info(repo_id, files_metadata=True)
source_files = []
for sibling in sorted(info.siblings or [], key=lambda item: item.rfilename):
source_files.append(
{
"repo_id": repo_id,
"filename": sibling.rfilename,
"size_bytes": int(getattr(sibling, "size", 0) or 0),
"source_sha": info.sha,
}
)
annotation_rows: list[dict[str, Any]] = []
source_metadata: list[dict[str, Any]] = []
for filename in ["goa_uniprot_all.gaf.gz", "goa_uniprot_all.gpa.gz"]:
rows, metadata = stream_rows(repo_id, filename, token, sample_rows_per_file)
annotation_rows.extend(rows)
source_metadata.append({"filename": filename, "sampled_rows": len(rows), "header_metadata": metadata})
if out_dir.exists():
shutil.rmtree(out_dir)
data_dir = out_dir / "data"
metadata_dir = out_dir / "metadata"
data_dir.mkdir(parents=True, exist_ok=True)
metadata_dir.mkdir(parents=True, exist_ok=True)
df = pd.DataFrame.from_records(annotation_rows, columns=ANNOTATION_COLUMNS)
df = df.sort_values(["split_bucket", "annotation_id"], kind="mergesort")
train = df[df["split_bucket"].ne(0)].sort_values("annotation_id", kind="mergesort")
test = df[df["split_bucket"].eq(0)].sort_values("annotation_id", kind="mergesort")
train.to_parquet(data_dir / "train-00000-of-00001.parquet", index=False, compression="zstd")
test.to_parquet(data_dir / "test-00000-of-00001.parquet", index=False, compression="zstd")
pd.DataFrame.from_records(source_files).to_parquet(metadata_dir / "source_files.parquet", index=False)
format_counts = df["source_format"].value_counts().to_dict()
aspect_counts = df["aspect"].fillna("missing").value_counts().to_dict()
evidence_counts = df["evidence_code"].value_counts().head(20).to_dict()
db_counts = df["db"].value_counts().to_dict()
summary = {
"source": repo_id,
"source_sha": info.sha,
"viewer_table_scope": "sample/index",
"sample_rows_per_annotation_file": int(sample_rows_per_file),
"annotation_sample_rows": int(len(df)),
"splits": {"train": int(len(train)), "test": int(len(test))},
"split_strategy": "deterministic sha256(annotation_id) % 10; bucket 0 is test, buckets 1-9 are train",
"source_files": source_files,
"source_metadata": source_metadata,
"format_counts": {str(k): int(v) for k, v in format_counts.items()},
"aspect_counts": {str(k): int(v) for k, v in aspect_counts.items()},
"top_evidence_codes": {str(k): int(v) for k, v in evidence_counts.items()},
"db_counts": {str(k): int(v) for k, v in db_counts.items()},
"columns": ANNOTATION_COLUMNS,
}
(out_dir / "dataset_summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
return summary
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--repo-id", default="LiteFold/GOA")
parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_GOA_processed"))
parser.add_argument("--sample-rows-per-file", type=int, default=50000)
args = parser.parse_args()
summary = build_dataset(args.repo_id, args.out_dir, args.sample_rows_per_file)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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