Datasets:
File size: 8,004 Bytes
fb0a48b | 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 | #!/usr/bin/env python3
"""Build viewer-friendly Parquet splits for LiteFold/PDB."""
from __future__ import annotations
import argparse
import hashlib
import json
import re
from datetime import date, datetime
from pathlib import Path
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from huggingface_hub import HfApi
ENTRY_COLUMNS = [
"pdb_id",
"classification",
"accession_date",
"title",
"source_organism",
"authors",
"raw_resolution",
"experimental_method",
]
def load_hf_token(env_path: Path) -> str | None:
if not env_path.exists():
return None
for line in env_path.read_text(encoding="utf-8").splitlines():
line = line.strip()
if not line or line.startswith("#") or "=" not in line:
continue
key, value = line.split("=", 1)
if key.strip() in {"HF_TOKEN", "HUGGINGFACE_HUB_TOKEN"}:
return value.strip().strip('"').strip("'")
return None
def parse_accession_date(value: str, current_year: int) -> str | None:
value = (value or "").strip()
if not value:
return None
try:
month, day, year = [int(part) for part in value.split("/")]
except ValueError:
return None
current_two_digit_year = current_year % 100
full_year = 2000 + year if year <= current_two_digit_year else 1900 + year
try:
return date(full_year, month, day).isoformat()
except ValueError:
return None
def parse_entries_idx(path: Path) -> pd.DataFrame:
rows = []
current_year = datetime.utcnow().year
with path.open("r", encoding="utf-8", errors="replace") as handle:
for line_number, line in enumerate(handle, start=1):
line = line.rstrip("\n")
if line_number <= 2 or not line:
continue
parts = line.split("\t")
if len(parts) < len(ENTRY_COLUMNS):
parts = parts + [""] * (len(ENTRY_COLUMNS) - len(parts))
elif len(parts) > len(ENTRY_COLUMNS):
parts = parts[: len(ENTRY_COLUMNS) - 1] + [" ".join(parts[len(ENTRY_COLUMNS) - 1 :])]
rows.append(dict(zip(ENTRY_COLUMNS, parts)))
df = pd.DataFrame.from_records(rows)
df["pdb_id"] = df["pdb_id"].str.lower()
df["accession_date_iso"] = df["accession_date"].map(
lambda value: parse_accession_date(value, current_year)
)
df["resolution_angstrom"] = pd.to_numeric(df["raw_resolution"], errors="coerce")
df["resolution_is_unknown"] = df["resolution_angstrom"].isna()
for column in ["classification", "title", "source_organism", "authors", "experimental_method"]:
df[column] = df[column].fillna("").str.strip()
return df
def mmcif_rows_from_hub(repo_id: str, token: str | None) -> pd.DataFrame:
api = HfApi(token=token)
info = api.dataset_info(repo_id, files_metadata=True)
records = []
for sibling in info.siblings or []:
path = sibling.rfilename
if not path.startswith("mmcif/") or not path.endswith(".cif.gz"):
continue
filename = Path(path).name
pdb_id = filename.removesuffix(".cif.gz").lower()
records.append(
{
"pdb_id": pdb_id,
"mmcif_path": path,
"mmcif_file_size_bytes": int(sibling.size) if sibling.size is not None else None,
"mmcif_blob_id": sibling.blob_id,
}
)
return pd.DataFrame.from_records(records)
def stable_bucket(value: str, buckets: int = 10) -> int:
digest = hashlib.sha256(value.encode("utf-8")).hexdigest()[:16]
return int(digest, 16) % buckets
def write_parquet(df: pd.DataFrame, path: Path) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
table = pa.Table.from_pandas(df, preserve_index=False)
pq.write_table(table, path, compression="zstd")
def build_dataset(raw_dir: Path, out_dir: Path, repo_id: str, token: str | None) -> dict:
entries = parse_entries_idx(raw_dir / "entries.idx")
mmcif = mmcif_rows_from_hub(repo_id, token)
if mmcif.empty:
raise RuntimeError(f"No mmCIF files found in {repo_id}")
df = mmcif.merge(entries, on="pdb_id", how="left", validate="one_to_one")
df["has_entries_idx_metadata"] = df["title"].notna()
for column in ["classification", "accession_date", "accession_date_iso", "title", "source_organism", "authors", "raw_resolution", "experimental_method"]:
df[column] = df[column].fillna("")
df["resolution_angstrom"] = df["resolution_angstrom"].astype("Float64")
df["resolution_is_unknown"] = df["resolution_angstrom"].isna()
df["pdb_url"] = "https://www.rcsb.org/structure/" + df["pdb_id"].str.upper()
df["rcsb_download_url"] = "https://files.rcsb.org/download/" + df["pdb_id"] + ".cif.gz"
df["split_bucket"] = df["pdb_id"].map(stable_bucket).astype("int64")
df["split"] = df["split_bucket"].map(lambda bucket: "test" if bucket == 0 else "train")
ordered_columns = [
"pdb_id",
"mmcif_path",
"mmcif_file_size_bytes",
"mmcif_blob_id",
"pdb_url",
"rcsb_download_url",
"classification",
"accession_date",
"accession_date_iso",
"title",
"source_organism",
"authors",
"raw_resolution",
"resolution_angstrom",
"resolution_is_unknown",
"experimental_method",
"has_entries_idx_metadata",
"split_bucket",
]
df = df[ordered_columns + ["split"]].sort_values(["split", "pdb_id"], kind="mergesort")
data_dir = out_dir / "data"
for split in ["train", "test"]:
split_df = df[df["split"].eq(split)].drop(columns=["split"])
write_parquet(split_df, data_dir / f"{split}-00000-of-00001.parquet")
metadata_dir = out_dir / "metadata"
write_parquet(entries.sort_values("pdb_id", kind="mergesort"), metadata_dir / "entries_idx.parquet")
method_counts = (
df["experimental_method"].replace("", "UNKNOWN").value_counts().head(20).to_dict()
)
class_counts = df["classification"].replace("", "UNKNOWN").value_counts().head(20).to_dict()
summary = {
"source": repo_id,
"full_entries_idx_rows": int(len(entries)),
"mmcif_rows_in_repo": int(len(df)),
"metadata_joined_rows": int(df["has_entries_idx_metadata"].sum()),
"splits": {
"train": int(df["split"].eq("train").sum()),
"test": int(df["split"].eq("test").sum()),
},
"split_strategy": "deterministic sha256(pdb_id) % 10; bucket 0 is test, buckets 1-9 are train",
"total_mmcif_size_bytes": int(df["mmcif_file_size_bytes"].fillna(0).sum()),
"resolution": {
"known_rows": int(df["resolution_angstrom"].notna().sum()),
"unknown_rows": int(df["resolution_angstrom"].isna().sum()),
"min": float(df["resolution_angstrom"].min(skipna=True)),
"median": float(df["resolution_angstrom"].median(skipna=True)),
"mean": float(df["resolution_angstrom"].mean(skipna=True)),
"max": float(df["resolution_angstrom"].max(skipna=True)),
},
"top_experimental_methods": method_counts,
"top_classifications": class_counts,
"columns": ordered_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("--raw-dir", type=Path, default=Path("LiteFold_PDB_raw"))
parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_PDB_processed"))
parser.add_argument("--repo-id", default="LiteFold/PDB")
parser.add_argument("--env-file", type=Path, default=Path(".env"))
args = parser.parse_args()
summary = build_dataset(args.raw_dir, args.out_dir, args.repo_id, load_hf_token(args.env_file))
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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