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"""Build viewer-friendly Parquet splits for LiteFold/AlphaFoldDB."""
from __future__ import annotations
import argparse
import json
import re
import shutil
from pathlib import Path
import duckdb
import pandas as pd
import pyarrow.parquet as pq
CSV_COLUMNS_SQL = (
"{"
"'uniprot_accession':'VARCHAR',"
"'first_residue_index':'BIGINT',"
"'last_residue_index':'BIGINT',"
"'alphafold_id':'VARCHAR',"
"'latest_version':'BIGINT'"
"}"
)
def sql_string(path: Path) -> str:
return "'" + path.as_posix().replace("'", "''") + "'"
def entry_source_sql(accession_csv: Path) -> str:
source = (
f"read_csv({sql_string(accession_csv)}, "
f"header=false, columns={CSV_COLUMNS_SQL}, sample_size=100000, "
f"strict_mode=false, parallel=false)"
)
return f"""
SELECT
uniprot_accession,
alphafold_id,
latest_version,
first_residue_index,
last_residue_index,
sequence_length,
fragment_number,
coalesce(fragment_number > 1, false) AS is_fragmented_prediction,
split_bucket
FROM (
SELECT
uniprot_accession,
alphafold_id,
latest_version::UTINYINT AS latest_version,
first_residue_index::INTEGER AS first_residue_index,
last_residue_index::INTEGER AS last_residue_index,
(last_residue_index - first_residue_index + 1)::INTEGER AS sequence_length,
try_cast(nullif(regexp_extract(alphafold_id, '-F([0-9]+)$', 1), '') AS INTEGER) AS fragment_number,
(hash(uniprot_accession) % 10)::UTINYINT AS split_bucket
FROM {source}
)
"""
def copy_split(
con: duckdb.DuckDBPyConnection,
base_sql: str,
split: str,
condition: str,
split_dir: Path,
target_file_size: str,
row_group_size: int,
) -> None:
split_dir.mkdir(parents=True, exist_ok=True)
copy_sql = f"""
COPY (
SELECT *
FROM ({base_sql})
WHERE {condition}
)
TO {sql_string(split_dir)}
(
FORMAT PARQUET,
COMPRESSION ZSTD,
ROW_GROUP_SIZE {row_group_size},
FILE_SIZE_BYTES {sql_string(Path(target_file_size))}
)
"""
con.execute(copy_sql)
files = sorted(
split_dir.glob("data_*.parquet"),
key=lambda path: int(re.search(r"data_(\d+)\.parquet$", path.name).group(1)),
)
total = len(files)
for index, path in enumerate(files):
path.rename(split_dir / f"{split}-{index:05d}-of-{total:05d}.parquet")
def write_archive_metadata(raw_dir: Path, out_dir: Path) -> dict:
metadata_path = raw_dir / "download_metadata.json"
if not metadata_path.exists():
return {}
records = json.loads(metadata_path.read_text(encoding="utf-8"))
df = pd.DataFrame.from_records(records)
if df.empty:
return {}
for column in [
"archive_name",
"species",
"common_name",
"reference_proteome",
"label",
"type",
]:
if column not in df.columns:
df[column] = None
if "latin_common_name" not in df.columns:
df["latin_common_name"] = None
df["archive_path"] = "latest/" + df["archive_name"]
df["size_gb"] = df["size_bytes"].astype("float64") / 1_000_000_000
ordered_columns = [
"archive_name",
"archive_path",
"type",
"species",
"common_name",
"latin_common_name",
"reference_proteome",
"label",
"num_predicted_structures",
"size_bytes",
"size_gb",
]
df = df[ordered_columns]
metadata_dir = out_dir / "metadata"
metadata_dir.mkdir(parents=True, exist_ok=True)
df.to_parquet(metadata_dir / "archive_metadata.parquet", index=False, compression="zstd")
return {
"archive_rows": int(len(df)),
"archive_types": df["type"].value_counts(dropna=False).to_dict(),
"archive_predicted_structures": int(df["num_predicted_structures"].sum()),
"archive_size_bytes": int(df["size_bytes"].sum()),
}
def parquet_row_count(paths: list[Path]) -> int:
return sum(pq.ParquetFile(path).metadata.num_rows for path in paths)
def build_dataset(raw_dir: Path, out_dir: Path, target_file_size: str, row_group_size: int) -> dict:
accession_csv = raw_dir / "accession_ids.csv"
if not accession_csv.exists():
raise FileNotFoundError(f"Missing {accession_csv}")
data_dir = out_dir / "data"
if data_dir.exists():
shutil.rmtree(data_dir)
data_dir.mkdir(parents=True, exist_ok=True)
con = duckdb.connect()
con.execute("SET preserve_insertion_order=false")
con.execute("PRAGMA disable_progress_bar")
base_sql = entry_source_sql(accession_csv)
copy_split(
con,
base_sql,
"train",
"split_bucket <> 0",
data_dir / "train",
target_file_size,
row_group_size,
)
copy_split(
con,
base_sql,
"test",
"split_bucket = 0",
data_dir / "test",
target_file_size,
row_group_size,
)
archive_summary = write_archive_metadata(raw_dir, out_dir)
train_files = sorted((data_dir / "train").glob("*.parquet"))
test_files = sorted((data_dir / "test").glob("*.parquet"))
split_counts = {
"train": parquet_row_count(train_files),
"test": parquet_row_count(test_files),
}
parquet_glob = sql_string(data_dir / "*" / "*.parquet")
stats_row = con.execute(
f"""
SELECT
count(*)::UBIGINT AS total_rows,
min(sequence_length)::INTEGER AS min_sequence_length,
approx_quantile(sequence_length, 0.5)::INTEGER AS median_sequence_length,
avg(sequence_length)::DOUBLE AS mean_sequence_length,
max(sequence_length)::INTEGER AS max_sequence_length,
sum(CASE WHEN is_fragmented_prediction THEN 1 ELSE 0 END)::UBIGINT AS fragmented_predictions,
sum(CASE WHEN fragment_number IS NULL THEN 1 ELSE 0 END)::UBIGINT AS rows_without_fragment_number,
min(latest_version)::UTINYINT AS min_latest_version,
max(latest_version)::UTINYINT AS max_latest_version
FROM read_parquet({parquet_glob})
"""
).fetchone()
version_counts = {
str(version): int(count)
for version, count in con.execute(
f"""
SELECT latest_version, count(*) AS row_count
FROM read_parquet({parquet_glob})
GROUP BY 1
ORDER BY 1
"""
).fetchall()
}
summary = {
"source": "LiteFold/AlphaFoldDB",
"table_source_file": "accession_ids.csv",
"split_strategy": "deterministic hash(uniprot_accession) % 10; bucket 0 is test, buckets 1-9 are train",
"splits": {
"train": split_counts.get("train", 0),
"test": split_counts.get("test", 0),
},
"total_rows": int(stats_row[0]),
"sequence_length": {
"min": int(stats_row[1]),
"median_approx": int(stats_row[2]),
"mean": float(stats_row[3]),
"max": int(stats_row[4]),
},
"fragmented_predictions": int(stats_row[5]),
"rows_without_fragment_number": int(stats_row[6]),
"latest_version": {
"min": int(stats_row[7]),
"max": int(stats_row[8]),
"counts": version_counts,
},
"parquet_files": {
"train": len(train_files),
"test": len(test_files),
},
"columns": [
"uniprot_accession",
"alphafold_id",
"latest_version",
"first_residue_index",
"last_residue_index",
"sequence_length",
"fragment_number",
"is_fragmented_prediction",
"split_bucket",
],
"archive_metadata": archive_summary,
}
(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_AlphaFoldDB_raw"))
parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_AlphaFoldDB_processed"))
parser.add_argument("--target-file-size", default="256MB")
parser.add_argument("--row-group-size", type=int, default=1_000_000)
args = parser.parse_args()
summary = build_dataset(args.raw_dir, args.out_dir, args.target_file_size, args.row_group_size)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
|