scripts/prepare_cath_dataset.py

#!/usr/bin/env python3
"""Build viewer-friendly Parquet splits for LiteFold/CATH."""

from __future__ import annotations

import argparse
import hashlib
import json
import re
from pathlib import Path

import pandas as pd


DOMAIN_COLUMNS = [
    "domain_id",
    "class_number",
    "architecture_number",
    "topology_number",
    "homologous_superfamily_number",
    "s35_cluster_id",
    "s60_cluster_id",
    "s95_cluster_id",
    "s100_cluster_id",
    "s100_sequence_count",
    "domain_length",
    "raw_structure_resolution_angstrom",
]


def iter_data_lines(path: Path):
    with path.open("r", encoding="utf-8") as handle:
        for line in handle:
            line = line.rstrip("\n")
            if not line or line.startswith("#"):
                continue
            yield line


def parse_domain_list(path: Path) -> pd.DataFrame:
    records = []
    for line in iter_data_lines(path):
        parts = line.split()
        if len(parts) != len(DOMAIN_COLUMNS):
            raise ValueError(f"Expected {len(DOMAIN_COLUMNS)} columns in {path}, got {len(parts)}: {line}")
        records.append(parts)

    df = pd.DataFrame(records, columns=DOMAIN_COLUMNS)
    int_columns = [
        "class_number",
        "architecture_number",
        "topology_number",
        "homologous_superfamily_number",
        "s35_cluster_id",
        "s60_cluster_id",
        "s95_cluster_id",
        "s100_cluster_id",
        "s100_sequence_count",
        "domain_length",
    ]
    for col in int_columns:
        df[col] = df[col].astype("int64")
    df["raw_structure_resolution_angstrom"] = df["raw_structure_resolution_angstrom"].astype("float64")
    df["structure_resolution_is_unknown"] = df["raw_structure_resolution_angstrom"].eq(999.0)
    df["structure_resolution_angstrom"] = df["raw_structure_resolution_angstrom"].mask(
        df["structure_resolution_is_unknown"]
    )
    df["structure_resolution_angstrom"] = df["structure_resolution_angstrom"].astype("Float64")

    df["pdb_id"] = df["domain_id"].str.slice(0, 4)
    df["chain_id"] = df["domain_id"].str.slice(4, 5)
    df["pdb_chain_id"] = df["domain_id"].str.slice(0, 5)
    df["domain_suffix"] = df["domain_id"].str.slice(5, 7)
    df["domain_index"] = df["domain_suffix"].astype("int64")

    df["class_code"] = df["class_number"].astype(str)
    df["architecture_code"] = df["class_code"] + "." + df["architecture_number"].astype(str)
    df["topology_code"] = df["architecture_code"] + "." + df["topology_number"].astype(str)
    df["homologous_superfamily_code"] = (
        df["topology_code"] + "." + df["homologous_superfamily_number"].astype(str)
    )
    df["cath_code"] = df["homologous_superfamily_code"]
    df["s35_cluster_key"] = df["homologous_superfamily_code"] + ":S35:" + df["s35_cluster_id"].astype(str)
    return df


def parse_names(path: Path) -> pd.DataFrame:
    records = []
    for line in iter_data_lines(path):
        if ":" not in line:
            continue
        left, name = line.split(":", 1)
        parts = left.split()
        if len(parts) < 2:
            continue
        records.append(
            {
                "cath_node_code": parts[0],
                "example_domain_id": parts[1],
                "cath_node_name": name.strip(),
            }
        )
    return pd.DataFrame.from_records(records)


_RANGE_SEGMENT_RE = re.compile(r"^\s*(-?\d+)(?:\([A-Za-z0-9]+\))?-(-?\d+)(?:\([A-Za-z0-9]+\))?\s*$")


def parse_range_summary(sequence_range: str) -> tuple[int | None, int | None, int]:
    starts: list[int] = []
    ends: list[int] = []
    segments = [segment for segment in sequence_range.split("_") if segment]
    for segment in segments:
        match = _RANGE_SEGMENT_RE.match(segment)
        if not match:
            continue
        starts.append(int(match.group(1)))
        ends.append(int(match.group(2)))
    if not starts:
        return None, None, len(segments)
    return min(starts), max(ends), len(segments)


def parse_fasta(path: Path) -> pd.DataFrame:
    records = []
    header: str | None = None
    sequence_chunks: list[str] = []

    def flush() -> None:
        if header is None:
            return
        try:
            _, version, payload = header.split("|", 2)
            domain_id, sequence_range = payload.split("/", 1)
        except ValueError as exc:
            raise ValueError(f"Unexpected FASTA header in {path}: {header}") from exc
        sequence = "".join(sequence_chunks)
        start, end, segment_count = parse_range_summary(sequence_range)
        records.append(
            {
                "domain_id": domain_id,
                "cath_version": version.replace("_", "."),
                "sequence": sequence,
                "sequence_length": len(sequence),
                "sequence_range": sequence_range,
                "sequence_range_start": start,
                "sequence_range_end": end,
                "sequence_segment_count": segment_count,
            }
        )

    with path.open("r", encoding="utf-8") as handle:
        for line in handle:
            line = line.strip()
            if not line:
                continue
            if line.startswith(">"):
                flush()
                header = line[1:]
                sequence_chunks = []
            else:
                sequence_chunks.append(line)
    flush()
    return pd.DataFrame.from_records(records)


def subset_domain_ids(path: Path) -> set[str]:
    return {line.split()[0] for line in iter_data_lines(path)}


def stable_hash_int(value: str) -> int:
    return int(hashlib.sha256(value.encode("utf-8")).hexdigest()[:16], 16)


def add_cluster_aware_split(df: pd.DataFrame, test_size: float) -> pd.DataFrame:
    cluster_counts = (
        df.groupby("s35_cluster_key", sort=False)
        .size()
        .rename("row_count")
        .reset_index()
    )
    cluster_counts["hash"] = cluster_counts["s35_cluster_key"].map(stable_hash_int)
    cluster_counts = cluster_counts.sort_values(["hash", "s35_cluster_key"], kind="mergesort")

    target_rows = round(len(df) * test_size)
    test_keys: set[str] = set()
    test_rows = 0
    for row in cluster_counts.itertuples(index=False):
        if test_rows >= target_rows:
            break
        test_keys.add(row.s35_cluster_key)
        test_rows += int(row.row_count)

    df = df.copy()
    df["split"] = df["s35_cluster_key"].map(lambda key: "test" if key in test_keys else "train")
    return df


def build_dataset(raw_dir: Path, out_dir: Path, test_size: float) -> dict:
    domains = parse_domain_list(raw_dir / "cath-domain-list.txt")
    names = parse_names(raw_dir / "cath-names.txt")
    names_by_code = names.set_index("cath_node_code")

    for level, code_col in [
        ("class", "class_code"),
        ("architecture", "architecture_code"),
        ("topology", "topology_code"),
        ("homologous_superfamily", "homologous_superfamily_code"),
    ]:
        domains[f"{level}_name"] = domains[code_col].map(names_by_code["cath_node_name"])
        domains[f"{level}_example_domain_id"] = domains[code_col].map(names_by_code["example_domain_id"])

    sequences = parse_fasta(raw_dir / "cath-domain-seqs.fa")
    df = domains.merge(sequences, on="domain_id", how="left", validate="one_to_one")

    missing_sequences = int(df["sequence"].isna().sum())
    if missing_sequences:
        raise ValueError(f"{missing_sequences} domain-list rows did not have FASTA sequences")

    for subset in ["S35", "S60", "S95", "S100"]:
        ids = subset_domain_ids(raw_dir / f"cath-domain-list-{subset}.txt")
        df[f"in_{subset.lower()}_nonredundant_subset"] = df["domain_id"].isin(ids)

    df = add_cluster_aware_split(df, test_size)

    ordered_columns = [
        "domain_id",
        "pdb_id",
        "chain_id",
        "pdb_chain_id",
        "domain_suffix",
        "domain_index",
        "cath_version",
        "cath_code",
        "class_number",
        "class_code",
        "class_name",
        "class_example_domain_id",
        "architecture_number",
        "architecture_code",
        "architecture_name",
        "architecture_example_domain_id",
        "topology_number",
        "topology_code",
        "topology_name",
        "topology_example_domain_id",
        "homologous_superfamily_number",
        "homologous_superfamily_code",
        "homologous_superfamily_name",
        "homologous_superfamily_example_domain_id",
        "s35_cluster_id",
        "s60_cluster_id",
        "s95_cluster_id",
        "s100_cluster_id",
        "s100_sequence_count",
        "s35_cluster_key",
        "domain_length",
        "raw_structure_resolution_angstrom",
        "structure_resolution_angstrom",
        "structure_resolution_is_unknown",
        "sequence",
        "sequence_length",
        "sequence_range",
        "sequence_range_start",
        "sequence_range_end",
        "sequence_segment_count",
        "in_s35_nonredundant_subset",
        "in_s60_nonredundant_subset",
        "in_s95_nonredundant_subset",
        "in_s100_nonredundant_subset",
        "split",
    ]
    df = df[ordered_columns].sort_values(["split", "domain_id"], kind="mergesort")

    data_dir = out_dir / "data"
    data_dir.mkdir(parents=True, exist_ok=True)
    split_counts = {}
    for split in ["train", "test"]:
        split_df = df[df["split"].eq(split)].drop(columns=["split"])
        split_counts[split] = len(split_df)
        split_df.to_parquet(
            data_dir / f"{split}-00000-of-00001.parquet",
            index=False,
            compression="zstd",
        )

    summary = {
        "source": "LiteFold/CATH",
        "cath_version": str(df["cath_version"].iloc[0]),
        "total_rows": len(df),
        "splits": split_counts,
        "test_size_requested": test_size,
        "split_strategy": "deterministic S35-cluster-aware split using sha256(s35_cluster_key)",
        "unique_s35_clusters": int(df["s35_cluster_key"].nunique()),
        "columns": [column for column in ordered_columns if column != "split"],
        "subset_rows": {
            "s35": int(df["in_s35_nonredundant_subset"].sum()),
            "s60": int(df["in_s60_nonredundant_subset"].sum()),
            "s95": int(df["in_s95_nonredundant_subset"].sum()),
            "s100": int(df["in_s100_nonredundant_subset"].sum()),
        },
    }
    (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_CATH_raw"))
    parser.add_argument("--out-dir", type=Path, default=Path("LiteFold_CATH_processed"))
    parser.add_argument("--test-size", type=float, default=0.10)
    args = parser.parse_args()

    summary = build_dataset(args.raw_dir, args.out_dir, args.test_size)
    print(json.dumps(summary, indent=2))


if __name__ == "__main__":
    main()