File size: 10,948 Bytes
e499a9b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 | #!/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()
|