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"""Create a deterministic, deduplicated DTA development sample and split manifests."""
from __future__ import annotations
import argparse
import hashlib
import json
import math
import random
import statistics
from collections import Counter, defaultdict
from pathlib import Path
import pyarrow.parquet as pq
from mitointeract_recovery import micromolar_to_paffinity
from mitointeract_recovery.chemistry import canonicalize_smiles, scaffold_id, stable_id
from mitointeract_recovery.splits import (
assign_grouped_splits,
assign_random_splits,
assert_group_disjoint,
)
COLUMNS = ["seq", "smiles", "smiles_can", "affinity_uM", "neg_log10_affinity_M"]
def stable_pair_id(sequence: str, smiles: str) -> str:
return stable_id("pair", f"{sequence}\0{smiles}")
def sample_raw_rows(
parquet_path: Path, candidate_count: int, seed: int
) -> tuple[list[dict], int]:
parquet = pq.ParquetFile(parquet_path)
total_rows = parquet.metadata.num_rows
if candidate_count > total_rows:
candidate_count = total_rows
wanted = sorted(random.Random(seed).sample(range(total_rows), candidate_count))
selected: list[dict] = []
pointer = 0
offset = 0
for batch in parquet.iter_batches(batch_size=8192, columns=COLUMNS):
batch_end = offset + batch.num_rows
local_indices: list[int] = []
while pointer < len(wanted) and wanted[pointer] < batch_end:
local_indices.append(wanted[pointer] - offset)
pointer += 1
if local_indices:
data = batch.to_pydict()
for local in local_indices:
selected.append({column: data[column][local] for column in COLUMNS})
offset = batch_end
if pointer == len(wanted):
break
if len(selected) != candidate_count:
raise RuntimeError(
f"requested {candidate_count} rows but selected {len(selected)}"
)
return selected, total_rows
def normalize_and_deduplicate(
raw_rows: list[dict], sample_size: int, seed: int
) -> tuple[list[dict], Counter]:
rejected = Counter()
pairs: dict[str, dict] = {}
targets: dict[str, list[float]] = defaultdict(list)
for raw in raw_rows:
sequence = "".join(str(raw.get("seq") or "").split()).upper()
source_smiles = str(raw.get("smiles_can") or raw.get("smiles") or "").strip()
if not sequence:
rejected["empty_sequence"] += 1
continue
try:
canonical = canonicalize_smiles(source_smiles)
except ValueError:
rejected["invalid_smiles"] += 1
continue
try:
affinity_um = float(raw["affinity_uM"])
published_paffinity = float(raw["neg_log10_affinity_M"])
calculated_paffinity = micromolar_to_paffinity(affinity_um)
except (TypeError, ValueError, OverflowError):
rejected["invalid_affinity"] += 1
continue
if (
not math.isfinite(published_paffinity)
or abs(calculated_paffinity - published_paffinity) > 1e-4
):
rejected["unit_mismatch"] += 1
continue
pair_id = stable_pair_id(sequence, canonical)
if pair_id not in pairs:
pairs[pair_id] = {
"pair_id": pair_id,
"protein_id": stable_id("protein", sequence),
"ligand_id": stable_id("ligand", canonical),
"scaffold_id": scaffold_id(canonical),
"sequence": sequence,
"smiles": canonical,
"protein_length": len(sequence),
"smiles_length": len(canonical),
}
targets[pair_id].append(published_paffinity)
normalized: list[dict] = []
for pair_id, row in pairs.items():
values = targets[pair_id]
normalized.append(
{
**row,
"paffinity": statistics.median(values),
"replicate_count": len(values),
"replicate_paffinity_range": max(values) - min(values),
}
)
normalized.sort(
key=lambda row: hashlib.sha256(f"{seed}:{row['pair_id']}".encode()).hexdigest()
)
if len(normalized) < sample_size:
raise RuntimeError(
f"only {len(normalized)} valid unique pairs remained; increase --candidate-multiplier"
)
return normalized[:sample_size], rejected
def write_jsonl(path: Path, rows: list[dict]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w") as handle:
for row in rows:
handle.write(json.dumps(row, sort_keys=True) + "\n")
def target_stats(rows: list[dict], assignments: dict[str, str]) -> dict:
result = {}
for split in ("train", "validation", "test"):
values = [
row["paffinity"] for row in rows if assignments[row["pair_id"]] == split
]
result[split] = {
"rows": len(values),
"mean_paffinity": statistics.mean(values),
"std_paffinity": statistics.stdev(values) if len(values) > 1 else 0.0,
"min_paffinity": min(values),
"max_paffinity": max(values),
}
return result
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--parquet", type=Path, required=True)
parser.add_argument(
"--dataset-revision",
default="11e49b7ece33d62afd7f65bc05ce60ad37f9ba7b",
)
parser.add_argument("--sample-size", type=int, default=10_000)
parser.add_argument("--candidate-multiplier", type=int, default=3)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--output-dir", type=Path, default=Path("artifacts/dev-10k"))
args = parser.parse_args()
if args.sample_size < 100:
raise ValueError("sample size must be at least 100")
raw_rows, source_rows = sample_raw_rows(
args.parquet,
args.sample_size * args.candidate_multiplier,
args.seed,
)
rows, rejected = normalize_and_deduplicate(raw_rows, args.sample_size, args.seed)
manifests = {
"random_pair": assign_random_splits(rows, seed=args.seed),
"cold_protein_exact": assign_grouped_splits(rows, "protein_id", seed=args.seed),
"cold_ligand_scaffold": assign_grouped_splits(
rows, "scaffold_id", seed=args.seed
),
}
assert_group_disjoint(rows, manifests["cold_protein_exact"], "protein_id")
assert_group_disjoint(rows, manifests["cold_ligand_scaffold"], "scaffold_id")
args.output_dir.mkdir(parents=True, exist_ok=True)
write_jsonl(args.output_dir / "sample.jsonl", rows)
for name, assignments in manifests.items():
write_jsonl(
args.output_dir / f"split-{name}.jsonl",
[
{"pair_id": pair_id, "split": split}
for pair_id, split in sorted(assignments.items())
],
)
with args.parquet.open("rb") as handle:
source_sha256 = hashlib.file_digest(handle, "sha256").hexdigest()
audit = {
"source_parquet": str(args.parquet),
"source_sha256": source_sha256,
"dataset": "jglaser/binding_affinity",
"dataset_revision": args.dataset_revision,
"source_rows": source_rows,
"candidate_rows": len(raw_rows),
"sample_rows": len(rows),
"seed": args.seed,
"target": {"column": "neg_log10_affinity_M", "unit": "pAffinity"},
"rejected": dict(rejected),
"unique_proteins": len({row["protein_id"] for row in rows}),
"unique_ligands": len({row["ligand_id"] for row in rows}),
"unique_scaffold_groups": len({row["scaffold_id"] for row in rows}),
"duplicate_measurements_in_sample": sum(
row["replicate_count"] - 1 for row in rows
),
"protein_length": {
"min": min(row["protein_length"] for row in rows),
"median": statistics.median(row["protein_length"] for row in rows),
"max": max(row["protein_length"] for row in rows),
"over_512": sum(row["protein_length"] > 512 for row in rows),
},
"splits": {
name: target_stats(rows, assignments)
for name, assignments in manifests.items()
},
"limitations": [
"the source target can combine Ki, Kd, IC50, and EC50; pAffinity is not pKd",
"assay type, relation operator, and source provenance are absent from the combined Parquet",
"cold_protein_exact is entity-disjoint but not sequence-similarity-clustered",
"scaffold groups use Bemis-Murcko with exact-molecule fallback for acyclic ligands",
"this development sample is not an external biological benchmark",
],
}
(args.output_dir / "audit.json").write_text(json.dumps(audit, indent=2) + "\n")
print(json.dumps(audit, indent=2))
if __name__ == "__main__":
main()
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