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"""Replay the frozen UltraMS MSnLib formula-level split (split A) from MSnLib.csv.
The split is defined by one rule: **every spectrum of a molecular formula belongs to
exactly one fold**. The official multiple-spectrum voting unit is the MSnLib source SMILES
string: 5,002 groups covering 57,436 of the 57,437 test spectra. The fold of each formula is the frozen assignment carried by the
`fold` column of `MSnLib.csv` (560,084 spectra, 21,107 molecular formulas), and the
per-fold counts are 16,885 / 2,110 / 2,112 formulas and 446,667 / 55,980 / 57,437
spectra for train / validation / test. This script recomputes the formula of every
spectrum with RDKit, verifies that the fold is formula-consistent, rebuilds the shipped
tables in memory and compares their SHA-256 with the frozen values. With
`--output-dir` it also writes the tables.
Usage
-----
# read-only replay + hash check (nothing written):
python3 build_split_a.py --csv /path/to/workspace/datasets/MSnLib/MSnLib.csv
# same, and write the tables:
python3 build_split_a.py --csv ... --output-dir replay_split_a
Requirements: Python >= 3.10 and RDKit 2026.03.5 (the frozen canonicalisation runtime).
Other RDKit patch releases can serialise the same molecule differently; use
`--lenient-rdkit` to downgrade the resulting table-hash mismatches to warnings.
"""
from __future__ import annotations
import argparse
import collections
import csv
import hashlib
import io
import json
import sys
from rdkit import Chem, RDLogger
from rdkit.Chem import rdMolDescriptors
RDLogger.DisableLog("rdApp.*")
csv.field_size_limit(10**9)
FOLD_ORDER = ["train", "val", "test"]
SOURCE_CSV_SHA256 = "be54b33039cf18675751ea3c6354b592177c899a0cd5df303ae616330133ec07"
EXPECTED = {
"n_data_rows": 560084,
"n_unique_formula": 21107,
"n_formula_train": 16885,
"n_formula_val": 2110,
"n_formula_test": 2112,
"n_spectra_train": 446667,
"n_spectra_val": 55980,
"n_spectra_test": 57437,
"n_voting_groups_official": 5002,
"n_voting_spectra_official": 57436,
"n_voting_groups_canonical": 4888,
"n_voting_spectra_canonical": 57436,
}
FROZEN = {
"formula_split.csv": (424350, "fcc44d1996d25306c884a6f8da30c1447fbe74f93c6fa12a3f271c0475a704b8"),
"row_formula_fold.csv": (13351630, "ac6113867fdda683304533f097fc7287c13271a2deac8e792d1316dcf00264ff"),
"test_spectra.csv": (7751575, "f2a870ba8ac9a54a36feaf1ecc12929ad9250ade2d082dc31454028c4b66b548"),
"val_spectra.csv": (7421554, "f4fa5e0f8875991bf9df2bcb67a05437271ee18c317f33d9108f3ec28a887a82"),
"test_molecules_voting.csv": (276185, "edad6ec7d6b6f9fc4763deac5245207aee767c78661532621c87acc5a9b1ee07"),
"test_molecules_voting_canonical_view.csv": (269502, "9fcd16ff4159e665833ea145848f94bee7c44115f4a858dd75fe486f15e92bd0"),
}
CANONICAL_DEPENDENT = {"test_spectra.csv", "val_spectra.csv", "test_molecules_voting.csv",
"test_molecules_voting_canonical_view.csv"}
def sha256(data: bytes) -> str:
return hashlib.sha256(data).hexdigest()
def compute(smiles):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None, None
return rdMolDescriptors.CalcMolFormula(mol), Chem.MolToSmiles(mol)
def replay(csv_path: str):
cache = {}
bad = []
bad_folds = collections.Counter()
fold_counts = collections.Counter()
formula_spectra = collections.Counter()
formula_folds = collections.defaultdict(set)
fold_formulas = collections.defaultdict(set)
formula_rows = []
test_rows = []
val_rows = []
with open(csv_path, newline="") as fh:
reader = csv.reader(fh)
header = next(reader)
col = {name: i for i, name in enumerate(header)}
for row_index, row in enumerate(reader):
smiles = row[col["smiles"]]
fold = row[col["fold"]]
if smiles not in cache:
cache[smiles] = compute(smiles)
formula, canon = cache[smiles]
if formula is None:
bad.append({"row_index": row_index, "smiles": smiles})
continue
if fold not in FOLD_ORDER:
bad_folds[fold] += 1
fold_counts[fold] += 1
formula_spectra[formula] += 1
formula_folds[formula].add(fold)
fold_formulas[fold].add(formula)
formula_rows.append((row_index, formula, fold))
if fold in ("test", "val"):
target = test_rows if fold == "test" else val_rows
target.append((row_index, smiles, row[col["precursor_mz"]], row[col["adduct"]],
row[col["collision_energy"]], fold, canon))
n_rows = len(formula_rows) + len(bad)
conflicts = {f: sorted(s) for f, s in formula_folds.items() if len(s) > 1}
# ---- tables (byte-identical to the frozen files) ----
fold_rank = {f: i for i, f in enumerate(FOLD_ORDER)}
buf = io.StringIO()
w = csv.writer(buf, lineterminator="\n")
w.writerow(["formula", "fold", "n_spectra"])
for formula, n in sorted(
formula_spectra.items(), key=lambda kv: (fold_rank.get(sorted(formula_folds[kv[0]])[0], 99), kv[0])
):
w.writerow([formula, sorted(formula_folds[formula])[0], n])
tables = {"formula_split.csv": buf.getvalue()}
buf = io.StringIO()
w = csv.writer(buf, lineterminator="\n")
w.writerow(["row_index", "formula", "fold"])
for row_index, formula, fold in formula_rows:
w.writerow([row_index, formula, fold])
tables["row_formula_fold.csv"] = buf.getvalue()
def spectra_csv(rows):
buf = io.StringIO()
w = csv.writer(buf, lineterminator="\n")
w.writerow(["row_index", "smiles", "precursor_mz", "adduct", "collision_energy", "fold",
"canonical_isomeric_smiles"])
for r in rows:
w.writerow([r[0], r[1], r[2], r[3], r[4], r[5], r[6]])
return buf.getvalue()
tables["test_spectra.csv"] = spectra_csv(test_rows)
tables["val_spectra.csv"] = spectra_csv(val_rows)
canon_groups = collections.Counter(r[6] for r in test_rows)
canon_ge2 = sorted((k, v) for k, v in canon_groups.items() if v >= 2)
buf = io.StringIO()
w = csv.writer(buf, lineterminator="\n")
w.writerow(["group_id", "canonical_isomeric_smiles", "n_spectra"])
for group_id, (canon, n) in enumerate(canon_ge2):
w.writerow([group_id, canon, n])
tables["test_molecules_voting_canonical_view.csv"] = buf.getvalue()
smiles_groups = collections.Counter(r[1] for r in test_rows)
smiles_ge2 = sorted((k, v) for k, v in smiles_groups.items() if v >= 2)
buf = io.StringIO()
w = csv.writer(buf, lineterminator="\n")
w.writerow(["group_id", "smiles", "n_spectra"])
for group_id, (smiles, n) in enumerate(smiles_ge2):
w.writerow([group_id, smiles, n])
tables["test_molecules_voting.csv"] = buf.getvalue()
summary = {
"n_data_rows": n_rows,
"n_unique_formula": len(formula_spectra),
"n_formula_by_fold": {f: len(fold_formulas[f]) for f in FOLD_ORDER},
"n_spectra_by_fold": {f: fold_counts[f] for f in FOLD_ORDER},
"n_formula_with_multiple_folds": len(conflicts),
"formula_conflicts": conflicts,
"n_rdkit_parse_failures": len(bad),
"parse_failure_examples": bad[:5],
"unexpected_folds": dict(bad_folds),
"n_voting_groups_official": len(smiles_ge2),
"n_voting_spectra_official": sum(n for _, n in smiles_ge2),
"n_voting_groups_canonical": len(canon_ge2),
"n_voting_spectra_canonical": sum(n for _, n in canon_ge2),
}
return tables, summary
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--csv", required=True, help="path to MSnLib.csv (560,084 data rows)")
parser.add_argument("--output-dir", default=None, help="write the tables here")
parser.add_argument("--lenient-rdkit", action="store_true",
help="downgrade canonical-string hash mismatches caused by a different RDKit release to warnings")
args = parser.parse_args()
digest = hashlib.sha256()
with open(args.csv, "rb") as fh:
while block := fh.read(8 << 20):
digest.update(block)
source_sha = digest.hexdigest()
print(f"source csv : {args.csv}")
print(f"source sha : {source_sha}")
if source_sha != SOURCE_CSV_SHA256:
print(f"[WARN] source SHA-256 differs from the frozen MSnLib.csv ({SOURCE_CSV_SHA256})")
tables, summary = replay(args.csv)
failures = []
actuals = {
"n_data_rows": summary["n_data_rows"],
"n_unique_formula": summary["n_unique_formula"],
"n_formula_train": summary["n_formula_by_fold"]["train"],
"n_formula_val": summary["n_formula_by_fold"]["val"],
"n_formula_test": summary["n_formula_by_fold"]["test"],
"n_spectra_train": summary["n_spectra_by_fold"]["train"],
"n_spectra_val": summary["n_spectra_by_fold"]["val"],
"n_spectra_test": summary["n_spectra_by_fold"]["test"],
"n_voting_groups_official": summary["n_voting_groups_official"],
"n_voting_spectra_official": summary["n_voting_spectra_official"],
"n_voting_groups_canonical": summary["n_voting_groups_canonical"],
"n_voting_spectra_canonical": summary["n_voting_spectra_canonical"],
}
for key, expected in EXPECTED.items():
actual = actuals[key]
ok = actual == expected
print(f"[{'PASS' if ok else 'FAIL'}] {key}: {actual:,} (expected {expected:,})")
if not ok:
failures.append(key)
if summary["n_formula_with_multiple_folds"]:
print(f"[FAIL] {summary['n_formula_with_multiple_folds']} formulas occur in more than one fold")
failures.append("formula_fold_consistency")
if summary["n_rdkit_parse_failures"]:
print(f"[FAIL] {summary['n_rdkit_parse_failures']} SMILES could not be parsed")
failures.append("rdkit_parse")
if summary["unexpected_folds"]:
print(f"[FAIL] unexpected fold labels: {summary['unexpected_folds']}")
failures.append("unexpected_folds")
canonical_diffs = []
for name, (expected_bytes, expected_sha) in FROZEN.items():
data = tables[name].encode()
got_sha = sha256(data)
ok = len(data) == expected_bytes and got_sha == expected_sha
print(f"[{'PASS' if ok else 'DIFF'}] {name}: {len(data):,} bytes sha256 {got_sha}")
if not ok:
if name in CANONICAL_DEPENDENT:
canonical_diffs.append(name)
else:
failures.append(name)
if args.output_dir:
import os
os.makedirs(args.output_dir, exist_ok=True)
for name, text in tables.items():
with open(os.path.join(args.output_dir, name), "w", newline="") as fh:
fh.write(text)
print(f"wrote {len(tables)} tables to {args.output_dir}")
print("\nsummary:")
print(json.dumps(summary, indent=2))
if canonical_diffs:
message = ("canonical-string tables differ from the frozen copies; the frozen tables were produced with "
"RDKit 2026.03.5 and a different patch release may serialise the same molecule differently")
if args.lenient_rdkit:
print(f"[WARN] {message}: {', '.join(canonical_diffs)}")
else:
print(f"[FAIL] {message}: {', '.join(canonical_diffs)}")
failures.extend(canonical_diffs)
return 1 if failures else 0
if __name__ == "__main__":
sys.exit(main())
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