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Build MolEdit dataset v2 — balanced, 20 samples per op type (13 types = 260 total).
Changes from v1:
- target_per_op is a fixed CLI arg (default 20), not derived from n_samples
- Uses a much larger candidate pool to handle rare op types
- Multi-pass: exhausts full pool before giving up on any op type
- Saves to a new output directory (default: ./data/pubchem_dataset_v2)
Usage:
python build_dataset_v2.py \
--input /home/dataset-assist-0/usr/lh/mzm/data/pubchem/CID-SMILES.gz.1 \
--output ./data/pubchem_dataset_v2 \
--target_per_op 20 \
--pool 20000 \
--seed 42
"""
import os, sys, json, random, argparse, subprocess, signal
sys.path.insert(0, os.path.dirname(__file__))
from rdkit import Chem
from rdkit.Chem import Draw, AllChem
from rdkit import RDLogger
RDLogger.DisableLog("rdApp.*")
from mol_corrupt import corrupt_molecule, CORRUPTION_TYPES
# ---------------------------------------------------------------------------
# Molecule filtering
# ---------------------------------------------------------------------------
def is_valid(mol, smiles):
if mol is None or "." in smiles:
return False
n = mol.GetNumAtoms()
if n < 5 or n > 60:
return False
if not any(a.GetAtomicNum() == 6 for a in mol.GetAtoms()):
return False
return True
# ---------------------------------------------------------------------------
# Streaming sample from gzip file
# ---------------------------------------------------------------------------
def stream_sample(filepath, pool_size, seed=42):
rng = random.Random(seed)
pool = []
proc = subprocess.Popen(
["zcat", filepath],
stdout=subprocess.PIPE,
stderr=subprocess.DEVNULL,
preexec_fn=lambda: signal.signal(signal.SIGPIPE, signal.SIG_DFL),
)
for raw in proc.stdout:
line = raw.decode("ascii", errors="ignore").strip()
parts = line.split("\t")
if len(parts) >= 2 and parts[0].isdigit():
pool.append((parts[0], parts[1]))
if len(pool) >= pool_size:
break
proc.kill(); proc.wait()
rng.shuffle(pool)
return pool
def render(smiles, path, size=(400, 300)):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return False
AllChem.Compute2DCoords(mol)
Draw.MolToFile(mol, path, size=size)
return True
# ---------------------------------------------------------------------------
# Main builder
# ---------------------------------------------------------------------------
def build(input_path, output_dir, target_per_op=20, pool_size=20000, seed=42):
img_dir = os.path.join(output_dir, "images")
os.makedirs(img_dir, exist_ok=True)
print(f"Sampling {pool_size} candidates from {input_path} ...")
raw_pool = stream_sample(input_path, pool_size, seed=seed)
print(f" Got {len(raw_pool)} candidates, filtering ...")
valid = []
for cid, smi in raw_pool:
mol = Chem.MolFromSmiles(smi)
if is_valid(mol, smi):
valid.append((cid, Chem.MolToSmiles(mol), mol))
print(f" {len(valid)} molecules passed filtering")
collected = {op: 0 for op in CORRUPTION_TYPES}
records = []
idx = 0
skipped = 0
# Pass through the pool repeatedly until all op types hit target
# or we've done max_passes full sweeps (to avoid infinite loop)
max_passes = 5
for pass_num in range(1, max_passes + 1):
remaining_ops = [op for op in CORRUPTION_TYPES if collected[op] < target_per_op]
if not remaining_ops:
break
print(f"\nPass {pass_num}: {len(remaining_ops)} op types still need samples")
random.seed(seed + pass_num)
random.shuffle(valid)
for cid, canonical, mol in valid:
if not remaining_ops:
break
for op_type in list(remaining_ops):
if collected[op_type] >= target_per_op:
remaining_ops.remove(op_type)
continue
result = corrupt_molecule(canonical, op_type=op_type, max_retries=15)
if result is None:
skipped += 1
continue
wrong_smi, wrong_smi_mapped, correction = result
item_id = f"{idx:06d}"
img_rel = os.path.join("images", f"{item_id}.png")
abs_img = os.path.join(output_dir, img_rel)
if not render(canonical, abs_img):
skipped += 1
continue
records.append({
"id": item_id,
"cid": cid,
"correct_smiles": canonical,
"wrong_smiles": wrong_smi,
"wrong_smiles_mapped": wrong_smi_mapped,
"image_path": img_rel,
"operation": correction,
})
collected[op_type] += 1
idx += 1
if idx % 50 == 0:
print(f" {idx} samples collected ...")
remaining_ops = [op for op in CORRUPTION_TYPES if collected[op] < target_per_op]
print("\nOp type coverage:")
for op in CORRUPTION_TYPES:
mark = "✓" if collected[op] >= target_per_op else f"✗ (only {collected[op]})"
print(f" {op:<35} {collected[op]:>4} {mark}")
json_path = os.path.join(output_dir, "dataset.json")
with open(json_path, "w") as f:
json.dump(records, f, indent=2, ensure_ascii=False)
print(f"\nDone: {len(records)} samples → {json_path} (skipped {skipped})")
return records
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input", default="/home/dataset-assist-0/usr/lh/mzm/data/pubchem/CID-SMILES.gz.1")
parser.add_argument("--output", default="./data/pubchem_dataset_v2")
parser.add_argument("--target_per_op", type=int, default=20, help="Samples per op type")
parser.add_argument("--pool", type=int, default=20000, help="Candidate pool size from input file")
parser.add_argument("--seed", type=int, default=42)
args = parser.parse_args()
build(
input_path=args.input,
output_dir=args.output,
target_per_op=args.target_per_op,
pool_size=args.pool,
seed=args.seed,
)
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