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import numpy as np
import pandas as pd
from pathlib import Path
SEED = 17
DATA_PROC = Path("data/processed")
DATA_PROC.mkdir(parents=True, exist_ok=True)
CONDITIONS = ["YPD", "YPD+EtOH_4pct", "YPD+H2O2_100uM"]
def build_labels(transporters: list, compounds: list, seed: int = SEED) -> pd.DataFrame:
"""
Parameters
----------
transporters : list of (name, ...) or plain name strings
compounds : list of (name, smiles, class) tuples
"""
rng = np.random.default_rng(seed)
rows = []
for t in transporters:
t_name = t if isinstance(t, str) else t[0]
base = 0.03
if t_name in ("PDR5", "SNQ2", "YOR1", "PDR15"): base = 0.06
if t_name == "ATM1": base = 0.05
for c_name, c_smi, c_cls in compounds:
p = base
if t_name in ("PDR5", "SNQ2") and c_cls in ("aromatic", "heterocycle"): p *= 2.5
if t_name == "ATM1" and c_name in ("H2O2", "ETHANOL"): p *= 3.0
if t_name == "YOR1" and c_cls == "alcohol": p *= 1.8
for assay in ("A1", "A2"):
rows.append({
"transporter": t_name,
"compound": c_name,
"y": int(rng.random() < min(p, 0.5)),
"assay_id": assay,
"condition": rng.choice(CONDITIONS),
"concentration": rng.choice(["1uM", "10uM", "50uM", "100uM"]),
"replicate": int(rng.integers(1, 4)),
"media": rng.choice(["YPD", "SD"]),
})
return pd.DataFrame(rows)
if __name__ == "__main__":
P = pd.read_csv(DATA_PROC / "protein.csv")
L = pd.read_csv(DATA_PROC / "ligand.csv")
transporters = P["transporter"].tolist()
compounds = list(zip(L["compound"], L.get("smiles", L["compound"]),
L.get("class", ["unknown"] * len(L))))
Y = build_labels(transporters, compounds, seed=SEED)
Y.to_csv(DATA_PROC / "labels.csv", index=False)
print(f"labels.csv shape={Y.shape} pos_rate={Y.y.mean():.3f} NaNs={Y.isna().any().any()}")
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