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()}")