from __future__ import annotations import argparse import json from pathlib import Path import sys ROOT_DIR = Path(__file__).resolve().parents[1] if str(ROOT_DIR) not in sys.path: sys.path.insert(0, str(ROOT_DIR)) import numpy as np import pandas as pd from libs.adaptive.clustering import cluster_ligands_butina from libs.adaptive.hyperclustering import hypercluster_representatives from libs.encoders.ligand_encoder import LigandEncoder from libs.utils.config import load_config from libs.utils.io_smiles import read_smiles_table def main() -> int: parser = argparse.ArgumentParser(description="Cluster ligands and derive hyperclusters") parser.add_argument("--config", default="configs/default.yaml") parser.add_argument("--output", default="results/clustering.csv") args = parser.parse_args() cfg = load_config(args.config) root = Path(__file__).resolve().parents[1] ligands = read_smiles_table(root / cfg["data"]["ligand_table"]) enc = LigandEncoder().encode_table(ligands) ligand_ids = [e.ligand_id for e in enc] fps = [e.fingerprint for e in enc] vectors = np.vstack([e.vector for e in enc]) id_to_idx = {lid: i for i, lid in enumerate(ligand_ids)} cluster_map = cluster_ligands_butina(ligand_ids, fps, cutoff=float(cfg["clustering"]["butina_cutoff"])) reps = {} for cid in sorted(set(cluster_map.values())): members = [lid for lid in ligand_ids if cluster_map[lid] == cid] reps[cid] = np.mean(np.vstack([vectors[id_to_idx[lid]] for lid in members]), axis=0) hyper = hypercluster_representatives(reps, int(cfg["clustering"]["n_hyperclusters"])) rows = [{"ligand_id": lid, "cluster_id": cluster_map[lid], "hypercluster_id": hyper.get(cluster_map[lid], -1)} for lid in ligand_ids] out = root / args.output out.parent.mkdir(parents=True, exist_ok=True) pd.DataFrame(rows).to_csv(out, index=False) print(json.dumps({"output": str(out), "n_clusters": len(set(cluster_map.values()))}, indent=2)) return 0 if __name__ == "__main__": raise SystemExit(main())