| |
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| if str(ROOT) not in sys.path: |
| sys.path.insert(0, str(ROOT)) |
|
|
| import numpy as np |
|
|
| from spec_rag.faiss_index import load_index |
| from spec_rag.io import load_embeddings, load_smiles, save_jsonl |
| from spec_rag.retrieval import build_smiles_to_index, recall_at_k, search_index |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| parser = argparse.ArgumentParser(description="Retrieve SMILES from FAISS index.") |
| parser.add_argument("--index-path", required=True) |
| parser.add_argument("--smiles-path", required=True) |
| parser.add_argument("--query-embeddings", required=True) |
| parser.add_argument("--out-jsonl", required=True) |
| parser.add_argument("--k", type=int, default=100) |
| parser.add_argument("--ground-truth-smiles", default=None) |
| return parser.parse_args() |
|
|
|
|
| def main() -> None: |
| args = parse_args() |
| index = load_index(args.index_path) |
| smiles = load_smiles(args.smiles_path) |
| queries = load_embeddings(args.query_embeddings) |
| scores, indices = search_index(index, queries, args.k) |
|
|
| rows = [] |
| for i in range(indices.shape[0]): |
| retrieved = [smiles[j] if j >= 0 else "" for j in indices[i].tolist()] |
| rows.append( |
| { |
| "query_id": i, |
| "indices": indices[i].tolist(), |
| "scores": scores[i].tolist(), |
| "smiles": retrieved, |
| } |
| ) |
| save_jsonl(args.out_jsonl, rows) |
|
|
| if args.ground_truth_smiles: |
| gt_smiles = load_smiles(args.ground_truth_smiles) |
| lookup = build_smiles_to_index(smiles) |
| gt_indices = [lookup.get(smi, -1) for smi in gt_smiles] |
| if not any(i >= 0 for i in gt_indices): |
| print("No ground-truth SMILES found in index.") |
| return |
| recall = recall_at_k(indices, gt_indices, args.k) |
| print(f"Recall@{args.k}: {recall:.4f}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|