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"""
Evaluate retrieval baselines against library indices.
Supported modes:
1) Oracle molecule-query retrieval (ChemBERTa / SMI-TED / FLARE-mol).
2) FLARE spectrum-query retrieval (query from FLARE spec encoder -> index_flare.faiss).
"""
from __future__ import annotations
import argparse
import json
import sys
from functools import lru_cache
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 tqdm import tqdm
from spec_rag.embeddings import SMILESEmbedder, l2_normalize
from spec_rag.faiss_index import load_index, index_search
from spec_rag.flare_encoder import FLAREMolEmbedder, FLARESpecEmbedder
from spec_rag.smited_encoder import load_smited_encoder
def load_meta(library_dir: Path):
if (library_dir / "meta.parquet").exists():
import pandas as pd
return pd.read_parquet(library_dir / "meta.parquet")
if (library_dir / "meta.jsonl").exists():
rows = []
with open(library_dir / "meta.jsonl") as f:
for line in f:
if line.strip():
rows.append(json.loads(line))
import pandas as pd
return pd.DataFrame(rows)
raise FileNotFoundError(f"No meta.parquet or meta.jsonl in {library_dir}")
@lru_cache(maxsize=200000)
def canonicalize_smiles(smiles: str) -> str:
text = str(smiles or "").strip()
if not text:
return ""
try:
from rdkit import Chem
except ImportError:
return text
mol = Chem.MolFromSmiles(text)
if mol is None:
return text
return Chem.MolToSmiles(mol, canonical=True)
def recall_at_k(indices: list, meta_df, gt_smiles: str, k: int) -> bool:
if not gt_smiles or not indices or meta_df is None:
return False
gt_canon = canonicalize_smiles(gt_smiles)
for i in indices[:k]:
if i < len(meta_df) and canonicalize_smiles(meta_df.iloc[i]["smiles"]) == gt_canon:
return True
return False
def tanimoto_at_1(top_smiles: str, gt_smiles: str) -> float | None:
try:
from rdkit import Chem
from rdkit.Chem import DataStructs
from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect
except ImportError:
return None
if not top_smiles or not gt_smiles:
return None
mol_gt = Chem.MolFromSmiles(gt_smiles)
mol_pred = Chem.MolFromSmiles(top_smiles)
if mol_gt is None or mol_pred is None:
return None
fp_gt = GetMorganFingerprintAsBitVect(mol_gt, 2, nBits=2048)
fp_pred = GetMorganFingerprintAsBitVect(mol_pred, 2, nBits=2048)
return DataStructs.TanimotoSimilarity(fp_gt, fp_pred)
def parse_args():
p = argparse.ArgumentParser(description="Evaluate retrieval baselines against library indices")
p.add_argument("--pred-jsonl", required=True, help="JSONL from retrieve_generate (has smiles_gt per line)")
p.add_argument("--library-dir", required=True, help="Library with index_chem.faiss, index_smi.faiss, meta")
p.add_argument("--despecbridge-path", default=None, help="De-SpecBridge path for SMI-TED encoder")
p.add_argument("--chemberta-model", default="Derify/ChemBERTa_augmented_pubchem_13m")
p.add_argument("--flare-repo", default=str(ROOT / "FLARE"))
p.add_argument("--flare-hparams", default=str(ROOT / "FLARE" / "experiments" / "20250913_optimized_filip-model" / "lightning_logs" / "version_0" / "hparams.yaml"))
p.add_argument("--flare-checkpoint", default=str(ROOT / "FLARE" / "pretrained_models" / "flare.ckpt"))
p.add_argument("--flare-batch-size", type=int, default=256)
p.add_argument("--with-flare-oracle", action="store_true", help="Run oracle retrieval using FLARE mol encoder + index_flare")
p.add_argument("--spec-tsv", default=None, help="TSV with spectra rows (for FLARE spec encoder eval)")
p.add_argument("--subformula-dir", default=None, help="Subformula directory for FLARE spec encoder eval")
p.add_argument("--spec-fold", default="test", help="Fold to evaluate in --spec-tsv (default: test)")
p.add_argument("--with-flare-spec", action="store_true", help="Run FLARE spectrum-encoder retrieval + index_flare")
p.add_argument("--K", type=int, default=100)
p.add_argument("--batch-size", type=int, default=64)
p.add_argument("--report", default=None, help="Write metrics JSON here")
p.add_argument("--device", default="cuda")
return p.parse_args()
def main():
args = parse_args()
library_dir = Path(args.library_dir)
meta_df = load_meta(library_dir)
rows = []
with open(args.pred_jsonl) as f:
for line in f:
if line.strip():
rows.append(json.loads(line))
smiles_gt_list = [r.get("smiles_gt", "") for r in rows]
n = len(smiles_gt_list)
if n == 0:
raise SystemExit("No rows in pred-jsonl")
index_chem = load_index(library_dir / "index_chem.faiss") if (library_dir / "index_chem.faiss").exists() else None
index_smi = load_index(library_dir / "index_smi.faiss") if (library_dir / "index_smi.faiss").exists() else None
index_flare = load_index(library_dir / "index_flare.faiss") if (library_dir / "index_flare.faiss").exists() else None
device = args.device
if device == "cuda":
try:
import torch
if not torch.cuda.is_available():
device = "cpu"
except Exception:
device = "cpu"
q_chem = None
if index_chem is not None:
print("Encoding test SMILES with ChemBERTa...")
embedder_chem = SMILESEmbedder(
model_name=args.chemberta_model,
device=device,
batch_size=args.batch_size,
normalize=False,
)
q_chem = embedder_chem.encode(smiles_gt_list)
q_chem = l2_normalize(q_chem).astype(np.float32)
q_smi = None
if index_smi is not None:
encode_smi = load_smited_encoder(despecbridge_path=args.despecbridge_path, device=device)
if encode_smi is None:
print("SMI-TED not available; skipping oracle_smi metrics.")
else:
print("Encoding test SMILES with SMI-TED...")
q_smi = encode_smi(smiles_gt_list, batch_size=args.batch_size)
q_smi = l2_normalize(q_smi).astype(np.float32)
K = args.K
results_chem = {"r1": 0, "r10": 0, "r50": 0, "tan": []}
results_smi = {"r1": 0, "r10": 0, "r50": 0, "tan": []}
if q_chem is not None and index_chem is not None:
scores_chem, indices_chem = index_search(index_chem, q_chem, K)
for i in range(n):
idx_list = indices_chem[i].tolist()
gt = smiles_gt_list[i]
if recall_at_k(idx_list, meta_df, gt, 1):
results_chem["r1"] += 1
if recall_at_k(idx_list, meta_df, gt, 10):
results_chem["r10"] += 1
if recall_at_k(idx_list, meta_df, gt, 50):
results_chem["r50"] += 1
if idx_list and idx_list[0] < len(meta_df):
top_smi = meta_df.iloc[idx_list[0]]["smiles"]
t = tanimoto_at_1(top_smi, gt)
if t is not None:
results_chem["tan"].append(t)
# SMI-TED oracle
if q_smi is not None and index_smi is not None:
scores_smi, indices_smi = index_search(index_smi, q_smi, K)
for i in range(n):
idx_list = indices_smi[i].tolist()
gt = smiles_gt_list[i]
if recall_at_k(idx_list, meta_df, gt, 1):
results_smi["r1"] += 1
if recall_at_k(idx_list, meta_df, gt, 10):
results_smi["r10"] += 1
if recall_at_k(idx_list, meta_df, gt, 50):
results_smi["r50"] += 1
if idx_list and idx_list[0] < len(meta_df):
top_smi = meta_df.iloc[idx_list[0]]["smiles"]
t = tanimoto_at_1(top_smi, gt)
if t is not None:
results_smi["tan"].append(t)
metrics = {
"n": n,
"K": K,
"oracle_chem": None,
"oracle_smi": None,
"oracle_flare": None,
"flare_spec_query": None,
}
if q_chem is not None and index_chem is not None:
metrics["oracle_chem"] = {
"Recall@1": results_chem["r1"] / n,
"Recall@10": results_chem["r10"] / n,
"Recall@50": results_chem["r50"] / n,
"Tanimoto@1_mean": sum(results_chem["tan"]) / len(results_chem["tan"]) if results_chem["tan"] else None,
}
if q_smi is not None:
metrics["oracle_smi"] = {
"Recall@1": results_smi["r1"] / n,
"Recall@10": results_smi["r10"] / n,
"Recall@50": results_smi["r50"] / n,
"Tanimoto@1_mean": sum(results_smi["tan"]) / len(results_smi["tan"]) if results_smi["tan"] else None,
}
if args.with_flare_oracle:
if index_flare is None:
print("index_flare.faiss not found; skipping oracle_flare.")
else:
print("Encoding test SMILES with FLARE mol encoder...")
flare_mol = FLAREMolEmbedder(
flare_repo=args.flare_repo,
hparams_pth=args.flare_hparams,
checkpoint_pth=args.flare_checkpoint,
device=device,
batch_size=args.flare_batch_size,
normalize=True,
)
q_flare = flare_mol.encode(smiles_gt_list).astype(np.float32)
scores_flare, indices_flare = index_search(index_flare, q_flare, K)
results_flare = {"r1": 0, "r10": 0, "r50": 0, "tan": []}
for i in range(n):
idx_list = indices_flare[i].tolist()
gt = smiles_gt_list[i]
if recall_at_k(idx_list, meta_df, gt, 1):
results_flare["r1"] += 1
if recall_at_k(idx_list, meta_df, gt, 10):
results_flare["r10"] += 1
if recall_at_k(idx_list, meta_df, gt, 50):
results_flare["r50"] += 1
if idx_list and idx_list[0] < len(meta_df):
top_smi = meta_df.iloc[idx_list[0]]["smiles"]
t = tanimoto_at_1(top_smi, gt)
if t is not None:
results_flare["tan"].append(t)
metrics["oracle_flare"] = {
"Recall@1": results_flare["r1"] / n,
"Recall@10": results_flare["r10"] / n,
"Recall@50": results_flare["r50"] / n,
"Tanimoto@1_mean": sum(results_flare["tan"]) / len(results_flare["tan"]) if results_flare["tan"] else None,
}
if args.with_flare_spec:
if index_flare is None:
print("index_flare.faiss not found; skipping flare_spec_query.")
elif not args.spec_tsv or not args.subformula_dir:
print("Need --spec-tsv and --subformula-dir for --with-flare-spec; skipping.")
else:
print("Encoding spectra with FLARE spec encoder...")
flare_spec = FLARESpecEmbedder(
flare_repo=args.flare_repo,
hparams_pth=args.flare_hparams,
checkpoint_pth=args.flare_checkpoint,
dataset_pth=args.spec_tsv,
subformula_dir_pth=args.subformula_dir,
fold=args.spec_fold,
device=device,
batch_size=args.batch_size,
normalize=True,
)
q_spec, gt_smiles, ids = flare_spec.encode()
if q_spec.shape[0] == 0:
print("No spectra encoded for flare_spec_query.")
else:
scores_spec, indices_spec = index_search(index_flare, q_spec.astype(np.float32), K)
m = q_spec.shape[0]
results_spec = {"n": m, "r1": 0, "r10": 0, "r50": 0, "tan": []}
for i in range(m):
idx_list = indices_spec[i].tolist()
gt = gt_smiles[i]
if recall_at_k(idx_list, meta_df, gt, 1):
results_spec["r1"] += 1
if recall_at_k(idx_list, meta_df, gt, 10):
results_spec["r10"] += 1
if recall_at_k(idx_list, meta_df, gt, 50):
results_spec["r50"] += 1
if idx_list and idx_list[0] < len(meta_df):
top_smi = meta_df.iloc[idx_list[0]]["smiles"]
t = tanimoto_at_1(top_smi, gt)
if t is not None:
results_spec["tan"].append(t)
metrics["flare_spec_query"] = {
"n": results_spec["n"],
"fold": args.spec_fold,
"Recall@1": results_spec["r1"] / results_spec["n"],
"Recall@10": results_spec["r10"] / results_spec["n"],
"Recall@50": results_spec["r50"] / results_spec["n"],
"Tanimoto@1_mean": sum(results_spec["tan"]) / len(results_spec["tan"]) if results_spec["tan"] else None,
}
print("Oracle retrieval metrics:", json.dumps(metrics, indent=2))
if args.report:
with open(args.report, "w") as f:
json.dump(metrics, f, indent=2)
print(f"Wrote {args.report}")
if __name__ == "__main__":
main()
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