File size: 4,867 Bytes
db32e07 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | #!/usr/bin/env python
"""
Self-retrieval sanity check: build a tiny FAISS index from test molecules only,
query with the same true molecule embeddings. Expected Recall@1 ≈ 1.0.
Run separately for ChemBERTa and SMI-TED. If either fails, that embedding pipeline is broken.
If both pass, the main issue is likely library coverage / eval mismatch.
"""
from __future__ import annotations
import argparse
import json
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.embeddings import SMILESEmbedder, l2_normalize
from spec_rag.faiss_index import build_hnsw_index, index_search
from spec_rag.smited_encoder import load_smited_encoder
def parse_args():
p = argparse.ArgumentParser(
description="Self-retrieval: index test molecules, query with same embeddings; expect Recall@1 ≈ 1.0"
)
p.add_argument("--pred-jsonl", required=True, help="JSONL with smiles_gt (e.g. candidates_*.jsonl)")
p.add_argument("--despecbridge-path", default=None, help="De-SpecBridge path for SMI-TED")
p.add_argument("--chemberta-model", default="Derify/ChemBERTa_augmented_pubchem_13m")
p.add_argument("--batch-size", type=int, default=64)
p.add_argument("--device", default="cuda")
p.add_argument("--report", default=None, help="Write metrics JSON here")
return p.parse_args()
def main():
args = parse_args()
rows = []
with open(args.pred_jsonl) as f:
for line in f:
if line.strip():
rows.append(json.loads(line))
smiles_list = [r.get("smiles_gt", "") for r in rows]
n = len(smiles_list)
if n == 0:
raise SystemExit("No rows in pred-jsonl")
device = args.device
if device == "cuda":
try:
import torch
if not torch.cuda.is_available():
device = "cpu"
except Exception:
device = "cpu"
results = {"n": n, "chemberta": None, "smited": None}
# --- ChemBERTa self-retrieval ---
print("ChemBERTa: encoding test SMILES...")
embedder = SMILESEmbedder(
model_name=args.chemberta_model,
device=device,
batch_size=args.batch_size,
normalize=False,
)
v_chem = embedder.encode(smiles_list)
v_chem = l2_normalize(v_chem).astype(np.float32)
print("ChemBERTa: building FAISS index from test vectors...")
index_chem = build_hnsw_index(v_chem, m=16, ef_construction=100, ef_search=64, metric="cosine")
scores_chem, indices_chem = index_search(index_chem, v_chem, k=1)
r1_chem = sum(
1 for i in range(n)
if indices_chem[i, 0] < n and smiles_list[indices_chem[i, 0]] == smiles_list[i]
) / n
results["chemberta"] = {"Recall@1": r1_chem}
print(f"ChemBERTa self-retrieval Recall@1: {r1_chem:.4f} (expected ≈ 1.0)")
# --- SMI-TED self-retrieval ---
encode_smi = load_smited_encoder(despecbridge_path=args.despecbridge_path, device=device)
if encode_smi is None:
print("SMI-TED not available; skipping SMI-TED self-retrieval.")
else:
print("SMI-TED: encoding test SMILES...")
v_smi = encode_smi(smiles_list, batch_size=args.batch_size)
v_smi = l2_normalize(v_smi).astype(np.float32)
print("SMI-TED: building FAISS index from test vectors...")
index_smi = build_hnsw_index(v_smi, m=16, ef_construction=100, ef_search=64, metric="cosine")
scores_smi, indices_smi = index_search(index_smi, v_smi, k=1)
r1_smi = sum(
1 for i in range(n)
if indices_smi[i, 0] < n and smiles_list[indices_smi[i, 0]] == smiles_list[i]
) / n
results["smited"] = {"Recall@1": r1_smi}
print(f"SMI-TED self-retrieval Recall@1: {r1_smi:.4f} (expected ≈ 1.0)")
# Interpretation
print("\nInterpretation:")
if results["chemberta"] is not None and results["chemberta"]["Recall@1"] < 0.99:
print(" - ChemBERTa Recall@1 << 1.0 → embedding/index pipeline may be broken.")
elif results["chemberta"] is not None:
print(" - ChemBERTa passed (Recall@1 ≈ 1.0).")
if results["smited"] is not None:
if results["smited"]["Recall@1"] < 0.99:
print(" - SMI-TED Recall@1 << 1.0 → SMI-TED embedding construction may be the issue.")
else:
print(" - SMI-TED passed (Recall@1 ≈ 1.0).")
if (results["chemberta"] and results["chemberta"]["Recall@1"] >= 0.99 and
results["smited"] and results["smited"]["Recall@1"] >= 0.99):
print(" - Both passed → main issue is likely library coverage / eval mismatch.")
if args.report:
with open(args.report, "w") as f:
json.dump(results, f, indent=2)
print(f"\nWrote {args.report}")
if __name__ == "__main__":
main()
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