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biology
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drug-discovery
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protein-protein-interaction
gene-essentiality
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6d1bbc7 | 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 | #!/usr/bin/env python3
"""Pre-annotate L2 candidates with LLM extraction for human correction.
Reads l2_candidates.jsonl, runs each abstract through an LLM using the
L2 zero-shot prompt, and saves the pre-annotated results for human review.
Usage:
python scripts/preannotate_l2.py --provider openai --model gpt-4o-mini
python scripts/preannotate_l2.py --provider anthropic --model claude-sonnet-4-6
Output:
exports/llm_benchmarks/l2_preannotated.jsonl
"""
import argparse
import json
import time
from pathlib import Path
from negbiodb.llm_client import LLMClient
from negbiodb.llm_eval import parse_l2_response
from negbiodb.llm_prompts import SYSTEM_PROMPT, format_l2_prompt
PROJECT_ROOT = Path(__file__).resolve().parent.parent
DATA_DIR = PROJECT_ROOT / "exports" / "llm_benchmarks"
CANDIDATES_FILE = DATA_DIR / "l2_candidates.jsonl"
OUTPUT_FILE = DATA_DIR / "l2_preannotated.jsonl"
def main():
parser = argparse.ArgumentParser(description="Pre-annotate L2 candidates")
parser.add_argument("--provider", default="openai",
choices=["openai", "gemini", "vllm", "anthropic"])
parser.add_argument("--model", default="gpt-4o-mini")
parser.add_argument("--api-base", default=None)
parser.add_argument("--api-key", default=None)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--max-tokens", type=int, default=2048)
args = parser.parse_args()
# Load candidates
candidates = []
with open(CANDIDATES_FILE) as f:
for line in f:
candidates.append(json.loads(line))
print(f"Loaded {len(candidates)} candidates")
# Resume: check existing output
completed = set()
if OUTPUT_FILE.exists():
with open(OUTPUT_FILE) as f:
for line in f:
rec = json.loads(line)
completed.add(rec["candidate_id"])
print(f" Resume: {len(completed)} already processed")
remaining = [c for c in candidates if c["candidate_id"] not in completed]
print(f" Remaining: {len(remaining)}")
if not remaining:
print("All candidates already processed.")
return
# Initialize client
print(f"\nInitializing: {args.model} ({args.provider})")
client = LLMClient(
provider=args.provider,
model=args.model,
api_base=args.api_base,
api_key=args.api_key,
temperature=args.temperature,
max_tokens=args.max_tokens,
)
# Process each candidate
start_time = time.time()
with open(OUTPUT_FILE, "a") as out_f:
for i, candidate in enumerate(remaining):
system, user = format_l2_prompt(
{"abstract_text": candidate["abstract_text"]},
config="zero-shot",
)
try:
response = client.generate(user, system)
except Exception as e:
print(f" Error on {candidate['candidate_id']}: {e}")
response = f"ERROR: {e}"
# Parse LLM extraction
extraction = parse_l2_response(response)
output_rec = {
**candidate,
"llm_response": response,
"llm_extraction": extraction,
"preannotation_model": args.model,
}
out_f.write(json.dumps(output_rec, ensure_ascii=False) + "\n")
out_f.flush()
if (i + 1) % 10 == 0:
elapsed = time.time() - start_time
rate = (i + 1) / elapsed * 60
print(f" Progress: {i + 1}/{len(remaining)} ({rate:.1f}/min)")
elapsed = time.time() - start_time
print(f"\nDone: {len(remaining)} processed in {elapsed:.0f}s")
print(f"Output: {OUTPUT_FILE}")
if __name__ == "__main__":
main()
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