#!/usr/bin/env python3 """Build GE-L3 reasoning dataset for LLM benchmark. Generates 200 records for LLM-as-Judge reasoning evaluation. Source: Gold/silver tier non-essential gene-cell_line pairs with gene descriptions. Balance: ~50% reference non-essential, ~50% context non-essential (essential elsewhere). Split: 20 fewshot + 20 val + 160 test Output: exports/ge_llm/ge_l3_dataset.jsonl Usage: PYTHONPATH=src python scripts_depmap/build_ge_l3_dataset.py """ from __future__ import annotations import argparse import logging import sys from pathlib import Path import numpy as np import pandas as pd logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") logger = logging.getLogger(__name__) PROJECT_ROOT = Path(__file__).resolve().parent.parent OUTPUT_DIR = PROJECT_ROOT / "exports" / "ge_llm" N_TOTAL = 200 N_REFERENCE_NE = 100 # Reference non-essential genes N_CONTEXT_NE = 100 # Context non-essential (essential elsewhere) MIN_DESC_LEN = 30 def _generate_gold_reasoning(row: pd.Series) -> str: """Generate template gold reasoning for fewshot examples.""" gene = row.get("gene_symbol", "UNKNOWN") cell_line = row.get("ccle_name") or row.get("model_id", "UNKNOWN") lineage = row.get("lineage", "unknown lineage") desc = row.get("description") or "unknown function" is_rne = row.get("is_reference_nonessential", 0) degree = row.get("gene_degree") effect = row.get("gene_effect_score") parts = [] parts.append( f"{gene} is described as: {desc[:200]}. " f"In {cell_line} ({lineage}), CRISPR knockout of {gene} shows no significant " f"effect on cell viability." ) if is_rne: parts.append( f"{gene} is part of the reference non-essential gene set, indicating " f"it is dispensable for cell survival across multiple cell types and " f"screening contexts." ) if degree and not pd.isna(degree) and degree > 100: parts.append( f"{gene} is non-essential in {int(degree)} cell lines in DepMap, " f"suggesting broad dispensability rather than lineage-specific tolerance." ) if effect is not None and not pd.isna(effect) and effect > -0.2: parts.append( f"The gene effect score ({effect:.3f}) is close to zero, indicating " f"minimal fitness impact upon knockout." ) return " ".join(parts) def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser(description="Build GE-L3 reasoning dataset.") parser.add_argument("--db", type=Path, default=PROJECT_ROOT / "data" / "negbiodb_depmap.db") parser.add_argument("--output", type=Path, default=OUTPUT_DIR / "ge_l3_dataset.jsonl") parser.add_argument("--seed", type=int, default=42) args = parser.parse_args(argv) from negbiodb_depmap.depmap_db import get_connection from negbiodb_depmap.llm_dataset import ( apply_max_per_gene, assign_splits, construct_l3_context, write_dataset_metadata, write_jsonl, ) rng = np.random.RandomState(args.seed) conn = get_connection(args.db) try: # Load gold/silver tier with gene descriptions df = pd.read_sql_query(""" SELECT nr.gene_id, nr.cell_line_id, g.gene_symbol, g.entrez_id, g.description, g.is_common_essential, g.is_reference_nonessential, c.model_id, c.ccle_name, c.lineage, c.primary_disease, nr.gene_effect_score, nr.dependency_probability, nr.evidence_type, nr.confidence_tier, p.gene_degree, p.cell_line_degree FROM ge_negative_results nr JOIN genes g ON nr.gene_id = g.gene_id JOIN cell_lines c ON nr.cell_line_id = c.cell_line_id LEFT JOIN gene_cell_pairs p ON nr.gene_id = p.gene_id AND nr.cell_line_id = p.cell_line_id WHERE nr.confidence_tier IN ('gold', 'silver') AND g.description IS NOT NULL AND LENGTH(g.description) >= ? ORDER BY RANDOM() LIMIT 2000 """, conn, params=(MIN_DESC_LEN,)) finally: conn.close() logger.info("Gold/silver with descriptions: %d records", len(df)) df = apply_max_per_gene(df, max_per_gene=5, seed=args.seed) # Split into reference non-essential vs context non-essential ref_ne = df[df["is_reference_nonessential"] == 1].copy() ctx_ne = df[df["is_reference_nonessential"] == 0].copy() logger.info("Reference NE: %d, Context NE: %d", len(ref_ne), len(ctx_ne)) selected = [] n_ref = min(N_REFERENCE_NE, len(ref_ne)) n_ctx = min(N_CONTEXT_NE, len(ctx_ne)) if n_ref > 0: selected.append(ref_ne.sample(n=n_ref, random_state=rng)) if n_ctx > 0: selected.append(ctx_ne.sample(n=n_ctx, random_state=rng)) # Fill remaining n_remaining = N_TOTAL - n_ref - n_ctx if n_remaining > 0: remaining = df[~df.index.isin(pd.concat(selected).index)] if len(remaining) > 0: n_fill = min(n_remaining, len(remaining)) selected.append(remaining.sample(n=n_fill, random_state=rng)) combined = pd.concat(selected, ignore_index=True) logger.info("Selected %d records for L3", len(combined)) # Assign splits combined = assign_splits(combined, ratios={"train": 0.1, "val": 0.1, "test": 0.8}) combined["split"] = combined["split"].replace({"train": "fewshot"}) # Build JSONL records records = [] for i, (_, row) in enumerate(combined.iterrows()): context = construct_l3_context(row) ne_type = "reference" if row.get("is_reference_nonessential", 0) else "context" rec = { "question_id": f"GEL3-{i:04d}", "task": "ge-l3", "split": row["split"], "difficulty": "medium", "context_text": context, "gold_answer": row.get("evidence_type", ""), "gold_category": ne_type, "metadata": { "gene_symbol": row.get("gene_symbol"), "model_id": row.get("model_id"), "lineage": row.get("lineage"), "confidence_tier": row.get("confidence_tier"), "is_reference_nonessential": int(row.get("is_reference_nonessential", 0)), "gene_degree": int(row["gene_degree"]) if pd.notna(row.get("gene_degree")) else None, }, } if row["split"] == "fewshot": rec["gold_reasoning"] = _generate_gold_reasoning(row) records.append(rec) write_jsonl(records, args.output) stats = { "n_total": len(records), "ne_type_distribution": { "reference": n_ref, "context": n_ctx, }, "split_distribution": dict(combined["split"].value_counts()), "seed": args.seed, } write_dataset_metadata(args.output.parent, "ge-l3", stats) logger.info("GE-L3 dataset built: %d records", len(records)) return 0 if __name__ == "__main__": sys.exit(main())