#!/usr/bin/env python3 """Prepare PPI ML benchmark datasets from exported negatives + HuRI positives. Produces (in exports/ppi/): - ppi_m1_balanced.parquet — 1:1 pos/neg (NegBioDB curated negatives) - ppi_m1_realistic.parquet — 1:10 pos/neg (NegBioDB curated negatives) - ppi_m1_uniform_random.parquet — 1:1 pos/neg (Exp 1 control A) - ppi_m1_degree_matched.parquet — 1:1 pos/neg (Exp 1 control B) - ppi_m1_balanced_ddb.parquet — M1 balanced + split_degree_balanced (Exp 4) Prerequisite: - exports/ppi/negbiodb_ppi_pairs.parquet (from export step) - data/ppi/huri/HI-union.tsv - data/ppi/huri/ensg_to_uniprot.tsv - data/negbiodb_ppi.db Usage: PYTHONPATH=src python scripts_ppi/prepare_exp_data.py """ from __future__ import annotations import argparse import logging import sys from pathlib import Path import pandas as pd logging.basicConfig( level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s", datefmt="%H:%M:%S", ) logger = logging.getLogger(__name__) ROOT = Path(__file__).parent.parent def main(argv: list[str] | None = None) -> int: parser = argparse.ArgumentParser(description="Prepare PPI ML benchmark datasets") parser.add_argument( "--data-dir", type=Path, default=ROOT / "exports" / "ppi", help="Directory for input/output parquets", ) parser.add_argument( "--db", type=Path, default=ROOT / "data" / "negbiodb_ppi.db", help="Path to negbiodb_ppi.db", ) parser.add_argument( "--huri-dir", type=Path, default=ROOT / "data" / "ppi" / "huri", help="Directory containing HI-union.tsv and ensg_to_uniprot.tsv", ) parser.add_argument("--seed", type=int, default=42) parser.add_argument("--skip-exp1", action="store_true") parser.add_argument("--skip-exp4", action="store_true") args = parser.parse_args(argv) data_dir: Path = args.data_dir db_path: Path = args.db huri_dir: Path = args.huri_dir # Verify inputs neg_parquet = data_dir / "negbiodb_ppi_pairs.parquet" huri_file = huri_dir / "HI-union.tsv" ensg_file = huri_dir / "ensg_to_uniprot.tsv" for p in [neg_parquet, huri_file, ensg_file, db_path]: if not p.exists(): logger.error("Required file missing: %s", p) return 1 data_dir.mkdir(parents=True, exist_ok=True) # ------------------------------------------------------------------ # Load negatives + positives # ------------------------------------------------------------------ from negbiodb_ppi.export import ( load_huri_positives_df, resolve_conflicts, build_m1_balanced, build_m1_realistic, add_degree_balanced_split, apply_ppi_m1_splits, generate_uniform_random_negatives, generate_degree_matched_negatives, control_pairs_to_df, ) logger.info("Loading negatives from %s", neg_parquet.name) neg_df = pd.read_parquet(neg_parquet) logger.info("Negatives: %d rows", len(neg_df)) # Filter to rows with sequences has_seq = neg_df["sequence_1"].notna() & neg_df["sequence_2"].notna() neg_df = neg_df[has_seq].reset_index(drop=True) logger.info("Negatives with sequences: %d rows", len(neg_df)) logger.info("Loading HuRI positives...") pos_df = load_huri_positives_df( ppi_path=huri_file, ensg_mapping_path=ensg_file, db_path=db_path, ) logger.info("Positives: %d rows", len(pos_df)) # Conflict resolution neg_df, pos_df, n_conflicts = resolve_conflicts(neg_df, pos_df) logger.info("After conflict resolution: %d neg, %d pos, %d conflicts removed", len(neg_df), len(pos_df), n_conflicts) # Positive pair set for exclusion positive_pairs = set(zip(pos_df["uniprot_id_1"], pos_df["uniprot_id_2"])) # ------------------------------------------------------------------ # M1 datasets (NegBioDB curated negatives) # ------------------------------------------------------------------ logger.info("Building M1 balanced (1:1)...") m1_bal = build_m1_balanced(neg_df, pos_df, seed=args.seed) m1_bal_path = data_dir / "ppi_m1_balanced.parquet" m1_bal.to_parquet(m1_bal_path, index=False) logger.info("Saved %d rows → %s", len(m1_bal), m1_bal_path.name) logger.info("Building M1 realistic (1:10)...") m1_real = build_m1_realistic(neg_df, pos_df, ratio=10, seed=args.seed) m1_real_path = data_dir / "ppi_m1_realistic.parquet" m1_real.to_parquet(m1_real_path, index=False) logger.info("Saved %d rows → %s", len(m1_real), m1_real_path.name) # ------------------------------------------------------------------ # Exp 1: Control negatives # ------------------------------------------------------------------ if not args.skip_exp1: n_pos = len(pos_df) logger.info("Generating %d uniform random negatives (Exp 1A)...", n_pos) uniform_pairs = generate_uniform_random_negatives( db_path, positive_pairs, n_samples=n_pos, seed=args.seed ) uniform_neg = control_pairs_to_df(uniform_pairs, db_path) m1_uniform = pd.concat([pos_df, uniform_neg], ignore_index=True) m1_uniform = apply_ppi_m1_splits(m1_uniform, seed=args.seed) uniform_path = data_dir / "ppi_m1_uniform_random.parquet" m1_uniform.to_parquet(uniform_path, index=False) logger.info("Saved %d rows → %s", len(m1_uniform), uniform_path.name) logger.info("Generating %d degree-matched negatives (Exp 1B)...", n_pos) deg_pairs = generate_degree_matched_negatives( db_path, positive_pairs, n_samples=n_pos, seed=args.seed ) deg_neg = control_pairs_to_df(deg_pairs, db_path) m1_deg = pd.concat([pos_df, deg_neg], ignore_index=True) m1_deg = apply_ppi_m1_splits(m1_deg, seed=args.seed) deg_path = data_dir / "ppi_m1_degree_matched.parquet" m1_deg.to_parquet(deg_path, index=False) logger.info("Saved %d rows → %s", len(m1_deg), deg_path.name) else: logger.info("Skipping Exp 1 (--skip-exp1)") # ------------------------------------------------------------------ # Exp 4: DDB split # ------------------------------------------------------------------ if not args.skip_exp4: logger.info("Building M1 balanced + DDB split (Exp 4)...") m1_ddb = add_degree_balanced_split( pd.read_parquet(m1_bal_path), seed=args.seed ) ddb_path = data_dir / "ppi_m1_balanced_ddb.parquet" m1_ddb.to_parquet(ddb_path, index=False) logger.info("Saved %d rows → %s", len(m1_ddb), ddb_path.name) else: logger.info("Skipping Exp 4 (--skip-exp4)") # ------------------------------------------------------------------ # Summary # ------------------------------------------------------------------ logger.info("=== Summary ===") for f in sorted(data_dir.glob("ppi_m1_*.parquet")): df = pd.read_parquet(f, columns=["Y"]) n_pos = (df["Y"] == 1).sum() n_neg = (df["Y"] == 0).sum() logger.info(" %s: %d rows (pos=%d, neg=%d)", f.name, len(df), n_pos, n_neg) logger.info("Done.") return 0 if __name__ == "__main__": sys.exit(main())