| |
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
|
|
| def sample_sequence(length: int, gc: float, rng: np.random.Generator) -> str: |
| p_gc = max(0.0, min(1.0, gc)) / 2.0 |
| p_at = (1.0 - max(0.0, min(1.0, gc))) / 2.0 |
| return "".join(rng.choice(list("ACGT"), size=length, p=[p_at, p_gc, p_gc, p_at]).tolist()) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description="Build a GC-matched random DNA negative baseline.") |
| parser.add_argument("--dataset_dir", required=True) |
| parser.add_argument("--split", default="valid") |
| parser.add_argument("--output_jsonl", required=True) |
| parser.add_argument("--num_samples", type=int, default=384) |
| parser.add_argument("--seed", type=int, default=42) |
| args = parser.parse_args() |
|
|
| rng = np.random.default_rng(args.seed) |
| df = pd.read_parquet(Path(args.dataset_dir) / f"{args.split}.parquet") |
| df = df.sample(n=min(args.num_samples, len(df)), random_state=args.seed).reset_index(drop=True) |
| output = Path(args.output_jsonl) |
| output.parent.mkdir(parents=True, exist_ok=True) |
|
|
| with open(output, "w", encoding="utf-8") as f: |
| for _, row in df.iterrows(): |
| ref = str(row["sequence"]).upper() |
| gc = (ref.count("G") + ref.count("C")) / len(ref) |
| seq = sample_sequence(len(ref), gc, rng) |
| f.write( |
| json.dumps( |
| { |
| "source": "generated", |
| "activity_bucket": "random_gc_matched", |
| "generated_sequence": seq, |
| "reference_sequence": ref, |
| "valid_dna": True, |
| "generated_bp_length": len(seq), |
| } |
| ) |
| + "\n" |
| ) |
| print(output) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
