| """Within-split diagnostics: duplicate sequences and label conflicts inside a |
| single split, plus repeat / low-complexity warnings that make coincidental |
| k-mer matches likely.""" |
| from __future__ import annotations |
|
|
| import argparse |
| import math |
| import sys |
| from collections import Counter, defaultdict |
| from pathlib import Path |
|
|
| import polars as pl |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
| from common import StageLog |
|
|
|
|
| def entropy(s: str) -> float: |
| if not s: |
| return 0.0 |
| c = Counter(s) |
| n = len(s) |
| return -sum((v / n) * math.log2(v / n) for v in c.values()) |
|
|
|
|
| def max_run(s: str) -> int: |
| best = cur = 1 |
| for i in range(1, len(s)): |
| cur = cur + 1 if s[i] == s[i - 1] else 1 |
| best = max(best, cur) |
| return best |
|
|
|
|
| def main() -> int: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--work", default="/workspace/nt-audit") |
| ap.add_argument("--pkg", default="/workspace/nt-revised-audit") |
| args = ap.parse_args() |
| work, pkg = Path(args.work), Path(args.pkg) |
| log = StageLog(pkg / "logs" / "stage.jsonl", "within_split") |
| rows = [] |
| for path in sorted((work / "prep").glob("*.parquet")): |
| task = path.stem |
| df = pl.read_parquet(path, columns=["split", "uid", "canon", "canon_sha", "label"]) |
| for split in ("train", "test"): |
| d = df.filter(pl.col("split") == split) |
| n = d.height |
| groups = defaultdict(list) |
| for r in d.iter_rows(named=True): |
| groups[r["canon_sha"]].append(r["label"]) |
| dup_seq = sum(len(v) - 1 for v in groups.values() if len(v) > 1) |
| conflicting = sum(1 for v in groups.values() if len(v) > 1 and len(set(v)) > 1) |
| sizes = sorted((len(v) for v in groups.values()), reverse=True) |
| seqs = d["canon"].to_list() |
| ents = [entropy(s) for s in seqs] |
| runs = [max_run(s) for s in seqs] |
| nfrac = [(s.count("N") / len(s)) if s else 0.0 for s in seqs] |
| rows.append({ |
| "task": task, "split": split, "rows": n, |
| "unique_sequences": len(groups), |
| "duplicate_sequence_rows": dup_seq, |
| "duplicate_groups": sum(1 for s in sizes if s > 1), |
| "largest_duplicate_group": sizes[0] if sizes else 0, |
| "groups_with_conflicting_labels": conflicting, |
| "rows_low_entropy_lt_1.5": sum(1 for e in ents if e < 1.5), |
| "rows_all_n": sum(1 for s in seqs if s and set(s) == {"N"}), |
| "rows_n_fraction_gt_0.1": sum(1 for f in nfrac if f > 0.1), |
| "mean_n_fraction": round(sum(nfrac) / n, 6) if n else None, |
| "rows_homopolymer_run_ge_20": sum(1 for r in runs if r >= 20), |
| "max_homopolymer_run": max(runs) if runs else None, |
| }) |
| out = pl.DataFrame(rows) |
| out.write_csv(pkg / "results" / "within_split.csv") |
| log.done(rows=len(rows)) |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| raise SystemExit(main()) |
|
|
