File size: 3,044 Bytes
44fc8dd | 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 | """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 # noqa: E402
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())
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