| """Illustrative study/content-grouped split, not an official benchmark protocol.""" |
| from __future__ import annotations |
|
|
| import argparse |
| import hashlib |
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| from hesrt_io import RELEASE_TAG, load_catalog |
|
|
|
|
| def assign_groups(frame: pd.DataFrame, seed: str) -> pd.DataFrame: |
| """Keep each GSE and any exact image/expression duplicates in one component.""" |
| frame = frame.reset_index(drop=True) |
| parent = list(range(len(frame))) |
| def root(i: int) -> int: |
| while parent[i] != i: |
| parent[i] = parent[parent[i]] |
| i = parent[i] |
| return i |
| for column in ["gse_id", "image_sha256", "expression_sha256"]: |
| first: dict[str, int] = {} |
| for i, value in enumerate(frame[column]): |
| if pd.isna(value) or not str(value).strip(): |
| continue |
| value = str(value) |
| if value in first: |
| parent[root(i)] = root(first[value]) |
| else: |
| first[value] = i |
| labels: dict[int, str] = {} |
| for i, gsm in enumerate(frame.gsm_id): |
| r = root(i) |
| labels[r] = min(labels.get(r, str(gsm)), str(gsm)) |
| result = frame[["gse_id", "gsm_id"]].copy() |
| result["leakage_group"] = [labels[root(i)] for i in range(len(frame))] |
| def split(group: str) -> str: |
| fraction = int(hashlib.sha256((seed + ":" + group).encode()).hexdigest()[:16], 16) / 2**64 |
| return "train" if fraction < 0.8 else "validation" if fraction < 0.9 else "test" |
| result["split"] = result.leakage_group.map(split) |
| return result |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--revision", default=RELEASE_TAG) |
| parser.add_argument("--seed", default="hesrt-example-0") |
| parser.add_argument("--output", type=Path, default=Path("study_split.csv")) |
| args = parser.parse_args() |
| frame, revision = load_catalog(args.revision) |
| result = assign_groups(frame, args.seed) |
| result["revision"] = revision |
| args.output.parent.mkdir(parents=True, exist_ok=True) |
| result.to_csv(args.output, index=False) |
| print(result.groupby("split").agg(samples=("gsm_id", "size"), studies=("gse_id", "nunique"))) |
| print("Approximate 80/10/10 group allocation; not stratified and not donor-disjoint certification.") |
| print("Resolve shared donors, near-duplicates, cohort overlap and task eligibility separately.") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
