| """Step 6a: render the Hugging Face dataset card from hf/card_template.md + computed tables. |
| |
| python -m fsbench.make_card --bench data/bench --sources configs/fundusshift_open.yaml --out hf/README.md |
| Placeholders: {{SPLIT_TABLE}} {{SOURCE_TABLE}} {{UNUSED_TABLE}} {{REUSE_TABLE}} {{CHECKS_TABLE}} {{N_*}} {{SHORTCUT}} |
| """ |
| import argparse |
| import json |
| import re |
| from pathlib import Path |
|
|
| import pandas as pd |
| import yaml |
|
|
| from .common import CLASSES |
|
|
| SPLITS = ["train", "validation", "calibration", "test", "test_ood", "test_openset"] |
|
|
|
|
| def md(df): |
| df = df.reset_index() if df.index.name or isinstance(df.index, pd.MultiIndex) else df |
| return "\n".join(["| " + " | ".join(map(str, df.columns)) + " |", "|" + "---|" * len(df.columns)] + |
| ["| " + " | ".join(map(str, r)) + " |" for r in df.itertuples(index=False)]) |
|
|
|
|
| def probe_section(path): |
| r = json.loads(Path(path).read_text()) |
| rows = [("validation (in-distribution)", "validation"), ("calibration", "calibration"), |
| ("**test** (held-out collections)", "test"), ("**test_ood** (Chákṣu, handheld)", "test_ood")] |
| classes = list(r["test"]["recall"]) if "classes" not in r else r["classes"] |
| t = f"| split | balanced acc | macro-F1 | recall {' / '.join(classes)} | worst collection (bAcc) |\n|---|---|---|---|---|\n" |
| for name, k in rows: |
| m = r[k] |
| rec = " / ".join(f"{m['recall'][c]:.2f}" if c in m["recall"] else "–" for c in classes) |
| w = min(m["per_source_bacc"], key=m["per_source_bacc"].get) |
| t += f"| {name} | {m['balanced_accuracy']:.3f} | {m['macro_f1']:.3f} | {rec} | {w} ({m['per_source_bacc'][w]:.3f}) |\n" |
| ps = "| test collection | bAcc |\n|---|---|\n" + "".join( |
| f"| {k} | {v:.3f} |\n" for k, v in sorted(r["test"]["per_source_bacc"].items(), key=lambda x: x[1])) |
| drop = r["validation"]["balanced_accuracy"] - r["test"]["balanced_accuracy"] |
| return (f"## Reference result: frozen-feature linear probe\n\n" |
| f"Frozen {r['model'].split('/')[-1]} features (CLS + mean patch token, 224 px), logistic regression with " |
| f"(collection, class)-balanced weights, C selected on validation (`code/baselines/linear_probe.py`). " |
| f"A difficulty check, not a tuned baseline.\n\n{t}\n{ps}\n" |
| f"Open-set AUROC (max-softmax, `test_openset` vs `test`): **{r['test_openset_auroc_msp']:.3f}**.\n\n" |
| f"Balanced accuracy drops by **{drop * 100:.1f} points** from validation to the held-out collections. " |
| f"Single-class collections (e.g. HYGD) report recall of that class." |
| + (f"\n\n**Task 3 (DR grading, same frozen features):** quadratic weighted kappa " |
| f"{r['dr_grading']['validation']['quadratic_kappa']:.3f} on validation vs " |
| f"**{r['dr_grading']['test']['quadratic_kappa']:.3f}** on held-out " |
| f"{', '.join(r['dr_grading']['test']['sources'])} (n = {r['dr_grading']['test']['n']})." |
| if "dr_grading" in r else "")) |
|
|
|
|
| def main(argv=None): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--bench", required=True) |
| ap.add_argument("--sources", required=True) |
| ap.add_argument("--template", default=str(Path(__file__).resolve().parents[1] / "hf/card_template.md")) |
| ap.add_argument("--out", required=True) |
| ap.add_argument("--probe", help="probe_metrics.json from baselines/linear_probe.py") |
| a = ap.parse_args(argv) |
| bench = Path(a.bench) |
| src = {s["id"]: s for s in yaml.safe_load(open(a.sources))["sources"]} |
| d = pd.read_csv(bench / "splits_final.csv", dtype=str, keep_default_na=False) |
| u = d[d.split.isin(SPLITS)] |
|
|
| st = pd.crosstab(u.split, u.label).reindex(index=SPLITS, fill_value=0) |
| st = st[[c for c in [*CLASSES, "OPENSET"] if c in st.columns]] |
| st["total"] = st.sum(axis=1) |
| st.loc["**all**"] = st.sum() |
| st.index.name = "split" |
|
|
| side = u.groupby("source").split.agg(lambda s: "/".join(sorted(set(s)))) |
| sc = pd.crosstab(u.source, u.label).reindex(columns=[c for c in [*CLASSES, "OPENSET"] if c in set(u.label)], |
| fill_value=0) |
| inv = d.groupby("source").size().rename("inventory") |
| sc = sc.join(inv) |
| sc.insert(0, "splits", side) |
| sc.insert(1, "country", [src[s].get("country", "") for s in sc.index]) |
| sc.insert(2, "device", [src[s].get("device", "") for s in sc.index]) |
| sc["licence"] = [src[s].get("licence", "") for s in sc.index] |
| order = {"train/validation/calibration": 0} |
| sc = sc.sort_values(["splits", "inventory"], ascending=[True, False]) |
| sc.index.name = "source" |
|
|
| rg = pd.crosstab(u.region, u.split).reindex(columns=SPLITS, fill_value=0) |
| rg.index.name = "region" |
| un = d[d.split == "unused"].unused_reason.value_counts().rename("images") |
| un.index.name = "reason" |
| reuse = pd.read_csv(bench / "cross_source_reuse.csv") |
| checks = re.search(r"## Leakage checks\n\n(.*?)\n\n", (bench / "audit_report.md").read_text(), re.S).group(1) |
| sc_auc = pd.read_csv(bench / "eda/shortcut_auc.csv") |
| eda = (bench / "eda/eda_report.md").read_text() |
| m = re.search(r"balanced accuracy \*\*([0-9.]+)\*\*\s*\(chance ([0-9.]+)", eda) |
|
|
| rep = {"{{SPLIT_TABLE}}": md(st), "{{PROBE_SECTION}}": probe_section(a.probe) if a.probe else "", "{{REGION_TABLE}}": md(rg), "{{SOURCE_TABLE}}": md(sc), "{{UNUSED_TABLE}}": md(un), |
| "{{REUSE_TABLE}}": md(reuse) if len(reuse) else "none detected", "{{CHECKS_TABLE}}": checks, |
| "{{N_INVENTORY}}": f"{len(d):,}", "{{N_RELEASED}}": f"{len(u):,}", "{{N_SOURCES}}": str(u.source.nunique()), |
| "{{SRC_BACC}}": m.group(1) if m else "?", "{{SRC_CHANCE}}": m.group(2) if m else "?", |
| "{{SHORTCUT_TABLE}}": md(sc_auc.sort_values("auc_trivial_features", ascending=False).round(3))} |
| text = Path(a.template).read_text() |
| for k, v in rep.items(): |
| text = text.replace(k, v) |
| Path(a.out).write_text(text) |
| print(f"card -> {a.out}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
