| """Step 3: source-held-out, group-aware splits. |
| |
| test / test_ood : every core image of a held-out source (role test / ood_test) |
| test_openset : held-out images whose condition is outside the core classes (label OPENSET) |
| train/val/cal : dev-source images, split by leakage group, stratified by (source, label) |
| cal -> cal_A / cal_B : disjoint halves (temperature scaling vs. acceptance thresholds) |
| unused : outside the core task, with the reason kept |
| |
| python -m fsbench.make_splits --bench data/bench [--fractions 0.8 0.1 0.1 --seed 2027] |
| """ |
| import argparse |
| from collections import Counter |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
|
|
|
|
| def _greedy_assign(groups, sizes, names, fracs, rng): |
| """Assign groups to splits so each split's image share tracks its target fraction (largest deficit first).""" |
| order = rng.permutation(len(groups)) |
| count = dict.fromkeys(names, 0) |
| total, out = 0, {} |
| for k in order: |
| total += sizes[k] |
| deficit = {s: f * total - count[s] for s, f in zip(names, fracs)} |
| s = max(names, key=lambda x: (deficit[x], -names.index(x))) |
| out[groups[k]] = s |
| count[s] += sizes[k] |
| return out |
|
|
|
|
| def _stratified(df, names, fracs, rng): |
| """df: one row per image with group_id, source, label. Returns {group_id: split}.""" |
| g = df.groupby("group_id").agg(source=("source", lambda s: Counter(s).most_common(1)[0][0]), |
| label=("label", lambda s: Counter(s).most_common(1)[0][0]), |
| n=("image_id", "size")) |
| out = {} |
| for _, sub in g.groupby(["source", "label"]): |
| out.update(_greedy_assign(list(sub.index), list(sub.n), names, fracs, rng)) |
| return out |
|
|
|
|
| def main(argv=None): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--bench", required=True) |
| ap.add_argument("--fractions", type=float, nargs=3, default=[0.8, 0.1, 0.1], metavar=("TRAIN", "VAL", "CAL")) |
| ap.add_argument("--seed", type=int, default=2027) |
| a = ap.parse_args(argv) |
| bench = Path(a.bench) |
| rng = np.random.default_rng(a.seed) |
| m = pd.read_csv(bench / "manifest.csv", dtype=str, keep_default_na=False) |
| m = m.merge(pd.read_csv(bench / "groups.csv", dtype=str), on="image_id", how="left", validate="1:1") |
| m["side"] = np.where(m.role.isin(["dev", "inventory"]), "dev", "test") |
|
|
| reason = pd.Series("", index=m.index) |
| reason[m.role == "inventory"] = "source_excluded_from_core" |
| reason[(reason == "") & (m.file_missing == "True")] = "file_missing" |
| reason[(reason == "") & (m.read_error != "")] = "unreadable" |
| reason[(reason == "") & (m.label == "")] = "label:" + m.label_reason |
| reason[(reason == "") & (m.label == "OPENSET") & (m.side == "dev")] = "label:openset_class_in_dev_source" |
|
|
| |
| test_groups = set(m.group_id[(m.side == "test") & (reason == "")]) |
| leak = (m.side == "dev") & m.group_id.isin(test_groups) & (reason == "") |
| reason[leak] = "duplicate_of_test_side_image" |
| |
| ood_leak = (m.role == "ood_test") & m.group_id.isin(set(m.group_id[(m.role == "test") & (reason == "")])) & (reason == "") |
| reason[ood_leak] = "duplicate_of_test_image" |
| |
| core = reason == "" |
| nlab = m[core].groupby("dup_group_id").label.nunique() |
| reason[m.dup_group_id.isin(nlab[nlab > 1].index) & core] = "duplicate_label_conflict" |
|
|
| m["split"] = "unused" |
| core = reason == "" |
| m.loc[core & (m.role == "test"), "split"] = "test" |
| m.loc[core & (m.role == "ood_test"), "split"] = "test_ood" |
| m.loc[core & (m.side == "test") & (m.label == "OPENSET"), "split"] = "test_openset" |
| dev = m[core & (m.side == "dev")] |
| m.loc[dev.index, "split"] = dev.group_id.map(_stratified(dev, ["train", "val", "cal"], a.fractions, rng)) |
| cal = m[m.split == "cal"] |
| m["cal_subset"] = "" |
| m.loc[cal.index, "cal_subset"] = cal.group_id.map(_stratified(cal, ["cal_A", "cal_B"], [0.5, 0.5], rng)) |
| m["unused_reason"] = reason |
|
|
| cols = ["image_id", "split", "cal_subset", "source", "family", "region", "country", "rel_path", "label", "native_label", |
| "label_reason", "normal_scope", "dr_grade", "patient_id", "eye", "group_id", "dup_group_id", "sha256", "unused_reason"] |
| m[cols].to_csv(bench / "splits.csv", index=False) |
| for s in ["train", "val", "cal", "test", "test_ood", "test_openset"]: |
| m[m.split == s][cols].to_csv(bench / f"split_{s}.csv", index=False) |
|
|
| print(m.split.value_counts().to_string()) |
| print("\nunused reasons:\n" + m[m.split == "unused"].unused_reason.value_counts().to_string()) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
