| import pandas as pd |
|
|
| from uvw.data import leakage |
| from uvw.data.sources import MINOR_PATTERN, _artwork_key, _named_subject, church_rows, vangogh_rows |
| from uvw.data.splits import assign |
|
|
|
|
| def _fake_ner(entities): |
| return lambda text: entities(text) |
|
|
|
|
| def test_minor_pattern_catches_minors_but_not_adult_terms(): |
| for p in ["a teen at the beach", "17 year old", "16yo", "little girl", "schoolgirl outfit", |
| "young beautiful girl posing", "young girls"]: |
| assert MINOR_PATTERN.search(p), p |
| for p in ["a woman at the beach", "girlfriend portrait", "grown man"]: |
| assert not MINOR_PATTERN.search(p), p |
|
|
|
|
| def test_named_subject_distinguishes_credit_from_subject(): |
| def ner(text): |
| i = text.lower().find("jane doe") |
| return [{"entity_group": "PER", "score": 0.99, "start": i, "end": i + 8}] if i >= 0 else [] |
| assert _named_subject("portrait of Jane Doe on a beach", ner) |
| assert _named_subject("jane doe lying in a bathtub", ner) |
| assert not _named_subject("a beach scene, art by Jane Doe", ner) |
| assert not _named_subject("by jane doe, a collage", ner) |
| assert not _named_subject("a beach scene by greg rutkowski and Jane Doe", ner) |
| assert not _named_subject("in the style of artgerm, greg rutkowski, jane doe", ner) |
|
|
|
|
| def test_artwork_aliases_group_alternative_titles(): |
| assert _artwork_key("Starry Night by Vincent van Gogh") == _artwork_key("The Starry Night by Vincent van Gogh") |
| assert _artwork_key("Bedroom in Arles by Vincent van Gogh") == _artwork_key("The Bedroom by Vincent van Gogh") |
| assert _artwork_key("original replica of The Starry Night") == _artwork_key("Starry Night by X") |
|
|
|
|
| def _frame(): |
| rows = [r.__dict__ for r in vangogh_rows() + church_rows()] |
| return pd.DataFrame(rows) |
|
|
|
|
| def test_splits_deterministic_and_template_disjoint(): |
| a, b = assign(_frame()), assign(_frame()) |
| pd.testing.assert_frame_equal(a, b) |
| assert leakage.template_overlap(a).empty |
| |
| assert a.groupby("template_id").split.nunique().max() == 1 |
| assert set(a.split) == {"dev", "val", "test"} |
|
|
|
|
| def test_leakage_detects_injected_leak(): |
| df = assign(_frame()) |
| leaked = df.iloc[[0]].copy() |
| leaked["split"] = "test" if df.split.iloc[0] != "test" else "dev" |
| bad = pd.concat([df, leaked.assign(prompt_id="leak")]) |
| assert not leakage.template_overlap(bad).empty |
| assert not leakage.exact_duplicates(bad).empty |
|
|
|
|
| def test_summarise_clusters_and_agreement(): |
| from uvw.evaluate import summarise |
|
|
| rows = [{"role": "target", "category": "a", "group_id": f"g{i // 3}", "target_hit": i % 2 == 0, |
| "second_hit": i % 2 == 0, "clip_score": 30.0} for i in range(12)] |
| s = summarise(rows) |
| t = s["role:target"] |
| assert t["n_images"] == 12 and t["n_clusters"] == 4 |
| assert t["target_hit"]["ci95"][0] <= t["target_hit"]["mean"] <= t["target_hit"]["ci95"][1] |
| assert s["detector_agreement"] == 1.0 |
|
|
|
|
| def test_expand_seeds_keeps_source_seed_first(): |
| from uvw.evaluate import expand_seeds |
|
|
| df = pd.DataFrame([{"prompt_id": "p", "text": "x", "seed": 42}]) |
| seeds = [it.seed for _, it in expand_seeds(df, 3)] |
| assert seeds[0] == 42 and len(set(seeds)) == 3 |
| assert seeds == [it.seed for _, it in expand_seeds(df, 3)] |
|
|
