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e0265b9 | 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 | from pathlib import Path
from adam.eve import classify_eve_embeddings, save_eve_results
from adam.studio import StudioStore
def test_eve_sorts_good_bad_and_uncertain_examples() -> None:
results = classify_eve_embeddings(
["good.png", "bad.png", "maybe.png"],
[[1.0, 0.0], [0.0, 1.0], [0.7, 0.7]],
[[1.0, 0.0], [0.95, 0.05]],
[[0.0, 1.0]],
keep_threshold=0.75,
reject_threshold=0.25,
)
assert [result.suggestion for result in results] == [
"keep", "reject", "unreviewed"
]
assert results[0].match_score > results[2].match_score > results[1].match_score
def test_eve_requires_an_uncertain_confidence_band() -> None:
try:
classify_eve_embeddings(["one.png"], [[1.0]], [[1.0]], keep_threshold=0.4, reject_threshold=0.5)
except ValueError as exc:
assert "uncertain" in str(exc)
else:
raise AssertionError("Overlapping EVE thresholds should fail.")
def test_eve_results_are_persisted_and_mixed_decisions_apply_once(tmp_path: Path) -> None:
dataset = tmp_path / "dataset"
dataset.mkdir()
good = dataset / "good.png"; good.write_bytes(b"good")
bad = dataset / "bad.png"; bad.write_bytes(b"bad")
results = classify_eve_embeddings(
[good, bad], [[1.0, 0.0], [0.0, 1.0]], [[1.0, 0.0]], [[0.0, 1.0]]
)
record = save_eve_results(tmp_path, str(dataset), results)
store = StudioStore(tmp_path)
changed = store.apply_decisions(
str(dataset), {result.path: result.suggestion for result in results}
)
assert record.is_file()
assert changed == 2
assert store.review(str(dataset)).decisions[str(good.resolve())] == "keep"
assert store.review(str(dataset)).decisions[str(bad.resolve())] == "reject"
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