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456264c | 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 | import numpy as np
import pytest
from pino.genre_benchmark import audit_split, benchmark_split, composition_features, decide
def row(genre, *components):
return {"genre": genre, "formula": [{"cas": cas, "weight_fraction": weight} for cas, weight in components]}
def test_composition_features_ignore_identity_and_order():
a = row("a", ("64-17-5", .8), ("x", .15), ("y", .05))
b = row("b", ("z", .05), ("q", .15), ("64-17-5", .8))
np.testing.assert_allclose(composition_features([a]), composition_features([b]))
def test_audit_rejects_shared_active_compounds():
with pytest.raises(ValueError, match="shared compounds"):
audit_split([row("a", ("x", 1))], [row("b", ("x", 1))])
def test_benchmark_and_negative_decision():
train = [row("a", ("a1", .1)), row("a", ("a2", .12)), row("b", ("b1", .8)), row("b", ("b2", .82))]
test = [row("a", ("a3", .11)), row("a", ("a4", .13)), row("b", ("b3", .79)), row("b", ("b4", .81))]
learned_train = np.zeros((4, 2))
learned_test = np.zeros((4, 2))
result = benchmark_split(train, test, learned_train, learned_test, bootstrap_samples=200, seed=4)
assert result["baseline_accuracy"] == 1.0
assert decide([result], minimum_splits=1)["decision"] == "negative_result"
def test_decision_requires_every_leakage_free_split_to_clear_ci():
good = {"leakage_audit": {"passed": True}, "paired_advantage": {"ci95_low": .01}}
weak = {"leakage_audit": {"passed": True}, "paired_advantage": {"ci95_low": 0.0}}
assert decide([good, good, good])["decision"] == "learned_representation_supported"
assert decide([good, good, weak])["decision"] == "negative_result"
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