def test_check_similarity_metric():
for metric in get_available_similarity_metrics():
check_similarity_metric(metric)
with pytest.raises(AssertionError) as ve:
output = check_similarity_metric("fail")
assert "fail not supported. Available similarity metrics:" in str(ve.value)
def test_evaluate_replicate_reproducibility():
similarity_metrics = get_available_similarity_metrics()
replicate_reproducibility_quantiles = [0.5, 0.95]
expected_result = {
"gene": {
"pearson": {"0.5": 0.431, "0.95": 0.056},
"kendall": {"0.5": 0.429, "0.95": 0.054},
"spearman": {"0.5": 0.429, "0.95": 0.055},
},
"compound": {
"pearson": {"0.5": 0.681, "0.95": 0.458},
"kendall": {"0.5": 0.679, "0.95": 0.463},
"spearman": {"0.5": 0.679, "0.95": 0.466},
},
}
for sim_metric in similarity_metrics:
for quant in replicate_reproducibility_quantiles:
gene_res = evaluate(
profiles=gene_profiles,
features=gene_features,
meta_features=gene_meta_features,
replicate_groups=gene_groups,
operation="replicate_reproducibility",
replicate_reproducibility_return_median_cor=False,
similarity_metric=sim_metric,
replicate_reproducibility_quantile=quant,
)
compound_res = evaluate(
profiles=compound_profiles,
features=compound_features,
meta_features=compound_meta_features,
replicate_groups=compound_groups,
operation="replicate_reproducibility",
replicate_reproducibility_return_median_cor=False,
similarity_metric=sim_metric,
replicate_reproducibility_quantile=quant,
)
assert (
np.round(gene_res, 3) == expected_result["gene"][sim_metric][str(quant)]
)
assert (
np.round(compound_res, 3)
== expected_result["compound"][sim_metric][str(quant)]
)
def test_get_available_similarity_metrics():
expected_result = ["pearson", "kendall", "spearman"]
assert expected_result == get_available_similarity_metrics()