(metrics.Recall(), sk_metrics.recall_score),
(metrics.MacroRecall(), partial(sk_metrics.recall_score, average='macro')),
(metrics.MicroRecall(), partial(sk_metrics.recall_score, average='micro')),
(metrics.WeightedRecall(),
partial(sk_metrics.recall_score, average='weighted')),
(metrics.FBeta(beta=.5), partial(sk_metrics.fbeta_score, beta=.5)),
(metrics.MacroFBeta(beta=.5),
partial(sk_metrics.fbeta_score, beta=.5, average='macro')),
(metrics.MicroFBeta(beta=.5),
partial(sk_metrics.fbeta_score, beta=.5, average='micro')),
(metrics.WeightedFBeta(beta=.5),
partial(sk_metrics.fbeta_score, beta=.5, average='weighted')),
(metrics.F1(), sk_metrics.f1_score),
(metrics.MacroF1(), partial(sk_metrics.f1_score, average='macro')),
(metrics.MicroF1(), partial(sk_metrics.f1_score, average='micro')),
(metrics.WeightedF1(), partial(sk_metrics.f1_score, average='weighted')),
(metrics.MCC(), sk_metrics.matthews_corrcoef),
(metrics.MAE(), sk_metrics.mean_absolute_error),
(metrics.MSE(), sk_metrics.mean_squared_error),
]
@pytest.mark.parametrize('metric, sk_metric', [
pytest.param(metric, sk_metric, id=f'{metric.__class__.__name__}')
for metric, sk_metric in TEST_CASES
])
@pytest.mark.filterwarnings('ignore::RuntimeWarning')
@pytest.mark.filterwarnings(
'ignore::sklearn.metrics.classification.UndefinedMetricWarning')
def test_metric(metric, sk_metric):
average="micro",
zero_division=0),
),
(
metrics.WeightedFBeta(beta=0.5),
partial(sk_metrics.fbeta_score,
beta=0.5,
average="weighted",
zero_division=0),
),
(metrics.F1(), partial(sk_metrics.f1_score, zero_division=0)),
(metrics.MacroF1(),
partial(sk_metrics.f1_score, average="macro", zero_division=0)),
(metrics.MicroF1(),
partial(sk_metrics.f1_score, average="micro", zero_division=0)),
(metrics.WeightedF1(),
partial(sk_metrics.f1_score, average="weighted", zero_division=0)),
(metrics.MCC(), sk_metrics.matthews_corrcoef),
(metrics.MAE(), sk_metrics.mean_absolute_error),
(metrics.MSE(), sk_metrics.mean_squared_error),
]
@pytest.mark.parametrize(
"metric, sk_metric",
[
pytest.param(metric, sk_metric, id=f"{metric.__class__.__name__}")
for metric, sk_metric in TEST_CASES
],
)
@pytest.mark.filterwarnings("ignore::RuntimeWarning")