average='macro'), [0, 1, 2, 2, 2], [0, 0, 2, 2, 1]),
(metrics.MicroPrecision(),
functools.partial(sk_metrics.precision_score,
average='micro'), [0, 1, 2, 2, 2], [0, 0, 2, 2, 1]),
(metrics.Recall(), sk_metrics.recall_score,
[True, False, True, True, True], [True, True, False, True, True]),
(metrics.MacroRecall(),
functools.partial(sk_metrics.recall_score,
average='macro'), [0, 1, 2, 2, 2], [0, 0, 2, 2, 1]),
(metrics.MicroRecall(),
functools.partial(sk_metrics.recall_score,
average='micro'), [0, 1, 2, 2, 2], [0, 0, 2, 2, 1]),
(metrics.FBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5),
[True, False, True, True, True], [True, True, False, True, True]),
(metrics.MacroFBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5,
average='macro'), [0, 1, 0, 2, 2], [0, 0, 1, 1, 2]),
(metrics.MicroFBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5,
average='micro'), [0, 1, 0, 2, 2], [0, 0, 1, 1, 2]),
(metrics.F1(), sk_metrics.f1_score, [True, False, True, True, True
], [True, True, False, True, True]),
(metrics.MacroF1(), functools.partial(
sk_metrics.f1_score, average='macro'), [0, 1, 2, 2,
2], [0, 0, 2, 2, 1]),
(metrics.MicroF1(), functools.partial(
sk_metrics.f1_score, average='micro'), [0, 1, 2, 2,
2], [0, 0, 2, 2, 1]),
(metrics.LogLoss(), sk_metrics.log_loss, [True, False, False, True
], [0.9, 0.1, 0.2, 0.65]),
sample_weights
)
TEST_CASES = [
(metrics.Accuracy(), sk_metrics.accuracy_score),
(metrics.Precision(), sk_metrics.precision_score),
(metrics.MacroPrecision(), functools.partial(sk_metrics.precision_score, average='macro')),
(metrics.MicroPrecision(), functools.partial(sk_metrics.precision_score, average='micro')),
(metrics.WeightedPrecision(), functools.partial(sk_metrics.precision_score, average='weighted')),
(metrics.Recall(), sk_metrics.recall_score),
(metrics.MacroRecall(), functools.partial(sk_metrics.recall_score, average='macro')),
(metrics.MicroRecall(), functools.partial(sk_metrics.recall_score, average='micro')),
(metrics.WeightedRecall(), functools.partial(sk_metrics.recall_score, average='weighted')),
(metrics.FBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5)),
(metrics.MacroFBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5, average='macro')),
(metrics.MicroFBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5, average='micro')),
(metrics.WeightedFBeta(beta=.5), functools.partial(sk_metrics.fbeta_score, beta=.5, average='weighted')),
(metrics.F1(), sk_metrics.f1_score),
(metrics.MacroF1(), functools.partial(sk_metrics.f1_score, average='macro')),
(metrics.MicroF1(), functools.partial(sk_metrics.f1_score, average='micro')),
(metrics.WeightedF1(), functools.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', TEST_CASES)
@pytest.mark.filterwarnings('ignore::RuntimeWarning')
@pytest.mark.filterwarnings('ignore::sklearn.metrics.classification.UndefinedMetricWarning')