TEST_CASES = [
(metrics.Accuracy(), sk_metrics.accuracy_score),
(metrics.Precision(), sk_metrics.precision_score),
(metrics.MacroPrecision(),
partial(sk_metrics.precision_score, average='macro')),
(metrics.MicroPrecision(),
partial(sk_metrics.precision_score, average='micro')),
(metrics.WeightedPrecision(),
partial(sk_metrics.precision_score, average='weighted')),
(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),
]
average="weighted",
zero_division=0),
),
(metrics.Recall(), partial(sk_metrics.recall_score, zero_division=0)),
(metrics.MacroRecall(),
partial(sk_metrics.recall_score, average="macro", zero_division=0)),
(metrics.MicroRecall(),
partial(sk_metrics.recall_score, average="micro", zero_division=0)),
(
metrics.WeightedRecall(),
partial(sk_metrics.recall_score, average="weighted", zero_division=0),
),
(metrics.FBeta(beta=0.5),
partial(sk_metrics.fbeta_score, beta=0.5, zero_division=0)),
(
metrics.MacroFBeta(beta=0.5),
partial(sk_metrics.fbeta_score,
beta=0.5,
average="macro",
zero_division=0),
),
(
metrics.MicroFBeta(beta=0.5),
partial(sk_metrics.fbeta_score,
beta=0.5,
average="micro",
zero_division=0),
),
(
metrics.WeightedFBeta(beta=0.5),
partial(sk_metrics.fbeta_score,