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,