(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') def test_metric(metric, sk_metric): # Check str works str(metric) for y_true, y_pred, sample_weights in generate_test_cases(metric=metric,
(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]), (metrics.CrossEntropy(), functools.partial(sk_metrics.log_loss, labels=[0, 1, 2]), [0, 1, 2, 2], [[0.29450637, 0.34216758, 0.36332605], [0.21290077, 0.32728332, 0.45981591], [0.42860913, 0.33380113, 0.23758974], [0.44941979, 0.32962558, 0.22095463]]), ( metrics.MCC(), sk_metrics.matthews_corrcoef, [True, True, True, False], [True, False, True, True], )]) @pytest.mark.filterwarnings('ignore::RuntimeWarning') @pytest.mark.filterwarnings( 'ignore::sklearn.metrics.classification.UndefinedMetricWarning') def test_metric(metric, sk_metric, y_true, y_pred): for i, (yt, yp) in enumerate(zip(y_true, y_pred)): if isinstance(yp, list): yp = dict(enumerate(yp)) metric.update(yt, yp)