(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)