def test_compose():
with pytest.raises(ValueError):
_ = metrics.MSE() + metrics.LogLoss()
with pytest.raises(ValueError):
_ = metrics.MSE() + metrics.MAE() + metrics.LogLoss()
def load_metrics():
"""Yields all the metrics."""
for name, obj in inspect.getmembers(
importlib.import_module('creme.metrics'), inspect.isclass):
if issubclass(obj, metrics.PerClass):
yield obj(metric=metrics.Precision())
continue
elif issubclass(obj, metrics.Rolling):
yield obj(metric=metrics.MSE(), window_size=42)
continue
elif name == 'RegressionMultiOutput':
yield obj(metric=metrics.MSE())
continue
try:
sig = inspect.signature(obj)
yield obj(
**{
param.name:
param.default if param.default != param.empty else 5
for param in sig.parameters.values()
})
except ValueError:
yield obj()
def test_compose():
metrics.MAE() + metrics.MSE()
metrics.Accuracy() + metrics.LogLoss()
metrics.Accuracy() + metrics.ConfusionMatrix()
with pytest.raises(ValueError):
_ = metrics.MSE() + metrics.LogLoss()
with pytest.raises(ValueError):
_ = metrics.MSE() + metrics.MAE() + metrics.LogLoss()
def load_metrics():
for name, obj in inspect.getmembers(
importlib.import_module('creme.metrics'), inspect.isclass):
if name == 'RegressionMultiOutput':
yield obj(metric=metrics.MSE())
continue
try:
sig = inspect.signature(obj)
yield obj(**{arg: 5 for arg in sig.parameters})
except ValueError:
yield obj()
def load_metrics():
for name, obj in inspect.getmembers(
importlib.import_module('creme.metrics'), inspect.isclass):
if name == 'RegressionMultiOutput':
yield obj(metric=metrics.MSE())
continue
try:
sig = inspect.signature(obj)
yield obj(
**{
param.name:
param.default if param.default != param.empty else 5
for param in sig.parameters.values()
})
except ValueError:
yield obj()
(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,
n=30):
m = copy.deepcopy(metric)
