def test_compose():
with pytest.raises(ValueError):
_ = metrics.MSE() + metrics.LogLoss()
with pytest.raises(ValueError):
_ = metrics.MSE() + metrics.MAE() + metrics.LogLoss()
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 default_metrics(self):
return [
metrics.Accuracy(),
metrics.LogLoss(),
metrics.Precision(),
metrics.Recall(),
metrics.F1()
]
def test_log_loss():
y_true = [True, False, False, True]
y_pred = [0.9, 0.1, 0.2, 0.65]
metric = metrics.LogLoss()
for i, (y_t, y_p) in enumerate(zip(y_true, y_pred)):
metric = metric.update(y_t, y_p)
if i >= 1:
assert metric.get() == sk_metrics.log_loss(y_true[:i + 1],
y_pred[:i + 1])
def test_log_loss():
metric = metrics.LogLoss()
y_true = [True, False, False, True]
y_pred = [0.9, 0.1, 0.2, 0.65]
for i, (yt, yp) in enumerate(zip(y_true, y_pred)):
metric.update(yt, yp)
if i >= 1:
assert math.isclose(metric.get(), sk_metrics.log_loss(y_true[:i + 1], y_pred[:i + 1]))
metric.revert(y_true[-1], y_pred[-1])
assert math.isclose(metric.get(), sk_metrics.log_loss(y_true[:-1], y_pred[:-1]))
[True, False, True, True, True], [True, True, False, True, True]),
(metrics.MacroFBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5,
average='macro'), [0, 1, 0, 2, 2], [0, 0, 1, 1, 2]),
(metrics.MicroFBeta(beta=0.5),
functools.partial(sk_metrics.fbeta_score, beta=0.5,
average='micro'), [0, 1, 0, 2, 2], [0, 0, 1, 1, 2]),
(metrics.F1(), sk_metrics.f1_score, [True, False, True, True, True
], [True, True, False, True, True]),
(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]])])
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)