def test_GroupNormalizer(kwargs, groups):
data = pd.DataFrame(dict(a=[1, 1, 2, 2, 3], b=[1.1, 1.1, 1.0, 5.0, 1.1]))
defaults = dict(
method="standard", log_scale=False, coerce_positive=False, center=True, log_zero_value=0.0, scale_by_group=False
)
defaults.update(kwargs)
kwargs = defaults
kwargs["groups"] = groups
kwargs["scale_by_group"] = kwargs["scale_by_group"] and len(kwargs["groups"]) > 0
if kwargs["coerce_positive"] and kwargs["log_scale"]:
with pytest.raises(AssertionError):
normalizer = GroupNormalizer(**kwargs)
else:
if kwargs["coerce_positive"]:
data.b = data.b - 2.0
normalizer = GroupNormalizer(**kwargs)
encoded = normalizer.fit_transform(data["b"], data)
test_data = dict(
prediction=torch.tensor([encoded.iloc[0]]),
target_scale=torch.tensor(normalizer.get_parameters([1])).unsqueeze(0),
)
if kwargs["coerce_positive"]:
assert (normalizer(test_data) >= 0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer(test_data), torch.tensor(data.b.iloc[0]), atol=1e-5
).all(), "Inverse transform should reverse transform"
def test_GroupNormalizer(kwargs, groups):
data = pd.DataFrame(dict(a=[1, 1, 2, 2, 3], b=[1.1, 1.1, 1.0, 5.0, 1.1]))
defaults = dict(method="standard",
transformation=None,
center=True,
scale_by_group=False)
defaults.update(kwargs)
kwargs = defaults
kwargs["groups"] = groups
kwargs["scale_by_group"] = kwargs["scale_by_group"] and len(
kwargs["groups"]) > 0
if kwargs.get("transformation") in ["relu", "softplus"]:
data.b = data.b - 2.0
normalizer = GroupNormalizer(**kwargs)
encoded = normalizer.fit_transform(data["b"], data)
test_data = dict(
prediction=torch.tensor([encoded[0]]),
target_scale=torch.tensor(normalizer.get_parameters([1])).unsqueeze(0),
)
if kwargs.get("transformation") in ["relu", "softplus", "log1p"]:
assert (normalizer(test_data) >=
0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer(test_data), torch.tensor(data.b.iloc[0]),
atol=1e-5).all(), "Inverse transform should reverse transform"