def test_EncoderNormalizer(kwargs):
data = torch.rand(100)
defaults = dict(method="standard",
log_scale=False,
coerce_positive=False,
center=True,
log_zero_value=0.0)
defaults.update(kwargs)
kwargs = defaults
if kwargs["coerce_positive"] and kwargs["log_scale"]:
with pytest.raises(AssertionError):
normalizer = EncoderNormalizer(**kwargs)
else:
normalizer = EncoderNormalizer(**kwargs)
if kwargs["coerce_positive"]:
data = data - 0.5
if kwargs["coerce_positive"]:
assert (
normalizer.inverse_transform(normalizer.fit_transform(data)) >=
0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer.inverse_transform(normalizer.fit_transform(data)),
data,
atol=1e-5).all(), "Inverse transform should reverse transform"
def test_EncoderNormalizer(kwargs):
data = torch.rand(100)
defaults = dict(method="standard", center=True)
defaults.update(kwargs)
kwargs = defaults
normalizer = EncoderNormalizer(**kwargs)
if kwargs.get("transformation") in ["relu", "softplus"]:
data = data - 0.5
if kwargs.get("transformation") in ["relu", "softplus", "log1p"]:
assert (normalizer.inverse_transform(normalizer.fit_transform(data)) >=
0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer.inverse_transform(normalizer.fit_transform(data)),
data,
atol=1e-5).all(), "Inverse transform should reverse transform"
def test_EncoderNormalizer(kwargs):
kwargs.setdefault("method", "standard")
kwargs.setdefault("center", True)
kwargs.setdefault("data", torch.rand(100))
data = kwargs.pop("data")
normalizer = EncoderNormalizer(**kwargs)
if kwargs.get("transformation") in ["relu", "softplus", "log1p"]:
assert (normalizer.inverse_transform(
torch.as_tensor(normalizer.fit_transform(data))) >=
0).all(), "Inverse transform should yield only positive values"
else:
assert torch.isclose(
normalizer.inverse_transform(
torch.as_tensor(normalizer.fit_transform(data))),
torch.as_tensor(data),
atol=1e-5,
).all(), "Inverse transform should reverse transform"
