def test_item_is_valid(channel_files: List[Optional[str]],
numerical_features: torch.Tensor,
categorical_features: torch.Tensor,
is_valid: bool) -> None:
c = ScalarDataSource(channel_files=channel_files,
numerical_non_image_features=numerical_features,
categorical_non_image_features=categorical_features,
label=torch.empty(0),
metadata=GeneralSampleMetadata(id="foo"))
assert c.is_valid() == is_valid
def _create(pos: int) -> ScalarDataSource:
z = torch.empty(0)
return ScalarDataSource(metadata=GeneralSampleMetadata(
id="", sequence_position=pos),
categorical_non_image_features=z,
label=z,
numerical_non_image_features=z,
channel_files=[])
def apply_source(source: ScalarDataSource) -> ScalarDataSource:
new_features = (source.numerical_non_image_features -
self.mean) / self.std
zero_or_nan = (self.std == 0.0) + torch.isnan(self.std)
new_features[zero_or_nan] = source.numerical_non_image_features[
zero_or_nan]
return source.clone_with_overrides(
numerical_non_image_features=new_features)