DataObject

class aixd.data.DataObject(name: str, dim: int, domain: Domain, unit: str | None = None, position: int | None = None, position_index: int | None = None, transformations: List[str] | List[DataObjectTransform] | None = None, type: str = 'any', dtype: str | None = None, flag_split_perdim: bool = False)

Bases: object

Master data object, to define each of the different building blocks that are going to be used to form the design parameters and performance attributes vectors.

Parameters:

• name (str) – Name of the data object.

• dim (int) – Dimensionality of the data object, or different columns to perform the split on.

• domain (Domain, optional) – Domain of the data object.

• unit (str, optional, default=None) – Unit of the data object (e.g. m, kg. m^2, m/s^2 etc.). Use ^ to indicate powers (e.g. m^2), and _ to indicate subscripts (e.g. m_1).

• position (int, optional, default=None) – Position of the data object in the vector.

• position_index (int, optional, default=None) – Index of the data object in the vector.

• transformations (Union[List[str], List[DataObjectTransform]], optional, default=None) – List of transformations to be applied to the data object.

• type (str, optional, default=”any”) – Name of the type of data object. Either real, categorical, integer, ordinal or any.

• dtype (str, optional, default=None) – The dtype of the numpy array that is expected.

• flag_split_perdim (bool, optional, default=False) – If True, object is split across dimensions in a DataBlock.

Methods

append_transformation	Adds the transformation at the end.
check_data_consistency	Check if the data is consistent with the defined domain.
copy	Returns a copy of the data object.
get_activation_outhead	Returns the activation function for approximating this feature.
get_loss_evaluation	Returns the evaluation loss function for this feature.
get_ml_heads	Returns two ML heads necessary for encoding and decoding the given data object.
get_objective	Returns the loss function for approximating this feature.
grid_samples	Returns grid samples from the domain of the data object.
has_name	Returns if the passed name match with the name of the DataObject.
inverse_transform	Inverse transformation of the data matrix according to specification DataObject.transformations.
plot_distrib	Plots the distribution of the passed data.
prepend_transformation	Adds the transformation at the start.
print_transf_norm	Prints an overview of the defined transformations.
random_samples	Returns random samples from the domain of the data object.
sample_around	Returns samples from the domain of the data object around the given centroid.
transform	Transforms the data matrix according to specification DataObject.transformations.
transform_is_fitted	Check if all transformations are fitted.
update_dobj_types
update_obj	Updates the domain of the data object with the passed data.