DataCategorical

class aixd.data.DataCategorical(name: str, dim: int = 1, domain: Options | None = None, position: int | None = None, position_index: int | None = None, dtype: str | None = None, flag_split_perdim: bool = False, **kwargs)

Bases: DataDiscrete

Categorical data type.

Parameters:

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

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

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

• position (int, optional) – Position of the data object in the vector.

• position_index (int, optional) – Index of the data object in the vector.

• dtype (str, optional) – The dtype of the numpy are of the expected data matrix.

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

Methods

copy	Returns a copy of the data object.
from_options	Class method to for initialization from list of integers
get_activation_outhead	Returns the activation function for approximating this feature.
get_loss_evaluation	Returns the loss function for approximating this feature.
get_ml_heads	Returns a fully-connected head with the appropriate number of in / out channels for encoding / decoding this feature
get_objective	Returns the loss function for approximating this feature.

Inherited Methods

append_transformation	Adds the transformation at the end.
check_data_consistency	Check if the data is consistent with the defined domain.
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.
is_data_transformed	Simple heuristic to determine if the data is transformed.
plot_distrib	Plots the distribution of the passed data as a Barplot.
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.