NumericExpansion

Converter

DashAI.back.converters.simple_converters.NumericExpansion

---

Derive a new numeric feature from each selected column via a unary function.

Applies one of log1p (ln(1+x)), square (x^2), or sqrt (sqrt(x)) to every numeric column in scope, appending one new column per input column named <operation>_<column>. Values outside the domain of the chosen function (x <= -1 for log1p, x < 0 for sqrt) become NaN in the corresponding output.

The original columns are left untouched. square preserves the input column's type (Integer stays Integer, Float stays Float), since squaring is exact for both; a missing value stays missing. log1p and sqrt always produce a Float column, since they can yield non-integer or NaN results even from integer input. The output type only depends on the operation and the input type, never on the values.

Parameters

operation : string, default=log1p

Unary numeric expansion to apply to each selected column: 'log1p' (ln(1+x)), 'square' (x^2), or 'sqrt' (sqrt(x)).

Methods

fit(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'NumericExpansion'

Defined on NumericExpansion

Identify which columns in x are numeric (Float or Integer).

Parameters

x : DashAIDataset

The dataset whose columns will be inspected.

y : DashAIDataset, optional

Ignored. Defaults to None.

Returns

NumericExpansion

The fitted converter instance (self).

get_output_type(self, column_name: str = None) -> DashAI.back.types.dashai_data_type.DashAIDataType

Defined on NumericExpansion

Return the output type for a given expanded column.

Parameters

column_name : str, optional

Name of the output column (e.g. "square_age"). Defaults to None.

Returns

DashAIDataType

An Integer type backed by pyarrow.int64(), or a Float type backed by pyarrow.float64().

infer_output_columns(self, inputs: List[Annotated[Union[DashAI.back.preprocessing.structure_types.ColumnItem, DashAI.back.preprocessing.structure_types.BlockItem], FieldInfo(annotation=NoneType, required=True, discriminator='kind')]]) -> DashAI.back.preprocessing.structure_types.StructureDelta

Defined on NumericExpansion

Estimate the output: the scope kept, plus one column per numeric one.

Parameters

inputs : list of ColumnItem | BlockItem

The dataset state items in this converter's scope.

Returns

StructureDelta

The unchanged inputs, plus an <operation>_<column> column for every Float or Integer input, as fit picks them.

transform(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> 'DashAIDataset'

Defined on NumericExpansion

Apply the configured unary expansion to the fitted numeric columns.

Parameters

x : DashAIDataset

The dataset to transform.

y : DashAIDataset, optional

Ignored. Defaults to None.

Returns

DashAIDataset

The dataset with one new <operation>_<column> column appended per fitted numeric column, typed Integer or Float depending on the source column and the operation (see class docstring).

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str

Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential

An instance of the requested credential component.

get_metadata(cls) -> 'Dict[str, Any]'

Defined on BaseConverter

Get metadata for the converter, used by the DashAI frontend.

Parameters

cls : type

The converter class (injected automatically by Python for classmethods).

Returns

Dict[str, Any]

Dictionary containing display name, short description, image preview path, category, icon, color, and whether the converter is supervised.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict

Dictionary representing the Json Schema of the component.

rows_to_keep(self, x: "'DashAIDataset'") -> 'List[int]'

Defined on BaseConverter

Positions of the rows of x that survive this converter.

Parameters

x : DashAIDataset

The converter's scope columns of one split.

Returns

list of int

0-based positions of the kept rows, in order.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict

A dictionary with the data provided by the user to initialize the model.

Returns

dict

A validated dictionary with the necessary objects.