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PolynomialFeatures

Converter
DashAI.back.converters.scikit_learn.PolynomialFeatures

Generate polynomial and interaction features up to a specified degree.

Given d input features, this converter produces all monomials of the form x_1^p_1 * x_2^p_2 * … * x_d^p_d where p_1 + … + p_d <= degree (and each p_i >= 0). For example, with two input features [a, b] and degree=2, the output is [1, a, b, a², ab, b²].

When interaction_only=True only cross-terms are retained (no squared or higher pure-power terms), which is useful when features are already on a meaningful scale and self-interactions are not informative.

Setting include_bias=True (the default) prepends a column of ones, acting as an intercept term for linear models that lack an explicit bias.

The order parameter controls whether the dense output array is stored in C (row-major) or Fortran (column-major) order. "F" order can speed up the transformation itself but may slow downstream estimators.

Output columns are typed as Float64 in DashAI.

Wraps sklearn.preprocessing.PolynomialFeatures.

References​

Parameters​

degree : integer, default=2
The degree of the polynomial features.
interaction_only : boolean, default=False
If True, only interaction features are produced: features that are products of at most degree distinct input features.
include_bias : boolean, default=True
If True (default), then include a bias column (a column of ones that act as an intercept term).
order : string, default=C
Order of output array in the dense case. 'F' order is faster to compute, but may slow down subsequent estimators.

Methods​

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

Defined on PolynomialFeatures

Return Float64 as the output type for all polynomial feature columns.

Parameters

column_name : str or None, optional
Name of the output column. Not used — all columns receive the same Float64 type. Default None.

Returns

Float
A DashAI Float type backed by pyarrow.float64().

changes_row_count(self) -> 'bool'

Defined on BaseConverter

Indicate whether this converter changes the number of dataset rows.

Returns

bool
True if the converter may add or remove rows, False otherwise.

fit(self, x: 'DashAIDataset', y: Optional[ForwardRef('DashAIDataset')] = None) -> DashAI.back.converters.base_converter.BaseConverter

Defined on SklearnWrapper

Fit the scikit-learn transformer to the data.

Parameters

x : DashAIDataset
The input dataset to fit the transformer on.
y : DashAIDataset, optional
Target values for supervised transformers. Defaults to None.

Returns

BaseConverter
The fitted transformer instance (self).

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.

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

Defined on SklearnWrapper

Transform the data using the fitted scikit-learn transformer.

Parameters

x : DashAIDataset
The input dataset to transform.
y : DashAIDataset, optional
Not used. Present for API consistency. Defaults to None.

Returns

DashAIDataset
The transformed dataset with updated DashAI column types.

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.