Skip to main content

OneHotEncoder

Converter
DashAI.back.converters.scikit_learn.OneHotEncoder

Encode categorical columns as binary indicator (one-hot) vectors.

For each input feature column every unique category value becomes a separate binary output column. Given a feature with k categories the encoding produces k columns (or k - 1 when drop is set) where exactly one column is 1 and the rest are 0:

  • Nominal categories without order — one-hot encoding treats all categories as equidistant, which is appropriate for unordered labels such as city names or product types.
  • Avoiding the dummy-variable trap — the drop parameter can remove one indicator column per feature so that the resulting matrix has full rank, which is required by unregularized linear models.
  • Infrequent categories — min_frequency and max_categories can group rare values into a single infrequent_categories bin, reducing dimensionality.

The total number of output columns equals the sum of unique category counts across all encoded input columns (minus dropped columns).

References​

Parameters​

categories : string, default=auto
The categories of each feature.
drop, default=None
Specifies a methodology to drop one of the categories per feature.
dtype : string, default=np.float64
Desired dtype of output.
handle_unknown : string, default=error
How to handle unknown categories during transform.
min_frequency, default=None
Minimum frequency of a category to be considered as frequent.
max_categories, default=None
Maximum number of categories to encode.
feature_name_combiner : string, default=concat
Method used to combine feature names.

Methods​

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

Defined on OneHotEncoder

Return the DashAI data type produced by this converter for a column.

Parameters

column_name : str, optional
Not used; all output columns share the same type. Defaults to None.

Returns

DashAIDataType
An Integer type backed by pyarrow.int64(), representing the binary indicator values (0 or 1).

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.