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OrdinalEncoder

Converter
DashAI.back.converters.scikit_learn.OrdinalEncoder

Encode categorical feature columns as integer ordinal codes.

For each input feature column every unique category value is mapped to a contiguous integer starting at 0. Given categories ["cold", "warm", "hot"] sorted alphabetically the default mapping would be cold -> 0, hot -> 1, warm -> 2; a custom category list can be supplied to impose a domain-specific order.

Unlike OneHotEncoder, ordinal encoding produces a single output column per input column and implicitly encodes a numerical order between the categories. This makes it appropriate when:

  • The categories have a meaningful rank (e.g. education level, severity score, shirt size).
  • The downstream model can exploit ordinal structure (e.g. tree-based models such as gradient-boosted trees or random forests).

For unordered nominal categories, OneHotEncoder is typically preferred because ordinal codes introduce a spurious ordering.

References​

Parameters​

categories : string, default=auto
Categories (unique values) per feature.
dtype : string, default=int64
Desired dtype of output.
handle_unknown : string, default=error
Whether to raise an error or use a specific encoded value when an unknown category is seen.
unknown_value, default=None
The value to use for unknown categories.
min_frequency, default=None
Minimum frequency of a category to be considered as frequent.
max_categories, default=None
Maximum number of categories to encode.

Methods​

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

Defined on OrdinalEncoder

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
A placeholder Integer type with the same dtype as the encoder's output. The actual categories will be set by sklearn_wrapper's transform method.

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 EncodingConverter

Encode x and append the result as new encoded_* columns.

Parameters

x : DashAIDataset
The dataset to transform (contains only the scope columns).
y : DashAIDataset, optional
Ignored. Defaults to None.

Returns

DashAIDataset
Dataset with the original scope columns preserved plus new encoded_* columns appended.

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.