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LabelEncoder

Converter
DashAI.back.converters.scikit_learn.LabelEncoder

Encode categorical labels as contiguous integer codes in [0, n_classes - 1].

Each unique label value is mapped to a unique integer in ascending order of the sorted class list. For example, given classes ["cat", "dog", "fish"] the mapping is cat -> 0, dog -> 1, fish -> 2.

Unlike OrdinalEncoder (which operates on feature columns), LabelEncoder is designed for target label columns. It is typically applied to the output column before training classifiers that require numeric class indices (e.g. gradient-boosted trees, support vector machines, or any model that indexes a class-weight array). The DashAI implementation extends the sklearn behaviour to support multiple columns and to preserve NaN values during transformation.

References​

Methods​

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

Defined on LabelEncoder

Fit a LabelEncoder for each eligible column in the dataset.

Parameters

x : DashAIDataset
Input dataset whose categorical/string columns will be encoded.
y : DashAIDataset or None, optional
Ignored. Present for API compatibility. Default None.

Returns

LabelEncoderConverter
The fitted converter instance (self).

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

Defined on LabelEncoder

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

Parameters

column_name : str, optional
The column name to look up in the fitted encoders. When provided and the encoder has been fitted, the returned type reflects the actual fitted classes. Defaults to None.

Returns

DashAIDataType
A Categorical type derived from the encoder's fitted classes. Returns a placeholder Categorical if the encoder has not been fitted for the given column.

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

Defined on LabelEncoder

Apply fitted label encoders to each eligible column, preserving NaN.

Parameters

x : DashAIDataset
Input dataset. Columns not seen during fit are left unchanged.
y : DashAIDataset or None, optional
Ignored. Present for API compatibility. Default None.

Returns

DashAIDataset
Dataset with categorical/string columns replaced by integer codes.

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.

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.

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.