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LabelBinarizer

Converter
DashAI.back.converters.scikit_learn.LabelBinarizer

Binarize a label column into a one-vs-all binary indicator matrix.

Given a flat array of class labels, this converter produces a 2-D integer matrix in which each column corresponds to one class. For a sample belonging to class k, column k is set to pos_label and all other columns are set to neg_label:

  • Binary classification — the output is a single column (shape (n_samples, 1)) because one column is sufficient to encode two classes.
  • Multiclass classification — the output has one column per class (shape (n_samples, n_classes)).

Label binarization is required by classifiers that natively expect a binary indicator matrix for their targets (e.g. multi-label SVMs), and is useful for computing one-vs-all metrics directly on the raw output.

References​

Parameters​

neg_label : integer, default=0
Value with which negative labels must be encoded.
pos_label : integer, default=1
Value with which positive labels must be encoded.

Methods​

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

Defined on LabelBinarizer

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 (0/1) or one-vs-all matrix values.

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.