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RandomUnderSamplerConverter

Converter
DashAI.back.converters.imbalanced_learn.RandomUnderSamplerConverter

Balance class distribution by randomly discarding majority-class samples.

Under-sampling addresses class imbalance by reducing the size of one or more over-represented classes rather than synthesising new minority-class examples. RandomUnderSampler achieves this by drawing a random subset of the majority class without replacement until the desired sampling_strategy ratio is reached.

The sampling_strategy parameter controls the resulting ratio between the minority and majority class after resampling. When set to "auto", the majority class is down-sampled until it matches the size of the next largest class. A float value specifies the desired minority-to-majority ratio directly.

Input column types are preserved in the output. Type determination is delegated to the transform method of the parent wrapper.

Wraps imblearn.under_sampling.RandomUnderSampler.

References​

Parameters​

sampling_strategy, default=auto
Sampling strategy (float or 'auto') to reduce majority class.
random_state, default=None
Seed for reproducibility.

Methods​

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

Defined on RandomUnderSamplerConverter

Not implemented; type preservation is handled in transform.

Parameters

column_name : str or None, optional
Name of the column whose output type is queried. Ignored because this method always raises. Default None.

changes_row_count(self) -> bool

Defined on ImbalancedLearnWrapper

Return True because all samplers add or remove rows.

Returns

bool
Always True.

fit(self, x: 'DashAIDataset', y: 'DashAIDataset') -> Type[DashAI.back.converters.base_converter.BaseConverter]

Defined on ImbalancedLearnWrapper

Resample the dataset by calling fit_resample and store the result.

Parameters

x : DashAIDataset
The input feature dataset.
y : DashAIDataset
The target label dataset (required; must be non-empty).

Returns

Type[BaseConverter]
The fitted sampler 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 ImbalancedLearnWrapper

Return the resampled dataset stored during fit.

Parameters

x : DashAIDataset
The original feature dataset (used only for type information).
y : DashAIDataset, optional
The original target dataset (used only for type information). Defaults to None.

Returns

DashAIDataset
The combined resampled dataset (features + target) produced by fit.

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.