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Normalizer

Converter
DashAI.back.converters.scikit_learn.Normalizer

Normalize each sample (row) independently to unit norm.

Unlike column-wise scalers such as StandardScaler or MinMaxScaler, this transformer operates along the sample axis. For each row vector x the transformation is::

x_normalized = x / ||x||_p

where p is chosen by the norm parameter:

  • "l2" (default) — Euclidean norm; the dot product of any two normalized samples equals the cosine of the angle between them.
  • "l1" — Manhattan norm; useful when the direction of the feature vector matters more than relative magnitudes.
  • "max" — divides by the largest absolute element; guarantees the output lies in [-1, 1].

Row-wise normalization is particularly effective for text classification and clustering algorithms that rely on the dot product or cosine similarity (e.g. k-means on TF-IDF vectors, linear SVMs on bag-of-words features).

References​

Parameters​

norm : string, default=l2
The norm to use to normalize each non-zero sample.
use_copy : boolean, default=True
Set to False to perform inplace row normalization.

Methods​

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

Defined on Normalizer

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 Float type backed by pyarrow.float64().

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.