iaml.actionables.normalize.act_normalizer¶

[STEP] Unit norm scaler (historical alias: ActNormalizer).

Attributes¶

ActNormalizer

Classes¶

ActUnitNormScaler

Scale each numeric row to unit norm; exposed as steps.UnitNormScaler.

Module Contents¶

class iaml.actionables.normalize.act_normalizer.ActUnitNormScaler¶

Bases: iaml.actionable.Actionable

Scale each numeric row to unit norm; exposed as steps.UnitNormScaler.

name: str = 'Unit Norm Scaler'¶: Step’s name.

columns: list[str] = None¶

normalizer: sklearn.preprocessing.Normalizer = None¶

configuration¶: Configuration of this step (equivalent to hyperparameters for models).

fit(dataset: iaml.dataset.Dataset)¶

Fits the step on the given dataset.

Parameters:: dataset (Dataset) – Features and labels.
Returns:: Fitted step.

transform(X: pandas.DataFrame) → pandas.DataFrame¶

Apply normalization

Parameters:: X (pd.DataFrame) – DataFrame to transform
Returns:: Transformed dataset

suitable(dataset: iaml.dataset.Dataset) → bool¶

Evaluates whether the step is suitable to run with the given dataset.

Parameters:: dataset (Dataset) – Dataset to evaluate on.
Returns:: Whether the step is suitable to run.

priorize(candidate: iaml.candidate.Candidate = None) → float¶

Evaluates the priority of the step within a pipeline. 0 means the execution of the step should not be prioritized, and 1 means it should be executed early in the pipeline.

Parameters:: candidate (Candidate, optional) – Candidate on which the priority should be evaluated.
Returns:: Value between 0 and 1.

iaml.actionables.normalize.act_normalizer.ActNormalizer¶