macrosynergy.learning.preprocessing.scalers.base_panel_scaler#

class BasePanelScaler(type='panel')[source]#

Bases: BaseEstimator, TransformerMixin, OneToOneFeatureMixin, ABC

Base class for scaling a panel of features in a learning pipeline.

Parameters:: type (str, default="panel") – The panel dimension over which the scaling is applied. Options are “panel” and “cross_section”.

Notes

Learning algorithms can benefit from scaling each feature to a similar range. This ensures they consider each feature equally in the model training process. It can also encourage faster convergence of an optimization algorithm.

fit(X, y=None)[source]#

Fit method to learn training set quantities for feature scaling.

Parameters:

X (pd.DataFrame) – The feature matrix.
y (pd.Series or pd.DataFrame, default=None) – The target vector.

Returns:

The fitted scaler.

Return type:

self

transform(X)[source]#

Transform method to scale the input data based on extracted training statistics.

Parameters:: X (pandas.DataFrame) – The feature matrix.
Returns:: X_transformed – The feature matrix with scaled features.
Return type:: pandas.DataFrame

abstract extract_statistics(X, feature)[source]#: Determine the relevant statistics for feature scaling.

abstract scale(X, feature, statistics)[source]#: Scale the input data based on the relevant statistics.