macrosynergy.learning.preprocessing.panel_selectors.panel_selectors#

class LarsSelector(n_factors=10, fit_intercept=False)[source]#

Bases: BasePanelSelector

determine_features(X, y)[source]#

Create feature mask based on the LARS algorithm.

Parameters:
Returns:

mask – Boolean mask of selected features.

Return type:

list

class LassoSelector(n_factors=10, positive=False)[source]#

Bases: BasePanelSelector

determine_features(X, y)[source]#

Create feature mask based on the LASSO-LARS algorithm.

Parameters:
Returns:

mask – Boolean mask of selected features.

Return type:

np.ndarray

class MapSelector(n_factors=None, significance_level=0.05, positive=False)[source]#

Bases: BasePanelSelector

determine_features(X, y)[source]#

Create feature mask based on the Macrosynergy panel test.

Parameters:
Returns:

mask – Boolean mask of selected features.

Return type:

np.ndarray

class KendallSignificanceSelector(alpha=0.05)[source]#

Bases: BasePanelSelector

Univariate statistical feature selection using Kendall correlation tests.

Future enhancements will include Bonferroni corrections for multiple testing.

Parameters:

alpha (float, default=0.05) – Significance level.

determine_features(X, y)[source]#

Create feature mask based on the Macrosynergy panel test.

Parameters:
Returns:

mask – Boolean mask of selected features.

Return type:

np.ndarray

class FactorAvailabilitySelector(min_cids=2, min_periods=36)[source]#

Bases: BasePanelSelector

determine_features(X, y)[source]#

Determine mask of selected features based on a training set pair (X, y).

Parameters:
Returns:

mask – Boolean mask of selected features.

Return type:

np.ndarray