`fracridge`.FracRidgeRegressor¶

class FracRidgeRegressor(fracs=None, fit_intercept=False, copy_X=True, tol=1e-10, jit=True)[source]¶

Parameters:

fracsfloat or sequence: The desired fractions of the parameter vector length, relative to OLS solution. Default: np.arange(.1, 1.1, .1)
fit_interceptbool, optional: Whether to fit an intercept term. Default: False.
copy_Xbool, optional: Whether to make a copy of the X matrix before fitting. Default: True.
tolfloat, optional.: Tolerance under which singular values of the X matrix are considered to be zero. Default: 1e-10.
jitbool, optional.: Whether to use jit-accelerated implementation. Default: True.

Attributes:

coef_ndarray, shape (p, f, b): The full estimated parameters across units of measurement for every desired fraction. Where p number of model parameters, f number of fractions and b number of targets.
alpha_ndarray, shape (f, b): The alpha coefficients associated with each solution. Where f number of fractions and b number of targets.

Examples

Generate random data:

>>> np.random.seed(1)
>>> y = np.random.randn(100)
>>> X = np.random.randn(100, 10)

Calculate coefficients with naive OLS:

>>> coef = np.linalg.inv(X.T @ X) @ X.T @ y

Initialize the estimator with a single fraction:

>>> fr = FracRidgeRegressor(fracs=0.3)

Fit estimator:

>>> fr.fit(X, y)
FracRidgeRegressor(fracs=0.3)

Check results:

>>> coef_ = fr.coef_
>>> alpha_ = fr.alpha_
>>> print(np.linalg.norm(coef_) / np.linalg.norm(coef))
0.29

__init__(fracs=None, fit_intercept=False, copy_X=True, tol=1e-10, jit=True)[source]¶

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

score(X, y)[source]¶: Score the fracridge fit

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: