fracridge.FracRidgeRegressor

class FracRidgeRegressor(fracs=None, fit_intercept=False, normalize=False, copy_X=True, tol=1e-10, jit=True)

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.

normalizebool, optional

Whether to normalize the columns of X. 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.

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

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.

__init__(fracs=None, fit_intercept=False, normalize=False, copy_X=True, tol=1e-10, jit=True)

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, sample_weight=None)

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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

Examples using fracridge.FracRidgeRegressor

Integrating FracRidge objects into sklearn pipelines

Comparing alpha and fracs

Coefficient paths and cross-validation curves