skglm.penalties.SLOPE¶

class skglm.penalties.SLOPE(alphas)[source]¶

Sorted L-One Penalized Estimation (SLOPE) penalty.

References

[1]

M. Bogdan, E. van den Berg, C. Sabatti, W. Su, E. Candes “SLOPE - Adaptive Variable Selection via Convex Optimization”, The Annals of Applied Statistics 9 (3): 1103-40 https://doi.org/10.1214/15-AOAS842

Attributes:

alphasarray, shape (n_features,): Contain regularization levels for every feature. When alphas contain a single unique value, SLOPE is equivalent to the ``L1``penalty.

__init__(alphas)[source]¶

Methods

`__init__`(alphas)
`generalized_support`(w)	Return a mask which is True for coefficients in the generalized support.
`get_spec`()	Specify the numba types of the class attributes.
`is_penalized`(n_features)	Return a binary mask with the penalized features.
`params_to_dict`()	Get the parameters to initialize an instance of the class.
`prox_vec`(x, stepsize)
`value`(w)	Compute the value of SLOPE at w.