skglm.datafits.LogisticGroup

class skglm.datafits.LogisticGroup(grp_ptr, grp_indices)

Logistic datafit used with group penalties.

The datafit reads:

`1 / n_"samples" sum_(i=1)^(n_"samples") log(1 + exp(-y_i (Xw)_i))`

Attributes:

grp_indicesarray, shape (n_features,)

The group indices stacked contiguously [grp1_indices, grp2_indices, ...].

grp_ptrarray, shape (n_groups + 1,)

The group pointers such that two consecutive elements delimit the indices of a group in grp_indices.

lipschitzarray, shape (n_groups,)

The lipschitz constants for each group.

__init__(grp_ptr, grp_indices)

Methods

__init__(grp_ptr, grp_indices)
full_grad_sparse(X_data, X_indptr, ...)
get_global_lipschitz(X, y)
get_global_lipschitz_sparse(X_data, ...)
get_lipschitz(X, y)
get_lipschitz_sparse(X_data, X_indptr, ...)
get_spec()	Specify the numba types of the class attributes.
gradient(X, y, Xw)
gradient_g(X, y, w, Xw, g)
gradient_scalar(X, y, w, Xw, j)
gradient_scalar_sparse(X_data, X_indptr, ...)
gradient_sparse(X_data, X_indptr, X_indices, ...)
initialize(X, y)	Pre-computations before fitting on X and y.
initialize_sparse(X_data, X_indptr, X_indices, y)	Pre-computations before fitting on X and y when X is a sparse matrix.
intercept_update_step(y, Xw)
inverse_link(Xw)	Inverse link function (identity by default).
params_to_dict()	Get the parameters to initialize an instance of the class.
raw_grad(y, Xw)	Compute gradient of datafit w.r.t Xw.
raw_hessian(y, Xw)	Compute Hessian of datafit w.r.t Xw.
value(y, w, Xw)	Value of datafit at vector w.