skglm.datafits.Logistic

class skglm.datafits.Logistic

Logistic datafit with labels in {-1, 1}.

The datafit reads:

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

Notes

The class is jit compiled at fit time using Numba compiler. This allows for faster computations.

__init__()

Methods

__init__()
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_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)
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.