lightning.regression.FistaRegressor¶

class lightning.regression.FistaRegressor(C=1.0, alpha=1.0, penalty='l1', multiclass=False, max_iter=100, max_steps=30, eta=2.0, sigma=1e-05, callback=None, verbose=0)¶

Estimator for learning linear classifiers by FISTA.

The objective functions considered take the form

minimize F(W) = C * L(W) + alpha * R(W),

where L(W) is a loss term and R(W) is a penalty term.

Parameters:

penalty : str, ‘l2’, ‘l1’, ‘l1/l2’ The penalty to be used. l2: ridge l1: lasso l1/l2: group lasso multiclass : bool Whether to use a direct multiclass formulation (True) or one-vs-rest (False). C : float Weight of the loss term. alpha : float Weight of the penalty term. max_iter : int Maximum number of iterations to perform. max_steps : int Maximum number of steps to use during the line search. sigma : float Constant used in the line search sufficient decrease condition. eta : float Decrease factor for line-search procedure. For example, eta=2. will decrease the step size by a factor of 2 at each iteration of the line-search routine. callback : callable Callback function. verbose : int Verbosity level.

Methods

fit(X, y)
get_params([deep])	Get parameters for this estimator.
n_nonzero([percentage])
predict(X)
score(X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(C=1.0, alpha=1.0, penalty='l1', multiclass=False, max_iter=100, max_steps=30, eta=2.0, sigma=1e-05, callback=None, verbose=0)¶

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

score(X, y, sample_weight=None)¶

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the regression sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual sum of squares ((y_true - y_true.mean()) ** 2).sum(). Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters:	X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True values for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.
Returns:	score : float R^2 of self.predict(X) wrt. y.

set_params(**params)¶

Set the parameters of this estimator.

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

Returns:	self :