lightning.regression.LinearSVR¶

class lightning.regression.LinearSVR(C=1.0, epsilon=0, loss='epsilon_insensitive', max_iter=1000, tol=0.001, fit_intercept=False, permute=True, warm_start=False, random_state=None, callback=None, n_calls=100, verbose=0)¶

Estimator for learning a linear support vector regressor by coordinate descent in the dual.

Parameters:

loss : str, ‘epsilon_insensitive’, ‘squared_epsilon_insensitive’ The loss function to be used. C : float Weight of the loss term. epsilon : float Parameter of the epsilon-insensitive loss. max_iter : int Maximum number of iterations to perform. tol : float Tolerance of the stopping criterion. fit_intercept : bool Whether to fit an intercept term or not. warm_start : bool Whether to activate warm-start or not. permute : bool Whether to permute coordinates or not before cycling. callback : callable Callback function. n_calls : int Frequency with which callback must be called. random_state : RandomState or int The seed of the pseudo random number generator to use. verbose : int Verbosity level.

Methods

fit(X, y)	Fit model according to X and 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, epsilon=0, loss='epsilon_insensitive', max_iter=1000, tol=0.001, fit_intercept=False, permute=True, warm_start=False, random_state=None, callback=None, n_calls=100, verbose=0)¶

fit(X, y)¶

Fit model according to X and y.

Parameters:	X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values.
Returns:	self : regressor Returns self.

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 :