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)[source]

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)[source]
fit(X, y)[source]

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 latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :