lightning.regression.CDRegressor

class lightning.regression.CDRegressor(C=1.0, alpha=1.0, loss='squared', penalty='l2', max_iter=50, tol=0.001, termination='violation_sum', shrinking=True, max_steps=30, sigma=0.01, beta=0.5, warm_start=False, debiasing=False, Cd=1.0, warm_debiasing=False, selection='cyclic', permute=True, callback=None, n_calls=100, random_state=None, verbose=0, n_jobs=1)[source]

Estimator for learning linear regressors by (block) coordinate descent.

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:

loss : str, ‘squared’

The loss function to be used.

penalty : str, ‘l2’, ‘l1’, ‘l1/l2’, ‘nnl1’, ‘nnl2’

The penalty to be used.

  • l2: ridge
  • l1: lasso
  • l1/l2: group lasso
  • nnl1: non-negative constraints + l1 penalty
  • nnl2: non-negative constraints + l2 penalty

For other parameters, see `CDClassifier`. :

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, alpha=1.0, loss='squared', penalty='l2', max_iter=50, tol=0.001, termination='violation_sum', shrinking=True, max_steps=30, sigma=0.01, beta=0.5, warm_start=False, debiasing=False, Cd=1.0, warm_debiasing=False, selection='cyclic', permute=True, callback=None, n_calls=100, random_state=None, verbose=0, n_jobs=1)[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] or [n_samples, n_targets]

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 :