sklearn.linear_model.Lars

class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True)[source]

Least Angle Regression model a.k.a. LAR

Read more in the User Guide.

Parameters

fit_intercept : bool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).

verbose : bool or int, default=False

Sets the verbosity amount

normalize : bool, default=True

This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False.

precompute : bool, ‘auto’ or array-like , default=’auto’

Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.

n_nonzero_coefs : int, default=500

Target number of non-zero coefficients. Use np.inf for no limit.

eps : float, optional

The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the tol parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization. By default, np.finfo(np.float).eps is used.

copy_X : bool, default=True

If True, X will be copied; else, it may be overwritten.

fit_path : bool, default=True

If True the full path is stored in the coef_path_ attribute. If you compute the solution for a large problem or many targets, setting fit_path to False will lead to a speedup, especially with a small alpha.

Attributes

alphas_

(array-like of shape (n_alphas + 1,) | list of n_targets such arrays) Maximum of covariances (in absolute value) at each iteration. n_alphas is either n_nonzero_coefs or n_features, whichever is smaller.

active_

(list, length = n_alphas | list of n_targets such lists) Indices of active variables at the end of the path.

coef_path_

(array-like of shape (n_features, n_alphas + 1) | list of n_targets such arrays) The varying values of the coefficients along the path. It is not present if the fit_path parameter is False.

coef_

(array-like of shape (n_features,) or (n_targets, n_features)) Parameter vector (w in the formulation formula).

intercept_

(float or array-like of shape (n_targets,)) Independent term in decision function.

n_iter_

(array-like or int) The number of iterations taken by lars_path to find the grid of alphas for each target.

Examples

>>> from sklearn import linear_model
>>> reg = linear_model.Lars(n_nonzero_coefs=1)
>>> reg.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
Lars(n_nonzero_coefs=1)
>>> print(reg.coef_)
[ 0. -1.11...]

Methods

fit(X, y[, Xy])

Fit the model using X, y as training data.

get_params([deep])

Get parameters for this estimator.

predict(X)

Predict using the linear model.

score(X, y[, sample_weight])

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

set_params(**params)

Set the parameters of this estimator.

__init__(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y, Xy=None)[source]

Fit the model using X, y as training data.

Parameters

X : array-like of shape (n_samples, n_features)

Training data.

y : array-like of shape (n_samples,) or (n_samples, n_targets)

Target values.

Xy : array-like of shape (n_samples,) or (n_samples, n_targets), default=None

Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.

Returns

self : object

returns an instance of self.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep : bool, default=True

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.

predict(X)[source]

Predict using the linear model.

Parameters

X : array_like or sparse matrix, shape (n_samples, n_features)

Samples.

Returns

C : array, shape (n_samples,)

Returns predicted values.

score(X, y, sample_weight=None)[source]

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

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The 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 of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns

score : float

R^2 of self.predict(X) wrt. y.

Notes

The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score. This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score directly or make a custom scorer with make_scorer (the built-in scorer 'r2' uses multioutput='uniform_average').

set_params(**params)[source]

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.

Parameters

**params : dict

Estimator parameters.

Returns

self : object

Estimator instance.