sklearn.linear_model
.LassoLars¶
-
class
sklearn.linear_model.
LassoLars
(alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)[source]¶ Lasso model fit with Least Angle Regression a.k.a. Lars
It is a Linear Model trained with an L1 prior as regularizer.
The optimization objective for Lasso is:
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Read more in the User Guide.
- Parameters
- alphafloat, default=1.0
Constant that multiplies the penalty term. Defaults to 1.0.
alpha = 0
is equivalent to an ordinary least square, solved byLinearRegression
. For numerical reasons, usingalpha = 0
with the LassoLars object is not advised and you should prefer the LinearRegression object.- fit_interceptbool, 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).
- verbosebool or int, default=False
Sets the verbosity amount
- normalizebool, 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 usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
.- precomputebool, ‘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.- max_iterint, default=500
Maximum number of iterations to perform.
- epsfloat, 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_Xbool, default=True
If True, X will be copied; else, it may be overwritten.
- fit_pathbool, default=True
If
True
the full path is stored in thecoef_path_
attribute. If you compute the solution for a large problem or many targets, settingfit_path
toFalse
will lead to a speedup, especially with a small alpha.- positivebool, default=False
Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients will not converge to the ordinary-least-squares solution for small values of alpha. Only coefficients up to the smallest alpha value (
alphas_[alphas_ > 0.].min()
when fit_path=True) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator.
- 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 eithermax_iter
,n_features
, or the number of nodes in the path with correlation greater thanalpha
, 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) or list
If a list is passed it’s expected to be one of n_targets such arrays. The varying values of the coefficients along the path. It is not present if the
fit_path
parameter isFalse
.- 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.
See also
Examples
>>> from sklearn import linear_model >>> reg = linear_model.LassoLars(alpha=0.01) >>> reg.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1]) LassoLars(alpha=0.01) >>> print(reg.coef_) [ 0. -0.963257...]
Methods
fit
(self, X, y[, Xy])Fit the model using X, y as training data.
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X)Predict using the linear model.
score
(self, X, y[, sample_weight])Return the coefficient of determination R^2 of the prediction.
set_params
(self, \*\*params)Set the parameters of this estimator.
-
__init__
(self, alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(self, X, y, Xy=None)[source]¶ Fit the model using X, y as training data.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training data.
- yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
- Xyarray-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
- selfobject
returns an instance of self.
-
get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
predict
(self, X)[source]¶ Predict using the linear model.
- Parameters
- Xarray_like or sparse matrix, shape (n_samples, n_features)
Samples.
- Returns
- Carray, shape (n_samples,)
Returns predicted values.
-
score
(self, 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
- Xarray-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.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor will usemultioutput='uniform_average'
from version 0.23 to keep consistent withr2_score
. This will influence thescore
method of all the multioutput regressors (except forMultiOutputRegressor
). To specify the default value manually and avoid the warning, please either callr2_score
directly or make a custom scorer withmake_scorer
(the built-in scorer'r2'
usesmultioutput='uniform_average'
).
-
set_params
(self, **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
- **paramsdict
Estimator parameters.
- Returns
- selfobject
Estimator instance.