sklearn.linear_model
.LassoLars¶
- class sklearn.linear_model.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, normalize='deprecated', precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None)[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=False
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 useStandardScaler
before callingfit
on an estimator withnormalize=False
.Changed in version 1.2: default changed from True to False in 1.2.
Deprecated since version 1.2:
normalize
was deprecated in version 1.2 and will be removed in 1.4.- 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, default=np.finfo(float).eps
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.- 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.- jitterfloat, default=None
Upper bound on a uniform noise parameter to be added to the
y
values, to satisfy the model’s assumption of one-at-a-time computations. Might help with stability.New in version 0.23.
- random_stateint, RandomState instance or None, default=None
Determines random number generation for jittering. Pass an int for reproducible output across multiple function calls. See Glossary. Ignored if
jitter
is None.New in version 0.23.
- Attributes:
- alphas_array-like of shape (n_alphas + 1,) or list of 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 withalpha >= alpha_min
, whichever is smaller. If this is a list of array-like, the length of the outer list isn_targets
.- active_list of length n_alphas or list of such lists
Indices of active variables at the end of the path. If this is a list of list, the length of the outer list is
n_targets
.- coef_path_array-like of shape (n_features, n_alphas + 1) or list of such arrays
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
. If this is a list of array-like, the length of the outer list isn_targets
.- 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.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
lars_path
Compute Least Angle Regression or Lasso path using LARS algorithm.
lasso_path
Compute Lasso path with coordinate descent.
Lasso
Linear Model trained with L1 prior as regularizer (aka the Lasso).
LassoCV
Lasso linear model with iterative fitting along a regularization path.
LassoLarsCV
Cross-validated Lasso, using the LARS algorithm.
LassoLarsIC
Lasso model fit with Lars using BIC or AIC for model selection.
sklearn.decomposition.sparse_encode
Sparse coding.
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. -0.955...]
Methods
fit
(X, y[, Xy])Fit the model using X, y as training data.
Get metadata routing of this object.
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 of the prediction.
set_fit_request
(*[, Xy])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.- fit(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_features,) or (n_features, 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_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(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:
- paramsdict
Parameter names mapped to their values.
- predict(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(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy
, 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 with shape
(n_samples, n_samples_fitted)
, wheren_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)
w.r.t.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_fit_request(*, Xy: Union[bool, None, str] = '$UNCHANGED$') LassoLars [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- Xystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
Xy
parameter infit
.
- Returns:
- selfobject
The updated object.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). 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:
- selfestimator instance
Estimator instance.
- set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') LassoLars [source]¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.