package sklearn

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package sklearn
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type tag = [
  1. | `LassoLars
]
type t = [ `BaseEstimator | `LassoLars | `MultiOutputMixin | `Object | `RegressorMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val as_regressor : t -> [ `RegressorMixin ] Obj.t
val as_multi_output : t -> [ `MultiOutputMixin ] Obj.t
val create : ?alpha:float -> ?fit_intercept:bool -> ?verbose:int -> ?normalize:bool -> ?precompute:[ `Arr of [> `ArrayLike ] Np.Obj.t | `Auto | `Bool of bool ] -> ?max_iter:int -> ?eps:float -> ?copy_X:bool -> ?fit_path:bool -> ?positive:bool -> ?jitter:float -> ?random_state:int -> unit -> t

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 :ref:`User Guide <least_angle_regression>`.

Parameters ---------- alpha : float, default=1.0 Constant that multiplies the penalty term. Defaults to 1.0. ``alpha = 0`` is equivalent to an ordinary least square, solved by :class:`LinearRegression`. For numerical reasons, using ``alpha = 0`` with the LassoLars object is not advised and you should prefer the LinearRegression object.

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 :class:`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.

max_iter : int, default=500 Maximum number of iterations to perform.

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.

positive : bool, 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.

jitter : float, 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.

random_state : int, RandomState instance or None (default) Determines random number generation for jittering. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Ignored if `jitter` is None.

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 ``max_iter``, ``n_features``, or the number of nodes in the path with correlation greater than ``alpha``, 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 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.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...

See also -------- lars_path lasso_path Lasso LassoCV LassoLarsCV LassoLarsIC sklearn.decomposition.sparse_encode

val fit : ?xy:[> `ArrayLike ] Np.Obj.t -> x:[> `ArrayLike ] Np.Obj.t -> y:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

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.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val predict : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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.

val score : ?sample_weight:[> `ArrayLike ] Np.Obj.t -> x:[> `ArrayLike ] Np.Obj.t -> y:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> float

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 uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

val alphas_ : t -> Py.Object.t

Attribute alphas_: get value or raise Not_found if None.

val alphas_opt : t -> Py.Object.t option

Attribute alphas_: get value as an option.

val active_ : t -> Py.Object.t

Attribute active_: get value or raise Not_found if None.

val active_opt : t -> Py.Object.t option

Attribute active_: get value as an option.

val coef_path_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute coef_path_: get value or raise Not_found if None.

val coef_path_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute coef_path_: get value as an option.

val coef_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute coef_: get value or raise Not_found if None.

val coef_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute coef_: get value as an option.

val intercept_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute intercept_: get value or raise Not_found if None.

val intercept_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute intercept_: get value as an option.

val n_iter_ : t -> Py.Object.t

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> Py.Object.t option

Attribute n_iter_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.