val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?alpha:float ->
?fit_intercept:bool ->
?verbose:int ->
?normalize:bool ->
?precompute:[ `Bool of bool | `Auto | `Arr of Arr.t ] ->
?max_iter:int ->
?eps:float ->
?copy_X:bool ->
?fit_path:bool ->
?positive:bool ->
unit ->
tLasso 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.
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
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 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 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.
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 :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.tAttribute alphas_: get value or raise Not_found if None.
val alphas_opt : t -> Py.Object.t optionAttribute alphas_: get value as an option.
val active_ : t -> Py.Object.tAttribute active_: get value or raise Not_found if None.
val active_opt : t -> Py.Object.t optionAttribute active_: get value as an option.
Attribute n_iter_: get value or raise Not_found if None.
Attribute n_iter_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.