val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?l1_ratio:[ `Float of float | `Ndarray of Ndarray.t ] ->
?eps:float ->
?n_alphas:int ->
?alphas:Ndarray.t ->
?fit_intercept:bool ->
?normalize:bool ->
?precompute:[ `Bool of bool | `Auto | `Ndarray of Ndarray.t ] ->
?max_iter:int ->
?tol:float ->
?cv:
[ `Int of int | `CrossValGenerator of Py.Object.t | `Ndarray of Ndarray.t ] ->
?copy_X:bool ->
?verbose:[ `Bool of bool | `Int of int ] ->
?n_jobs:[ `Int of int | `None ] ->
?positive:bool ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?selection:string ->
unit ->
tElastic Net model with iterative fitting along a regularization path.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <elastic_net>`.
Parameters ---------- l1_ratio : float or array of floats, optional float between 0 and 1 passed to ElasticNet (scaling between l1 and l2 penalties). For ``l1_ratio = 0`` the penalty is an L2 penalty. For ``l1_ratio = 1`` it is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a combination of L1 and L2 This parameter can be a list, in which case the different values are tested by cross-validation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in ``.1, .5, .7,
.9, .95, .99, 1``
eps : float, optional Length of the path. ``eps=1e-3`` means that ``alpha_min / alpha_max = 1e-3``.
n_alphas : int, optional Number of alphas along the regularization path, used for each l1_ratio.
alphas : numpy array, optional List of alphas where to compute the models. If None alphas are set automatically
fit_intercept : boolean 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).
normalize : boolean, optional, 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 use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``.
precompute : True | False | 'auto' | array-like 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, optional The maximum number of iterations
tol : float, optional The tolerance for the optimization: if the updates are smaller than ``tol``, the optimization code checks the dual gap for optimality and continues until it is smaller than ``tol``.
cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are:
For integer/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here.
.. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold.
copy_X : boolean, optional, default True If ``True``, X will be copied; else, it may be overwritten.
verbose : bool or integer Amount of verbosity.
n_jobs : int or None, optional (default=None) Number of CPUs to use during the cross validation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
positive : bool, optional When set to ``True``, forces the coefficients to be positive.
random_state : int, RandomState instance or None, optional, default None The seed of the pseudo random number generator that selects a random feature to update. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``selection`` == 'random'.
selection : str, default 'cyclic' If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to 'random') often leads to significantly faster convergence especially when tol is higher than 1e-4.
Attributes ---------- alpha_ : float The amount of penalization chosen by cross validation
l1_ratio_ : float The compromise between l1 and l2 penalization chosen by cross validation
coef_ : array, shape (n_features,) | (n_targets, n_features) Parameter vector (w in the cost function formula),
intercept_ : float | array, shape (n_targets, n_features) Independent term in the decision function.
mse_path_ : array, shape (n_l1_ratio, n_alpha, n_folds) Mean square error for the test set on each fold, varying l1_ratio and alpha.
alphas_ : numpy array, shape (n_alphas,) or (n_l1_ratio, n_alphas) The grid of alphas used for fitting, for each l1_ratio.
n_iter_ : int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Examples -------- >>> from sklearn.linear_model import ElasticNetCV >>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=2, random_state=0) >>> regr = ElasticNetCV(cv=5, random_state=0) >>> regr.fit(X, y) ElasticNetCV(cv=5, random_state=0) >>> print(regr.alpha_) 0.199... >>> print(regr.intercept_) 0.398... >>> print(regr.predict([0, 0])) 0.398...
Notes ----- For an example, see :ref:`examples/linear_model/plot_lasso_model_selection.py <sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py>`.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortran-contiguous numpy array.
The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. More specifically, the optimization objective is::
1 / (2 * n_samples) * ||y - Xw||^2_2
If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to::
a * L1 + b * L2
for::
alpha = a + b and l1_ratio = a / (a + b).
See also -------- enet_path ElasticNet
Fit linear model with coordinate descent
Fit is on grid of alphas and best alpha estimated by cross-validation.
Parameters ---------- X : array-like, shape (n_samples, n_features) Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output, X can be sparse.
y : array-like, shape (n_samples,) or (n_samples, n_targets) Target values
val get_params : ?deep:bool -> t -> Py.Object.tGet 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:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict 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 alpha_ : t -> floatAttribute alpha_: see constructor for documentation
val l1_ratio_ : t -> floatAttribute l1_ratio_: see constructor for documentation
val n_iter_ : t -> intAttribute n_iter_: see constructor for documentation
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 : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.