package sklearn
Module Linear_model.MultiTaskLassoCVSource
Source
val create :
?eps:float ->
?n_alphas:int ->
?alphas:Arr.t ->
?fit_intercept:bool ->
?normalize:bool ->
?max_iter:int ->
?tol:float ->
?copy_X:bool ->
?cv:[ `I of int | `CrossValGenerator of Py.Object.t | `Arr of Arr.t ] ->
?verbose:int ->
?n_jobs:int ->
?random_state:int ->
?selection:string ->
unit ->
tMulti-task Lasso model trained with L1/L2 mixed-norm as regularizer.
See glossary entry for :term:`cross-validation estimator`.
The optimization objective for MultiTaskLasso is::
(1 / (2 * n_samples)) * ||Y - XW||^Fro_2 + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt\sum_j w_{ij^2
}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
.. versionadded:: 0.15
Parameters ---------- 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
alphas : array-like, optional List of alphas where to compute the models. If not provided, 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``.
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``.
copy_X : boolean, optional, default True If ``True``, X will be copied; else, it may be overwritten.
cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds.
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
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.
verbose : bool or integer Amount of verbosity.
n_jobs : int or None, optional (default=None) Number of CPUs to use during the cross validation. Note that this is used only if multiple values for l1_ratio are given. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
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 ---------- intercept_ : array, shape (n_tasks,) Independent term in decision function.
coef_ : array, shape (n_tasks, n_features) Parameter vector (W in the cost function formula). Note that ``coef_`` stores the transpose of ``W``, ``W.T``.
alpha_ : float The amount of penalization chosen by cross validation
mse_path_ : array, shape (n_alphas, n_folds) mean square error for the test set on each fold, varying alpha
alphas_ : numpy array, shape (n_alphas,) The grid of alphas used for fitting.
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 MultiTaskLassoCV >>> from sklearn.datasets import make_regression >>> from sklearn.metrics import r2_score >>> X, y = make_regression(n_targets=2, noise=4, random_state=0) >>> reg = MultiTaskLassoCV(cv=5, random_state=0).fit(X, y) >>> r2_score(y, reg.predict(X)) 0.9994... >>> reg.alpha_ 0.5713... >>> reg.predict(X:1,) array([153.7971..., 94.9015...])
See also -------- MultiTaskElasticNet ElasticNetCV MultiTaskElasticNetCV
Notes ----- The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortran-contiguous numpy array.
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
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'``).
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.
Pretty-print the object to a formatter.