type t = [ `BaseEstimator | `GraphicalLassoCV | `Object ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?alphas:Py.Object.t ->
?n_refinements:int ->
?cv:
[ `BaseCrossValidator of [> `BaseCrossValidator ] Np.Obj.t
| `Arr of [> `ArrayLike ] Np.Obj.t
| `I of int ] ->
?tol:float ->
?enet_tol:float ->
?max_iter:int ->
?mode:[ `Cd | `Lars ] ->
?n_jobs:int ->
?verbose:int ->
?assume_centered:bool ->
unit ->
tSparse inverse covariance w/ cross-validated choice of the l1 penalty.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
.. versionchanged:: v0.20 GraphLassoCV has been renamed to GraphicalLassoCV
Parameters ---------- alphas : int or array-like of shape (n_alphas,), dtype=float, default=4 If an integer is given, it fixes the number of points on the grids of alpha to be used. If a list is given, it gives the grid to be used. See the notes in the class docstring for more details. Range is (0, inf] when floats given.
n_refinements : int, default=4 The number of times the grid is refined. Not used if explicit values of alphas are passed. Range is 1, inf).
cv : int, cross-validation generator or iterable, default=None
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:: 0.20
``cv`` default value if None changed from 3-fold to 5-fold.
tol : float, default=1e-4
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped. Range is (0, inf.
enet_tol : float, default=1e-4 The tolerance for the elastic net solver used to calculate the descent direction. This parameter controls the accuracy of the search direction for a given column update, not of the overall parameter estimate. Only used for mode='cd'. Range is (0, inf].
max_iter : int, default=100 Maximum number of iterations.
mode : 'cd', 'lars', default='cd' The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where number of features is greater than number of samples. Elsewhere prefer cd which is more numerically stable.
n_jobs : int, default=None number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
.. versionchanged:: v0.20 `n_jobs` default changed from 1 to None
verbose : bool, default=False If verbose is True, the objective function and duality gap are printed at each iteration.
assume_centered : bool, default=False If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation.
Attributes ---------- location_ : ndarray of shape (n_features,) Estimated location, i.e. the estimated mean.
covariance_ : ndarray of shape (n_features, n_features) Estimated covariance matrix.
precision_ : ndarray of shape (n_features, n_features) Estimated precision matrix (inverse covariance).
alpha_ : float Penalization parameter selected.
cv_alphas_ : list of shape (n_alphas,), dtype=float All penalization parameters explored.
grid_scores_ : ndarray of shape (n_alphas, n_folds) Log-likelihood score on left-out data across folds.
n_iter_ : int Number of iterations run for the optimal alpha.
Examples -------- >>> import numpy as np >>> from sklearn.covariance import GraphicalLassoCV >>> true_cov = np.array([0.8, 0.0, 0.2, 0.0],
... [0.0, 0.4, 0.0, 0.0],
... [0.2, 0.0, 0.3, 0.1],
... [0.0, 0.0, 0.1, 0.7]) >>> np.random.seed(0) >>> X = np.random.multivariate_normal(mean=0, 0, 0, 0, ... cov=true_cov, ... size=200) >>> cov = GraphicalLassoCV().fit(X) >>> np.around(cov.covariance_, decimals=3) array([0.816, 0.051, 0.22 , 0.017],
[0.051, 0.364, 0.018, 0.036],
[0.22 , 0.018, 0.322, 0.094],
[0.017, 0.036, 0.094, 0.69 ]) >>> np.around(cov.location_, decimals=3) array(0.073, 0.04 , 0.038, 0.143)
See Also -------- graphical_lasso, GraphicalLasso
Notes ----- The search for the optimal penalization parameter (alpha) is done on an iteratively refined grid: first the cross-validated scores on a grid are computed, then a new refined grid is centered around the maximum, and so on.
One of the challenges which is faced here is that the solvers can fail to converge to a well-conditioned estimate. The corresponding values of alpha then come out as missing values, but the optimum may be close to these missing values.
val error_norm :
?norm:[ `Frobenius | `Spectral ] ->
?scaling:bool ->
?squared:bool ->
comp_cov:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatComputes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
Parameters ---------- comp_cov : array-like of shape (n_features, n_features) The covariance to compare with.
norm : 'frobenius', 'spectral', default='frobenius' The type of norm used to compute the error. Available error types:
scaling : bool, default=True If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.
squared : bool, default=True Whether to compute the squared error norm or the error norm. If True (default), the squared error norm is returned. If False, the error norm is returned.
Returns ------- result : float The Mean Squared Error (in the sense of the Frobenius norm) between `self` and `comp_cov` covariance estimators.
val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> tFits the GraphicalLasso covariance model to X.
Parameters ---------- X : array-like of shape (n_samples, n_features) Data from which to compute the covariance estimate
y : Ignored Not used, present for API consistence purpose.
Returns ------- self : object
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.
Getter for the precision matrix.
Returns ------- precision_ : array-like of shape (n_features, n_features) The precision matrix associated to the current covariance object.
Computes the squared Mahalanobis distances of given observations.
Parameters ---------- X : array-like of shape (n_samples, n_features) The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit.
Returns ------- dist : ndarray of shape (n_samples,) Squared Mahalanobis distances of the observations.
val score :
?y:Py.Object.t ->
x_test:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatComputes the log-likelihood of a Gaussian data set with `self.covariance_` as an estimator of its covariance matrix.
Parameters ---------- X_test : array-like of shape (n_samples, n_features) Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is the number of features. X_test is assumed to be drawn from the same distribution than the data used in fit (including centering).
y : Ignored Not used, present for API consistence purpose.
Returns ------- res : float The likelihood of the data set with `self.covariance_` as an estimator of its covariance matrix.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.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.
Attribute location_: get value or raise Not_found if None.
Attribute covariance_: get value or raise Not_found if None.
Attribute covariance_: get value as an option.
Attribute precision_: get value or raise Not_found if None.
Attribute precision_: get value as an option.
val alpha_ : t -> floatAttribute alpha_: get value or raise Not_found if None.
val alpha_opt : t -> float optionAttribute alpha_: get value as an option.
val cv_alphas_ : t -> Py.Object.tAttribute cv_alphas_: get value or raise Not_found if None.
val cv_alphas_opt : t -> Py.Object.t optionAttribute cv_alphas_: get value as an option.
Attribute grid_scores_: get value or raise Not_found if None.
Attribute grid_scores_: get value as an option.
val n_iter_ : t -> intAttribute n_iter_: get value or raise Not_found if None.
val n_iter_opt : t -> int optionAttribute 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 : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.