type t = [ `BaseEstimator | `GraphicalLasso | `Object ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?alpha:float ->
?mode:[ `Cd | `Lars ] ->
?tol:float ->
?enet_tol:float ->
?max_iter:int ->
?verbose:int ->
?assume_centered:bool ->
unit ->
tSparse inverse covariance estimation with an l1-penalized estimator.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
.. versionchanged:: v0.20 GraphLasso has been renamed to GraphicalLasso
Parameters ---------- alpha : float, default=0.01 The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance. Range is (0, inf].
mode : 'cd', 'lars', default='cd' The Lasso solver to use: coordinate descent or LARS. Use LARS for very sparse underlying graphs, where p > n. Elsewhere prefer cd which is more numerically stable.
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 The maximum number of iterations.
verbose : bool, default=False If verbose is True, the objective function and dual gap are plotted 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 pseudo inverse matrix.
n_iter_ : int Number of iterations run.
Examples -------- >>> import numpy as np >>> from sklearn.covariance import GraphicalLasso >>> 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 = GraphicalLasso().fit(X) >>> np.around(cov.covariance_, decimals=3) array([0.816, 0.049, 0.218, 0.019],
[0.049, 0.364, 0.017, 0.034],
[0.218, 0.017, 0.322, 0.093],
[0.019, 0.034, 0.093, 0.69 ]) >>> np.around(cov.location_, decimals=3) array(0.073, 0.04 , 0.038, 0.143)
See Also -------- graphical_lasso, GraphicalLassoCV
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 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 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.