package sklearn

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type tag = [
  1. | `GraphicalLasso
]
type t = [ `BaseEstimator | `GraphicalLasso | `Object ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val create : ?alpha:float -> ?mode:[ `Cd | `Lars ] -> ?tol:float -> ?enet_tol:float -> ?max_iter:int -> ?verbose:int -> ?assume_centered:bool -> unit -> t

Sparse 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 -> float

Computes 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:

  • 'frobenius' (default): sqrt(tr(A^t.A))
  • 'spectral': sqrt(max(eigenvalues(A^t.A)) where A is the error ``(comp_cov - self.covariance_)``.

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 -> t

Fits 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

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val get_precision : [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Getter for the precision matrix.

Returns ------- precision_ : array-like of shape (n_features, n_features) The precision matrix associated to the current covariance object.

val mahalanobis : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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 -> float

Computes 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 -> t

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.

val location_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute location_: get value or raise Not_found if None.

val location_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute location_: get value as an option.

val covariance_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute covariance_: get value or raise Not_found if None.

val covariance_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute covariance_: get value as an option.

val precision_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute precision_: get value or raise Not_found if None.

val precision_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute precision_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

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