sklearn.covariance.GraphLassoCV

Warning

DEPRECATED

class sklearn.covariance.GraphLassoCV(*args, **kwargs)

Sparse inverse covariance w/ cross-validated choice of the l1 penalty.

See glossary entry for cross-validation estimator.

This class implements the Graphical Lasso algorithm.

Read more in the User Guide.

Parameters:

alphas : integer, or list positive float, optional

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.

n_refinements : strictly positive integer

The number of times the grid is refined. Not used if explicit values of alphas are passed.

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 3-fold cross-validation,
• integer, to specify the number of folds.
• CV splitter,
• An iterable yielding (train, test) splits as arrays of indices.

For integer/None inputs KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

Changed in version 0.20: cv default value if None will change from 3-fold to 5-fold in v0.22.

tol : positive float, optional

The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped.

enet_tol : positive float, optional

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’.

max_iter : integer, optional

Maximum number of iterations.

mode : {‘cd’, ‘lars’}

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 or None, optional (default=None)

number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verbose : boolean, optional

If verbose is True, the objective function and duality gap are printed at each iteration.

assume_centered : Boolean

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:

covariance_ : numpy.ndarray, shape (n_features, n_features)

Estimated covariance matrix.

precision_ : numpy.ndarray, shape (n_features, n_features)

Estimated precision matrix (inverse covariance).

alpha_ : float

Penalization parameter selected.

cv_alphas_ : list of float

All penalization parameters explored.

grid_scores_ : 2D numpy.ndarray (n_alphas, n_folds)

Log-likelihood score on left-out data across folds.

n_iter_ : int

Number of iterations run for the optimal alpha.

See also

graph_lasso, GraphLasso

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.

Methods

error_norm(comp_cov[, norm, scaling, squared])	Computes the Mean Squared Error between two covariance estimators.
fit(X[, y])	Fits the GraphicalLasso covariance model to X.
get_params([deep])	Get parameters for this estimator.
get_precision()	Getter for the precision matrix.
mahalanobis(X)	Computes the squared Mahalanobis distances of given observations.
score(X_test[, y])	Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.
set_params(**params)	Set the parameters of this estimator.

__init__(*args, **kwargs)

DEPRECATED: The ‘GraphLassoCV’ was renamed to ‘GraphicalLassoCV’ in version 0.20 and will be removed in 0.22.

error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)

Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).

Parameters:

comp_cov : array-like, shape = [n_features, n_features]

The covariance to compare with.

norm : str

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

If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.

squared : bool

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:

The Mean Squared Error (in the sense of the Frobenius norm) between

`self` and `comp_cov` covariance estimators.

fit(X, y=None)

Fits the GraphicalLasso covariance model to X.

Parameters:

X : ndarray, shape (n_samples, n_features)

Data from which to compute the covariance estimate

y : (ignored)

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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.

get_precision()

Getter for the precision matrix.

Returns:

precision_ : array-like

The precision matrix associated to the current covariance object.

grid_scores

DEPRECATED: Attribute grid_scores was deprecated in version 0.19 and will be removed in 0.21. Use grid_scores_ instead

mahalanobis(X)

Computes the squared Mahalanobis distances of given observations.

Parameters:

X : array-like, 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 : array, shape = [n_samples,]

Squared Mahalanobis distances of the observations.

score(X_test, y=None)

Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix.

Parameters:

X_test : array-like, 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

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.

set_params(**params)

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.

Returns:

self