8.2.6. sklearn.covariance.GraphLassoCV¶
- class sklearn.covariance.GraphLassoCV(alphas=4, n_refinements=4, cv=None, tol=0.0001, max_iter=100, mode='cd', n_jobs=1, verbose=False)¶
Sparse inverse covariance w/ cross-validated choice of the l1 penality
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 time the grid is refined. Not used if explicit values of alphas are passed.
cv : crossvalidation generator, optional
see sklearn.cross_validation module. If None is passed, default to a 3-fold strategy
tol: positive float, optional :
The tolerance to declare convergence: if the dual gap goes below this value, iterations are stopped
max_iter: integer, optional :
The maximum number of iterations
mode: {‘cd’, ‘lars’} :
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.
n_jobs: int, optional :
number of jobs to run in parallel (default 1)
verbose: boolean, optional :
If verbose is True, the objective function and dual gap are print at each iteration
See also
Notes
The search for the optimal alpha is done on an iteratively refined grid: first the cross-validated scores on a grid are computed, then a new refined grid is center around the maximum...
One of the challenges that we have to face 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.
Attributes
covariance_ array-like, shape (n_features, n_features) Estimated covariance matrix precision_ array-like, shape (n_features, n_features) Estimated precision matrix (inverse covariance). alpha_: float Penalization parameter selected cv_alphas_: list of float All the penalization parameters explored cv_scores: 2D array (n_alphas, n_folds) The log-likelihood score on left-out data across the folds. Methods
error_norm(comp_cov[, norm, scaling, squared]) Computes the Mean Squared Error between two covariance estimators. fit(X[, y]) mahalanobis(observations) Computes the mahalanobis distances of given observations. score(X_test[, assume_centered]) 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 the estimator. - __init__(alphas=4, n_refinements=4, cv=None, tol=0.0001, max_iter=100, mode='cd', n_jobs=1, verbose=False)¶
- 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. :
- mahalanobis(observations)¶
Computes the mahalanobis distances of given observations.
The provided observations are assumed to be centered. One may want to center them using a location estimate first.
Parameters : observations: array-like, shape = [n_observations, n_features] :
The observations, the Mahalanobis distances of the which we compute.
Returns : mahalanobis_distance: array, shape = [n_observations,] :
Mahalanobis distances of the observations.
- score(X_test, assume_centered=False)¶
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.
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 the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :