sklearn.covariance.GraphLasso¶
- class sklearn.covariance.GraphLasso(alpha=0.01, mode='cd', tol=0.0001, max_iter=100, verbose=False, assume_centered=False)¶
Sparse inverse covariance estimation with an l1-penalized estimator.
Parameters : alpha : positive float, optional
The regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance.
cov_init : 2D array (n_features, n_features), optional
The initial guess for the covariance.
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.
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.
verbose : boolean, optional
If verbose is True, the objective function and dual gap are plotted at each iteration.
See also
Attributes
covariance_ array-like, shape (n_features, n_features) Estimated covariance matrix precision_ array-like, shape (n_features, n_features) Estimated pseudo inverse matrix. Methods
error_norm(comp_cov[, norm, scaling, squared]) Computes the Mean Squared Error between two covariance estimators. fit(X[, y]) get_params([deep]) Get parameters for this estimator. get_precision() Getter for the precision matrix. mahalanobis(observations) Computes the Mahalanobis distances of given observations. score(X_test[, y]) Computes the log-likelihood of a Gaussian data set with set_params(**params) Set the parameters of this estimator. - __init__(alpha=0.01, mode='cd', tol=0.0001, max_iter=100, verbose=False, assume_centered=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. :
- 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.
- 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. Observations are assumed to be drawn from the same distribution than the data used in fit (including centering).
Returns : mahalanobis_distance : array, shape = [n_observations,]
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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :