skglm.GraphicalLasso

class skglm.GraphicalLasso(alpha=1.0, weights=None, algo='dual', max_iter=100, tol=1e-08, warm_start=False, inner_tol=0.0001, verbose=False, solver='barebones')

Block Coordinate Descent (BCD) solver for the Graphical Lasso (GLasso) problem.

Implements the sparse inverse covariance estimation algorithm from Friedman et al. (2008) and its weighted/primal variant from Mazumder et al. (2012), supporting both primal and dual coordinate descent.

The problem is defined as:

`\Theta^* = \underset{\Theta \succ 0}{\arg\min}\ -\log\det(\Theta) + \langle S, \Theta \rangle + \alpha \sum_{i \ne j} w_{ij} |\Theta_{ij}|`

where `S` is the empirical covariance matrix, `\Theta` the precision matrix, `\alpha` the regularisation strength, and `w_{ij}` optional positive weights (uniform when weights=None).

Parameters:

alphafloat, default=1.0

Regularization strength.

weightsndarray or None, default=None

Symmetric matrix of penalty weights, or None for uniform weights.

algo{‘dual’, ‘primal’}, default=’dual’

Algorithm variant to use.

max_iterint, default=100

Maximum number of coordinate descent iterations.

tolfloat, default=1e-8

Convergence tolerance.

warm_startbool, default=False

Use previous solution as initialization (primal only).

inner_tolfloat, default=1e-4

Tolerance for inner solver.

verbosebool, default=False

Print convergence info.

Attributes:

precision_ndarray

Estimated precision (inverse covariance) matrix.

covariance_ndarray

Estimated covariance matrix.

n_iter_int

Number of iterations run.

References

[1]

Friedman et al., Biostatistics, 2008. https://doi.org/10.1093/biostatistics/kxm045

[2]

Mazumder et al., Electron. J. Statist., 2012. https://doi.org/10.1214/12-EJS740

__init__(alpha=1.0, weights=None, algo='dual', max_iter=100, tol=1e-08, warm_start=False, inner_tol=0.0001, verbose=False, solver='barebones')

Methods

__init__([alpha, weights, algo, max_iter, ...])
fit(X[, y, mode])	Fit the GraphicalLasso model.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_fit_request(*[, mode])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.