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MAINT parameter validation for covariance.graphical_lasso #25911

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MAINT parameter validation for covariance.graphical_lasso #25911

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5baeb46
Add parameter validation to covariance.graphical_lasso.
genvalen Mar 20, 2023
3c8d9f5
Update comment location.
genvalen Mar 20, 2023
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25 changes: 20 additions & 5 deletions sklearn/covariance/_graph_lasso.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@
check_scalar,
)
from ..utils.parallel import delayed, Parallel
from ..utils._param_validation import Interval, StrOptions
from ..utils._param_validation import validate_params, Interval, StrOptions

# mypy error: Module 'sklearn.linear_model' has no attribute '_cd_fast'
from ..linear_model import _cd_fast as cd_fast # type: ignore
Expand Down Expand Up @@ -78,6 +78,21 @@ def alpha_max(emp_cov):


# The g-lasso algorithm
@validate_params(
{
"emp_cov": ["array-like"],
"alpha": [Real],
"cov_init": ["array-like", None],
"mode": [StrOptions({"cd", "lars"})],
"tol": [Interval(Real, 0, None, closed="right")],
"enet_tol": [Interval(Real, 0, None, closed="right")],
"max_iter": [Interval(Integral, 0, None, closed="left")],
"verbose": ["verbose"],
"return_costs": ["boolean"],
"eps": [Real],
"return_n_iter": ["boolean"],
}
)
def graphical_lasso(
emp_cov,
alpha,
Expand All @@ -101,15 +116,15 @@ def graphical_lasso(

Parameters
----------
emp_cov : ndarray of shape (n_features, n_features)
emp_cov : array-like of shape (n_features, n_features)
Empirical covariance from which to compute the covariance estimate.

alpha : float
The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.
Range is (0, inf].

cov_init : array of shape (n_features, n_features), default=None
cov_init : array-like of shape (n_features, n_features), default=None
The initial guess for the covariance. If None, then the empirical
covariance is used.

Expand Down Expand Up @@ -149,10 +164,10 @@ def graphical_lasso(

Returns
-------
covariance : ndarray of shape (n_features, n_features)
covariance : array-like of shape (n_features, n_features)
The estimated covariance matrix.

precision : ndarray of shape (n_features, n_features)
precision : array-like of shape (n_features, n_features)
The estimated (sparse) precision matrix.

costs : list of (objective, dual_gap) pairs
Expand Down
1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -117,6 +117,7 @@ def _check_function_param_validation(
"sklearn.cluster.cluster_optics_xi",
"sklearn.cluster.ward_tree",
"sklearn.covariance.empirical_covariance",
"sklearn.covariance.graphical_lasso",
"sklearn.covariance.shrunk_covariance",
"sklearn.datasets.dump_svmlight_file",
"sklearn.datasets.fetch_20newsgroups",
Expand Down