scikit-learn · Anthony22-dev · Jan 12, 2023
diff --git a/sklearn/covariance/_graph_lasso.py b/sklearn/covariance/_graph_lasso.py
@@ -14,6 +14,7 @@
 import numpy as np
 from scipy import linalg
 from joblib import Parallel
+from ..utils._param_validation import Interval, HasMethods, StrOptions, validate_params

 from . import empirical_covariance, EmpiricalCovariance, log_likelihood

@@ -78,6 +79,21 @@ def alpha_max(emp_cov):
    return np.max(np.abs(A))


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

diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -122,6 +122,7 @@ def _check_function_param_validation(
    "sklearn.model_selection.train_test_split",
    "sklearn.random_projection.johnson_lindenstrauss_min_dim",
    "sklearn.svm.l1_min_c",
+    "covariance.graphical_lasso",
 ]