MAINT Parameters validation for sklearn.covariance.log_likelihood (sc…

…ikit-learn#26539) Co-authored-by: Guillaume Lemaitre <[email protected]>
neurodata · Jun 29, 2023 · 315a04c · 315a04c
1 parent fbdc7b3
commit 315a04c
Show file tree

Hide file tree

Showing 2 changed files with 8 additions and 0 deletions.
diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py
@@ -23,6 +23,13 @@
 from ..utils.extmath import fast_logdet
 
 
+@validate_params(
+ {
+ "emp_cov": [np.ndarray],
+ "precision": [np.ndarray],
+ },
+ prefer_skip_nested_validation=True,
+)
 def log_likelihood(emp_cov, precision):
  """Compute the sample mean of the log_likelihood under a covariance model.
 

diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
@@ -139,6 +139,7 @@ def _check_function_param_validation(
  "sklearn.cluster.ward_tree",
  "sklearn.covariance.empirical_covariance",
  "sklearn.covariance.ledoit_wolf_shrinkage",
+ "sklearn.covariance.log_likelihood",
  "sklearn.covariance.shrunk_covariance",
  "sklearn.datasets.clear_data_home",
  "sklearn.datasets.dump_svmlight_file",