DOC Ensures that log_likelihood passes numpydoc validation (#21438)

genvalen · web-flow · commit c020c35212da · 2021-10-24T11:09:50.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -31,7 +31,6 @@
     "sklearn.cluster._spectral.spectral_clustering",
     "sklearn.compose._column_transformer.make_column_transformer",
     "sklearn.covariance._empirical_covariance.empirical_covariance",
-    "sklearn.covariance._empirical_covariance.log_likelihood",
     "sklearn.covariance._graph_lasso.graphical_lasso",
     "sklearn.covariance._robust_covariance.fast_mcd",
     "sklearn.covariance._shrunk_covariance.ledoit_wolf",
diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py
@@ -22,11 +22,11 @@
 
 
 def log_likelihood(emp_cov, precision):
-    """Computes the sample mean of the log_likelihood under a covariance model
+    """Compute the sample mean of the log_likelihood under a covariance model.
 
-    computes the empirical expected log-likelihood (accounting for the
-    normalization terms and scaling), allowing for universal comparison (beyond
-    this software package)
+    Computes the empirical expected log-likelihood, allowing for universal
+    comparison (beyond this software package), and accounts for normalization
+    terms and scaling.
 
     Parameters
     ----------
