DOC Ensures that roc_auc_score passes numpydoc validation (scikit-learn#23505)

madinak · Madina Kasymova · adrinjalali · ogrisel · commit 0f67a6c33b51 · 2022-07-11T15:15:23.000+02:00
* DOC Ensures that roc_auc_score passes numpydoc validation

* apply review

Co-authored-by: Madina Kasymova &lt;madina.kasymova@dida.do&gt;
Co-authored-by: Adrin Jalali &lt;adrin.jalali@gmail.com&gt;
diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py
@@ -372,7 +372,7 @@ def roc_auc_score(
     multi_class="raise",
     labels=None,
 ):
-    """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
+    """Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \
     from prediction scores.
 
     Note: this implementation can be used with binary, multiclass and
@@ -471,6 +471,16 @@ class scores must correspond to the order of ``labels``,
     Returns
     -------
     auc : float
+        Area Under the Curve score.
+
+    See Also
+    --------
+    average_precision_score : Area under the precision-recall curve.
+    roc_curve : Compute Receiver operating characteristic (ROC) curve.
+    RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
+        (ROC) curve given an estimator and some data.
+    RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
+        (ROC) curve given the true and predicted values.
 
     References
     ----------
@@ -493,15 +503,6 @@ class scores must correspond to the order of ``labels``,
             Machine Learning, 45(2), 171-186.
             <http://link.springer.com/article/10.1023/A:1010920819831>`_
 
-    See Also
-    --------
-    average_precision_score : Area under the precision-recall curve.
-    roc_curve : Compute Receiver operating characteristic (ROC) curve.
-    RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
-        (ROC) curve given an estimator and some data.
-    RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
-        (ROC) curve given the true and predicted values.
-
     Examples
     --------
     Binary case:
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -40,7 +40,6 @@
     "sklearn.metrics._plot.precision_recall_curve.plot_precision_recall_curve",
     "sklearn.metrics._ranking.coverage_error",
     "sklearn.metrics._ranking.dcg_score",
-    "sklearn.metrics._ranking.roc_auc_score",
     "sklearn.metrics._ranking.roc_curve",
     "sklearn.metrics._ranking.top_k_accuracy_score",
     "sklearn.metrics._regression.mean_pinball_loss",
