From bfc68a9c6abd70945d7a3d37532acb3042d904b5 Mon Sep 17 00:00:00 2001
From: Gabriel Stefanini Vicente <gabrielusvicente@gmail.com>
Date: Fri, 22 Oct 2021 15:45:23 -0400
Subject: [PATCH] DOC Ensures that PassiveAggressiveRegressor passes numpydoc
 validation

---
 maint_tools/test_docstrings.py              |  1 -
 sklearn/linear_model/_passive_aggressive.py | 27 ++++++++++-----------
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index 3afc2b9e5cc18..a404077988aac 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -16,7 +16,6 @@
     "LabelSpreading",
     "MultiTaskElasticNetCV",
     "OrthogonalMatchingPursuitCV",
-    "PassiveAggressiveRegressor",
     "SpectralCoclustering",
     "SpectralEmbedding",
     "StackingRegressor",
diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py
index fc5286c235a70..65f754ba35f55 100644
--- a/sklearn/linear_model/_passive_aggressive.py
+++ b/sklearn/linear_model/_passive_aggressive.py
@@ -301,7 +301,7 @@ def fit(self, X, y, coef_init=None, intercept_init=None):
 
 
 class PassiveAggressiveRegressor(BaseSGDRegressor):
-    """Passive Aggressive Regressor
+    """Passive Aggressive Regressor.
 
     Read more in the :ref:`User Guide <passive_aggressive>`.
 
@@ -352,7 +352,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
     shuffle : bool, default=True
         Whether or not the training data should be shuffled after each epoch.
 
-    verbose : integer, default=0
+    verbose : int, default=0
         The verbosity level.
 
     loss : str, default="epsilon_insensitive"
@@ -416,6 +416,17 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
         Number of weight updates performed during training.
         Same as ``(n_iter_ * n_samples)``.
 
+    See Also
+    --------
+    SGDRegressor : Linear model fitted by minimizing a regularized
+        empirical loss with SGD.
+
+    References
+    ----------
+    Online Passive-Aggressive Algorithms
+    <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
+    K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).
+
     Examples
     --------
     >>> from sklearn.linear_model import PassiveAggressiveRegressor
@@ -432,18 +443,6 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
     [-0.02306214]
     >>> print(regr.predict([[0, 0, 0, 0]]))
     [-0.02306214]
-
-    See Also
-    --------
-    SGDRegressor : Linear model fitted by minimizing a regularized
-        empirical loss with SGD.
-
-    References
-    ----------
-    Online Passive-Aggressive Algorithms
-    <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
-    K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).
-
     """
 
     def __init__(