From d6e24ee79fc4d7119b714b16e988c29b2fcbeb95 Mon Sep 17 00:00:00 2001
From: Gabriel Stefanini Vicente <gabrielusvicente@gmail.com>
Date: Sat, 23 Oct 2021 07:00:25 -0400
Subject: [PATCH] DOC Ensures that LabelSpreading passes numpydoc validation
 (#21414)

---
 maint_tools/test_docstrings.py                |  1 -
 sklearn/semi_supervised/_label_propagation.py | 22 +++++++++----------
 2 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
index e855a1fde99b2..5ba1ed2b2c041 100644
--- a/maint_tools/test_docstrings.py
+++ b/maint_tools/test_docstrings.py
@@ -9,7 +9,6 @@
 
 # List of modules ignored when checking for numpydoc validation.
 DOCSTRING_IGNORE_LIST = [
-    "LabelSpreading",
     "MultiTaskElasticNetCV",
     "SpectralCoclustering",
     "SpectralEmbedding",
diff --git a/sklearn/semi_supervised/_label_propagation.py b/sklearn/semi_supervised/_label_propagation.py
index ef4226558fbcb..1e3684797ebe6 100644
--- a/sklearn/semi_supervised/_label_propagation.py
+++ b/sklearn/semi_supervised/_label_propagation.py
@@ -476,7 +476,7 @@ def fit(self, X, y):
 
 
 class LabelSpreading(BaseLabelPropagation):
-    """LabelSpreading model for semi-supervised learning
+    """LabelSpreading model for semi-supervised learning.
 
     This model is similar to the basic Label Propagation algorithm,
     but uses affinity matrix based on the normalized graph Laplacian
@@ -546,6 +546,16 @@ class LabelSpreading(BaseLabelPropagation):
     n_iter_ : int
         Number of iterations run.
 
+    See Also
+    --------
+    LabelPropagation : Unregularized graph based semi-supervised learning.
+
+    References
+    ----------
+    Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
+    Bernhard Schoelkopf. Learning with local and global consistency (2004)
+    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219
+
     Examples
     --------
     >>> import numpy as np
@@ -559,16 +569,6 @@ class LabelSpreading(BaseLabelPropagation):
     >>> labels[random_unlabeled_points] = -1
     >>> label_prop_model.fit(iris.data, labels)
     LabelSpreading(...)
-
-    References
-    ----------
-    Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston,
-    Bernhard Schoelkopf. Learning with local and global consistency (2004)
-    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219
-
-    See Also
-    --------
-    LabelPropagation : Unregularized graph based semi-supervised learning.
     """
 
     _variant = "spreading"