DOC Ensures that K/RadiusNeighborsRegressor passes numpydoc validation (#20378)

tomasrhughes · thomasjpfan · cmaureir · web-flow · commit 2480f5af2fe0 · 2021-07-20T19:48:22.000+02:00
Co-authored-by: Thomas J. Fan &lt;thomasjpfan@gmail.com&gt;
Co-authored-by: Cristián Maureira-Fredes &lt;cmaureir@users.noreply.github.com&gt;
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -106,7 +106,6 @@
     "RBFSampler",
     "RFE",
     "RadiusNeighborsClassifier",
-    "RadiusNeighborsRegressor",
     "RadiusNeighborsTransformer",
     "RandomTreesEmbedding",
     "RandomizedSearchCV",
diff --git a/sklearn/neighbors/_base.py b/sklearn/neighbors/_base.py
@@ -1,4 +1,4 @@
-"""Base and mixin classes for nearest neighbors"""
+"""Base and mixin classes for nearest neighbors."""
 # Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
 #          Fabian Pedregosa <fabian.pedregosa@inria.fr>
 #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
@@ -88,7 +88,7 @@ def _check_weights(weights):
 
 
 def _get_weights(dist, weights):
-    """Get the weights from an array of distances and a parameter ``weights``
+    """Get the weights from an array of distances and a parameter ``weights``.
 
     Parameters
     ----------
@@ -135,7 +135,7 @@ def _get_weights(dist, weights):
 
 
 def _is_sorted_by_data(graph):
-    """Returns whether the graph's non-zero entries are sorted by data
+    """Return whether the graph's non-zero entries are sorted by data.
 
     The non-zero entries are stored in graph.data and graph.indices.
     For each row (or sample), the non-zero entries can be either:
@@ -162,7 +162,7 @@ def _is_sorted_by_data(graph):
 
 
 def _check_precomputed(X):
-    """Check precomputed distance matrix
+    """Check precomputed distance matrix.
 
     If the precomputed distance matrix is sparse, it checks that the non-zero
     entries are sorted by distances. If not, the matrix is copied and sorted.
@@ -223,7 +223,7 @@ def _check_precomputed(X):
 
 
 def _kneighbors_from_graph(graph, n_neighbors, return_distance):
-    """Decompose a nearest neighbors sparse graph into distances and indices
+    """Decompose a nearest neighbors sparse graph into distances and indices.
 
     Parameters
     ----------
@@ -275,7 +275,7 @@ def extract(a):
 
 
 def _radius_neighbors_from_graph(graph, radius, return_distance):
-    """Decompose a nearest neighbors sparse graph into distances and indices
+    """Decompose a nearest neighbors sparse graph into distances and indices.
 
     Parameters
     ----------
@@ -589,7 +589,7 @@ def _pairwise(self):
 
 
 def _tree_query_parallel_helper(tree, *args, **kwargs):
-    """Helper for the Parallel calls in KNeighborsMixin.kneighbors
+    """Helper for the Parallel calls in KNeighborsMixin.kneighbors.
 
     The Cython method tree.query is not directly picklable by cloudpickle
     under PyPy.
@@ -598,10 +598,10 @@ def _tree_query_parallel_helper(tree, *args, **kwargs):
 
 
 class KNeighborsMixin:
-    """Mixin for k-neighbors searches"""
+    """Mixin for k-neighbors searches."""
 
     def _kneighbors_reduce_func(self, dist, start, n_neighbors, return_distance):
-        """Reduce a chunk of distances to the nearest neighbors
+        """Reduce a chunk of distances to the nearest neighbors.
 
         Callback to :func:`sklearn.metrics.pairwise.pairwise_distances_chunked`
 
@@ -851,7 +851,8 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode="connectivity"):
 
         See Also
         --------
-        NearestNeighbors.radius_neighbors_graph: Computes a graph of neighbors.
+        NearestNeighbors.radius_neighbors_graph : Compute the (weighted) graph
+            of Neighbors for points in X.
 
         Examples
         --------
@@ -901,7 +902,7 @@ def kneighbors_graph(self, X=None, n_neighbors=None, mode="connectivity"):
 
 
 def _tree_query_radius_parallel_helper(tree, *args, **kwargs):
-    """Helper for the Parallel calls in RadiusNeighborsMixin.radius_neighbors
+    """Helper for the Parallel calls in RadiusNeighborsMixin.radius_neighbors.
 
     The Cython method tree.query_radius is not directly picklable by
     cloudpickle under PyPy.
@@ -910,10 +911,10 @@ def _tree_query_radius_parallel_helper(tree, *args, **kwargs):
 
 
 class RadiusNeighborsMixin:
-    """Mixin for radius-based neighbors searches"""
+    """Mixin for radius-based neighbors searches."""
 
     def _radius_neighbors_reduce_func(self, dist, start, radius, return_distance):
-        """Reduce a chunk of distances to the nearest neighbors
+        """Reduce a chunk of distances to the nearest neighbors.
 
         Callback to :func:`sklearn.metrics.pairwise.pairwise_distances_chunked`
 
diff --git a/sklearn/neighbors/_regression.py b/sklearn/neighbors/_regression.py
@@ -1,4 +1,4 @@
-"""Nearest Neighbor Regression"""
+"""Nearest Neighbor Regression."""
 
 # Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
 #          Fabian Pedregosa <fabian.pedregosa@inria.fr>
@@ -263,7 +263,7 @@ class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBa
         queries.
 
     weights : {'uniform', 'distance'} or callable, default='uniform'
-        weight function used in prediction.  Possible values:
+        Weight function used in prediction.  Possible values:
 
         - 'uniform' : uniform weights.  All points in each neighborhood
           are weighted equally.
@@ -300,7 +300,7 @@ class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBa
         (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
 
     metric : str or callable, default='minkowski'
-        the distance metric to use for the tree.  The default metric is
+        The distance metric to use for the tree.  The default metric is
         minkowski, and with p=2 is equivalent to the standard Euclidean
         metric. See the documentation of :class:`DistanceMetric` for a
         list of available metrics.
@@ -338,6 +338,20 @@ class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBa
     n_samples_fit_ : int
         Number of samples in the fitted data.
 
+    See Also
+    --------
+    NearestNeighbors : Regression based on nearest neighbors.
+    KNeighborsRegressor : Regression based on k-nearest neighbors.
+    KNeighborsClassifier : Classifier based on the k-nearest neighbors.
+    RadiusNeighborsClassifier : Classifier based on neighbors within a given radius.
+
+    Notes
+    -----
+    See :ref:`Nearest Neighbors <neighbors>` in the online documentation
+    for a discussion of the choice of ``algorithm`` and ``leaf_size``.
+
+    https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
+
     Examples
     --------
     >>> X = [[0], [1], [2], [3]]
@@ -348,20 +362,6 @@ class RadiusNeighborsRegressor(RadiusNeighborsMixin, RegressorMixin, NeighborsBa
     RadiusNeighborsRegressor(...)
     >>> print(neigh.predict([[1.5]]))
     [0.5]
-
-    See Also
-    --------
-    NearestNeighbors
-    KNeighborsRegressor
-    KNeighborsClassifier
-    RadiusNeighborsClassifier
-
-    Notes
-    -----
-    See :ref:`Nearest Neighbors <neighbors>` in the online documentation
-    for a discussion of the choice of ``algorithm`` and ``leaf_size``.
-
-    https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
     """
 
     def __init__(
@@ -410,7 +410,7 @@ def fit(self, X, y):
         return self._fit(X, y)
 
     def predict(self, X):
-        """Predict the target for the provided data
+        """Predict the target for the provided data.
 
         Parameters
         ----------
