DOC Ensures that SparseRandomProjection passes numpydoc validation (#21297)

baam25simo · glemaitre · web-flow · commit 948582d8e8ad · 2021-10-20T17:28:43.000+02:00
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
@@ -22,7 +22,6 @@
     "MultiTaskLassoCV",
     "OrthogonalMatchingPursuitCV",
     "PassiveAggressiveRegressor",
-    "SparseRandomProjection",
     "SpectralBiclustering",
     "SpectralCoclustering",
     "SpectralEmbedding",
diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
@@ -617,23 +617,10 @@ class SparseRandomProjection(BaseRandomProjection):
 
         .. versionadded:: 1.0
 
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from sklearn.random_projection import SparseRandomProjection
-    >>> rng = np.random.RandomState(42)
-    >>> X = rng.rand(100, 10000)
-    >>> transformer = SparseRandomProjection(random_state=rng)
-    >>> X_new = transformer.fit_transform(X)
-    >>> X_new.shape
-    (100, 3947)
-    >>> # very few components are non-zero
-    >>> np.mean(transformer.components_ != 0)
-    0.0100...
-
     See Also
     --------
-    GaussianRandomProjection
+    GaussianRandomProjection : Reduce dimensionality through Gaussian
+        random projection.
 
     References
     ----------
@@ -645,6 +632,19 @@ class SparseRandomProjection(BaseRandomProjection):
     .. [2] D. Achlioptas, 2001, "Database-friendly random projections",
            https://users.soe.ucsc.edu/~optas/papers/jl.pdf
 
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.random_projection import SparseRandomProjection
+    >>> rng = np.random.RandomState(42)
+    >>> X = rng.rand(100, 10000)
+    >>> transformer = SparseRandomProjection(random_state=rng)
+    >>> X_new = transformer.fit_transform(X)
+    >>> X_new.shape
+    (100, 3947)
+    >>> # very few components are non-zero
+    >>> np.mean(transformer.components_ != 0)
+    0.0100...
     """
 
     def __init__(
