DOC Ensures that TruncatedSVD passes numpydoc validation (scikit-learn#20946)

genvalen · web-flow · commit ba7e6fbd100d · 2021-09-06T11:29:36.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -84,7 +84,6 @@
     "StackingRegressor",
     "TheilSenRegressor",
     "TransformedTargetRegressor",
-    "TruncatedSVD",
     "TweedieRegressor",
     "VarianceThreshold",
 ]
diff --git a/sklearn/decomposition/_truncated_svd.py b/sklearn/decomposition/_truncated_svd.py
@@ -64,7 +64,7 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
         multiple function calls.
         See :term:`Glossary <random_state>`.
 
-    tol : float, default=0.
+    tol : float, default=0.0
         Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
         SVD solver.
 
@@ -96,6 +96,29 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
 
         .. versionadded:: 1.0
 
+    See Also
+    --------
+    DictionaryLearning : Find a dictionary that sparsely encodes data.
+    FactorAnalysis : A simple linear generative model with
+        Gaussian latent variables.
+    IncrementalPCA : Incremental principal components analysis.
+    KernelPCA : Kernel Principal component analysis.
+    NMF : Non-Negative Matrix Factorization.
+    PCA : Principal component analysis.
+
+    Notes
+    -----
+    SVD suffers from a problem called "sign indeterminacy", which means the
+    sign of the ``components_`` and the output from transform depend on the
+    algorithm and random state. To work around this, fit instances of this
+    class to data once, then keep the instance around to do transformations.
+
+    References
+    ----------
+    Finding structure with randomness: Stochastic algorithms for constructing
+    approximate matrix decompositions
+    Halko, et al., 2009 (arXiv:909) https://arxiv.org/pdf/0909.4061.pdf
+
     Examples
     --------
     >>> from sklearn.decomposition import TruncatedSVD
@@ -114,23 +137,6 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
     0.2102...
     >>> print(svd.singular_values_)
     [35.2410...  4.5981...   4.5420...  4.4486...  4.3288...]
-
-    See Also
-    --------
-    PCA
-
-    References
-    ----------
-    Finding structure with randomness: Stochastic algorithms for constructing
-    approximate matrix decompositions
-    Halko, et al., 2009 (arXiv:909) https://arxiv.org/pdf/0909.4061.pdf
-
-    Notes
-    -----
-    SVD suffers from a problem called "sign indeterminacy", which means the
-    sign of the ``components_`` and the output from transform depend on the
-    algorithm and random state. To work around this, fit instances of this
-    class to data once, then keep the instance around to do transformations.
     """
 
     def __init__(
@@ -157,6 +163,7 @@ def fit(self, X, y=None):
             Training data.
 
         y : Ignored
+            Not used, present here for API consistency by convention.
 
         Returns
         -------
@@ -175,6 +182,7 @@ def fit_transform(self, X, y=None):
             Training data.
 
         y : Ignored
+            Not used, present here for API consistency by convention.
 
         Returns
         -------

Original file line number	Diff line number	Diff line change
`@@ -84,7 +84,6 @@`
`84`	`84`	`"StackingRegressor",`
`85`	`85`	`"TheilSenRegressor",`
`86`	`86`	`"TransformedTargetRegressor",`
`87`		`- "TruncatedSVD",`
`88`	`87`	`"TweedieRegressor",`
`89`	`88`	`"VarianceThreshold",`
`90`	`89`	`]`