DOC Docstring validation in Pipeline estimator
@@ -28,7 +28,8 @@ __all__ = ['Pipeline', 'FeatureUnion', 'make_pipeline', 'make_union']
class Pipeline(_BaseComposition):
- """Pipeline of transforms with a final estimator.
+ """
+ Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be 'transforms', that is, they
@@ -63,7 +64,7 @@ class Pipeline(_BaseComposition):
inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.
- verbose : boolean, optional
+ verbose : bool, default=False
If True, the time elapsed while fitting each step will be printed as it
is completed.
@@ -73,9 +74,9 @@ class Pipeline(_BaseComposition):
Read-only attribute to access any step parameter by user given name.
Keys are step names and values are steps parameters.
- See also
+ See Also
--------
- sklearn.pipeline.make_pipeline : convenience function for simplified
+ sklearn.pipeline.make_pipeline : Convenience function for simplified
pipeline construction.
Examples
@@ -671,11 +672,11 @@ def make_pipeline(*steps, **kwargs):
inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.
- verbose : boolean, optional
+ verbose : boolean, default=False
If True, the time elapsed while fitting each step will be printed as it
is completed.
- See also
+ See Also
--------
sklearn.pipeline.Pipeline : Class for creating a pipeline of
transforms with a final estimator.
@@ -782,9 +783,9 @@ class FeatureUnion(TransformerMixin, _BaseComposition):
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
- See also
+ See Also
--------
- sklearn.pipeline.make_union : convenience function for simplified
+ sklearn.pipeline.make_union : Convenience function for simplified
feature union construction.
Examples
@@ -994,7 +995,8 @@ class FeatureUnion(TransformerMixin, _BaseComposition):
def make_union(*transformers, **kwargs):
- """Construct a FeatureUnion from the given transformers.
+ """
+ Construct a FeatureUnion from the given transformers.
This is a shorthand for the FeatureUnion constructor; it does not require,
and does not permit, naming the transformers. Instead, they will be given
@@ -1018,7 +1020,7 @@ def make_union(*transformers, **kwargs):
-------
f : FeatureUnion
- See also
+ See Also
--------
sklearn.pipeline.FeatureUnion : Class for concatenating the results
of multiple transformer objects.
