8000 DOC document that last step is never cached in pipeline by windiana42 · Pull Request #25995 · scikit-learn/scikit-learn · GitHub
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Merged
merged 4 commits into from
Mar 28, 2023
Merged

DOC document that last step is never cached in pipeline #25995

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55a4bfd
#23112: fix documentation last step is not cached
windiana42 Mar 28, 2023
71854e6
Merge remote-tracking branch 'upstream/main' into fix_doc_23112
windiana42 Mar 28, 2023
307a2bd
Update doc/modules/compose.rst
windiana42 Mar 28, 2023
099a7e3
suggestion: emphasize on last step transformer
windiana42 Mar 28, 2023
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2 changes: 1 addition & 1 deletion doc/modules/compose.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -198,7 +198,7 @@ after calling ``fit``.
This feature is used to avoid computing the fit transformers within a pipeline
if the parameters and input data are identical. A typical example is the case of
a grid search in which the transformers can be fitted only once and reused for
each configuration.
each configuration. The last step will never be cached, even if it is a transformer.

The parameter ``memory`` is needed in order to cache the transformers.
``memory`` can be either a string containing the directory where to cache the
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28 changes: 14 additions & 14 deletions sklearn/pipeline.py
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Expand Up @@ -80,13 +80,13 @@ class Pipeline(_BaseComposition):
estimator.

memory : str or object with the joblib.Memory interface, default=None
Used to cache the fitted transformers of the pipeline. By default,
no caching is performed. If a string is given, it is the path to
the caching directory. Enabling caching triggers a clone of
the transformers before fitting. Therefore, the transformer
instance given to the pipeline cannot be inspected
directly. Use the attribute ``named_steps`` or ``steps`` to
inspect estimators within the pipeline. Caching the
Used to cache the fitted transformers of the pipeline. The last step
will never be cached, even if it is a transformer. By default, no
caching is performed. If a string is given, it is the path to the
caching directory. Enabling caching triggers a clone of the transformers
before fitting. Therefore, the transformer instance given to the
pipeline cannot be inspected directly. Use the attribute ``named_steps``
or ``steps`` to inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.

verbose : bool, default=False
Expand Down Expand Up @@ -858,13 +858,13 @@ def make_pipeline(*steps, memory=None, verbose=False):
List of the scikit-learn estimators that are chained together.

memory : str or object with the joblib.Memory interface, default=None
Used to cache the fitted transformers of the pipeline. By default,
no caching is performed. If a string is given, it is the path to
the caching directory. Enabling caching triggers a clone of
the transformers before fitting. Therefore, the transformer
instance given to the pipeline cannot be inspected
directly. Use the attribute ``named_steps`` or ``steps`` to
inspect estimators within the pipeline. Caching the
Used to cache the fitted transformers of the pipeline. The last step
will never be cached, even if it is a transformer. By default, no
caching is performed. If a string is given, it is the path to the
caching directory. Enabling caching triggers a clone of the transformers
before fitting. Therefore, the transformer instance given to the
pipeline cannot be inspected directly. Use the attribute ``named_steps``
or ``steps`` to inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming.

verbose : bool, default=False
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