10000 DOC Ensures that sklearn.base.clone passes numpydoc validation by kkatottn · Pull Request #21557 · scikit-learn/scikit-learn · GitHub
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DOC Ensures that sklearn.base.clone passes numpydoc validation #21557

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Merged
merged 5 commits into from
Nov 22, 2021
Merged

DOC Ensures that sklearn.base.clone passes numpydoc validation #21557

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4f5beb9
sklearn clone numpydoc validation fix
kkatottn Nov 4, 2021
dc5e184
adjusting docstring and reverting earlier unrelated change
kkatottn Nov 5, 2021
3ec9c0a
Update sklearn/base.py
kkatottn Nov 15, 2021
22f2700
Update sklearn/base.py
kkatottn Nov 15, 2021
393eb27
Merge branch 'main' into numpydoc-clone-validation
kkatottn Nov 15, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -12,7 +12,6 @@
numpydoc_validation = pytest.importorskip("numpydoc.validate")

FUNCTION_DOCSTRING_IGNORE_LIST = [
"sklearn.base.clone",
"sklearn.cluster._affinity_propagation.affinity_propagation",
"sklearn.cluster._kmeans.kmeans_plusplus",
"sklearn.cluster._mean_shift.estimate_bandwidth",
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25 changes: 15 additions & 10 deletions sklearn/base.py
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Expand Up @@ -31,29 +31,34 @@


def clone(estimator, *, safe=True):
"""Constructs a new unfitted estimator with the same parameters.
"""Construct a new unfitted estimator with the same parameters.

Clone does a deep copy of the model in an estimator
without actually copying attached data. It yields a new estimator
without actually copying attached data. It returns a new estimator
with the same parameters that has not been fitted on any data.

If the estimator's `random_state` parameter is an integer (or if the
estimator doesn't have a `random_state` parameter), an *exact clone* is
returned: the clone and the original estimator will give the exact same
results. Otherwise, *statistical clone* is returned: the clone might
yield different results from the original estimator. More details can be
found in :ref:`randomness`.

Parameters
----------
estimator : {list, tuple, set} of estimator instance or a single \
estimator instance
The estimator or group of estimators to be cloned.

safe : bool, default=True
If safe is False, clone will fall back to a deep copy on objects
that are not estimators.

Returns
-------
estimator : object
The deep copy of the input, an estimator if input is an estimator.

Notes
-----
If the estimator's `random_state` parameter is an integer (or if the
estimator doesn't have a `random_state` parameter), an *exact clone* is
returned: the clone and the original estimator will give 62A3 the exact same
results. Otherwise, *statistical clone* is returned: the clone might
return different results from the original estimator. More details can be
found in :ref:`randomness`.
"""
estimator_type = type(estimator)
# XXX: not handling dictionaries
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