SLEP017: Clone Override Protocol with __sklearn_clone__

Author:Joel Nothman
Type:Standards Track


The ability to clone Scikit-learn estimators – removing any state due to previous fitting – is essential to ensuring estimator configurations are reusable across multiple instances in cross validation. A centralised implementation of sklearn.base.clone() regards an estimator’s constructor parameters as the state that should be copied. This proposal allows for an estimator class to implement custom cloning functionality with a __sklearn_clone__ method, which will default to the current clone behaviour.

Detailed description

Cloning estimators is one way that Scikit-learn ensures that there is no data leakage across data splits in cross-validation: by only copying an estimator’s configuration, with no data from previous fitting, the estimator must fit with a cold start. Cloning an estimator often also occurs prior to parallelism, ensuring that a minimal version of the estimator – without a large stored model – is serialised and distributed.

Cloning is currently governed by the implementation of sklearn.base.clone(), which recursively descends and copies the parameters of the passed object. For an estimator, it constructs a new instance of the estimator’s class, passing to it cloned versions of the parameter values returned by its get_params. It then performs some sanity checks to ensure that the values passed to the construtor are identical to what is then returned by the clone’s get_params.

The current equivalence between constructor parameters and what is cloned means that whenever an estimator or library developer deems it necessary to have further configuration of an estimator reproduced in a clone, they must include this configuration as a constructor parameter.

Cases where this need has been raised in Scikit-learn development include:

  • ensuring metadata requests are cloned with an estimator
  • ensuring parameter spaces are cloned with an estimator
  • building a simple wrapper that can “freeze” a pre-fitted estimator
  • allowing existing options for using prefitted models in ensembles to work under cloning

The current design also limits the ability for an estimator developer to define an exception to the sanity checks (see #15371).

This proposal empowers estimator developers to extend the base implementation of clone by providing a __sklearn_clone__ method, which clone will delegate to when available. The default implementaton will match current clone behaviour. It will be provided through BaseEstimator.__sklearn_clone__ but also provided for estimators not inheriting from BaseEstimator.

This shifts the paradigm from clone being a fixed operation that Scikit-learn must be able to perform on an estimator to clone being a behaviour that each Scikit-learn compatible estimator may implement.

Developers that define __sklearn_clone__ are expected to be responsible in maintaintaining the fundamental properties of cloning. Ordinarily, they can achieve this through use of super().__sklearn_clone__. Core behaviours, such as constructor parameters being preserved through clone operations, can be ensured through estimator checks.


Implementing this SLEP will require:

  1. Factoring out clone_parametrized from clone, being the portion of its implementation that handles objects with get_params.
  2. Modifying clone to call __sklearn_clone__ when available on an object with get_params, or clone_parametrized when not available.
  3. Defining BaseEstimator.__sklearn_clone__ to call clone_parametrized.
  4. Documenting the above.

Backward compatibility

No breakage.


Instead of allowing estimators to overwrite the entire clone process, the core clone process could be obligatory, with the ability for an estimator class to customise additional steps.

One API would allow for an estimator class to provide __sklearn__post_clone__(self, source) for operations in addition to the core cloning, or __sklearn__clone_attrs__ could be defined on a class to specify additional attributes that should be copied for that class and its descendants.

Alternative solutions include continuing to force developers into providing sometimes-awkward constructor parameters for any clonable material, and Scikit-learn core developers having the exceptional ability to extend the clone function as needed.


#5080 raised the proposal of polymorphism for clone as the right way to provide an object-oriented API, and as a way to enable the implementation of wrappers around estimators for model memoisation and freezing. The naming of __sklearn_clone__ was further proposed and discussed in #21838.

Making cloning more flexible either enables or simplifies the design and implementation of several features, including wrapping pre-fitted estimators, and providing estimator configuration through methods without adding new constructor arguments (e.g. through mixins).

Related issues include:

  • #6451, #8710, #19848: CalibratedClassifierCV with prefitted base estimator
  • #7382: VotingClassifier with prefitted base estimator
  • #16748: Stacking estimator with prefitted base estimator
  • #8370, #9464: generic estimator wrapper for model freezing
  • #5082: configuring parameter search spaces
  • #16079: configuring the routing of sample-aligned metadata
  • #16185: configuring selected parameters to not be deep-copied

Under the incumbent monolithic clone implementation, designing such additional per-estimator configuration requires resolving whether to:

  • adjust the monolithic clone to account for the new configuration attributes (an option only available to the Scikit-learn core developer team);
  • add constructor attributes for each new configuration option; or
  • not clone estimator configurations, and accept that some use cases may not be possible.

A more flexible cloning operation provides a simpler pattern for adding new configuration options through mixins. It should be noted that adding new capabilities to all estimators remains possible only through modifying the default __sklearn_clone__ implementation.

There are, however, notable concerns in relation to this proposal. Introducing a generic clone handler on each estimator gives a developer complete freedom to disregard existing conventions regarding parameter setting and construction in Scikit-learn. In this vein, objections to #5080 cited the notion that “clone has a simple contract,” and that “extension to it would open the door to violations of that contract” [2].

While these objections identify considerable risks, many public libraries include developers regularly working around Scikit-learn conventions and contracts, in part because developers are backed into a “design corner”, wherein it is not always obvious how to build an acceptable UX while adhering to established conventions; in this case, that everything to be cloned must go into __init__. This proposal paves a road for how developers can solve functionality UX limitations in the core library, rather than inviting custom workarounds.

References and Footnotes

[1]Each SLEP must either be explicitly labeled as placed in the public domain (see this SLEP as an example) or licensed under the Open Publication License.
[2]Gael Varoquaux’s comments on #5080 in 2015