[ENH] refactor _clone to a plugin structure

skbase/base/_base.py

@@ -60,6 +60,7 @@ class name: BaseEstimator
 from typing import List
 
 from skbase._exceptions import NotFittedError
+from skbase.base._clone_base import _check_clone, _clone
 from skbase.base._pretty_printing._object_html_repr import _object_html_repr
 from skbase.base._tagmanager import _FlagManager
 
@@ -175,7 +176,7 @@ def clone(self):
         ------
         RuntimeError if the clone is non-conforming, due to faulty ``__init__``.
         """
-        self_clone = _clone(self)
+        self_clone = _clone(self, base_cls=BaseObject)
         if self.get_config()["check_clone"]:
             _check_clone(original=self, clone=self_clone)
         return self_clone
@@ -1653,107 +1654,3 @@ def _get_fitted_params(self):
             fitted parameters, keyed by names of fitted parameter
         """
         return self._get_fitted_params_default()
-
-
-# Adapted from sklearn's `_clone_parametrized()`
-def _clone(estimator, *, safe=True):
-    """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 returns a new estimator
-    with the same parameters that has not been fitted on any data.
-
-    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 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)
-    if estimator_type is dict:
-        return {k: _clone(v, safe=safe) for k, v in estimator.items()}
-    if estimator_type in (list, tuple, set, frozenset):
-        return estimator_type([_clone(e, safe=safe) for e in estimator])
-    elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
-        if not safe:
-            return deepcopy(estimator)
-        else:
-            if isinstance(estimator, type):
-                raise TypeError(
-                    "Cannot clone object. "
-                    + "You should provide an instance of "
-                    + "scikit-learn estimator instead of a class."
-                )
-            else:
-                raise TypeError(
-                    "Cannot clone object '%s' (type %s): "
-                    "it does not seem to be a scikit-learn "
-                    "estimator as it does not implement a "
-                    "'get_params' method." % (repr(estimator), type(estimator))
-                )
-
-    klass = estimator.__class__
-    new_object_params = estimator.get_params(deep=False)
-    for name, param in new_object_params.items():
-        new_object_params[name] = _clone(param, safe=False)
-    new_object = klass(**new_object_params)
-    params_set = new_object.get_params(deep=False)
-
-    # quick sanity check of the parameters of the clone
-    for name in new_object_params:
-        param1 = new_object_params[name]
-        param2 = params_set[name]
-        if param1 is not param2:
-            raise RuntimeError(
-                "Cannot clone object %s, as the constructor "
-                "either does not set or modifies parameter %s" % (estimator, name)
-            )
-
-    # This is an extension to the original sklearn implementation
-    if isinstance(estimator, BaseObject) and estimator.get_config()["clone_config"]:
-        new_object.set_config(**estimator.get_config())
-
-    return new_object
-
-
-def _check_clone(original, clone):
-    from skbase.utils.deep_equals import deep_equals
-
-    self_params = original.get_params(deep=False)
-
-    # check that all attributes are written to the clone
-    for attrname in self_params.keys():
-        if not hasattr(clone, attrname):
-            raise RuntimeError(
-                f"error in {original}.clone, __init__ must write all arguments "
-                f"to self and not mutate them, but {attrname} was not found. "
-                f"Please check __init__ of {original}."
-            )
-
-    clone_attrs = {attr: getattr(clone, attr) for attr in self_params.keys()}
-
-    # check equality of parameters post-clone and pre-clone
-    clone_attrs_valid, msg = deep_equals(self_params, clone_attrs, return_msg=True)
-    if not clone_attrs_valid:
-        raise RuntimeError(
-            f"error in {original}.clone, __init__ must write all arguments "
-            f"to self and not mutate them, but this is not the case. "
-            f"Error on equality check of arguments (x) vs parameters (y): {msg}"
-        )

skbase/base/_clone_base.py

@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+# copyright: skbase developers, BSD-3-Clause License (see LICENSE file)
+# Elements of BaseObject reuse code developed in scikit-learn. These elements
+# are copyrighted by the scikit-learn developers, BSD-3-Clause License. For
+# conditions see https://github.com/scikit-learn/scikit-learn/blob/main/COPYING
+"""Logic and plugins for cloning objects.
+
+This module contains logic for cloning objects:
+
+_clone(estimator, *, safe=True, plugins=None) - central entry point for cloning
+_check_clone(original, clone) - validation utility to check clones
+
+Default plugins for _clone are stored in _clone_plugins:
+
+DEFAULT_CLONE_PLUGINS - list with default plugins for cloning
+
+Each element of DEFAULT_CLONE_PLUGINS inherits from BaseCloner, with methods:
+
+* check(obj) -> boolean - fast checker whether plugin applies
+* clone(obj) -> type(obj) - method to clone obj
+"""
+__all__ = ["_clone", "_check_clone"]
+
+from skbase.base._clone_plugins import DEFAULT_CLONE_PLUGINS
+
+
+# Adapted from sklearn's `_clone_parametrized()`
+def _clone(estimator, *, safe=True, clone_plugins=None, base_cls=None):
+    """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 returns a new estimator
+    with the same parameters that has not been fitted on any data.
+
+    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.
+    clone_plugins : list of BaseCloner clone plugins, concrete descendant classes.
+        Must implement ``_check`` and ``_clone`` method, see ``BaseCloner`` interface.
+        If passed, will work through clone plugins in ``clone_plugins``
+        before working through ``DEFAULT_CLONE_PLUGINS``. To override
+        a cloner in ``DEAULT_CLONE_PLUGINS``, simply ensure a cloner with
+        the same ``_check`` logis is present in ``clone_plugins``.
+    base_cls : reference to BaseObject
+        Reference to the BaseObject class from skbase.base._base.
+        Present for easy reference, fast imports, and potential extensions.
+
+    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 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`.
+    """
+    # handle cloning plugins:
+    # if no plugins provided by user, work through the DEFAULT_CLONE_PLUGINS
+    # if provided by user, work through user provided plugins first, then defaults
+    if clone_plugins is not None:
+        all_plugins = clone_plugins.copy()
+        all_plugins.append(DEFAULT_CLONE_PLUGINS.copy())
+    else:
+        all_plugins = DEFAULT_CLONE_PLUGINS
+
+    for cloner_plugin in all_plugins:
+        cloner = cloner_plugin(safe=safe, clone_plugins=all_plugins, base_cls=base_cls)
+        # we clone with the first plugin in the list that:
+        # 1. claims it is applicable, via check
+        # 2. does not produce an Exception when cloning
+        if cloner.check(obj=estimator):
+            return cloner.clone(obj=estimator)
+
+    raise RuntimeError(
+        "Error in skbase _clone, catch-all plugin did not catch all "
+        "remaining cases. This is likely due to custom modification of the module."
+    )
+
+
+def _check_clone(original, clone):
+    """Check that clone is a valid clone of original.
+
+    Called from BaseObject.clone to validate the clone, if
+    the config flag check_clone is set to True.
+
+    Parameters
+    ----------
+    original : object
+        The original object.
+    clone : object
+        The cloned object.
+
+    Raises
+    ------
+    RuntimeError
+        If the clone is not a valid clone of the original.
+    """
+    from skbase.utils.deep_equals import deep_equals
+
+    self_params = original.get_params(deep=False)
+
+    # check that all attributes are written to the clone
+    for attrname in self_params.keys():
+        if not hasattr(clone, attrname):
+            raise RuntimeError(
+                f"error in {original}.clone, __init__ must write all arguments "
+                f"to self and not mutate them, but {attrname} was not found. "
+                f"Please check __init__ of {original}."
+            )
+
+    clone_attrs = {attr: getattr(clone, attr) for attr in self_params.keys()}
+
+    # check equality of parameters post-clone and pre-clone
+    clone_attrs_valid, msg = deep_equals(self_params, clone_attrs, return_msg=True)
+    if not clone_attrs_valid:
+        raise RuntimeError(
+            f"error in {original}.clone, __init__ must write all arguments "
+            f"to self and not mutate them, but this is not the case. "
+            f"Error on equality check of arguments (x) vs parameters (y): {msg}"
+        )