[MNT] Refactor .clone()

skbase/base/_base.py

@@ -63,7 +63,7 @@
 from skbase.base._pretty_printing._object_html_repr import _object_html_repr
 from skbase.base._tagmanager import _FlagManager
 
-__author__: List[str] = ["mloning", "RNKuhns", "fkiraly"]
+__author__: List[str] = ["fkiraly", "mloning", "RNKuhns", "tpvasconcelos"]
 __all__: List[str] = ["BaseEstimator", "BaseObject"]
 
 
@@ -157,113 +157,11 @@
         -----
         If successful, equal in value to ``type(self)(**self.get_params(deep=False))``.
         """
-        self_params = self.get_params(deep=False)
-        self_clone = self._clone(self)
-
-        # if checking the clone is turned off, return now
-        if not self.get_config()["check_clone"]:
-            return self_clone
-
-        from skbase.utils.deep_equals import deep_equals
-
-        # check that all attributes are written to the clone
-        for attrname in self_params.keys():
-            if not hasattr(self_clone, attrname):
-                raise RuntimeError(
-                    f"error in {self}.clone, __init__ must write all arguments "
-                    f"to self and not mutate them, but {attrname} was not found. "
-                    f"Please check __init__ of {self}."
-                )
-
-        clone_attrs = {attr: getattr(self_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 {self}.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}"
-            )
-
+        self_clone = _clone(self)
+        if self.get_config()["check_clone"]:
+            _check_clone(original=self, clone=self_clone)
         return self_clone
 
-    # copied from sklearn
-    def _clone(self, 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)
-        # XXX: not handling dictionaries
-        if estimator_type in (list, tuple, set, frozenset):
-            return estimator_type([self._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] = self._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
-
     @classmethod
     def _get_init_signature(cls):
         """Get class init signature.
@@ -1405,3 +1303,106 @@
             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)
+    # XXX: not handling dictionaries
+    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/tests/conftest.py

@@ -178,6 +178,10 @@
 SKBASE_FUNCTIONS_BY_MODULE = SKBASE_PUBLIC_FUNCTIONS_BY_MODULE.copy()
 SKBASE_FUNCTIONS_BY_MODULE.update(
     {
+        "skbase.base._base": (
+            "_clone",
+            "_check_clone",
+        ),
         "skbase.base._pretty_printing._object_html_repr": (
             "_get_visual_block",
             "_object_html_repr",