MpjxSSKJrJr \"SS\55r\"SS\\55r\"SS\\55rg) )Protocolruntime_checkablec$\rSrSrSrSrSrSrg) _BaseCallbackz Protocol for the base callbacks.cg)aMethod called at the beginning of the fit method of the estimator. For auto-propagated callbacks, this method is called only once, before running the fit method of the outermost estimator. Parameters ---------- estimator : estimator instance The estimator calling this callback hook. context : `sklearn.callback.CallbackContext` instance Context of the corresponding task. This is usually the root context of the estimator but it can be an intermediate context if the estimator is a sub-estimator of a meta-estimator. Nself estimatorcontexts R/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/sklearn/callback/_base.pysetup_BaseCallback.setup cg)aMethod called after finishing the fit method of the estimator. For auto-propagated callbacks, this method is called only once, after finishing the fit method of the outermost estimator. Parameters ---------- estimator : estimator instance The estimator calling this callback hook. context : `sklearn.callback.CallbackContext` instance Context of the corresponding task. This is usually the root context of the estimator but it can be an intermediate context if the estimator is a sub-estimator of a meta-estimator. Nr r s rteardown_BaseCallback.teardownrrr N)__name__ __module__ __qualname____firstlineno____doc__rr__static_attributes__r rrrrs* " rrc@\rSrSrSrSSSSS.SjrSSSSS.SjrSrg) FitCallback.zMProtocol for the callbacks evaluated on tasks during the fit of an estimator.N)Xymetadatafitted_estimatorcg)apMethod called at the beginning of each fit task of the estimator. Parameters ---------- estimator : estimator instance The estimator calling this callback hook. context : `sklearn.callback.CallbackContext` instance Context of the corresponding task. X : array-like The training data at this task. y : array-like The training target values at this task. metadata : dict Training metadata at this task, e.g. sample weights. fitted_estimator : estimator instance A new instance of the estimator that is ready to predict, transform, etc ... as if fit had stopped at the beginning of this task. Nr r r r rr r!r"s ron_fit_task_beginFitCallback.on_fit_task_begin2rrcg)aMethod called at the end of each fit task of the estimator. Parameters ---------- estimator : estimator instance The estimator calling this callback hook. context : `sklearn.callback.CallbackContext` instance Context of the corresponding task. X : array-like The training data at this task. y : array-like The training target values at this task. metadata : dict Training metadata at this task, e.g. sample weights. fitted_estimator : estimator instance A new instance of the estimator that is ready to predict, transform, etc ... as if fit had stopped at the end of this task. Returns ------- stop : bool Whether or not to stop the current level of iterations at this task. Nr r$s ron_fit_task_endFitCallback.on_fit_task_endTrrr )rrrrrr%r(rr rrrr.s1W    N  % % rrc(\rSrSrSr\S5rSrg)AutoPropagatedCallback|zProtocol for the auto-propagated callbacks An auto-propagated callback is a callback that is meant to be set on a top-level estimator and that is automatically propagated to its sub-estimators (if any). cg)zThe maximum number of nested estimators at which the callback should be propagated. If set to None, the callback is propagated to sub-estimators at all nesting levels. Nr )r s rmax_propagation_depth,AutoPropagatedCallback.max_propagation_depthrrr N)rrrrrpropertyr.rr rrr+r+|s   rr+N)typingrrrrr+r rrr2sd/# H# # LJ -J J Z ]H  r