:jxddlmZmZeGddeZeGddeeZeGddeeZy) )Protocolruntime_checkableceZdZdZdZdZy) _BaseCallbackz Protocol for the base callbacks.cy)aqMethod 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 k/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/sklearn/callback/_base.pysetupz_BaseCallback.setup cy)anMethod 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. Nrr s r teardownz_BaseCallback.teardownrrN)__name__ __module__ __qualname____doc__rrrrr rrs* " rrc4eZdZdZddddddZddddddZy) FitCallbackzMProtocol for the callbacks evaluated on tasks during the fit of an estimator.N)Xymetadatafitted_estimatorcy)aMethod 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. Nrr r r rrrrs r on_fit_task_beginzFitCallback.on_fit_task_begin2rrcy)ajMethod 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. Nrrs r on_fit_task_endzFitCallback.on_fit_task_endTrr)rrrrrr!rrr rr.s,W    N  % rrc eZdZdZedZy)AutoPropagatedCallbackzProtocol 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). cy)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 r max_propagation_depthz,AutoPropagatedCallback.max_propagation_depthrrN)rrrrpropertyr%rrr r#r#|s   rr#N)typingrrrrr#rrr r(sd/# H# # LJ -J J Z ]H  r