Q3jK^ddlZddlmZddlmZmZddlmZGddZedZ dZ y) N)contextmanager)AutoPropagatedCallback FitCallback)CallbackContextc eZdZdZdZ ddZy)CallbackSupportMixinzMixin class to add callback support to an estimator. .. document the private method .. automethod:: _init_callback_context ctd|Ds td|rt||_|S|jj dd|S)aSet callbacks for the estimator. Parameters ---------- *callbacks : callback instances The callbacks to set. Returns ------- self : estimator instance The estimator instance itself. c3bK|]'}t|txrt|t )ywN) isinstancertype).0callbacks O/DATA/.local/lib/python3.12/site-packages/sklearn/callback/_callback_support.py z5CallbackSupportMixin.set_callbacks..s3 & x - Pj46P2P P%s-/z?callbacks must be instances following the FitCallback protocol._skl_callbacksN)all TypeErrorlistr__dict__pop)self callbackss r set_callbacksz"CallbackSupportMixin.set_callbackssa &   Q  "&y/D   MM  . 5 c4tj||||||_g|_t |dgD]W}t |t r t|dr |jj||j||jY|jS)aPInitialize the callback context for the estimator and setup its callbacks. This method should be called once, at the beginning of the fit method. It will only setup callbacks that are not propagated from a meta-estimator. Parameters ---------- task_name : str, default="fit" The name of the root task. task_id : int, default=0 Identifier for the root task. max_subtasks : int or None, default=0 The maximum number of subtasks that can be children of the root task. None means the maximum number of subtasks is not known in advance. 0 means it's a leaf. sequential_subtasks : bool, default=True Whether the root context has sequential subtasks. If True, children contexts of the root context, created via `subcontext`, will have automatically assigned consecutive integer task_ids starting from 0. Returns ------- callback_fit_ctx : CallbackContext The root callback context for the estimator. ) estimator task_nametask_id max_subtaskssequential_subtasksr_parent_callback_ctxrcontext) r_from_estimator_callback_fit_ctx_skl_callbacks_to_teardowngetattrr rhasattrappendsetup)rrrr r!rs r_init_callback_contextz+CallbackSupportMixin._init_callback_context0s@"1!@!@% 3 " +-'&6;H8%;<D"89//66x@t7M7MN<%%%rN)fitrrT)__name__ __module__ __qualname____doc__rr,rrrr s >OS5&rrc#HK dt|drWg}|jD] } |j||j"|`|`t |dk(r|d|r td|yy#t$r}|j |Yd}~id}~wwxYw#t|dr~g}|jD]G} |j||j"#t$r}|j |Yd}~Ad}~wwxYw|`|`t |dk(r|d|r td|wwxYww)aContext manager to manage callback lifecycle around estimator fit. The context manager is responsible for calling the callbacks `teardown` hook in a `try finally` block, which guarantees that callbacks teardown will always be called, whether the estimator's fit exits successfully or not. Parameters ---------- estimator : estimator instance The estimator being fitted. Yields ------ None. Nr&r#rz/The following callback teardown errors occurred)r)r'teardownr& Exceptionr*lenExceptionGroup)rteardown_errorsrexcs rcallback_management_contextr;hsj" 91 2 O%@@0%%"+Y5P5P&A4+?#q(%a(($E# 3!0#**3//0 91 2 O%@@0%%"+Y5P5P&!0#**3//0 A4+?#q(%a(($E# 3snD"BD"A,)D", B5B D" BD"D2CD C4 C/ *D/C4 4+DD"cBtjfd}|S)a6Decorator to run the method within a callback context manager. This decorator is responsible for calling the callbacks `teardown` hooks of callbacks in a `try finally` block, which guarantees that callbacks teardown will always be called, whether the estimator's method exits successfully or not. It will only teardown callbacks that are not propagated from a meta-estimator. Parameters ---------- method : method The method to decorate. Returns ------- decorated_method : method The decorated method. c^t|5|g|i|cdddS#1swYyxYwr )r;)rargskwargsmethods rcallback_managed_methodz/with_callbacks..callback_managed_methods) ( 3)5d5f54 3 3s #,) functoolswraps)r@rAs` rwith_callbacksrDs((__V66 #"r) rB contextlibrsklearn.callback._baserr"sklearn.callback._callback_contextrrr;rDr2rrrHs:%F>Z&Z&z%%P#r