qtjl)dZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZmZddlmZddlmZdd lmZdd lmZ dd Zdd ZGd de e eZdZGdde eeZdS)z)Base class for ensemble-based estimators.)ABCMetaabstractmethodN)effective_n_jobs) BaseEstimatorMetaEstimatorMixinclone is_classifier is_regressor)Bunchcheck_random_state)get_tags)_print_elapsed_time)_routing_enabled)_BaseCompositionctsd|vr t||5||||ddddn #1swxYwYn#t$rD}dt |vr-td|jj|d}~wwxYwt||5|j||fi|dddn #1swxYwY|S)z7Private function used to fit an estimator within a job. sample_weight)rNz+unexpected keyword argument 'sample_weight'z8Underlying estimator {} does not support sample weights.)rrfit TypeErrorstrformat __class____name__) estimatorXy fit_paramsmessage_clsnamemessageexcs T/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/ensemble/_base.py_fit_single_estimatorr!s   ./Z"?"? $_g>> O O a*_2M NNN O O O O O O O O O O O O O O O   <CHHNUU!+4   !' : : . . IM!Q - -* - - - . . . . . . . . . . . . . . . sLAA AAAAA B)%?B$$B)<CCCc>t|}i}t|dD]V}|dks|dr9|t jt jj||<W|r|j di|dSdS)aSet fixed random_state parameters for an estimator. Finds all parameters ending ``random_state`` and sets them to integers derived from ``random_state``. Parameters ---------- estimator : estimator supporting get/set_params Estimator with potential randomness managed by random_state parameters. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the generation of the random integers. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Notes ----- This does not necessarily set *all* ``random_state`` attributes that control an estimator's randomness, only those accessible through ``estimator.get_params()``. ``random_state``s not controlled include those belonging to: * cross-validation splitters * ``scipy.stats`` rvs Tdeep random_state__random_stateN) r sorted get_paramsendswithrandintnpiinfoint32max set_params)rr%to_setkeys r _set_random_statesr31s8&l33L Fi***5566GG . CLL1A$B$B &..rx/A/A/EFFF3K ' &&v&&&&&''cfeZdZdZe d deddZd dZd dZd Z d Z d Z dS) BaseEnsembleaBase class for all ensemble classes. Warning: This class should not be used directly. Use derived classes instead. Parameters ---------- estimator : object The base estimator from which the ensemble is built. n_estimators : int, default=10 The number of estimators in the ensemble. estimator_params : list of str, default=tuple() The list of attributes to use as parameters when instantiating a new base estimator. If none are given, default parameters are used. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. estimators_ : list of estimators The collection of fitted base estimators. N ) n_estimatorsestimator_paramsc0||_||_||_dSN)rr8r9)selfrr8r9s r __init__zBaseEnsemble.__init__rs!#( 0r4c>|j|j|_dS||_dS)zMCheck the base estimator. Sets the `estimator_` attributes. N)r estimator_)r<defaults r _validate_estimatorz BaseEnsemble._validate_estimators$ > %"nDOOO%DOOOr4Tctj}|jdifdjD|t |||rj||S)zMake and configure a copy of the `estimator_` attribute. Warning: This method should be used to properly instantiate new sub-estimators. c2i|]}|t|Sr')getattr).0pr<s r z0BaseEnsemble._make_estimator..s%SSS74#3#3SSSr4Nr')rr?r0r9r3 estimators_append)r<rIr%rs` r _make_estimatorzBaseEnsemble._make_estimators $/**  TTSSSST=RSSSTTT  # y, 7 7 7  /   # #I . . .r4c*t|jS)z0Return the number of estimators in the ensemble.)lenrHr<s r __len__zBaseEnsemble.__len__s4#$$$r4c|j|S)z.Return the index'th estimator in the ensemble.)rH)r<indexs r __getitem__zBaseEnsemble.__getitem__s&&r4c*t|jS)z0Return iterator over estimators in the ensemble.)iterrHrMs r __iter__zBaseEnsemble.__iter__sD$%%%r4r;)TN) r __module__ __qualname____doc__rtupler=rArJrNrQrTr'r4r r6r6Ws4 1 1 1 1 1^ 1 &&&&"%%%'''&&&&&r4r6) metaclassc&tt||}tj|||zt}|d||zxxdz cc<tj|}||dg|zfS)z;Private function used to partition estimators between jobs.)dtypeNr)minrr,fullintcumsumtolist)r8n_jobsn_estimators_per_jobstartss r _partition_estimatorsres!&))< 8 8F76<6+AMMM0<&00111Q6111 Y+ , ,F '..001# 2G GGr4cjeZdZdZedZedZdZfdZ d fd Z fdZ xZ S) _BaseHeterogeneousEnsembleaBase class for heterogeneous ensemble of learners. Parameters ---------- estimators : list of (str, estimator) tuples The ensemble of estimators to use in the ensemble. Each element of the list is defined as a tuple of string (i.e. name of the estimator) and an estimator instance. An estimator can be set to `'drop'` using `set_params`. Attributes ---------- estimators_ : list of estimators The elements of the estimators parameter, having been fitted on the training data. If an estimator has been set to `'drop'`, it will not appear in `estimators_`. c>tdit|jS)zDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` r')r dict estimatorsrMs r named_estimatorsz+_BaseHeterogeneousEnsemble.named_estimatorss"--tDO,,---r4c||_dSr;rj)r<rjs r r=z#_BaseHeterogeneousEnsemble.__init__s $r4c t|jdkstd|jDstdt |j\}}||t d|D}|stdt|rtnt}|D]M}|dkrE||s:td |j j |j ddN||fS) Nrc3K|];}t|ttfot|dtVzB_BaseHeterogeneousEnsemble._validate_estimators..sW0 0  teT] + + H 47C0H0H0 0 0 0 0 0 r4zfInvalid 'estimators' attribute, 'estimators' should be a non-empty list of (string, estimator) tuples.c3"K|] }|dkV dS)dropNr'rEests r rszB_BaseHeterogeneousEnsemble._validate_estimators..s&@@cC6M@@@@@@r4zHAll estimators are dropped. At least one is required to be an estimator.ruz The estimator {} should be a {}.) rLrjall ValueErrorzip_validate_namesanyr r rrr)r<namesrj has_estimatoris_estimator_typerws r _validate_estimatorsz/_BaseHeterogeneousEnsemble._validate_estimatorssK t  1 $ $C0 0 0 0 0 - - $@  1z U###@@Z@@@@@  &  .;4-@-@RMMl  Cf}}%6%6s%;%;} 6== .0A0J1220N j  r4c :tjdi||S)a Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with `get_params()`. Note that you can directly set the parameters of the estimators contained in `estimators`. Parameters ---------- **params : keyword arguments Specific parameters using e.g. `set_params(parameter_name=new_value)`. In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to 'drop'. Returns ------- self : object Estimator instance. rjrm)super _set_params)r<paramsrs r r0z%_BaseHeterogeneousEnsemble.set_paramss', 33F333 r4TcJtd|S)a< Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the `estimators` parameter. Parameters ---------- deep : bool, default=True Setting it to True gets the various estimators and the parameters of the estimators as well. Returns ------- params : dict Parameter and estimator names mapped to their values or parameter names mapped to their values. rjr#)r _get_params)r<r$rs r r)z%_BaseHeterogeneousEnsemble.get_paramss"&ww""_BaseHeterogeneousEnsemble.__sklearn_tags__..,s[,,:=Q69I9IQ  +55t,,,,,,r4c3pK|]1}|ddkrt|djjndV2dSr)r rsparservs r rsz>_BaseHeterogeneousEnsemble.__sklearn_tags__..0s[))7:!f6F6FQ  +22D))))))r4)r__sklearn_tags__ryrjrrr Exception)r<tagsrs r rz+_BaseHeterogeneousEnsemble.__sklearn_tags__)sww'')) (+,,?,,,))DO %&)))?)))&&DO " "    D    sAA44 BB)T) rrUrVrWpropertyrkrr=rr0r)r __classcell__)rs@r rgrgs$..X.%%^%!!!@2<<<<<<*r4rg)NNr;)rWabcrrnumpyr,joblibr sklearn.baserrrr r sklearn.utilsr r sklearn.utils._tagsr sklearn.utils._user_interfacersklearn.utils.metadata_routingrsklearn.utils.metaestimatorsrr!r3r6rergr'r4r rs// ('''''''######43333333((((((======;;;;;;999999@D0#'#'#'#'LQ&Q&Q&Q&Q&%}Q&Q&Q&Q&h H H HAAAAA(GAAAAAAr4