Kil)SrSSKJrJr SSKrSSKJr SSKJ r J r J r J r J r SSKJrJr SSKJr SSKJr SS KJr SS KJr SS jrSS jr"S S\ \ \S9rSr"SS\ \\S9rg)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)_BaseCompositionc[5(d0SU;a*[XE5 URXUSS9 SSS5 U$[XE5 UR"X40UD6 SSS5 U$!,(df  U$=f![aDnS[ U5;a/[SR UR R55UeeSnAff=f!,(df  U$=f)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 X/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/ensemble/_base.py_fit_single_estimatorr!s   /Z"? $_> a*_2M N? ! : MM! -* -; ?>  <CHNUU!++44   ; : s? A;A)A; C ) A83A;8A;; C ?CC  CcJ[U5n0n[URSS95H`nUS:XdURS5(dM!UR [ R "[ R5R5X#'Mb U(aUR"S0UD6 gg)aeSet fixed random_state parameters for an estimator. Finds all parameters ending ``random_state`` and sets them to integers derived from ``random_state``. Parameters ---------- estimator : estimator supporting get/set_params Estimator with potential randomness managed by random_state parameters. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the generation of the random integers. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Notes ----- This does not necessarily set *all* ``random_state`` attributes that control an estimator's randomness, only those accessible through ``estimator.get_params()``. ``random_state``s not controlled include those belonging to: * cross-validation splitters * ``scipy.stats`` rvs Tdeep random_state__random_stateN) r sorted get_paramsendswithrandintnpiinfoint32max set_params)rr%to_setkeys r _set_random_statesr31s8&l3L Fi***56 . CLL1A$B$B&..rxx/A/E/EFFK7&v&ch\rSrSrSr\S S\"5S.Sjj5rS SjrSSjr S r S r S r S r g) BaseEnsembleWaBase class for all ensemble classes. Warning: This class should not be used directly. Use derived classes instead. Parameters ---------- estimator : object The base estimator from which the ensemble is built. n_estimators : int, default=10 The number of estimators in the ensemble. estimator_params : list of str, default=tuple() The list of attributes to use as parameters when instantiating a new base estimator. If none are given, default parameters are used. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. estimators_ : list of estimators The collection of fitted base estimators. N ) n_estimatorsestimator_paramsc(XlX lX0lgN)rr9r:)selfrr9r:s r __init__BaseEnsemble.__init__rs#( 0r4cNURbURUlgXlg)z=Check the base estimator. Sets the `estimator_` attributes. N)r estimator_)r=defaults r _validate_estimator BaseEnsemble._validate_estimators >> %"nnDO%Or4c [UR5nUR"S0URVs0sHoD[ X5_M snD6 Ub [ X25 U(aUR RU5 U$s snf)zMake and configure a copy of the `estimator_` attribute. Warning: This method should be used to properly instantiate new sub-estimators. r')rrAr0r:getattrr3 estimators_append)r=rHr%rps r _make_estimatorBaseEnsemble._make_estimatorss $//* TT=R=RS=R74#3 3=RST  # y 7     # #I . TsA> c,[UR5$)z0Return the number of estimators in the ensemble.)lenrGr=s r __len__BaseEnsemble.__len__s4##$$r4c URU$)z.Return the index'th estimator in the ensemble.)rG)r=indexs r __getitem__BaseEnsemble.__getitem__s&&r4c,[UR5$)z0Return iterator over estimators in the ensemble.)iterrGrNs r __iter__BaseEnsemble.__iter__sD$$%%r4)rrAr:r9r<)TN)r __module__ __qualname____firstlineno____doc__rtupler>rCrJrOrSrW__static_attributes__r'r4r r6r6WsH4 1 1 1 &"%'&r4r6) metaclassc[[U5U5n[R"XU-[S9nUSX-===S- sss&[R "U5nXR 5S/UR 5-4$)z;Private function used to partition estimators between jobs.)dtypeNr)minrr,fullintcumsumtolist)r9n_jobsn_estimators_per_jobstartss r _partition_estimatorsrkst!&)< 8F7766+AM0<01Q61 YY+ ,F ..01# 2G GGr4cn^\rSrSrSr\S5r\S5rSr U4Sjr S U4Sjjr U4Sjr S r U=r$) _BaseHeterogeneousEnsembleaIBase 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>[S0[UR5D6$)zgDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` r')r dict estimatorsrNs r named_estimators+_BaseHeterogeneousEnsemble.named_estimatorss-tDOO,--r4cXlgr<rq)r=rqs r r>#_BaseHeterogeneousEnsemble.__init__s$r4c [UR5S:Xd![SUR55(d [S5e[ UR6upUR U5 [ SU55nU(d [S5e[U5(a[O[nUHTnUS:wdM U"U5(aM[SRURRURSS55e X4$) Nrc3# UH5n[U[[45=(a [US[5v M7 g7f)rN) isinstancer]listr).0items r B_BaseHeterogeneousEnsemble._validate_estimators..s50 ' teT] + H 47C0H H's=?zfInvalid 'estimators' attribute, 'estimators' should be a non-empty list of (string, estimator) tuples.c3*# UH oS:gv M g7f)dropNr'r{ests r r}r~s@Zc6MZszHAll estimators are dropped. At least one is required to be an estimator.rz The estimator {} should be a {}.) rMrqall ValueErrorzip_validate_namesanyr r rrr)r=namesrq has_estimatoris_estimator_typers r _validate_estimators/_BaseHeterogeneousEnsemble._validate_estimatorss t 1 $C0 0 - - @  1 U#@Z@@ &  .;4-@-@MlCf}%6s%;%; 6== ..0A0J0J120N  r4c (>[TU]"S0UD6 U$)a3 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. ru)super _set_params)r=paramsrs r r0%_BaseHeterogeneousEnsemble.set_paramss, 3F3 r4c >[TU]SUS9$)a Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the `estimators` parameter. Parameters ---------- deep : bool, default=True Setting it to True gets the various estimators and the parameters of the estimators as well. Returns ------- params : dict Parameter and estimator names mapped to their values or parameter names mapped to their values. rqr#)r _get_params)r=r$rs r r)%_BaseHeterogeneousEnsemble.get_paramss&w"[TU]5n[SUR55URl[SUR55URlU$![a U$f=f)Nc3|# UH2nUSS:wa"[US5RROSv M4 g7frbrTN)r input_tags allow_nanrs r r}>_BaseHeterogeneousEnsemble.__sklearn_tags__..,s<,*C:=Q69IQ ++55tS*:<c3|# UH2nUSS:wa"[US5RROSv M4 g7fr)r rsparsers r r}r0s<)*C7:!f6FQ ++22DP*r)r__sklearn_tags__rrqrrr Exception)r=tagsrs r r+_BaseHeterogeneousEnsemble.__sklearn_tags__)s}w') (+,??,)DOO %&))??)&DOO "     sAA)) A76A7ru)T)rrYrZr[r\propertyrrrr>rr0r)rr^ __classcell__)rs@r rmrmsJ$..%%!@2<*r4rm)NNr<)r\abcrrnumpyr,joblibr sklearn.baserrrr r sklearn.utilsr r sklearn.utils._tagsr sklearn.utils._user_interfacersklearn.utils.metadata_routingrsklearn.utils.metaestimatorsrr!r3r6rkrmr'r4r rsn/ (#4(=;9@D0#'LQ&%}Q&h HA(GAr4