h$,fU'dZddlmZmZddlmZddlZddlm Z ddl m Z m Z m Z mZmZddlmZmZmZdd lmZdd lmZ dd Zdd ZGd de e eZdZGdde eeZy)z)Base class for ensemble-based estimators.)ABCMetaabstractmethod)ListN)effective_n_jobs) BaseEstimatorMetaEstimatorMixinclone is_classifier is_regressor)Bunch_print_elapsed_timecheck_random_state _safe_tags)_BaseCompositionc~|, t||5|j|||ddd|St||5|j||ddd|S#1swY4xYw#t$rB}dt|vr/tdj |j j |d}~wwxYw#1swY|SxYw)z7Private function used to fit an estimator within a job.N) sample_weightz+unexpected keyword argument 'sample_weight'z8Underlying estimator {} does not support sample weights.)rfit TypeErrorstrformat __class____name__) estimatorXyrmessage_clsnamemessageexcs 6lib/python3.12/site-packages/sklearn/ensemble/_base.py_fit_single_estimatorr"s  $_g> A a- @ A !' : MM!Q   A A <CHNUU!++44    s8 A$AA$B2A!A$$ B/-=B**B/2B<c4t|}i}t|jdD]X}|dk(s|jds|j t j t jj||<Z|r|jdi|yy)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) rsorted get_paramsendswithrandintnpiinfoint32max set_params)rr&to_setkeys r!_set_random_statesr4(s8&l3L Fi***56G . CLL1A$B&..rxx/A/E/EFF3KG &v&cneZdZUdZgZeeed<e d de ddZ d dZ d dZ d Z d Zd Zy) 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. _required_parametersN ) n_estimatorsestimator_paramsc.||_||_||_yN)rr:r;)selfrr:r;s r!__init__zBaseEnsemble.__init__ls#( 0r5cN|j|j|_y||_y)zMCheck the base estimator. Sets the `estimator_` attributes. N)r estimator_)r>defaults r!_validate_estimatorz BaseEnsemble._validate_estimator}s >> %"nnDO%DOr5c t|j}|jdi|jDcic]}|t ||c}| t |||r|j j||Scc}w)zMake and configure a copy of the `estimator_` attribute. Warning: This method should be used to properly instantiate new sub-estimators. r()r rAr1r;getattrr4 estimators_append)r>rGr&rps r!_make_estimatorzBaseEnsemble._make_estimatorsu $//*  TT=R=RS74#3 3ST  # y, 7     # #I . TsA8 c,t|jS)z0Return the number of estimators in the ensemble.)lenrFr>s r!__len__zBaseEnsemble.__len__s4##$$r5c |j|S)z.Return the index'th estimator in the ensemble.)rF)r>indexs r! __getitem__zBaseEnsemble.__getitem__s&&r5c,t|jS)z0Return iterator over estimators in the ensemble.)iterrFrLs r!__iter__zBaseEnsemble.__iter__sD$$%%r5r=)TN)r __module__ __qualname____doc__r8rr__annotations__rtupler?rCrIrMrPrSr(r5r!r7r7NsW6')$s)( 1 1 1 &"%'&r5r7) metaclassctt||}tj|||zt}|d||zxxxdz ccctj |}||j dg|j zfS)z;Private function used to partition estimators between jobs.)dtypeNr)minrr-fullintcumsumtolist)r:n_jobsn_estimators_per_jobstartss r!_partition_estimatorsres{!&)< 8F776<6+AM0<&01Q61 YY+ ,F '..01# 2G GGr5c`eZdZdZdgZedZedZdZ fdZ d fd Z dZ 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_`. estimatorsc>tdit|jS)zDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` r()r dictrhrLs r!named_estimatorsz+_BaseHeterogeneousEnsemble.named_estimatorss-tDOO,--r5c||_yr=rh)r>rhs r!r?z#_BaseHeterogeneousEnsemble.__init__s $r5c t|jdk(r tdt|j\}}|j |t d|D}|s tdt |rt nt}|D]L}|dk7s ||rtdj|jj|jdd||fS)NrzfInvalid 'estimators' attribute, 'estimators' should be a non-empty list of (string, estimator) tuples.c3&K|] }|dk7 yw)dropNr(.0ests r! zB_BaseHeterogeneousEnsemble._validate_estimators..s@cC6M@szHAll estimators are dropped. At least one is required to be an estimator.rpz The estimator {} should be a {}.) rKrh ValueErrorzip_validate_namesanyr r rrr)r>namesrh has_estimatoris_estimator_typerss r!_validate_estimatorsz/_BaseHeterogeneousEnsemble._validate_estimatorss t 1 $@  1z U#@Z@@ &  .;4-@Ml Cf}%6s%; 6== ..0A0J0J120N j  r5c &t|di||S)a Set the parameters of an estimator from the ensemble. Valid parameter keys can be listed with `get_params()`. Note that you can directly set the parameters of the estimators contained in `estimators`. Parameters ---------- **params : keyword arguments Specific parameters using e.g. `set_params(parameter_name=new_value)`. In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to 'drop'. Returns ------- self : object Estimator instance. rm)super _set_params)r>paramsrs r!r1z%_BaseHeterogeneousEnsemble.set_paramss, 3F3 r5c&t|d|S)a< Get the parameters of an estimator from the ensemble. Returns the parameters given in the constructor as well as the estimators contained within the `estimators` parameter. Parameters ---------- deep : bool, default=True Setting it to True gets the various estimators and the parameters of the estimators as well. Returns ------- params : dict Parameter and estimator names mapped to their values or parameter names mapped to their values. rhr$)r _get_params)r>r%rs r!r*z%_BaseHeterogeneousEnsemble.get_params s&w".$s647q6V3C 3q6";/Ms%'F)preserves_dtyper)allrh Exception)r>rs r! _more_tagsz%_BaseHeterogeneousEnsemble._more_tags"sG ??I$&I>>  I  s # 11)T)rrTrUrVr8propertyrkrr?r}r1r*r __classcell__)rs@r!rgrgsL$)> ..%%!:2<* ?r5rg)NNNr=)rVabcrrtypingrnumpyr-joblibrbaserr r r r utilsr rr utils._tagsrutils.metaestimatorsrr"r4r7rergr(r5r!rsh/ (#XXBB$3HL,#'LT&%}T&n H{?(G{?r5