o i/)@sdZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZmZddlmZdd lmZdd lmZdd lmZ dd d ZdddZGddde e edZddZGddde eedZdS)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 CstsId|vrIz$t|||j|||ddWdW|S1s$wYW|StyH}zdt|vrCtd|jj|d}~wwt|||j||fi|Wd|S1sdwY|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__) estimatorXyZ fit_paramsZmessage_clsnamemessageexcre/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/sklearn/ensemble/_base.py_fit_single_estimators4       rcCsht|}i}t|jddD]}|dks|dr%|ttjj||<q|r2|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_stateZ__random_stateNr) r sorted get_paramsendswithrandintnpZiinfoZint32max set_params)rr"Zto_setkeyrrr_set_random_states+sr+c@sVeZdZdZe ddedddZdddZdd d Zd d Z ddZ ddZ 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_paramscCs||_||_||_dSN)rr.r/)selfrr.r/rrr__init__ls  zBaseEnsemble.__init__cCs |jdur |j|_dS||_dS)zMCheck the base estimator. Sets the `estimator_` attributes. N)r estimator_)r1defaultrrr_validate_estimator}s   z BaseEnsemble._validate_estimatorTcsPtj}|jdifddjD|durt|||r&j||S)zMake and configure a copy of the `estimator_` attribute. Warning: This method should be used to properly instantiate new sub-estimators. csi|]}|t|qSr)getattr).0pr1rr sz0BaseEnsemble._make_estimator..Nr)rr3r)r/r+ estimators_append)r1r<r"rrr9r_make_estimators    zBaseEnsemble._make_estimatorcC t|jS)z0Return the number of estimators in the ensemble.)lenr;r9rrr__len__ zBaseEnsemble.__len__cCs |j|S)z.Return the index'th estimator in the ensemble.)r;)r1indexrrr __getitem__rAzBaseEnsemble.__getitem__cCr>)z0Return iterator over estimators in the ensemble.)iterr;r9rrr__iter__rAzBaseEnsemble.__iter__r0)TN) r __module__ __qualname____doc__rtupler2r5r=r@rCrErrrrr,Qs   r,) metaclasscCs\tt||}tj|||td}|d||d7<t|}||dg|fS)z;Private function used to partition estimators between jobs.)ZdtypeNr)minrr'fullintZcumsumtolist)r.Zn_jobsZn_estimators_per_jobZstartsrrr_partition_estimatorss  rPcsZeZdZdZeddZeddZddZfdd Z dfd d Z fd dZ Z 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_`. cCstdit|jS)zDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` Nr)r dict estimatorsr9rrrnamed_estimatorssz+_BaseHeterogeneousEnsemble.named_estimatorscCs ||_dSr0rS)r1rSrrrr2rAz#_BaseHeterogeneousEnsemble.__init__cCst|jdkstdd|jDstdt|j\}}||tdd|D}|s0tdt|r6tnt}|D]}|dkrS||sStd |j j |j d dq:||fS) Nrcss,|]}t|ttfot|dtVqdS)rN) isinstancerIlistr)r7itemrrr s  zB_BaseHeterogeneousEnsemble._validate_estimators..zfInvalid 'estimators' attribute, 'estimators' should be a non-empty list of (string, estimator) tuples.css|]}|dkVqdS)dropNrr7estrrrrYszHAll estimators are dropped. At least one is required to be an estimator.rZz The estimator {} should be a {}.) r?rSall ValueErrorzipZ_validate_namesanyr r rrr)r1namesrSZ has_estimatorZis_estimator_typer\rrr_validate_estimatorss. z/_BaseHeterogeneousEnsemble._validate_estimatorsc stjdi||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. rSNrU)superZ _set_params)r1paramsrrrr)sz%_BaseHeterogeneousEnsemble.set_paramsTcstjd|dS)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. rSr )rdZ _get_params)r1r!rfrrr$sz%_BaseHeterogeneousEnsemble.get_paramscsVt}ztdd|jD|j_tdd|jD|j_W|Sty*Y|Sw)Ncs0|]}|ddkrt|djjndVqdSrKrZTN)r input_tags allow_nanr[rrrrY&  z>_BaseHeterogeneousEnsemble.__sklearn_tags__..csrgrh)r risparser[rrrrY*rk)rd__sklearn_tags__r^rSrirjrl Exception)r1tagsrfrrrm#s   z+_BaseHeterogeneousEnsemble.__sklearn_tags__)T) rrFrGrHpropertyrTrr2rcr)r$rm __classcell__rrrfrrQs   rQ)NNr0)rHabcrrnumpyr'Zjoblibrbaserrrr r utilsr r Z utils._tagsr Zutils._user_interfacerZutils.metadata_routingrZutils.metaestimatorsrrr+r,rPrQrrrrs$      &T