U 9%en+@sdZddlZddlmZmZddlmZddlZddl m Z ddl m Z m Z mZmZmZddlmZmZmZmZdd lmZdd d Zdd d ZGddde e edZddZGddde eedZdS)z)Base class for ensemble-based estimators.N)ABCMetaabstractmethod)List)effective_n_jobs) BaseEstimatorMetaEstimatorMixinclone is_classifier is_regressor)Bunch_print_elapsed_timecheck_random_state deprecated)_BaseCompositionc Cs|dk rzz*t|||j|||dW5QRXWqtk rv}z&dt|krdtd|jj|W5d}~XYqXn"t|||||W5QRX|S)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.)r fit TypeErrorstrformat __class____name__) estimatorXyrZmessage_clsnamemessageexcrU/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/ensemble/_base.py_fit_single_estimators"   rcCs`t|}i}t|jddD].}|dks2|dr|ttjj||<q|r\|j f|dS)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_stateN) rsorted get_paramsendswithrandintnpZiinfoZint32max set_params)rr"Zto_setkeyrrr_set_random_states(sr+c@s|eZdZUdZgZeeed<edde ddddZ dd d Z e d e d d ZdddZddZddZddZdS) 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. base_estimator : object, default="deprecated" Use `estimator` instead. .. deprecated:: 1.2 `base_estimator` is deprecated and will be removed in 1.4. Use `estimator` instead. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. base_estimator_ : estimator The base estimator from which the ensemble is grown. .. deprecated:: 1.2 `base_estimator_` is deprecated and will be removed in 1.4. Use `estimator_` instead. estimators_ : list of estimators The collection of fitted base estimators. _required_parametersN r) n_estimatorsestimator_paramsbase_estimatorcCs||_||_||_||_dSN)rr/r0r1)selfrr/r0r1rrr__init__zs zBaseEnsemble.__init__cCsl|jdk r|jdkrtd|jdk r0|j|_n8|jdkrbtdt|jdk rZ|j|_qh||_n||_dS)zMCheck the base estimator. Sets the `estimator_` attributes. N)NrzEBoth `estimator` and `base_estimator` were set. Only set `estimator`.rzV`base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.)rr1 ValueError estimator_warningswarn FutureWarning)r3defaultrrr_validate_estimators"      z BaseEnsemble._validate_estimatorzoAttribute `base_estimator_` was deprecated in version 1.2 and will be removed in 1.4. Use `estimator_` instead.cCs|jS)z$Estimator used to grow the ensemble.)r6r3rrrbase_estimator_szBaseEnsemble.base_estimator_TcsLtj}|jffddjD|dk r8t|||rHj||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).0pr<rr sz0BaseEnsemble._make_estimator..N)r r6r)r0r+ estimators_append)r3rCr"rrr<r_make_estimators   zBaseEnsemble._make_estimatorcCs t|jS)z0Return the number of estimators in the ensemble.)lenrBr<rrr__len__szBaseEnsemble.__len__cCs |j|S)z.Return the index'th estimator in the ensemble.)rB)r3indexrrr __getitem__szBaseEnsemble.__getitem__cCs t|jS)z0Return iterator over estimators in the ensemble.)iterrBr<rrr__iter__szBaseEnsemble.__iter__)N)N)TN)r __module__ __qualname____doc__r-rr__annotations__rtupler4r;rpropertyr=rDrFrHrJrrrrr,Ns& )   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  rWcsTeZdZdZdgZeddZeddZddZ fd d Z dfd d Z 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_`. estimatorscCstft|jS)zDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` )r dictrYr<rrrnamed_estimatorssz+_BaseHeterogeneousEnsemble.named_estimatorscCs ||_dSr2)rY)r3rYrrrr4sz#_BaseHeterogeneousEnsemble.__init__cCst|jdkrtdt|j\}}||tdd|D}|sLtdt|rXtnt}|D]2}|dkr`||s`td|j j |j ddq`||fS) NrzfInvalid 'estimators' attribute, 'estimators' should be a non-empty list of (string, estimator) tuples.css|]}|dkVqdS)dropNr)r?estrrr szB_BaseHeterogeneousEnsemble._validate_estimators..zHAll estimators are dropped. At least one is required to be an estimator.r\z The estimator {} should be a {}.) rErYr5zipZ_validate_namesanyr r rrr)r3namesrYZ has_estimatorZis_estimator_typer]rrr_validate_estimatorss*  z/_BaseHeterogeneousEnsemble._validate_estimatorsc stjd||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. rY)rY)superZ _set_params)r3paramsrrrr)"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. rYr )rdZ _get_params)r3r!rfrrr$;sz%_BaseHeterogeneousEnsemble.get_params)T) rrKrLrMr-rPr[rr4rcr)r$ __classcell__rrrfrrXs   rX)NNN)N)rMr7abcrrtypingrnumpyr'Zjoblibrbaserrr r r utilsr r rrZutils.metaestimatorsrrr+r,rWrXrrrrs*     &