a j)@sdZddlmZmZddlZddlmZddlm Z m Z m Z m Z m Z ddlmZmZddlmZdd lmZdd lmZdd lmZdd 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 Cstsd|vrzBt||$|j|||ddWdn1sD0YWqty}z.dt|vrtd|jj|WYd}~qd}~00n>t||$|j||fi|Wdn1s0Y|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_clsnamemessageexcr`. 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) r sorted get_paramsendswithrandintnpZiinfoZint32max set_params)rr"Zto_setkeyrrr_set_random_states+sr+c@sTeZdZdZeddedddZdddZdd 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|_n||_dS)zMCheck the base estimator. Sets the `estimator_` attributes. N)r estimator_)r1defaultrrr_validate_estimator}s  z BaseEnsemble._validate_estimatorTcsPtj}|jfifddjD|durz0BaseEnsemble._make_estimator..N)rr3r)r/r+ estimators_append)r1r=r"rrr9r_make_estimators    zBaseEnsemble._make_estimatorcCs t|jS)z0Return the number of estimators in the ensemble.)lenr<r9rrr__len__szBaseEnsemble.__len__cCs |j|S)z.Return the index'th estimator in the ensemble.)r<)r1indexrrr __getitem__szBaseEnsemble.__getitem__cCs t|jS)z0Return iterator over estimators in the ensemble.)iterr<r9rrr__iter__szBaseEnsemble.__iter__)N)N)TN) r __module__ __qualname____doc__rtupler2r5r>r@rBrDrrrrr,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  rOcsZeZdZdZeddZeddZddZfdd 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_`. cCstfit|jS)zDictionary to access any fitted sub-estimators by name. Returns ------- :class:`~sklearn.utils.Bunch` )r dict estimatorsr9rrrnamed_estimatorssz+_BaseHeterogeneousEnsemble.named_estimatorscCs ||_dSr0)rR)r1rRrrrr2sz#_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)dropNrr7estrrr r;zB_BaseHeterogeneousEnsemble._validate_estimators..zHAll estimators are dropped. At least one is required to be an estimator.rTz The estimator {} should be a {}.) r?rR ValueErrorzipZ_validate_namesanyr r rrr)r1namesrRZ has_estimatorZis_estimator_typerVrrr_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. rR)rR)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. rRr )r^Z _get_params)r1r!r`rrr$ sz%_BaseHeterogeneousEnsemble.get_paramscsVt}z4tdd|jD|j_tdd|jD|j_WntyPYn0|S)Ncss.|]&}|ddkr"t|djjndVqdSrJrTTN)r input_tags allow_nanrUrrrrW#sz>_BaseHeterogeneousEnsemble.__sklearn_tags__..css.|]&}|ddkr"t|djjndVqdSra)r rbsparserUrrrrW's)r^__sklearn_tags__allrRrbrcrd Exception)r1tagsr`rrre s   z+_BaseHeterogeneousEnsemble.__sklearn_tags__)T) rrErFrGpropertyrSrr2r]r)r$re __classcell__rrr`rrPs   rP)NN)N)rGabcrrnumpyr'Zjoblibrbaserrrr r utilsr r Z utils._tagsr Zutils._user_interfacerZutils.metadata_routingrZutils.metaestimatorsrrr+r,rOrPrrrrs"       &T