>[gdZddlZddlZddlmZmZddlmZddlmZddl m Z ddl Z ddl mZmZmZdd lmZmZdd lmZmZdd lmZmZmZmZdd lmZdd lmZm Z m!Z!ddl"m#Z#ddl$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0m1Z1ddl2m3Z3ddl4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;ddlZ>ddgZ?e je jjZCdZDdZEdZFdZGdZHdZIdZJGd d!e=e"ZKGd#deeKZLGd$deeKZMy)%zBagging meta-estimator.N)ABCMetaabstractmethod)partial)Integralwarn)ClassifierMixinRegressorMixin _fit_context)accuracy_scorer2_score)DecisionTreeClassifierDecisionTreeRegressor)Bunch_safe_indexingcheck_random_state column_or_1d)indices_to_mask) HasMethodsInterval RealNotInt)get_tags)MetadataRouter MethodMapping_raise_for_params_routing_enabledget_routing_for_objectprocess_routing) available_if)check_classification_targets)Paralleldelayed)sample_without_replacement)_check_method_params_check_sample_weight_deprecate_positional_args_estimator_hascheck_is_fittedhas_fit_parameter validate_data) BaseEnsemble_partition_estimatorsBaggingClassifierBaggingRegressorcP|r|jd||}|St|||}|S)zDraw randomly sampled indices.r) random_state)randintr$)r2 bootstrap n_population n_samplesindicess T/var/www/html/bid-api/venv/lib/python3.12/site-packages/sklearn/ensemble/_bagging.py_generate_indicesr98s<&&q, B N - ),  NcXt|}t||||}t||||}||fS)z)Randomly draw feature and sample indices.)rr9) r2bootstrap_featuresbootstrap_samples n_featuresr6 max_features max_samplesfeature_indicessample_indicess r8_generate_bagging_indicesrCEsG&l3L((*lO''KN N **r:c z|j\} } |j} |j} |j} |j}t |j d}|xs| | k7}g}g}t |j d}ts|s|jd tdt|D]}|dkDrtd|dz||fz||}|jd|}|rt|j| }n |j}t||| | | | | \}}|j!}tr(t#|j }|j%d d }n|}|rxt'|j)dd|j!}| rt+j,|| }||z}nt/|| }d ||<||d<|r |dd|fn|} || |fi|n;t1||}!t1||} t3|||}|r | dd|f} || |!fi||j5||j5|||fS)zBPrivate function used to build a batch of estimators within a job. check_input sample_weightNz`The base estimator doesn't support sample weight, but sample_weight is passed to the fit method.r,z?Building estimator %d of %d for this parallel run (total %d)...F)appendr2)rEfitrF) minlengthr)paramsr7)shape _max_features _max_samplesr4r<r* estimator_rget ValueErrorrangeprint_make_estimatorrrHrCcopyrconsumesr&popnpbincountrrr%rG)" n_estimatorsensembleXyseedstotal_n_estimatorsverboserE fit_paramsr6r>r?r@r4r<has_check_inputrequires_feature_indexing estimatorsestimators_featuressupport_sample_weightir2 estimator estimator_fitfeaturesr7 fit_params_request_or_routerconsumes_sample_weightcurr_sample_weight sample_countsnot_indices_maskX_y_s" r8_parallel_build_estimatorsrs]sGGIz))L''K""I!44'(;(;]KO 2 Plj6PJ.h.A.A?S   !jnn_&E&Q (  <  Q; Qq5,(:;<  Qx ,,E ,U #IMM{KM%MMM6        '!oo'    6x7J7J K %6%?%?)& "&; " !!56"df  " Gy I "m3"$3GY$G#G 78"#34+=K (#<1h;!B "a /; / 7+B7+B.qgVK(8 _ "b 0K 0)$""8,O!R * **r:c |jd}tj||f}t||D]\}}t |dru|j |dd|f}|t |jk(r||z }H|dd|jfxx|ddtt |jfz cc<|j|dd|f} t|D]} || | | fxxdz cc<|S)zBPrivate function used to compute (proba-)predictions within a job.r predict_probaNr,) rLrXzerosziphasattrrulenclasses_rRpredict) rdrer\ n_classesr6probarhrjproba_estimator predictionsrgs r8_parallel_predict_probars I HHi+ ,E":/BC 8 9o .'55a8 nEOC 2 233(a+++,uS!3!34551, $++AakN;K9%aQ'(A-(&! D& Lr:c |jd}tj||f}|jtj tj |t }t||D]\}}|j|dd|f} |t|jk(rtj|| }Mtj|dd|jf| ddtt|jf|dd|jf<tj||j} tj|dd| ftj |dd| f<|S)z@Private function used to compute log probabilities within a job.rdtypeN)rLrXemptyfillinfarangeintrwpredict_log_probaryrz logaddexprR setdiff1d) rdrer\r|r6 log_proba all_classesrhrjlog_proba_estimatormissings r8_parallel_predict_log_probars1 I)Y/0I NNBFF7))IS1K":/BC 8'99!AxK.I I../ / Y0CDI02||!Y///0#AuS1C1C-D'E$EF0Ia+++ , ll; 0B0BCG$&LL1g:1F$PIaj ! D r:c@tfdt||DS)z8Private function used to compute decisions within a job.c3TK|]\}}|jdd|f!ywN)decision_function.0rhrjr\s r8 z._parallel_decision_function..s1#G Ix ##AakN3#G%(sumrwrdrer\s `r8_parallel_decision_functionr% #&z3F#G r:c@tfdt||DS)z:Private function used to compute predictions within a job.c3TK|]\}}|jdd|f!ywr)r{rs r8rz/_parallel_predict_regression.. s1#G Ix !AxK.)#Grrrs `r8_parallel_predict_regressionr rr:c eZdZUdZeddgdgeedddgeedddeeddd geedddeeddd gd gd gd gd gdegd gd gd Ze e d<e d"dddddddddd fd Z e deddddZdZ d#dZe dZdZdZedZdZe d Zfd!ZxZS)$ BaseBaggingzBase class for Bagging meta-estimator. Warning: This class should not be used directly. Use derived classes instead. rHr{Nr,left)closedrrightbooleanr2r` rhrZr@r?r4r< oob_score warm_startn_jobsr2r`_parameter_constraints?TF r@r?r4r<rrrr2r`c t |||||_||_||_||_||_||_| |_| |_ | |_ y)N)rhrZ) super__init__r@r?r4r<rrrr2r` selfrhrZr@r?r4r<rrrr2r` __class__s r8rzBaseBagging.__init__,sb %  '(""4"$ ( r:z1.7)version)prefer_skip_nested_validationrIc t||dt|||ddgddd\}}|t||d}||d <|j||fd |ji|S) aBuild a Bagging ensemble of estimators from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression). sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting. **fit_params : dict Parameters to pass to the underlying estimators. .. versionadded:: 1.5 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : object Fitted estimator. rHcsrcscNFT) accept_sparserensure_all_finite multi_outputrrFr@)rr+r&_fitr@)rr\r]rFras r8rHzBaseBagging.fitMs}N *dE2   %.# 1  $0NM*7J 'tyyAJ4+;+;JzJJr:ciSrrs r8_parallel_argszBaseBagging._parallel_argss r:c z tj}jd}|_j j j trtdfi|n;tt|_ d|vr|djjd<||j_ | j}n5t|t j"st%|jdz}|jdkDr t'd|_tj*t j"r j*} nz } | dkr-t'd jBtEj>fz| dk(r tGd StI| jJ\} tM j8rBtEj>dkDr*|jOtPtEj>|jOtP| _)tUd| jVdjY f dt[| D} xj>t]t^j`jcd| Dz c_xj@t]t^j`jcd| Dz c_ j6rjeS)a0Build a Bagging ensemble of estimators from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression). max_samples : int or float, default=None Argument to use instead of self.max_samples. max_depth : int, default=None Override value used when constructing base estimator. Only supported if the base estimator has a max_depth parameter. check_input : bool, default=True Override value used when fitting base estimator. Only supported if the base estimator has a check_input parameter for fit function. If the meta-estimator already checks the input, set this value to False to prevent redundant input validation. **fit_params : dict, default=None Parameters to pass to the :term:`fit` method of the underlying estimator. Returns ------- self : object Fitted estimator. rrH)rHrFz max_samples must be <= n_samplesz"max_features must be <= n_featuresr,z6Out of bag estimation only available if bootstrap=Truez6Out of bag estimate only available if warm_start=False oob_score_ estimators_zTn_estimators=%d must be larger or equal to len(estimators_)=%d when warm_start==TruezJWarm-start fitting without increasing n_estimators does not fit new trees.)sizerr`c 3 K|]O}tt| ||dz jjj Qyw)r,)r`rEraN)r#rsr`rhrH) rrgr\rErZ routed_paramsr^rstartsr_r]s r8rz#BaseBagging._fit..sq # 0G. /QfQi&Q-0" '(2266  #sAAc3&K|] }|d yw)rNrrts r8rz#BaseBagging._fit..')D 1!A$ c3&K|] }|d ywr,Nrrs r8rz#BaseBagging._fit..*rrr)3rr2rL _n_samples _validate_y_validate_estimator_get_estimatorrrrrhrHrO max_depthr@ isinstancenumbersrrrQrNr?floatn_features_in_maxrMr4rrrxrrestimators_features_rZryrr.rrr3MAX_INT_seedsr"r`rrRlist itertoolschain from_iterable_set_oob_score)rr\r]r@rrErar2r6r?n_more_estimatorsr all_resultsrZrr^rr_s``` ` @@@@@r8rzBaseBagging._fitsX*$*;*;< GGAJ #   Q    !4!4!67  +D%F:FM!GM&+ &;M #*,?I#@ ''++O<  (1DOO %  **KK)9)9:kAGGAJ67K  #?@ @( d'')9)9 :,,L ))5 1t0043F3FFGL $-- -AB B1c,/0 *~~$..UV V ??t~~UV V 4 &4??gdM&B!D (*D % --D4D4D0EE q <$$c$*:*:&;<=  ! # ! K(= t{{( $ f!. ??s4#3#34q8  s43C3C/D E$$W3D$E  h 4<< 373F3F3H  6]  $ D OO ) ))D )D D   !!T OO ) ))D )D D&  ! >>   1 % r:cy)z+Calculate out of bag predictions and score.Nr)rr\r]s r8rzBaseBagging._set_oob_score2r:ctt|jdk(s|jddk(r t|dS|S)Nr,Tr)ryrLrrr]s r8rzBaseBagging._validate_y6s2 qww<1  a- -r:c #K|jD]X}t||j|j|j|j |j |j\}}||fZywr)rrCr<r4rrrMrN)rseedrArBs r8_get_estimators_indicesz#BaseBagging._get_estimators_indices;shKKD/H''##""!!/ +O^">1 1 sA)A+cT|jDcgc]\}}| c}}Scc}}w)a The subset of drawn samples for each base estimator. Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples. Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected. )r)r_rBs r8estimators_samples_zBaseBagging.estimators_samples_Ls,9=8T8T8VW8V#41n8VWWWs $ct|jj}|j|j t jdd|S)ajGet metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.5 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. )ownerrH)calleecaller)rhmethod_mapping)rr__name__addrr)rrouters r8get_metadata_routingz BaseBagging.get_metadata_routing[sQ dnn&=&=> ))+(?..eE.J   r:cy)z"Resolve which estimator to return.Nrrs r8rzBaseBagging._get_estimatorprr:ct|}t|jjj |j_|Sr)r__sklearn_tags__rr input_tags allow_nan)rtagsrs r8rzBaseBagging.__sklearn_tags__ts;w')$,T-@-@-B$C$N$N$X$X! r:N )NNT)r __module__ __qualname____doc__rrrrrdict__annotations__rrr'r rHrrrrrpropertyrrrr __classcell__rs@r8rrs!%!34d;!(AtFCD Xq$v 6 ZAg 6 Xq$v 6 ZAg 6  [(k[ k"'(;#$D(  @ .&+*.3K / 3Kj fP:: 2" X X*11r:r) metaclassc eZdZdZ ddddddddddd fd Zd Zd Zd Zd Zd Z dZ e e dddZ xZS)r/aA Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. Read more in the :ref:`User Guide `. .. versionadded:: 0.15 Parameters ---------- estimator : object, default=None The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a :class:`~sklearn.tree.DecisionTreeClassifier`. .. versionadded:: 1.2 `base_estimator` was renamed to `estimator`. n_estimators : int, default=10 The number of base estimators in the ensemble. max_samples : int or float, default=1.0 The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details). - If int, then draw `max_samples` samples. - If float, then draw `max_samples * X.shape[0]` samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details). - If int, then draw `max_features` features. - If float, then draw `max(1, int(max_features * n_features_in_))` features. bootstrap : bool, default=True Whether samples are drawn with replacement. If False, sampling without replacement is performed. bootstrap_features : bool, default=False Whether features are drawn with replacement. oob_score : bool, default=False Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary `. .. versionadded:: 0.17 *warm_start* constructor parameter. n_jobs : int, default=None The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. random_state : int, RandomState instance or None, default=None Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a `random_state` attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. verbose : int, default=0 Controls the verbosity when fitting and predicting. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. .. versionadded:: 1.2 `base_estimator_` was renamed to `estimator_`. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 estimators_ : list of estimators The collection of fitted base estimators. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected. estimators_features_ : list of arrays The subset of drawn features for each base estimator. classes_ : ndarray of shape (n_classes,) The classes labels. n_classes_ : int or list The number of classes. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True. oob_decision_function_ : ndarray of shape (n_samples, n_classes) Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_decision_function_` might contain NaN. This attribute exists only when ``oob_score`` is True. See Also -------- BaggingRegressor : A Bagging regressor. References ---------- .. [1] L. Breiman, "Pasting small votes for classification in large databases and on-line", Machine Learning, 36(1), 85-103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. Examples -------- >>> from sklearn.svm import SVC >>> from sklearn.ensemble import BaggingClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_samples=100, n_features=4, ... n_informative=2, n_redundant=0, ... random_state=0, shuffle=False) >>> clf = BaggingClassifier(estimator=SVC(), ... n_estimators=10, random_state=0).fit(X, y) >>> clf.predict([[0, 0, 0, 0]]) array([1]) NrTFrrc :t |||||||||| | |  yNrrrrs r8rzBaggingClassifier.__init__"8 %#%1!%  r:cF|j tS|jSzEResolve which estimator to return (default is DecisionTreeClassifier))rhrrs r8rz BaggingClassifier._get_estimator?s >> !)+ +~~r:c  |jd}|j}tj||f}t |j |j |jD]\}}}t||} t|dr/|| ddfxx|j|| ddfdd|fz cc<O|j|| ddfdd|f} d} t|D]} | | s || | | fxxdz cc<| dz } !|jddk(jr td||jdddtj fz } t#|tj$|d}| |_||_y)Nrrur,axis{Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.)rL n_classes_rXrvrwrrrrrxrur{rRranyrnewaxisr argmaxoob_decision_function_r)rr\r]r6rrrhsamplesrjmaskpjrgoob_decision_functionrs r8rz BaggingClassifier._set_oob_scoreEsGGAJ __ hh :67 ,/   d668Q8Q- (Iw$GY77Dy/2D!G$ (?(?tQwZH-)$ %%qqz1h;&?@y)AAw#AqtG,1,Q*- ( OOO #q ( - - / 9  !,koo1o.Eam.T T"1bii !&DE &;##r:ct|d}t|tj|d\|_}t |j|_|S)NTr)return_inverse)rr!rXuniquerzryrrs r8rzBaggingClassifier._validate_ylsB  &$Q'99Qt< qdmm,r:c|j|}|jjtj|ddS)a_Predict class for X. The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a ``predict_proba`` method, then it resorts to voting. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- y : ndarray of shape (n_samples,) The predicted classes. r,rr)rurztakerXr)rr\predicted_probabilitiys r8r{zBaggingClassifier.predictts<$"&!3!3A!6}}!!299-C!#LTU!VVr:c@ttddgdddtjj\}}t d|j djfdt|D}t|jz }|S) aPredict class probabilities for X. The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a ``predict_proba`` method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- p : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. rrNFrrrresetrc 3K|]R}ttj||dzj||dzjTywr)r#rrrrrrgr\rrs r8rz2BaggingClassifier.predict_proba..sl # -G+ ,  VAE];))&)fQUmD   #AAr) r)r+r.rZrr"r`rrRr)rr\rr all_probar}rs`` @r8ruzBaggingClassifier.predict_probas,     %.#  2$2C2CT[[Q6 H 4<< 373F3F3H  6]   I!2!22 r:cttjdrtddgdddt j j \}}t|jfdt|D}|d }td t|D]}tj|||}|tjj z}|Stjj}|S) aPredict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the base estimators in the ensemble. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- p : ndarray of shape (n_samples, n_classes) The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. rrrNFr$rc 3K|]R}ttj||dzj||dzjTywr)r#rrrrr's r8rz6BaggingClassifier.predict_log_proba..smJ'A 534$$VAYA?--fQi&Q-HOO  'r(rr,)r)rxrOr+r.rZrr"r`rRryrXrlogru)rr\rr all_log_probarrrs`` @r8rz#BaggingClassifier.predict_log_probas&  4??$7 8$en"' A!6d6G6G U FAvIHFDLLIJvJM&a(I1c-01LLM!4DE 2  1 12 2I t11!45Ir:r)rrh) delegatesc"ttddgdddtjj\}}t |j fdt|D}t|jz }|S)aAverage of the decision functions of the base classifiers. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- score : ndarray of shape (n_samples, k) The decision function of the input samples. The columns correspond to the classes in sorted order, as they appear in the attribute ``classes_``. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. rrNFr$rc 3K|]G}ttj||dzj||dzIywr)r#rrrr's r8rz6BaggingClassifier.decision_function..sfF # 1G/ 0  VAE];))&)fQUmD  #A A r)r+r.rZrr"r`rRr)rr\rr all_decisions decisionsrs`` @r8rz#BaggingClassifier.decision_functions(     %.#  2$2C2CT[[Q6E EF 6] F   &):):: r:r)rrrrrrrrr{rurr r(rrrs@r8r/r/zseR   : %$NW*3j7r*6RS,,r:c PeZdZdZ d dddddddddd fd Zd Zd Zd ZxZS) r0aA Bagging regressor. A Bagging regressor is an ensemble meta-estimator that fits base regressors each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. Read more in the :ref:`User Guide `. .. versionadded:: 0.15 Parameters ---------- estimator : object, default=None The base estimator to fit on random subsets of the dataset. If None, then the base estimator is a :class:`~sklearn.tree.DecisionTreeRegressor`. .. versionadded:: 1.2 `base_estimator` was renamed to `estimator`. n_estimators : int, default=10 The number of base estimators in the ensemble. max_samples : int or float, default=1.0 The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details). - If int, then draw `max_samples` samples. - If float, then draw `max_samples * X.shape[0]` samples. max_features : int or float, default=1.0 The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details). - If int, then draw `max_features` features. - If float, then draw `max(1, int(max_features * n_features_in_))` features. bootstrap : bool, default=True Whether samples are drawn with replacement. If False, sampling without replacement is performed. bootstrap_features : bool, default=False Whether features are drawn with replacement. oob_score : bool, default=False Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True. warm_start : bool, default=False When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary `. n_jobs : int, default=None The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. random_state : int, RandomState instance or None, default=None Controls the random resampling of the original dataset (sample wise and feature wise). If the base estimator accepts a `random_state` attribute, a different seed is generated for each instance in the ensemble. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. verbose : int, default=0 Controls the verbosity when fitting and predicting. Attributes ---------- estimator_ : estimator The base estimator from which the ensemble is grown. .. versionadded:: 1.2 `base_estimator_` was renamed to `estimator_`. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 estimators_ : list of estimators The collection of fitted sub-estimators. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by an array of the indices selected. estimators_features_ : list of arrays The subset of drawn features for each base estimator. oob_score_ : float Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when ``oob_score`` is True. oob_prediction_ : ndarray of shape (n_samples,) Prediction computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, `oob_prediction_` might contain NaN. This attribute exists only when ``oob_score`` is True. See Also -------- BaggingClassifier : A Bagging classifier. References ---------- .. [1] L. Breiman, "Pasting small votes for classification in large databases and on-line", Machine Learning, 36(1), 85-103, 1999. .. [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140, 1996. .. [3] T. Ho, "The random subspace method for constructing decision forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998. .. [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. Examples -------- >>> from sklearn.svm import SVR >>> from sklearn.ensemble import BaggingRegressor >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_samples=100, n_features=4, ... n_informative=2, n_targets=1, ... random_state=0, shuffle=False) >>> regr = BaggingRegressor(estimator=SVR(), ... n_estimators=10, random_state=0).fit(X, y) >>> regr.predict([[0, 0, 0, 0]]) array([-2.8720...]) NrTFrrc :t |||||||||| | |  yr r rs r8rzBaggingRegressor.__init__r r:c"ttddgdddtjj\}}t |j fdt|D}t|jz }|S)a%Predict regression target for X. The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. 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