-iSrSSKrSSKrSSKJrJr SSKJr SSKJr SSK J r SSK r SSK JrJrJr SS KJrJr SS KJrJr SS KJrJrJrJr SS KJr SS KJrJ r J!r! SSK"J#r# SSK$J%r%J&r&J'r'J(r(J)r)J*r* SSK+J,r, SSK-J.r. SSK/J0r0J1r1 SSK2J3r3 SSK4J5r5J6r6J7r7J8r8J9r9J:r: SSK;J\ R~"\ R5RrBSrCSrDSrES%SjrFSrGSrHSrI"S S!\<\S"9rJ"S#S\\J5rK"S$S\\J5rLg)&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_estimator_hascheck_is_fittedhas_fit_parameter validate_data) BaseEnsemble_partition_estimatorsBaggingClassifierBaggingRegressorcPU(aURSX#5nU$[X#US9nU$)zDraw randomly sampled indices.r) random_state)randintr$)r1 bootstrap n_population n_samplesindicess L/var/www/html/venv/lib/python3.13/site-packages/sklearn/ensemble/_bagging.py_generate_indicesr86s8&&q,B N - ,  NcN[U5n[XX55n[XXF5nXx4$)z)Randomly draw feature and sample indices.)rr8) r1bootstrap_featuresbootstrap_samples n_featuresr5 max_features max_samplesfeature_indicessample_indicess r7_generate_bagging_indicesrBCs;&l3L(*O'N  **r9c URupURn URn URn URn[ UR S5nU=(d X:gn/n/n[ UR S5n[5(d$U(dURS5b [S5e[U5GHnUS:a[SUS-X4-5 UUnURSUS9nU(a[URUS 9nO URn[UUU U U U U 5unnUR!5n[5(a([#UR 5nUR%S S 5nOUnU(a['UR)SS5U5R!5nU (a[*R,"UU S 9nUU-nO[/UU 5)nS UU'UUS'U(a USS2U4OUn U"U U40UD6 O=[1UU5n![1UU5n [3UUUS9nU(a U SS2U4n U"U U!40UD6 UR5U5 UR5U5 GM UU4$)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)appendr1)rDfit)rE) minlengthr)paramsr6)shape _max_features _max_samplesr3r;r) estimator_rget ValueErrorrangeprint_make_estimatorrrGrBcopyrconsumesr&popnpbincountrrr%rF)" n_estimatorsensembleXyseedstotal_n_estimatorsverboserD fit_paramsr5r=r>r?r3r;has_check_inputrequires_feature_indexing estimatorsestimators_featuressupport_sample_weightir1 estimator estimator_fitfeaturesr6 fit_params_request_or_routerconsumes_sample_weightcurr_sample_weight sample_countsnot_indices_maskX_y_s" r7_parallel_build_estimatorsrq[sGGI))L''K""I!44'(;(;]KO 2 Pl6PJ.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)$""8,O!R * **r9c *URSn[R"Xc45n[X5Hup[ US5(aUR "USS2U 440U=(d 0D6n U[ UR5:XaXz- nMXUSS2UR4==U SS2[[ UR554- ss'MUR"USS2U 440U=(d 0D6n [U5Hn X|X4==S- ss'M M U$)zBPrivate function used to compute (proba-)predictions within a job.r predict_probaNr+) rJrVzerosziphasattrrslenclasses_rPpredict) rbrcrZ n_classespredict_paramspredict_proba_paramsr5probarfrhproba_estimator predictionsres r7_parallel_predict_probars I HHi+ ,E":C 9o . .'55!X+#1#7ROC 2 233(a+++,uS!3!34551, $++!X+#7#=2K9%'(A-(&) D. Lr9c URSn[R"XS45nUR[R*5 [R "U[ S9n[X5GHupUR"USS2U 440UD6n U[UR5:Xa[R"Xj5nMQ[R"USS2UR4U SS2[[UR5545USS2UR4'[R"XxR5n [R"USS2U 4[R*5USS2U 4'GM U$)z@Private function used to compute log probabilities within a job.r)dtypeN)rJrVemptyfillinfarangeintrupredict_log_probarwrx logaddexprP setdiff1d) rbrcrZrzrIr5 log_proba all_classesrfrhlog_proba_estimatormissings r7_parallel_predict_log_probars* I)/0I NNBFF7))IS1K":C '99!AxK.SFS I../ / YDI02||!Y///0#AuS1C1C-D'E$EF0Ia+++ , ll;0B0BCG$&LL1g:1F$PIaj ! D r9cD^^[UU4Sj[X555$)z8Private function used to compute decisions within a job.c3\># UH!upUR"TSS2U440TD6v M# g7fNdecision_function.0rfrhrZrIs r7 ._parallel_decision_function.. s3#G I ##AakN=f=#G),sumrurbrcrZrIs ``r7_parallel_decision_functionr " #&z#G r9cD^^[UU4Sj[X555$)z:Private function used to compute predictions within a job.c3\># UH!upUR"TSS2U440TD6v M# g7frryrs r7r/_parallel_predict_regression..s3#G I !AxK.3F3#Grrrs ``r7_parallel_predict_regressionrrr9c t^\rSrSr%Sr\"SS/5S/\"\SSSS9/\"\SSSS9\"\S SS S9/\"\SSSS9\"\S SS S9/S /S /S /S /S\/S /S /S. r \ \ S'\ S!SSSSSSSSS S. U4Sjjj5r \"SS9S"Sj5rSrS#Sjr\ S5rSrSr\S5rSr\ S5rU4SjrS rU=r$)$ BaseBaggingizvBase class for Bagging meta-estimator. Warning: This class should not be used directly. Use derived classes instead. rGryNr+left)closedrrightbooleanr1r^ rfrXr?r>r3r; oob_score warm_startn_jobsr1r^_parameter_constraints?TF r?r>r3r;rrrr1r^c >[T U]UUS9 X0lX@lXPlX`lXplXlXlXl Xl g)N)rfrX) super__init__r?r>r3r;rrrr1r^ selfrfrXr?r>r3r;rrrr1r^ __class__s r7rBaseBagging.__init__5sP %  '(""4"$ ( r9)prefer_skip_nested_validationc [X@S5 [UUUSS/SSSS9upUR"UU4URUS.UD6$) aBuild a Bagging ensemble of estimators from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression). sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting. **fit_params : dict Parameters to pass to the underlying estimators. .. versionadded:: 1.5 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : object Fitted estimator. rGcsrcscNFT) accept_sparserensure_all_finite multi_output)r?rE)rr*_fitr?)rrZr[rEr_s r7rGBaseBagging.fitSslJ *E2   %.# yy  (('     r9c0$rrs r7_parallel_argsBaseBagging._parallel_argss r9c  ^^^^^^^^^[TR5nTRSn U TlTR T5mTR TR 55 UbXgS'[5(a[TS40UD6mO:[5m[US9Tl SU;aUSTRRS'UbUTRl Uc TRnO:[U[ R"5(d[%UTRS-5nUTRS:a ['S5eUTl[TR*[ R"5(a TR*n OA[TR*[,5(a"[%TR*TR.-5n W TR.:a ['S5e[1S[%U 55n U TlTR4(dTR6(a ['S5eTR8(aTR6(a ['S 5e[;TS 5(aTR8(aT?TR8(a[;TS 5(d/Tl/Tl TRB[ETR>5- n U S:a-['S TRB[ETR>54-5eU S:Xa [GS 5 T$[IU TRJ5un mm[MT5mTR8(a@[ETR>5S:a'URO[P[ETR>5S9 URO[PU S9mTTl)[USU TRVS.TRY5D6"UUUUUUUUU4 Sj[[U 555n T=R>[][^R`RcSU 555- slT=R@[][^R`RcSU 555- sl TR6(aTReTT5 T$)a2Build 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. 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, default=None Parameters to pass to the :term:`fit` method of the underlying estimator. Returns ------- self : object Fitted estimator. rrErG)rGz 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> # UHMn[[5"TUTTT TTUTUS-T TRTTRRS9 v MO g7f)r+)r^rDr_N)r#rqr^rfrG) rrerZrDrX routed_paramsr\rstartsr]r[s r7r#BaseBagging._fit..%si # . /QfQi&Q-0" '(2266 #sAAc3*# UH oSv M g7f)rNrrts r7rr6)D 1A$ c3*# UH oSv M g7f)r+Nrrs r7rr9rrr)3rr1rJ _n_samples _validate_y_validate_estimator_get_estimatorrrrrfrGrM max_depthr? isinstancenumbersrrrOrLr>floatn_features_in_maxrKr3rrrvrrestimators_features_rXrwrr-rrr2MAX_INT_seedsr"r^rrPlist itertoolschain from_iterable_set_oob_score)rrZr[r?rrDrEr_r1r5r>n_more_estimatorsr all_resultsrXrr\rr]s``` ` @@@@@r7rBaseBagging._fitsd*$*;*;< GGAJ #   Q    !4!4!67  $*7 '   +D%F:FM!GM&+ &;M #*,?I#@ ''++O<  (1DOO %  **KK)9)9::kAGGAJ67K  #?@ @( d'')9)9 : :,,L ))5 1 1t0043F3FFGL $-- -AB B1c,/0 *~~$..UV V ??t~~UV V 4 & &4??gdM&B&B!D (*D % --D4D4D0EE q <$$c$*:*:&;<=  ! # ! K(= t{{( $ f!. ??s4#3#34q8  s43C3C/D E$$W3D$E  4<< 373F3F3H  6]  $ D OO ) ))D )D D   !!T OO ) ))D )D D&  ! >>   1 % r9cg)z+Calculate out of bag predictions and score.Nr)rrZr[s r7rBaseBagging._set_oob_scoreAr9cr[UR5S:XdURSS:Xa [USS9$U$)Nr+Tr)rwrJrrr[s r7rBaseBagging._validate_yEs2 qww<1  a- -r9c ## URHWn[UURURURUR UR UR5up#X#4v MY g7fr)rrBr;r3rrrKrL)rseedr@rAs r7_get_estimators_indices#BaseBagging._get_estimators_indicesJscKKD/H''##""!!/ +O"1 1 sA)A+cXUR5VVs/sHupUPM snn$s snnf)a The subset of drawn samples for each base estimator. Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples. Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected. )r)r_rAs r7estimators_samples_BaseBagging.estimators_samples_[s*9=8T8T8VW8V#418VWWWs&cd[URRS9n[5nUR SSS9R SSS9 [ UR 5S5(aUR SSS9R SSS9 OUR SSS9R SSS9 [ UR 5S5(aUR SSS9 O@[ UR 5S5(aUR SSS9 OUR SSS9 UR UR 5US9 U$) a"Get 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. )ownerrG)callercalleerrsryr)rfmethod_mapping)rr__name__raddrvr)rrouterrs r7get_metadata_routing BaseBagging.get_metadata_routingjsB dnn&=&=>&%6::&/B ; 4&&(/ : :"")O"LPP*?Q"")I"FJJ*9K 4&&(*= > >   &9BU  Vt**,o>>""*=o"V""*=i"P T002> R r9cg)z"Resolve which estimator to return.Nrrs r7rBaseBagging._get_estimatorrr9c>[TU]5n[UR55RR URl[UR55RR URlU$r)r__sklearn_tags__rr input_tagssparse allow_nan)rtagsrs r7rBaseBagging.__sklearn_tags__saw')!)$*=*=*?!@!K!K!R!R$,T-@-@-B$C$N$N$X$X! r9)rKrLrrr3r;rrr>r?rrr1r^rN r)NNTN)r __module__ __qualname____firstlineno____doc__rrrrrdict__annotations__rrr rGrrrrrpropertyrrrr__static_attributes__ __classcell__rs@r7rrs{!%!34d;!(AtFCD Xq$v 6 ZAg 6 Xq$v 6 ZAg 6  [(k[ k"'(;#$D(  :&+4 4 lob:: 2" X X8t11r9r) metaclassc ^\rSrSrSrSSSSSSSSSSS. U4S jjjrS rS rS rS r Sr Sr \ "\ "SSS95S5rSrU=r$)r.iaA 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 4>[T U]UUUUUUUUU U U S9 gNrrrrs r7rBaggingClassifier.__init__W8 %#%1!%  r9cHURc [5$UR$zEResolve which estimator to return (default is DecisionTreeClassifier))rfrrs r7r BaggingClassifier._get_estimatorts >> !)+ +~~r9c URSnURn[R"X445n[ UR UR UR5Hupgn[Xs5)n [US5(a.XYSS24==URXSS24SS2U45- ss'MQURXSS24SS2U45n Sn [U5H#n X(dMX\X4==S- ss'U S- n M% M URSS9S:HR5(a [S5 XURSS9SS2[R 4- n [#U[R$"USS95nXlXlg)Nrrsr+axis{Some inputs do not have OOB scores. This probably means too few estimators were used to compute any reliable oob estimates.)rJ n_classes_rVrtrurrrrrvrsryrPranyrnewaxisr argmaxoob_decision_function_r)rrZr[r5r%rrfsamplesrhmaskpjreoob_decision_functionrs r7r BaggingClassifier._set_oob_scorezsoGGAJ __ hh 67 ,/   d668Q8Q- (I$G77Dy/22!G$ (?(?QwZH-)$ %%qqz1h;&?@y)Aww#qtG,1,Q*- ( OOO #q ( - - / / 9  !,oo1o.Eam.T T"1bii !&DE &;##r9c[USS9n[U5 [R"USS9uUln[ UR5UlU$)NTr)return_inverse)rr!rVuniquerxrwr%rs r7rBaggingClassifier._validate_ysB  &$Q'99Qt< qdmm,r9c [X S5 UR"U40UD6nURR[R "USS9SS9$)auPredict class for X. The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a ``predict_proba`` method, then it resorts to voting. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. **params : dict Parameters routed to the `predict_proba` (if available) or the `predict` method (otherwise) of the sub-estimators via the metadata routing API. .. versionadded:: 1.7 Only available if `sklearn.set_config(enable_metadata_routing=True)` is set. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y : ndarray of shape (n_samples,) The predicted classes. ryr+r"r)rrsrxtakerVr()rrZrIpredicted_probabilitiys r7ryBaggingClassifier.predictsK: & 2!%!3!3A!@!@}}!!299-C!#LTU!VVr9c ^^^^[UTS5 [T5 [TTSS/SSSS9m[5(a[ TS40UD6mO [ 5m[ [ 5S9Tl[TRTR5up4m[S UTRS.TR5D6"UUUU4S j[U555n[U5TR- nU$) 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. **params : dict Parameters routed to the `predict_proba` (if available) or the `predict` method (otherwise) of the sub-estimators via the metadata routing API. .. versionadded:: 1.7 Only available if `sklearn.set_config(enable_metadata_routing=True)` is set. See :ref:`Metadata Routing User Guide ` for more details. 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_`. rsrrNFrrrreset)rsrc 3(># UHn[[5"TRTUTUS-TRTUTUS-TTRTR R SS5TR R SS5S9v M g7f)r+ryNrs)r{r|)r#rrrr%rfrNrrerZrrrs r7r2BaggingClassifier.predict_proba..s # + ,  VAE];))&)fQUmD,66::9dK%2%<%<%@%@RV%W  #sBBr)rr(r*rrrrfr-rXrr"r^rrPr) rrZrIrr all_probar}rrs `` @@r7rsBaggingClassifier.predict_probasB &$8    %.#     +D/LVLM!GM&+%'&BM #2$2C2CT[[Q6 4<< 373F3F3H  6]   I!2!22 r9c ^^^^ [UTS5 [T5 [TRS5(a[ TTSS/SSSS9m[ 5(a[ TS40UD6mO [5m[[5S9Tl[TRTR5up4m [UTRS9"UUUU 4S j[U555nUS n[S [U55Hn[ R""XeU5nM U[ R$"TR5-nU$[ R$"TR&"T40UD65nU$) aNPredict 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. **params : dict Parameters routed to the `predict_log_proba`, the `predict_proba` or the `proba` method of the sub-estimators via the metadata routing API. The routing is tried in the mentioned order depending on whether this method is available on the sub-estimator. .. versionadded:: 1.7 Only available if `sklearn.set_config(enable_metadata_routing=True)` is set. See :ref:`Metadata Routing User Guide ` for more details. 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_`. rrrNFr9)rrc 3># UHfn[[5"TRTUTUS-TRTUTUS-TTRTR R S9v Mh g7fr+)rIN)r#rrrr%rfrr<s r7r6BaggingClassifier.predict_log_proba..Msy J'A34$$VAYA?--fQi&Q-HOO(22DD 'sA.A1rr+)rr(rvrMr*rrrrfr-rXrr"r^rPrwrVrlogrs) rrZrIrr all_log_probarr-rrs `` @@r7r#BaggingClassifier.predict_log_probasE@ &$(;< 4??$7 8 8$en"' A !! /6I TV T % */%'*J '!6d6G6G U Fv$FDLLI Jv J M&a(I1c-01LL!4DE 2  1 12 2I t11!>v>?Ir9r)rrf) delegatesc ^^^^[UTS5 [T5 [TTSS/SSSS9m[5(a[ TS40UD6mO [ 5m[ [ 5S9Tl[TRTR5up4m[UTRS9"UUUU4S j[U555n[U5TR- nU$) 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. **params : dict Parameters routed to the `decision_function` method of the sub-estimators via the metadata routing API. .. versionadded:: 1.7 Only available if `sklearn.set_config(enable_metadata_routing=True)` is set. See :ref:`Metadata Routing User Guide ` for more details. 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``. rrrNFr9rrc 3># UH[n[[5"TRTUTUS-TRTUTUS-TTRR S9v M] g7frB)r#rrrrfrr<s r7r6BaggingClassifier.decision_function..srF # / 0  VAE];))&)fQUmD$..@@   #A#A&rr(r*rrrrfr-rXrr"r^rPr) rrZrIrr all_decisions decisionsrrs `` @@r7r#BaggingClassifier.decision_functiones> &$(;<    %.#     +D2EPPM!GM&+eg&FM #2$2C2CT[[Q6  EF 6]F   &):):: r9)rxr%r)rr )rr rrrrrrrryrsrr r'rrrrs@r7r.r.seR    : %$N WDHTN`*6RS@@r9c ^^\rSrSrSrSSSSSSSSSSS. U4S jjjrS rS rS rS r U=r $)r/iaA 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 4>[T U]UUUUUUUUU U U S9 grrrs r7rBaggingRegressor.__init__Jrr9c ^^^^[UTS5 [T5 [TTSS/SSSS9m[5(a[ TS40UD6mO [ 5m[ [ 5S9Tl[TRTR5up4m[UTRS9"UUUU4S j[U555n[U5TR- nU$) aPredict 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. **params : dict Parameters routed to the `predict` method of the sub-estimators via the metadata routing API. .. versionadded:: 1.7 Only available if `sklearn.set_config(enable_metadata_routing=True)` is set. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y : ndarray of shape (n_samples,) The predicted values. ryrrNFr9rrc 3># UH[n[[5"TRTUTUS-TRTUTUS-TTRR S9v M] g7frB)r#rrrrfryr<s r7r+BaggingRegressor.predict..srB # 0 1  VAE];))&)fQUmD$..66   #rKrL) rrZrIrr all_y_haty_hatrrs `` @@r7ryBaggingRegressor.predictgs8 &$ 2    %.#     +D)FvFM!GM&+EG&> !(* *~~r9)rZrr ) rr rrrrryrrrrrs@r7r/r/sS\@    :?B38r9)NN)Mrrrabcrr functoolsrrwarningsrnumpyrVbaser r r metricsr rtreerrutilsrrrr utils._maskrutils._param_validationrrr utils._tagsrutils.metadata_routingrrrrrrutils.metaestimatorsr utils.multiclassr!utils.parallelr"r# utils.randomr$utils.validationr%r&r'r(r)r*_baser,r-__all__iinfoint32rrr8rBrqrrrrrr.r/rr9r7rts '@@.@ *FF"0;.57  2 3 ((288   +0m+j #L2R,'Rj yyx]~{]r9