:iSrSSKrSSKJr SSKJr SSKJr SSKJr SSK J r SSK J r S S K Jr S S KJrJrJrJrJrJr S S KJrJr S S KJr \R6"S5 SS/r\"\R:5R<r\"\5\"S5:aSSKJ r OSSK!J r "SS\\RD5r"SSjr#Sr$SSjr%\"S\&\"S/5/S/S.SS9SSS.Sj5r'g)z The :mod:`imblearn.pipeline` module implements utilities to build a composite estimator, as a chain of transforms, samples and estimators. N)pipeline)clone)Bunch) parse_version) available_if) check_memory)_ParamsValidationMixin)METHODSMetadataRouter MethodMapping_raise_for_params_routing_enabledprocess_routing) HasMethodsvalidate_params) _fit_context fit_resamplePipeline make_pipelinez1.5)_print_elapsed_timec^\rSrSr%SrSS\\"S/5/S/S.r\\ S'S r S(U4S jjr S)S jr \ "S S 9S*Sj5rSr\"\5\ "S S 9S*Sj55r\"\R&"S55S5rSr\"\5\ "S S 9S*Sj55r\"\R&"S55\ "S S 9S*Sj55r\"\R&"S55S5r\"\R&"S55S5r\"\R&"S55S5r\"\R&"S55S5rSr\"\5S 5rS!r\"\5S"5r\"\R&"S#55S)S$j5r S%r!S&r"S'r#U=r$$)+r.aPipeline of transforms and resamples with a final estimator. Sequentially apply a list of transforms, sampling, and a final estimator. Intermediate steps of the pipeline must be transformers or resamplers, that is, they must implement fit, transform and sample methods. The samplers are only applied during fit. The final estimator only needs to implement fit. The transformers and samplers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``. Parameters ---------- steps : list List of (name, transform) tuples (implementing fit/transform/fit_resample) that are chained, in the order in which they are chained, with the last object an estimator. memory : Instance of joblib.Memory or str, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Attributes ---------- named_steps : :class:`~sklearn.utils.Bunch` Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters. classes_ : ndarray of shape (n_classes,) The classes labels. n_features_in_ : int Number of features seen during first step `fit` method. See Also -------- make_pipeline : Helper function to make pipeline. Notes ----- See :ref:`sphx_glr_auto_examples_pipeline_plot_pipeline_classification.py` .. warning:: A surprising behaviour of the `imbalanced-learn` pipeline is that it breaks the `scikit-learn` contract where one expects `estimmator.fit_transform(X, y)` to be equivalent to `estimator.fit(X, y).transform(X)`. The semantic of `fit_resample` is to be applied only during the fit stage. Therefore, resampling will happen when calling `fit_transform` while it will only happen on the `fit` stage when calling `fit` and `transform` separately. Practically, `fit_transform` will lead to a resampled dataset while `fit` and `transform` will not. Examples -------- >>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split as tts >>> from sklearn.decomposition import PCA >>> from sklearn.neighbors import KNeighborsClassifier as KNN >>> from sklearn.metrics import classification_report >>> from imblearn.over_sampling import SMOTE >>> from imblearn.pipeline import Pipeline >>> X, y = make_classification(n_classes=2, class_sep=2, ... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0, ... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10) >>> print(f'Original dataset shape {Counter(y)}') Original dataset shape Counter({1: 900, 0: 100}) >>> pca = PCA() >>> smt = SMOTE(random_state=42) >>> knn = KNN() >>> pipeline = Pipeline([('smt', smt), ('pca', pca), ('knn', knn)]) >>> X_train, X_test, y_train, y_test = tts(X, y, random_state=42) >>> pipeline.fit(X_train, y_train) Pipeline(...) >>> y_hat = pipeline.predict(X_test) >>> print(classification_report(y_test, y_hat)) precision recall f1-score support 0 0.87 1.00 0.93 26 1 1.00 0.98 0.99 224 accuracy 0.98 250 macro avg 0.93 0.99 0.96 250 weighted avg 0.99 0.98 0.98 250 no_validationNcacheboolean)stepsmemoryverbose_parameter_constraintsc F[UR6upURU5 USSnUSnUHnUbUS:XaM[US5=(a [US5n[US5nU=(d U(+nU(a[ SU<S[ U5<S35eU(aU(a[ S U-5e[ U[R5(dM[ S 5e Ub6US:wa/[US5(d[ S U<S[ U5<S 35eggg) N passthroughfit transformrzAll intermediate steps of the chain should be estimators that implement fit and transform or fit_resample (but not both) or be a string 'passthrough' 'z' (type z ) doesn't)zAll intermediate steps of the chain should be estimators that implement fit and transform or fit_resample. '%s' implements both)z;All intermediate steps of the chain should not be PipelineszLLast step of Pipeline should implement fit or be the string 'passthrough'. 'z ) doesn't) zipr_validate_nameshasattr TypeErrortype isinstancerr) selfnames estimators transformers estimatort is_transfomer is_sampleris_not_transfomer_or_samplers T/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/imblearn/pipeline.py_validate_stepsPipeline._validate_stepss5, U#"#2 rN AyA.#Au-I'![2IM N3J0=0K+L (+23DG=-123!X..//Q3>  !]*Iu--d9o/ .+ "cN>[TU]X5nU(a [SU5$U$)zGenerate (idx, (name, trans)) tuples from self.steps. When `filter_passthrough` is `True`, 'passthrough' and None transformers are filtered out. When `filter_resample` is `True`, estimator with a method `fit_resample` are filtered out. c*[USS5(+$)Nr"rr()xs r5 Pipeline._iter..s"~(F$Fr8)super_iterfilter)r, with_finalfilter_passthroughfilter_resampleit __class__s r5r@Pipeline._iters)W]: : FK KIr8c [UR5UlUR5 [UR5nUR [ 5nUR [5nURSSSS9Hupxn U bU S:Xa'[SURU55 SSS5 M6[US5(aURcU n O [U 5n [U S5(d[U S5(a U"U UUSSURU5X8S9upO0[U S 5(aU"U UUSURU5X8S9upn UW 4URU'M X4$!,(df  N=f) NF)rBrCrDr#rlocationr% fit_transform)message_clsnamemessageparamsr)listrr6rrr_fit_transform_one_fit_resample_oner@r _log_messager(rIr) r,Xy routed_paramsrfit_transform_one_cachedfit_resample_one_cachedstep_idxname transformercloned_transformerfitted_transformers r5_fit Pipeline._fits$**%  dkk*#)<<0B#C "(,,/@"A+/::,6, 'HK"k]&B(T5F5Fx5PQRQvz**v/F&1"%*;%7");777"O<<)A&$. --h7(.)%%+^<<+B&$. --h7(. ,((%)*<#=DJJx O, Pt IRQs %E'' E5 Fprefer_skip_nested_validationc nURSUS9nURXU5upV[SUR[ UR 5S- 55 UR S:wa4X@R SSnUR R"XV40USD6 SSS5 U$!,(df  U$=f) aFit the model. Fit all the transforms/samplers one after the other and transform/sample the data, then fit the transformed/sampled data using the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True` is set via :func:`~sklearn.set_config`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : Pipeline This estimator. r$methodpropsrr r#r"rN)_check_method_paramsr\rrQlenr_final_estimatorr$)r,rRrSrMrTXtytlast_step_paramss r5r$ Pipeline.fits`11f1M 1/ T->->s4::QR?R-S T$$ 5#0B1B#C %%))"L4DU4KLU U T s AB%% B4cURS:H=(d3 [URS5=(d [URS5$)Nr#r%rJrfr(r,s r5_can_fit_transformPipeline._can_fit_transformPs?  ! !] 2 ?t,,k: ?t,,o> r8c URSUS9nURXU5upVURn[SUR [ UR 5S- 55 US:Xa UsSSS5 $X@R SSn[US5(aUR"XV40USD6sSSS5 $UR"XR40US D6R"U40US D6sSSS5 $!,(df  g=f) aFit the model and transform with the final estimator. Fits all the transformers/samplers one after the other and transform/sample the data, then uses fit_transform on transformed data with the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples. rJrarr r#Nr"rr$r%) rdr\rfrrQrerr(rJr$r% r,rRrSrMrTrgrh last_stepris r5rJPipeline.fit_transformWs`11PV1W 1/)) T->->s4::QR?R-S TM)U T -ZZ^A->? y/22 ...? U T!}}RF.>u.EFPP*;7U T Ts"C+4:C+8)C++ C9predictc Un[5(dMURSS9HupEnURU5nM URSSR"U40UD6$[ US40UD6nURSS9H%upEnUR"U40XuRD6nM' URSSR"U40XpRSSRD6$)aTransform the data, and apply `predict` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict` method. Only valid if the final estimator implements `predict`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the ``predict`` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True` is set via :func:`~sklearn.set_config`. See :ref:`Metadata Routing User Guide ` for more details. Note that while this may be used to return uncertainties from some models with ``return_std`` or ``return_cov``, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. Returns ------- y_pred : ndarray Result of calling `predict` on the final estimator. FrBr"r rtr)rr@r%rrtrr,rRrMrg_rXr%rTs r5rtPipeline.predicts^!!&*jjEj&B"((,'C::b>!$,,R:6: :(iB6B "&***"> AY$$RI=+>+H+HIB#?zz"~a ((X}ZZ^A=N/O/W/WXXr8cZURS:H=(d [URS5$)Nr#rrlrms r5_can_fit_resamplePipeline._can_fit_resamples,$$ 5   ! !>:  r8c URSUS9nURXU5upVURn[SUR [ UR 5S- 55 US:Xa UsSSS5 $X@R SSn[US5(aUR"XV40USD6sSSS5 $SSS5 g!,(df  g=f) aeFit the model and sample with the final estimator. Fits all the transformers/samplers one after the other and transform/sample the data, then uses fit_resample on transformed data with the final estimator. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters passed to the ``fit`` method of each step, where each parameter name is prefixed such that parameter ``p`` for step ``s`` has key ``s__p``. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples. yt : array-like of shape (n_samples, n_transformed_features) Transformed target. rrarr r#Nr"r) rdr\rfrrQrerr(rrqs r5rPipeline.fit_resamplesf11v1V 1/)) T->->s4::QR?R-S TM)U T -ZZ^A->? y.11 --.~> U T2 U T Ts"C4:C C fit_predictc tURSUS9nURXU5upVX@RSSn[SUR [ UR5S- 55 URSSR "XV40URS05D6nSSS5 U$!,(df  W$=f)aApply `fit_predict` of last step in pipeline after transforms. Applies fit_transforms of a pipeline to the data, followed by the fit_predict method of the final estimator in the pipeline. Valid only if the final estimator implements fit_predict. Parameters ---------- X : iterable Training data. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Training targets. Must fulfill label requirements for all steps of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the ``predict`` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Note that while this may be used to return uncertainties from some models with ``return_std`` or ``return_cov``, uncertainties that are generated by the transformations in the pipeline are not propagated to the final estimator. Returns ------- y_pred : ndarray of shape (n_samples,) The predicted target. rrar"rrr N)rdr\rrrQrerget) r,rRrSrMrTrgrhparams_last_stepy_preds r5rPipeline.fit_predictsl11f1U 1/(B):; T->->s4::QR?R-S TZZ^B'33*..}bAFU U T s *4B(( B7 predict_probac Un[5(dMURSS9HupEnURU5nM URSSR"U40UD6$[ US40UD6nURSS9H%upEnUR"U40XuRD6nM' URSSR"U40XpRSSRD6$)aTransform the data, and apply `predict_proba` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_proba` method. Only valid if the final estimator implements `predict_proba`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the `predict_proba` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_proba` on the final estimator. Frvr"r rr)rr@r%rrrrws r5rPipeline.predict_proba[sT!!&*jjEj&B"((,'C::b>!$222@@ @(oHH "&***"> AY$$RI=+>+H+HIB#?zz"~a ..   2q 12@@  r8decision_functionc v[X S5 [US40UD6nUnURSS9H9upVnUR"U40UR U05R S05D6nM; UR SSR "U40UR UR SS05R S05D6$)aTransform the data, and apply `decision_function` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `decision_function` method. Only valid if the final estimator implements `decision_function`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of string -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_score : ndarray of shape (n_samples, n_classes) Result of calling `decision_function` on the final estimator. rFrvr%r"r r)rrr@r%rrrr,rRrMrTrgrxrXr%s r5rPipeline.decision_functions< &(;<(.ALVL "&***"> AY$$#''b155k2FB#?zz"~a 22  ##DJJrN1$5r:>>?RTVW  r8 score_samplescUnURSS9Hu p4URU5nM URSSRU5$)aTransform the data, and apply `score_samples` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score_samples` method. Only valid if the final estimator implements `score_samples`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : ndarray of shape (n_samples,) Result of calling `score_samples` on the final estimator. Frvr"r )r@r%rr)r,rRrgrxrYs r5rPipeline.score_samplessQ(!%u!= Aq&&r*B">zz"~a ..r22r8predict_log_probac Un[5(dMURSS9HupEnURU5nM URSSR"U40UD6$[ US40UD6nURSS9H%upEnUR"U40XuRD6nM' URSSR"U40XpRSSRD6$)aTransform the data, and apply `predict_log_proba` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `predict_log_proba` method. Only valid if the final estimator implements `predict_log_proba`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object - If `enable_metadata_routing=False` (default): Parameters to the `predict_log_proba` called at the end of all transformations in the pipeline. - If `enable_metadata_routing=True`: Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 0.20 .. versionchanged:: 1.4 Parameters are now passed to the ``transform`` method of the intermediate steps as well, if requested, and if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_log_proba : ndarray of shape (n_samples, n_classes) Result of calling `predict_log_proba` on the final estimator. Frvr"r rr)rr@r%rrrrws r5rPipeline.predict_log_probasT!!&*jjEj&B"((,'C::b>!$66rDVD D(.ALVL "&***"> AY$$RI=+>+H+HIB#?zz"~a 22   2q 12DD  r8cZURS:H=(d [URS5$)Nr#r%rlrms r5_can_transformPipeline._can_transforms,$$ 5   ! !;:  r8c [X S5 [US40UD6nUnUR5H%upVnUR"U40X6RD6nM' U$)aTransform the data, and apply `transform` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `transform` method. Only valid if the final estimator implements `transform`. This also works where final estimator is `None` in which case all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_transformed_features) Transformed data. r%)rrr@r%rs r5r%Pipeline.transforms_B & 4(kDVD "&**, AY$$RI=+>+H+HIB#/ r8cB[SUR555$)Nc3@# UHu p[US5v M g7f)inverse_transformNr;).0rxr1s r5 2Pipeline._can_inverse_transform..DsO,wq!71122,s)allr@rms r5_can_inverse_transformPipeline._can_inverse_transformCsO$**,OOOr8c [X S5 [US40UD6n[[UR 555nUH%upVnUR "U40X6R D6nM' U$)aApply `inverse_transform` for each step in a reverse order. All estimators in the pipeline must support `inverse_transform`. Parameters ---------- Xt : array-like of shape (n_samples, n_transformed_features) Data samples, where ``n_samples`` is the number of samples and ``n_features`` is the number of features. Must fulfill input requirements of last step of pipeline's ``inverse_transform`` method. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- Xt : ndarray of shape (n_samples, n_features) Inverse transformed data, that is, data in the original feature space. r)rrreversedrNr@r)r,rgrMrT reverse_iterrxrXr%s r5rPipeline.inverse_transformFsr< &(;<(.ALVL TZZ\ 23 ". AY,,#);;B#/ r8scorec Un[5(dVURSS9HupgnURU5nM 0n UbX9S'URSSR"XR40U D6$[ US4SU0UD6n UnURSS9H%upgnUR"U40XRD6nM' URSSR"XR40XRSSRD6$)amTransform the data, and apply `score` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `score` method. Only valid if the final estimator implements `score`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. y : iterable, default=None Targets used for scoring. Must fulfill label requirements for all steps of the pipeline. sample_weight : array-like, default=None If not None, this argument is passed as ``sample_weight`` keyword argument to the ``score`` method of the final estimator. **params : dict of str -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- score : float Result of calling `score` on the final estimator. Frv sample_weightr"r rr)rr@r%rrr) r,rRrSrrMrgrxrXr% score_paramsrTs r5rPipeline.scoreps J!!&*jjEj&B"((,'CL(0=_-::b>!$**2ALA A( ' )6 :@ "&***"> AY$$RI=+>+H+HIB#?zz"~a &&rW jjnQ>O0P0V0VWWr8c[URRS9nURSSS9GHdup#n[ 5n[ US5(a;UR SSS9R SSS9R SSS9R S SS9 OrUR SSS9R SS S9R SSS9R SS S9R SSS9R SS S9R S SS9R S S S9 UR S S S9R S S S9R S S S9R S S S9R SS S9R S S S9R SSS9R SS S9R S S S9 UR "SSU0X40D6 GMg URSupgUbUS:XaU$[ 5n[ US5(aUR SSS9R S SS9 O:UR SSS9R SS S9R S SS9R S S S9 UR SSS9R S S S9R SSS9R S S S9R S S S9R SSS9R S S S9R SSS9R SSS9R S S S9 UR "SSU0Xg0D6 U$)aGet metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. Returns ------- routing : MetadataRouter A :class:`~utils.metadata_routing.MetadataRouter` encapsulating routing information. )ownerFT)rBrCrJr$)callercalleerrr%rtrrrrrmethod_mappingr"r#)r rF__name__r@r r(addr)r,routerrxrXtransr final_name final_ests r5get_metadata_routingPipeline.get_metadata_routings dnn&=&=>#jjEdjSNAU*_Nuo.."&&eO&LSSHS oSFSSG#&&eE&BSkS:SS>S SDS eS<S kSBSuS=S{SC"")K"HIk:OK@/ D/ DK </8KLGK8N;? JJ Fn F FGTJ!% 2   ] :M' 9o . .""//"RVV)/W ""%">E+6N59N;?   eE  : S )S 4 S mS < SS @ S+4GS H S+4GS H S KS 8 S+4GS H SS 0 S~S >  L.LZ4KL r8c [5(a[X40UDUD6nU$[S0URVVVs0sH+upVUcM U[S0[Vs0sHo0_M snD6_M- snnnD6nUR 5HWupSU;a[ SRU55eURSS5upjXUSU 'XUSU 'XUSU 'MY U$s snfs snnnf)N__zPipeline.fit does not accept the {} parameter. You can pass parameters to specific steps of your pipeline using the stepname__parameter format, e.g. `Pipeline.fit(X, y, logisticregression__sample_weight=sample_weight)`.r r$rJrr) rrrrr items ValueErrorformatsplit) r,rbrckwargsrTrXstepfit_params_stepspnamepvalparams r5rdPipeline._check_method_paramss   +DLELVLM $ '+jj&0 FD%EG"DG&2:G"DEE&0   %{{} u$$,-3F5M $kk$2 7;&u-e4BF&7>?C&}5e< -$ #)#Es C"C" C C"C")r)TTT)NN)N)%r __module__ __qualname____firstlineno____doc__strrr dict__annotations__r6r@r\rr$rnrrJr_final_estimator_hasrtr{rrrrrrrr%rrrrrd__static_attributes__ __classcell__)rFs@r5rr.s0hV!j'34;$D1f  1h&+2 2h $%&+: & :x(// :;9Y<9Yv #$&+8 % 8t(// >?&+9 @ 9|(//@A6 B6 p(//0CDE* F* X(//@A3B30(//0CDE6 F6 p .!("(TP()'*'R(//895X:5XrUn$$r8c [X45 UR"X40URS05D6upgXgU4sSSS5 $!,(df  g=f)Nr)rrr)samplerrRrSrKrLrMX_resy_ress r5rPrPs@ _ 6++ASFJJ~r4RS W$ 7 6 6s )? A cNUR"U40URD6nUcU$XS-$)aCall transform and apply weight to output. Parameters ---------- transformer : estimator Estimator to be used for transformation. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input data to be transformed. y : ndarray of shape (n_samples,) Ignored. weight : float Weight to be applied to the output of the transformation. params : dict Parameters to be passed to the transformer's ``transform`` method. This should be of the form ``process_routing()["step_name"]``. )r%)rYrRrSweightrMress r5_transform_oner&s1,    6V%5%5 6C ~ <r8c pU=(d 0n[XE5 [US5(a$UR"X40URS05D6nODUR"X40URS05D6R "U40URS05D6nSSS5 UcWU4$WU-U4$!,(df  N=f)z Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. ``params`` needs to be of the form ``process_routing()["step_name"]``. rJr$r%N)rr(rJrr$r%)rYrRrSrrKrLrMrs r5rOrOCs\rF _ 6 ; 0 0++ATFJJPR4STC//!@&**UB*?@JJZZ R0C 7~K < $$ 7 6s A:B'' B5rrrrTr^Fc>[[R"U5XS9$)aConstruct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of estimators A list of estimators. memory : None, str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. Returns ------- p : Pipeline Returns an imbalanced-learn `Pipeline` instance that handles samplers. See Also -------- imblearn.pipeline.Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) r)rr_name_estimators)rrrs r5rr[s` H--e4V UUr8)NN)(rsklearnr sklearn.baser sklearn.utilsrsklearn.utils.fixesrsklearn.utils.metaestimatorsrsklearn.utils.validationrbaser utils._metadata_requestsr r r rrrutils._param_validationrr utils.fixesrappend__all__ __version__ base_versionsklearn_versionrsklearn.utils._user_interfacerrPrrOrrrr8r5rs-51(A%~  ' 3 34AA!M%$881An$%x'8'8n$b%<IM%0c:wi01ykJ"&"&u,V ,Vr8