:irSrSSKJrJr SSKJrJr SSKrSSK J r SSK J r J r Jr SSKJr SS KJr SS KJr SS KJr SS KJr SS KJrJr SSKJrJr SSKJ r SSK!J"r" SSK#J$r$ SSK%J&r&J'r'J(r(J)r)J*r* SSK+J,r,J-r- SSK.J/r/J0r0 SSK1J2r2J3r3 /SQr4Sr5"SS\,5r6Sr7SSS.Sjr8S%Sjr9S&Sjr:S&S jr;"S!S"\ \,5r[URT5 gNT)getattr_final_estimator)selfattrs S/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/sklearn/pipeline.pycheck#_final_estimator_has..check1s%%t,)r-r/s` r._final_estimator_hasr3,s  Lr1c\rSrSr%SrS/r\\"\5/S\ \ "S/5/S/S.r \ \ S'SS S .S jrSS .S jrS:SjrSrSrS;SjrSrSr\S5r\S5r\S5rSrSrSr#9aY A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`estimator` only needs to implement `fit`. The transformers 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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` shows how to grid search on a pipeline using `'__'` as a separator in the parameter names. Read more in the :ref:`User Guide `. .. versionadded:: 0.5 Parameters ---------- steps : list of tuples List of (name of step, estimator) tuples that are to be chained in sequential order. To be compatible with the scikit-learn API, all steps must define `fit`. All non-last steps must also define `transform`. See :ref:`Combining Estimators ` for more details. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. 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` Dictionary-like object, with the following attributes. 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. Only exist if the last step of the pipeline is a classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying first estimator in `steps` exposes such an attribute when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if the underlying estimator exposes such an attribute when fit. .. versionadded:: 1.0 See Also -------- make_pipeline : Convenience function for simplified pipeline construction. Examples -------- >>> from sklearn.svm import SVC >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.datasets import make_classification >>> from sklearn.model_selection import train_test_split >>> from sklearn.pipeline import Pipeline >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... random_state=0) >>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())]) >>> # The pipeline can be used as any other estimator >>> # and avoids leaking the test set into the train set >>> pipe.fit(X_train, y_train).score(X_test, y_test) 0.88 >>> # An estimator's parameter can be set using '__' syntax >>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test) 0.76 stepsNcachebooleanr6memoryverbose_parameter_constraintsFr:r;c(XlX lX0lgNr9)r,r6r:r;s r.__init__Pipeline.__init__s   r1 transformcLUR5Hu p#[X1S9 M U$)aTSet the output container when `"transform"` and `"fit_transform"` are called. Calling `set_output` will set the output of all estimators in `steps`. Parameters ---------- transform : {"default", "pandas", "polars"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `"polars"`: Polars output - `None`: Transform configuration is unchanged .. versionadded:: 1.4 `"polars"` option was added. Returns ------- self : estimator instance Estimator instance. rB)_iterr)r,rC_steps r. set_outputPipeline.set_outputs%.**,JAq T 7' r1c"URSUS9$)aGet parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `steps` of the `Pipeline`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. r6deep _get_paramsr,rLs r. get_paramsPipeline.get_paramss"d33r1c *UR"S0UD6 U$)aSet the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Note that you can directly set the parameters of the estimators contained in `steps`. Parameters ---------- **kwargs : dict Parameters of this estimator or parameters of estimators contained in `steps`. Parameters of the steps may be set using its name and the parameter name separated by a '__'. Returns ------- self : object Pipeline class instance. )r6 _set_paramsr,kwargss r. set_paramsPipeline.set_paramss& +F+ r1c [UR6upURU5 USSnUSnUH^nUbUS:XaM[US5(d[US5(a[US5(aMC[ SU<S[ U5<S35e Ub6US:wa/[US5(d[ S U<S[ U5<S35eggg) N passthroughfit fit_transformrCznAll intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' '' (type ) doesn'tzLLast step of Pipeline should implement fit or be the string 'passthrough'. ')zipr6_validate_nameshasattr TypeErrortype)r,names estimators transformers estimatorts r._validate_stepsPipeline._validate_stepss, U#"#2 rN AyA.Au%%O)D)DW;NN 1247<   !]*Iu---6tIH .+ "r1c## [UR5nU(dUS-n[[URSU55H*unupVU(dXEU4v MUcMUS:wdM$XEU4v M, g7f)z Generate (idx, (name, trans)) tuples from self.steps When filter_passthrough is True, 'passthrough' and None transformers are filtered out. rrNr[)lenr6 enumerater)r, with_finalfilter_passthroughstopidxnametranss r.rEPipeline._itersj4:: AID"+F4::q$,G"H C$%&&"u '=&& #IsAA3 A3( A3c,[UR5$)z$ Returns the length of the Pipeline )rmr6r,s r.__len__Pipeline.__len__$s4::r1c,[U[5(aMURS;a [S5eUR UR UUR URS9$UR Uup#U$![a URUs$f=f)aReturns a sub-pipeline or a single estimator in the pipeline Indexing with an integer will return an estimator; using a slice returns another Pipeline instance which copies a slice of this Pipeline. This copy is shallow: modifying (or fitting) estimators in the sub-pipeline will affect the larger pipeline and vice-versa. However, replacing a value in `step` will not affect a copy. )rNz*Pipeline slicing only supports a step of 1r=) isinstanceslicerG ValueError __class__r6r:r;rc named_steps)r,indrsests r. __getitem__Pipeline.__getitem__*s c5 ! !xxy( !MNN>> 3 T\\"  ) 3ID  )##C( ( )s$A77BBc:URSSR$)NrZr)r6_estimator_typerws r.rPipeline._estimator_type@szz"~a 000r1c>[S0[UR5D6$)zAccess the steps by name. Read-only attribute to access any step by given name. Keys are steps names and values are the steps objects.r2)rdictr6rws r.rPipeline.named_stepsDs(tDJJ'((r1clURSSnUcS$U$![[[4a gf=f)NrZrr[)r6r}AttributeErrorrc)r,rhs r.r+Pipeline._final_estimatorMsD  2q)I$-$5= D9 DNI6    s33cUR(dgURUup#SUS-[UR5U4-$)N(step %d of %d) Processing %sr)r;r6rm)r,step_idxrsrFs r. _log_messagePipeline._log_messageYs;||**X&.(Q,DJJQU1VVVr1c [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)`.rr\r] fit_predictr2) rrrr6ritemsr}formatsplit) r,methodpropsrV routed_paramsrsrGfit_params_stepspnamepvalparams r._check_method_paramsPipeline._check_method_params`s   +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"c [UR5UlUR5 [UR5nUR [ 5nURSSS9HupgnUbUS:Xa'[SURU55 SSS5 M6[US5(aURcUn O [U5n U"U UUSSURU5X7S9upXz4URU'M U$!,(df  No=f)NFrorpr[r#locationmessage_clsnamemessageparams) listr6rjr"r:r7_fit_transform_onerErrrbrr ) r,Xyrr:fit_transform_one_cachedrrs transformercloned_transformerfitted_transformers r._fit Pipeline._fits $**%  dkk*#)<<0B#C +/::,6, 'HK"k]&B(T5F5Fx5PQRQvz**v/F&1"%*;%7"$<" *))(3$*% !A%)#=DJJx 5, 6/RQs C<< D )prefer_skip_nested_validationc jURSUS9nURXU5n[SUR[ UR 5S- 55 UR S:wa4X@R SSnUR R"XR40USD6 SSS5 U$!,(df  U$=f) aFit the model. Fit all the transformers one after the other and sequentially transform the data. Finally, fit the transformed 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 : object Pipeline with fitted steps. r\rrr#rr[rZrN)rrrrrmr6r+r\)r,rrrrXtlast_step_paramss r.r\ Pipeline.fits^11f1M YYq] + T->->s4::QR?R-S T$$ 5#0B1B#C %%))"K3CE3JKU  U T  s AB## B2cURS:H=(d3 [URS5=(d [URS5$)Nr[rCr]r+rbrws r._can_fit_transformPipeline._can_fit_transforms?  ! !] 2 ?t,,k: ?t,,o> r1c URSUS9nURXU5nURn[SUR [ UR 5S- 55 US:Xa UsSSS5 $X@R SSn[US5(aUR"XR40USD6sSSS5 $UR"XR40US D6R"U40US D6sSSS5 $!,(df  g=f) a$Fit the model and transform with the final estimator. Fit all the transformers one after the other and sequentially transform the data. Only valid if the final estimator either implements `fit_transform` or `fit` and `transform`. 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 : ndarray of shape (n_samples, n_transformed_features) Transformed samples. r]rr#rr[NrZrr\rC) rrr+rrrmr6rbr]r\rC)r,rrrrr last_steprs r.r]Pipeline.fit_transforms`11PV1W YYq] +)) T->->s4::QR?R-S TM)U T -ZZ^A->? y/22 ..-o> U T!}}RF.>u.EFPP*;7U T Ts C)2:C)6)C)) C7predictc 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. FrorZrrr)rrErCr6rrr,rrrrFrsrCrs r.rPipeline.predict%s^!!&*jjEj&B"((,'C::b>!$,,R:6: :(iB6B "&***"> AY$$RI=+>+H+HIB#?zz"~a ((X}ZZ^A=N/O/W/WXXr1rc pURSUS9nURXU5nX@RSSn[SUR [ UR5S- 55 URSSR "XR40URS05D6nSSS5 U$!,(df  W$=f)aTransform the data, and apply `fit_predict` with the final estimator. Call `fit_transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `fit_predict` method. Only valid 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 Result of calling `fit_predict` on the final estimator. rrrZrr#rN)rrr6rrrmrget)r,rrrrrparams_last_stepy_preds r.rPipeline.fit_predictasn11f1U YYq] +(B):; T->->s4::QR?R-S TZZ^A&22)--mR@FU U T s (4B&& B5 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. FrrZrrr)rrErCr6rrrs r.rPipeline.predict_probasT!!&*jjEj&B"((,'C::b>!$222@@ @(oHH "&***"> AY$$RI=+>+H+HIB#?zz"~a ..   2q 12@@  r1decision_functionc v[X S5 [US40UD6nUnURSS9H9upVnUR"U40UR U05R S05D6nM; UR SSR "U40UR UR SS05R S05D6$)aTransform the data, and apply `decision_function` with the final estimator. Call `transform` of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls `decision_function` method. Only valid if the final estimator implements `decision_function`. Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **params : dict of string -> object Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them. .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y_score : ndarray of shape (n_samples, n_classes) Result of calling `decision_function` on the final estimator. rFrrCrZrr)rrrErCrr6rr,rrrrrFrsrCs r.rPipeline.decision_functions< &(;<(.ALVL "&***"> AY$$#''b155k2FB#?zz"~a 22  ##DJJrN1$5r:>>?RTVW  r1 score_samplescUnURSS9Hu p4URU5nM URSSRU5$)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. FrrZr)rErCr6r)r,rrrFrs r.rPipeline.score_samplessQ(!%u!= Aq&&r*B">zz"~a ..r22r1predict_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. FrrZrrr)rrErCr6rrrs r.rPipeline.predict_log_proba!sT!!&*jjEj&B"((,'C::b>!$66rDVD D(.ALVL "&***"> AY$$RI=+>+H+HIB#?zz"~a 22   2q 12DD  r1cZURS:H=(d [URS5$)Nr[rCrrws r._can_transformPipeline._can_transformZs,$$ 5   ! !;:  r1c [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. rC)rrrErCrs r.rCPipeline.transform_s_B & 4(kDVD "&**, AY$$RI=+>+H+HIB#/ r1cB[SUR555$)Nc3@# UHu p[US5v M g7f)inverse_transformN)rb).0rFris r. 2Pipeline._can_inverse_transform..sO,wq!71122,s)allrErws r._can_inverse_transformPipeline._can_inverse_transformsO$**,OOOr1)rc [X0S5 [X5n[US40UD6n[[ UR 555nUH%upgnUR "U40XGR D6nM' U$)aApply `inverse_transform` for each step in a reverse order. All estimators in the pipeline must support `inverse_transform`. Parameters ---------- X : 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. 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. .. deprecated:: 1.5 `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead. **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)rrrreversedrrEr) r,rrrr reverse_iterrFrsrCs r.rPipeline.inverse_transformsuN &(;< .q 5(.ALVL TZZ\ 23 ". AY++AW1D1V1VWA#/r1scorec 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. Fr sample_weightrZrrr)rrErCr6rr) r,rrrrrrFrsrC score_paramsrs r.rPipeline.scores J!!&*jjEj&B"((,'CL(0=_-::b>!$**2ALA A( ' )6 :@ "&***"> AY$$RI=+>+H+HIB#?zz"~a &&rW jjnQ>O0P0V0VWWr1c:URSSR$)z@The classes labels. Only exist if the last step is a classifier.rZr)r6classes_rws r.rPipeline.classes_szz"~a )))r1cSSSS.0n[URSSS5US'[URSSS5US'U$![[[4a N9f=f![[[4a U$f=f) N _xfail_checkszkPipeline changes the `steps` parameter, which it shouldn't.Therefore this test is x-fail until we fix this.)check_dont_overwrite_parameters!check_estimators_overwrite_paramsrrpairwiserZ multioutput)rr6r}rrc)r,tagss r. _more_tagsPipeline._more_tagss GG    )$**Q-*:JGD   ",TZZ^A-> "ND   NI6   NI6    s"A A% A"!A"%A>=A>cUnUR5HBup4n[US5(d[SRU55eUR U5nMD U$)aGet output feature names for transformation. Transform input features using the pipeline. Parameters ---------- input_features : array-like of str or None, default=None Input features. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. get_feature_names_outzpEstimator {} does not provide get_feature_names_out. Did you mean to call pipeline[:-1].get_feature_names_out()?)rErbrrr)r,input_featuresfeature_names_outrFrsrCs r.rPipeline.get_feature_names_outsd+"&**, AY9&=>>$ &, !* ? ?@Q R #/! r1c:URSSR$)z7Number of features seen during first step `fit` method.rr)r6n_features_in_rws r.rPipeline.n_features_in_5szz!}Q...r1c:URSSR$)z6Names of features seen during first step `fit` method.rr)r6feature_names_in_rws r.rPipeline.feature_names_in_;szz!}Q111r1c\[URSS5 g![a gf=f)z'Indicate whether pipeline has been fit.rZrTF)r!r6r rws r.__sklearn_is_fitted__Pipeline.__sklearn_is_fitted__As3  DJJrN1- .  s  ++c[UR6upSnURVVs/sH upEU"XE5PM nnnUVs/sHn[U5PM nn[SUUUSS9$s snnfs snf)NcTUbUS:XaUS3$USURR3$)Nr[z : passthroughz: )r~__name__)rsrs r. _get_name-Pipeline._sk_visual_block_.._get_namePs7{c]2}--V2cmm4456 6r1serialF)re name_details dash_wrapped)r`r6strr)r,rFrfr rsrrer s r._sk_visual_block_Pipeline._sk_visual_block_MsuTZZ(  7 8` on how the routing mechanism works. Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. ownerFTrr]r\callercalleerrCrrrrrrmethod_mappingrZr[r2)rr~r rErrbaddr6)r,routerrFrsrtr final_name final_ests r.get_metadata_routingPipeline.get_metadata_routing`s dnn&=&=>#jjEdjSNAU*_Nuo.."&&eO&LSSHS oSF#&&eE&BSkS:SS>S SDS eS<S kSB"")K"HIk:OK@/ D/ DK </8KLGK8 JJ Fn F F?TB!% 2   ] :M' 9o . .   oo  N   eE  : > >[ ?    eE  : S )S 4 S mS < SS @ S+4GS H S+4GS H S KS 8 S+4GS H SS 0  L.LZ4KL r1)r:r6r;T)TT)NNr?)7r  __module__ __qualname____firstlineno____doc___required_parametersrrtuplerrr<r__annotations__r@rHrPrWrjrErxrpropertyrrr+rrrr r\rrr]r3rrrrrrrrCrrrrrrrrrrr__static_attributes__r2r1r.r#r#9sbJ$9u &j'34;$D )-e '+64&,!F'" ,11))  W$>$L&+2 2h $%&+: & :x&y129Y39Yv&}56&+: 7 :x&786 96 p&':;<* =* X&783930&':;<6 =6 p .!("(TP()0d0*0d&w/05X15Xn**<!4// 22  &Ir1r#cUVs/sH=n[U[5(aUO"[U5RR 5PM? nn[ [ 5n[X5HupEX5==S- ss'M [UR55HupgUS:XdM X6 M [[[U555H+nX(nXS;dMX(==SX5-- ss'X5==S-ss'M- [[X 55$s snf)zGenerate names for estimators.rz-%d) r{rrdr lowerrintr`rrrrangerm) rfrhre namecountrrskvis r._name_estimatorsr1s $ #I 3// T)_5M5M5S5S5UU#  C I+ 1,Y__&' 6 (eC O, -x   H / /H Oq O . E& ''% sADFr=c([[U5XS9$)aConstruct a :class:`Pipeline` from the given estimators. This is a shorthand for the :class:`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 Estimator objects List of the scikit-learn estimators that are chained together. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. 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 a scikit-learn :class:`Pipeline` object. See Also -------- Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.pipeline import make_pipeline >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) r=)r#r1)r:r;r6s r.r%r%sZ $U+F LLr1cNUR"U40URD6nUcU$XS-$)aCall transform and apply weight to output. Parameters ---------- transformer : estimator Estimator to be used for transformation. X : {array-like, sparse matrix} of shape (n_samples, n_features) Input data to be transformed. y : ndarray of shape (n_samples,) Ignored. weight : float Weight to be applied to the output of the transformation. params : dict Parameters to be passed to the transformer's ``transform`` method. This should be of the form ``process_routing()["step_name"]``. rB)rrrweightrress r._transform_oner6s1,    6V%5%5 6C ~ <r1c pU=(d 0n[XE5 [US5(a$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"]``. r]r\rCN)rrbr]rr\rC)rrrr4rrrr5s r.rrs\rF _ 6 ; 0 0++ATFJJPR4STC//!@&**UB*?@JJZZ R0C 7~K < $$ 7 6s A:B'' B5cz[XE5 UR"X40USD6sSSS5 $!,(df  g=f)z* Fits ``transformer`` to ``X`` and ``y``. r\N)rr\)rrrr4rrrs r._fit_oner9)s- _ 6q5ve}5 7 6 6s, :c^\rSrSrSrS/rSSSSS.SjrSS .U4S jjr\S 5r S!S jr S r Sr Sr SrS"SjrSrS"SjrS"SjrSrSrSrSrSr\S5r\S5rSrSrSrSrS rU=r$)#r$i1a4Concatenates results of multiple transformer objects. This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. A transformer may be replaced entirely by setting the parameter with its name to another transformer, removed by setting to 'drop' or disabled by setting to 'passthrough' (features are passed without transformation). Read more in the :ref:`User Guide `. .. versionadded:: 0.13 Parameters ---------- transformer_list : list of (str, transformer) tuples List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer. The transformer can be 'drop' for it to be ignored or can be 'passthrough' for features to be passed unchanged. .. versionadded:: 1.1 Added the option `"passthrough"`. .. versionchanged:: 0.22 Deprecated `None` as a transformer in favor of 'drop'. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None transformer_weights : dict, default=None Multiplicative weights for features per transformer. Keys are transformer names, values the weights. Raises ValueError if key not present in ``transformer_list``. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. verbose_feature_names_out : bool, default=True If True, :meth:`get_feature_names_out` will prefix all feature names with the name of the transformer that generated that feature. If False, :meth:`get_feature_names_out` will not prefix any feature names and will error if feature names are not unique. .. versionadded:: 1.5 Attributes ---------- named_transformers : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. Read-only attribute to access any transformer parameter by user given name. Keys are transformer names and values are transformer parameters. .. versionadded:: 1.2 n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying first transformer in `transformer_list` exposes such an attribute when 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:: 1.3 See Also -------- make_union : Convenience function for simplified feature union construction. Examples -------- >>> from sklearn.pipeline import FeatureUnion >>> from sklearn.decomposition import PCA, TruncatedSVD >>> union = FeatureUnion([("pca", PCA(n_components=1)), ... ("svd", TruncatedSVD(n_components=2))]) >>> X = [[0., 1., 3], [2., 2., 5]] >>> union.fit_transform(X) array([[-1.5 , 3.0..., -0.8...], [ 1.5 , 5.7..., 0.4...]]) >>> # An estimator's parameter can be set using '__' syntax >>> union.set_params(svd__n_components=1).fit_transform(X) array([[-1.5 , 3.0...], [ 1.5 , 5.7...]]) For a more detailed example of usage, see :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`. transformer_listNFT)n_jobstransformer_weightsr;verbose_feature_names_outc@XlX lX0lX@lXPlgr?)r;r<r=r;r>)r,r;r<r=r;r>s r.r@FeatureUnion.__init__s !1 #6  )B&r1rBch>[TU]US9 UR5Hup#n[X1S9 M U$)aSet the output container when `"transform"` and `"fit_transform"` are called. `set_output` will set the output of all estimators in `transformer_list`. Parameters ---------- transform : {"default", "pandas", "polars"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `"polars"`: Polars output - `None`: Transform configuration is unchanged Returns ------- self : estimator instance Estimator instance. rB)superrHrEr)r,rCrFrGr~s r.rHFeatureUnion.set_outputs6( Y/**,JAQ T 7' r1c>[S0[UR5D6$)Nr2)rrr;rws r.named_transformersFeatureUnion.named_transformerss3tD11233r1c"URSUS9$)aGet parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `transformer_list` of the `FeatureUnion`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. r;rKrMrOs r.rPFeatureUnion.get_paramss$ 2>>r1c *UR"S0UD6 U$)aSet the parameters of this estimator. Valid parameter keys can be listed with ``get_params()``. Note that you can directly set the parameters of the estimators contained in `transformer_list`. Parameters ---------- **kwargs : dict Parameters of this estimator or parameters of estimators contained in `transform_list`. Parameters of the transformers may be set using its name and the parameter name separated by a '__'. Returns ------- self : object FeatureUnion class instance. )r;rSrUs r.rWFeatureUnion.set_paramss& 6v6 r1c [UR6upURU5 UH[nUS;aM [US5(d[US5(a[US5(aM@[ SU<S[ U5<S35e g)N)dropr[r\r]rCz4All estimators should implement fit and transform. 'r^r_)r`r;rarbrcrd)r,rergris r._validate_transformers#FeatureUnion._validate_transformerss!4#8#89 U#A++Au%%O)D)DW;NN ;.sJ4I4Iz"Attempting to weight transformer "z-", but it is not present in transformer_list.)r=setr;r})r,transformer_namesrss r._validate_transformer_weights*FeatureUnion._validate_transformer_weightss['' JD4I4IJJ,,D, 8?AA-r1c## UR=(d 0RnURH(up#US:XaM US:Xa [SS9nX#U"U54v M* g7f)zO Generate (name, trans, weight) tuples excluding None and 'drop' transformers. rLr[z one-to-one)rN)r=rr;r )r, get_weightrsrts r.rEFeatureUnion._iters[ ..4"99 00KD %+lK 4 01 1 1sAAc $/nUR5Hjup4n[US5(d0[S[U5<S[ U5R <S35eUR U5nURX645 Ml URU5$)zGet output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Input features. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. rz Transformer z (type z)) does not provide get_feature_names_out.) rErbrrrdr rappend!_add_prefix_for_feature_names_out)r,r"transformer_with_feature_names_outrsrtrFrs r.r"FeatureUnion.get_feature_names_outs.0*"jjlND5"9::$4y$u+"6"68!& ; ;N K  . 5 5t6O P+55 .  r1c`UR(a?[[R"SU555n[R "U[ S9$[[R"SU555nURS5VVs/sHupEUS:dM UPM nnnUR5 U(a>[U5S:Xa[USS5SSS -nO [U5n[S US 35e[R"UVVs/sHupUPM snn5$s snnfs snnf) aiAdd prefix for feature names out that includes the transformer names. Parameters ---------- transformer_with_feature_names_out : list of tuples of (str, array-like of str) The tuple consistent of the transformer's name and its feature names out. Returns ------- feature_names_out : ndarray of shape (n_features,), dtype=str Transformed feature names. c3B^# UHumnU4SjU5v M g7f)c32># UH nTSU3v M g7f)rNr2)rr0rss r.rKFeatureUnion._add_prefix_for_feature_names_out...Ls?->vRs^->sNr2)rrrss @r.rAFeatureUnion._add_prefix_for_feature_names_out..Ks($3U//@->??3Us)dtypec3*# UH upUv M g7fr?r2)rrFss r.rrcUsQ.Pda.PrRrNrZz, ...]zOutput feature names: z[ are not unique. Please set verbose_feature_names_out=True to add prefixes to feature names)r>rr from_iterablenpasarrayobjectr most_commonsortrmrr} concatenate) r,r]refeature_names_countrscount top_6_overlap names_reprrFs r.r\.FeatureUnion._add_prefix_for_feature_names_out;s5  ) )##$3U$E ::e62 2&   Q.PQ Q %8$C$CA$F $F[T%RS)D$F    =!Q&!r!23CR88C  / ( 5RR  ~~!C D!CgaT!C D  ! " Es D$D$D* c [5(a[US40UD6nO3[5nURHupV[0S9XE'X4UlM UR X[ U5nU(dU$URU5 U$)aFit all transformers using X. Parameters ---------- X : iterable or array-like, depending on transformers Input data, used to fit transformers. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. **fit_params : dict, default=None - If `enable_metadata_routing=False` (default): Parameters directly passed to the `fit` methods of the sub-transformers. - If `enable_metadata_routing=True`: Parameters safely routed to the `fit` methods of the sub-transformers. See :ref:`Metadata Routing User Guide ` for more details. .. versionchanged:: 1.5 `**fit_params` can be routed via metadata routing API. Returns ------- self : object FeatureUnion class instance. r\r\)rrrr;r\_parallel_funcr9_update_transformer_list)r,rr fit_paramsrrsrFrgs r.r\FeatureUnion.fitks:   +D%F:FM"GM00&+m #*4d#'1**1=I K %%l3 r1c [5(a[US40UD6nOe[5nURHKupV[ US5(a[0S9XE'X4UlM,[0S9XE'[0S9XE'X4UlMM URX[U5nU(d%[R"URSS45$[U6upURU 5 URU5$)aFit all transformers, transform the data and concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. y : array-like of shape (n_samples, n_outputs), default=None Targets for supervised learning. **params : dict, default=None - If `enable_metadata_routing=False` (default): Parameters directly passed to the `fit` methods of the sub-transformers. - If `enable_metadata_routing=True`: Parameters safely routed to the `fit` methods of the sub-transformers. See :ref:`Metadata Routing User Guide ` for more details. .. versionchanged:: 1.5 `**params` can now be routed via metadata routing API. Returns ------- X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) The `hstack` of results of transformers. `sum_n_components` is the sum of `n_components` (output dimension) over transformers. r])r]rvrBr)rrrr;rbr]r\rwrrjzerosshaper`rx_hstack) r,rrrrrsobjresultsXsrgs r.r]FeatureUnion.fit_transforms>   +D/LVLM"GM!22 300*/b*AM'8>$'5*/B-M'*/"*=M'.4$'+3%%a,> N88QWWQZO, ,= %%l3||Br1c4UR(dgSX#U4-$)Nr)r;)r,rsrrtotals r.rFeatureUnion._log_messages||.#d1CCCr1c^^^^^^[TR5TlTR5 TR5 [TR 55m[ TR S9"UUUUUU4Sj[TS555$)z Runs func in parallel on X and yr<c3># UH<unup#n[T5"UTT USTRX![T 55TUS9v M> g7f)r$rN)r rrm) rrrrsrr4rfuncrr,rgrs r.r.FeatureUnion._parallel_func..s] , 5O00d DM .))$S5FG$T* 5OsAAr)rr;rMrUrErr<rn)r,rrrrrgs`````@r.rwFeatureUnion._parallel_funcso $T%:%: ; ##% **,DJJL) t{{+ , , 5>lA4N ,  r1c ^^[X S5 [5(a[US40UD6mO+[5mURHup4[0S9TU'M [ UR S9"UU4SjUR555nU(d%[R"TRSS45$URU5$)aTransform X separately by each transformer, concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. **params : dict, default=None Parameters routed to the `transform` method of the sub-transformers via the metadata routing API. See :ref:`Metadata Routing User Guide ` for more details. .. versionadded:: 1.5 Returns ------- X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) The `hstack` of results of transformers. `sum_n_components` is the sum of `n_components` (output dimension) over transformers. rCrBrc 3^># UH"upn[[5"UTSUTUS9v M$ g7f)N)r)r r6)rrsrtr4rrs r.r)FeatureUnion.transform.. s5* '3#V N #E1dF=QUCV W'3s*-r) rrrrr;rr<rErjr|r}r~)r,rrrsrFrrs ` @r.rCFeatureUnion.transforms, & 4   +D+HHM"GM00&+b&9 d#1T[[ )* '+zz|*  88QWWQZO, ,||Br1c*^[SU5mT(a+[U4SjU55(aTRU5$[SU55(a&[R"U5R 5nU$[ R"U5nU$)NrCc3F># UHnTRU5v M g7fr?)is_supported_container)rradapters r.r'FeatureUnion._hstack..sIb799!<>"% % .2. . .r"((*B 2B r1c[U5nURVVs/sHup#X#S:XaUO [U54PM snnURSS&gs snnf)NrL)iterr;next)r,rgrsolds r.rx%FeatureUnion._update_transformer_listsSL) "22$ 2 -3T,-? @2$ a $ s A c:URSSR$)z+Number of features seen during :term:`fit`.rr)r;rrws r.rFeatureUnion.n_features_in_&s $$Q'*999r1c:URSSR$)z*Names of features seen during :term:`fit`.rr)r;rrws r.rFeatureUnion.feature_names_in_-s $$Q'*<<[UR6up[SX!S9$)Nparallel)re)r`r;r)r,rergs r.rFeatureUnion._sk_visual_block_9s"!4#8#89J BBr1c`[U[5(d [S5eURU$)zReturn transformer with name.zOnly string keys are supported)r{rKeyErrorrE)r,rss r.rFeatureUnion.__getitem__=s,$$$;< <&&t,,r1c 4[URRS9nURHjup#UR"S0X#0DS[ 5R SSS9R SSS9R SSS9R SSS9R SSS90D6 Ml U$)a"Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.5 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. rrr\rr]rCr2)rr~r r;rr)r,rrsrs r.r!FeatureUnion.get_metadata_routingCs dnn&=&=>!%!6!6 D JJ % ,E%0OODOE:OK@K < "7 r1)r<r;r=r;r>rr?) r r r!r"r#r$r@rHr'rErPrWrMrUrErr\r\r]rrwrCr~rxrrrrrrr( __classcell__)r~s@r.r$r$1seN//  "& C'+244?(,$  2 8. `-^5 nD  (( T  :: ==  C- r1r$r<r;c([[U5XS9$)a<Construct a :class:`FeatureUnion` from the given transformers. This is a shorthand for the :class:`FeatureUnion` constructor; it does not require, and does not permit, naming the transformers. Instead, they will be given names automatically based on their types. It also does not allow weighting. Parameters ---------- *transformers : list of estimators One or more estimators. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None. verbose : bool, default=False If True, the time elapsed while fitting each transformer will be printed as it is completed. Returns ------- f : FeatureUnion A :class:`FeatureUnion` object for concatenating the results of multiple transformer objects. See Also -------- FeatureUnion : Class for concatenating the results of multiple transformer objects. Examples -------- >>> from sklearn.decomposition import PCA, TruncatedSVD >>> from sklearn.pipeline import make_union >>> make_union(PCA(), TruncatedSVD()) FeatureUnion(transformer_list=[('pca', PCA()), ('truncatedsvd', TruncatedSVD())]) r)r$r1)r<r;rgs r.r&r&asZ (6v WWr1r?)NN)>r# collectionsrr itertoolsrrnumpyrjscipyrbaser r r exceptionsr preprocessingr utilsrutils._estimator_html_reprrutils._metadata_requestsrutils._param_validationrrutils._set_outputrr utils._tagsrutils._user_interfacerutils.deprecationrutils.metadata_routingrrrrrutils.metaestimatorsrrutils.parallelrr utils.validationr!r"__all__r3r#r1r%r6rr9r$r&r2r1r.rsW-#77&.4-7$6AA-; E ppf#(0"&u-M`<IM%06m#%5m`&*5-Xr1