U !`@s2dZddlmZddlmZddlZddlmZddl m Z ddl m Z m Z dd lmZdd lmZdd lmZmZdd lmZdd lmZddlmZddlmZddlmZddlmZddddgZGdddeZ ddZ!dddddZ"ddZ#d&dd Z$d'd!d"Z%Gd#dde eZ&ddd$d%dZ'dS)(z The :mod:`sklearn.pipeline` module implements utilities to build a composite estimator, as a chain of transforms and estimators. ) defaultdict)isliceN)sparse)Parallel)cloneTransformerMixin) _VisualBlock)if_delegate_has_method)Bunch_print_elapsed_time) deprecated) _safe_tags) check_memory)_deprecate_positional_args)delayed)_BaseCompositionPipeline FeatureUnion make_pipeline make_unionc@seZdZdZdgZedddddZdHd d Zd d Zd dZ dIddZ ddZ ddZ e ddZe ddZe ddZddZddZdJdd ZdKd!d"ZdLd#d$Zed%d&d'd(Zed%d&dMd)d*Zed%d&d+d,Zed%d&d-d.Zed%d&d/d0Zed%d&d1d2Ze d3d4Zd5d6Ze d7d8Zd9d:Z ed%d&dNd;d<Z!e d=d>Z"d?d@Z#e$dAe dBdCZ%e dDdEZ&dFdGZ'dS)Ora+ Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final 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``. Read more in the :ref:`User Guide `. .. versionadded:: 0.5 Parameters ---------- steps : list List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator. memory : 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. 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. 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) Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())]) >>> pipe.score(X_test, y_test) 0.88 stepsNFmemoryverbosecCs||_||_||_|dSN)rrr_validate_steps)selfrrrr/lib/python3.8/site-packages/sklearn/pipeline.py__init__qszPipeline.__init__TcCs|jd|dS)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. rdeepZ _get_paramsrr"rrr get_paramsxszPipeline.get_paramscKs|jd||S)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`. Returns ------- self r)rZ _set_paramsrkwargsrrr set_paramss zPipeline.set_paramscCst|j\}}|||dd}|d}|D]H}|dks0|dkrFq0t|dsZt|drdt|ds0td|t|fq0|dk r|dkrt|dstd|t|fdS)N passthroughfit fit_transform transformzAll intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' '%s' (type %s) doesn'tzaLast step of Pipeline should implement fit or be the string 'passthrough'. '%s' (type %s) doesn't)zipr_validate_nameshasattr TypeErrortype)rnames estimators transformers estimatortrrrrs*    zPipeline._validate_stepsccsht|j}|s|d8}tt|jd|D]:\}\}}|sF|||fVq(|dk r(|dkr(|||fVq(dS)z Generate (idx, (name, trans)) tuples from self.steps When filter_passthrough is True, 'passthrough' and None transformers are filtered out. rrNr+)lenr enumerater)r with_finalfilter_passthroughstopidxnametransrrr_iters zPipeline._itercCs t|jS)z4 Returns the length of the Pipeline )r9rrrrr__len__szPipeline.__len__cCslt|tr6|jdkrtd|j|j||j|jdSz|j|\}}Wntk rf|j |YSX|S)aReturns a sub-pipeline or a single esimtator 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) isinstanceslicestep ValueError __class__rrrr2 named_steps)rZindr?estrrr __getitem__s  zPipeline.__getitem__cCs|jddjS)Nr*r)r_estimator_typerBrrrrLszPipeline._estimator_typecCstft|jSr)r dictrrBrrrrIszPipeline.named_stepscCs|jdd}|dkrdS|S)Nr*rr+)r)rr7rrr_final_estimatorszPipeline._final_estimatorcCs0|js dS|j|\}}d|dt|j|fS)N(step %d of %d) Processing %sr)rrr9)rstep_idxr?rFrrr _log_messageszPipeline._log_messagecKsXdd|jD}|D]:\}}d|kr6td||dd\}}||||<q|S)NcSsi|]\}}|dk r|iqSrr).0r?rFrrr sz.Pipeline._check_fit_params..__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)ritemsrGformatsplit)r fit_paramsfit_params_stepsZpnameZpvalrFZparamrrr_check_fit_paramsszPipeline._check_fit_paramsc Kst|j|_|t|j}|t}|jdddD]\}}}|dksP|dkrxtd| |W5QRq6W5QRXt |dr|j dkr|} qt |} n,t |dr|j dkr|} qt |} nt |} || ||dfd| |d||\}} || f|j|<q6|S)NF)r;r<r+rlocationcachedirmessage_clsnamemessage)listrrrrcache_fit_transform_onerAr rQr1r[rr\) rXyrYrZfit_transform_one_cachedrPr? transformerZcloned_transformerZfitted_transformerrrr_fit sJ          z Pipeline._fitc Kst|jf|}|j||f|}td|t|jd4|jdkrf||jdd}|jj||f|W5QRX|S)aFit the model Fit all the transforms one after the other and transform the data, then 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. **fit_params : dict of string -> object 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``. Returns ------- self : Pipeline This estimator rrr+r*r)rZrfr rQr9rrNr,)rrcrdrXrYXtfit_params_last_steprrrr,:s  z Pipeline.fitc Ks|jf|}|j||f|}|j}td|t|jdx|dkrV|W5QRS||jdd}t|dr|j||f|W5QRS|j ||f| |W5QRSW5QRXdS)aFit the model and transform with the final estimator Fits all the transforms one after the other and transforms 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. **fit_params : dict of string -> object 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``. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) Transformed samples rrr+r*rr-N) rZrfrNr rQr9rr1r-r,r.)rrcrdrXrYrgZ last_steprhrrrr-^s    zPipeline.fit_transformrN)ZdelegatecKs>|}|jddD]\}}}||}q|jddj|f|S)aApply transforms to the data, and predict with the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. **predict_params : dict of string -> object Parameters to the ``predict`` called at the end of all transformations in the pipeline. 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. .. versionadded:: 0.20 Returns ------- y_pred : array-like Fr;r*)rAr.rpredict)rrcZpredict_paramsrg_r?r.rrrrjs zPipeline.predictc Ksr|jf|}|j||f|}||jdd}td|t|jd |jddj||f|}W5QRX|S)aoApplies 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. **fit_params : dict of string -> object 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``. Returns ------- y_pred : array-like r*rrr)rZrfrr rQr9 fit_predict)rrcrdrXrYrgrhZy_predrrrrls zPipeline.fit_predictcCs:|}|jddD]\}}}||}q|jdd|S)aHApply transforms, and predict_proba of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_proba : array-like of shape (n_samples, n_classes) Frir*)rAr.r predict_probarrcrgrkr?r.rrrrms zPipeline.predict_probacCs:|}|jddD]\}}}||}q|jdd|S)aLApply transforms, and decision_function of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : array-like of shape (n_samples, n_classes) Frir*)rAr.rdecision_functionrnrrrros zPipeline.decision_functioncCs:|}|jddD]\}}}||}q|jdd|S)a<Apply transforms, and score_samples of the final estimator. 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,) Frir*)rAr.r score_samples)rrcrgrkrerrrrps zPipeline.score_samplescCs:|}|jddD]\}}}||}q|jdd|S)aLApply transforms, and predict_log_proba of the final estimator Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of first step of the pipeline. Returns ------- y_score : array-like of shape (n_samples, n_classes) Frir*)rAr.rpredict_log_probarnrrrrqs zPipeline.predict_log_probacCs|jdkr|jj|jS)aApply transforms, and transform with the final estimator This also works where final estimator is ``None``: all prior transformations are applied. Parameters ---------- X : iterable Data to transform. Must fulfill input requirements of first step of the pipeline. Returns ------- Xt : array-like of shape (n_samples, n_transformed_features) r+)rNr. _transformrBrrrr.s zPipeline.transformcCs&|}|D]\}}}||}q |Sr)rAr.)rrcrgrkr.rrrrr-s zPipeline._transformcCs |D]\}}}|jq|jS)a<Apply inverse transformations in 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. Returns ------- Xt : array-like of shape (n_samples, n_features) )rAinverse_transform_inverse_transform)rrkr.rrrrs3szPipeline.inverse_transformcCs2|}tt|}|D]\}}}||}q|Sr)reversedr`rArs)rrcrgZ reverse_iterrkr.rrrrtKs  zPipeline._inverse_transformc CsT|}|jddD]\}}}||}qi}|dk r:||d<|jddj||f|S)auApply transforms, and score with the final estimator 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. Returns ------- score : float FriN sample_weightr*)rAr.rscore) rrcrdrvrgrkr?r.Z score_paramsrrrrwRs zPipeline.scorecCs|jddjS)Nr*)rclasses_rBrrrrxpszPipeline.classes_cCsdt|jdddiS)NZpairwiserr)rrrBrrr _more_tagstszPipeline._more_tagszaAttribute _pairwise was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26).cCst|jddddS)Nrr _pairwiseF)getattrrrBrrrrzzszPipeline._pairwisecCs|jddjSNrr)rn_features_in_rBrrrr}szPipeline.n_features_in_csJt|j\}}ddfdd|jD}dd|D}td|||ddS) NcSs,|dks|dkr|dS|d|jjS)Nr+z : passthroughz: )rH__name__)r?rJrrr _get_names z-Pipeline._sk_visual_block_.._get_namecsg|]\}}||qSrr)rRr?rJrrr sz.Pipeline._sk_visual_block_..cSsg|] }t|qSr)str)rRrJrrrrsserialF)r4 name_detailsZ dash_wrapped)r/rr )rrkr5r4rrrr_sk_visual_block_szPipeline._sk_visual_block_)T)TT)N)N)N)N)NN)(r~ __module__ __qualname____doc___required_parametersrr r%r)rrArCrKpropertyrLrIrNrQrZrfr,r-r rjrlrmrorprqr.rrrsrtrwrxryr rzr}rrrrrr%sdI       0 $ *  $          cCsdd|D}tt}t||D]\}}||d7<q t|D]\}}|dkrF||=qFttt|D]<}||}||krn||d||7<||d8<qntt||S)zGenerate names for estimators.cSs(g|] }t|tr|n t|jqSr)rDrr3r~lower)rRr7rrrrsz$_name_estimators..rz-%d)rintr/r`rUruranger9)r5r4Z namecountrJr?kvirrr_name_estimatorssrFrcGstt|||dS)a Construct a Pipeline from the given estimators. This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically. Parameters ---------- *steps : list of estimators. memory : str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. verbose : bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed. See Also -------- Pipeline : Class for creating a pipeline of transforms with a final estimator. Examples -------- >>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())]) Returns ------- p : Pipeline r)rr)rrrrrrrs*cKs||}|dkr|S||Sr)r.)rercrdweightrXresrrr_transform_ones rc Ksdt||8t|dr(|j||f|}n|j||f||}W5QRX|dkrX||fS|||fS)z Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned with the fitted transformer. If ``weight`` is not ``None``, the result will be multiplied by ``weight``. r-N)r r1r-r,r.)rercrdrr^r_rXrrrrrbs   rbc Ks2t|||j||f|W5QRSQRXdS)z2 Fits ``transformer`` to ``X`` and ``y``. N)r r,)rercrdrr^r_rXrrr_fit_ones rc@seZdZdZdgZeddddddZd'd d Zd d Zd dZ ddZ ddZ ddZ d(ddZ d)ddZddZddZddZdd Zd!d"Zed#d$Zd%d&ZdS)*raConcatenates 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, or removed by setting to 'drop'. Read more in the :ref:`User Guide `. .. versionadded:: 0.13 Parameters ---------- transformer_list : list of (string, 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 tranformer can be 'drop' for it to be ignored. .. 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. 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...]]) transformer_listNF)n_jobstransformer_weightsrcCs$||_||_||_||_|dSr)rrrr_validate_transformers)rrrrrrrrr Gs zFeatureUnion.__init__TcCs|jd|dS)aGet parameters for this estimator. Returns the parameters given in the constructor as well as the estimators contained within the `transformer_list` of the `FeatureUnion`. Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns ------- params : mapping of string to any Parameter names mapped to their values. rr!r#r$rrrr%PszFeatureUnion.get_paramscKs|jd||S)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 `tranformer_list`. Returns ------- self r)rr&r'rrrr)ds zFeatureUnion.set_paramscCsbt|j\}}|||D]@}|dkr*qt|ds>t|drHt|dstd|t|fqdS)Ndropr,r-r.zIAll estimators should implement fit and transform. '%s' (type %s) doesn't)r/rr0r1r2r3)rr4r6r8rrrrrs  z#FeatureUnion._validate_transformerscCsF|js dStdd|jD}|jD]}||kr$td|dq$dS)Ncss|]\}}|VqdSrr)rRr?rkrrr sz=FeatureUnion._validate_transformer_weights..z"Attempting to weight transformer "z-", but it is not present in transformer_list.)rsetrrG)rZtransformer_namesr?rrr_validate_transformer_weightss  z*FeatureUnion._validate_transformer_weightscs |jpijfdd|jDS)zg Generate (name, trans, weight) tuples excluding None and 'drop' transformers. c3s(|] \}}|dkr|||fVqdS)rNr)rRr?r@Z get_weightrrrsz%FeatureUnion._iter..)rgetrrBrrrrAs  zFeatureUnion._itercs\g}|D]J\}}t|ds:tdtt|jf|fdd|Dq |S)zGet feature names from all transformers. Returns ------- feature_names : list of strings Names of the features produced by transform. get_feature_namesz.)rAr1AttributeErrorrr3r~extendr)rZ feature_namesr@rrrrrs  zFeatureUnion.get_feature_namescKs&||||t}|s|S|||S)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. Returns ------- self : FeatureUnion This estimator )_parallel_funcr_update_transformer_list)rrcrdrXr6rrrr,s  zFeatureUnion.fitcKsH||||t}|s(t|jddfSt|\}}||||S)a[Fit 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. Returns ------- X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. r)rrbnpzerosshaper/r_hstack)rrcrdrXZresultsXsr6rrrr-s   zFeatureUnion.fit_transformcCs|js dSd|||fS)NrO)r)rr?r>ZtotalrrrrQszFeatureUnion._log_messagecsVtj_ttjdfddtdDS)z Runs func in parallel on X and yrc 3sF|]>\}\}}}t||fd||tdVqdS)rr]N)rrQr9)rRr>r?rerrcrXfuncrr6rdrrrs z.FeatureUnion._parallel_func..r)r`rrrrArrr:)rrcrdrXrrrrrs  zFeatureUnion._parallel_funccsDt|jdfdd|D}|s:tjddfS||S)aTransform X separately by each transformer, concatenate results. Parameters ---------- X : iterable or array-like, depending on transformers Input data to be transformed. Returns ------- X_t : array-like or sparse matrix of shape (n_samples, sum_n_components) hstack of results of transformers. sum_n_components is the sum of n_components (output dimension) over transformers. rc3s&|]\}}}tt|d|VqdSr)rr)rRr?r@rrcrrrsz)FeatureUnion.transform..r)rrrArrrr)rrcrrrrr.s zFeatureUnion.transformcCs0tdd|Dr"t|}n t|}|S)Ncss|]}t|VqdSr)rZissparserrrrr sz'FeatureUnion._hstack..)anyrZhstackZtocsrr)rrrrrr s zFeatureUnion._hstackcs*tfdd|jD|jdd<dS)Ncs(g|] \}}||dkr|ntfqS)r)next)rRr?oldr6rrrsz9FeatureUnion._update_transformer_list..)iterr)rr6rrrrs z%FeatureUnion._update_transformer_listcCs|jddjSr|)rr}rBrrrr}szFeatureUnion.n_features_in_cCst|j\}}td||dS)NZparallel)r4)r/rr )rr4r6rrrrszFeatureUnion._sk_visual_block_)T)N)N)r~rrrrrr r%r)rrrArr,r-rQrr.rrrr}rrrrrr s.;      rrcGstt|||dS)a( Construct a FeatureUnion from the given transformers. This is a shorthand for the 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 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 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)rr)rrr6rrrr s *)rN)rN)(r collectionsr itertoolsrZnumpyrZscipyrZjoblibrbaserrZutils._estimator_html_reprr Zutils.metaestimatorsr Zutilsr r Zutils.deprecationr Z utils._tagsrZutils.validationrrZ utils.fixesrr__all__rrrrrbrrrrrrrsD             u-