Ki6SrSSKJrJr SSKrSSKJrJrJ r J r J r SSK J r SSKJrJr SSKJrJr SSKJrJrJrJrJr SS KJrJrJrJr SS KJ r SS K!J"r"J#r# "S S \ \\5r$g)z Sequential feature selection )IntegralRealN) BaseEstimatorMetaEstimatorMixin _fit_contextclone is_classifier) SelectorMixin) check_scoringget_scorer_names)check_cvcross_val_score)MetadataRouter MethodMapping_raise_for_params_routing_enabledprocess_routing) HasMethodsInterval RealNotInt StrOptions)get_tags)check_is_fitted validate_datac $^\rSrSr%Sr\"S/5/\"S15\"\SSSS9\"\ SS S S9/S \"\ S S S S9/\"S S 15/S \"\ "\ "555\ /S /S \ /S.r\\S'SS S S SS S.Sjr\"SS9SSj5rSrSrU4SjrSrSrU=r$)SequentialFeatureSelector"aTransformer that performs Sequential Feature Selection. This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a feature subset in a greedy fashion. At each stage, this estimator chooses the best feature to add or remove based on the cross-validation score of an estimator. In the case of unsupervised learning, this Sequential Feature Selector looks only at the features (X), not the desired outputs (y). Read more in the :ref:`User Guide `. .. versionadded:: 0.24 Parameters ---------- estimator : estimator instance An unfitted estimator. n_features_to_select : "auto", int or float, default="auto" If `"auto"`, the behaviour depends on the `tol` parameter: - if `tol` is not `None`, then features are selected while the score change does not exceed `tol`. - otherwise, half of the features are selected. If integer, the parameter is the absolute number of features to select. If float between 0 and 1, it is the fraction of features to select. .. versionadded:: 1.1 The option `"auto"` was added in version 1.1. .. versionchanged:: 1.3 The default changed from `"warn"` to `"auto"` in 1.3. tol : float, default=None If the score is not incremented by at least `tol` between two consecutive feature additions or removals, stop adding or removing. `tol` can be negative when removing features using `direction="backward"`. `tol` is required to be strictly positive when doing forward selection. It can be useful to reduce the number of features at the cost of a small decrease in the score. `tol` is enabled only when `n_features_to_select` is `"auto"`. .. versionadded:: 1.1 direction : {'forward', 'backward'}, default='forward' Whether to perform forward selection or backward selection. scoring : str or callable, default=None Scoring method to use for cross-validation. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)`` that returns a single value. See :ref:`scoring_callable` for details. - `None`: the `estimator`'s :ref:`default evaluation criterion ` is used. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. In all other cases, :class:`~sklearn.model_selection.KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. n_jobs : int, default=None Number of jobs to run in parallel. When evaluating a new feature to add or remove, the cross-validation procedure is parallel over the folds. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. Attributes ---------- n_features_in_ : int Number of features seen during :term:`fit`. Only defined if the underlying estimator 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:: 1.0 n_features_to_select_ : int The number of features that were selected. support_ : ndarray of shape (n_features,), dtype=bool The mask of selected features. See Also -------- GenericUnivariateSelect : Univariate feature selector with configurable strategy. RFE : Recursive feature elimination based on importance weights. RFECV : Recursive feature elimination based on importance weights, with automatic selection of the number of features. SelectFromModel : Feature selection based on thresholds of importance weights. Examples -------- >>> from sklearn.feature_selection import SequentialFeatureSelector >>> from sklearn.neighbors import KNeighborsClassifier >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> knn = KNeighborsClassifier(n_neighbors=3) >>> sfs = SequentialFeatureSelector(knn, n_features_to_select=3) >>> sfs.fit(X, y) SequentialFeatureSelector(estimator=KNeighborsClassifier(n_neighbors=3), n_features_to_select=3) >>> sfs.get_support() array([ True, False, True, True]) >>> sfs.transform(X).shape (150, 3) fitautorright)closedNneitherforwardbackward cv_object estimatorn_features_to_selecttol directionscoringcvn_jobs_parameter_constraints)r)r*r+r,r-r.cXXlX lX0lX@lXPlX`lXplgNr')selfr(r)r*r+r,r-r.s g/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/feature_selection/_sequential.py__init__"SequentialFeatureSelector.__init__s(#$8!"  F)prefer_skip_nested_validationc R[X0S5 UR5n[UUSSURR(+S9nUR SnUR S:Xa#URb US- UlOUS-UlO[UR [5(a-UR U:a [S5eUR UlO;[UR [5(a[XPR -5UlURb+URS :aURS :Xa [S 5e[UR U[#UR$5S 9n['UR$5n[(R*"U[,S 9nUR S:XdURS :Xa URO XPR- n [(R.*n URSL=(a UR S:Hn [15(a [3US40UD6 [5U 5H;n UR6"XqX&U40UD6upU (aX- UR:a O Un SX'M= URS:XaU)nXlUR8R;5UlU$)a.Learn the features to select from X. Parameters ---------- X : array-like of shape (n_samples, n_features) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of predictors. y : array-like of shape (n_samples,), default=None Target values. This parameter may be ignored for unsupervised learning. **params : dict, default=None Parameters to be passed to the underlying `estimator`, `cv` and `scorer` objects. .. versionadded:: 1.6 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : object Returns the instance itself. rcsc) accept_sparseensure_min_featuresensure_all_finiter rNz*n_features_to_select must be < n_features.rr$z:tol must be strictly positive when doing forward selection classifier)shapedtypeTr%)r__sklearn_tags__r input_tags allow_nanrAr)r*n_features_to_select_ isinstancer ValueErrorrintr+r r-r r(rnpzerosboolinfrrrange_get_best_new_feature_scoresupport_sum)r3Xyparamstags n_featuresr-cloned_estimator current_mask n_iterations old_scoreis_auto_select_new_feature_idx new_scores r4rSequentialFeatureSelector.fitsYD &.$$&    !"&//";";;  WWQZ  $ $ .xx#.8!^*-71_* 118 < <((J6 !MNN)-)B)BD & 114 8 8),Z:S:S-S)TD & 88 DHHqLT^^y5PL dggq]4>>-J K 0 xxj= ((F2dnn 6Q  & &888  VVG -U$2K2Kv2U    D% 26 2|$A)-)I)I QL*>Z '(=L$ %)]]%6%6%8" r7c :^ [R"U)5n0m UHgnUR5n SX'URS:XaU )n USS2U 4n [ UU UUUR UR US9R5T U'Mi [T U 4SjS9n U T U 4$)NTr%)r-r,r.rTc>TU$r2) feature_idxscoress r4GSequentialFeatureSelector._get_best_new_feature_score..Is f[>Qr7)key) rJ flatnonzerocopyr+rr,r.meanmax) r3r(rRrSr-rXrTcandidate_feature_indicesrccandidate_maskX_newr]rds @r4rO5SequentialFeatureSelector._get_best_new_feature_score2s %'NNL=$A!4K)..0N*.N '~~+"0a'(E"1 {{#df ;  5f*QR 777r7c0[U5 UR$r2)rrP)r3s r4_get_support_mask+SequentialFeatureSelector._get_support_maskLs}}r7c>[TU]5n[UR5RR URl[UR5RR URlU$r2)superrCrr(rDrEsparse)r3rU __class__s r4rC*SequentialFeatureSelector.__sklearn_tags__PsWw')$,T^^$<$G$G$Q$Q!!)$..!9!D!D!K!K r7c[US9nURUR[5RSSS9S9 UR[ UR [ UR5S9[5RSSS9S9 UR[URURS9[5RSS S9S 9 U$) a"Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.6 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. )ownerr)callercallee)r(method_mappingr?split)splitterr|)r,score)scorerr|) raddr(rr r-r r r,)r3routers r4get_metadata_routing.SequentialFeatureSelector.get_metadata_routingVs d+ nn(?..eE.J    dgg-2OP(?..eG.L     F(?..eG.L   r7) r-r+r(r)rFr.r,rPr*r2)__name__ __module__ __qualname____firstlineno____doc__rrrrrrsetr callabler/dict__annotations__r5rrrOrqrCr__static_attributes__ __classcell__)rvs@r4rr"sDN!%)* x ZAg 6 Xq$y 9! htT4 BC )Z!89:*S)9);%<=xHm" $D $$  &&+b bH84 r7r)%rnumbersrrnumpyrJ sklearn.baserrrrr sklearn.feature_selection._baser sklearn.metricsr r sklearn.model_selectionr r sklearn.utils._metadata_requestsrrrrrsklearn.utils._param_validationrrrrsklearn.utils._tagsrsklearn.utils.validationrrrrbr7r4rsS#:;=YX(CO /A=Or7