-iUSSKrSSKJrJr SSKJr SSKrSSKJrJ r J r J r J r SSK JrJrJr SSKJrJrJrJr SSKJrJrJrJrJr SS KJr SS KJrJ r J!r! S /r""S S \ \ \5r#g) N)IntegralReal)warn) BaseEstimatorClassifierMixinMetaEstimatorMixin _fit_contextclone)Bunchget_tags safe_mask) HasMethodsHiddenInterval StrOptions)MetadataRouter MethodMapping_raise_for_params_routing_enabledprocess_routing) available_if)_estimator_hascheck_is_fitted validate_dataSelfTrainingClassifierc ^\rSrSr%SrS\"S/5/\"S/5\"\"S155/\"\ SSSS 9/\"S S 15/\"\ S SSS 9/\"\ S SSS 9S/S/S.r \ \ S'S#SjrSr\"SS9S5r\"\"S55S5r\"\"S55S5r\"\"S55S5r\"\"S55S5r\"\"S55S5rS rU4S!jrS"rU=r$)$r aSelf-training classifier. This :term:`metaestimator` allows a given supervised classifier to function as a semi-supervised classifier, allowing it to learn from unlabeled data. It does this by iteratively predicting pseudo-labels for the unlabeled data and adding them to the training set. The classifier will continue iterating until either max_iter is reached, or no pseudo-labels were added to the training set in the previous iteration. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object An estimator object implementing `fit` and `predict_proba`. Invoking the `fit` method will fit a clone of the passed estimator, which will be stored in the `estimator_` attribute. .. versionadded:: 1.6 `estimator` was added to replace `base_estimator`. base_estimator : estimator object An estimator object implementing `fit` and `predict_proba`. Invoking the `fit` method will fit a clone of the passed estimator, which will be stored in the `estimator_` attribute. .. deprecated:: 1.6 `base_estimator` was deprecated in 1.6 and will be removed in 1.8. Use `estimator` instead. threshold : float, default=0.75 The decision threshold for use with `criterion='threshold'`. Should be in [0, 1). When using the `'threshold'` criterion, a :ref:`well calibrated classifier ` should be used. criterion : {'threshold', 'k_best'}, default='threshold' The selection criterion used to select which labels to add to the training set. If `'threshold'`, pseudo-labels with prediction probabilities above `threshold` are added to the dataset. If `'k_best'`, the `k_best` pseudo-labels with highest prediction probabilities are added to the dataset. When using the 'threshold' criterion, a :ref:`well calibrated classifier ` should be used. k_best : int, default=10 The amount of samples to add in each iteration. Only used when `criterion='k_best'`. max_iter : int or None, default=10 Maximum number of iterations allowed. Should be greater than or equal to 0. If it is `None`, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled. verbose : bool, default=False Enable verbose output. Attributes ---------- estimator_ : estimator object The fitted estimator. classes_ : ndarray or list of ndarray of shape (n_classes,) Class labels for each output. (Taken from the trained `estimator_`). transduction_ : ndarray of shape (n_samples,) The labels used for the final fit of the classifier, including pseudo-labels added during fit. labeled_iter_ : ndarray of shape (n_samples,) The iteration in which each sample was labeled. When a sample has iteration 0, the sample was already labeled in the original dataset. When a sample has iteration -1, the sample was not labeled in any iteration. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_iter_ : int The number of rounds of self-training, that is the number of times the base estimator is fitted on relabeled variants of the training set. termination_condition_ : {'max_iter', 'no_change', 'all_labeled'} The reason that fitting was stopped. - `'max_iter'`: `n_iter_` reached `max_iter`. - `'no_change'`: no new labels were predicted. - `'all_labeled'`: all unlabeled samples were labeled before `max_iter` was reached. See Also -------- LabelPropagation : Label propagation classifier. LabelSpreading : Label spreading model for semi-supervised learning. References ---------- :doi:`David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL '95). Association for Computational Linguistics, Stroudsburg, PA, USA, 189-196. <10.3115/981658.981684>` Examples -------- >>> import numpy as np >>> from sklearn import datasets >>> from sklearn.semi_supervised import SelfTrainingClassifier >>> from sklearn.svm import SVC >>> rng = np.random.RandomState(42) >>> iris = datasets.load_iris() >>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3 >>> iris.target[random_unlabeled_points] = -1 >>> svc = SVC(probability=True, gamma="auto") >>> self_training_model = SelfTrainingClassifier(svc) >>> self_training_model.fit(iris.data, iris.target) SelfTrainingClassifier(...) Nfit deprecatedgg?left)closed thresholdk_bestrverbose) estimatorbase_estimatorr# criterionr$max_iterr&_parameter_constraintsFcXXlX0lX@lXPlX`lXplX lgN)r'r#r)r$r*r&r()selfr'r(r#r)r$r*r&s Y/var/www/html/venv/lib/python3.13/site-packages/sklearn/semi_supervised/_self_training.py__init__SelfTrainingClassifier.__init__s*#""   -cXURc7URS:wa'[UR5n[S[5 U$URcURS:Xa [ S5eURbURS:wa [ S5e[UR5nU$)zcGet the estimator. Returns ------- estimator_ : estimator object The cloned estimator object. r zg`base_estimator` has been deprecated in 1.6 and will be removed in 1.8. Please use `estimator` instead.zFYou must pass an estimator to SelfTrainingClassifier. Use `estimator`.zLYou must pass only one estimator to SelfTrainingClassifier. Use `estimator`.)r'r(r r FutureWarning ValueError)r. estimator_s r/_get_estimator%SelfTrainingClassifier._get_estimators >> !d&9&9\&It223J ?  &^^ #(;(;|(KX ^^ 'D,?,?<,O$  t~~.Jr2)prefer_skip_nested_validationc * [X0S5 UR5Ul[UUU/SQSS9upURR S;a [ S5eUS:gn[R"U5(a[R"S[5 URS :XaOURURS [R"U5- :a[R"S [5 [!5(a[#US40UD6nO[%[%0S 9S 9n[R&"U5Ul[R*"US5UlS UR,U'S Ul[R"U5(GdbUR0bUR.UR0:Ga:U=R.S- slURR2"U[5X5UR(U40UR6R2D6 URR9U[5X)55nURR:[R<"USS9n[R>"USS9nURS:XaXR@:n Oj[CURURS 5n XRS :Xa[RD"U[FS9n O[RH"U*U 5SU n [RJ"U)5S U n XyUR(U 'SXK'UR.UR,U 'U RS S :XaSUl&OURN(a)[QSUR.SU RS S35 [R"U5(d-UR0cGMUR.UR0:aGM:UR.UR0:XaSUl&[R"U5(aSUl&URR2"U[5X5UR(U40UR6R2D6 URR:UlU$)a Fit self-training classifier using `X`, `y` as training data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. y : {array-like, sparse matrix} of shape (n_samples,) Array representing the labels. Unlabeled samples should have the label -1. **params : dict Parameters to pass to the underlying estimators. .. versionadded:: 1.6 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- self : object Fitted estimator. r)csrcsclildokF) accept_sparseensure_all_finite)USz~y has dtype string. If you wish to predict on string targets, use dtype object, and use -1 as the label for unlabeled samples.zy contains no unlabeled samplesr$rzsk_best is larger than the amount of unlabeled samples. All unlabeled samples will be labeled in the first iteration)rr'Nr%)axisr#)dtypeT no_changezEnd of iteration z, added z new labels.r* all_labeled))rr7r6rrFkindr5npallwarningsr UserWarningr)r$shapesumrrr copy transduction_ full_like labeled_iter_n_iter_r*rrr' predict_probaclasses_argmaxmaxr#min ones_likebool argpartitionnonzerotermination_condition_r&print) r.Xyparams has_label routed_paramsprobpred max_probaselected n_to_select selected_fulls r/rSelfTrainingClassifier.fits@ &.--/  6#   77<<: %7  G 66)   MM;[ I >>X % KK!''!*rvvi'88 8 MM*     +D%B6BM!EbM:MWWQZ\\!R0()9% &&## MM !T\\DMM%A LLA L OO  )A)*""9-  ))-- ??009Q 3K1LMD??++BIId,CDDt!,I~~,$~~5!$++yq/AB //!"44!||ITBH " z;G UHJJ z215h?M15D  } -'+I $04 D  } -""1%*.9+||' ~6+11!45\CQ&&## MM !T\\DMM%AX <<4== (*4D ' 66)  *7D '  i% &   y ) %%)) 00  r2predictc [U5 [X S5 [5(a[US40UD6nO[ [ 0S9S9n[ UUSSSS9nUR R"U40URRD6$)aePredict the classes of `X`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. **params : dict of str -> object Parameters to pass to the underlying estimator's ``predict`` method. .. 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 ------- y : ndarray of shape (n_samples,) Array with predicted labels. rl)rlrDTFr?r@reset) rrrrr rr6rlr'r.r`rbrds r/rlSelfTrainingClassifier.predictrs0 & 2   +D)FvFM!E",=>M   #  &&qLM,C,C,K,KLLr2rUc [U5 [X S5 [5(a[US40UD6nO[ [ 0S9S9n[ UUSSSS9nUR R"U40URRD6$)aPredict probability for each possible outcome. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. **params : dict of str -> object Parameters to pass to the underlying estimator's ``predict_proba`` method. .. versionadded:: 1.6 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y : ndarray of shape (n_samples, n_features) Array with prediction probabilities. rU)rUrDTFrn) rrrrr rr6rUr'rps r/rU$SelfTrainingClassifier.predict_probas2 &8   +D/LVLM!E,CDM   #  ,,QX-2I2I2W2WXXr2decision_functionc [U5 [X S5 [5(a[US40UD6nO[ [ 0S9S9n[ UUSSSS9nUR R"U40URRD6$)aCall decision function of the `estimator`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. **params : dict of str -> object Parameters to pass to the underlying estimator's ``decision_function`` method. .. versionadded:: 1.6 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- y : ndarray of shape (n_samples, n_features) Result of the decision function of the `estimator`. rt)rtrDTFrn) rrrrr rr6rtr'rps r/rt(SelfTrainingClassifier.decision_function2 &(;<   +D2EPPM!EB,GHM   #  00  ((::  r2predict_log_probac [U5 [X S5 [5(a[US40UD6nO[ [ 0S9S9n[ UUSSSS9nUR R"U40URRD6$)aPredict log probability for each possible outcome. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. **params : dict of str -> object Parameters to pass to the underlying estimator's ``predict_log_proba`` method. .. 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 ------- y : ndarray of shape (n_samples, n_features) Array with log prediction probabilities. rx)rxrDTFrn) rrrrr rr6rxr'rps r/rx(SelfTrainingClassifier.predict_log_probarwr2scorec [U5 [X0S5 [5(a[US40UD6nO[ [ 0S9S9n[ UUSSSS9nUR R"X40URRD6$)aCall score on the `estimator`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. y : array-like of shape (n_samples,) Array representing the labels. **params : dict of str -> object Parameters to pass to the underlying estimator's ``score`` method. .. versionadded:: 1.6 Only available if `enable_metadata_routing=True`, which can be set by using ``sklearn.set_config(enable_metadata_routing=True)``. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- score : float Result of calling score on the `estimator`. r{)r{rDTFrn) rrrrr rr6r{r')r.r`rarbrds r/r{SelfTrainingClassifier.score!s6 &0   +D'DVDM!EO` on how the routing mechanism works. .. versionadded:: 1.6 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. )ownerr)calleecallerr{rlrUrtrx)r'method_mapping)r __class____name__addr'r)r.routers r/get_metadata_routing+SelfTrainingClassifier.get_metadata_routingNs dnn&=&=> nnE%0GE2Ii8OOD/8KL/8KLGG4   r2c>[TU]5nURb8[UR5RR URlU$r-)super__sklearn_tags__r'r input_tagssparse)r.tagsrs r/r'SelfTrainingClassifier.__sklearn_tags__ls@w') >> %%-dnn%=%H%H%O%ODOO " r2) r(rVr)r'r6r$rSr*rTr^r#rQr&)Nr g?r# rF)r __module__ __qualname____firstlineno____doc__rrrrrrr+dict__annotations__r0r7r rrrrlrUrtrxr{rr__static_attributes__ __classcell__)rs@r/rr s~HJw/0 w  :|n- . tS#f=> +x!89:Haf=>h4?F;$D&#-(B&+E EN.+,'M-'MR.12(Y3(YT.!456* 7* X.!456* 7* X.)**L+*LX<r2)$rLnumbersrrrnumpyrJbaserrr r r utilsr r rutils._param_validationrrrrutils.metadata_routingrrrrrutils.metaestimatorsrutils.validationrrr__all__rr2r/rsY"/.NN0MM # $ Q _.@-Q r2