-iyhSSKJr SSKJr SSKJr SSKJr SSKJ r SSK J r Sr "S S \5r g ) )deepcopy) BaseEstimator)NotFittedError)get_tags) available_if)check_is_fittedc^U4SjnU$)zKCheck that final_estimator has `attr`. Used together with `available_if`. c2>[URT5 gNT)getattr estimator)selfattrs I/var/www/html/venv/lib/python3.13/site-packages/sklearn/frozen/_frozen.pycheck_estimator_has..checks%)rrs` r_estimator_hasr s  Lrct\rSrSrSrSr\"\"S55S5rSr Sr Sr S r S r SS jrS rS rg)FrozenEstimatoraEstimator that wraps a fitted estimator to prevent re-fitting. This meta-estimator takes an estimator and freezes it, in the sense that calling `fit` on it has no effect. `fit_predict` and `fit_transform` are also disabled. All other methods are delegated to the original estimator and original estimator's attributes are accessible as well. This is particularly useful when you have a fitted or a pre-trained model as a transformer in a pipeline, and you'd like `pipeline.fit` to have no effect on this step. Parameters ---------- estimator : estimator The estimator which is to be kept frozen. See Also -------- None: No similar entry in the scikit-learn documentation. Examples -------- >>> from sklearn.datasets import make_classification >>> from sklearn.frozen import FrozenEstimator >>> from sklearn.linear_model import LogisticRegression >>> X, y = make_classification(random_state=0) >>> clf = LogisticRegression(random_state=0).fit(X, y) >>> frozen_clf = FrozenEstimator(clf) >>> frozen_clf.fit(X, y) # No-op FrozenEstimator(estimator=LogisticRegression(random_state=0)) >>> frozen_clf.predict(X) # Predictions from `clf.predict` array(...) cXlgNr)rrs r__init__FrozenEstimator.__init__>s"r __getitem__c:URR"U0UD6$)z__getitem__ is defined in :class:`~sklearn.pipeline.Pipeline` and :class:`~sklearn.compose.ColumnTransformer`. )rr)rargskwargss rrFrozenEstimator.__getitem__As ~~))4:6::rcVUS;a[US35e[URU5$)N) fit_predict fit_transformz( is not available for frozen estimators.)AttributeErrorr r)rnames r __getattr__FrozenEstimator.__getattr__Hs1 3 3 D6)Q!RS St~~t,,rcU$rrrs r__sklearn_clone__!FrozenEstimator.__sklearn_clone__Os rcP[UR5 g![a gf=f)NTF)r rrr,s r__sklearn_is_fitted__%FrozenEstimator.__sklearn_is_fitted__Rs(  DNN +  s  %%c0[UR5 U$)aNo-op. As a frozen estimator, calling `fit` has no effect. Parameters ---------- X : object Ignored. y : object Ignored. *args : tuple Additional positional arguments. Ignored, but present for API compatibility with `self.estimator`. **kwargs : dict Additional keyword arguments. Ignored, but present for API compatibility with `self.estimator`. Returns ------- self : object Returns the instance itself. )r r)rXyr!r"s rfitFrozenEstimator.fitYs4 ' rc ^URSS5nUbX lU(a [S5eg)aSet the parameters of this estimator. The only valid key here is `estimator`. You cannot set the parameters of the inner estimator. Parameters ---------- **kwargs : dict Estimator parameters. Returns ------- self : FrozenEstimator This estimator. rNzYou cannot set parameters of the inner estimator in a frozen estimator since calling `fit` has no effect. You can use `frozenestimator.estimator.set_params` to set parameters of the inner estimator.)popr ValueError)rr"rs r set_paramsFrozenEstimator.set_paramsvs9 JJ{D1  &N   rcSUR0$)aGet parameters for this estimator. Returns a `{"estimator": estimator}` dict. The parameters of the inner estimator are not included. Parameters ---------- deep : bool, default=True Ignored. Returns ------- params : dict Parameter names mapped to their values. rr)rdeeps r get_paramsFrozenEstimator.get_paramss T^^,,rcP[[UR55nSUlU$r )rrr _skip_test)rtagss r__sklearn_tags__ FrozenEstimator.__sklearn_tags__s!01 rrN)T)__name__ __module__ __qualname____firstlineno____doc__rrrrr)r-r0r5r:r>rC__static_attributes__rrrrrsN D#./0;1; -:6-$rrN)copyrbaser exceptionsrutilsrutils.metaestimatorsrutils.validationr rrrrrrQs, '/. KmKr