o `^hy@sdddlmZddlmZddlmZddlmZddlm Z ddl m Z dd Z Gd d d eZ d S) )deepcopy) BaseEstimator)NotFittedError)get_tags) available_if)check_is_fittedcsfdd}|S)zSCheck that final_estimator has `attr`. Used together with `available_if`. cst|jdSNT)getattr estimatorselfattrT/home/air/shanriGPT/back/venv/lib/python3.10/site-packages/sklearn/frozen/_frozen.pychecks z_estimator_has..checkr)rrrrr_estimator_has s rc@sfeZdZdZddZeedddZddZd d Z d d Z d dZ ddZ dddZ ddZdS)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(...) cCs ||_dSNr )r r rrr__init__>s zFrozenEstimator.__init__ __getitem__cOs|jj|i|S)z__getitem__ is defined in :class:`~sklearn.pipeline.Pipeline` and :class:`~sklearn.compose.ColumnTransformer`. )r r)r argskwargsrrrrAszFrozenEstimator.__getitem__cCs"|dvr t|dt|j|S)N) fit_predict fit_transformz( is not available for frozen estimators.)AttributeErrorr r )r namerrr __getattr__Hs zFrozenEstimator.__getattr__cCs|Srrr rrr__sklearn_clone__Osz!FrozenEstimator.__sklearn_clone__cCs&zt|jWdStyYdSw)NTF)rr rr rrr__sklearn_is_fitted__Rs   z%FrozenEstimator.__sklearn_is_fitted__cOst|j|S)aGNo-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. )rr )r XyrrrrrfitYs zFrozenEstimator.fitcKs*|dd}|dur ||_|rtddS)aZSet 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. r NzYou 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)r rr rrr set_paramsvs zFrozenEstimator.set_paramsTcCs d|jiS)ahGet 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. r r)r deeprrr get_paramss zFrozenEstimator.get_paramscCstt|j}d|_|Sr )rrr _skip_test)r tagsrrr__sklearn_tags__sz FrozenEstimator.__sklearn_tags__N)T)__name__ __module__ __qualname____doc__rrrrrr r!r$r'r)r,rrrrrs"    rN)copyrbaser exceptionsrutilsrutils.metaestimatorsrutils.validationrrrrrrrs