-iBSSKrSSKJr SSKrSSKJrJrJr SSK J r SSK J r SSK JrJr SSKJr SS KJrJrJrJrJrJrJrJrJr S r"S S \\5rg) N)partial) BaseEstimatorTransformerMixin _fit_context) StrOptions) _VisualBlock)_get_adapter_from_container_get_output_config) available_if) _allclose_dense_sparse_check_feature_names_check_feature_names_in_check_n_features_get_feature_names _is_pandas_df _is_polars_df check_array validate_datacU$)zThe identity function.)Xs ^/var/www/html/venv/lib/python3.13/site-packages/sklearn/preprocessing/_function_transformer.py _identityrs Hc ^\rSrSr%Sr\S/\S/S/S/S/\\"S15S/\S/\S/S.r\\ S'SSSS SSSS .S jjr S r S r \ "S S9SSj5rSrSr\"S5SSj5rSSjrSrU4SjrSS.SjrSrSrSrU=r$)FunctionTransformer#awConstructs a transformer from an arbitrary callable. A FunctionTransformer forwards its X (and optionally y) arguments to a user-defined function or function object and returns the result of this function. This is useful for stateless transformations such as taking the log of frequencies, doing custom scaling, etc. Note: If a lambda is used as the function, then the resulting transformer will not be pickleable. .. versionadded:: 0.17 Read more in the :ref:`User Guide `. Parameters ---------- func : callable, default=None The callable to use for the transformation. This will be passed the same arguments as transform, with args and kwargs forwarded. If func is None, then func will be the identity function. inverse_func : callable, default=None The callable to use for the inverse transformation. This will be passed the same arguments as inverse transform, with args and kwargs forwarded. If inverse_func is None, then inverse_func will be the identity function. validate : bool, default=False Indicate that the input X array should be checked before calling ``func``. The possibilities are: - If False, there is no input validation. - If True, then X will be converted to a 2-dimensional NumPy array or sparse matrix. If the conversion is not possible an exception is raised. .. versionchanged:: 0.22 The default of ``validate`` changed from True to False. accept_sparse : bool, default=False Indicate that func accepts a sparse matrix as input. If validate is False, this has no effect. Otherwise, if accept_sparse is false, sparse matrix inputs will cause an exception to be raised. check_inverse : bool, default=True Whether to check that or ``func`` followed by ``inverse_func`` leads to the original inputs. It can be used for a sanity check, raising a warning when the condition is not fulfilled. .. versionadded:: 0.20 feature_names_out : callable, 'one-to-one' or None, default=None Determines the list of feature names that will be returned by the `get_feature_names_out` method. If it is 'one-to-one', then the output feature names will be equal to the input feature names. If it is a callable, then it must take two positional arguments: this `FunctionTransformer` (`self`) and an array-like of input feature names (`input_features`). It must return an array-like of output feature names. The `get_feature_names_out` method is only defined if `feature_names_out` is not None. See ``get_feature_names_out`` for more details. .. versionadded:: 1.1 kw_args : dict, default=None Dictionary of additional keyword arguments to pass to func. .. versionadded:: 0.18 inv_kw_args : dict, default=None Dictionary of additional keyword arguments to pass to inverse_func. .. versionadded:: 0.18 Attributes ---------- 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 See Also -------- MaxAbsScaler : Scale each feature by its maximum absolute value. StandardScaler : Standardize features by removing the mean and scaling to unit variance. LabelBinarizer : Binarize labels in a one-vs-all fashion. MultiLabelBinarizer : Transform between iterable of iterables and a multilabel format. Notes ----- If `func` returns an output with a `columns` attribute, then the columns is enforced to be consistent with the output of `get_feature_names_out`. Examples -------- >>> import numpy as np >>> from sklearn.preprocessing import FunctionTransformer >>> transformer = FunctionTransformer(np.log1p) >>> X = np.array([[0, 1], [2, 3]]) >>> transformer.transform(X) array([[0. , 0.6931], [1.0986, 1.3862]]) Nboolean one-to-onefunc inverse_funcvalidate accept_sparse check_inversefeature_names_outkw_args inv_kw_args_parameter_constraintsFT)r$r%r&r'r(r)cdXlX lX0lX@lXPlX`lXplXlgNr!) selfr"r#r$r%r&r'r(r)s r__init__FunctionTransformer.__init__s1 ( **!2 &rcUR(a[XURUS9$U(a[XUS9 [ XUS9 U$)N)r%resetr1)r$rr%rr)r-rr1s r _check_input FunctionTransformer._check_inputs= == 8J8JRWX X  dU 3  6rc [SS[SURSS-55nURUR X55n[ US5(aUR /nO[ US5(a URn[SW55(d [S5e[XU5(d[R"S [5 gg) z1Check that func and inverse_func are the inverse.Nrddtypedtypesc3# UHKn[U[R5=(a% [R"U[R5v MM g7fr,) isinstancenpr8 issubdtypenumber).0ds r ?FunctionTransformer._check_inverse_transform..s5 MSJq"(( # C a(C CVsAAzL'check_inverse' is only supported when all the elements in `X` is numerical.zThe provided functions are not strictly inverse of each other. If you are sure you want to proceed regardless, set 'check_inverse=False'.)slicemaxshapeinverse_transform transformhasattrr8r9all ValueErrorr warningswarn UserWarning)r-r idx_selected X_round_tripr9s r_check_inverse_transform,FunctionTransformer._check_inverse_transformsT4Q c0A)BC --dnnQ_.MN 1g  ggYF Q ! !XXF MS     &ao|DD MM.  Er)prefer_skip_nested_validationcURUSS9nUR(a+URbURbUR U5 U$)aFit transformer by checking X. If ``validate`` is ``True``, ``X`` will be checked. Parameters ---------- X : {array-like, sparse-matrix} of shape (n_samples, n_features) if `validate=True` else any object that `func` can handle Input array. y : Ignored Not used, present here for API consistency by convention. Returns ------- self : object FunctionTransformer class instance. Tr2)r3r&r"r#rP)r-rys rfitFunctionTransformer.fitsI(   at  ,   tyy'8D.s.4?SJsC((Ks)X_output X_originalrZinplacezThe output generated by `func` have different column names than the ones provided by `get_feature_names_out`. Got output with columns names: z' and `get_feature_names_out` returned: z. The column names can be overridden by setting `set_output(transform='pandas')` or `set_output(transform='polars')` such that the column names are set to the names provided by `get_feature_names_out`.zWhen `set_output` is configured to be '{0}', `func` should return a {0} DataFrame to follow the `set_output` API or `feature_names_out` should be defined.pandaspolars)r3 _transformr"r(r rHr'get_feature_names_outlistrZgetattrrrIr create_containerrJrrKrLformatr) r-rout output_configr'feature_names_insame_feature_names_in_outnot_all_str_columnsadapterwarn_msgs rrGFunctionTransformer.transforms   au  -ooaiioF*;=gF 3 " "t'='='I!% : : < CKK D):$;;$+*,>q,A$ -=D,H-'T$N#++&N')+..47KK.+'#-0C9#>G!22!$#& 1 % 3C%::>s{{:K9LM= : : <=>?T T    ! ! )&  (s1C1C hooh78 (*=3E3E hooh78 rcUR(a[XRS9nURXRUR S9$)aBTransform X using the inverse function. Parameters ---------- X : {array-like, sparse-matrix} of shape (n_samples, n_features) if `validate=True` else any object that `inverse_func` can handle Input array. Returns ------- X_original : array-like, shape (n_samples, n_features) Transformed input. )r%rX)r$rr%rer#r))r-rs rrF%FunctionTransformer.inverse_transform@s: ==A-?-?@Aq'8'8$BRBRSSrcURSL$r,)r'r-s rFunctionTransformer.Rst55TArc$[US5(dUb [X5nURS:XaUnOF[UR5(aURX5nO[ SUR<S35e[ R "U[S9$)aoGet output feature names for transformation. This method is only defined if `feature_names_out` is not None. Parameters ---------- input_features : array-like of str or None, default=None Input feature names. - If `input_features` is None, then `feature_names_in_` is used as the input feature names. If `feature_names_in_` is not defined, then names are generated: `[x0, x1, ..., x(n_features_in_ - 1)]`. - If `input_features` is array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. - If `feature_names_out` is 'one-to-one', the input feature names are returned (see `input_features` above). This requires `feature_names_in_` and/or `n_features_in_` to be defined, which is done automatically if `validate=True`. Alternatively, you can set them in `func`. - If `feature_names_out` is a callable, then it is called with two arguments, `self` and `input_features`, and its return value is returned by this method. n_features_in_r zfeature_names_out=z is invalid. It must either be "one-to-one" or a callable with two arguments: the function transformer and an array-like of input feature names. The callable must return an array-like of output feature names.)r8)rHrr'callablerJr<asarrayobject)r-input_features names_outs rrf)FunctionTransformer.get_feature_names_outRs@ 4) * *n.H4TJN  ! !\ 1&I d,, - -..tDI$T%;%;$>?++ zz)622rc<Uc[nU"U40U(aUD6$0D6$r,)r)r-rr"r(s rreFunctionTransformer._transforms& <DA6W'66"66rcg)z2Return True since FunctionTransfomer is stateless.Trrvs r__sklearn_is_fitted__)FunctionTransformer.__sklearn_is_fitted__src>[TU]5nUR(+UlSUlUR(+=(d UR UR lU$)NF)super__sklearn_tags__r$ no_validation requires_fitr% input_tagssparse)r-tags __class__s rr$FunctionTransformer.__sklearn_tags__sJw')!%.!%)]]!2!Hd6H6H r)rGcR[US5(d0UlXRS'U$)a>Set output container. See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` for an example on how to use the API. Parameters ---------- transform : {"default", "pandas", "polars"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `"polars"`: Polars output - `None`: Transform configuration is unchanged .. versionadded:: 1.4 `"polars"` option was added. Returns ------- self : estimator instance Estimator instance. _sklearn_output_configrG)rHr)r-rGs r set_outputFunctionTransformer.set_outputs-0t566*,D '3<##K0 rc([URS5(aURR$[UR[5(a URRR$URR RS3$)z?Get the name display of the `func` used in HTML representation.__name__z(...))rHr"rr;rrrvs r_get_function_name&FunctionTransformer._get_function_namesf 499j ) )99%% % dii ) )99>>** *))%%../u55rc L[SUUR5[U5SSS9$)Nsingler)names name_details name_captiondoc_link_label)r rr]rvs r_sk_visual_block_%FunctionTransformer._sk_visual_block_s.  ))+T.0   r) rr%r&r'r"r)r#r(r$)NNr,)r __module__ __qualname____firstlineno____doc__r{rdictr*__annotations__r.r3rPrrUrGrFr rfrerrrrr__static_attributes__ __classcell__)rs@rrr#sod4 !4(K##& L>(BDI$<d| $D ' '* >560FPT$AB-3C-3^7 '+<6  rr)rK functoolsrnumpyr<baserrrutils._param_validationrutils._repr_html.estimatorr utils._set_outputr r utils.metaestimatorsr utils.validationr rrrrrrrrrrrrrrsK@@050    ^ *M^ r