]j9pSSKrSSKJr SSKJrJr SSKrSSKJ r SSK J r SSK J r SrSr\"S S \55r"S S 5r"S S5r"SS5r\"5r\R+\"55 \R+\"55 SrSSjrSSjrSrSrSr"SS5rSS.Sjrg)Nwraps)Protocolruntime_checkable)issparse) get_config) available_ifcz[R"U5$![an[SUSUS35UeSnAff=f)zCheck library is installed.zSetting output container to 'z ' requires z to be installedN) importlib import_module ImportError)libraryexcs V/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sklearn/utils/_set_output.pycheck_library_installedrsR&&w// +G9KyI   s :5:cV[U5(aU"5$U$![a gf=fN)callable Exceptioncolumnss r get_columnsrs5 9  N  s  ((c<\rSrSr%\\S'S SjrSrSrSr Sr g) ContainerAdapterProtocol# container_libcg)aRCreate container from `X_output` with additional metadata. Parameters ---------- X_output : {ndarray, dataframe} Data to wrap. X_original : {ndarray, dataframe} Original input dataframe. This is used to extract the metadata that should be passed to `X_output`, e.g. pandas row index. columns : callable, ndarray, or None The column names or a callable that returns the column names. The callable is useful if the column names require some computation. If `None`, then no columns are passed to the container's constructor. inplace : bool, default=False Whether or not we intend to modify `X_output` in-place. However, it does not guarantee that we return the same object if the in-place operation is not possible. Returns ------- wrapped_output : container_type `X_output` wrapped into the container type. N)selfX_output X_originalrinplaces rcreate_container)ContainerAdapterProtocol.create_container'cg)zReturn True if X is a supported container. Parameters ---------- Xs: container Containers to be checked. Returns ------- is_supported_container : bool True if X is a supported container. Nr)rXs ris_supported_container/ContainerAdapterProtocol.is_supported_containerCr%r&cg)zRename columns in `X`. Parameters ---------- X : container Container which columns is updated. columns : ndarray of str Columns to update the `X`'s columns with. Returns ------- updated_container : container Container with new names. Nrrr(rs rrename_columns'ContainerAdapterProtocol.rename_columnsQr%r&cg)zStack containers horizontally (column-wise). Parameters ---------- Xs : list of containers List of containers to stack. Returns ------- stacked_Xs : container Stacked containers. Nr)rXss rhstackContainerAdapterProtocol.hstackbr%r&rN)F) __name__ __module__ __qualname____firstlineno__str__annotations__r#r)r-r1__static_attributes__rr&rrr#s 8   "  r&rc4\rSrSrSrS SjrSrSrSrSr g) PandasAdapterqpandasc~[S5n[U5nU(a[XR5(dq[XR5(a URnO5[X%RUR 45(a URnOSnURXU(+S9nUbUR X5$U$)Nr=)indexcopy)rr isinstance DataFramer?Seriesr-)rr r!rr"pdr?s rr#PandasAdapter.create_containerts $X .g&j<<@@ (LL11 Jryy(ABB"((||HG |LH  &&x9 9r&cB[S5n[XR5$)Nr=rrArB)rr(rDs rr)$PandasAdapter.is_supported_container $X .!\\**r&cX!lU$rrr,s rr-PandasAdapter.rename_columns r&c8[S5nURUSS9$)Nr=)axisrconcat)rr0rDs rr1PandasAdapter.hstacks $X .yy!y$$r&rNT r3r4r5r6rr#r)r-r1r9rr&rr;r;qsM0+ %r&r;c4\rSrSrSrS SjrSrSrSrSr g) PolarsAdapterpolarsc[S5n[U5n[U[R5(aUR 5OUnU(a[XR 5(dUR XSS9$UbURX5$U$)NrXrow)schemaorient)rrrAnpndarraytolistrBr-)rr r!rr"pls rr#PolarsAdapter.create_containerst $X .g&&0"**&E&E'.."7j<<@@<<<G G  &&x9 9r&cB[S5n[XR5$)NrXrG)rr(r`s rr)$PolarsAdapter.is_supported_containerrIr&cX!lU$rrr,s rr-PolarsAdapter.rename_columnsrLr&c8[S5nURUSS9$)NrX horizontal)howrP)rr0r`s rr1PolarsAdapter.hstacks $X .yyy..r&rNrSrTrr&rrVrVsM + /r&rVc0\rSrSrSr\S5rSrSrg)ContainerAdaptersManagerc0Ulgradaptersrs r__init__!ContainerAdaptersManager.__init__s  r&c4S1[UR5-$)Ndefault)setrorps rsupported_outputs*ContainerAdaptersManager.supported_outputss{S///r&c4XRUR'gr)ror)radapters rregister!ContainerAdaptersManager.registers/6 g++,r&rnN) r3r4r5r6rqpropertyrvrzr9rr&rrkrks 007r&rkcURRRS5Sn[RU$![ a@n[ [RR55n[SUSU<S35UeSnAff=f)zGet the adapter that knows how to handle such container. See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more details. .rzZThe container does not have a registered adapter in scikit-learn. Available adapters are: z" while the container provided is: N) __class__r4splitADAPTERS_MANAGERroKeyErrorlistkeys ValueError) container module_nameravailable_adapterss r_get_adapter_from_containerrs %%0066s;A>K((55 !"2";";"@"@"BC ''9&:;%= +   s= B;BBcf[X5Sn[RU$![a gf=f)zGet container adapter.denseN)_get_output_configrror)method estimator dense_configs r_get_container_adapterrs9%f8AL((66 s # 00c[US05nX;aX nO[5US3n[RnX4;a[ S[ U5SU35eSU0$)aGet output config based on estimator and global configuration. Parameters ---------- method : {"transform"} Estimator's method for which the output container is looked up. estimator : estimator instance or None Estimator to get the output configuration from. If `None`, check global configuration is used. Returns ------- config : dict Dictionary with keys: - "dense": specifies the dense container for `method`. This can be `"default"` or `"pandas"`. _sklearn_output_config_outputzoutput config must be in z, got r)getattrrrrvrsorted)rrest_sklearn_output_configrrvs rrrsx(!( 3KR P *08 !|vhg$67 (::,'/@(A'B& W   \ ""r&c[X5nUSS:Xd[U5(dU$USn[U5(a[SUR 5S35e[ R UnURUUURS9$)aWrap output with container based on an estimator's or global config. Parameters ---------- method : {"transform"} Estimator's method to get container output for. data_to_wrap : {ndarray, dataframe} Data to wrap with container. original_input : {ndarray, dataframe} Original input of function. estimator : estimator instance Estimator with to get the output configuration from. Returns ------- output : {ndarray, dataframe} If the output config is "default" or the estimator is not configured for wrapping return `data_to_wrap` unchanged. If the output config is "pandas", return `data_to_wrap` as a pandas DataFrame. rrtzmThe transformer outputs a scipy sparse matrix. Try to set the transformer output to a dense array or disable z- output with set_output(transform='default').r) r_auto_wrap_is_configuredrr capitalizerror#get_feature_names_out)r data_to_wraporiginal_inputr output_configrrys r_wrap_data_with_containerr s2'v9MW*2J92U2U )L  M&&())V X  '' 5G  # #// $ r&c4^^[T5UU4Sj5nU$)z@Wrapper used by `_SetOutputMixin` to automatically wrap methods.c>T"X/UQ70UD6n[U[5(aM[TUSX5/USSQ7n[[ U5S5(a[ U5R U5$U$[TXAU5$)NrrN_make)rAtuplerhasattrtyper)rr(argskwargsr return_tuplefrs rwrapped$_wrap_method_output..wrapped:s24262 lE * **&,q/1Kab!L tL)733L)// == ($GGr&r)rrrs`` r_wrap_method_outputr7s$ 1XHH Nr&c\[US[55n[US5=(a SU;$)zReturn True if estimator is configured for auto-wrapping the transform method. `_SetOutputMixin` sets `_sklearn_auto_wrap_output_keys` to `set()` if auto wrapping is manually disabled. _sklearn_auto_wrap_output_keysr transform)rrur)rauto_wrap_output_keyss rrrNs6 $I/OQTQVW 23 1 0 0r&cT^\rSrSrSrSU4Sjjr\"\5SS.Sj5rSr U=r $) _SetOutputMixini[aHMixin that dynamically wraps methods to return container based on config. Currently `_SetOutputMixin` wraps `transform` and `fit_transform` and configures it based on `set_output` of the global configuration. `set_output` is only defined if `get_feature_names_out` is defined and `auto_wrap_output_keys` is the default value. rc >[TU]"S0UD6 [U[5(dUb [ S5eUc[ 5UlgSSS.n[ 5UlUR5HiupE[X5(aXQ;aMUR RU5 X@R;aMH[[X5U5n[XU5 Mk g)Nz6auto_wrap_output_keys must be None or a tuple of keys.r)r fit_transformr)super__init_subclass__rArrruritemsradd__dict__rrsetattr)clsrr method_to_keyrkeywrapped_methodrs rr!_SetOutputMixin.__init_subclass__es !+F+ ,e 4 48M8UUV V (14C . %( .1U*(..0KF3''3+K  . . 2 23 7\\)01EsKN C 01r&Nc\UcU$[US5(d0UlXRS'U$)a>Set output container. See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` for an example on how to use the API. Parameters ---------- transform : {"default", "pandas", "polars"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `"polars"`: Polars output - `None`: Transform configuration is unchanged .. versionadded:: 1.4 `"polars"` option was added. Returns ------- self : estimator instance Estimator instance. rr)rr)rrs r set_output_SetOutputMixin.set_outputs82  Kt566*,D '3<##K0 r&)r)r) r3r4r5r6__doc__rr rrr9 __classcell__)rs@rrr[s+1@*+&*,r&rrc[US5=(d [US5=(a USLnU(dg[US5(d[SUS35eURUS9$)aSafely call estimator.set_output and error if it not available. This is used by meta-estimators to set the output for child estimators. Parameters ---------- estimator : estimator instance Estimator instance. transform : {"default", "pandas", "polars"}, default=None Configure output of the following estimator's methods: - `"transform"` - `"fit_transform"` If `None`, this operation is a no-op. Returns ------- estimator : estimator instance Estimator instance. rrNrzUnable to configure output for z' because `set_output` is not available.r)rrr)rrset_output_for_transforms r_safe_set_outputrsw. 'y+>  ?+E 0E $  9l + +-i[9       )  44r&r)r functoolsrtypingrrnumpyr] scipy.sparsersklearn._configrsklearn.utils._available_ifr rrrr;rVrkrrzrrrrrrrrrr&rrs.!&4J xJ J Z'%'%T//> 7 7,--/*-/*$ #F+\. JJZ.2$5r&