-i9pSSKrSSKJr SSKJrJr SSKrSSKJ r SSK J r SSK J r S rS r\"S S \55r"S S5r"SS5r"SS5r\"5r\R+\"55 \R+\"55 SrSSjrSSjrSrSrSr"SS5rSS.Sjrg)Nwraps)Protocolruntime_checkable)issparse) get_config) available_ifcz[R"U5$![an[SUSUS35UeSnAff=f)zCheck library is installed.zSetting output container to 'z ' requires z to be installedN) importlib import_module ImportError)libraryexcs L/var/www/html/venv/lib/python3.13/site-packages/sklearn/utils/_set_output.pycheck_library_installedrsR&&w// +G9KyI   s :5:cV[U5(aU"5$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 )r!Xs 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. Nr r!r*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 )r!Xss rhstackContainerAdapterProtocol.hstackbr'r(r N)F) __name__ __module__ __qualname____firstlineno__str__annotations__r%r+r/r3__static_attributes__r r(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$)Nr?)indexcopy)rr isinstance DataFramerASeriesr/)r!r"r#rr$pdrAs rr%PandasAdapter.create_containerts $X .g&j<<@@ (LL11 Jryy(ABB"((||HG |LH  &&x9 9r(cB[S5n[XR5$)Nr?rrCrD)r!r*rFs rr+$PandasAdapter.is_supported_container $X .!\\**r(cX!lU$rrr.s rr/PandasAdapter.rename_columns r(c8[S5nURUSS9$)Nr?r )axisrconcat)r!r2rFs rr3PandasAdapter.hstacks $X .yy!y$$r(r NT r5r6r7r8rr%r+r/r3r;r r(rr=r=qsM0+ %r(r=c4\rSrSrSrS SjrSrSrSrSr g) PolarsAdapterpolarsc[S5n[U5n[U[R5(aUR 5OUnU(a[XR 5(dUR XSS9$UbURX5$U$)NrYrow)schemaorient)rrrCnpndarraytolistrDr/)r!r"r#rr$pls rr%PolarsAdapter.create_containerst $X .g&&0"**&E&E'.."7j<<@@<<<G G  &&x9 9r(cB[S5n[XR5$)NrYrI)r!r*ras rr+$PolarsAdapter.is_supported_containerrKr(cX!lU$rrr.s rr/PolarsAdapter.rename_columnsrNr(c8[S5nURUSS9$)NrY horizontal)howrQ)r!r2ras rr3PolarsAdapter.hstacks $X .yyy..r(r NrTrUr r(rrWrWsM + /r(rWc0\rSrSrSr\S5rSrSrg)ContainerAdaptersManagerc0Ulgradaptersr!s r__init__!ContainerAdaptersManager.__init__s  r(c4S1[UR5-$)Ndefault)setrprqs rsupported_outputs*ContainerAdaptersManager.supported_outputss{S///r(c4XRUR'gr)rpr)r!adapters rregister!ContainerAdaptersManager.registers/6 g++,r(roN) r5r6r7r8rrpropertyrwr{r;r r(rrlrls 007r(rlcURRRS5Sn[RU$![ a@n[ [RR55n[SUSU<S35UeSnAff=f)zGet the adapter that knows how to handle such container. See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more details. .rzZThe container does not have a registered adapter in scikit-learn. Available adapters are: z" while the container provided is: N) __class__r6splitADAPTERS_MANAGERrpKeyErrorlistkeys ValueError) container module_nameravailable_adapterss r_get_adapter_from_containerrs %%0066s;A>K((55 !"2";";"@"@"BC ''9&:;%= +   s= B;BBcf[X5Sn[RU$![a gf=f)zGet container adapter.denseN)_get_output_configrrpr)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)getattrr rrwrsorted)rrest_sklearn_output_configrrws 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. rruzmThe 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 capitalizerrpr%get_feature_names_out)r data_to_wraporiginal_inputr output_configrrzs 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$[TXAU5$)Nrr _make)rCtuplerhasattrtyper)r!r*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)rrvr)rauto_wrap_output_keyss rrrNs6 $I/OQTQVW 23 1 0 0r(cT^\rSrSrSrSU4Sjjr\"\5SS.Sj5rSr U=r $) _SetOutputMixini[aHMixin that dynamically wraps methods to return container based on config. Currently `_SetOutputMixin` wraps `transform` and `fit_transform` and configures it based on `set_output` of the global configuration. `set_output` is only defined if `get_feature_names_out` is defined and `auto_wrap_output_keys` is the default value. rc >[TU]"S0UD6 [U[5(dUb [ S5eUc[ 5UlgSSS.n[ 5UlUR5HiupE[X5(aXQ;aMUR RU5 X@R;aMH[[X5U5n[XU5 Mk g)Nz6auto_wrap_output_keys must be None or a tuple of keys.r)r fit_transformr )super__init_subclass__rCrrrvritemsradd__dict__rrsetattr)clsrr method_to_keyrkeywrapped_methodrs rr!_SetOutputMixin.__init_subclass__es !+F+ ,e 4 48M8UUV V (14C . %( .1U*(..0KF3''3+K  . . 2 23 7\\)01EsKN C 01r(Nc\UcU$[US5(d0UlXRS'U$)a>Set output container. See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` for an example on how to use the API. Parameters ---------- transform : {"default", "pandas", "polars"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `"polars"`: Polars output - `None`: Transform configuration is unchanged .. versionadded:: 1.4 `"polars"` option was added. Returns ------- self : estimator instance Estimator instance. rr)rr)r!rs r set_output_SetOutputMixin.set_outputs82  Kt566*,D '3<##K0 r()r)r) r5r6r7r8__doc__rr rrr; __classcell__)rs@rrr[s+1@*+&*,r(rrc[US5=(d [US5=(a USLnU(dg[US5(d[SUS35eURUS9$)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.sparser_configr _available_ifr rrrr=rWrlrr{rrrrrrrrr r(rrs.! 'J xJ J Z'%'%T//> 7 7,--/*-/*$ #F+\. JJZ.2$5r(