a j)<@sddlZddlmZddlmZmZddlZddlm Z ddl m Z ddl m Z dd lmZd d Zd d ZeGdddeZGdddZGdddZGdddZeZeeeeddZd'ddZd(ddZddZddZd d!ZGd"d#d#Zdd$d%d&ZdS))Nwraps)Protocolruntime_checkable)issparse) get_config) available_if)2_create_pandas_dataframe_from_non_pandas_containerc CsNz t|WStyH}z$td|d|d|WYd}~n d}~00dS)zCheck library is installed.zSetting output container to 'z ' requires z to be installedN) importlib import_module ImportError)Zlibraryexcr?E:\soccer\ml_env\Lib\site-packages\sklearn/utils/_set_output.pycheck_library_installeds rcCs*t|r&z|WSty$YdS0|SN)callable Exceptioncolumnsrrr get_columnss  rc@s8eZdZUeed<d ddZddZddZd d Zd S) ContainerAdapterProtocol container_libFcCsdS)aCreate container from `X_output` with additional metadata. Parameters ---------- X_output : {ndarray, dataframe} Data to wrap. X_original : {ndarray, dataframe} Original input dataframe. This is used to extract the metadata that should be passed to `X_output`, e.g. pandas row index. columns : callable, ndarray, or None The column names or a callable that returns the column names. The callable is useful if the column names require some computation. If `None`, then no columns are passed to the container's constructor. inplace : bool, default=False Whether or not we intend to modify `X_output` in-place. However, it does not guarantee that we return the same object if the in-place operation is not possible. Returns ------- wrapped_output : container_type `X_output` wrapped into the container type. Nr)selfX_output X_originalrinplacerrrcreate_container(sz)ContainerAdapterProtocol.create_containercCsdS)a Return True if X is a supported container. Parameters ---------- Xs: container Containers to be checked. Returns ------- is_supported_container : bool True if X is a supported container. Nr)rXrrris_supported_containerDsz/ContainerAdapterProtocol.is_supported_containercCsdS)aQRename columns in `X`. Parameters ---------- X : container Container which columns is updated. columns : ndarray of str Columns to update the `X`'s columns with. Returns ------- updated_container : container Container with new names. Nrrr rrrrrename_columnsRsz'ContainerAdapterProtocol.rename_columnscCsdS)aStack containers horizontally (column-wise). Parameters ---------- Xs : list of containers List of containers to stack. Returns ------- stacked_Xs : container Stacked containers. Nr)rXsrrrhstackcszContainerAdapterProtocol.hstackN)F) __name__ __module__ __qualname__str__annotations__rr!r#r%rrrrr$s  rc@s2eZdZdZd ddZddZddZd d Zd S) PandasAdapterpandasTcCsttd}t|}|r t||js\t||jr4|j}nt||jrH|j}nd}t||| d}|durp|||S|S)Nr,)r indexcopy)rr isinstance DataFramer-r r#)rrrrrpdr-rrrrus   zPandasAdapter.create_containercCstd}t||jS)Nr,rr/r0)rr r1rrrr!sz$PandasAdapter.is_supported_containercCs ||_|Srrr"rrrr#szPandasAdapter.rename_columnscCstd}|j|ddS)Nr,r )Zaxisrconcat)rr$r1rrrr%szPandasAdapter.hstackN)Tr&r'r(rrr!r#r%rrrrr+rs  r+c@s2eZdZdZd ddZddZddZd d Zd S) PolarsAdapterpolarsTcCs`td}t|}t|tjr$|n|}|r8t||jsH|j||ddS|dur\|||S|S)Nr7row)ZschemaZorient)rrr/npZndarraytolistr0r#)rrrrrplrrrrs zPolarsAdapter.create_containercCstd}t||jS)Nr7r2)rr r;rrrr!sz$PolarsAdapter.is_supported_containercCs ||_|Srrr"rrrr#szPolarsAdapter.rename_columnscCstd}|j|ddS)Nr7 horizontal)howr3)rr$r;rrrr%szPolarsAdapter.hstackN)Tr5rrrrr6s  r6c@s(eZdZddZeddZddZdS)ContainerAdaptersManagercCs i|_dSr)adaptersrrrr__init__sz!ContainerAdaptersManager.__init__cCsdht|jBS)Ndefault)setr?r@rrrsupported_outputssz*ContainerAdaptersManager.supported_outputscCs||j|j<dSr)r?r)radapterrrrregistersz!ContainerAdaptersManager.registerN)r&r'r(rApropertyrDrFrrrrr>s r>c Csn|jjdd}z tj|WStyh}z2ttj}td|d|d|WYd}~n d}~00dS)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__r'splitADAPTERS_MANAGERr?KeyErrorlistkeys ValueError) container module_namerZavailable_adaptersrrr_get_adapter_from_containers rRcCs4t||d}z tj|WSty.YdS0dS)zGet container adapter.denseN)_get_output_configrKr?rL)method estimator dense_configrrr_get_container_adapters   rXcCs\t|di}||vr||}nt|d}tj}||vrTtdt|d|d|iS)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 rS)getattrrrKrDrOsorted)rUrVZest_sklearn_output_configrWrDrrrrTs  rTcCsbt||}|ddkst|s"|S|d}t|rFtd|dtj|}|j|||jdS)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. rSrBzmThe 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) rT_auto_wrap_is_configuredrrO capitalizerKr?rget_feature_names_out)rU data_to_wrapZoriginal_inputrVZ output_configrWrErrr_wrap_data_with_container s   racstfdd}|S)z@Wrapper used by `_SetOutputMixin` to automatically wrap methods.csr||g|Ri|}t|trdt|d||g|ddR}tt|dr`t||S|St|||S)Nrr _make)r/tuplerahasattrtyperb)rr argskwargsr`Z return_tuplefrUrrwrapped=s  z$_wrap_method_output..wrappedr)rirUrjrrhr_wrap_method_output:srkcCs t|dt}t|dod|vS)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)r[rCrd)rVauto_wrap_output_keysrrrr]Qs r]cs8eZdZdZdfdd ZeeddddZZS) _SetOutputMixina\Mixin 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. rmc stjfi|t|ts,|dus,td|dur@t|_dSddd}t|_|D]P\}}t||rZ||vrvqZ|j |||j vrqZt t |||}t |||qZdS)Nz6auto_wrap_output_keys must be None or a tuple of keys.rm)rm fit_transform)super__init_subclass__r/rcrOrCrlitemsrdadd__dict__rkr[setattr)clsrnrgZ method_to_keyrUkeyZwrapped_methodrIrrrshs*  z!_SetOutputMixin.__init_subclass__NcCs*|dur |St|dsi|_||jd<|S)aSet 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. NrYrm)rdrY)rrmrrr set_outputs   z_SetOutputMixin.set_output)rp) r&r'r(__doc__rsr r]r{ __classcell__rrrzrro^s  rorpcCsJt|dpt|do|du}|s$dSt|ds>td|d|j|dS)a)Safely 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. rmrqNr{zUnable to configure output for z' because `set_output` is not available.rp)rdrOr{)rVrmZset_output_for_transformrrr_safe_set_outputs    r~)N)N) r functoolsrtypingrrnumpyr9Z scipy.sparser_configrZ _available_ifr fixesr rrrr+r6r>rKrFrRrXrTrarkr]ror~rrrrs2       M,    #. M