rtj:ddlZddlmZddlmZmZddlZddlm Z ddl m Z ddl m Z dZdZeGd d eZGd d ZGd dZGddZeZeeeedZddZddZdZdZdZGddZdddZdS)Nwraps)Protocolruntime_checkable)issparse) get_config) available_ifc~ tj|S#t$r}td|d|d|d}~wwxYw)zCheck library is installed.zSetting output container to 'z ' requires z to be installedN) importlib import_module ImportError)libraryexcs W/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/utils/_set_output.pycheck_library_installedrsp&w///  G        s <7<ct|r5 |S#t$r}dt|vrYd}~dSd}~wwxYw|S)Nz&does not provide get_feature_names_out)callableAttributeErrorstr)columnses r get_columnsrsj 799    73q66AAttttt   Ns  A==Ac4eZdZUeed<ddZdZdZdZdS) ContainerAdapterProtocol container_libFcdS)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. N)selfX_output X_originalrinplaces rcreate_containerz)ContainerAdapterProtocol.create_container*cdS)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)rXs ris_supported_containerz/ContainerAdapterProtocol.is_supported_containerFr#r$cdS)aQRename columns in `X`. Parameters ---------- X : container Container which columns is updated. columns : ndarray of str Columns to update the `X`'s columns with. Returns ------- updated_container : container Container with new names. Nrrr&rs rrename_columnsz'ContainerAdapterProtocol.rename_columnsTr#r$cdS)aStack containers horizontally (column-wise). Parameters ---------- Xs : list of containers List of containers to stack. Returns ------- stacked_Xs : container Stacked containers. Nr)rXss rhstackzContainerAdapterProtocol.hstacker#r$N)F) __name__ __module__ __qualname__r__annotations__r"r'r*r-rr$rrr&sc    8       "      r$rc,eZdZdZddZdZdZdZdS) PandasAdapterpandasTcXtd}t|}|rt||js\t||jr|j}n&t||j|jfr|j}nd}|||| }||||S|S)Nr4)indexcopy)rr isinstance DataFramer6Seriesr*)rrr rr!pdr6s rr"zPandasAdapter.create_containerws $X . .g&& Mj2<@@ M (BL11  Jry(ABB "(||HEG |LLH  &&x99 9r$cJtd}t||jS)Nr4rr8r9)rr&r;s rr'z$PandasAdapter.is_supported_container! $X . .!R\***r$c||_|SNrr)s rr*zPandasAdapter.rename_columns r$cNtd}||dS)Nr4)axisrconcat)rr,r;s rr-zPandasAdapter.hstacks% $X . .yy!y$$$r$NTr.r/r0rr"r'r*r-rr$rr3r3tsZM0+++ %%%%%r$r3c,eZdZdZddZdZdZdZdS) PolarsAdapterpolarsTc0td}t|}t|tjr|n|}|rt||js|||dS||||S|S)NrLrow)schemaorient)rrr8npndarraytolistr9r*)rrr rr!pls rr"zPolarsAdapter.create_containers $X . .g&&&0"*&E&ER'.."""7 Hj2<@@ H<<<GG G  &&x99 9r$cJtd}t||jS)NrLr=)rr&rTs rr'z$PolarsAdapter.is_supported_containerr>r$c||_|Sr@rAr)s rr*zPolarsAdapter.rename_columnsrBr$cNtd}||dS)NrL horizontal)howrF)rr,rTs rr-zPolarsAdapter.hstacks% $X . .yyy...r$NrHrIrr$rrKrKsZM    +++ /////r$rKc0eZdZdZedZdZdS)ContainerAdaptersManagerci|_dSr@)adaptersrs r__init__z!ContainerAdaptersManager.__init__s  r$c2dht|jzS)Ndefault)setr]r^s rsupported_outputsz*ContainerAdaptersManager.supported_outputss{S////r$c$||j|j<dSr@)r]r)radapters rregisterz!ContainerAdaptersManager.registers/6 g+,,,r$N)r.r/r0r_propertyrcrfrr$rr[r[sM00X077777r$r[c|jjdd} tj|S#t $rG}t tj}td|d|d|d}~wwxYw)zGet the adapter that knows how to handle such container. See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more details. .rzZThe container does not have a registered adapter in scikit-learn. Available adapters are: z" while the container provided is: N) __class__r/splitADAPTERS_MANAGERr]KeyErrorlistkeys ValueError) container module_nameravailable_adapterss r_get_adapter_from_containerrts %066s;;A>K(55 !"2";"@"@"B"BCC +'9 + +% + + +    s9 B ABB cvt||d} tj|S#t$rYdSwxYw)zGet container adapter.denseN)_get_output_configrlr]rm)method estimator dense_configs r_get_container_adapterr{sK%fi88AL(66 tts * 88ct|di}||vr ||}nt|d}tj}||vr"t dt |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 rv)getattrrrlrcrpsorted)rxryest_sklearn_output_configrzrcs rrwrws(!( 3KR P P ***08 !||v$6$6$67 (:,,, W/@(A(A W W W W    \ ""r$c2t||}|ddkst|s|S|d}t|r%td|dt j|}||||jS)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. rvrazmThe 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').rA) rw_auto_wrap_is_configuredrrp capitalizerlr]r"get_feature_names_out)rx data_to_wraporiginal_inputry output_configrzres r_wrap_data_with_containerr s2'vy99MW**2J92U2U* )L   X&&(( X X X   ' 5G  # #/ $  r$c@tfd}|S)z@Wrapper used by `_SetOutputMixin` to automatically wrap methods.c6||g|Ri|}t|tret|d||g|ddR}tt |dr"t ||S|St|||S)NrrD_make)r8tuplerhasattrtyper)rr&argskwargsr return_tuplefrxs rwrappedz$_wrap_method_output..wrapped=sqq24222622 lE * * *&,q/1dKKabb!L tL))733 >L))// === (q$GGGr$r)rrxrs`` r_wrap_method_outputr:sA 1XXHHHHHXH Nr$cdt|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)rrbr)ryauto_wrap_output_keyss rrrQs? $I/OQTQVQVWW 233 1 0 0r$cReZdZdZdfd ZeedddZxZS)_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. rc tjdi|t|ts|t d|t |_dSddd}t |_|D]m\}}t||r||vr|j |||j vr>tt|||}t|||ndS)Nz6auto_wrap_output_keys must be None or a tuple of keys.r)r fit_transformr)super__init_subclass__r8rrprbritemsradd__dict__rrsetattr)clsrr method_to_keyrxkeywrapped_methodrjs rrz!_SetOutputMixin.__init_subclass__hs$!!++F+++ ,e 4 4 W8M8UUVV V (14C . F%(  .1UU*(..00 1 1KFC3'' 36K+K+K  . 2 23 7 7 7S\))0f1E1EsKKN C 0 0 0 0 1 1r$NcP||St|dsi|_||jd<|S)aSet output container. Refer to the :ref:`user guide ` for more details and :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` for an example on how to use the API. Parameters ---------- transform : {"default", "pandas", "polars"}, default=None Configure output of `transform` and `fit_transform`. - `"default"`: Default output format of a transformer - `"pandas"`: DataFrame output - `"polars"`: Polars output - `None`: Transform configuration is unchanged .. versionadded:: 1.4 `"polars"` option was added. Returns ------- self : estimator instance Estimator instance. Nr}r)rr})rrs r set_outputz_SetOutputMixin.set_outputs<4  Kt566 -*,D '3<#K0 r$)r) r.r/r0__doc__rr rr __classcell__)rjs@rrr^s|111111@\*++&*    ,+     r$rrct|dpt|do|du}|sdSt|dstd|d||S)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. rrNrzUnable to configure output for z' because `set_output` is not available.r)rrpr)ryrset_output_for_transforms r_safe_set_outputrs. 'y+>>  ?++E 0E $  9l + +  i         )  4 44r$r@)r functoolsrtypingrrnumpyrQ scipy.sparsersklearn._configrsklearn.utils._available_ifr rrrr3rKr[rlrfrtr{rwrrrrrrr$rrsZ........!!!!!!&&&&&&444444   J J J J J xJ J J Z'%'%'%'%'%'%'%'%T////////> 7 7 7 7 7 7 7 7,+----//***--//***$ # # # #F+++\.   KKKKKKKK\.2$5$5$5$5$5$5$5r$