{Kg:vddlZddlmZddlmZmZddlZddlm Z ddl m Z ddl m Z dd lmZd Zd ZeGd d eZGddZGddZGddZeZej/eej/edZddZddZdZdZdZGddZdddZy)Nwraps)Protocolruntime_checkable)issparse) get_config) available_if)2_create_pandas_dataframe_from_non_pandas_containercv tj|S#t$r}td|d|d|d}~wwxYw)zCheck library is installed.zSetting output container to 'z ' requires z to be installedN) importlib import_module ImportError)libraryexcs ]/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/utils/_set_output.pycheck_library_installedr sR&&w// +G9KyI   s 838cJt|r |S|S#t$rYywxYwN)callable Exceptioncolumnss r get_columnsrs2 9  N  s  ""c2eZdZUeed<ddZdZdZdZy)ContainerAdapterProtocol container_libcy)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%cy)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 )r!Xs ris_supported_containerz/ContainerAdapterProtocol.is_supported_containerAr&r'cy)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. Nr r!r)rs rrename_columnsz'ContainerAdapterProtocol.rename_columnsOr&r'cy)aStack containers horizontally (column-wise). Parameters ---------- Xs : list of containers List of containers to stack. Returns ------- stacked_Xs : container Stacked containers. Nr )r!Xss rhstackzContainerAdapterProtocol.hstack`r&r'N)F) __name__ __module__ __qualname__str__annotations__r%r*r-r0r r'rrr!s 8   "  r'rc*eZdZdZddZdZdZdZy) PandasAdapterpandasc8td}t|}|rt||jsWt||jr |j}n%t||jr |j}nd}t ||| }||j ||S|S)Nr8)r)indexcopy)rr isinstance DataFramer:r r-)r!r"r#rr$pdr:s rr%zPandasAdapter.create_containerrs $X .g&j2<<@ (BLL1 J 5"((J%'kH  &&x9 9r'cDtd}t||jS)Nr8rr<r=)r!r)r>s rr*z$PandasAdapter.is_supported_container $X .!R\\**r'c||_|Srrr,s rr-zPandasAdapter.rename_columns r'c>td}|j|dS)Nr8r )axisrconcat)r!r/r>s rr0zPandasAdapter.hstacks $X .yy!y$$r'NTr1r2r3rr%r*r-r0r r'rr7r7osM4+ %r'r7c*eZdZdZddZdZdZdZy) PolarsAdapterpolarsc td}t|}t|tjr|j n|}|rt||j s|j ||dS||j||S|S)NrLrow)schemaorient)rrr<npndarraytolistr=r-)r!r"r#rr$pls rr%zPolarsAdapter.create_containerst $X .g&&0"**&E'.."7j2<<@<<<G G  &&x9 9r'cDtd}t||jS)NrLr@)r!r)rTs rr*z$PolarsAdapter.is_supported_containerrAr'c||_|Srrr,s rr-zPolarsAdapter.rename_columnsrCr'c>td}|j|dS)NrL horizontal)howrF)r!r/rTs rr0zPolarsAdapter.hstacks $X .yyy..r'NrHrIr r'rrKrKsM + /r'rKc(eZdZdZedZdZy)ContainerAdaptersManagerci|_yr)adaptersr!s r__init__z!ContainerAdaptersManager.__init__s  r'c4dht|jzS)Ndefault)setr]r^s rsupported_outputsz*ContainerAdaptersManager.supported_outputss{S///r'c6||j|j<yr)r]r)r!adapters rregisterz!ContainerAdaptersManager.registers/6 g++,r'N)r1r2r3r_propertyrcrfr r'rr[r[s 007r'r[c|jjjdd} tj|S#t $r?}t tjj}td|d|d|d}~wwxYw)zGet the adapter that knows how to handle such container. See :class:`sklearn.utils._set_output.ContainerAdapterProtocol` for more details. .rzZThe container does not have a registered adapter in scikit-learn. Available adapters are: z" while the container provided is: N) __class__r2splitADAPTERS_MANAGERr]KeyErrorlistkeys ValueError) container module_nameravailable_adapterss r_get_adapter_from_containerrts %%0066s;A>K((55 !"2";";"@"@"BC ''9&:;%= +   s= B:BBcft||d} tj|S#t$rYywxYw)zGet container adapter.denseN)_get_output_configrlr]rm)method estimator dense_configs r_get_container_adapterr{s;%fi8AL((66 s $ 00ct|di}||vr||}nt|d}tj}||vrt 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)getattrr rlrcrpsorted)rxryest_sklearn_output_configrzrcs rrwrws(!( 3KR P **08 !|vhg$67 (::,,'/@(A'B& W   \ ""r'ct||}|ddk(s t|s|S|d}t|rtd|j dt j |}|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').r) rw_auto_wrap_is_configuredrrp capitalizerlr]r%get_feature_names_out)rx data_to_wraporiginal_inputry output_configrzres r_wrap_data_with_containerr s2'vy9MW*2J92U )L  M&&())V X  '' 5G  # #// $ r'c2tfd}|S)z@Wrapper used by `_SetOutputMixin` to automatically wrap methods.c||g|i|}t|trIt|d||g|dd}tt |drt |j |S|St|||S)Nrr _make)r<tuplerhasattrtyper)r!r)argskwargsr return_tuplefrxs rwrappedz$_wrap_method_output..wrapped:sq24262 lE **&,q/1dKab!L tL)73L)// == (q$GGr'r)rrxrs`` r_wrap_method_outputr7s$ 1XHH Nr'cPt|dt}t|dxrd|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 rrrNs6 $I/OQTQVW 23 1 0 0r'cFeZdZdZdfd 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 t|di|t|ts | t d|t |_yddd}t |_|jD]c\}}t||r||vr|j j|||jvrAtt|||}t|||ey)Nz6auto_wrap_output_keys must be None or a tuple of keys.r)r fit_transformr )super__init_subclass__r<rrprbritemsradd__dict__rrsetattr)clsrr method_to_keyrxkeywrapped_methodrjs rrz!_SetOutputMixin.__init_subclass__es !+F+ ,e 48M8UUV V (14C . %( .1U*(..0KFC3'36K+K  . . 2 23 7S\\)0f1EsKN C 01r'NcR||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. r}r)rr})r!rs r set_outputz_SetOutputMixin.set_outputs72  Kt56*,D '3<##K0 r')r) r1r2r3__doc__rr rr __classcell__)rjs@rrr[s+1@*+&*,r'rrct|dxst|dxr|du}|syt|dstd|d|j|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_outputrsw0  ;' " 9o . " T ! $  9l +-i[9       )  44r'r) r functoolsrtypingrrnumpyrQ scipy.sparser_configr _available_ifr fixesr rrrr7rKr[rlrfrtr{rwrrrrrr r'rrs.! 'EJ xJ J Z)%)%X//> 7 7,--/*-/*$ #F+\. JJZ.2&5r'