ó
    †ñ:iÀ  ã                   ó®   • S SK rS SKrS SKrS SKJr   " S S5      rS rS rS r	S r
S	 rS
 rS rS rS rS rS rS rS rS rS rS rS rS rS rg)é    N)ÚStandardScalerc                   ó2   • \ rS rSrSrSS jrS	S jrS rSrg)
Ú	KerasWrapé   z\A wrapper that allows us to set parameters in the constructor and do a reset before fitting.c                 óT   • Xl         X l        X0l        S U l        [	        5       U l        g ©N)ÚmodelÚepochsÚflatten_outputÚinit_weightsr   Úscaler)Úselfr	   r
   r   s       ÚX/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/shap/benchmark/models.pyÚ__init__ÚKerasWrap.__init__
   s$   € ØŒ
ØŒØ,ÔØ ˆÔÜ$Ó&ˆ�ó    c                 ó&  • U R                   c   U R                  R                  5       U l         O%U R                  R                  U R                   5        U R                  R                  U5        U R                  R                  XU R                  US9$ )N)r
   Úverbose)r   r	   Úget_weightsÚset_weightsr   Úfitr
   )r   ÚXÚyr   s       r   r   ÚKerasWrap.fit   sh   € Ø×ÑÑ$Ø $§
¡
× 6Ñ 6Ó 8ˆDÕà�J‰J×"Ñ" 4×#4Ñ#4Ô5Ø�‰�‰˜ÔØ�z‰z�~‰~˜a¨4¯;©;Àˆ~ÐHÐHr   c                 óâ   • U R                   R                  U5      nU R                  (       a)  U R                  R	                  U5      R                  5       $ U R                  R	                  U5      $ r   )r   Ú	transformr   r	   ÚpredictÚflatten)r   r   s     r   r   ÚKerasWrap.predict   sR   € Ø�K‰K×!Ñ! !Ó$ˆØ××Ø—:‘:×%Ñ% aÓ(×0Ñ0Ó2Ð2à—:‘:×%Ñ% aÓ(Ð(r   )r
   r   r   r	   r   N)F)r   )	Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r   r   r   Ú__static_attributes__© r   r   r   r      s   † Ùfô'ôIõ)r   r   c                  ó<   • [         R                  R                  SS9$ ©úLasso Regressionçš™™™™™¹?©Úalpha©ÚsklearnÚlinear_modelÚLassor&   r   r   Úcorrgroups60__lassor1   $   ó   € ä×Ñ×%Ñ%¨CÐ%Ð0Ð0r   c                  ó<   • [         R                  R                  SS9$ ©úRidge Regressiong      ð?r+   ©r.   r/   ÚRidger&   r   r   Úcorrgroups60__ridger8   )   r2   r   c                  ó>   • [         R                  R                  SSS9$ )úDecision Treer   é   ©Úrandom_stateÚ	max_depth©r.   ÚtreeÚDecisionTreeRegressorr&   r   r   Úcorrgroups60__decision_treerB   .   ó   € ô �<‰<×-Ñ-¸1ÈÐ-ÐJÐJr   c                  ó>   • [         R                  R                  SSS9$ ©úRandom Forestéd   r   ©r=   ©r.   ÚensembleÚRandomForestRegressorr&   r   r   Úcorrgroups60__random_forestrL   4   ó   € ä×Ñ×1Ñ1°#ÀAÐ1ÐFÐFr   c                  ó0   • SSK n U R                  SSSSSS9$ )úGradient Boosted Treesr   Nr;   é2   r*   é   ©r>   Ún_estimatorsÚlearning_rateÚn_jobsr=   ©ÚxgboostÚXGBRegressor©rW   s    r   Úcorrgroups60__gbmrZ   9   s%   € ãð ×Ñ¨!¸"ÈCÐXYÐhiÐÐjÐjr   c                  ó(  • SSK n U R                  R                  R                  5       nUR	                  U R                  R
                  R                  SSSS95        UR	                  U R                  R
                  R                  SSS95        UR	                  U R                  R
                  R                  SSS95        UR	                  U R                  R
                  R                  S	5      5        UR                  S
SS/S9  [        USSS9$ )ú4-Layer Neural Networkr   Né    Úrelué<   ©Ú
activationÚ	input_dimé   ©ra   é   ÚadamÚmean_squared_error©Ú	optimizerÚlossÚmetricsé   T©r   )	Ú
tensorflowÚkerasÚmodelsÚ
SequentialÚaddÚlayersÚDenseÚcompiler   )Útfr	   s     r   Úcorrgroups60__ffnnrw   A   sÎ   € ãà�H‰H�O‰O×&Ñ&Ó(€EØ	‡I�Iˆb�h‰h�o‰o×#Ñ# B°6ÀRÐ#ÐHÔIØ	‡I�Iˆb�h‰h�o‰o×#Ñ# B°6Ð#Ð:Ô;Ø	‡I�Iˆb�h‰h�o‰o×#Ñ# B°6Ð#Ð:Ô;Ø	‡I�Iˆb�h‰h�o‰o×#Ñ# AÓ&Ô'à	‡M�M˜FÐ)=ÐH\ÐG]€MÑ^ä�U˜B¨tÑ4Ð4r   c                  ó<   • [         R                  R                  SS9$ r(   r-   r&   r   r   Úindependentlinear60__lassory   P   r2   r   c                  ó<   • [         R                  R                  SS9$ r4   r6   r&   r   r   Úindependentlinear60__ridger{   U   r2   r   c                  ó>   • [         R                  R                  SSS9$ )r:   r   é   r<   r?   r&   r   r   Ú"independentlinear60__decision_treer~   Z   rC   r   c                  ó>   • [         R                  R                  SSS9$ rE   rI   r&   r   r   Ú"independentlinear60__random_forestr€   `   rM   r   c                  ó0   • SSK n U R                  SSSSSS9$ )rO   r   Nr;   rG   r*   rQ   rR   rV   rY   s    r   Úindependentlinear60__gbmr‚   e   s%   € ãð ×Ñ¨!¸#ÈSÐYZÐijÐÐkÐkr   c                  ó  • SSK Jn   SSKJn  U" 5       nUR	                  U " SSSS95        UR	                  U " SSS	95        UR	                  U " SSS	95        UR	                  U " S
5      5        UR                  SSS/S9  [        USSS9$ )r\   r   )rt   ©rq   r]   r^   r_   r`   rc   rd   re   rf   rg   rh   rl   Trm   )Útensorflow.keras.layersrt   Útensorflow.keras.modelsrq   rr   ru   r   )rt   rq   r	   s      r   Úindependentlinear60__ffnnr‡   m   s€   € å-Ý2á‹L€EØ	‡I�I‰e�B 6°RÑ8Ô9Ø	‡I�I‰e�B 6Ñ*Ô+Ø	‡I�I‰e�B 6Ñ*Ô+Ø	‡I�I‰e�A‹hÔà	‡M�M˜FÐ)=ÐH\ÐG]€MÑ^ä�U˜B¨tÑ4Ð4r   c                  óZ   ^ • [         R                  R                  SSS9m U 4S jT l        T $ )r)   Úl1gü©ñÒMb`?)ÚpenaltyÚCc                 ó4   >• TR                  U 5      S S 2S4   $ ©Nre   ©Úpredict_proba©r   r	   s    €r   Ú<lambda>Úcric__lasso.<locals>.<lambda>‚   ó   ø€ ˜e×1Ñ1°!Ó4²Q¸°TÒ:r   ©r.   r/   ÚLogisticRegressionr   ©r	   s   @r   Úcric__lassor—   }   s,   ø€ ä× Ñ ×3Ñ3¸DÀEÐ3ÐJ€Eô ;€E„Mà€Lr   c                  óX   ^ • [         R                  R                  SS9m U 4S jT l        T $ )r5   Úl2)rŠ   c                 ó4   >• TR                  U 5      S S 2S4   $ r�   rŽ   r�   s    €r   r‘   Úcric__ridge.<locals>.<lambda>Œ   r“   r   r”   r–   s   @r   Úcric__ridgerœ   ‡   s*   ø€ ä× Ñ ×3Ñ3¸DÐ3ÐA€Eô ;€E„Mà€Lr   c                  óZ   ^ • [         R                  R                  SSS9m U 4S jT l        T $ )r:   r   r}   r<   c                 ó4   >• TR                  U 5      S S 2S4   $ r�   rŽ   r�   s    €r   r‘   Ú%cric__decision_tree.<locals>.<lambda>–   r“   r   )r.   r@   ÚDecisionTreeClassifierr   r–   s   @r   Úcric__decision_treer¡   ‘   s*   ø€ ä�L‰L×/Ñ/¸QÈ!Ð/ÐL€Eô ;€E„Mà€Lr   c                  óZ   ^ • [         R                  R                  SSS9m U 4S jT l        T $ )rF   rG   r   rH   c                 ó4   >• TR                  U 5      S S 2S4   $ r�   rŽ   r�   s    €r   r‘   Ú%cric__random_forest.<locals>.<lambda>    r“   r   )r.   rJ   ÚRandomForestClassifierr   r–   s   @r   Úcric__random_forestr¦   ›   s,   ø€ ä×Ñ×3Ñ3°CÀaÐ3ÐH€Eô ;€E„Mà€Lr   c            	      óp   ^• SSK n U R                  SSSSSSS9mTR                  Tl        U4S	 jTl        T$ )
rO   r   Né   i�  g{®Gáz„?gš™™™™™É?rQ   )r>   rS   rT   Ú	subsamplerU   r=   c                 ó$   >• TR                  U SS9$ )NT)Úoutput_margin)Ú__orig_predictr�   s    €r   r‘   Úcric__gbm.<locals>.<lambda>²   s   ø€ ˜e×2Ñ2°1ÀDÐ2ÑIr   )rW   ÚXGBClassifierr   r¬   )rW   r	   s    @r   Ú	cric__gbmr¯   ¥   sF   ø€ ãð
 ×!Ñ!Ø #°TÀSÐQRÐabð "ð €Eð
 !Ÿ=™=€EÔÜI€E„Mà€Lr   c                  óH  • SSK Jn Jn  SSKJn  U" 5       nUR                  U " SSSS95        UR                  U" S5      5        UR                  U " SSS	95        UR                  U" S5      5        UR                  U " S
SS	95        UR                  SSS/S9  [        USSS9$ )r\   r   )rt   ÚDropoutr„   é
   r^   iP  r`   g      à?rd   re   Úsigmoidrf   Úbinary_crossentropyÚaccuracyrh   rl   Trm   )r…   rt   r±   r†   rq   rr   ru   r   )rt   r±   rq   r	   s       r   Ú
cric__ffnnr¶   ·   sŽ   € ç6Ý2á‹L€EØ	‡I�I‰e�B 6°SÑ9Ô:Ø	‡I�I‰g�c‹lÔØ	‡I�I‰e�B 6Ñ*Ô+Ø	‡I�I‰g�c‹lÔØ	‡I�I‰e�A )Ñ,Ô-à	‡M�M˜FÐ)>ÈÈ€MÑUä�U˜B¨tÑ4Ð4r   c                  ó   • Sn Sn[         R                  " X45      nUR                    [         R                  " U 5      nSUS'   SUS'   SUS'   SUS'   SUSSS24'   S	US'   [        R                  R                  SS
9nUR                  X#5        U$ )r:   i@B é   re   )r   r   rQ   r   )re   re   é   r}   )r>   )ÚnpÚzerosÚshaper.   r@   rA   r   )ÚNÚMr   r   Ú	xor_models        r   Úhuman__decision_treerÀ   È   s›   € ð 	€AØ	€AÜ
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1ò
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kò5ò1ò
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