ó
    uñ:i‹  ã                   óV  • S r SSKJrJrJr  SSKrSSKrSSKJ	r	  SSK
JrJrJr  SSKJrJr  SS	KJr  SS
KJrJr  S\S\S\R.                  S\R.                  S\\   SS4S jrS\S\S\S\R.                  S\R.                  S\\   SS4S jrS\S\SS4S jrS\SS4S jrS\SS4S jrg)z$Tests for compatiblity with sklearn.é    )ÚCallableÚOptionalÚTypeNé   )ÚDMatrix)ÚXGBClassifierÚXGBRegressorÚXGBRFRegressoré   )Úget_california_housingÚmake_batches)Úmake_recoded)ÚDeviceÚassert_allcloseÚtree_methodÚdeviceÚXÚyÚas_frameÚreturnc                 ób  • [        SSSU US9nUR                  X#S9  UR                  USS9nUb  U" U5      n[        SSSU US9nUR                  X#US	9  UR                  X&S
9n[        SSSU US9n	U	R                  X#S9  U	R                  U5      n
[        R                  R                  XŠ5        g)z�
Parameters
----------

as_frame: A callable function to convert margin into DataFrame, useful for different
df implementations.
ç333333Ó?r   é   ©Úlearning_rateÚrandom_stateÚn_estimatorsr   r   ©r   r   T©Úoutput_marginN©r   r   Úbase_margin©r"   é   )r   ÚfitÚpredictÚnpÚtestingr   )r   r   r   r   r   Úmodel_0ÚmarginÚmodel_1Úpredictions_1Úcls_2Úpredictions_2s              Ú[/srv/projetos/modelo_ml_acdoc/venv/lib/python3.13/site-packages/xgboost/testing/with_skl.pyÚ run_boost_from_prediction_binaryr0      sÓ   € ô ØØØØØñ€Gð ‡K�K�!€KÑØ�_‰_˜Q¨dˆ_Ð3€FØÑÙ˜&Ó!ˆäØØØØØñ€Gð ‡K�K�! f€KÑ-Ø—O‘O A�OÐ:€MäØØØØØñ€Eð 
‡I�I�€IÑØ—M‘M !Ó$€MÜ‡J�J×Ñ˜}Õ<ó    Ú	estimatorc                 ó4  • U " SSSUUS9nUR                  X4S9  UR                  5       R                  USS9nUb  U" U5      nU " SSSUUS9nUR                  X4US	9  UR                  5       R                  [	        X7S
9SS9n	U " SSSUUS9n
U
R                  X4S9  U
R                  5       R                  USS9n[        U	S5      (       a  U	R                  5       n	[        US5      (       a  UR                  5       n[        R                  R                  X›SS9  g)z.Boosting from prediction with multi-class clf.r   r   r   r   r   r*   )Úpredict_typeNr!   r#   Tr   r$   Úgetg�íµ ÷Æ°>)Úatol)
r%   Úget_boosterÚinplace_predictr&   r   Úhasattrr5   r'   r(   r   )r2   r   r   r   r   r   r)   r*   r+   r,   Úmodel_2r.   s               r/   Ú&run_boost_from_prediction_multi_clasasr;   A   sD  € ñ ØØØØØñ€Gð ‡K�K�!€KÑØ× Ñ Ó"×2Ñ2°1À8Ð2ÐL€FØÑÙ˜&Ó!ˆáØØØØØñ€Gð ‡K�K�! f€KÑ-Ø×'Ñ'Ó)×1Ñ1Ü�Ñ&°dð 2ð €Mñ ØØØØØñ€Gð ‡K�K�!€KÑØ×'Ñ'Ó)×9Ñ9¸!È(Ð9ÐS€Mäˆ}˜e×$Ñ$Ø%×)Ñ)Ó+ˆÜˆ}˜e×$Ñ$Ø%×)Ñ)Ó+ˆÜ‡J�J×Ñ˜}À$ÐÒGr1   c                 óö  • SSK Jn  SSKJn  [	        5       u  pE[
        R                  R                  S5      nU" SSUS9nUR                  XE5       HH  u  p‰[        SXS	9R                  XH   XX   5      n
U
R                  XI   5      nXY   nU" X¼5      S
:  a  MH   e   [        US9n[        R                  " [        5         UR                  SS9  UR                  XE5        SSS5        g! , (       d  f       g= f)z"Testwith the cali housing dataset.r   )Úmean_squared_error)ÚKFoldéÊ  r   T)Ún_splitsÚshuffler   é*   )r   r   r   é#   ©r   é
   )Úearly_stopping_roundsN)Úsklearn.metricsr=   Úsklearn.model_selectionr>   r   r'   ÚrandomÚRandomStateÚsplitr
   r%   r&   ÚpytestÚraisesÚNotImplementedErrorÚ
set_params)r   r   r=   r>   r   r   ÚrngÚkfÚtrain_indexÚ
test_indexÚ	xgb_modelÚpredsÚlabelsÚrfregs                 r/   Úrun_housing_rf_regressionrX   t   sÚ   € å2Ý-ä!Ó#�D€AÜ
�)‰)×
Ñ
 Ó
%€CÙ	˜ 4°cÑ	:€BØ#%§8¡8¨A¦>ÑˆÜ"Ø¨ñ
ç
‰#ˆa‰n˜a™nÓ
-ð 	ð ×!Ñ! !¡-Ó0ˆØ‘ˆÙ! %Ó0°2Õ5Ð5Ð5ñ $2ô  &Ñ)€EÜ	�ŠÔ*Õ	+Ø×Ñ¨rÐÑ2Ø�	‰	�!Œ÷ 
,×	+Ö	+ús   Ã !C*Ã*
C8c                 ó”  • [        U SS9u  pn  n[        SSU S9nUR                  XX#4/S9  UR                  5       nUR	                  5       nUR                  5       R                  5       (       a   e[        SSU S9nUR                  X#XqU4/S9  UR                  5       nUR	                  5       nUR                  5       S:X  d   eUR                  5       R                  5       (       a   e[        SSU S9nUR                  XX#4/S9  UR                  5       n	[        R                  R                  U	S	   S
   US	   S
   US	   S
   -   5        [        R                  R                  UR                  U5      UR                  U5      5        [        R                  R                  UR                  U5      UR                  U5      5        g)z)Test re-coding for training continuation.é   )Ú
n_featuresTr   )Úenable_categoricalr   r   )Úeval_set)rT   r]   r   Úvalidation_0ÚrmseN)r   r	   r%   Úevals_resultr7   Úget_categoriesÚemptyÚnum_boosted_roundsr'   r(   r   r&   Úapply)
r   ÚencÚreencr   Ú_ÚregÚ	results_0ÚboosterÚ	results_1Ú	results_2s
             r/   Úrun_recodingrm   Š   sœ  € ä& v¸"Ñ=Ñ€C��1�aÜ
¨$¸QÀvÑ
N€CØ‡G�GˆC˜u˜j˜\€GÑ*Ø× Ñ Ó"€Ià�o‰oÓ€GØ×%Ñ%Ó'×-Ñ-×/Ñ/Ð/Ð/ä
¨$¸QÀvÑ
N€CØ‡G�GˆE ¸°8°*€GÑ=Ø× Ñ Ó"€Ià�o‰oÓ€GØ×%Ñ%Ó'¨1Ó,Ð,Ð,Ø×%Ñ%Ó'×-Ñ-×/Ñ/Ð/Ð/ä
¨$¸QÀvÑ
N€CØ‡G�GˆC˜u˜j˜\€GÑ*Ø× Ñ Ó"€Iä‡J�J×ÑØ�.Ñ! &Ñ)Ø�.Ñ! &Ñ)¨I°nÑ,EÀfÑ,MÑMôô
 ‡J�J×Ñ˜sŸ{™{¨5Ó1°3·;±;¸sÓ3CÔDÜ‡J�J×Ñ˜sŸy™y¨Ó/°·±¸3³Õ@r1   c           	      ó>  • SSK JnJn  [        SSSSS9 Vs/ s H  o3S   PM	     snu  pEn[	        U S9nUR                  XEUS	9  UR                  nUR                  [        R                  :X  d   eUS   S
:  d   e[	        SU S9nUR                  XEUS	9  UR                  n[        U[        R                  5      (       d   eUR                  [        R                  :X  d   eUS   S
:  d   eSn	U" SSSU	SSS9u  pE[        SSU S9n
U
R                  XE5        U
R                  n[        U[        R                  5      (       d   e[        U5      S:X  d   eUS:¬  R                  5       (       d   e[        R                  R!                  [#        U5      S5        [        R$                  " U	[        R                  S9U	-  nU S:X  a  SSKnUR)                  U5      n[        SUS9n
U
R                  XE5        [!        XU
R                  5        U" SSSU	S9u  pE[        R$                  " U	[        R                  S9S-  nU S:X  a  SSKnUR)                  U5      n[        US9n
U
R                  XE5        [!        XU
R                  5        U
R*                  S:X  d   egs  snf )zTests for the intercept.r   )Úmake_classificationÚmake_multilabel_classificationé   é   r   F)Úuse_cupyrD   )Úsample_weightg      à?Úgblinear)rj   r   r   r?   é€   rZ   )r   Ú	n_samplesr[   Ú	n_classesÚn_informativeÚn_redundantÚgbtreezmulti:softprob)rj   Ú	objectiver   g        g      ð?)ÚshapeÚdtypeÚcudaN)r|   Ú
base_score)r   rw   r[   rx   r   )r€   zbinary:logistic)Úsklearn.datasetsro   rp   r   r	   r%   Ú
intercept_r~   r'   Úfloat32Ú
isinstanceÚndarrayr   ÚlenÚallr(   r   ÚsumÚonesÚcupyÚarrayr|   )r   ro   rp   Úvr   r   Úwrh   Úresultrx   ÚclfÚ	interceptÚcps                r/   Úrun_interceptr’   ©   sY  € çTä)¨#¨q°!¸eÒDÓEÒD˜�ŒtÑDÑE�G€Aˆ!Ü
˜fÑ
%€CØ‡G�GˆA €GÑ"Ø�^‰^€FØ�<‰<œ2Ÿ:™:Ó%Ð%Ð%Ø�!‰9�s‹?Ðˆ?ä
˜z°&Ñ
9€CØ‡G�GˆA €GÑ"Ø�^‰^€FÜ�fœbŸj™j×)Ñ)Ð)Ð)Ø�<‰<œ2Ÿ:™:Ó%Ð%Ð%Ø�!‰9�s‹?Ðˆ?à€IÙØØØØØØñ�D€Aô  Ð4DÈVÑ
T€CØ‡G�GˆA„MØ�^‰^€FÜ�fœbŸj™j×)Ñ)Ð)Ð)Üˆv‹;˜!ÓÐÐØ�c‰M×Ñ× Ñ Ð Ð Ü‡J�J×Ñœs 6›{¨CÔ0ô —’˜y´·±Ñ<¸yÑH€IØ�ÓÛà—H‘H˜YÓ'ˆ	ä
Ð"2¸yÑ
I€CØ‡G�GˆA„MÜ�F s§~¡~Ô6á)Ø S°RÀ9ñ�D€Aô
 —’˜y´·±Ñ<¸qÑ@€IØ�ÓÛà—H‘H˜YÓ'ˆ	ä
 9Ñ
-€CØ‡G�GˆA„MÜ�F s§~¡~Ô6Ø�=‰=Ð-Ó-Ð-Ñ-ùòu Fs   —J)Ú__doc__Útypingr   r   r   Únumpyr'   rL   Úcorer   Úsklearnr   r	   r
   Údatar   r   Úordinalr   Úutilsr   r   Ústrr…   r0   r;   rX   rm   r’   © r1   r/   Ú<module>r�      s  ðá *ç +Ñ +ã Û å ß AÑ Aß 6Ý !ß *ð.=Øð.=àð.=ð 
‡z�zð.=ð 
‡z�zð	.=ð
 �xÑ ð.=ð 
ô.=ðb0HØð0Hàð0Hð ð0Hð 
‡z�zð	0Hð
 
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