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z&This module contains utility routines.   )is_classifier   )
_BinMapperlightgbmNc                 C   s  |dvrt d||  }|d dkrt d|d r!tddd	|d
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| r_|dIi |	S |dIi |	S )Ja  Return an unfitted estimator from another lib with matching hyperparams.

    This utility function takes care of renaming the sklearn parameters into
    their LightGBM, XGBoost or CatBoost equivalent parameters.

    # unmapped XGB parameters:
    # - min_samples_leaf
    # - min_data_in_bin
    # - min_split_gain (there is min_split_loss though?)

    # unmapped Catboost parameters:
    # max_leaves
    # min_*
    )r   xgboostcatboostz:accepted libs are lightgbm, xgboost, and catboost.  got {}Zlossautozaauto loss is not accepted. We need to know if the problem is binary or multiclass classification.Zearly_stoppingz%Early stopping should be deactivated.Zregression_l2Zregression_l1   binaryZ
multiclassgammapoisson)Zsquared_errorZabsolute_errorlog_lossr   r   	objectivelearning_raten_estimatorsZmax_iterZ
num_leavesZmax_leaf_nodes	max_depthZmin_data_in_leafZmin_samples_leaf
reg_lambdaZl2_regularizationmax_binZmax_binsZmin_data_in_binr   Zmin_sum_hessian_in_leafgMbP?Zmin_split_gain    	verbosityverbose
   iZboost_from_averageTZenable_bundleFZsubsample_for_binZpoisson_max_delta_stepg-q=Zfeature_fraction_bynodeZmax_featuresr   Nz
reg:linearZ LEAST_ABSOLUTE_DEV_NOT_SUPPORTEDzreg:logisticzmulti:softmaxz	reg:gammazcount:poissonhistZ	lossguide)Ztree_methodZgrow_policyr   r   r   Z
max_leavesr   lambdar   Zmin_child_weightr   ZsilentZn_jobsZcolsample_bynodeZRMSEZ LEAST_ASBOLUTE_DEV_NOT_SUPPORTEDZLoglossZ
MultiClassZPoissonZMedianZNewton)	Zloss_functionr   Z
iterationsdepthr   r   Zfeature_border_typeZleaf_estimation_methodr   r   )LGBMClassifierLGBMRegressorr   )XGBClassifierXGBRegressor)CatBoostClassifierCatBoostRegressor )
ValueErrorformat
get_paramsNotImplementedErrorr   Z	subsampleboolr   r   r   r   r   r   r   r   r    r!   )Z	estimatorlibZ	n_classesZsklearn_paramsZlightgbm_loss_mappingZlightgbm_paramsZxgboost_loss_mappingZxgboost_paramsZcatboost_loss_mappingZcatboost_paramsr   r   r   r   r    r!   r"   r"   h/home/kim/smarthome/.venv/lib/python3.10/site-packages/sklearn/ensemble/_hist_gradient_boosting/utils.pyget_equivalent_estimator
   s   	
	

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

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r*   )r   N)__doc__baser   Zbinningr   r*   r"   r"   r"   r)   <module>   s    