

import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def       a__					(		UpperCAmelCase							:      dict      )  ->						tuple:
  return (data["data"], data["target"])

def       a__					(		UpperCAmelCase							:      np.ndarray    ,			UpperCAmelCase							:      np.ndarray    ,			UpperCAmelCase							:      np.ndarray      )  ->						np.ndarray:
  UpperCAmelCase	:							List[str]						=			XGBRegressor(verbosity=0    ,			random_state=42      )
  xgb.fit(UpperCAmelCase    ,			UpperCAmelCase      )
  # Predict target for test data
  UpperCAmelCase	:							Any						=			xgb.predict(UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			predictions.reshape(len(UpperCAmelCase      )    ,			1      )
  return predictions

def       a__					(		)  ->						None:
  UpperCAmelCase	:							str						=			fetch_california_housing()
  UpperCAmelCase	:							List[str]						=			data_handling(UpperCAmelCase      )
  UpperCAmelCase	:							Optional[Any]						=			train_test_split(
      UpperCAmelCase    ,			UpperCAmelCase    ,			test_size=0.25    ,			random_state=1      )
  UpperCAmelCase	:							str						=			xgboost(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )
  # Error printing
  print(f'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase    ,			UpperCAmelCase      )}'''      )
  print(f'''Mean Square Error  : {mean_squared_error(UpperCAmelCase    ,			UpperCAmelCase      )}'''      )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()








from ..utils import DummyObject, requires_backends





class  __UpperCAmelCase					(	metaclass=lowerCamelCase__						):
   UpperCamelCase       					=					["""onnx"""]



   def __init__(							self							:					int,		*__A							:					Optional[Any],		**__A							:					Dict						):
     requires_backends(self,		['''onnx''']						)



   @classmethod
   def 	__magic_name__   (							cls							:					Any,		*__A							:					Any,		**__A							:					Dict						):
     requires_backends(cls,		['''onnx''']						)





   @classmethod
   def 	__magic_name__   (							cls							:					Tuple,		*__A							:					List[str],		**__A							:					List[str]						):
     requires_backends(cls,		['''onnx''']						)





