



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    lowerCamelCase_(lowerCamelCase_    ) ->       tuple:
						return (data["data"], data["target"])

def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    ) ->       np.ndarray:
						UpperCAmelCase         =     XGBRegressor(verbosity=0    ,   random_state=42    )
						xgb.fit(lowerCamelCase_    ,   lowerCamelCase_    )
						# Predict target for test data
						UpperCAmelCase         =     xgb.predict(lowerCamelCase_    )
						UpperCAmelCase         =     predictions.reshape(len(lowerCamelCase_    )    ,   1    )
						return predictions

def    lowerCamelCase_() ->       None:
						UpperCAmelCase         =     fetch_california_housing()
						UpperCAmelCase       ,		UpperCAmelCase         =     data_handling(lowerCamelCase_    )
						UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase       ,		UpperCAmelCase         =     train_test_split(
						    lowerCamelCase_    ,   lowerCamelCase_    ,   test_size=0.25    ,   random_state=1    )
						UpperCAmelCase         =     xgboost(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_    )
						# Error printing
						print(F'Mean Absolute Error : {mean_absolute_error(lowerCamelCase_    ,   lowerCamelCase_    )}'    )
						print(F'Mean Square Error  : {mean_squared_error(lowerCamelCase_    ,   lowerCamelCase_    )}'    )


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)
		main()








from ..utils import DummyObject, requires_backends





class  __magic_name__   (							metaclass=A__						):
		lowercase      :       int          =['''note_seq''']






		def __init__(						self      :     List[Any]				,		*UpperCamelCase__      :     Tuple				,		**UpperCamelCase__      :     Dict      )				->					int:
								'''simple docstring'''







								requires_backends(self				,		["note_seq"]      )






		@classmethod
		def SCREAMING_SNAKE_CASE_			(						cls      :     List[str]				,		*UpperCamelCase__      :     Dict				,		**UpperCamelCase__      :     str      )				->					Optional[int]:
								'''simple docstring'''







								requires_backends(cls				,		["note_seq"]      )







		@classmethod
		def SCREAMING_SNAKE_CASE_			(						cls      :     List[Any]				,		*UpperCamelCase__      :     Optional[int]				,		**UpperCamelCase__      :     Optional[Any]      )				->					Optional[int]:
								'''simple docstring'''







								requires_backends(cls				,		["note_seq"]      )



