
'''simple docstring'''
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_     (						__SCREAMING_SNAKE_CASE						:  dict )				->       List[Any]:





  """simple docstring"""

  return (data["data"], data["target"])







def        A_     (						__SCREAMING_SNAKE_CASE						:  np.ndarray			,     __SCREAMING_SNAKE_CASE						:  np.ndarray			,     __SCREAMING_SNAKE_CASE						:  np.ndarray )				->       Any:





  """simple docstring"""

  __A		:     Tuple												=		XGBRegressor(verbosity=0			,     random_state=42 )
  xgb.fit(snake_case__			,     snake_case__ )
  # Predict target for test data
  __A		:     Tuple												=		xgb.predict(snake_case__ )
  __A		:     List[str]												=		predictions.reshape(len(snake_case__ )			,     1 )
  return predictions







def        A_     (						)				->       Tuple:





  """simple docstring"""

  __A		:     int												=		fetch_california_housing()
  __A							,					__A		:     List[str]												=		data_handling(snake_case__ )
  __A							,					__A							,					__A							,					__A		:     List[Any]												=		train_test_split(
      snake_case__			,     snake_case__			,     test_size=0.25			,     random_state=1 )
  __A		:     List[str]												=		xgboost(snake_case__			,     snake_case__			,     snake_case__ )
  # Error printing
  print(F"Mean Absolute Error : {mean_absolute_error(snake_case__			,     snake_case__ )}" )
  print(F"Mean Square Error  : {mean_squared_error(snake_case__			,     snake_case__ )}" )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()

'''simple docstring'''
A__   :		List[Any] 		=[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages



