


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 							_UpperCAmelCase				(UpperCamelCase__     :  dict						):
       return (data["data"], data["target"])
def 							_UpperCAmelCase				(UpperCamelCase__     :  np.ndarray				,							UpperCamelCase__     :  np.ndarray				,							UpperCamelCase__     :  np.ndarray						):
       _A      :       str				    = XGBRegressor(verbosity=0				,							random_state=42						)
       xgb.fit(UpperCamelCase__				,							UpperCamelCase__						)
       # Predict target for test data
       _A      :       int				    = xgb.predict(UpperCamelCase__						)
       _A      :       int				    = predictions.reshape(len(UpperCamelCase__						)				,							1						)
       return predictions
def 							_UpperCAmelCase				():
       _A      :       List[str]				    = fetch_california_housing()
       _A    ,			_A      :       Tuple				    = data_handling(UpperCamelCase__						)
       _A    ,			_A    ,			_A    ,			_A      :       Optional[Any]				    = train_test_split(
           UpperCamelCase__				,							UpperCamelCase__				,							test_size=0.25				,							random_state=1						)
       _A      :       List[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 __future__ import annotations
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  list[int]				,							UpperCamelCase__     :  int						):
       _A      :       Dict				    = list(range(len(UpperCamelCase__						)						)						)
       _A      :       Any				    = [v / w for v, w in zip(UpperCamelCase__				,							UpperCamelCase__						)]
       index.sort(key=lambda UpperCamelCase__						: ratio[i]				,							reverse=UpperCamelCase__						)

       _A      :       float				    = 0
       _A      :       list[float]				    = [0] * len(UpperCamelCase__						)
       for i in index:
              if weight[i] <= capacity:
                     _A      :       Union[str, Any]				    = 1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     _A      :       Optional[Any]				    = capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
    import doctest

    doctest.testmod()
