

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  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		fetch_california_housing()
    __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		data_handling(_lowerCAmelCase     )
    __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:str        =		train_test_split(
        _lowerCAmelCase  ,      _lowerCAmelCase  ,      test_size=0.25  ,      random_state=1     )
    __UpperCamelCase					:str        =		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()






def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''     )

    for cell_n in range(1  ,      len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __UpperCamelCase					:Union[str, Any]        =		grid[0]

    for row_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        __UpperCamelCase					:Optional[int]        =		grid[row_n]
        __UpperCamelCase					:Dict        =		fill_row(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Any        =		grid[row_n]

    return grid[-1][-1]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    current_row[0] += row_above[0]
    for cell_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]  ,      row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()





