
'''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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    int     			=						XGBRegressor(verbosity=0,		random_state=4_2       )
    xgb.fit(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    # Predict target for test data
    snake_case_ :    int     			=						xgb.predict(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Union[str, Any]     			=						predictions.reshape(len(__SCREAMING_SNAKE_CASE       ),		1       )
    return predictions

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    List[str]     			=						fetch_california_housing()
    snake_case_			, snake_case_ :    Optional[Any]     			=						data_handling(__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_			, snake_case_			, snake_case_ :    int     			=						train_test_split(
        __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		test_size=0.25,		random_state=1       )
    snake_case_ :    Optional[int]     			=						xgboost(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    # Error printing
    print(f'Mean Absolute Error : {mean_absolute_error(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )}'       )
    print(f'Mean Square Error  : {mean_squared_error(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )}'       )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()






'''simple docstring'''
from __future__ import annotations

import math

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if depth < 0:
        raise ValueError("Depth cannot be less than 0"       )

    if len(__SCREAMING_SNAKE_CASE       ) == 0:
        raise ValueError("Scores cannot be empty"       )

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

    return min(
        minimax(depth + 1,		node_index * 2,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		minimax(depth + 1,		node_index * 2 + 1,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ),		)

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    int     			=						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    snake_case_ :    Union[str, Any]     			=						math.log(len(__SCREAMING_SNAKE_CASE       ),		2       )
    print("Optimal value : ",		end=""       )
    print(minimax(0,		0,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




