



from __future__ import annotations

from typing import Any






def  lowerCamelCase_	(      lowerCAmelCase__	:	list[Any]				)       ->							Optional[int]:

							'''simple docstring'''






							create_state_space_tree(lowerCAmelCase__			,	[]			,	0				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	list[Any]			,	lowerCAmelCase__	:	list[Any]			,	lowerCAmelCase__	:	int				)       ->							Any:

							'''simple docstring'''






							if index == len(lowerCAmelCase__				):
														print(lowerCAmelCase__				)
														return

							create_state_space_tree(lowerCAmelCase__			,	lowerCAmelCase__			,	index + 1				)
							current_subsequence.append(sequence[index]				)
							create_state_space_tree(lowerCAmelCase__			,	lowerCAmelCase__			,	index + 1				)
							current_subsequence.pop()


if __name__ == "__main__":
				__snake_case     :list[Any]			       =[3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(['A', 'B', 'C'])
				generate_all_subsequences(seq)





'''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 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							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()

