






'''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 	__magic_name__( _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = XGBRegressor(verbosity=0						,      random_state=42					)
						xgb.fit(_A						,      _A					)
						# Predict target for test data
						UpperCamelCase__         = xgb.predict(_A					)
						UpperCamelCase__         = predictions.reshape(len(_A					)						,      1					)
						return predictions







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = fetch_california_housing()
						UpperCamelCase__    ,   UpperCamelCase__         = data_handling(_A					)
						UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = train_test_split(
						    _A						,      _A						,      test_size=0.2_5						,      random_state=1					)
						UpperCamelCase__         = xgboost(_A						,      _A						,      _A					)
						# Error printing
						print(f"Mean Absolute Error : {mean_absolute_error(_A						,      _A					)}"					)
						print(f"Mean Square Error  : {mean_squared_error(_A						,      _A					)}"					)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()











'''simple docstring'''


from collections import defaultdict







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = 1
						UpperCamelCase__         = True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(_A					)
						if ret % 2 == 0:
												cuts.append(_A					)
						return ret







def 	__magic_name__( ):
						'''simple docstring'''





						dfs(1					)


if __name__ == "__main__":
					lowerCamelCase_	,     lowerCamelCase_				: int    	=							10, 9
					lowerCamelCase_				: int    	=							defaultdict(list)
					lowerCamelCase_				: dict[int, bool]    	=							{}
					lowerCamelCase_				: list[int]    	=							[]
					lowerCamelCase_				: Union[str, Any]    	=							0
					lowerCamelCase_				: Tuple    	=							[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)
