




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 					_a							(					lowerCAmelCase )->	tuple:
		return (data["data"], data["target"])


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	np.ndarray:
		SCREAMING_SNAKE_CASE_					      =							XGBRegressor(verbosity=0    ,						random_state=42 )
		xgb.fit(UpperCamelCase__    ,						UpperCamelCase__ )
		# Predict target for test data
		SCREAMING_SNAKE_CASE_					      =							xgb.predict(UpperCamelCase__ )
		SCREAMING_SNAKE_CASE_					      =							predictions.reshape(len(UpperCamelCase__ )    ,						1 )
		return predictions


def 					_a							(					)->	None:
		SCREAMING_SNAKE_CASE_					      =							fetch_california_housing()
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							data_handling(UpperCamelCase__ )
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							train_test_split(
		    UpperCamelCase__    ,						UpperCamelCase__    ,						test_size=0.2_5    ,						random_state=1 )
		SCREAMING_SNAKE_CASE_					      =							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()



def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	return [
	    {
	        0: [1, 2],
	        1: [0, 2],
	        2: [0, 1, 3, 5],
	        3: [2, 4],
	        4: [3],
	        5: [2, 6, 8],
	        6: [5, 7],
	        7: [6, 8],
	        8: [5, 7],
	    },
	    {
	        0: [6],
	        1: [9],
	        2: [4, 5],
	        3: [4],
	        4: [2, 3],
	        5: [2],
	        6: [0, 7],
	        7: [6],
	        8: [],
	        9: [1],
	    },
	    {
	        0: [4],
	        1: [6],
	        2: [],
	        3: [5, 6, 7],
	        4: [0, 6],
	        5: [3, 8, 9],
	        6: [1, 3, 4, 7],
	        7: [3, 6, 8, 9],
	        8: [5, 7],
	        9: [5, 7],
	    },
	    {
	        0: [1, 3],
	        1: [0, 2, 4],
	        2: [1, 3, 4],
	        3: [0, 2, 4],
	        4: [1, 2, 3],
	    },
	][index]





def 	_lowerCAmelCase					( UpperCamelCase__:  dict[int, list[int]]    )			-> list[tuple[int, int]]:





	"""simple docstring"""







	A             =					0
	A             =					len(UpperCamelCase__    )  # No of vertices in graph
	A             =					[0] * n
	A             =					[False] * n

	def dfs(UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Any    ):
		A             =					True
		A             =					id_
		id_ += 1
		for to in graph[at]:
			if to == parent:
				pass
			elif not visited[to]:
				dfs(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       id_    )
				A             =					min(low[at]				,       low[to]    )
				if id_ <= low[to]:
					bridges.append((at, to) if at < to else (to, at)    )
			else:
				# This edge is a back edge and cannot be a bridge
				A             =					min(low[at]				,       low[to]    )

	A             =					[]
	for i in range(UpperCamelCase__    ):
		if not visited[i]:
			dfs(UpperCamelCase__				,       -1				,       UpperCamelCase__				,       id_    )
	return bridges


if __name__ == "__main__":
				import doctest

				doctest.testmod()



