

'''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 							_lowerCAmelCase (      __magic_name__	:    dict	)							->     tuple:
						return (data["data"], data["target"])






def 							_lowerCAmelCase (      __magic_name__	:    np.ndarray      ,   __magic_name__	:    np.ndarray      ,   __magic_name__	:    np.ndarray	)							->     np.ndarray:
						lowercase   :       Optional[int]								=XGBRegressor(verbosity=0      ,   random_state=42	)
						xgb.fit(__magic_name__      ,   __magic_name__	)
						# Predict target for test data
						lowercase   :       List[str]								=xgb.predict(__magic_name__	)
						lowercase   :       List[str]								=predictions.reshape(len(__magic_name__	)      ,   1	)
						return predictions






def 							_lowerCAmelCase (      )							->     None:
						lowercase   :       str								=fetch_california_housing()
						lowercase		,  lowercase   :       str								=data_handling(__magic_name__	)
						lowercase		,  lowercase		,  lowercase		,  lowercase   :       Optional[int]								=train_test_split(
						    __magic_name__      ,   __magic_name__      ,   test_size=0.2_5      ,   random_state=1	)
						lowercase   :       Dict								=xgboost(__magic_name__      ,   __magic_name__      ,   __magic_name__	)
						# Error printing
						print(f'''Mean Absolute Error : {mean_absolute_error(__magic_name__      ,   __magic_name__	)}'''	)
						print(f'''Mean Square Error  : {mean_squared_error(__magic_name__      ,   __magic_name__	)}'''	)


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							main()








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int = 1000000	)							->     int:
						lowercase   :       Dict								=set(range(3      ,   __magic_name__      ,   2	)	)
						primes.add(2	)
						for p in range(3      ,   __magic_name__      ,   2	):
												if p not in primes:
																		continue
												primes.difference_update(set(range(p * p      ,   __magic_name__      ,   __magic_name__	)	)	)

						lowercase   :       List[Any]								=[float(__magic_name__	) for n in range(limit + 1	)]

						for p in primes:
												for n in range(__magic_name__      ,   limit + 1      ,   __magic_name__	):
																		phi[n] *= 1 - 1 / p

						return int(sum(phi[2:]	)	)


if __name__ == "__main__":
							print(f'''{solution() = }''')





