



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__						(							_SCREAMING_SNAKE_CASE       :			dict    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			np.ndarray				,     _SCREAMING_SNAKE_CASE       :			np.ndarray				,     _SCREAMING_SNAKE_CASE       :			np.ndarray    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		fetch_california_housing()
				__a     ,					__a       =		data_handling(_SCREAMING_SNAKE_CASE    )
				__a     ,					__a     ,					__a     ,					__a       =		train_test_split(
				    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     test_size=0.25				,     random_state=1    )
				__a       =		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()





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if index == number_of_items:
								return 0
				__a       =		0
				__a       =		0
				__a       =		knapsack(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     index + 1    )
				if weights[index] <= max_weight:
								__a       =		values[index] + knapsack(
								    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     max_weight - weights[index]				,     index + 1    )
				return max(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
