




'''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()




def      A__					(			__A  :						str     ,				__A  :						str			)					->str:

      if not (isinstance(__A     ,				__A			) and isinstance(__A     ,				__A			)):
            raise ValueError('''longest_common_substring() takes two strings for inputs'''			)

      __A    =len(__A			)
      __A    =len(__A			)

      __A    =[[0] * (texta_length + 1) for _ in range(texta_length + 1			)]
      __A    =0
      __A    =0

      for i in range(1     ,				texta_length + 1			):
            for j in range(1     ,				texta_length + 1			):
                  if texta[i - 1] == texta[j - 1]:
                        __A    =1 + dp[i - 1][j - 1]
                        if dp[i][j] > ans_length:
                              __A    =i
                              __A    =dp[i][j]

      return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


