
"""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 	__a     (		__lowerCamelCase  ):
   return (data["data"], data["target"])







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Tuple			     =    XGBRegressor(verbosity=0,		random_state=42  )
   xgb.fit(__lowerCamelCase,		__lowerCamelCase  )
   # Predict target for test data
   UpperCAmelCase_					:					Union[str, Any]			     =    xgb.predict(__lowerCamelCase  )
   UpperCAmelCase_					:					int			     =    predictions.reshape(len(__lowerCamelCase  ),		1  )
   return predictions







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    fetch_california_housing()
   UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    data_handling(__lowerCamelCase  )
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    train_test_split(
       __lowerCamelCase,		__lowerCamelCase,		test_size=0.25,		random_state=1  )
   UpperCAmelCase_					:					int			     =    xgboost(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )
   # Error printing
   print(f"""Mean Absolute Error : {mean_absolute_error(__lowerCamelCase,		__lowerCamelCase  )}"""  )
   print(f"""Mean Square Error  : {mean_squared_error(__lowerCamelCase,		__lowerCamelCase  )}"""  )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()






'''simple docstring'''
import re

from filelock import FileLock


try:
  import nltk

  UpperCamelCase__    					=      True
except (ImportError, ModuleNotFoundError):
  UpperCamelCase__    					=      False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)





def 					a__       (					lowerCAmelCase__						)    ->	str:
    re.sub('''<n>'''  ,					''''''  ,					lowerCAmelCase__						)  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(lowerCAmelCase__						)						)
