




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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> np.ndarray:
       lowerCamelCase   :      List[str]			 =					XGBRegressor(verbosity=0 ,random_state=42    )
       xgb.fit(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Predict target for test data
       lowerCamelCase   :      List[Any]			 =					xgb.predict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					predictions.reshape(len(_SCREAMING_SNAKE_CASE    ) ,1    )
       return predictions

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					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()












from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   {'configuration_mmbt': ['MMBTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__       :      List[Any]  =   ['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
       from .configuration_mmbt import MMBTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
       import sys

       SCREAMING_SNAKE_CASE__       :      int  =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






