





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__ (					lowercase__  ):






   '''simple docstring'''






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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=XGBRegressor(verbosity=0  ,    random_state=4_2  )
   xgb.fit(lowercase__  ,    lowercase__  )
   # Predict target for test data
   UpperCAmelCase_											=xgb.predict(lowercase__  )
   UpperCAmelCase_											=predictions.reshape(len(lowercase__  )  ,    1  )
   return predictions




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											=fetch_california_housing()
   UpperCAmelCase_						,       UpperCAmelCase_											=data_handling(lowercase__  )
   UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=train_test_split(
       lowercase__  ,    lowercase__  ,    test_size=0.25  ,    random_state=1  )
   UpperCAmelCase_											=xgboost(lowercase__  ,    lowercase__  ,    lowercase__  )
   # Error printing
   print(F'Mean Absolute Error : {mean_absolute_error(lowercase__  ,    lowercase__  )}'  )
   print(F'Mean Square Error  : {mean_squared_error(lowercase__  ,    lowercase__  )}'  )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()











from typing import TYPE_CHECKING

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


__lowercase  :   Optional[Any]						      ={
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[Any]						      =[
         """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GroupViTModel""",
         """GroupViTPreTrainedModel""",
         """GroupViTTextModel""",
         """GroupViTVisionModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __lowercase  :   List[str]						      =[
         """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGroupViTModel""",
         """TFGroupViTPreTrainedModel""",
         """TFGroupViTTextModel""",
         """TFGroupViTVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_groupvit import (
         GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         GroupViTConfig,
         GroupViTOnnxConfig,
         GroupViTTextConfig,
         GroupViTVisionConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_groupvit import (
              GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              GroupViTModel,
              GroupViTPreTrainedModel,
              GroupViTTextModel,
              GroupViTVisionModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_groupvit import (
              TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGroupViTModel,
              TFGroupViTPreTrainedModel,
              TFGroupViTTextModel,
              TFGroupViTVisionModel,
          )

else:
     import sys

     __lowercase  :   str						      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




