

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__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





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

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   List[Any]      =       fetch_california_housing()
     lowercase__    :   Any      =       data_handling(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   str      =       train_test_split(
         SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     test_size=0.25	,     random_state=1			)
     lowercase__    :   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_tf_available, is_torch_available


_UpperCAmelCase		:  int	=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  List[str]	=   [
         "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "SwinForImageClassification",
         "SwinForMaskedImageModeling",
         "SwinModel",
         "SwinPreTrainedModel",
         "SwinBackbone",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFSwinForImageClassification",
         "TFSwinForMaskedImageModeling",
         "TFSwinModel",
         "TFSwinPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swin import (
              SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
              SwinModel,
              SwinPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_swin import (
              TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
              TFSwinModel,
              TFSwinPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  int	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



