

from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     A_      (					lowerCAmelCase__  ):


    '''simple docstring'''






    def __init__(self   ,					lowercase__   ,					lowercase__ = None   ,					lowercase__ = None   ,					lowercase__ = False   ,					lowercase__ = False   ,					lowercase__ = None   ,					lowercase__ = None   ,					**lowercase__   ,					)   ->					Dict:
        super().__init__(
            features=UpperCamelCase__   ,					cache_dir=UpperCamelCase__   ,					keep_in_memory=UpperCamelCase__   ,					streaming=UpperCamelCase__   ,					num_proc=UpperCamelCase__   ,					**UpperCamelCase__   ,					)
        __UpperCAmelCase     =	Generator(
            cache_dir=UpperCamelCase__   ,					features=UpperCamelCase__   ,					generator=UpperCamelCase__   ,					gen_kwargs=UpperCamelCase__   ,					**UpperCamelCase__   ,					)






    def    lowerCAmelCase_  (self     )   ->					List[Any]:
        # Build iterable dataset
        if self.streaming:
            __UpperCAmelCase     =	self.builder.as_streaming_dataset(split='''train'''     )
        # Build regular (map-style) dataset
        else:
            __UpperCAmelCase     =	None
            __UpperCAmelCase     =	None
            __UpperCAmelCase     =	None
            __UpperCAmelCase     =	None

            self.builder.download_and_prepare(
                download_config=UpperCamelCase__   ,					download_mode=UpperCamelCase__   ,					verification_mode=UpperCamelCase__   ,					base_path=UpperCamelCase__   ,					num_proc=self.num_proc   ,					)
            __UpperCAmelCase     =	self.builder.as_dataset(
                split='''train'''   ,					verification_mode=UpperCamelCase__   ,					in_memory=self.keep_in_memory     )
        return dataset










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()






