from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream


class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = False				,						UpperCAmelCase = False				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   List[str]:
								super().__init__(
								    features=UpperCAmelCase				,						cache_dir=UpperCAmelCase				,						keep_in_memory=UpperCAmelCase				,						streaming=UpperCAmelCase				,						num_proc=UpperCAmelCase				,						**UpperCAmelCase				,						)
								__a		 =       Generator(
								    cache_dir=UpperCAmelCase				,						features=UpperCAmelCase				,						generator=UpperCAmelCase				,						gen_kwargs=UpperCAmelCase				,						**UpperCAmelCase				,						)







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

													self.builder.download_and_prepare(
													    download_config=UpperCAmelCase				,						download_mode=UpperCAmelCase				,						verification_mode=UpperCAmelCase				,						base_path=UpperCAmelCase				,						num_proc=self.num_proc				,						)
													__a		 =       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 							lowerCAmelCase(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       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()




