
"""simple docstring"""





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



def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					XGBRegressor(verbosity=0    ,							random_state=42							)
		xgb.fit(UpperCamelCase_    ,							UpperCamelCase_							)
		# Predict target for test data
		__SCREAMING_SNAKE_CASE				=					xgb.predict(UpperCamelCase_							)
		__SCREAMING_SNAKE_CASE				=					predictions.reshape(len(UpperCamelCase_							)    ,							1							)
		return predictions



def 		_lowerCAmelCase							(							):
		__SCREAMING_SNAKE_CASE				=					fetch_california_housing()
		__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE				=					data_handling(UpperCamelCase_							)
		__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE				=					train_test_split(
		    UpperCamelCase_    ,							UpperCamelCase_    ,							test_size=0.25    ,							random_state=1							)
		__SCREAMING_SNAKE_CASE				=					xgboost(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_							)
		# Error printing
		print(f"Mean Absolute Error : {mean_absolute_error(UpperCamelCase_    ,							UpperCamelCase_							)}"							)
		print(f"Mean Square Error  : {mean_squared_error(UpperCamelCase_    ,							UpperCamelCase_							)}"							)


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							main()










import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				warnings.warn(
				    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use PerceiverImageProcessor instead.'      ,     SCREAMING_SNAKE_CASE      ,     )
				super().__init__(*SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
