





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_A)
class        snake_case_		(			_A):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		lowerCamelCase    :str						=     field(default="summarization"						,					metadata={"include_in_asdict_even_if_is_default": True})
		lowerCamelCase    :ClassVar[Features]						=     Features({"text": Value("string")})
		lowerCamelCase    :ClassVar[Features]						=     Features({"summary": Value("string")})
		lowerCamelCase    :str						=     "text"
		lowerCamelCase    :str						=     "summary"







		@property
		def 			__lowercase   (		self   )				->   Dict[str, str]:
				return {self.text_column: "text", self.summary_column: "summary"}








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   : Dict								=XGBRegressor(verbosity=0							,       random_state=4_2		)
		xgb.fit(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)
		# Predict target for test data
		lowerCamelCase   : List[str]								=xgb.predict(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase   : List[Any]								=predictions.reshape(len(SCREAMING_SNAKE_CASE_		)							,       1		)
		return predictions


def 					A__	(       )   ->     None:
		lowerCamelCase   : Union[str, Any]								=fetch_california_housing()
		lowerCamelCase		,   lowerCamelCase   : Optional[Any]								=data_handling(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : str								=train_test_split(
		    SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       test_size=0.2_5							,       random_state=1		)
		lowerCamelCase   : str								=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()

