



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_      (	__a     :				int		):

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

def 			A_      (	__a     :				Any ,		__a     :				Optional[int] ,		__a     :				Optional[int]		):

    """simple docstring"""
    a__				      =		XGBRegressor(verbosity=0 ,		random_state=42		)
    xgb.fit(__a ,		__a		)
    # Predict target for test data
    a__				      =		xgb.predict(__a		)
    a__				      =		predictions.reshape(len(__a		) ,		1		)
    return predictions

def 			A_      (	):

    """simple docstring"""
    a__				      =		fetch_california_housing()
    a__				      =		data_handling(__a		)
    a__				      =		train_test_split(
        __a ,		__a ,		test_size=0.2_5 ,		random_state=1		)
    a__				      =		xgboost(__a ,		__a ,		__a		)
    # Error printing
    print(F'''Mean Absolute Error : {mean_absolute_error(__a ,		__a		)}'''		)
    print(F'''Mean Square Error  : {mean_squared_error(__a ,		__a		)}'''		)


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
 main()






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

