



'''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 A__   (					UpperCAmelCase_							):
						return (data["data"], data["target"])






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       XGBRegressor(verbosity=0  ,  random_state=4_2							)
						xgb.fit(UpperCAmelCase_  ,  UpperCAmelCase_							)
						# Predict target for test data
						_UpperCamelCase :		Optional[Any]			    =       xgb.predict(UpperCAmelCase_							)
						_UpperCamelCase :		Tuple			    =       predictions.reshape(len(UpperCAmelCase_							)  ,  1							)
						return predictions






def A__   (					):
						_UpperCamelCase :		List[str]			    =       fetch_california_housing()
						_UpperCamelCase    ,      _UpperCamelCase :		str			    =       data_handling(UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Tuple			    =       train_test_split(
						    UpperCAmelCase_  ,  UpperCAmelCase_  ,  test_size=0.25  ,  random_state=1							)
						_UpperCamelCase :		Dict			    =       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()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


