






"""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 			_snake_case    (					lowerCamelCase__     :   dict  )    ->     tuple:
       return (data["data"], data["target"])

def 			_snake_case    (					lowerCamelCase__     :   np.ndarray    ,						lowerCamelCase__     :   np.ndarray    ,						lowerCamelCase__     :   np.ndarray  )    ->     np.ndarray:
       lowerCamelCase_	:							Any									=XGBRegressor(verbosity=0    ,						random_state=42  )
       xgb.fit(lowerCamelCase__    ,						lowerCamelCase__  )
       # Predict target for test data
       lowerCamelCase_	:							Optional[Any]									=xgb.predict(lowerCamelCase__  )
       lowerCamelCase_	:							str									=predictions.reshape(len(lowerCamelCase__  )    ,						1  )
       return predictions

def 			_snake_case    (					)    ->     None:
       lowerCamelCase_	:							Tuple									=fetch_california_housing()
       lowerCamelCase_	:							Union[str, Any]									=data_handling(lowerCamelCase__  )
       lowerCamelCase_	:							List[str]									=train_test_split(
           lowerCamelCase__    ,						lowerCamelCase__    ,						test_size=0.25    ,						random_state=1  )
       lowerCamelCase_	:							Optional[Any]									=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()











"""simple docstring"""



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							Optional[int]									=ArgumentParser("Accelerate CLI tool"    ,						usage="accelerate <command> [<args>]"    ,						allow_abbrev=lowerCamelCase__  )
							lowerCamelCase_	:							Optional[int]									=parser.add_subparsers(help="accelerate command helpers"  )

							# Register commands
							get_config_parser(subparsers=lowerCamelCase__  )
							env_command_parser(subparsers=lowerCamelCase__  )
							launch_command_parser(subparsers=lowerCamelCase__  )
							tpu_command_parser(subparsers=lowerCamelCase__  )
							test_command_parser(subparsers=lowerCamelCase__  )

							# Let's go
							lowerCamelCase_	:							int									=parser.parse_args()

							if not hasattr(lowerCamelCase__    ,						"func"  ):
														parser.print_help()
														exit(1  )

							# Run
							args.func(lowerCamelCase__  )


if __name__ == "__main__":
						main()



