
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(  SCREAMING_SNAKE_CASE__				) ->    tuple:
       return (data["data"], data["target"])







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







def 						_snake_case(  ) ->    None:
       lowercase							:     str       	=      fetch_california_housing()
       lowercase	,			lowercase							:     str       	=      data_handling(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      train_test_split(
           SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	test_size=0.25					,	random_state=1				)
       lowercase							:     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()





from typing import TYPE_CHECKING

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


_lowercase: Union[str, Any]      			=		{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: Dict      			=		["BridgeTowerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: int      			=		[
				    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BridgeTowerForContrastiveLearning",
				    "BridgeTowerForImageAndTextRetrieval",
				    "BridgeTowerForMaskedLM",
				    "BridgeTowerModel",
				    "BridgeTowerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				_lowercase: Optional[Any]      			=		_LazyModule(__name__, globals()["__file__"], _import_structure)
