


"""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 			UpperCamelCase (     __magic_name__					:							dict					)       ->  tuple:
		"""simple docstring"""




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






def 			UpperCamelCase (     __magic_name__					:							np.ndarray   ,   __magic_name__					:							np.ndarray   ,   __magic_name__					:							np.ndarray					)       ->  np.ndarray:
		"""simple docstring"""




		lowercase__					      =		XGBRegressor(verbosity=0   ,   random_state=42					)
		xgb.fit(_snake_case   ,   _snake_case					)
		# Predict target for test data
		lowercase__					      =		xgb.predict(_snake_case					)
		lowercase__					      =		predictions.reshape(len(_snake_case					)   ,   1					)
		return predictions






def 			UpperCamelCase (     )       ->  None:
		"""simple docstring"""




		lowercase__					      =		fetch_california_housing()
		lowercase__					      =		data_handling(_snake_case					)
		lowercase__					      =		train_test_split(
		    _snake_case   ,   _snake_case   ,   test_size=0.2_5   ,   random_state=1					)
		lowercase__					      =		xgboost(_snake_case   ,   _snake_case   ,   _snake_case					)
		# Error printing
		print(f'''Mean Absolute Error : {mean_absolute_error(_snake_case   ,   _snake_case					)}'''					)
		print(f'''Mean Square Error  : {mean_squared_error(_snake_case   ,   _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


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

