






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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    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()







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:		int		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Any   				=    get_size_dict(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: Optional[int]   				=    do_resize
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Dict   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    do_center_crop
         UpperCamelCase_: Tuple   				=    crop_size
         UpperCamelCase_: Optional[int]   				=    size
         UpperCamelCase_: Dict   				=    resample
         UpperCamelCase_: Tuple   				=    rescale_factor
         UpperCamelCase_: Tuple   				=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size=size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           UpperCamelCase_: Any   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase							)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
         return center_crop(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase							):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: str   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Dict   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: List[str]   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    resample if resample is not None else self.resample
         UpperCamelCase_: str   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Dict   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Any   				=    size if size is not None else self.size
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if not is_batched(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    [images]

         if not valid_images(_lowerCamelCase							):
           raise ValueError(
               'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
               'torch.Tensor, tf.Tensor or jax.ndarray.'							)

         if do_resize and size is None:
           raise ValueError('Size must be specified if do_resize is True.'							)

         if do_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'							)

         if do_rescale and rescale_factor is None:
           raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

         # All transformations expect numpy arrays.
         UpperCamelCase_: List[str]   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: List[str]   				=    [self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: List[str]   				=    [self.center_crop(image=_lowerCamelCase  ,      size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: int   				=    [self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    [self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: Optional[int]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)