





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				(	lowerCamelCase:   dict )	->						tuple:







      '''simple docstring'''






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



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray )	->						np.ndarray:







      '''simple docstring'''






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



def _a				(	)	->						None:







      '''simple docstring'''






      __A          =				fetch_california_housing()
      __A   ,						__A          =				data_handling(lowerCamelCase )
      __A   ,						__A   ,						__A   ,						__A          =				train_test_split(
          lowerCamelCase					,				lowerCamelCase					,				test_size=0.25					,				random_state=1 )
      __A          =				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()












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(lowerCamelCase					,				'''wb''' ) as f:
            f.write(json.dumps(lowerCamelCase ).encode('''utf-8''' ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()





