



"""simple docstring"""
import json
import sys


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						List[str]:







		'''simple docstring'''


		with open(lowerCamelCase_ ,  encoding="""utf-8"""							) as f:
				lowercase_								= json.load(lowerCamelCase_							)

		lowercase_								= ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

		for benchmark_name in sorted(lowerCamelCase_							):
				lowercase_								= results[benchmark_name]

				lowercase_								= benchmark_name.split("""/"""							)[-1]
				output_md.append(F'''### Benchmark: {benchmark_file_name}'''							)

				lowercase_								= '| metric |'
				lowercase_								= '|--------|'
				lowercase_								= '| new / old (diff) |'
				for metric_name in sorted(lowerCamelCase_							):
						lowercase_								= benchmark_res[metric_name]
						lowercase_								= metric_vals['new']
						lowercase_								= metric_vals.get("""old""" ,  lowerCamelCase_							)
						lowercase_								= metric_vals.get("""diff""" ,  lowerCamelCase_							)

						lowercase_								= F''' {new_val:f}''' if isinstance(lowerCamelCase_ ,  (int, float)							) else 'None'

						if old_val is not None:
								val_str += F''' / {old_val:f}''' if isinstance(lowerCamelCase_ ,  (int, float)							) else "None"
						if dif_val is not None:
								val_str += F''' ({dif_val:f})''' if isinstance(lowerCamelCase_ ,  (int, float)							) else "None"

						title += " " + metric_name + " |"
						lines += "---|"
						value += val_str + " |"

				output_md += [title, lines, value, " "]

		output_md.append("""</details>"""							)

		with open(lowerCamelCase_ ,  """w""" ,  encoding="""utf-8"""							) as f:
				f.writelines("""\n""".join(lowerCamelCase_							)							)


if __name__ == "__main__":
							UpperCAmelCase						:		Dict			= sys.argv[1]
							UpperCAmelCase						:		int			= sys.argv[2]

							format_json_to_md(input_json_file, output_md_file)






"""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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= 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()

