


"""simple docstring"""


import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			XGBClassifier()
			classifier.fit(_UpperCamelCase    , _UpperCamelCase      )
			return classifier


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			load_iris()
			__lowerCAmelCase    ,	__lowerCAmelCase				    =			data_handling(_UpperCamelCase      )
			__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			train_test_split(
			    _UpperCamelCase    , _UpperCamelCase    , test_size=0.25      )

			__lowerCAmelCase				    =			iris["target_names"]

			# Create an XGBoost Classifier from the training data
			__lowerCAmelCase				    =			xgboost(_UpperCamelCase    , _UpperCamelCase      )

			# Display the confusion matrix of the classifier with both training and test sets
			ConfusionMatrixDisplay.from_estimator(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , display_labels=_UpperCamelCase    , cmap="Blues"    , normalize="true"    , )
			plt.title("Normalized Confusion Matrix - IRIS Dataset"      )
			plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )					)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""",     type=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

  # positional
  parser.add_argument(
      """training_script""",     type=A_,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ),     )

  # rest from the training program
  parser.add_argument("""training_script_args""",     nargs=A_					)

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

  # Import training_script as a module.
  __magic_name__				     =       Path(args.training_script					)
  sys.path.append(str(script_fpath.parent.resolve()					)					)
  __magic_name__				     =       script_fpath.stem
  __magic_name__				     =       importlib.import_module(A_					)

  # Patch sys.argv
  __magic_name__				     =       [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores					)]

  xmp.spawn(mod._mp_fn,     args=(),     nprocs=args.num_cores					)


if __name__ == "__main__":
    main()

