





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      _A       (		SCREAMING_SNAKE_CASE     :   dict						):





  """simple docstring"""



  return (data["data"], data["target"])
def      _A       (		SCREAMING_SNAKE_CASE     :   np.ndarray       ,       SCREAMING_SNAKE_CASE     :   np.ndarray						):





  """simple docstring"""



  a__	:					Tuple										=XGBClassifier()
  classifier.fit(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  return classifier
def      _A       (		):





  """simple docstring"""



  a__	:					Optional[Any]										=load_iris()
  a__					,							a__	:					Union[str, Any]										=data_handling(SCREAMING_SNAKE_CASE						)
  a__					,							a__					,							a__					,							a__	:					int										=train_test_split(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       test_size=0.2_5						)

  a__	:					List[str]										=iris["target_names"]

  # Create an XGBoost Classifier from the training data
  a__	:					Union[str, Any]										=xgboost(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

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


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()













from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class 				__lowerCAmelCase       :
      pass






