






'''simple docstring'''



from __future__ import annotations

from typing import Any



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						list[Any]     )	->      Union[str, Any]:

   """simple docstring"""
   create_state_space_tree(__a,       [],       0     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						list[Any],       SCREAMING_SNAKE_CASE__:						list[Any],       SCREAMING_SNAKE_CASE__:						int     )	->      Optional[int]:

   """simple docstring"""



   if index == len(__a     ):
      print(__a     )
      return

   create_state_space_tree(__a,       __a,       index + 1     )
   current_subsequence.append(sequence[index]     )
   create_state_space_tree(__a,       __a,       index + 1     )
   current_subsequence.pop()


if __name__ == "__main__":
 __UpperCamelCase   :							int         =   [3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(["""A""", """B""", """C"""])
 generate_all_subsequences(seq)




"""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     UpperCAmelCase_					(				__a   :			dict						):




			'''simple docstring'''



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

def     UpperCAmelCase_					(				__a   :			np.ndarray      ,							__a   :			np.ndarray						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    XGBClassifier()
			classifier.fit(__a      ,							__a						)
			return classifier

def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					List[str]	       =    load_iris()
			_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    data_handling(__a						)
			_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					int	       =    train_test_split(
			    __a      ,							__a      ,							test_size=0.2_5						)

			_lowerCamelCase					:					Optional[Any]	       =    iris['target_names']

			# Create an XGBoost Classifier from the training data
			_lowerCamelCase					:					Tuple	       =    xgboost(__a      ,							__a						)

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


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





