





from math import loga






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				if a < 0:
								raise ValueError('''Input value must be a positive integer'''			)
				elif isinstance(__lowerCamelCase			,    __lowerCamelCase			):
								raise TypeError('''Input value must be a \'int\' type'''			)
				return 0 if (a == 0) else int(loga(a & -a			)			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()







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			)		->						tuple:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  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()
