



from bisect import bisect
from itertools import accumulate






def     a(      A       :    List[str] ,     A       :    int ,     A       :    Tuple ,     A       :    Any       )		->       str:
					"""simple docstring"""





					a					     =   sorted(zip(A ,     A       ) ,     key=lambda A       : x[0] / x[1] ,     reverse=A       )
					a     ,				a					     =   [i[0] for i in r], [i[1] for i in r]
					a					     =   list(accumulate(A       )       )
					a					     =   bisect(A ,     A       )
					return (
					    0
					    if k == 0
					    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
					    if k != n
					    else sum(vl[:k]       )
					)


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     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





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






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   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()
