





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 							SCREAMING_SNAKE_CASE__						(    __a   ):
	return (data["data"], data["target"])


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			int											=	XGBClassifier()
	classifier.fit(__a   , __a   )
	return classifier


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Dict											=	load_iris()
	snake_case_						,snake_case_		:			int											=	data_handling(__a   )
	snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Tuple											=	train_test_split(
	    __a   , __a   , test_size=0.25   )

	snake_case_		:			str											=	iris['target_names']

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








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

