




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_ (						lowerCamelCase ):
				return (data["data"], data["target"])




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=XGBClassifier()
				classifier.fit(lowerCamelCase						,  lowerCamelCase )
				return classifier




def 		lowerCAmelCase_ (						):
				__magic_name__						:						Dict										=load_iris()
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=data_handling(lowerCamelCase )
				__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[int]										=train_test_split(
				    lowerCamelCase						,  lowerCamelCase						,  test_size=0.2_5 )

				__magic_name__						:						Any										=iris["""target_names"""]

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

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


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							main()










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




