
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_						)		->     tuple:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_						)		->     XGBClassifier:







				"""simple docstring"""



				__A             =			XGBClassifier()
				classifier.fit(a_		,       a_						)
				return classifier


def 						UpperCAmelCase      (     )		->     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()


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			int							,*A :			Dict							,**A :			Dict			):
					warnings.warn(
					    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
					    " use CLIPImageProcessor instead."							,A							,)
					super().__init__(*A							,**A			)
