

"""simple docstring"""




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      ):
 '''simple docstring'''





 return (data["data"], data["target"])
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					XGBClassifier()
 classifier.fit(__UpperCamelCase							,    __UpperCamelCase      )
 return classifier
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Optional[int]					   =					load_iris()
 UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					data_handling(__UpperCamelCase      )
 UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					train_test_split(
     __UpperCamelCase							,    __UpperCamelCase							,    test_size=0.25      )

 UpperCAmelCase__					:  str					   =					iris["""target_names"""]

 # Create an XGBoost Classifier from the training data
 UpperCAmelCase__					:  Any					   =					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()








"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  Union[str, Any]					   =					comments[0] if len(__UpperCamelCase      ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="""closed"""      )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       """This issue has been automatically marked as stale because it has not had """
       """recent activity. If you think this still needs to be addressed """
       """please comment on this thread.\n\nPlease note that issues that do not follow the """
       """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
       """are likely to be ignored."""      )


if __name__ == "__main__":
      main()





