
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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    tuple:
       return (data["data"], data["target"])







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    XGBClassifier:
       lowercase							:     Tuple       	=      XGBClassifier()
       classifier.fit(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       return classifier







def 						_snake_case(  ) ->    None:
       lowercase							:     Optional[Any]       	=      load_iris()
       lowercase	,			lowercase							:     Tuple       	=      data_handling(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      train_test_split(
           SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	test_size=0.25				)

       lowercase							:     Union[str, Any]       	=      iris["""target_names"""]

       # Create an XGBoost Classifier from the training data
       lowercase							:     Dict       	=      xgboost(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       # Display the confusion matrix of the classifier with both training and test sets
       ConfusionMatrixDisplay.from_estimator(
           SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	display_labels=SCREAMING_SNAKE_CASE__					,	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 collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

       # Validation
       def count_nodes(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left				) + count_nodes(node.right				) + 1

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_coins(node.left				) + count_coins(node.right				) + node.data

       if count_nodes(SCREAMING_SNAKE_CASE__				) != count_coins(SCREAMING_SNAKE_CASE__				):
              raise ValueError("""The nodes number should be same as the number of coins"""				)

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0					,	1				)

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
