



"""simple docstring"""
from __future__ import annotations

_UpperCAmelCase     :   List[str]     =			1.6021e-19  # units = C





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			):
   if (conductivity, electron_conc, mobility).count(0 ) != 1:
      raise ValueError("""You cannot supply more or less than 2 values""" )
   elif conductivity < 0:
      raise ValueError("""Conductivity cannot be negative""" )
   elif electron_conc < 0:
      raise ValueError("""Electron concentration cannot be negative""" )
   elif mobility < 0:
      raise ValueError("""mobility cannot be negative""" )
   elif conductivity == 0:
      return (
          "conductivity",
          mobility * electron_conc * ELECTRON_CHARGE,
      )
   elif electron_conc == 0:
      return (
          "electron_conc",
          conductivity / (mobility * ELECTRON_CHARGE),
      )
   else:
      return (
          "mobility",
          conductivity / (electron_conc * ELECTRON_CHARGE),
      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()








"""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      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

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


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



