


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					(    lowerCAmelCase_ )    ->tuple:
   return (data["data"], data["target"])
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->XGBClassifier:
   UpperCAmelCase      					=  XGBClassifier()
   classifier.fit(lowerCAmelCase_	,    lowerCAmelCase_ )
   return classifier
def 	_UpperCamelCase					(    )    ->None:
   UpperCAmelCase      					=  load_iris()
   UpperCAmelCase					,	UpperCAmelCase      					=  data_handling(lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase      					=  train_test_split(
       lowerCAmelCase_	,    lowerCAmelCase_	,    test_size=0.25 )

   UpperCAmelCase      					=  iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   UpperCAmelCase      					=  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()







from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''image_processor''', '''tokenizer''']
      UpperCamelCase										=							'''BridgeTowerImageProcessor'''
      UpperCamelCase										=							('''RobertaTokenizer''', '''RobertaTokenizerFast''')





      def __init__(		self    :       int       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       List[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         super().__init__(__lowerCamelCase       ,							__lowerCamelCase							)





      def __call__(		self    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[bool, str, PaddingStrategy] = False       ,							__lowerCamelCase    :       Union[bool, str, TruncationStrategy] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       int = 0       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> BatchEncoding:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(
             text=__lowerCamelCase       ,							add_special_tokens=__lowerCamelCase       ,							padding=__lowerCamelCase       ,							truncation=__lowerCamelCase       ,							max_length=__lowerCamelCase       ,							stride=__lowerCamelCase       ,							pad_to_multiple_of=__lowerCamelCase       ,							return_token_type_ids=__lowerCamelCase       ,							return_attention_mask=__lowerCamelCase       ,							return_overflowing_tokens=__lowerCamelCase       ,							return_special_tokens_mask=__lowerCamelCase       ,							return_offsets_mapping=__lowerCamelCase       ,							return_length=__lowerCamelCase       ,							verbose=__lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							**__lowerCamelCase       ,							)
         # add pixel_values + pixel_mask
         UpperCAmelCase      					=  self.image_processor(
             __lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_center_crop=__lowerCamelCase       ,							**__lowerCamelCase							)
         encoding.update(__lowerCamelCase							)

         return encoding





      def 							_lowercase					(		self    :       Optional[Any]       ,							*__lowerCamelCase    :       Optional[int]       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







         return self.tokenizer.batch_decode(*__lowerCamelCase       ,							**__lowerCamelCase							)





      def 							_lowercase					(		self    :       Dict       ,							*__lowerCamelCase    :       Any       ,							**__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         return self.tokenizer.decode(*__lowerCamelCase       ,							**__lowerCamelCase							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer.model_input_names
         UpperCAmelCase      					=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



