






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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    XGBClassifier:
  UpperCamelCase_: Optional[int]   				=    XGBClassifier()
  classifier.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return classifier

def   snake_case ()	->    None:
  UpperCamelCase_: str   				=    load_iris()
  UpperCamelCase_				,UpperCamelCase_: List[str]   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: List[str]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25  )

  UpperCamelCase_: Tuple   				=    iris['target_names']

  # Create an XGBoost Classifier from the training data
  UpperCamelCase_: Tuple   				=    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()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_		:		Tuple		     =			{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			['FunnelTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			[
       'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FunnelBaseModel',
       'FunnelForMaskedLM',
       'FunnelForMultipleChoice',
       'FunnelForPreTraining',
       'FunnelForQuestionAnswering',
       'FunnelForSequenceClassification',
       'FunnelForTokenClassification',
       'FunnelModel',
       'FunnelPreTrainedModel',
       'load_tf_weights_in_funnel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		str		     =			[
       'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFunnelBaseModel',
       'TFFunnelForMaskedLM',
       'TFFunnelForMultipleChoice',
       'TFFunnelForPreTraining',
       'TFFunnelForQuestionAnswering',
       'TFFunnelForSequenceClassification',
       'TFFunnelForTokenClassification',
       'TFFunnelModel',
       'TFFunnelPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
   from .tokenization_funnel import FunnelTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_funnel_fast import FunnelTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_funnel import (
          FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
          FunnelPreTrainedModel,
          load_tf_weights_in_funnel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_funnel import (
          TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFunnelBaseModel,
          TFFunnelForMaskedLM,
          TFFunnelForMultipleChoice,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForSequenceClassification,
          TFFunnelForTokenClassification,
          TFFunnelModel,
          TFFunnelPreTrainedModel,
      )

else:
   import sys

   A_		:		Any		     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)