

'''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			(  __snake_case	:						dict			)			->				Tuple:






						"""simple docstring"""





						return (data["data"], data["target"])
def 		__lowerCamelCase			(  __snake_case	:						np.ndarray,						__snake_case	:						np.ndarray			)			->				List[str]:






						"""simple docstring"""





						A__	:  Dict           =XGBClassifier()
						classifier.fit(__a,						__a			)
						return classifier
def 		__lowerCamelCase			(  )			->				Dict:






						"""simple docstring"""





						A__	:  List[str]           =load_iris()
						A__	:  Optional[Any]           =data_handling(__a			)
						A__	:  int           =train_test_split(
						    __a,						__a,						test_size=0.25			)

						A__	:  Optional[Any]           =iris['target_names']

						# Create an XGBoost Classifier from the training data
						A__	:  Tuple           =xgboost(__a,						__a			)

						# Display the confusion matrix of the classifier with both training and test sets
						ConfusionMatrixDisplay.from_estimator(
						    __a,						__a,						__a,						display_labels=__a,						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 typing import TYPE_CHECKING

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


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





