



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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      XGBClassifier:
		_a     :      int        =							XGBClassifier()
		classifier.fit(lowerCAmelCase_						,			lowerCAmelCase_  )
		return classifier



def      __lowerCamelCase     (      )			->      None:
		_a     :      Optional[Any]        =							load_iris()
		_a       ,					_a     :      List[Any]        =							data_handling(lowerCAmelCase_  )
		_a       ,					_a       ,					_a       ,					_a     :      int        =							train_test_split(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			test_size=0.25  )

		_a     :      str        =							iris['target_names']

		# Create an XGBoost Classifier from the training data
		_a     :      Any        =							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()









'''simple docstring'''

# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
    '''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CpmAntForCausalLM''',
							    '''CpmAntModel''',
							    '''CpmAntPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
							from .tokenization_cpmant import CpmAntTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_cpmant import (
														    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CpmAntForCausalLM,
														    CpmAntModel,
														    CpmAntPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




