.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/20_basic/example_classification.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_20_basic_example_classification.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_20_basic_example_classification.py:


==============
Classification
==============

The following example shows how to fit a simple classification model with
*auto-sklearn*.

.. GENERATED FROM PYTHON SOURCE LINES 10-18

.. code-block:: default

    from pprint import pprint

    import sklearn.datasets
    import sklearn.metrics

    import autosklearn.classification









.. GENERATED FROM PYTHON SOURCE LINES 19-21

Data Loading
============

.. GENERATED FROM PYTHON SOURCE LINES 21-27

.. code-block:: default


    X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
        X, y, random_state=1
    )








.. GENERATED FROM PYTHON SOURCE LINES 28-30

Build and fit a classifier
==========================

.. GENERATED FROM PYTHON SOURCE LINES 30-38

.. code-block:: default


    automl = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=120,
        per_run_time_limit=30,
        tmp_folder="/tmp/autosklearn_classification_example_tmp",
    )
    automl.fit(X_train, y_train, dataset_name="breast_cancer")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    AutoSklearnClassifier(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
                          per_run_time_limit=30, time_left_for_this_task=120,
                          tmp_folder='/tmp/autosklearn_classification_example_tmp')



.. GENERATED FROM PYTHON SOURCE LINES 39-41

View the models found by auto-sklearn
=====================================

.. GENERATED FROM PYTHON SOURCE LINES 41-44

.. code-block:: default


    print(automl.leaderboard())





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

              rank  ensemble_weight                 type      cost   duration
    model_id                                                                 
    7            1             0.16          extra_trees  0.014184   1.569340
    27           2             0.04          extra_trees  0.014184   2.449368
    16           4             0.04    gradient_boosting  0.021277   1.235045
    21           5             0.06          extra_trees  0.021277   1.586606
    30           3             0.04          extra_trees  0.021277  12.410941
    2            6             0.02        random_forest  0.028369   1.892178
    3            7             0.08                  mlp  0.028369   1.077336
    6            8             0.02                  mlp  0.028369   1.222855
    11           9             0.02        random_forest  0.028369   2.290498
    14          11             0.02                  mlp  0.028369   2.054393
    22          10             0.06    gradient_boosting  0.028369   1.379215
    5           16             0.02        random_forest  0.035461   2.209646
    8           15             0.02        random_forest  0.035461   2.130122
    12          14             0.02    gradient_boosting  0.035461   1.431612
    18          13             0.02        random_forest  0.035461   2.392527
    31          12             0.08        random_forest  0.035461   1.798755
    9           17             0.04          extra_trees  0.042553   1.930847
    28          19             0.08         bernoulli_nb  0.070922   1.001414
    33          18             0.02        decision_tree  0.070922   8.978891
    34          20             0.02  k_nearest_neighbors  0.070922   0.897243
    20          22             0.02   passive_aggressive  0.078014   0.737455
    32          21             0.02    gradient_boosting  0.078014   1.069623
    29          23             0.08                  mlp  0.134752   2.241808




.. GENERATED FROM PYTHON SOURCE LINES 45-47

Print the final ensemble constructed by auto-sklearn
====================================================

.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: default


    pprint(automl.show_models(), indent=4)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    {   2: {   'balancing': Balancing(random_state=1),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d62ef550>,
               'cost': 0.028368794326241176,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d6436250>,
               'ensemble_weight': 0.02,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d62ef490>,
               'model_id': 2,
               'rank': 1,
               'sklearn_classifier': RandomForestClassifier(max_features=5, n_estimators=512, n_jobs=1,
                           random_state=1, warm_start=True)},
        3: {   'balancing': Balancing(random_state=1),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d61a4640>,
               'cost': 0.028368794326241176,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d684ebb0>,
               'ensemble_weight': 0.08,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d61a4be0>,
               'model_id': 3,
               'rank': 2,
               'sklearn_classifier': MLPClassifier(activation='tanh', alpha=0.0001363185819149026, beta_1=0.999,
                  beta_2=0.9, early_stopping=True,
                  hidden_layer_sizes=(115, 115, 115),
                  learning_rate_init=0.00018009776276177523, max_iter=32,
                  n_iter_no_change=32, random_state=1, verbose=0, warm_start=True)},
        5: {   'balancing': Balancing(random_state=1, strategy='weighting'),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d48108b0>,
               'cost': 0.03546099290780147,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d6403880>,
               'ensemble_weight': 0.02,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d5f64c10>,
               'model_id': 5,
               'rank': 3,
               'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=3, min_samples_leaf=2,
                           n_estimators=512, n_jobs=1, random_state=1,
                           warm_start=True)},
        6: {   'balancing': Balancing(random_state=1, strategy='weighting'),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d4824e50>,
               'cost': 0.028368794326241176,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d62e5640>,
               'ensemble_weight': 0.02,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d4824c10>,
               'model_id': 6,
               'rank': 4,
               'sklearn_classifier': MLPClassifier(alpha=0.0017940473175767063, beta_1=0.999, beta_2=0.9,
                  early_stopping=True, hidden_layer_sizes=(101, 101),
                  learning_rate_init=0.0004684917334431039, max_iter=32,
                  n_iter_no_change=32, random_state=1, verbose=0, warm_start=True)},
        7: {   'balancing': Balancing(random_state=1),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d45bd160>,
               'cost': 0.014184397163120588,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d605d910>,
               'ensemble_weight': 0.16,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d47c2ee0>,
               'model_id': 7,
               'rank': 5,
               'sklearn_classifier': ExtraTreesClassifier(max_features=34, min_samples_leaf=3, min_samples_split=11,
                         n_estimators=512, n_jobs=1, random_state=1,
                         warm_start=True)},
        8: {   'balancing': Balancing(random_state=1, strategy='weighting'),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d5ff72b0>,
               'cost': 0.03546099290780147,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d5f8d070>,
               'ensemble_weight': 0.02,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d45e0fa0>,
               'model_id': 8,
               'rank': 6,
               'sklearn_classifier': RandomForestClassifier(max_features=2, min_samples_leaf=2, n_estimators=512,
                           n_jobs=1, random_state=1, warm_start=True)},
        9: {   'balancing': Balancing(random_state=1, strategy='weighting'),
               'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d41d5e80>,
               'cost': 0.04255319148936165,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d47ec880>,
               'ensemble_weight': 0.04,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d614d880>,
               'model_id': 9,
               'rank': 7,
               'sklearn_classifier': ExtraTreesClassifier(max_features=9, min_samples_split=10, n_estimators=512,
                         n_jobs=1, random_state=1, warm_start=True)},
        11: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d416d2e0>,
                'cost': 0.028368794326241176,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d457fd00>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d4160e80>,
                'model_id': 11,
                'rank': 8,
                'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=23, min_samples_leaf=7,
                           n_estimators=512, n_jobs=1, random_state=1,
                           warm_start=True)},
        12: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d3f33070>,
                'cost': 0.03546099290780147,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d4620bb0>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d3f6f370>,
                'model_id': 12,
                'rank': 9,
                'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
                                   l2_regularization=0.005326508887463406,
                                   learning_rate=0.060800813211425456, max_iter=512,
                                   max_leaf_nodes=6, min_samples_leaf=5,
                                   n_iter_no_change=5, random_state=1,
                                   validation_fraction=None, warm_start=True)},
        14: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d2670820>,
                'cost': 0.028368794326241176,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d426de50>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d26b6340>,
                'model_id': 14,
                'rank': 10,
                'sklearn_classifier': MLPClassifier(activation='tanh', alpha=2.5550223982458062e-06, beta_1=0.999,
                  beta_2=0.9, hidden_layer_sizes=(54, 54, 54),
                  learning_rate_init=0.00027271287919467994, max_iter=256,
                  n_iter_no_change=32, random_state=1, validation_fraction=0.0,
                  verbose=0, warm_start=True)},
        16: {   'balancing': Balancing(random_state=1, strategy='weighting'),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d261cac0>,
                'cost': 0.021276595744680882,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d40ffe50>,
                'ensemble_weight': 0.04,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d261c940>,
                'model_id': 16,
                'rank': 11,
                'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
                                   l2_regularization=3.387912939529945e-10,
                                   learning_rate=0.30755227194768237, max_iter=128,
                                   max_leaf_nodes=60, min_samples_leaf=39,
                                   n_iter_no_change=18, random_state=1,
                                   validation_fraction=None, warm_start=True)},
        18: {   'balancing': Balancing(random_state=1, strategy='weighting'),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d40245b0>,
                'cost': 0.03546099290780147,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d268c400>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d247c2b0>,
                'model_id': 18,
                'rank': 12,
                'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=3, n_estimators=512,
                           n_jobs=1, random_state=1, warm_start=True)},
        20: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d62b5220>,
                'cost': 0.07801418439716312,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d26b6f70>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d62b9760>,
                'model_id': 20,
                'rank': 13,
                'sklearn_classifier': PassiveAggressiveClassifier(C=0.14268277711454813, max_iter=32, random_state=1,
                                tol=0.0002600768160857831, warm_start=True)},
        21: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d23da280>,
                'cost': 0.021276595744680882,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d255b880>,
                'ensemble_weight': 0.06,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d24356d0>,
                'model_id': 21,
                'rank': 14,
                'sklearn_classifier': ExtraTreesClassifier(criterion='entropy', max_features=4, min_samples_leaf=2,
                         min_samples_split=15, n_estimators=512, n_jobs=1,
                         random_state=1, warm_start=True)},
        22: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d1eb1b80>,
                'cost': 0.028368794326241176,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d24823d0>,
                'ensemble_weight': 0.06,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1f865b0>,
                'model_id': 22,
                'rank': 15,
                'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
                                   l2_regularization=8.057778875694463e-05,
                                   learning_rate=0.09179220974965213, max_iter=256,
                                   max_leaf_nodes=200, n_iter_no_change=18,
                                   random_state=1,
                                   validation_fraction=0.14295295806077554,
                                   warm_start=True)},
        27: {   'balancing': Balancing(random_state=1, strategy='weighting'),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d1cb3c10>,
                'cost': 0.014184397163120588,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d614d250>,
                'ensemble_weight': 0.04,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1cb3a90>,
                'model_id': 27,
                'rank': 16,
                'sklearn_classifier': ExtraTreesClassifier(max_features=134, min_samples_leaf=12, min_samples_split=4,
                         n_estimators=512, n_jobs=1, random_state=1,
                         warm_start=True)},
        28: {   'balancing': Balancing(random_state=1, strategy='weighting'),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d1b88e50>,
                'cost': 0.07092198581560283,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d2095340>,
                'ensemble_weight': 0.08,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1b88430>,
                'model_id': 28,
                'rank': 17,
                'sklearn_classifier': BernoulliNB(alpha=0.011056975175744176)},
        29: {   'balancing': Balancing(random_state=1, strategy='weighting'),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d1908fa0>,
                'cost': 0.13475177304964536,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d1fa2670>,
                'ensemble_weight': 0.08,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1908c40>,
                'model_id': 29,
                'rank': 18,
                'sklearn_classifier': MLPClassifier(alpha=0.0007119897774330087, beta_1=0.999, beta_2=0.9,
                  hidden_layer_sizes=(51, 51, 51),
                  learning_rate_init=0.00028079049815589414, max_iter=256,
                  n_iter_no_change=32, random_state=1, validation_fraction=0.0,
                  verbose=0, warm_start=True)},
        30: {   'balancing': Balancing(random_state=1, strategy='weighting'),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d187da00>,
                'cost': 0.021276595744680882,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d1cbbd00>,
                'ensemble_weight': 0.04,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d187d910>,
                'model_id': 30,
                'rank': 19,
                'sklearn_classifier': ExtraTreesClassifier(max_features=5390, min_samples_leaf=7, n_estimators=512,
                         n_jobs=1, random_state=1, warm_start=True)},
        31: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d17d6bb0>,
                'cost': 0.03546099290780147,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d1ab23d0>,
                'ensemble_weight': 0.08,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d17d6760>,
                'model_id': 31,
                'rank': 20,
                'sklearn_classifier': RandomForestClassifier(criterion='entropy', max_features=2, min_samples_leaf=10,
                           min_samples_split=7, n_estimators=512, n_jobs=1,
                           random_state=1, warm_start=True)},
        32: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d15ad460>,
                'cost': 0.07801418439716312,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d192dd60>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d158dbe0>,
                'model_id': 32,
                'rank': 21,
                'sklearn_classifier': HistGradientBoostingClassifier(early_stopping=True,
                                   l2_regularization=6.118619248990061e-06,
                                   learning_rate=0.9217672331809242, max_iter=512,
                                   max_leaf_nodes=223, min_samples_leaf=122,
                                   n_iter_no_change=6, random_state=1,
                                   validation_fraction=None, warm_start=True)},
        33: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d1021a60>,
                'cost': 0.07092198581560283,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d189ed00>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d10219a0>,
                'model_id': 33,
                'rank': 22,
                'sklearn_classifier': DecisionTreeClassifier(criterion='entropy', max_depth=30, min_samples_leaf=9,
                           min_samples_split=14, random_state=1)},
        34: {   'balancing': Balancing(random_state=1),
                'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7f05d0f03bb0>,
                'cost': 0.07092198581560283,
                'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d165c1c0>,
                'ensemble_weight': 0.02,
                'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d0fdbcd0>,
                'model_id': 34,
                'rank': 23,
                'sklearn_classifier': KNeighborsClassifier(p=1)}}




.. GENERATED FROM PYTHON SOURCE LINES 51-53

Get the Score of the final ensemble
===================================

.. GENERATED FROM PYTHON SOURCE LINES 53-56

.. code-block:: default


    predictions = automl.predict(X_test)
    print("Accuracy score:", sklearn.metrics.accuracy_score(y_test, predictions))




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Accuracy score: 0.958041958041958





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 2 minutes  1.815 seconds)


.. _sphx_glr_download_examples_20_basic_example_classification.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/automl/auto-sklearn/master?urlpath=lab/tree/notebooks/examples/20_basic/example_classification.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: example_classification.py <example_classification.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: example_classification.ipynb <example_classification.ipynb>`


.. only:: html

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