.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/40_advanced/example_feature_types.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_40_advanced_example_feature_types.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_40_advanced_example_feature_types.py:


=============
Feature Types
=============

In *auto-sklearn* it is possible to specify the feature types of a dataset when calling
the method :meth:`fit() <autosklearn.classification.AutoSklearnClassifier.fit>` by
specifying the argument ``feat_type``.
The following example demonstrates a way it can be done.

Additionally, you can provide a properly formatted pandas DataFrame, and the feature
types will be automatically inferred, as demonstrated in
:ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py`.

.. GENERATED FROM PYTHON SOURCE LINES 16-25

.. code-block:: default

    import numpy as np

    import sklearn.model_selection
    import sklearn.datasets
    import sklearn.metrics

    import autosklearn.classification









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Data Loading
============
Load Australian dataset from https://www.openml.org/d/40981

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.. code-block:: default

    bunch = data = sklearn.datasets.fetch_openml(data_id=40981, as_frame=True)
    y = bunch["target"].to_numpy()
    X = bunch["data"].to_numpy(np.float)

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

    # Auto-sklearn can automatically recognize categorical/numerical data from a pandas
    # DataFrame. This example highlights how the user can provide the feature types,
    # when using numpy arrays, as there is no per-column dtype in this case.
    # feat_type is a list that tags each column from a DataFrame/ numpy array / list
    # with the case-insensitive string categorical or numerical, accordingly.
    feat_type = [
        "Categorical" if x.name == "category" else "Numerical" for x in bunch["data"].dtypes
    ]





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

 .. code-block:: none

    /home/runner/work/auto-sklearn/auto-sklearn/examples/40_advanced/example_feature_types.py:31: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
    Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
      X = bunch["data"].to_numpy(np.float)




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

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.. code-block:: default


    cls = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=30,
        # Bellow two flags are provided to speed up calculations
        # Not recommended for a real implementation
        initial_configurations_via_metalearning=0,
        smac_scenario_args={"runcount_limit": 1},
    )
    cls.fit(X_train, y_train, X_test, y_test, feat_type=feat_type)





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

 .. code-block:: none


    AutoSklearnClassifier(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
                          initial_configurations_via_metalearning=0,
                          per_run_time_limit=3,
                          smac_scenario_args={'runcount_limit': 1},
                          time_left_for_this_task=30)



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

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.. code-block:: default


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




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

 .. code-block:: none

    Accuracy score 0.8786127167630058





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

   **Total running time of the script:** ( 0 minutes  25.381 seconds)


.. _sphx_glr_download_examples_40_advanced_example_feature_types.py:

.. only:: html

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


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      .. image:: images/binder_badge_logo.svg
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        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_