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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_basic_run_n_jobs.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_basic_run_n_jobs.py:


Parallelize Julearn
============================

In this example we will parallelize outer cross-validation
and/or inner cross-validation for hyperparameter search.

.. include:: ../../links.inc

.. GENERATED FROM PYTHON SOURCE LINES 10-16

.. code-block:: default

    # Authors: Sami Hamdan <s.hamdan@fz-juelich.de>
    #
    # License: AGPL
    from seaborn import load_dataset
    from julearn import run_cross_validation








.. GENERATED FROM PYTHON SOURCE LINES 17-18

prepare some simple standard input

.. GENERATED FROM PYTHON SOURCE LINES 18-25

.. code-block:: default

    df_iris = load_dataset("iris")
    df_iris = df_iris[df_iris['species'].isin(['versicolor', 'virginica'])]

    X = ['sepal_length', 'sepal_width', 'petal_length']
    y = 'species'









.. GENERATED FROM PYTHON SOURCE LINES 26-27

run without any parallelization

.. GENERATED FROM PYTHON SOURCE LINES 27-32

.. code-block:: default

    scores = run_cross_validation(
        X=X, y=y, data=df_iris, model='svm',
        model_params=dict(svm__C=[1, 2, 3]))









.. GENERATED FROM PYTHON SOURCE LINES 33-38

To add parallelization to the outer cross-validation we
will add the n_jobs argument to run_cross_validation.
We can use verbose > 0 to get more information
about the parallelization done.
Here, I will set the parallel jobs to 2.

.. GENERATED FROM PYTHON SOURCE LINES 38-45

.. code-block:: default

    scores = run_cross_validation(
        X=X, y=y, data=df_iris, model='svm',
        model_params=dict(svm__C=[1, 2, 3]),
        n_jobs=2, verbose=3
    )









.. GENERATED FROM PYTHON SOURCE LINES 46-51

We can also parallelize over the hyperparameter search/inner cv.
This will work by using the n_jobs argument of the searcher itself, e.g.
by default `sklearn.model_selection.GridSearchCV`.
To adjust the parameters of the search we have to use the search_params
inside of the model_params like this:

.. GENERATED FROM PYTHON SOURCE LINES 51-62

.. code-block:: default

    model_params = dict(
        svm__C=[1, 2, 3],
        search_params=dict(n_jobs=2, verbose=3)
    )

    scores = run_cross_validation(
        X=X, y=y, data=df_iris, model='svm',
        model_params=model_params
    )






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

 .. code-block:: none

    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits
    Fitting 5 folds for each of 3 candidates, totalling 15 fits




.. GENERATED FROM PYTHON SOURCE LINES 63-67

Depending on your resources you can use n_jobs for outer cv, inner cv or
even as a model_parameter for some models like `rf`.
Additionally, you can also use the scikitlearns `parallel_backend` for
parallelization.


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

   **Total running time of the script:** ( 0 minutes  9.662 seconds)


.. _sphx_glr_download_auto_examples_basic_run_n_jobs.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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