.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_02_decoding_plot_mixed_gambles_frem.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_auto_examples_02_decoding_plot_mixed_gambles_frem.py:


FREM on Jimura et al "mixed gambles" dataset.
==================================================

In this example, we use fast ensembling of regularized models (FREM) to
solve a regression problem, predicting the gain level corresponding to each
beta maps regressed from mixed gambles experiment. FREM uses an implicit
spatial regularization through fast clustering and aggregates a high number
of  estimators trained on various splits of the training set, thus returning
a very robust decoder at a lower computational cost than other spatially
regularized methods.

To have more details, see: :ref:`frem`.

Load the data from the Jimura mixed-gamble experiment
---------------------------------------------------------------------------


.. code-block:: default

    from nilearn.datasets import fetch_mixed_gambles
    data = fetch_mixed_gambles(n_subjects=16)

    zmap_filenames = data.zmaps
    behavioral_target = data.gain
    mask_filename = data.mask_img








Fit FREM
---------------------------------------------------------------------------
We compare both of these models to a pipeline ensembling many models



.. code-block:: default

    from nilearn.decoding import FREMRegressor
    frem = FREMRegressor('svr', cv=10)

    frem.fit(zmap_filenames, behavioral_target)

    # Visualize FREM weights
    # ---------------------------------------------------------------------------

    from nilearn.plotting import plot_stat_map
    plot_stat_map(frem.coef_img_['beta'], title="FREM", display_mode="yz",
                  cut_coords=[20, -2], threshold=.2)




.. image:: /auto_examples/02_decoding/images/sphx_glr_plot_mixed_gambles_frem_001.png
    :alt: plot mixed gambles frem
    :class: sphx-glr-single-img


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

 Out:

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    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is bigger than the volume of a standard human brain. This object is probably not tuned to be used on such data.
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    /home/varoquau/dev/scikit-learn/sklearn/svm/_base.py:246: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.
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      warnings.warn('Solver terminated early (max_iter=%i).'

    <nilearn.plotting.displays.YZSlicer object at 0x7f8bfc614370>



We can observe that the coefficients map learnt by FREM is structured,
due to the spatial regularity imposed by working on clusters and model
ensembling. Although these maps have been thresholded for display, they are
not sparse (i.e. almost all voxels have non-zero coefficients). See also this
:ref:`other example <sphx_glr_auto_examples_02_decoding_plot_haxby_frem.py>`
using FREM, and related :ref:`section of user guide <frem>`.


Example use of TV-L1 SpaceNet
---------------------------------------------------------------------------
:ref:`SpaceNet<space_net>` is another method available in Nilearn to decode
with spatially sparse models. Depending on the penalty that is used,
it yields either very structured maps (TV-L1) or unstructured maps
(graph_net). Because of their heavy computational costs, these methods are
not demonstrated on this example but you can try them easily if you have a
few minutes. Example code is included below.



.. code-block:: default


    from nilearn.decoding import SpaceNetRegressor

    # We use the regressor object since the task is to predict a continuous
    # variable (gain of the gamble).

    tv_l1 = SpaceNetRegressor(mask=mask_filename, penalty="tv-l1",
                              eps=1e-1,  # prefer large alphas
                              memory="nilearn_cache")
    # tv_l1.fit(zmap_filenames, behavioral_target)
    # plot_stat_map(tv_l1.coef_img_, title="TV-L1", display_mode="yz",
    #               cut_coords=[20, -2])








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

   **Total running time of the script:** ( 1 minutes  19.568 seconds)


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