.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_02_decoding_plot_haxby_different_estimators.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_haxby_different_estimators.py:


Different classifiers in decoding the Haxby dataset
=====================================================

Here we compare different classifiers on a visual object recognition
decoding task.

Loading the data
-----------------


.. code-block:: default


    # We start by loading data using nilearn dataset fetcher
    from nilearn import datasets
    from nilearn.image import get_data
    # by default 2nd subject data will be fetched
    haxby_dataset = datasets.fetch_haxby()

    # print basic information on the dataset
    print('First subject anatomical nifti image (3D) located is at: %s' %
          haxby_dataset.anat[0])
    print('First subject functional nifti image (4D) is located at: %s' %
          haxby_dataset.func[0])

    # load labels
    import numpy as np
    import pandas as pd
    labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
    stimuli = labels['labels']

    # identify resting state (baseline) labels in order to be able to remove them
    resting_state = (stimuli == 'rest')

    # extract the indices of the images corresponding to some condition or task
    task_mask = np.logical_not(resting_state)

    # find names of remaining active labels
    categories = stimuli[task_mask].unique()

    # extract tags indicating to which acquisition run a tag belongs
    session_labels = labels['chunks'][task_mask]


    # Load the fMRI data
    # For decoding, standardizing is often very important
    mask_filename = haxby_dataset.mask_vt[0]
    func_filename = haxby_dataset.func[0]


    # Because the data is in one single large 4D image, we need to use
    # index_img to do the split easily.
    from nilearn.image import index_img
    fmri_niimgs = index_img(func_filename, task_mask)
    classification_target = stimuli[task_mask]





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

 Out:

 .. code-block:: none

    First subject anatomical nifti image (3D) located is at: /home/varoquau/nilearn_data/haxby2001/subj2/anat.nii.gz
    First subject functional nifti image (4D) is located at: /home/varoquau/nilearn_data/haxby2001/subj2/bold.nii.gz




Training the decoder
---------------------


.. code-block:: default


    # Then we define the various classifiers that we use
    classifiers = ['svc_l2', 'svc_l1', 'logistic_l1',
                   'logistic_l2', 'ridge_classifier']

    # Here we compute prediction scores and run time for all these
    # classifiers
    import time
    from nilearn.decoding import Decoder
    from sklearn.model_selection import LeaveOneGroupOut

    cv = LeaveOneGroupOut()
    classifiers_data = {}

    for classifier_name in sorted(classifiers):
        classifiers_data[classifier_name] = {}
        print(70 * '_')

        # The decoder has as default score the `roc_auc`
        decoder = Decoder(estimator=classifier_name, mask=mask_filename,
                          standardize=True, cv=cv)
        t0 = time.time()
        decoder.fit(fmri_niimgs, classification_target, groups=session_labels)

        classifiers_data[classifier_name] = {}
        classifiers_data[classifier_name]['score'] = decoder.cv_scores_
        classifiers_data[classifier_name]['map'] = decoder.coef_img_['house']

        print("%10s: %.2fs" % (classifier_name, time.time() - t0))
        for category in categories:
            print("    %14s vs all -- AUC: %1.2f +- %1.2f" % (
                category,
                np.mean(classifiers_data[classifier_name]['score'][category]),
                np.std(classifiers_data[classifier_name]['score'][category]))
            )

        # Adding the average performance per estimator
        scores = classifiers_data[classifier_name]['score']
        scores['AVERAGE'] = np.mean(list(scores.values()), axis=0)
        classifiers_data[classifier_name]['score'] = scores





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

 Out:

 .. code-block:: none

    ______________________________________________________________________
    logistic_l1: 12.69s
              scissors vs all -- AUC: 0.92 +- 0.05
                  face vs all -- AUC: 0.98 +- 0.02
                   cat vs all -- AUC: 0.96 +- 0.04
                  shoe vs all -- AUC: 0.92 +- 0.08
                 house vs all -- AUC: 1.00 +- 0.00
          scrambledpix vs all -- AUC: 0.99 +- 0.01
                bottle vs all -- AUC: 0.90 +- 0.08
                 chair vs all -- AUC: 0.91 +- 0.06
    ______________________________________________________________________
    logistic_l2: 50.69s
              scissors vs all -- AUC: 0.91 +- 0.08
                  face vs all -- AUC: 0.97 +- 0.04
                   cat vs all -- AUC: 0.97 +- 0.03
                  shoe vs all -- AUC: 0.92 +- 0.09
                 house vs all -- AUC: 1.00 +- 0.00
          scrambledpix vs all -- AUC: 0.96 +- 0.11
                bottle vs all -- AUC: 0.82 +- 0.18
                 chair vs all -- AUC: 0.89 +- 0.18
    ______________________________________________________________________
    ridge_classifier: 25.69s
              scissors vs all -- AUC: 0.91 +- 0.08
                  face vs all -- AUC: 0.96 +- 0.03
                   cat vs all -- AUC: 0.91 +- 0.07
                  shoe vs all -- AUC: 0.91 +- 0.07
                 house vs all -- AUC: 1.00 +- 0.00
          scrambledpix vs all -- AUC: 0.99 +- 0.01
                bottle vs all -- AUC: 0.85 +- 0.10
                 chair vs all -- AUC: 0.91 +- 0.06
    ______________________________________________________________________
        svc_l1: 27.25s
              scissors vs all -- AUC: 0.92 +- 0.05
                  face vs all -- AUC: 0.98 +- 0.03
                   cat vs all -- AUC: 0.96 +- 0.04
                  shoe vs all -- AUC: 0.92 +- 0.07
                 house vs all -- AUC: 1.00 +- 0.00
          scrambledpix vs all -- AUC: 0.99 +- 0.01
                bottle vs all -- AUC: 0.89 +- 0.08
                 chair vs all -- AUC: 0.93 +- 0.04
    ______________________________________________________________________
        svc_l2: 62.57s
              scissors vs all -- AUC: 0.90 +- 0.09
                  face vs all -- AUC: 0.96 +- 0.05
                   cat vs all -- AUC: 0.96 +- 0.04
                  shoe vs all -- AUC: 0.91 +- 0.08
                 house vs all -- AUC: 1.00 +- 0.00
          scrambledpix vs all -- AUC: 0.96 +- 0.10
                bottle vs all -- AUC: 0.82 +- 0.17
                 chair vs all -- AUC: 0.87 +- 0.16




Visualization
--------------


.. code-block:: default


    # Then we make a rudimentary diagram
    import matplotlib.pyplot as plt
    plt.figure(figsize=(6, 6))

    all_categories = np.sort(np.hstack([categories, 'AVERAGE']))
    tick_position = np.arange(len(all_categories))
    plt.yticks(tick_position + 0.25, all_categories)
    height = 0.1

    for i, (color, classifier_name) in enumerate(zip(['b', 'm', 'k', 'r', 'g'],
                                                     classifiers)):
        score_means = [
            np.mean(classifiers_data[classifier_name]['score'][category])
            for category in all_categories
        ]

        plt.barh(tick_position, score_means,
                 label=classifier_name.replace('_', ' '),
                 height=height, color=color)
        tick_position = tick_position + height

    plt.xlabel('Classification accuracy (AUC score)')
    plt.ylabel('Visual stimuli category')
    plt.xlim(xmin=0.5)
    plt.legend(loc='lower left', ncol=1)
    plt.title(
        'Category-specific classification accuracy for different classifiers')
    plt.tight_layout()

    # We can see that for a fixed penalty the results are similar between the svc
    # and the logistic regression. The main difference relies on the penalty
    # ($\ell_1$ and $\ell_2$). The sparse penalty works better because we are in
    # an intra-subject setting.




.. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_different_estimators_001.png
    :alt: Category-specific classification accuracy for different classifiers
    :class: sphx-glr-single-img





Visualizing the face vs house map
---------------------------------


.. code-block:: default


    # Finally, we plot the face vs house map for the different classifiers
    # Use the average EPI as a background
    from nilearn.image import mean_img
    mean_epi_img = mean_img(func_filename)

    # Restrict the decoding to face vs house
    condition_mask = np.logical_or(stimuli == b'face', stimuli == b'house')
    stimuli = stimuli[condition_mask]
    fmri_niimgs_condition = index_img(func_filename, condition_mask)

    from nilearn.plotting import plot_stat_map, show

    for classifier_name in sorted(classifiers):
        coef_img = classifiers_data[classifier_name]['map']
        threshold = np.max(np.abs(get_data(coef_img))) * 1e-3
        plot_stat_map(
            coef_img, bg_img=mean_epi_img, display_mode='z', cut_coords=[-15],
            threshold=threshold,
            title='%s: face vs house' % classifier_name.replace('_', ' '))

    show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_different_estimators_002.png
          :alt: plot haxby different estimators
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_different_estimators_003.png
          :alt: plot haxby different estimators
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_different_estimators_004.png
          :alt: plot haxby different estimators
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_different_estimators_005.png
          :alt: plot haxby different estimators
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_different_estimators_006.png
          :alt: plot haxby different estimators
          :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    /home/varoquau/dev/nilearn/nilearn/plotting/displays.py:1608: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
      ax = fh.add_axes([fraction * index * (x1 - x0) + x0, y0,





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

   **Total running time of the script:** ( 3 minutes  13.594 seconds)


.. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_different_estimators.py:


.. only :: html

 .. container:: sphx-glr-footer
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