.. only:: html

    .. note::
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        Click :ref:`here <sphx_glr_download_auto_examples_02_decoding_plot_haxby_full_analysis.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_full_analysis.py:


ROI-based decoding analysis in Haxby et al. dataset
=====================================================

In this script we reproduce the data analysis conducted by
Haxby et al. in "Distributed and Overlapping Representations of Faces and
Objects in Ventral Temporal Cortex".

Specifically, we look at decoding accuracy for different objects in
three different masks: the full ventral stream (mask_vt), the house
selective areas (mask_house) and the face selective areas (mask_face),
that have been defined via a standard GLM-based analysis.

Load and prepare the data
-----------------------------------


.. code-block:: default


    # Fetch data using nilearn dataset fetcher
    from nilearn import datasets
    # by default we fetch 2nd subject data for analysis
    haxby_dataset = datasets.fetch_haxby()
    func_filename = haxby_dataset.func[0]

    # 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' %
          func_filename)

    # Load nilearn NiftiMasker, the practical masking and unmasking tool
    from nilearn.input_data import NiftiMasker

    # load labels
    import pandas as pd
    labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
    stimuli = labels['labels']
    # identify resting state labels in order to be able to remove them
    task_mask = (stimuli != 'rest')

    # 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]

    # apply the task_mask to  fMRI data (func_filename)
    from nilearn.image import index_img
    task_data = index_img(func_filename, 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




Decoding on the different masks
--------------------------------

The classifier used here is a support vector classifier (svc). We use
class:`nilearn.decoding.Decoder` and specify the classifier.


.. code-block:: default

    import numpy as np
    from nilearn.decoding import Decoder

    # Make a data splitting object for cross validation
    from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
    cv = LeaveOneGroupOut()








We use :class:`sklearn.dummy.DummyClassifier` as a baseline.


.. code-block:: default

    from sklearn.dummy import DummyClassifier
    dummy_classifier = DummyClassifier()

    mask_names = ['mask_vt', 'mask_face', 'mask_house']

    mask_scores = {}
    mask_chance_scores = {}

    for mask_name in mask_names:
        print("Working on %s" % mask_name)
        # For decoding, standardizing is often very important
        mask_filename = haxby_dataset[mask_name][0]
        masker = NiftiMasker(mask_img=mask_filename, standardize=True)
        masked_timecourses = masker.fit_transform(func_filename)[task_mask]
        mask_scores[mask_name] = {}
        mask_chance_scores[mask_name] = {}

        for category in categories:
            print("Processing %s %s" % (mask_name, category))
            classification_target = (stimuli[task_mask] == category)
            # Specify the classifier to the decoder object.
            # With the decoder we can input the masker directly.
            # We are using the svc_l1 here because it is intra subject.
            decoder = Decoder(estimator='svc_l1', cv=cv,
                              mask=masker, scoring='roc_auc')
            decoder.fit(task_data, classification_target, groups=session_labels)
            mask_scores[mask_name][category] = decoder.cv_scores_[1]
            print("Scores: %1.2f +- %1.2f" % (
                  np.mean(mask_scores[mask_name][category]),
                  np.std(mask_scores[mask_name][category])))

            mask_chance_scores[mask_name][category] = cross_val_score(
                dummy_classifier,
                masked_timecourses,
                classification_target,
                cv=cv,
                groups=session_labels,
                scoring="roc_auc",
            )






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

 Out:

 .. code-block:: none

    Working on mask_vt
    Processing mask_vt scissors
    Scores: 0.92 +- 0.05
    Processing mask_vt face
    Scores: 0.98 +- 0.03
    Processing mask_vt cat
    Scores: 0.96 +- 0.04
    Processing mask_vt shoe
    Scores: 0.92 +- 0.07
    Processing mask_vt house
    Scores: 1.00 +- 0.00
    Processing mask_vt scrambledpix
    Scores: 0.99 +- 0.01
    Processing mask_vt bottle
    Scores: 0.89 +- 0.08
    Processing mask_vt chair
    Scores: 0.93 +- 0.04
    Working on mask_face
    Processing mask_face scissors
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.70 +- 0.16
    Processing mask_face face
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.90 +- 0.06
    Processing mask_face cat
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.76 +- 0.12
    Processing mask_face shoe
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.73 +- 0.17
    Processing mask_face house
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.71 +- 0.16
    Processing mask_face scrambledpix
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.87 +- 0.09
    Processing mask_face bottle
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.67 +- 0.17
    Processing mask_face chair
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    /home/varoquau/dev/nilearn/nilearn/decoding/decoder.py:491: UserWarning: After clustering and screening, the decoding model will be trained only on 30 features. Consider raising clustering_percentile or screening_percentile parameters
      warnings.warn(
    Scores: 0.63 +- 0.10
    Working on mask_house
    Processing mask_house scissors
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.83 +- 0.08
    Processing mask_house face
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.90 +- 0.07
    Processing mask_house cat
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.86 +- 0.09
    Processing mask_house shoe
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.82 +- 0.12
    Processing mask_house house
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 1.00 +- 0.00
    Processing mask_house scrambledpix
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.96 +- 0.05
    Processing mask_house bottle
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.86 +- 0.10
    Processing mask_house chair
    /home/varoquau/dev/nilearn/nilearn/_utils/param_validation.py:191: UserWarning: Brain mask is smaller than .5% of the volume human brain. This object is probably not tuned tobe used on such data.
      screening_percentile_ = _adjust_screening_percentile(
    Scores: 0.90 +- 0.10




We make a simple bar plot to summarize the results
---------------------------------------------------


.. code-block:: default

    import matplotlib.pyplot as plt
    from nilearn.plotting import show

    plt.figure()

    tick_position = np.arange(len(categories))
    plt.xticks(tick_position, categories, rotation=45)

    for color, mask_name in zip('rgb', mask_names):
        score_means = [np.mean(mask_scores[mask_name][category])
                       for category in categories]
        plt.bar(tick_position, score_means, label=mask_name,
                width=.25, color=color)

        score_chance = [np.mean(mask_chance_scores[mask_name][category])
                        for category in categories]
        plt.bar(tick_position, score_chance,
                width=.25, edgecolor='k', facecolor='none')

        tick_position = tick_position + .2

    plt.ylabel('Classification accurancy (AUC score)')
    plt.xlabel('Visual stimuli category')
    plt.ylim(0.3, 1)
    plt.legend(loc='lower right')
    plt.title('Category-specific classification accuracy for different masks')
    plt.tight_layout()


    show()



.. image:: /auto_examples/02_decoding/images/sphx_glr_plot_haxby_full_analysis_001.png
    :alt: Category-specific classification accuracy for different masks
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 2 minutes  32.632 seconds)


.. _sphx_glr_download_auto_examples_02_decoding_plot_haxby_full_analysis.py:


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