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

    Click :ref:`here <sphx_glr_download_auto_examples_plot_coroICAexample.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_coroICAexample.py:


====================
Minimalistic example
====================

Demonstrate basic use of :class:`coroica.CoroICA`



.. image:: /auto_examples/images/sphx_glr_plot_coroICAexample_001.png
    :class: sphx-glr-single-img





.. code-block:: default

    import numpy as np
    from coroica import CoroICA, UwedgeICA
    from matplotlib import pyplot as plt
    from sklearn.decomposition import FastICA
    from sklearn.linear_model import LinearRegression
    from sklearn.model_selection import cross_val_predict
    from sklearn.pipeline import Pipeline

    # generate data
    X = np.random.randn(500, 25)
    y = np.random.randn(500,)

    group_index = np.zeros(500,)
    group_index[150:] = 1

    X[:, :10] = X[:, :10] + 2 * y.reshape(-1, 1)

    X[:150, 5:20] += 3 * np.random.randn(150, 15).dot(np.random.randn(15, 15))
    X[150:, 5:20] += 5 * np.random.randn(350, 15).dot(np.random.randn(15, 15))

    # define coroICA-based pipeline
    model_coroICA = Pipeline(steps=[
        ('coroICA', CoroICA(n_components=10,
                            timelags=[5, 10],
                            max_matrices='no_partitions',
                            pairing='allpairs')),
        ('regression', LinearRegression())])

    # get cross-validated predictions with coroICA-based pipeline
    y_hat_coroICA = cross_val_predict(
        model_coroICA,
        X,
        y,
        fit_params={'coroICA__group_index': group_index})

    # define uwedgeICA-based pipeline (second-order-based, ignores groupstructure)
    model_uwedgeICA = Pipeline(steps=[
        ('uwedgeICA', UwedgeICA(n_components=10)),
        ('regression', LinearRegression())])

    # get cross-validated predictions with uwedgeICA-based pipeline
    y_hat_uwedgeICA = cross_val_predict(
        model_uwedgeICA,
        X,
        y)

    # define pooled fastica-based pipeline (ignores groupstructure)
    model_fastica = Pipeline(steps=[
        ('fastica', FastICA(n_components=10)),
        ('regression', LinearRegression())])

    # get cross-validated predictions with pooled fastica-based pipeline
    y_hat_fastica = cross_val_predict(
        model_fastica,
        X,
        y)

    # for comparison plot scatter of predictions against the true y
    plt.plot(y, y_hat_coroICA,
             '.',
             label='coroICA (correlation with true y {:.2f})'.format(
                 np.corrcoef(y, y_hat_coroICA)[0, 1]))

    plt.plot(y, y_hat_uwedgeICA,
             '.',
             label='uwedgeICA (correlation with true y {:.2f})'.format(
                 np.corrcoef(y, y_hat_uwedgeICA)[0, 1]))

    plt.plot(y, y_hat_fastica,
             '.',
             label='pooled fastica (correlation with true y {:.2f})'.format(
                 np.corrcoef(y, y_hat_fastica)[0, 1]))

    plt.title('y vs y_hat')
    plt.legend(loc='best')
    plt.grid(True)
    plt.xlabel('true y')
    plt.ylabel('y_hat')

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  9.423 seconds)


.. _sphx_glr_download_auto_examples_plot_coroICAexample.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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