.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\feature_selection\SI-SeqFS-DA.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_feature_selection_SI-SeqFS-DA.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_feature_selection_SI-SeqFS-DA.py:


SI for Sequential Feature Selection after Optimal Transport-based Domain Adaptation
===================================================================================
This example demonstrates how to perform statistical inference for sequential feature selection (SeqFS) after applying Optimal Transport-based Domain Adaptation. The implementation follows the methodology proposed by Loc et al. (2025) [4].
[4] Loc, D. T., Loi, N. T., & Duy, V. N. L. (2025). Statistical Inference for Sequential Feature Selection after Domain Adaptation. arXiv preprint arXiv:2501.09933.

.. GENERATED FROM PYTHON SOURCE LINES 7-18

.. code-block:: Python


    # Author: Duong Tan Loc

    from pythonsi import Pipeline
    from pythonsi.feature_selection import SequentialFeatureSelection
    from pythonsi import Data
    from pythonsi.test_statistics import SFS_DATestStatistic
    from pythonsi.domain_adaptation import OptimalTransportDA
    import numpy as np
    import matplotlib.pyplot as plt








.. GENERATED FROM PYTHON SOURCE LINES 19-21

Define the pipeline
-------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-44

.. code-block:: Python



    def SI_SeqFS_DA(k) -> Pipeline:
        xs = Data()
        ys = Data()

        xt = Data()
        yt = Data()

        OT = OptimalTransportDA()
        x_tilde, y_tilde = OT.run(xs=xs, ys=ys, xt=xt, yt=yt)

        seqfs = SequentialFeatureSelection(k, direction="forward")
        active_set = seqfs.run(x_tilde, y_tilde)
        return Pipeline(
            inputs=(xs, ys, xt, yt),
            output=active_set,
            test_statistic=SFS_DATestStatistic(xs=xs, ys=ys, xt=xt, yt=yt),
        )


    my_pipeline = SI_SeqFS_DA(3)








.. GENERATED FROM PYTHON SOURCE LINES 45-47

Generate data
--------------

.. GENERATED FROM PYTHON SOURCE LINES 47-62

.. code-block:: Python



    def gen_data(n, p, true_beta):
        x = np.random.normal(loc=0, scale=1, size=(n, p))
        true_beta = true_beta.reshape(-1, 1)

        mu = x.dot(true_beta)
        Sigma = np.identity(n)
        Y = mu + np.random.normal(loc=0, scale=1, size=(n, 1))
        return x, Y, Sigma


    xs, ys, sigma_s = gen_data(150, 5, np.asarray([0, 0, 0, 0, 0]))
    xt, yt, sigma_t = gen_data(25, 5, np.asarray([0, 0, 0, 0, 0]))








.. GENERATED FROM PYTHON SOURCE LINES 63-65

Run the pipeline
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 65-73

.. code-block:: Python


    selected_features, p_values = my_pipeline(
        inputs=[xs, ys, xt, yt], covariances=[sigma_s, sigma_t]
    )

    print("Selected features: ", selected_features)
    print("P-values: ", p_values)





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

 .. code-block:: none

    Selected features:  [3, 4, 1]
    P-values:  [0.38409760959246575, 0.5261348213591066, 0.8038003268473604]




.. GENERATED FROM PYTHON SOURCE LINES 74-75

Plot the p-values

.. GENERATED FROM PYTHON SOURCE LINES 75-80

.. code-block:: Python

    plt.figure()
    plt.bar(range(len(p_values)), p_values)
    plt.xlabel("Feature index")
    plt.ylabel("P-value")
    plt.show()



.. image-sg:: /auto_examples/feature_selection/images/sphx_glr_SI-SeqFS-DA_001.png
   :alt: SI SeqFS DA
   :srcset: /auto_examples/feature_selection/images/sphx_glr_SI-SeqFS-DA_001.png
   :class: sphx-glr-single-img






.. _sphx_glr_download_auto_examples_feature_selection_SI-SeqFS-DA.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: SI-SeqFS-DA.ipynb <SI-SeqFS-DA.ipynb>`

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

      :download:`Download Python source code: SI-SeqFS-DA.py <SI-SeqFS-DA.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: SI-SeqFS-DA.zip <SI-SeqFS-DA.zip>`


.. only:: html

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

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