.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\feature_selection\SFS_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_SFS_DA.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_feature_selection_SFS_DA.py:


SI for Feature Selection after Optimal Transport-based Domain Adaptation
========================================================================

This example demonstrates how to perform statistical inference for feature selection after applying Optimal Transport-based Domain Adaptation. The implementation is based on the paper by Loi et al. (2025) [3].
[3] Loi, N. T., Loc, D. T., & Duy, V. N. L. (2025). "Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation." In International Conference on Artificial Intelligence and Statistics, pp. 1747-1755. PMLR, 2025.

.. GENERATED FROM PYTHON SOURCE LINES 8-19

.. code-block:: Python


    # Author: Tran Tuan Kiet

    from pythonsi import Pipeline
    from pythonsi.feature_selection import LassoFeatureSelection
    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 20-22

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

.. GENERATED FROM PYTHON SOURCE LINES 22-45

.. code-block:: Python



    def SFS_DA() -> 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)

        lasso = LassoFeatureSelection(lambda_=10)
        active_set = lasso.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 = SFS_DA()








.. GENERATED FROM PYTHON SOURCE LINES 46-48

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

.. GENERATED FROM PYTHON SOURCE LINES 48-63

.. 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 64-66

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

.. GENERATED FROM PYTHON SOURCE LINES 66-74

.. 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:  [0 1 2 3]
    P-values:  [0.5424014971318711, 0.21260386674254184, 0.15564751961626722, 0.41546429041302224]




.. GENERATED FROM PYTHON SOURCE LINES 75-76

Plot the p-values

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.. 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_SFS_DA_001.png
   :alt: SFS DA
   :srcset: /auto_examples/feature_selection/images/sphx_glr_SFS_DA_001.png
   :class: sphx-glr-single-img






.. _sphx_glr_download_auto_examples_feature_selection_SFS_DA.py:

.. only:: html

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

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

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

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

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

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

      :download:`Download zipped: SFS_DA.zip <SFS_DA.zip>`


.. only:: html

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

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