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

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

.. _sphx_glr_auto_examples_plot_FOCISelector_progression.py:


=============================
FOCISelector: T_n progression
=============================

This example fits the FOCISelector on synthetic data and plots the cumulative
T_n values along the selection path.

.. GENERATED FROM PYTHON SOURCE LINES 9-48



.. image-sg:: /auto_examples/images/sphx_glr_plot_FOCISelector_progression_001.png
   :alt: FOCISelector: cumulative T_n over selection steps
   :srcset: /auto_examples/images/sphx_glr_plot_FOCISelector_progression_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from pyFOCI import FOCISelector

    random_state = np.random.RandomState(0)
    n, p = 1000, 30
    X = random_state.normal(size=(n, p))

    # Additive signal across multiple features
    # -> incremental improvements with each addition
    y = (
        np.sin(2.0 * X[:, 0])
        + X[:, 1]
        + (X[:, 2] ** 2 - 1.0)
        + np.tanh(X[:, 3])
        + (X[:, 4] > 0).astype(float)
        + 0.1 * random_state.normal(size=n)  # small noise
    )

    selector = FOCISelector(max_features=6, random_state=0)
    selector.fit(X, y)

    feat_idx = list(selector.selected_indices_)
    Tn_vals = list(selector.Tn_path_)

    # Labels: directly prepend "x" to the indices
    labels = [f"step {i+1}: x{j}" for i, j in enumerate(feat_idx)]

    m = len(Tn_vals)
    plt.figure(figsize=(8, 4))
    plt.bar(range(m), Tn_vals, color="tab:orange", edgecolor="black", linewidth=0.5)
    plt.title("FOCISelector: cumulative T_n over selection steps")
    plt.ylabel("T_n(S_k)")
    plt.xlabel("Selection steps")
    plt.xticks(ticks=range(m), labels=labels, rotation=45, ha="right", fontsize=9)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.775 seconds)


.. _sphx_glr_download_auto_examples_plot_FOCISelector_progression.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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