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

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

.. _sphx_glr_auto_examples_plot_stability_scores.py:


===========================
Plotting stability scores
===========================

An example plot of the stability scores for each variable after fitting :class:`stability_selection.stability_selection.StabilitySelection`


.. image:: /auto_examples/images/sphx_glr_plot_stability_scores_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    Selected variables are:
    -----------------------
    Variable 1: [180], score 0.790
    Variable 2: [495], score 1.000
    Variable 3: [523], score 1.000
    Variable 4: [524], score 1.000
    Variable 5: [647], score 1.000




|


.. code-block:: default

    import numpy as np

    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from sklearn.utils import check_random_state
    from stability_selection import StabilitySelection, plot_stability_path


    def _generate_dummy_classification_data(p=1000, n=1000, k=5, random_state=123321):

        rng = check_random_state(random_state)

        X = rng.normal(loc=0.0, scale=1.0, size=(n, p))
        betas = np.zeros(p)
        important_betas = np.sort(rng.choice(a=np.arange(p), size=k))
        betas[important_betas] = rng.uniform(size=k)

        probs = 1 / (1 + np.exp(-1 * np.matmul(X, betas)))
        y = (probs > 0.5).astype(int)

        return X, y, important_betas


    if __name__ == '__main__':
        n, p, k = 500, 1000, 5

        X, y, important_betas = _generate_dummy_classification_data(n=n, k=k)

        base_estimator = Pipeline([
            ('scaler', StandardScaler()),
            ('model', LogisticRegression(penalty='l1'))
        ])
        selector = StabilitySelection(base_estimator=base_estimator, lambda_name='model__C',
                                      lambda_grid=np.logspace(-5, -1, 50))
        selector.fit(X, y)

        fig, ax = plot_stability_path(selector)
        fig.show()

        selected_variables = selector.get_support(indices=True)
        selected_scores = selector.stability_scores_.max(axis=1)

        print('Selected variables are:')
        print('-----------------------')

        for idx, (variable, score) in enumerate(zip(selected_variables, selected_scores[selected_variables])):
            print('Variable %d: [%d], score %.3f' % (idx + 1, variable, score))


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

   **Total running time of the script:** ( 1 minutes  24.598 seconds)


.. _sphx_glr_download_auto_examples_plot_stability_scores.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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