.. only:: html

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

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

    .. _sphx_glr_auto_examples_example_logistic_group_lasso.py:


GroupLasso for logistic regression
==================================

A sample script for group lasso regression

Setup
-----


.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np

    from group_lasso import LogisticGroupLasso

    np.random.seed(0)
    LogisticGroupLasso.LOG_LOSSES = True



Set dataset parameters
----------------------


.. code-block:: default

    group_sizes = [np.random.randint(10, 20) for i in range(50)]
    active_groups = [np.random.randint(2) for _ in group_sizes]
    groups = np.concatenate([size * [i] for i, size in enumerate(group_sizes)])
    num_coeffs = sum(group_sizes)
    num_datapoints = 10000
    noise_std = 1



Generate data matrix
--------------------


.. code-block:: default

    X = np.random.standard_normal((num_datapoints, num_coeffs))



Generate coefficients
---------------------


.. code-block:: default

    w = np.concatenate(
        [
            np.random.standard_normal(group_size) * is_active
            for group_size, is_active in zip(group_sizes, active_groups)
        ]
    )
    w = w.reshape(-1, 1)
    true_coefficient_mask = w != 0
    intercept = 2



Generate regression targets
---------------------------


.. code-block:: default

    y_true = X @ w + intercept
    y = y_true + np.random.randn(*y_true.shape) * noise_std
    p = 1 / (1 + np.exp(-y))
    p_true = 1 / (1 + np.exp(-y_true))
    c = np.random.binomial(1, p_true)



View noisy data and compute maximum accuracy
--------------------------------------------


.. code-block:: default

    plt.figure()
    plt.plot(p, p_true, ".")
    plt.xlabel("Noisy probabilities")
    plt.ylabel("Noise-free probabilities")
    # Use noisy y as true because that is what we would have access
    # to in a real-life setting.
    best_accuracy = ((p_true > 0.5) == c).mean()



Generate estimator and train it
-------------------------------


.. code-block:: default

    gl = LogisticGroupLasso(
        groups=groups,
        group_reg=0.05,
        l1_reg=0,
        scale_reg="inverse_group_size",
        subsampling_scheme=1,
        supress_warning=True,
    )

    gl.fit(X, c)



Extract results and compute performance metrics
-----------------------------------------------


.. code-block:: default


    # Extract info from estimator
    pred_c = gl.predict(X)
    sparsity_mask = gl.sparsity_mask_
    w_hat = gl.coef_

    # Compute performance metrics
    accuracy = (pred_c == c).mean()

    # Print results
    print(f"Number variables: {len(sparsity_mask)}")
    print(f"Number of chosen variables: {sparsity_mask.sum()}")
    print(f"Accuracy: {accuracy}, best possible accuracy = {best_accuracy}")



Visualise regression coefficients
---------------------------------


.. code-block:: default

    coef = gl.coef_[:, 1] - gl.coef_[:, 0]
    plt.figure()
    plt.plot(w / np.linalg.norm(w), ".", label="True weights")
    plt.plot(
        coef / np.linalg.norm(coef), ".", label="Estimated weights",
    )

    plt.figure()
    plt.plot([w.min(), w.max()], [coef.min(), coef.max()], "gray")
    plt.scatter(w, coef, s=10)
    plt.ylabel("Learned coefficients")
    plt.xlabel("True coefficients")

    plt.figure()
    plt.plot(gl.losses_)

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_example_logistic_group_lasso.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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