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

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

.. _sphx_glr_auto_examples_compare_group_lasso_performance.py:


=============================================================
Rudimentary performance comparison of groupyr and group-lasso
=============================================================

This example performs a rudimentary performance comparison of the groupyr and
group-lasso packages for various problem sizes. Speedup is defined as the
ratio of time to fit a `group-lasso.GroupLasso` model to the time to fit a
`groupyr.SGL` model.

In addition to `groupyr`, you will need to install the `group-lasso` package
using
```
pip install group-lasso
```

.. GENERATED FROM PYTHON SOURCE LINES 17-79

.. code-block:: default

    import groupyr as gpr
    import matplotlib.pyplot as plt
    import numpy as np

    from group_lasso import GroupLasso
    from sklearn.model_selection import train_test_split
    from timeit import timeit
    from tqdm.auto import tqdm

    n_features = [5, 10, 25, 50, 75, 100, 150, 200, 250]
    speedup = []

    for n_features_per_group in tqdm(n_features):
        X, y, groups, coef = gpr.datasets.make_group_regression(
            n_samples=400,
            n_groups=50,
            n_informative_groups=5,
            n_features_per_group=n_features_per_group,
            n_informative_per_group=int(0.8 * n_features_per_group),
            noise=200,
            coef=True,
            random_state=10,
            shuffle=True,
        )

        gl_groups = np.concatenate(
            [[idx] * len(grp) for idx, grp in enumerate(groups)]
        ).reshape([-1, 1])

        X_train, X_test, y_train, y_test = train_test_split(
            X, y, test_size=0.25, random_state=10
        )

        sgl = gpr.SGL(groups=groups, l1_ratio=0.5, alpha=1.0, max_iter=1000, tol=1e-3)

        gl = GroupLasso(
            groups=gl_groups,
            group_reg=0.5,
            l1_reg=0.5,
            frobenius_lipschitz=True,
            scale_reg="group_size",
            n_iter=1000,
            tol=1e-3,
            supress_warning=True,
        )

        gpr_time = timeit("sgl.fit(X_train, y_train)", globals=globals(), number=3)
        gl_time = timeit("gl.fit(X_train, y_train)", globals=globals(), number=3)

        speedup.append(gpr_time / gl_time)

    fig, ax = plt.subplots(1, 1, figsize=(8, 5))

    _ = ax.plot(np.array(n_features) * 50, 1 / np.array(speedup))
    _ = ax.set_xlabel(r"Number of features", fontsize=16)
    _ = ax.set_ylabel(r"$t$(group-lasso) / $t$(groupyr)", fontsize=16)
    _ = ax.set_title(
        "\n".join([r"Rudimentary performance comparison", r"of groupyr and group-lasso"]),
        fontsize=16,
    )

    fig.savefig("./groupyr_speedup.pdf", bbox_inches="tight")


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

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


.. _sphx_glr_download_auto_examples_compare_group_lasso_performance.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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