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

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

.. _sphx_glr_auto_examples_plot_runtime_vs_performance.py:


==================================================
MILP Solve Time vs. Feature & Sample Dimensions
==================================================

This example evaluates how execution time scales with feature count :math:`p`
and sample size :math:`n` when optimizing exact ternary weights via MILP.

.. GENERATED FROM PYTHON SOURCE LINES 9-43



.. image-sg:: /auto_examples/images/sphx_glr_plot_runtime_vs_performance_001.png
   :alt: TlmMilpClassifier Execution Time Scaling
   :srcset: /auto_examples/images/sphx_glr_plot_runtime_vs_performance_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import time
    import matplotlib.pyplot as plt
    from sklearn.datasets import make_classification
    from tlmnet import TlmMilpClassifier

    feature_counts = [5, 10, 15, 20, 25]
    sample_sizes = [50, 100, 200]
    results: dict[int, list[float]] = {n: [] for n in sample_sizes}

    for n in sample_sizes:
        for p in feature_counts:
            X, y = make_classification(
                n_samples=n, n_features=p, n_informative=min(p, 3), random_state=42
            )
            clf = TlmMilpClassifier(max_features=5, C=1.0)

            start = time.time()
            clf.fit(X, y)
            elapsed = time.time() - start

            results[n].append(elapsed)

    plt.figure(figsize=(8, 5))
    for n in sample_sizes:
        plt.plot(feature_counts, results[n], marker="o", label=f"n = {n} samples")

    plt.xlabel("Number of Features (p)")
    plt.ylabel("MILP Solve Time (seconds)")
    plt.title("TlmMilpClassifier Execution Time Scaling")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 10.278 seconds)


.. _sphx_glr_download_auto_examples_plot_runtime_vs_performance.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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