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

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

.. _sphx_glr_auto_examples_plot_feature_selection_breast_cancer.py:


=======================================================
Exact L0 Sparsity Budgeting on Breast Cancer Dataset
=======================================================

Demonstrates exact cardinality constraints via ``max_features`` against
an L1 Logistic Regression baseline on the Breast Cancer dataset.

.. GENERATED FROM PYTHON SOURCE LINES 9-64



.. image-sg:: /auto_examples/images/sphx_glr_plot_feature_selection_breast_cancer_001.png
   :alt: Exact L0 Budget vs. L1 Penalty
   :srcset: /auto_examples/images/sphx_glr_plot_feature_selection_breast_cancer_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    from sklearn.datasets import load_breast_cancer
    from sklearn.feature_selection import SelectKBest, f_classif
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import StandardScaler
    from tlmnet import TlmMilpClassifier

    data = load_breast_cancer()
    X, y = data.data, data.target
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=42, stratify=y
    )

    k_budgets = range(1, 10)
    tlm_scores, lr_scores = [], []

    for k in k_budgets:
        # TLM Exact L0
        pipe_tlm = Pipeline(
            [
                ("select", SelectKBest(f_classif, k=15)),
                ("scaler", StandardScaler()),
                ("tlm", TlmMilpClassifier(max_features=k, C=1.0)),
            ]
        )
        pipe_tlm.fit(X_train, y_train)
        tlm_scores.append(pipe_tlm.score(X_test, y_test))

        # L1 Logistic Regression Baseline (Updated for Scikit-Learn 1.8+ API)
        pipe_lr = Pipeline(
            [
                ("select", SelectKBest(f_classif, k=k)),
                ("scaler", StandardScaler()),
                (
                    "lr",
                    LogisticRegression(solver="saga", l1_ratio=1.0, C=1.0, max_iter=5000),
                ),
            ]
        )
        pipe_lr.fit(X_train, y_train)
        lr_scores.append(pipe_lr.score(X_test, y_test))

    plt.figure(figsize=(8, 5))
    plt.plot(k_budgets, tlm_scores, "s-", label="Exact MILP (Test)")
    plt.plot(k_budgets, lr_scores, "o--", label="L1 Logistic Regression (Test)")
    plt.xlabel("Feature Budget")
    plt.ylabel("Test Accuracy")
    plt.title("Exact L0 Budget vs. L1 Penalty")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 4.798 seconds)


.. _sphx_glr_download_auto_examples_plot_feature_selection_breast_cancer.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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