.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/01_plot_gaussiannb_equivalence.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_01_plot_gaussiannb_equivalence.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_01_plot_gaussiannb_equivalence.py:


=====================================================
MixedNB Equivalence with GaussianNB
=====================================================

This example demonstrates that :class:`skbn.mixed_nb.MixedNB`
produces identical results to :class:`sklearn.naive_bayes.GaussianNB`
when all features are continuous (Gaussian).

The plot shows the decision boundaries for both classifiers. As expected,
the boundaries are identical, and the predicted probabilities for the
dataset are all-close.

.. GENERATED FROM PYTHON SOURCE LINES 14-110



.. image-sg:: /auto_examples/images/sphx_glr_01_plot_gaussiannb_equivalence_001.png
   :alt: Equivalence of MixedNB and GaussianNB on Continuous Data, 1. scikit-learn GaussianNB, 2. skbn MixedNB (auto-detected)
   :srcset: /auto_examples/images/sphx_glr_01_plot_gaussiannb_equivalence_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    GaussianNB vs MixedNB Equivalence Check: Probabilities are identical.






|

.. code-block:: Python


    # Author: The scikit-bayes Developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    from numpy.testing import assert_allclose
    from sklearn.datasets import make_classification
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.naive_bayes import GaussianNB

    from skbn.mixed_nb import MixedNB

    # 1. Generate a 2D Gaussian dataset
    X, y = make_classification(
        n_samples=100,
        n_features=2,
        n_informative=2,
        n_redundant=0,
        n_clusters_per_class=1,
        random_state=42,
    )

    # 2. Fit both classifiers
    gnb = GaussianNB()
    gnb.fit(X, y)
    probs_gnb = gnb.predict_proba(X)

    # MixedNB will auto-detect both features as 'gaussian'
    mnb = MixedNB()
    mnb.fit(X, y)
    probs_mnb = mnb.predict_proba(X)

    # 3. Assert equivalence
    try:
        assert_allclose(probs_gnb, probs_mnb, rtol=1e-7, atol=1e-7)
        equivalence_message = "Probabilities are identical."
    except AssertionError as e:
        equivalence_message = f"Probabilities are NOT identical:\n{e}"

    print(f"GaussianNB vs MixedNB Equivalence Check: {equivalence_message}")

    # 4. Plot decision boundaries
    fig, axes = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

    models = [gnb, mnb]
    titles = ["1. scikit-learn GaussianNB", "2. skbn MixedNB (auto-detected)"]

    for ax, model, title in zip(axes, models, titles):
        # Plot Decision Boundary - VIRIDIS
        DecisionBoundaryDisplay.from_estimator(
            model,
            X,
            ax=ax,
            response_method="predict_proba",
            plot_method="pcolormesh",
            shading="auto",
            alpha=0.8,
            cmap="viridis",
        )

        # Overlay real data points with consistent style
        # Class 0 -> Indigo
        ax.scatter(
            X[y == 0, 0],
            X[y == 0, 1],
            c="indigo",
            marker="o",
            s=40,
            alpha=0.8,
            edgecolors="w",
            linewidth=0.8,
            label="Class 0",
        )

        # Class 1 -> Gold
        ax.scatter(
            X[y == 1, 0],
            X[y == 1, 1],
            c="gold",
            marker="^",
            s=40,
            alpha=0.9,
            edgecolors="k",
            linewidth=0.5,
            label="Class 1",
        )

        ax.set_title(title, fontsize=12)

    # Add Legend to the first plot
    axes[0].legend(loc="lower right")

    fig.suptitle("Equivalence of MixedNB and GaussianNB on Continuous Data", fontsize=16)
    plt.tight_layout()
    plt.subplots_adjust(top=0.85)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.375 seconds)


.. _sphx_glr_download_auto_examples_01_plot_gaussiannb_equivalence.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 01_plot_gaussiannb_equivalence.ipynb <01_plot_gaussiannb_equivalence.ipynb>`

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

      :download:`Download Python source code: 01_plot_gaussiannb_equivalence.py <01_plot_gaussiannb_equivalence.py>`

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

      :download:`Download zipped: 01_plot_gaussiannb_equivalence.zip <01_plot_gaussiannb_equivalence.zip>`


.. only:: html

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

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