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

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

.. _sphx_glr_auto_examples_plot_k_sensitivity.py:


==============================================================
Analyzing DenMune's Sensitivity to the `k_nearest` Parameter
==============================================================

The `k_nearest` parameter is the most critical hyperparameter in the DenMune
algorithm. The original paper claims that the algorithm is stable over a wide
range of `k`.

This example investigates this claim by running DenMune on a dataset with
varying values of `k_nearest` and plotting the resulting clustering quality, as
measured by the Adjusted Rand Index (ARI).

A stable algorithm should exhibit a plateau of high ARI scores across a range
of `k` values, rather than a single sharp peak.

.. GENERATED FROM PYTHON SOURCE LINES 17-26

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import make_blobs
    from sklearn.metrics import adjusted_rand_score
    from sklearn.preprocessing import StandardScaler

    from denmune_skl import DenMune








.. GENERATED FROM PYTHON SOURCE LINES 27-30

Generate a dataset
------------------
We create a dataset with clear, but slightly overlapping, clusters.

.. GENERATED FROM PYTHON SOURCE LINES 30-33

.. code-block:: Python

    X, y = make_blobs(n_samples=400, centers=4, cluster_std=1.2, random_state=42)
    X = StandardScaler().fit_transform(X)








.. GENERATED FROM PYTHON SOURCE LINES 34-38

Test a range of `k_nearest` values
-----------------------------------
We iterate through a list of `k` values, fit a DenMune model for each,
and store the number of found clusters and the ARI score.

.. GENERATED FROM PYTHON SOURCE LINES 38-56

.. code-block:: Python


    k_values = range(5, 51, 2)
    ari_scores = []
    n_clusters_found = []

    for k in k_values:
        model = DenMune(k_nearest=k, reduce_dims=False, random_state=42)
        labels = model.fit_predict(X)

        # We only score if more than one cluster is found (and not just noise).
        if model.n_clusters_ > 1:
            score = adjusted_rand_score(y, labels)
        else:
            score = 0.0  # Assign a score of 0 if only one cluster or all noise is found.

        ari_scores.append(score)
        n_clusters_found.append(model.n_clusters_)








.. GENERATED FROM PYTHON SOURCE LINES 57-59

Plot the sensitivity analysis results
-------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 59-92

.. code-block:: Python

    fig, ax1 = plt.subplots(figsize=(10, 6))

    # Plot ARI scores
    color = "tab:blue"
    ax1.set_xlabel("k_nearest")
    ax1.set_ylabel("Adjusted Rand Index (ARI)", color=color)
    ax1.plot(k_values, ari_scores, color=color, marker="o", label="ARI Score")
    ax1.tick_params(axis="y", labelcolor=color)
    ax1.set_ylim(0, 1.05)
    ax1.grid(True, linestyle="--", alpha=0.6)

    # Create a second y-axis for the number of clusters
    ax2 = ax1.twinx()
    color = "tab:red"
    ax2.set_ylabel("Number of Clusters Found", color=color)
    ax2.plot(
        k_values,
        n_clusters_found,
        color=color,
        linestyle="--",
        marker="x",
        label="Num Clusters",
    )
    ax2.tick_params(axis="y", labelcolor=color)
    # Set y-axis ticks to be integers
    ax2.set_yticks(np.arange(0, max(n_clusters_found) + 2))

    fig.tight_layout()
    plt.title("DenMune Sensitivity to k_nearest")
    plt.show()

    # An ideal result shows a wide plateau where the ARI is high and stable, and the
    # number of clusters found is correct (4 in this case).



.. image-sg:: /auto_examples/images/sphx_glr_plot_k_sensitivity_001.png
   :alt: DenMune Sensitivity to k_nearest
   :srcset: /auto_examples/images/sphx_glr_plot_k_sensitivity_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.475 seconds)


.. _sphx_glr_download_auto_examples_plot_k_sensitivity.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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