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

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

.. _sphx_glr_auto_examples_custom_reducer.py:


===================================================================
DenMune with a Custom Dimensionality Reducer (UMAP)
===================================================================

DenMune's `dim_reducer` parameter is flexible, allowing you to pass not just
pre-defined strings ('tsne', 'pca') but also any scikit-learn compatible
estimator instance.

This example shows how to use UMAP (`umap-learn`) as the dimensionality
reducer. This is particularly useful as UMAP is often faster than t-SNE and can
be better at preserving global data structure.

Note: To run this example, you must have the `umap-learn` library installed.
`pip install umap-learn`

.. GENERATED FROM PYTHON SOURCE LINES 17-33

.. code-block:: Python


    import matplotlib.pyplot as plt
    from sklearn.datasets import make_blobs

    # Try to import UMAP. If it fails, skip the example.
    try:
        from umap import UMAP
    except ImportError:
        print("UMAP not found. Skipping this example.")
        # sphinx-gallery will not run the rest of the script if it exits with code 0
        import sys

        sys.exit(0)

    from denmune_skl import DenMune


.. GENERATED FROM PYTHON SOURCE LINES 34-38

Generate high-dimensional data
------------------------------
We create a high-dimensional dataset that requires reduction to be clustered
effectively by DenMune's 2D-focused approach.

.. GENERATED FROM PYTHON SOURCE LINES 38-42

.. code-block:: Python

    X_high_dim, y = make_blobs(
        n_samples=500, n_features=50, centers=5, cluster_std=2.5, random_state=42
    )


.. GENERATED FROM PYTHON SOURCE LINES 43-48

Initialize DenMune with a UMAP instance
-----------------------------------------------
We create an instance of `UMAP` and pass it directly to the `dim_reducer`
parameter of DenMune. DenMune will then use this instance for its
dimensionality reduction step.

.. GENERATED FROM PYTHON SOURCE LINES 48-61

.. code-block:: Python


    umap_reducer = UMAP(n_components=2, n_neighbors=15, min_dist=0.1, random_state=42)

    model = DenMune(
        k_nearest=30,
        reduce_dims=True,
        target_dims=2,  # This will be ignored, but is a required parameter
        dim_reducer=umap_reducer,
        random_state=42,
    )

    labels = model.fit_predict(X_high_dim)


.. GENERATED FROM PYTHON SOURCE LINES 62-66

Visualize the results on the projected data
-------------------------------------------
The clustering is performed on the 2D data projected by UMAP. We can access
this projected data via the `projected_X_` attribute.

.. GENERATED FROM PYTHON SOURCE LINES 66-87

.. code-block:: Python


    X_projected = model.projected_X_

    plt.figure(figsize=(10, 8))

    # Plot projected data colored by DenMune labels
    plt.subplot(1, 2, 1)
    plt.scatter(X_projected[:, 0], X_projected[:, 1], c=labels, s=20, cmap="viridis")
    plt.title(f"DenMune Labels (k={model.k_nearest})\n(Projected by UMAP)")
    plt.xlabel("UMAP Component 1")
    plt.ylabel("UMAP Component 2")

    # Plot projected data colored by true labels for comparison
    plt.subplot(1, 2, 2)
    plt.scatter(X_projected[:, 0], X_projected[:, 1], c=y, s=20, cmap="viridis")
    plt.title("True Labels\n(Projected by UMAP)")
    plt.xlabel("UMAP Component 1")

    plt.suptitle("DenMune with Custom UMAP Reducer")
    plt.tight_layout(rect=[0, 0.03, 1, 0.95])
    plt.show()


.. _sphx_glr_download_auto_examples_custom_reducer.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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