.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_optics.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_cluster_plot_optics.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_cluster_plot_optics.py:


===================================
Demo of OPTICS clustering algorithm
===================================

.. currentmodule:: sklearn

Finds core samples of high density and expands clusters from them.
This example uses data that is generated so that the clusters have
different densities.

The :class:`~cluster.OPTICS` is first used with its Xi cluster detection
method, and then setting specific thresholds on the reachability, which
corresponds to :class:`~cluster.DBSCAN`. We can see that the different
clusters of OPTICS's Xi method can be recovered with different choices of
thresholds in DBSCAN.

.. GENERATED FROM PYTHON SOURCE LINES 19-109



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_optics_001.png
   :alt: Reachability Plot, Automatic Clustering OPTICS, Clustering at 0.5 epsilon cut DBSCAN, Clustering at 2.0 epsilon cut DBSCAN
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_optics_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.gridspec as gridspec
    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.cluster import OPTICS, cluster_optics_dbscan

    # Generate sample data

    np.random.seed(0)
    n_points_per_cluster = 250

    C1 = [-5, -2] + 0.8 * np.random.randn(n_points_per_cluster, 2)
    C2 = [4, -1] + 0.1 * np.random.randn(n_points_per_cluster, 2)
    C3 = [1, -2] + 0.2 * np.random.randn(n_points_per_cluster, 2)
    C4 = [-2, 3] + 0.3 * np.random.randn(n_points_per_cluster, 2)
    C5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)
    C6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)
    X = np.vstack((C1, C2, C3, C4, C5, C6))

    clust = OPTICS(min_samples=50, xi=0.05, min_cluster_size=0.05)

    # Run the fit
    clust.fit(X)

    labels_050 = cluster_optics_dbscan(
        reachability=clust.reachability_,
        core_distances=clust.core_distances_,
        ordering=clust.ordering_,
        eps=0.5,
    )
    labels_200 = cluster_optics_dbscan(
        reachability=clust.reachability_,
        core_distances=clust.core_distances_,
        ordering=clust.ordering_,
        eps=2,
    )

    space = np.arange(len(X))
    reachability = clust.reachability_[clust.ordering_]
    labels = clust.labels_[clust.ordering_]

    plt.figure(figsize=(10, 7))
    G = gridspec.GridSpec(2, 3)
    ax1 = plt.subplot(G[0, :])
    ax2 = plt.subplot(G[1, 0])
    ax3 = plt.subplot(G[1, 1])
    ax4 = plt.subplot(G[1, 2])

    # Reachability plot
    colors = ["g.", "r.", "b.", "y.", "c."]
    for klass, color in enumerate(colors):
        Xk = space[labels == klass]
        Rk = reachability[labels == klass]
        ax1.plot(Xk, Rk, color, alpha=0.3)
    ax1.plot(space[labels == -1], reachability[labels == -1], "k.", alpha=0.3)
    ax1.plot(space, np.full_like(space, 2.0, dtype=float), "k-", alpha=0.5)
    ax1.plot(space, np.full_like(space, 0.5, dtype=float), "k-.", alpha=0.5)
    ax1.set_ylabel("Reachability (epsilon distance)")
    ax1.set_title("Reachability Plot")

    # OPTICS
    colors = ["g.", "r.", "b.", "y.", "c."]
    for klass, color in enumerate(colors):
        Xk = X[clust.labels_ == klass]
        ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
    ax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], "k+", alpha=0.1)
    ax2.set_title("Automatic Clustering\nOPTICS")

    # DBSCAN at 0.5
    colors = ["g.", "r.", "b.", "c."]
    for klass, color in enumerate(colors):
        Xk = X[labels_050 == klass]
        ax3.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
    ax3.plot(X[labels_050 == -1, 0], X[labels_050 == -1, 1], "k+", alpha=0.1)
    ax3.set_title("Clustering at 0.5 epsilon cut\nDBSCAN")

    # DBSCAN at 2.
    colors = ["g.", "m.", "y.", "c."]
    for klass, color in enumerate(colors):
        Xk = X[labels_200 == klass]
        ax4.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
    ax4.plot(X[labels_200 == -1, 0], X[labels_200 == -1, 1], "k+", alpha=0.1)
    ax4.set_title("Clustering at 2.0 epsilon cut\nDBSCAN")

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.340 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_optics.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_optics.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_optics.recommendations


.. only:: html

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

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