.. 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 ` to download the full example code. or to run this example in your browser via JupyterLite or 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 1.560 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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_optics.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/cluster/plot_optics.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_optics.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_optics.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_optics.zip ` .. include:: plot_optics.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_