.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/manifold/plot_compare_methods.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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_manifold_plot_compare_methods.py: ========================================= Comparison of Manifold Learning methods ========================================= An illustration of dimensionality reduction on the S-curve dataset with various manifold learning methods. For a discussion and comparison of these algorithms, see the :ref:`manifold module page ` For a similar example, where the methods are applied to a sphere dataset, see :ref:`sphx_glr_auto_examples_manifold_plot_manifold_sphere.py` Note that the purpose of the MDS is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-dimensional space, unlike other manifold-learning algorithms, it does not seeks an isotropic representation of the data in the low-dimensional space. .. GENERATED FROM PYTHON SOURCE LINES 21-84 .. image:: /auto_examples/manifold/images/sphx_glr_plot_compare_methods_001.png :alt: Manifold Learning with 1000 points, 10 neighbors, LLE (0.13 sec), LTSA (0.25 sec), Hessian LLE (0.38 sec), Modified LLE (0.26 sec), Isomap (0.6 sec), MDS (1.8 sec), SE (0.089 sec), t-SNE (10 sec) :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/circleci/project/sklearn/utils/validation.py:70: FutureWarning: Pass n_neighbors=10, n_components=2 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error warnings.warn(f"Pass {args_msg} as keyword args. From version " LLE: 0.13 sec LTSA: 0.25 sec Hessian LLE: 0.38 sec Modified LLE: 0.26 sec Isomap: 0.6 sec MDS: 1.8 sec SE: 0.089 sec t-SNE: 10 sec | .. code-block:: default # Author: Jake Vanderplas -- print(__doc__) from collections import OrderedDict from functools import partial from time import time import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib.ticker import NullFormatter from sklearn import manifold, datasets # Next line to silence pyflakes. This import is needed. Axes3D n_points = 1000 X, color = datasets.make_s_curve(n_points, random_state=0) n_neighbors = 10 n_components = 2 # Create figure fig = plt.figure(figsize=(15, 8)) fig.suptitle("Manifold Learning with %i points, %i neighbors" % (1000, n_neighbors), fontsize=14) # Add 3d scatter plot ax = fig.add_subplot(251, projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral) ax.view_init(4, -72) # Set-up manifold methods LLE = partial(manifold.LocallyLinearEmbedding, n_neighbors, n_components, eigen_solver='auto') methods = OrderedDict() methods['LLE'] = LLE(method='standard') methods['LTSA'] = LLE(method='ltsa') methods['Hessian LLE'] = LLE(method='hessian') methods['Modified LLE'] = LLE(method='modified') methods['Isomap'] = manifold.Isomap(n_neighbors, n_components) methods['MDS'] = manifold.MDS(n_components, max_iter=100, n_init=1) methods['SE'] = manifold.SpectralEmbedding(n_components=n_components, n_neighbors=n_neighbors) methods['t-SNE'] = manifold.TSNE(n_components=n_components, init='pca', random_state=0) # Plot results for i, (label, method) in enumerate(methods.items()): t0 = time() Y = method.fit_transform(X) t1 = time() print("%s: %.2g sec" % (label, t1 - t0)) ax = fig.add_subplot(2, 5, 2 + i + (i > 3)) ax.scatter(Y[:, 0], Y[:, 1], c=color, cmap=plt.cm.Spectral) ax.set_title("%s (%.2g sec)" % (label, t1 - t0)) ax.xaxis.set_major_formatter(NullFormatter()) ax.yaxis.set_major_formatter(NullFormatter()) ax.axis('tight') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 14.554 seconds) .. _sphx_glr_download_auto_examples_manifold_plot_compare_methods.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_compare_methods.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_compare_methods.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_compare_methods.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_