.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.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_semi_supervised_plot_label_propagation_structure.py:


==============================================
Label Propagation learning a complex structure
==============================================

Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Because both label groups
lie inside their own distinct shape, we can see that the labels
propagate correctly around the circle.



.. image:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_001.png
    :class: sphx-glr-single-img






.. code-block:: default

    print(__doc__)

    # Authors: Clay Woolam <clay@woolam.org>
    #          Andreas Mueller <amueller@ais.uni-bonn.de>
    # License: BSD

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.semi_supervised import LabelSpreading
    from sklearn.datasets import make_circles

    # generate ring with inner box
    n_samples = 200
    X, y = make_circles(n_samples=n_samples, shuffle=False)
    outer, inner = 0, 1
    labels = np.full(n_samples, -1.)
    labels[0] = outer
    labels[-1] = inner

    # #############################################################################
    # Learn with LabelSpreading
    label_spread = LabelSpreading(kernel='knn', alpha=0.8)
    label_spread.fit(X, labels)

    # #############################################################################
    # Plot output labels
    output_labels = label_spread.transduction_
    plt.figure(figsize=(8.5, 4))
    plt.subplot(1, 2, 1)
    plt.scatter(X[labels == outer, 0], X[labels == outer, 1], color='navy',
                marker='s', lw=0, label="outer labeled", s=10)
    plt.scatter(X[labels == inner, 0], X[labels == inner, 1], color='c',
                marker='s', lw=0, label='inner labeled', s=10)
    plt.scatter(X[labels == -1, 0], X[labels == -1, 1], color='darkorange',
                marker='.', label='unlabeled')
    plt.legend(scatterpoints=1, shadow=False, loc='upper right')
    plt.title("Raw data (2 classes=outer and inner)")

    plt.subplot(1, 2, 2)
    output_label_array = np.asarray(output_labels)
    outer_numbers = np.where(output_label_array == outer)[0]
    inner_numbers = np.where(output_label_array == inner)[0]
    plt.scatter(X[outer_numbers, 0], X[outer_numbers, 1], color='navy',
                marker='s', lw=0, s=10, label="outer learned")
    plt.scatter(X[inner_numbers, 0], X[inner_numbers, 1], color='c',
                marker='s', lw=0, s=10, label="inner learned")
    plt.legend(scatterpoints=1, shadow=False, loc='upper right')
    plt.title("Labels learned with Label Spreading (KNN)")

    plt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.621 seconds)

**Estimated memory usage:**  8 MB


.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
      :width: 150 px


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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