.. _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.


.. code-block:: python

    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 label_propagation
    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.ones(n_samples)
    labels[0] = outer
    labels[-1] = inner







Learn with LabelSpreading


.. code-block:: python

    label_spread = label_propagation.LabelSpreading(kernel='knn', alpha=1.0)
    label_spread.fit(X, labels)







Plot output labels


.. code-block:: python

    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()



.. image:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_001.png
    :align: center




**Total running time of the script:**
(0 minutes 0.128 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_label_propagation_structure.ipynb <plot_label_propagation_structure.ipynb>`