.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/semi_supervised/plot_label_propagation_structure.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_semi_supervised_plot_label_propagation_structure.py>`
        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_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.

.. GENERATED FROM PYTHON SOURCE LINES 13-18

.. code-block:: default


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


.. GENERATED FROM PYTHON SOURCE LINES 19-23

We generate a dataset with two concentric circles. In addition, a label
is associated with each sample of the dataset that is: 0 (belonging to
the outer circle), 1 (belonging to the inner circle), and -1 (unknown).
Here, all labels but two are tagged as unknown.

.. GENERATED FROM PYTHON SOURCE LINES 23-34

.. code-block:: default


    import numpy as np
    from sklearn.datasets import make_circles

    n_samples = 200
    X, y = make_circles(n_samples=n_samples, shuffle=False)
    outer, inner = 0, 1
    labels = np.full(n_samples, -1.0)
    labels[0] = outer
    labels[-1] = inner


.. GENERATED FROM PYTHON SOURCE LINES 35-36

Plot raw data

.. GENERATED FROM PYTHON SOURCE LINES 36-67

.. code-block:: default

    import matplotlib.pyplot as plt

    plt.figure(figsize=(4, 4))
    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="center")
    _ = plt.title("Raw data (2 classes=outer and inner)")


.. GENERATED FROM PYTHON SOURCE LINES 68-70

The aim of :class:`~sklearn.semi_supervised.LabelSpreading` is to associate
a label to sample where the label is initially unknown.

.. GENERATED FROM PYTHON SOURCE LINES 71-76

.. code-block:: default

    from sklearn.semi_supervised import LabelSpreading

    label_spread = LabelSpreading(kernel="knn", alpha=0.8)
    label_spread.fit(X, labels)


.. GENERATED FROM PYTHON SOURCE LINES 77-79

Now, we can check which labels have been associated with each sample
when the label was unknown.

.. GENERATED FROM PYTHON SOURCE LINES 79-106

.. code-block:: default

    output_labels = label_spread.transduction_
    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.figure(figsize=(4, 4))
    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="center")
    plt.title("Labels learned with Label Spreading (KNN)")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.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/semi_supervised/plot_label_propagation_structure.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/semi_supervised/plot_label_propagation_structure.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

      :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>`_