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

    Click :ref:`here <sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_versus_svm_iris.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_semi_supervised_plot_label_propagation_versus_svm_iris.py:


=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================

Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.

This demonstrates Label Propagation learning a good boundary
even with a small amount of labeled data.




.. code-block:: python

    print(__doc__)

    # Authors: Clay Woolam <clay@woolam.org>
    # License: BSD

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import datasets
    from sklearn import svm
    from sklearn.semi_supervised import label_propagation

    rng = np.random.RandomState(0)

    iris = datasets.load_iris()

    X = iris.data[:, :2]
    y = iris.target

    # step size in the mesh
    h = .02

    y_30 = np.copy(y)
    y_30[rng.rand(len(y)) < 0.3] = -1
    y_50 = np.copy(y)
    y_50[rng.rand(len(y)) < 0.5] = -1
    # we create an instance of SVM and fit out data. We do not scale our
    # data since we want to plot the support vectors
    ls30 = (label_propagation.LabelSpreading().fit(X, y_30),
            y_30)
    ls50 = (label_propagation.LabelSpreading().fit(X, y_50),
            y_50)
    ls100 = (label_propagation.LabelSpreading().fit(X, y), y)
    rbf_svc = (svm.SVC(kernel='rbf').fit(X, y), y)

    # create a mesh to plot in
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    # title for the plots
    titles = ['Label Spreading 30% data',
              'Label Spreading 50% data',
              'Label Spreading 100% data',
              'SVC with rbf kernel']

    color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}

    for i, (clf, y_train) in enumerate((ls30, ls50, ls100, rbf_svc)):
        # Plot the decision boundary. For that, we will assign a color to each
        # point in the mesh [x_min, x_max]x[y_min, y_max].
        plt.subplot(2, 2, i + 1)
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
        plt.axis('off')

        # Plot also the training points
        colors = [color_map[y] for y in y_train]
        plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')

        plt.title(titles[i])

    plt.suptitle("Unlabeled points are colored white", y=0.1)
    plt.show()

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


.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_versus_svm_iris.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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