.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.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_semi_supervised_versus_svm_iris.py:


===============================================================================
Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset
===============================================================================

A comparison for the decision boundaries generated on the iris dataset
by Label Spreading, Self-training and SVM.

This example demonstrates that Label Spreading and Self-training can learn
good boundaries even when small amounts of labeled data are available.

Note that Self-training with 100% of the data is omitted as it is functionally
identical to training the SVC on 100% of the data.

.. GENERATED FROM PYTHON SOURCE LINES 16-87



.. image:: /auto_examples/semi_supervised/images/sphx_glr_plot_semi_supervised_versus_svm_iris_001.png
    :alt: Unlabeled points are colored white, Label Spreading 30% data, Self-training 30% data, Label Spreading 50% data, Self-training 50% data, Label Spreading 100% data, SVC with rbf kernel
    :class: sphx-glr-single-img





.. code-block:: default

    print(__doc__)

    # Authors: Clay Woolam   <clay@woolam.org>
    #          Oliver Rausch <rauscho@ethz.ch>
    # License: BSD

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import datasets
    from sklearn.svm import SVC
    from sklearn.semi_supervised import LabelSpreading
    from sklearn.semi_supervised import SelfTrainingClassifier


    iris = datasets.load_iris()

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

    # step size in the mesh
    h = .02

    rng = np.random.RandomState(0)
    y_rand = rng.rand(y.shape[0])
    y_30 = np.copy(y)
    y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled
    y_50 = np.copy(y)
    y_50[y_rand < 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 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')
    ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')
    ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')

    # the base classifier for self-training is identical to the SVC
    base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)
    st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),
            y_30, 'Self-training 30% data')
    st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),
            y_50, 'Self-training 50% data')

    rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')

    # 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))

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

    classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)
    for i, (clf, y_train, title) in enumerate(classifiers):
        # 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(3, 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(title)

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


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

   **Total running time of the script:** ( 0 minutes  2.020 seconds)


.. _sphx_glr_download_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py:


.. only :: html

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


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

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