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

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

.. _sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py:


========================================
Label Propagation digits active learning
========================================

Demonstrates an active learning technique to learn handwritten digits
using label propagation.

We start by training a label propagation model with only 10 labeled points,
then we select the top five most uncertain points to label. Next, we train
with 15 labeled points (original 10 + 5 new ones). We repeat this process
four times to have a model trained with 30 labeled examples. Note you can
increase this to label more than 30 by changing `max_iterations`. Labeling
more than 30 can be useful to get a sense for the speed of convergence of
this active learning technique.

A plot will appear showing the top 5 most uncertain digits for each iteration
of training. These may or may not contain mistakes, but we will train the next
model with their true labels.



.. code-block:: python

    print(__doc__)

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

    import numpy as np
    import matplotlib.pyplot as plt
    from scipy import stats

    from sklearn import datasets
    from sklearn.semi_supervised import label_propagation
    from sklearn.metrics import classification_report, confusion_matrix

    digits = datasets.load_digits()
    rng = np.random.RandomState(0)
    indices = np.arange(len(digits.data))
    rng.shuffle(indices)

    X = digits.data[indices[:330]]
    y = digits.target[indices[:330]]
    images = digits.images[indices[:330]]

    n_total_samples = len(y)
    n_labeled_points = 10
    max_iterations = 5

    unlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]
    f = plt.figure()

    for i in range(max_iterations):
        if len(unlabeled_indices) == 0:
            print("No unlabeled items left to label.")
            break
        y_train = np.copy(y)
        y_train[unlabeled_indices] = -1

        lp_model = label_propagation.LabelSpreading(gamma=0.25, max_iter=5)
        lp_model.fit(X, y_train)

        predicted_labels = lp_model.transduction_[unlabeled_indices]
        true_labels = y[unlabeled_indices]

        cm = confusion_matrix(true_labels, predicted_labels,
                              labels=lp_model.classes_)

        print("Iteration %i %s" % (i, 70 * "_"))
        print("Label Spreading model: %d labeled & %d unlabeled (%d total)"
              % (n_labeled_points, n_total_samples - n_labeled_points,
                 n_total_samples))

        print(classification_report(true_labels, predicted_labels))

        print("Confusion matrix")
        print(cm)

        # compute the entropies of transduced label distributions
        pred_entropies = stats.distributions.entropy(
            lp_model.label_distributions_.T)

        # select up to 5 digit examples that the classifier is most uncertain about
        uncertainty_index = np.argsort(pred_entropies)[::-1]
        uncertainty_index = uncertainty_index[
            np.in1d(uncertainty_index, unlabeled_indices)][:5]

        # keep track of indices that we get labels for
        delete_indices = np.array([])

        # for more than 5 iterations, visualize the gain only on the first 5
        if i < 5:
            f.text(.05, (1 - (i + 1) * .183),
                   "model %d\n\nfit with\n%d labels" %
                   ((i + 1), i * 5 + 10), size=10)
        for index, image_index in enumerate(uncertainty_index):
            image = images[image_index]

            # for more than 5 iterations, visualize the gain only on the first 5
            if i < 5:
                sub = f.add_subplot(5, 5, index + 1 + (5 * i))
                sub.imshow(image, cmap=plt.cm.gray_r, interpolation='none')
                sub.set_title("predict: %i\ntrue: %i" % (
                    lp_model.transduction_[image_index], y[image_index]), size=10)
                sub.axis('off')

            # labeling 5 points, remote from labeled set
            delete_index, = np.where(unlabeled_indices == image_index)
            delete_indices = np.concatenate((delete_indices, delete_index))

        unlabeled_indices = np.delete(unlabeled_indices, delete_indices)
        n_labeled_points += len(uncertainty_index)

    f.suptitle("Active learning with Label Propagation.\nRows show 5 most "
               "uncertain labels to learn with the next model.", y=1.15)
    plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2,
                        hspace=0.85)
    plt.show()

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


.. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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