.. _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. 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. .. image:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_digits_active_learning_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: Iteration 0 ______________________________________________________________________ Label Spreading model: 10 labeled & 320 unlabeled (330 total) precision recall f1-score support 0 0.00 0.00 0.00 24 1 0.49 0.90 0.63 29 2 0.88 0.97 0.92 31 3 0.00 0.00 0.00 28 4 0.00 0.00 0.00 27 5 0.89 0.49 0.63 35 6 0.86 0.95 0.90 40 7 0.75 0.92 0.83 36 8 0.54 0.79 0.64 33 9 0.41 0.86 0.56 37 avg / total 0.52 0.63 0.55 320 Confusion matrix [[26 1 0 0 1 0 1] [ 1 30 0 0 0 0 0] [ 0 0 17 6 0 2 10] [ 2 0 0 38 0 0 0] [ 0 3 0 0 33 0 0] [ 7 0 0 0 0 26 0] [ 0 0 2 0 0 3 32]] Iteration 1 ______________________________________________________________________ Label Spreading model: 15 labeled & 315 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 23 1 0.61 0.59 0.60 29 2 0.91 0.97 0.94 31 3 1.00 0.56 0.71 27 4 0.79 0.88 0.84 26 5 0.89 0.46 0.60 35 6 0.86 0.95 0.90 40 7 0.97 0.92 0.94 36 8 0.54 0.84 0.66 31 9 0.70 0.81 0.75 37 avg / total 0.82 0.80 0.79 315 Confusion matrix [[23 0 0 0 0 0 0 0 0 0] [ 0 17 1 0 2 0 0 1 7 1] [ 0 1 30 0 0 0 0 0 0 0] [ 0 0 0 15 0 0 0 0 10 2] [ 0 3 0 0 23 0 0 0 0 0] [ 0 0 0 0 1 16 6 0 2 10] [ 0 2 0 0 0 0 38 0 0 0] [ 0 0 2 0 1 0 0 33 0 0] [ 0 5 0 0 0 0 0 0 26 0] [ 0 0 0 0 2 2 0 0 3 30]] Iteration 2 ______________________________________________________________________ Label Spreading model: 20 labeled & 310 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 23 1 0.68 0.59 0.63 29 2 0.91 0.97 0.94 31 3 0.96 1.00 0.98 23 4 0.81 1.00 0.89 25 5 0.89 0.46 0.60 35 6 0.86 0.95 0.90 40 7 0.97 0.92 0.94 36 8 0.68 0.84 0.75 31 9 0.75 0.81 0.78 37 avg / total 0.85 0.84 0.83 310 Confusion matrix [[23 0 0 0 0 0 0 0 0 0] [ 0 17 1 0 2 0 0 1 7 1] [ 0 1 30 0 0 0 0 0 0 0] [ 0 0 0 23 0 0 0 0 0 0] [ 0 0 0 0 25 0 0 0 0 0] [ 0 0 0 1 1 16 6 0 2 9] [ 0 2 0 0 0 0 38 0 0 0] [ 0 0 2 0 1 0 0 33 0 0] [ 0 5 0 0 0 0 0 0 26 0] [ 0 0 0 0 2 2 0 0 3 30]] Iteration 3 ______________________________________________________________________ Label Spreading model: 25 labeled & 305 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 23 1 0.70 0.85 0.77 27 2 1.00 0.90 0.95 31 3 1.00 1.00 1.00 23 4 1.00 1.00 1.00 25 5 0.96 0.74 0.83 34 6 1.00 0.95 0.97 40 7 0.90 1.00 0.95 35 8 0.83 0.81 0.82 31 9 0.75 0.83 0.79 36 avg / total 0.91 0.90 0.90 305 Confusion matrix [[23 0 0 0 0 0 0 0 0 0] [ 0 23 0 0 0 0 0 0 4 0] [ 0 1 28 0 0 0 0 2 0 0] [ 0 0 0 23 0 0 0 0 0 0] [ 0 0 0 0 25 0 0 0 0 0] [ 0 0 0 0 0 25 0 0 0 9] [ 0 2 0 0 0 0 38 0 0 0] [ 0 0 0 0 0 0 0 35 0 0] [ 0 5 0 0 0 0 0 0 25 1] [ 0 2 0 0 0 1 0 2 1 30]] Iteration 4 ______________________________________________________________________ Label Spreading model: 30 labeled & 300 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 23 1 0.77 0.88 0.82 26 2 1.00 0.90 0.95 31 3 1.00 1.00 1.00 23 4 1.00 1.00 1.00 25 5 0.94 0.97 0.95 32 6 1.00 0.97 0.99 39 7 0.90 1.00 0.95 35 8 0.89 0.81 0.85 31 9 0.94 0.89 0.91 35 avg / total 0.94 0.94 0.94 300 Confusion matrix [[23 0 0 0 0 0 0 0 0 0] [ 0 23 0 0 0 0 0 0 3 0] [ 0 1 28 0 0 0 0 2 0 0] [ 0 0 0 23 0 0 0 0 0 0] [ 0 0 0 0 25 0 0 0 0 0] [ 0 0 0 0 0 31 0 0 0 1] [ 0 1 0 0 0 0 38 0 0 0] [ 0 0 0 0 0 0 0 35 0 0] [ 0 5 0 0 0 0 0 0 25 1] [ 0 0 0 0 0 2 0 2 0 31]] | .. code-block:: python print(__doc__) # Authors: Clay Woolam # 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 unlabeled_indices = np.arange(n_total_samples)[n_labeled_points:] f = plt.figure() for i in range(5): 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 five digit examples that the classifier is most uncertain about uncertainty_index = uncertainty_index = np.argsort(pred_entropies)[-5:] # keep track of indices that we get labels for delete_indices = np.array([]) 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] sub = f.add_subplot(5, 5, index + 1 + (5 * i)) sub.imshow(image, cmap=plt.cm.gray_r) 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 += 5 f.suptitle("Active learning with Label Propagation.\nRows show 5 most " "uncertain labels to learn with the next model.") plt.subplots_adjust(0.12, 0.03, 0.9, 0.8, 0.2, 0.45) plt.show() **Total running time of the script:** (0 minutes 1.908 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_label_propagation_digits_active_learning.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_label_propagation_digits_active_learning.ipynb `