.. _example_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:: images/plot_label_propagation_digits_active_learning_001.png :align: center **Script output**:: 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]] **Python source code:** :download:`plot_label_propagation_digits_active_learning.py ` .. literalinclude:: plot_label_propagation_digits_active_learning.py :lines: 18- **Total running time of the example:** 1.34 seconds ( 0 minutes 1.34 seconds)