.. _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 <plot_label_propagation_digits_active_learning.py>`
.. literalinclude:: plot_label_propagation_digits_active_learning.py
:lines: 18-
**Total running time of the example:** 1.49 seconds
( 0 minutes 1.49 seconds)