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.

Active learning with Label Propagation. Rows show 5 most uncertain labels to learn with the next model., predict: 1 true: 1, predict: 2 true: 1, predict: 1 true: 1, predict: 1 true: 1, predict: 3 true: 3, predict: 4 true: 4, predict: 4 true: 4, predict: 4 true: 4, predict: 8 true: 2, predict: 8 true: 7, predict: 2 true: 2, predict: 9 true: 5, predict: 9 true: 5, predict: 5 true: 9, predict: 7 true: 7, predict: 8 true: 8, predict: 1 true: 8, predict: 3 true: 3, predict: 4 true: 4, predict: 8 true: 8, predict: 1 true: 1, predict: 1 true: 1, predict: 7 true: 7, predict: 7 true: 7, predict: 1 true: 1

Out:

Iteration 0 ______________________________________________________________________
Label Spreading model: 40 labeled & 290 unlabeled (330 total)
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        22
           1       0.78      0.69      0.73        26
           2       0.93      0.93      0.93        29
           3       1.00      0.89      0.94        27
           4       0.92      0.96      0.94        23
           5       0.96      0.70      0.81        33
           6       0.97      0.97      0.97        35
           7       0.94      0.91      0.92        33
           8       0.62      0.89      0.74        28
           9       0.73      0.79      0.76        34

    accuracy                           0.87       290
   macro avg       0.89      0.87      0.87       290
weighted avg       0.88      0.87      0.87       290

Confusion matrix
[[22  0  0  0  0  0  0  0  0  0]
 [ 0 18  2  0  0  0  1  0  5  0]
 [ 0  0 27  0  0  0  0  0  2  0]
 [ 0  0  0 24  0  0  0  0  3  0]
 [ 0  1  0  0 22  0  0  0  0  0]
 [ 0  0  0  0  0 23  0  0  0 10]
 [ 0  1  0  0  0  0 34  0  0  0]
 [ 0  0  0  0  0  0  0 30  3  0]
 [ 0  3  0  0  0  0  0  0 25  0]
 [ 0  0  0  0  2  1  0  2  2 27]]
Iteration 1 ______________________________________________________________________
Label Spreading model: 45 labeled & 285 unlabeled (330 total)
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        22
           1       0.79      1.00      0.88        22
           2       1.00      0.93      0.96        29
           3       1.00      1.00      1.00        26
           4       0.92      0.96      0.94        23
           5       0.96      0.70      0.81        33
           6       1.00      0.97      0.99        35
           7       0.94      0.91      0.92        33
           8       0.77      0.86      0.81        28
           9       0.73      0.79      0.76        34

    accuracy                           0.90       285
   macro avg       0.91      0.91      0.91       285
weighted avg       0.91      0.90      0.90       285

Confusion matrix
[[22  0  0  0  0  0  0  0  0  0]
 [ 0 22  0  0  0  0  0  0  0  0]
 [ 0  0 27  0  0  0  0  0  2  0]
 [ 0  0  0 26  0  0  0  0  0  0]
 [ 0  1  0  0 22  0  0  0  0  0]
 [ 0  0  0  0  0 23  0  0  0 10]
 [ 0  1  0  0  0  0 34  0  0  0]
 [ 0  0  0  0  0  0  0 30  3  0]
 [ 0  4  0  0  0  0  0  0 24  0]
 [ 0  0  0  0  2  1  0  2  2 27]]
Iteration 2 ______________________________________________________________________
Label Spreading model: 50 labeled & 280 unlabeled (330 total)
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        22
           1       0.85      1.00      0.92        22
           2       1.00      1.00      1.00        28
           3       1.00      1.00      1.00        26
           4       0.87      1.00      0.93        20
           5       0.96      0.70      0.81        33
           6       1.00      0.97      0.99        35
           7       0.94      1.00      0.97        32
           8       0.92      0.86      0.89        28
           9       0.73      0.79      0.76        34

    accuracy                           0.92       280
   macro avg       0.93      0.93      0.93       280
weighted avg       0.93      0.92      0.92       280

Confusion matrix
[[22  0  0  0  0  0  0  0  0  0]
 [ 0 22  0  0  0  0  0  0  0  0]
 [ 0  0 28  0  0  0  0  0  0  0]
 [ 0  0  0 26  0  0  0  0  0  0]
 [ 0  0  0  0 20  0  0  0  0  0]
 [ 0  0  0  0  0 23  0  0  0 10]
 [ 0  1  0  0  0  0 34  0  0  0]
 [ 0  0  0  0  0  0  0 32  0  0]
 [ 0  3  0  0  1  0  0  0 24  0]
 [ 0  0  0  0  2  1  0  2  2 27]]
Iteration 3 ______________________________________________________________________
Label Spreading model: 55 labeled & 275 unlabeled (330 total)
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        22
           1       0.85      1.00      0.92        22
           2       1.00      1.00      1.00        27
           3       1.00      1.00      1.00        26
           4       0.87      1.00      0.93        20
           5       0.96      0.87      0.92        31
           6       1.00      0.97      0.99        35
           7       1.00      1.00      1.00        31
           8       0.92      0.86      0.89        28
           9       0.88      0.85      0.86        33

    accuracy                           0.95       275
   macro avg       0.95      0.95      0.95       275
weighted avg       0.95      0.95      0.95       275

Confusion matrix
[[22  0  0  0  0  0  0  0  0  0]
 [ 0 22  0  0  0  0  0  0  0  0]
 [ 0  0 27  0  0  0  0  0  0  0]
 [ 0  0  0 26  0  0  0  0  0  0]
 [ 0  0  0  0 20  0  0  0  0  0]
 [ 0  0  0  0  0 27  0  0  0  4]
 [ 0  1  0  0  0  0 34  0  0  0]
 [ 0  0  0  0  0  0  0 31  0  0]
 [ 0  3  0  0  1  0  0  0 24  0]
 [ 0  0  0  0  2  1  0  0  2 28]]
Iteration 4 ______________________________________________________________________
Label Spreading model: 60 labeled & 270 unlabeled (330 total)
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        22
           1       0.96      1.00      0.98        22
           2       1.00      0.96      0.98        27
           3       0.96      1.00      0.98        25
           4       0.86      1.00      0.93        19
           5       0.96      0.87      0.92        31
           6       1.00      0.97      0.99        35
           7       1.00      1.00      1.00        31
           8       0.92      0.96      0.94        25
           9       0.88      0.85      0.86        33

    accuracy                           0.96       270
   macro avg       0.95      0.96      0.96       270
weighted avg       0.96      0.96      0.96       270

Confusion matrix
[[22  0  0  0  0  0  0  0  0  0]
 [ 0 22  0  0  0  0  0  0  0  0]
 [ 0  0 26  1  0  0  0  0  0  0]
 [ 0  0  0 25  0  0  0  0  0  0]
 [ 0  0  0  0 19  0  0  0  0  0]
 [ 0  0  0  0  0 27  0  0  0  4]
 [ 0  1  0  0  0  0 34  0  0  0]
 [ 0  0  0  0  0  0  0 31  0  0]
 [ 0  0  0  0  1  0  0  0 24  0]
 [ 0  0  0  0  2  1  0  0  2 28]]

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 LabelSpreading
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 = 40
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 = LabelSpreading(gamma=0.25, max_iter=20)
    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([], dtype=int)

    # 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 0.416 seconds)

Gallery generated by Sphinx-Gallery