Label Propagation digits active learningΒΆ

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.

../../_images/sphx_glr_plot_label_propagation_digits_active_learning_001.png

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 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 = 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 = label_propagation.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 0.462 seconds)

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