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/scikit-learnLabel 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.
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 0.51 0.86 0.64 29
2 0.83 0.97 0.90 31
3 0.00 0.00 0.00 28
4 0.00 0.00 0.00 27
5 0.85 0.49 0.62 35
6 0.84 0.95 0.89 40
7 0.70 0.92 0.80 36
8 0.57 0.76 0.65 33
9 0.41 0.86 0.55 37
avg / total 0.51 0.62 0.54 320
Confusion matrix
[[25 3 0 0 0 0 1]
[ 1 30 0 0 0 0 0]
[ 0 0 17 7 0 1 10]
[ 2 0 0 38 0 0 0]
[ 0 3 0 0 33 0 0]
[ 8 0 0 0 0 25 0]
[ 0 0 3 0 0 2 32]]
Iteration 1 ______________________________________________________________________
Label Spreading model: 15 labeled & 315 unlabeled (330 total)
precision recall f1-score support
0 0.00 0.00 0.00 24
1 0.51 0.75 0.61 28
2 0.91 0.97 0.94 31
3 0.00 0.00 0.00 28
4 0.00 0.00 0.00 27
5 0.84 0.97 0.90 33
6 1.00 0.95 0.97 40
7 0.75 0.92 0.83 36
8 0.46 0.81 0.59 31
9 0.43 0.78 0.56 37
avg / total 0.53 0.66 0.58 315
Confusion matrix
[[21 0 0 0 0 6 1]
[ 1 30 0 0 0 0 0]
[ 0 0 32 0 0 0 1]
[ 2 0 0 38 0 0 0]
[ 0 3 0 0 33 0 0]
[ 6 0 0 0 0 25 0]
[ 0 0 6 0 0 2 29]]
Iteration 2 ______________________________________________________________________
Label Spreading model: 20 labeled & 310 unlabeled (330 total)
precision recall f1-score support
0 1.00 1.00 1.00 22
1 0.67 0.71 0.69 28
2 0.94 0.97 0.95 31
3 0.00 0.00 0.00 28
4 0.85 0.92 0.88 24
5 0.89 0.97 0.93 33
6 1.00 0.95 0.97 40
7 1.00 0.92 0.96 36
8 0.50 0.81 0.62 31
9 0.67 0.78 0.72 37
avg / total 0.76 0.81 0.78 310
Confusion matrix
[[22 0 0 0 0 0 0 0 0]
[ 0 20 0 1 0 0 0 6 1]
[ 0 1 30 0 0 0 0 0 0]
[ 0 1 0 22 0 0 0 1 0]
[ 0 0 0 0 32 0 0 0 1]
[ 0 2 0 0 0 38 0 0 0]
[ 0 0 2 1 0 0 33 0 0]
[ 0 6 0 0 0 0 0 25 0]
[ 0 0 0 2 4 0 0 2 29]]
Iteration 3 ______________________________________________________________________
Label Spreading model: 25 labeled & 305 unlabeled (330 total)
precision recall f1-score support
0 1.00 1.00 1.00 22
1 0.68 0.85 0.75 27
2 1.00 0.90 0.95 31
3 1.00 0.77 0.87 26
4 1.00 0.92 0.96 24
5 0.89 0.97 0.93 33
6 1.00 0.97 0.99 39
7 0.95 1.00 0.97 35
8 0.66 0.81 0.72 31
9 0.97 0.78 0.87 37
avg / total 0.91 0.90 0.90 305
Confusion matrix
[[22 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 20 0 0 0 0 6 0]
[ 0 1 0 0 22 0 0 0 1 0]
[ 0 0 0 0 0 32 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 6 0 0 0 0 0 0 25 0]
[ 0 2 0 0 0 4 0 0 2 29]]
Iteration 4 ______________________________________________________________________
Label Spreading model: 30 labeled & 300 unlabeled (330 total)
precision recall f1-score support
0 1.00 1.00 1.00 22
1 0.68 0.85 0.75 27
2 1.00 0.87 0.93 31
3 0.92 1.00 0.96 23
4 1.00 0.92 0.96 24
5 0.97 0.94 0.95 33
6 1.00 0.97 0.99 39
7 0.95 1.00 0.97 35
8 0.81 0.81 0.81 31
9 0.94 0.86 0.90 35
avg / total 0.93 0.92 0.92 300
Confusion matrix
[[22 0 0 0 0 0 0 0 0 0]
[ 0 23 0 0 0 0 0 0 4 0]
[ 0 1 27 1 0 0 0 2 0 0]
[ 0 0 0 23 0 0 0 0 0 0]
[ 0 1 0 0 22 0 0 0 1 0]
[ 0 0 0 0 0 31 0 0 0 2]
[ 0 1 0 0 0 0 38 0 0 0]
[ 0 0 0 0 0 0 0 35 0 0]
[ 0 6 0 0 0 0 0 0 25 0]
[ 0 2 0 1 0 1 0 0 1 30]]
print(__doc__) # Authors: Clay Woolam <[email protected]> # 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 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=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 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([]) # 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 1.361 seconds)
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http://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html