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# Label Propagation Digits Active Learning in Scikit-learn

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.

### Version¶

In [1]:
import sklearn
sklearn.__version__

Out[1]:
'0.18.1'

### Imports¶

This tutorial imports classification_report and confusion_matrix.

In [2]:
print(__doc__)

import plotly.plotly as py
import plotly.graph_objs as go
from plotly import tools

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

Automatically created module for IPython interactive environment


### Calculations¶

In [3]:
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

unlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]


### Plot Results¶

In [4]:
data = []
text = []
titles = []

def matplotlib_to_plotly(cmap, pl_entries):
h = 1.0/(pl_entries-1)
pl_colorscale = []

for k in range(pl_entries):
C = map(np.uint8, np.array(cmap(k*h)[:3])*255)
pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])

return pl_colorscale

In [5]:
for i in range(5):
y_train = np.copy(y)
y_train[unlabeled_indices] = -1

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 five digit examples that the classifier is most uncertain about
uncertainty_index = uncertainty_index = np.argsort(pred_entropies)[-5:]

# keep track of indices that we get labels for
delete_indices = np.array([])
data.append([])
text.append("model %d<br>fit with<br>%d labels" % ((i + 1), i * 5 + 10))
for index, image_index in enumerate(uncertainty_index):
image = images[image_index]

trace = go.Heatmap(z=image, showscale=False,
colorscale=matplotlib_to_plotly(plt.cm.gray_r, 5))
data[i].append(trace)

titles.append('predict: %i<br>true: %i' % (
lp_model.transduction_[image_index], y[image_index]))

# 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 += 5

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]]

In [10]:
fig = tools.make_subplots(rows=5, cols=5,
subplot_titles=tuple(titles),
print_grid=False)

for i in range(0, len(data)):
for j in range(0, len(data[i])):
fig.append_trace(data[i][j], i+1, j+1)

for i in map(str,range(1, 26)):
y = 'yaxis' + i
x = 'xaxis' + i

fig['layout'][y].update(autorange='reversed',
showticklabels=False, ticks='')
fig['layout'][x].update(showticklabels=False, ticks='')

j = 0
for i in map(str,range(1, 26, 5)):
y = 'yaxis' + i
x = 'xaxis' + i

fig['layout'][y].update(title=text[j],
autorange='reversed',
showticklabels=False, ticks='')
j+=1
fig['layout'].update(height=1000, title="Active learning with Label Propagation.<br>"
+"Rows show 5 most uncertain labels to learn with the next model.",
margin=dict(t=200))

In [11]:
py.iplot(fig)

The draw time for this plot will be slow for all clients.

Out[11]:

Authors:

      Clay Woolam <clay@woolam.org>



      BSD