plot_label_propogation.py

 # learning a complex internal structure to demonstrate manifold learning. 
 # The outer circle should be labeled red and the inner circle blue. 
 # Because both label groups lie inside their own distinct shape, we can see 
 # that the labels propagate correctly around the circle.

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt
from sklearn.semi_supervised import label_propagation
from sklearn.datasets import make_circles

# generate ring with inner box
n_samples = 200
X, y = make_circles(n_samples=n_samples, shuffle=False)
outer, inner = 0, 1
labels = -np.ones(n_samples)
labels[0] = outer
labels[-1] = inner

###############################################################################
# Learn with LabelSpreading
label_spread = label_propagation.LabelSpreading(kernel='knn', alpha=1.0)
label_spread.fit(X, labels)

###############################################################################
# Plot output labels
output_labels = label_spread.transduction_
plt.figure(figsize=(8.5, 4))
plt.subplot(1, 2, 1)
plot_outer_labeled, = plt.plot(X[labels == outer, 0],
                               X[labels == outer, 1], 'rs')
plot_unlabeled, = plt.plot(X[labels == -1, 0], X[labels == -1, 1], 'g.')
plot_inner_labeled, = plt.plot(X[labels == inner, 0],
                               X[labels == inner, 1], 'bs')
plt.legend((plot_outer_labeled, plot_inner_labeled, plot_unlabeled),
           ('Outer Labeled', 'Inner Labeled', 'Unlabeled'), 'upper left',
           numpoints=1, shadow=False)
plt.title("Raw data (2 classes=red and blue)")

plt.subplot(1, 2, 2)
output_label_array = np.asarray(output_labels)
outer_numbers = np.where(output_label_array == outer)[0]
inner_numbers = np.where(output_label_array == inner)[0]
plot_outer, = plt.plot(X[outer_numbers, 0], X[outer_numbers, 1], 'rs')
plot_inner, = plt.plot(X[inner_numbers, 0], X[inner_numbers, 1], 'bs')
plt.legend((plot_outer, plot_inner), ('Outer Learned', 'Inner Learned'),
           'upper left', numpoints=1, shadow=False)
plt.title("Labels learned with Label Spreading (KNN)")

plt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92)
plt.show()