scikit-learn/examples/semi_supervised/plot_label_propagation_structure.py

"""
==============================================
Label Propagation learning a complex structure
==============================================

Example of LabelPropagation 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__)

# Authors: Clay Woolam <clay@woolam.org>
#          Andreas Mueller <amueller@ais.uni-bonn.de>
# Licence: BSD

import numpy as np
import pylab as pl
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_
pl.figure(figsize=(8.5, 4))
pl.subplot(1, 2, 1)
plot_outer_labeled, = pl.plot(X[labels == outer, 0],
                              X[labels == outer, 1], 'rs')
plot_unlabeled, = pl.plot(X[labels == -1, 0], X[labels == -1, 1], 'g.')
plot_inner_labeled, = pl.plot(X[labels == inner, 0],
                              X[labels == inner, 1], 'bs')
pl.legend((plot_outer_labeled, plot_inner_labeled, plot_unlabeled),
          ('Outer Labeled', 'Inner Labeled', 'Unlabeled'), 'upper left',
          numpoints=1, shadow=False)
pl.title("Raw data (2 classes=red and blue)")

pl.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, = pl.plot(X[outer_numbers, 0], X[outer_numbers, 1], 'rs')
plot_inner, = pl.plot(X[inner_numbers, 0], X[inner_numbers, 1], 'bs')
pl.legend((plot_outer, plot_inner), ('Outer Learned', 'Inner Learned'),
          'upper left', numpoints=1, shadow=False)
pl.title("Labels learned with Label Spreading (KNN)")

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