def cluster_data():
examples = [pages[page][2] for page in pages]
kmeans_results = kmeans.runkmeans_sklearn(examples, range(1, 16))
x = sorted(kmeans_results.keys())
y = [kmeans_results[key].inertia_ for key in x]
plt.plot(x, y)
plt.xlabel('Number of Clusters')
plt.ylabel('Loss')
plt.title('K-means Clustering\nLoss vs. Number of Clusters')
plt.savefig('kmeans_cluster_num_graph.png')
plt.show()
def set_up_links_and_features(pages):
print 'total articles:', len(pages)
ref = {}
examples = []
for i, (page, value) in enumerate(pages.iteritems()):
val = value[0]
links = get_links_from_text(pages, val)
if not isinstance(val, basestring):
features = {}
else:
features = extract_features(val, links)
if i % 1000 == 0:
print 'i = ', i
pages[page] = (val, links, features)
ref[page] = i
examples.append(features)
kmeans_results = kmeans.runkmeans_sklearn(examples, [NUM_CLUSTERS])
labs = kmeans_results[NUM_CLUSTERS].labels_
for page, value in pages.iteritems():
features = value[2]
cluster = labs[ref[page]]
for i in range(NUM_CLUSTERS):
features['IN_CLUSTER_'+str(i)] = 1 if cluster == i else 0
