# Introduction to Page Rank

Around 25 minutes into this lecture, there is some good discussion of the PageRank algorithm. I have always wanted to code up a basic version of this algorithm, so this is a great excuse. This algorithm is probably one of the cleanest examples of Markov Chains that I have seen, and obviously its application was quite successful.

In [1]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

In [2]:
from utils import progress_bar_downloader
import os

dlname = 'pages.zip'
#This will unzip into a directory called pages
if not os.path.exists('./%s' % dlname):
os.system('unzip %s' % dlname)
else:

pages.zip already downloaded!


### Building the Matrix¶

To build the link matrix (basically an adjacency matrix for web pages), we need to look at the links referenced by every single page. For every page referenced by a page, we will add a 1 to the associated column. Adding a small term eps to all entries, in order guarantee the matrix is fully connected, we will then have a stochastic matrix which is suitable for Markov chain simulations!

In [3]:
#Quick and dirty link parsing as per http://www.cs.ubc.ca/~nando/540b-2011/lectures/book540.pdf
for fname in os.listdir(dlname[:-4]):
f = open(dlname[:-4] + '/' + fname)
while True:
p = line.partition('<a href="http://')[2]
if p == '':
break
url, _, line = p.partition('\">')
f.close()

In [4]:
import numpy as np
import matplotlib.pyplot as plt
G = np.zeros((num_pages, num_pages))

#Assign identity numbers to each page, along with a reverse lookup
idx = {}
lookup = {}
idx[k] = n
lookup[n] = k

#Go through all keys, and add a 1 for each link to another page
for e in v:
G[idx[k],idx[e]] = 1

#Add a small value (epsilon) to ensure a fully connected graph
eps = 1. / num_pages
G += eps * np.ones((num_pages, num_pages))
G = G / np.sum(G, axis=1)

In [5]:
#Now we run the Markov Chain until it converges from random initialization
init = np.random.rand(1, num_pages)
init = init / np.sum(init)
probs = [init]
p = init
for i in range(100):
p = np.dot(p, G)
probs.append(p)

for i in range(num_pages):
plt.plot([step[0, i] for step in probs], label=lookup[i], lw=2)

plt.legend()

Out[5]:
<matplotlib.legend.Legend at 0x106699710>

### Turn the Beat Around¶

Now that the PageRank for each page is calculated, how can we actually perform a search?

We simply need to create an index of every word in a page. When we search for words, we will then sort the output by the PageRank of those pages, thus ordering the links by the importance we associated with that page.

In [6]:
search = {}
for fname in os.listdir(dlname[:-4]):
f = open(dlname[:-4] + '/' + fname)
if '<' in line or '>' in line:
continue
words = line.strip().split(' ')
words = filter(lambda x: x != '', words)
#Remove references like [1], [2]
words = filter(lambda x: not ('[' in x or ']' in x), words)

for word in words:
if word in search:
if fname in search[word]:
search[word][fname] += 1
else:
search[word][fname] = 1
else:
search[word] = {fname: 1}
f.close()


### Ranking The Results¶

With words indexed, we can now complete the task. Searching for a particular word (in this case, 'film'), we get back all the pages with references and counts. Sorting these so that the highest pagerank comes first, we see the Googley(TM) result for our tiny web.

In [7]:
def get_pr(fname):
return probs[-1][0, idx[fname]]

r = search['film']
print(sorted(r, reverse=True, key=get_pr))

['martinscorcese.html', 'jenniferaniston.html', 'bradpitt.html', 'jonvoight.html']


This is a neat application of Markov chains and a great learning experience. Though this notebook did not touch on the eigendecomposition approaches and features of PageRank, it is most definitely worth looking into - check out the paper The \$25,000,000,000 Dollar Eigenvector.

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