/usr/lib/python2.7/dist-packages/networkx/algorithms/link_analysis/tests/test_pagerank.py is in python-networkx 1.8.1-0ubuntu3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | #!/usr/bin/env python
from nose.tools import *
from nose import SkipTest
from nose.plugins.attrib import attr
import random
import networkx
# Example from
# A. Langville and C. Meyer, "A survey of eigenvector methods of web
# information retrieval." http://citeseer.ist.psu.edu/713792.html
class TestPageRank:
def setUp(self):
G=networkx.DiGraph()
edges=[(1,2),(1,3),\
(3,1),(3,2),(3,5),\
(4,5),(4,6),\
(5,4),(5,6),\
(6,4)]
G.add_edges_from(edges)
self.G=G
self.G.pagerank=dict(zip(G,
[0.03721197,0.05395735,0.04150565,
0.37508082,0.20599833, 0.28624589]))
def test_pagerank(self):
G=self.G
p=networkx.pagerank(G,alpha=0.9,tol=1.e-08)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
nstart = dict((n,random.random()) for n in G)
p=networkx.pagerank(G,alpha=0.9,tol=1.e-08, nstart=nstart)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
assert_raises(networkx.NetworkXError,networkx.pagerank,G,
max_iter=0)
@attr('numpy')
def test_numpy_pagerank(self):
try:
import numpy
except ImportError:
raise SkipTest('numpy not available.')
G=self.G
p=networkx.pagerank_numpy(G,alpha=0.9)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
personalize = dict((n,random.random()) for n in G)
p=networkx.pagerank_numpy(G,alpha=0.9, personalization=personalize)
@attr('numpy')
def test_google_matrix(self):
try:
import numpy.linalg
except ImportError:
raise SkipTest('numpy not available.')
G=self.G
M=networkx.google_matrix(G,alpha=0.9)
e,ev=numpy.linalg.eig(M.T)
p=numpy.array(ev[:,0]/ev[:,0].sum())[:,0]
for (a,b) in zip(p,self.G.pagerank.values()):
assert_almost_equal(a,b)
personalize = dict((n,random.random()) for n in G)
M=networkx.google_matrix(G,alpha=0.9, personalization=personalize)
_ = personalize.pop(1)
assert_raises(networkx.NetworkXError,networkx.google_matrix,G,
personalization=personalize)
def test_scipy_pagerank(self):
G=self.G
try:
import scipy
except ImportError:
raise SkipTest('scipy not available.')
p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08)
for n in G:
assert_almost_equal(p[n],G.pagerank[n],places=4)
personalize = dict((n,random.random()) for n in G)
p=networkx.pagerank_scipy(G,alpha=0.9,tol=1.e-08,
personalization=personalize)
assert_raises(networkx.NetworkXError,networkx.pagerank_scipy,G,
max_iter=0)
def test_personalization(self):
G=networkx.complete_graph(4)
personalize={0:1,1:1,2:4,3:4}
answer={0:0.1,1:0.1,2:0.4,3:0.4}
p=networkx.pagerank(G,alpha=0.0,personalization=personalize)
for n in G:
assert_almost_equal(p[n],answer[n],places=4)
_ = personalize.pop(0)
assert_raises(networkx.NetworkXError,networkx.pagerank,G,
personalization=personalize)
@attr('numpy')
def test_empty(self):
try:
import numpy
except ImportError:
raise SkipTest('numpy not available.')
G=networkx.Graph()
assert_equal(networkx.pagerank(G),{})
assert_equal(networkx.pagerank_numpy(G),{})
assert_equal(networkx.google_matrix(G).shape,(0,0))
def test_empty_scipy(self):
try:
import scipy
except ImportError:
raise SkipTest('scipy not available.')
G=networkx.Graph()
assert_equal(networkx.pagerank_scipy(G),{})
|