from __future__ import print_function
import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
from pattern.graph import Graph, CENTRALITY
# A graph is a network of nodes (or concepts)
# connected to each other with edges (or links).
g = Graph()
for n in ("tree", "nest", "bird", "fly", "insect", "ant"):
g.add_node(n)
g.add_edge("tree", "nest") # Trees have bird nests.
g.add_edge("nest", "bird") # Birds live in nests.
g.add_edge("bird", "fly") # Birds eat flies.
g.add_edge("ant", "bird") # Birds eat ants.
g.add_edge("fly", "insect") # Flies are insects.
g.add_edge("insect", "ant") # Ants are insects.
g.add_edge("ant", "tree") # Ants crawl on trees.
# From tree => fly: tree => ant => bird => fly
print(g.shortest_path(g.node("tree"), g.node("fly")))
print(g.shortest_path(g.node("nest"), g.node("ant")))
print()
# Which nodes get the most traffic?
for n in sorted(g.nodes, key=lambda n: n.centrality, reverse=True):
print('%.2f' % n.centrality, n)
import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
from pattern.graph import Graph, CENTRALITY
# A graph is a network of nodes (or concepts)
# connected to each other with edges (or links).
g = Graph()
for n in ("tree", "nest", "bird", "fly", "insect", "ant"):
g.add_node(n)
g.add_edge("tree", "nest") # Trees have bird nests.
g.add_edge("nest", "bird") # Birds live in nests.
g.add_edge("bird", "fly") # Birds eat flies.
g.add_edge("ant", "bird") # Birds eat ants.
g.add_edge("fly", "insect") # Flies are insects.
g.add_edge("insect", "ant") # Ants are insects.
g.add_edge("ant", "tree") # Ants crawl on trees.
# From tree => fly: tree => ant => bird => fly
print(g.shortest_path(g.node("tree"), g.node("fly")))
print(g.shortest_path(g.node("nest"), g.node("ant")))
print()
# Which nodes get the most traffic?
for n in sorted(g.nodes, key=lambda n: n.centrality, reverse=True):
print('%.2f' % n.centrality, n)
class WebCrawler():
def __init__(self, args, depth=1):
self.links = [WebPage(x) for x in args.url]
self.depth = depth
self.historyDb = WebsiteDatabase()
self.done = False
self.options = args
self.results = {link.url.domain: Result() for link in self.links}
self.cloudIndexer = CloudSearchIndexer.forDomainIndex("websites")
if args.graph or args.rank:
self.webGraph = Graph(distance=30.0)
for link in self.links:
self.webGraph.add_node(link.url.domain,
radius=15,
fill=(1, 0, 0, 0.5))
def __del__(self):
self.cloudIndexer._commitToAmazon()
def crawl(self):
if len(self.links) < 1:
self.done = True
self.finish()
return
site = self.links.pop(0)
if self.historyDb.wasPageVisited(site):
print 'reading data'
site = self.historyDb.readWebPage(site.url.string,
isExternal=site.isExternal,
depth=site.depth)
else:
print 'downloading'
try:
site.downloadContent()
except HTTP404NotFound:
return self.fail(site, "404 not found")
except URLTimeout:
return self.fail(site, "Timeout error")
except URLError as err:
return self.fail(site, str(err))
connected = True
if site.depth == self.depth:
connected = False
self.historyDb.insertWebpage(site, connection=connected)
self.historyDb.appendSession(site)
for link in site.getLinks():
if self.isValidForQueue(link):
if link.isExternal and (self.options.graph
or self.options.rank):
self.addDomainNode(link)
if site.depth < self.depth:
self.links.append(link)
elif not link.isExternal and site.depth < self.depth:
self.links.insert(0, link)
if not self.historyDb.wasPageVisited(site):
self.visit(site)
site.cleanCashedData()
def isValidForQueue(self, link):
if link not in self.links and not link.url.anchor:
if self.historyDb.isInThisSession(link):
self.historyDb.insertRelation(link.parent, link)
else:
return True
return False
def addDomainNode(self, page):
match = re.search("\.", page.url.domain)
if not match:
return
if page.parent.url.domain == page.url.domain:
return
if self.webGraph.node(page.url.domain) is None:
self.webGraph.add_node(page.url.domain, radius=15)
if self.webGraph.edge(page.parent.url.domain, page.url.domain) is None:
self.webGraph.add_edge(page.parent.url.domain,
page.url.domain,
weight=0.0,
type='is-related-to')
def visit(self, page):
print 'visited: ', page.url.string, ' domain: ', page.url.domain, 'graph', self.options.graph
self.cloudIndexer.addDocument(page)
if page.isExternal and self.options.graph and page.url.domain not in self.results.keys(
):
self.webGraph.node(page.url.domain).fill = (0, 1, 0, 0.5)
try:
if self.options.text:
self.results[page.url.domain].wordStats += page.countWords()
if self.options.a:
links = [link.url.string for link in page.getLinks()]
self.results[page.url.domain].links.update(links)
if self.options.image:
self.results[page.url.domain].images.update(page.getImages())
if self.options.script:
self.results[page.url.domain].scripts.update(page.getScripts())
except Exception as e:
print "Error parsing document: ", type(e).__name__ + ': ' + str(e)
def fail(self, link, error):
print 'failed:', link.url.string, 'err: ', error
def finish(self):
"""Print all results and calculate cosine similarity between all provided ur;s"""
self.historyDb.clearSession()
with Emitter(self.options.console, self.options.file) as output:
for key, value in self.results.iteritems():
output.emitLine(key)
value.emit(output)
if len(self.results
) > 1 and self.options.text and self.options.cos:
combinations = [
list(x)
for x in itertools.combinations(self.results.keys(), 2)
]
for pair in combinations:
cosValue = self.results[pair[0]].cosineSimilarity(
self.results[pair[1]])
output.emitLine(
u"cos similarity between:{0} and {1} = {2}".format(
pair[0], pair[1], cosValue))
output.emitLine('')
#output.emitLine("max depth: " + str(max(site.depth for site in self.history)))
#output.emitLine("sites visited: " + str(len(self.history)))
if self.options.graph:
self.webGraph.eigenvector_centrality()
self.webGraph.export('graph',
directed=True,
width=2200,
height=1600,
repulsion=10)
if self.options.rank:
ranks = self.calculatePageRank()
output.emitLine('')
output.emit(ranks)
def calculatePageRank(self):
adjMap = adjacency(self.webGraph, directed=True, stochastic=True)
domains = adjMap.keys()
M = np.zeros((len(domains), len(domains)))
for idx, domain in enumerate(domains):
connections = adjMap[domain].keys()
for connection in connections:
M[idx, domains.index(connection)] = adjMap[domain][connection]
M = np.transpose(M)
#M = np.array([[0,0,0,0,1], [0.5,0,0,0,0], [0.5,0,0,0,0], [0,1,0.5,0,0], [0,0,0.5,1,0]])
#M = np.array([[0, 0.5, 0],[0.5,0.5, 0], [0.5, 0, 0]])
pageScores = self.executeComputations(M)
print pageScores
ranks = dict(zip(domains, pageScores))
ranks = sorted(ranks.items(), key=operator.itemgetter(1))
return ranks
def executeComputations(self, M):
damping = 0.80
error = 0.0000001
N = M.shape[0]
v = np.ones(N)
v = v / np.linalg.norm(v, 1)
last_v = np.full(N, np.finfo(float).max)
for i in range(0, N):
if sum(M[:, i]) == 0:
M[:, i] = np.full(N, 1.0 / N)
M_hat = np.multiply(M, damping) + np.full((N, N), (1 - damping) / N)
while np.linalg.norm(v - last_v) > error:
last_v = v
v = np.matmul(M_hat, v)
return np.round(v, 6)