def get_pattern_data(search_param):
twitter = Twitter(language='en')
for tweet in twitter.search(search_param, cached=True):
print(plaintext(tweet.text).encode('ascii', 'ignore').decode('utf-8'))
g = Graph()
for i in range(10):
for result in twitter.search(search_param, start=i + 1, count=50):
s = result.text.lower()
s = plaintext(s)
s = parsetree(s)
p = '{NP} (VP) ' + search_param + ' {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0, 0, 0, 0.75)) # R,G,B,A
#if len(g)>0:
# g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('data', directed=False, weighted=0.6)
def get_pattern_data(search_param):
twitter = Twitter(language='en')
for tweet in twitter.search(search_param, cached=True):
print(plaintext(tweet.text).encode('ascii', 'ignore').decode('utf-8'))
g = Graph()
for i in range(10):
for result in twitter.search(search_param, start=i+1,count=50):
s = result.text.lower()
s = plaintext(s)
s = parsetree(s)
p = '{NP} (VP) ' +search_param+ ' {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
#if len(g)>0:
# g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('data', directed=False, weighted=0.6)
def compare_visualization(product_sku, compare_phrase):
all_reviews = ReviewInfo.objects.all().filter(sku=product_sku)
g = Graph()
count = 0.0
for e in all_reviews :
s = e.comment.lower()
s = plaintext(s)
s = parsetree(s)
#p = '{NP} (VP) faster than {NP}'
p = '{NP} (VP) ' + compare_phrase + ' {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
count += 1.0
print count/len(all_reviews), '\r'
if len(g) > 0:
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:80]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('static/compare_visualization', directed=True, weighted=2.0)
return True
else:
return False
def make_graph(dgram, n, numWord):
if n == 1:
graph = Graph(distance=4.0)
center = graph.add_node(' ', radius=0)
center.fill = (0,0,0,0)
for gram in dgram:
key = gram
w = dgram[gram] / numWord
node = graph.add_node(key, centrality=w, radius=dgram[gram] + 1)
node.fill = (0, 0.5, 1, node.radius * 0.1)
graph.add_edge(center, node, length=2000/node.radius, stroke=(0,0,0,0)) # R,G,B,A
graph.export('/home/matrx63/Web/monogram', pack=False, width='2000', height='2000', frames=5000, ipf=30)
def render_graph(self, domains):
"""renders graph output"""
g = Graph()
for domain in domains.keys():
if domain in self.cat_dict:
categories = self.cat_dict[domain]
stroke = (0, 0, 0, 0.5)
if 'right' in categories:
stroke = (255, 0, 0, 1)
elif 'right_center' in categories:
stroke = (255, 0, 0, .5)
if 'left' in categories:
stroke = (0, 0, 255, 1)
elif 'left_center' in categories:
stroke = (0, 0, 255, .5)
if 'least_biased' in categories:
stroke = (0, 255, 0, 1)
fill = (128, 128, 0, 0.1)
dub_cats = [
'fake', 'questionable', 'clickbait', 'unreliable', 'conspiracy'
]
score = len([c for c in categories if c in dub_cats])
if score:
fill = (0, 0, 0, float(score) / 5)
g.add_node(domain,
radius=len(domains[domain]) * 6,
stroke=stroke,
strokewidth=6,
fill=fill,
font_size=30)
pairs = self.pairwise(domains.keys())
for x, y in pairs:
x_queries = set(domains[x])
y_queries = set(domains[y])
intersection = len(x_queries.intersection(y_queries))
if intersection > 0:
max_rad = max(len(domains[x]), len(domains[y])) + 1000
g.add_edge(x, y, length=max_rad, strokewidth=intersection)
path = 'graph'
g.export(path,
encoding='utf-8',
distance=6,
directed=False,
width=1400,
height=900)
def visualize_rel(self):
orderedPairs = []
for i in range(len(self.subject_object_dict)):
orderedPair = list(
itertools.product(
self.subject_object_dict["S" + str(i + 1)][0],
self.subject_object_dict["S" + str(i + 1)][1]))
orderedPairs.append(orderedPair)
g = Graph()
for node in (orderedPairs):
for n1, n2 in node:
g.add_node(n1)
g.add_node(n2)
g.add_edge(n1, n2, weight=0.0, type='is-related-to')
g.export('FeatureRelations', directed=True)
orig_stdout = sys.stdout
gn = file('GraphNodeWeights.txt', 'a')
sys.stdout = gn
for n in sorted(g.nodes, key=lambda n: n.weight):
print '%.2f' % n.weight, n
sys.stdout = orig_stdout
gn.close()
class LogStatGraph:
def __init__(self, name=None):
self.name = None
self.graph = Graph()
def load(self, log_stat):
if self.name is None:
self.name = log_stat.repo_name
for commit in log_stat.commits:
author_email = commit.ae
self.graph.add_node(author_email, fill=BLACK_50)
for diffstat in commit.diffstats:
file_path = diffstat["file_path"]
self.graph.add_node(file_path, stroke=BLACK_25, text=BLACK_15)
self.graph.add_edge(author_email, file_path, stroke=BLACK_25)
def prune(self, depth=0):
self.graph.prune(depth)
def export(self, path=None, **kwargs):
if path is None:
path = self.name
self.graph.export(path, directed=True, weighted=True, **kwargs)
#g = g.split()[0]
#print 'NUMBER OF NODES ' + str(len(g.nodes))
#node_removed=0
#for node in g.nodes:
# if node.weight <= 0.07:
# print node_removed
# node_removed+=1
# g.remove(node)
# g = g.split()[0]
# g.eigenvector_centrality()
#print '\nNUMBER OF NODES ' + str(len(g.nodes))
#for graph i g.split():
# graph.export('hashtags' + str(i),title='Hashtags Network',width=800, height=600, directed=False,repulsion=60)
# i+=1
#
#
#on a que le sous graphe
#g.split()[0].export('hashtags' ,title='Hashtags Network',width=900, height=550, directed=False,k=7,repulsion=60)
#g.export('hashtags' ,title='Hashtags Network',width=900, height=550, directed=False,k=7,repulsion=60)
g.export('sound', directed=True)
#g.serialize()
print '\n\ngraph exported'
import os
import sys
sys.path.insert(0, os.path.join("..", ".."))
from pattern.graph import Graph, WEIGHT, CENTRALITY, DEGREE, DEFAULT
from random import choice, random
# This example demonstrates how a graph visualization can be exported to GraphML,
# a file format that can be opened in Gephi (https://gephi.org).
g = Graph()
# Random nodes.
for i in range(50):
g.add_node(i)
# Random edges.
for i in range(75):
node1 = choice(g.nodes)
node2 = choice(g.nodes)
g.add_edge(node1, node2,
weight=random())
g.prune(0)
# This node's label is different from its id.
# FIXME this fails if the 1 has been pruned
# g[1].text.string = "home"
# By default, Graph.export() exports to HTML,
# but if we give it a filename that ends in .graphml it will export to GraphML.
g.export(os.path.join(os.path.dirname(__file__), "test.graphml"))
for i in range(1, 10):
# Set cached=False for live results:
for result in Twitter(language="en").search("\"is the new\"", start=i, count=100, cached=True):
s = result.text
s = s.replace("\n", " ")
s = s.lower()
s = s.replace("is the new", "NEW")
s = s.split(" ")
try:
i = s.index("NEW")
A = s[i - 1].strip("?!.:;,#@\"'")
B = s[i + 1].strip("?!.:;,#@\"'")
# Exclude common phrases such as "this is the new thing".
if A and B and A not in ("it", "this", "here", "what", "why", "where"):
comparisons.append((A, B))
except:
pass
g = Graph()
for A, B in comparisons:
e = g.add_edge(B, A) # "A is the new B": A <= B
e.weight += 0.1
print(("%s => %s" % (B, A)).encode('utf-8'))
# Not all nodes will be connected, there will be multiple subgraphs.
# Simply take the largest subgraph for our visualization.
g = g.split()[0]
g.export("trends", weighted=True, directed=True)
# This node's label is different from its id.
# We'll make it a hyperlink, see the href attribute at the bottom.
# FIXME this fails if the 1 has been pruned
# g[1].text.string = "home"
# The export() command generates a folder with an index.html,
# that displays the graph using an interactive, force-based spring layout.
# You can drag the nodes around - open index.html in a browser and try it out!
# The layout can be tweaked in many ways:
g.export(os.path.join(os.path.dirname(__file__), "test"),
width=700, # <canvas> width.
height=500, # <canvas> height.
frames=500, # Number of frames of animation.
directed=True, # Visualize eigenvector centrality as an edge arrow?
weighted=0.5, # Visualize betweenness centrality as a node shadow?
# Keep clusters close together + visualize node weight as node radius?
pack=True,
distance=10, # Average edge length.
k=4.0, # Force constant.
force=0.01, # Force dampener.
repulsion=50, # Force radius.
# INLINE, DEFAULT, None or the path to your own stylesheet.
stylesheet=DEFAULT,
javascript=None,
# Node.id => URL
href={"1": "http://www.clips.ua.ac.be/pages/pattern-graph"},
css={"1": "node-link-docs"} # Node.id => CSS class.
)
from pattern.web import Bing, plaintext
from pattern.en import parsetree
from pattern.search import search
from pattern.graph import Graph
g = Graph()
for i in range(10):
# for result in Bing().search('"more important than"', start=i+1,
for result in Bing().search('"is less important than"', start=i+1,
count=50):
s = result.text.lower()
s = plaintext(s)
s = parsetree(s)
#p = '{NP} (VP) more important than {NP}'
p = '{NP} (VP) is less important than {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('test', directed=True, weighted=0.6)
for i in range(1,10):
# Set cached=False for live results:
for result in Twitter(language="en").search("\"is the new\"", start=i, count=100, cached=True):
s = result.text
s = s.replace("\n", " ")
s = s.lower()
s = s.replace("is the new", "NEW")
s = s.split(" ")
try:
i = s.index("NEW")
A = s[i-1].strip("?!.:;,#@\"'")
B = s[i+1].strip("?!.:;,#@\"'")
# Exclude common phrases such as "this is the new thing".
if A and B and A not in ("it", "this", "here", "what", "why", "where"):
comparisons.append((A,B))
except:
pass
g = Graph()
for A, B in comparisons:
e = g.add_edge(B, A) # "A is the new B": A <= B
e.weight += 0.1
print B, "=>", A
# Not all nodes will be connected, there will be multiple subgraphs.
# Simply take the largest subgraph for our visualization.
g = g.split()[0]
g.export("trends", weighted=True, directed=True, overwrite=True)
for i in range(1,10):
# Set cached=False for live results:
for result in Twitter(language="en").search("\"is the new\"", start=i, count=100, cached=True):
s = result.text
s = s.replace("\n", " ")
s = s.lower()
s = s.replace("is the new", "NEW")
s = s.split(" ")
try:
i = s.index("NEW")
A = s[i-1].strip("?!.:;,#@\"'")
B = s[i+1].strip("?!.:;,#@\"'")
# Exclude common phrases such as "this is the new thing".
if A and B and A not in ("it", "this", "here", "what", "why", "where"):
comparisons.append((A,B))
except:
pass
g = Graph()
for A, B in comparisons:
e = g.add_edge(B, A) # "A is the new B": A <= B
e.weight += 0.1
print B, "=>", A
# Not all nodes will be connected, there will be multiple subgraphs.
# Simply take the largest subgraph for our visualization.
g = g.split()[0]
g.export("trends", weighted=True, directed=True, overwrite=True)
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)
from pattern.graph import Graph
import webbrowser
g = Graph()
n1 = "asdasd"
n2 = "two"
n3 = "three"
n4 = "four"
n5 = "five"
g.add_node(n1)
g.add_node(n2)
g.add_node(n3)
g.add_node(n4)
g.add_node(n5)
g.add_edge(n2, n3)
g.add_edge(n3, n4)
g.add_edge(n4, n5)
"""for n1, n2 in (
('cat', 'tail'), ('cat', 'purr'), ('purr', 'sound'),
('dog', 'tail'), ('dog', 'bark'), ('bark', 'sound')):
g.add_node(n1)
g.add_node(n2)
g.add_edge(n1, n2, weight=0.0, type='is-related-to')"""
g.export('sound')
webbrowser.open(
u"file:///Users/tobiasfuma/Desktop/FirmenbuchCrawler/sound/index.html")
g = Graph()
for i in range(10):
for result in Bing().search('"more important than"', start=i+1, count=50):
s = r.text.lower()
s = plaintext(s)
s = parsetree(s)
p = '{NP} (VP) more important than {NP}'
for m in search(p, s):
x = m.group(1).string # NP left
y = m.group(2).string # NP right
if x not in g:
g.add_node(x)
if y not in g:
g.add_node(y)
g.add_edge(g[x], g[y], stroke=(0,0,0,0.75)) # R,G,B,A
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sort by Node.weight.
n.fill = (0, 0.5, 1, 0.75 * n.weight)
g.export('test', directed=True, weighted=0.6)
