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 subGraph(self):
# Take the largest subgraph.
h = self.g.split()[0]
# Sort by Node.weight.i = 1
i = 0
newGraph = Graph()
for n in h.sorted()[:30]:
i += 1
n.fill = (0, 0.5, 1, 0.75 * n.weight)
logger.debug(u"i:%d=%s" % (i, n))
newGraph.add_node(n.id)
logger.debug(u"edges : %s" % n.edges)
for e in n.edges:
logger.debug(u"edge1 : %s, edge2 : %s" % (e.node1.id, e.node2.id))
if e.node1.id == n.id:
newGraph.add_node(e.node2.id)
else:
newGraph.add_node(e.node1.id)
newGraph.add_edge(e.node1.id, e.node2.id, stroke=(0, 0, 0, 0.75))
h = newGraph.split()
return h
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 make_graph(cls, parse, enhanced=True):
edge_map, node_map = {}, {}
g = Graph()
root = None
for child, parent, arc in parse:
if arc == 'root':
root = child
if not enhanced:
arc = arc.split(':')[0]
if child not in node_map:
node_map[child] = Node(child)
child = node_map[child]
if parent not in node_map:
node_map[parent] = Node(parent)
parent = node_map[parent]
if parent.id != child.id:
g.add_edge(parent, child, type=arc)
return g, edge_map, node_map, root
def make_graph(cls, parse, enhanced=True):
edge_map, node_map = {}, {}
g = Graph()
root = None
for child, parent, arc in parse:
if arc == 'root':
root = child
if not enhanced:
arc = arc.split(':')[0]
if child not in node_map:
node_map[child] = Node(child)
child = node_map[child]
if parent not in node_map:
node_map[parent] = Node(parent)
parent = node_map[parent]
if parent.id != child.id:
g.add_edge(parent, child, type=arc)
return g, edge_map, node_map, root
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)
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"))
import os, sys; sys.path.insert(0, os.path.join("..", "..", ".."))
from pattern.graph import Graph, CENTRALITY
# Simple Graph demonstration.
g = Graph()
for n in ("tree", "nest", "bird", "fly", "insect", "ant"):
g.add_node(n)
g.add_edge("tree", "nest")
g.add_edge("nest", "bird")
g.add_edge("bird", "fly")
g.add_edge("fly", "insect")
g.add_edge("insect", "ant")
g.add_edge("ant", "tree")
g.add_edge("ant", "bird")
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?
print g.sorted(order=CENTRALITY)
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)
def add_edge(self, id1, id2, *args, **kwargs):
""" Returns a Relation between two concepts (Edge subclass).
"""
self._properties = None
kwargs.setdefault("base", Relation)
return Graph.add_edge(self, id1, id2, *args, **kwargs)
\t<style type="text/css">
\t\t%s
\t</style>
</head>
<body>
\t%s
</body>
</html>
'''.strip()
def webpage(graph, **kwargs):
s1 = graph.serialize(CSS, **kwargs)
s2 = graph.serialize(CANVAS, **kwargs)
return template % (s1.replace("\n", "\n\t\t"), s2.replace("\n", "\n\t"))
# Create a graph:
g = Graph()
g.add_node("cat")
g.add_node("dog")
g.add_edge("cat", "dog")
# To make this work as a cgi-bin script, uncomment the following lines:
# !/usr/bin/env python
#import cgi
# import cgitb; cgitb.enable() # Debug mode.
# print "Content-type: text/html"
print(webpage(g, width=500, height=500))
comparisons = []
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]
export(g, "trends", weight=True, weighted=True, directed=True, overwrite=True)
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)
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)
# using the HTML5 <canvas> tag and Javascript.
# All properties (e.g., stroke color) of nodes and edges are ported.
g = Graph()
# Random nodes.
for i in range(50):
g.add_node(id=str(i + 1),
radius=5,
stroke=(0, 0, 0, 1),
text = (0, 0, 0, 1))
# Random edges.
for i in range(75):
node1 = choice(g.nodes)
node2 = choice(g.nodes)
g.add_edge(node1, node2,
length=1.0,
weight=random(),
stroke=(0, 0, 0, 1))
for node in g.sorted()[:20]:
# More blue = more important.
node.fill = (0.6, 0.8, 1.0, 0.8 * node.weight)
g.prune(0)
# 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.
import os, sys
sys.path.append(os.path.join("..", "..", ".."))
from pattern.graph import Graph, CENTRALITY
# Simple Graph demonstration.
g = Graph()
for n in ("tree", "nest", "bird", "fly", "insect", "ant"):
g.add_node(n)
g.add_edge("tree", "nest")
g.add_edge("nest", "bird")
g.add_edge("bird", "fly")
g.add_edge("fly", "insect")
g.add_edge("insect", "ant")
g.add_edge("ant", "tree")
g.add_edge("ant", "bird")
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?
print g.sorted(order=CENTRALITY)
</html>
'''.strip()
def webpage(graph, head="", style="", body=("",""), **kwargs):
""" The head, style and body parameters can be used to insert custom HTML in the template.
You can pass any optional parameter that can also be passed to render().
"""
s1 = render(graph, type=STYLE, **kwargs)
s2 = render(graph, type=CANVAS, **kwargs)
# Fix HTML source indentation:
# f1 = indent each line
# f2 = indent first line
f1 = lambda s, t="\t": s.replace("\n","\n"+t)
f2 = lambda s, t="\t": ("\n%s%s" % (t,s.lstrip())).rstrip()
return template % (
f2(head), f1(s1), f2(style, "\t\t"), f1(body[0]), f1("\n"+s2), f2(body[1]))
# Create a graph:
g = Graph()
g.add_node("cat")
g.add_node("dog")
g.add_edge("cat", "dog")
# To make this work as a cgi-bin script, uncomment the following lines:
##!/usr/bin/env python
#import cgi
#import cgitb; cgitb.enable() # Debug mode.
#print "Content-type: text/html"
print webpage(g, width=300, height=300)
s = plaintext(r.description.lower())
print s
s = Sentence(parse(s))
print s
p = Pattern.fromstring('NP (VP) more important than NP')
for m in p.search(s):
a = m.constituents(p[+0])[-1] # Left NP.
b = m.constituents(p[-1])[+0] # Right NP.
a = (isinstance(a, Chunk) and a.head or a).string
b = (isinstance(b, Chunk) and b.head or b).string
if a and b:
if a not in g:
g.add_node(a, radius=5, stroke=(0, 0, 0, 0.8))
if b not in g:
g.add_node(b, radius=5, stroke=(0, 0, 0, 0.8))
g.add_edge(g[b], g[a], stroke=(0, 0, 0, 0.6))
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sorted by Node.weight.
n.fill = (0.0, 0.5, 1.0, 0.7 * n.weight)
export(g,
'test',
directed=True,
weighted=0.6,
distance=6,
force=0.05,
repulsion=150)
import os
os.system('ls -lR test/')
# ------------------------------------------------------------------------------------
# Another approach is to create a network of movies linked by tropes (or vice versa).
# A network can be represented as a graph with nodes (= things)
# and edges (connections between things).
# http://www.clips.ua.ac.be/pages/pattern-graph
from pattern.graph import Graph
g = Graph()
for movie, tropes in movies.items():
g.add_node(movie)
for trope in tropes:
g.add_node(trope)
g.add_edge(movie, trope) # connection between movie <=> trope
# What nodes directly connect to a given trope?
for node in g["Teach Him Anger"].links:
print node
# What is the shortest path between two nodes in the network?
print
print g.shortest_path("Cinderella", "Alien")
# Cinderella => Race Against the Clock => The X-Files => Absurdly Spacious Sewer => Alien
# Could we transform this into a tweet? For example:
# "I just watched Alien vs. Cinderella...
# a mind-blowing race against the clock in an absurdly spacious sewer!"
def add_edge(self, id1, id2, *args, **kwargs):
""" Returns a Relation between two concepts (Edge subclass).
"""
self._properties = None
kwargs.setdefault("base", Relation)
return Graph.add_edge(self, id1, id2, *args, **kwargs)
row = str(row[0])
for word in row.split():
token = word.lower()
if token.startswith('#'):
# : if graph contains. Augmenter le poids. fontweight
if g.add_node(token, radius=3, fill=(0, 0, 0, 0.4)) is not None:
g.add_node(token).radius += 0.003
g.add_node(token).text.fontsize += 0.03
if token not in list_hashtag:
list_hashtag.append(token)
combinaison = combinliste(list_hashtag, 2)
for paire in combinaison:
#si le poids existe alors on augmente le poids (voir la valeur de retour de add_edge.)
if g.add_edge(paire[0],
paire[1],
strokewidth=0.1,
stroke=(0, 0, 0, 0.4),
weight=0.0,
type='is-related-to') is not None:
g.add_edge(paire[0], paire[1]).strokewidth += 0.06
listStroke = list(g.add_edge(paire[0], paire[1]).stroke)
listStroke[3] += 0.1
g.add_edge(paire[0], paire[1]).stroke = tuple(listStroke)
tweets_treated += 1
print tweets_treated
#elagage
#
#g = g.split()[0]
#print 'NUMBER OF NODES ' + str(len(g.nodes))
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)
from pattern.graph import export, WEIGHT, CENTRALITY
from random import choice, random
# This example demonstrates how a graph visualization can be exported to HTML,
# using the HTML5 <canvas> tag and Javascript.
# All properties (e.g. stroke color) of nodes and edges are ported.
g = Graph()
# Random nodes.
for i in range(50):
g.add_node(id=str(i + 1), radius=5, stroke=(0, 0, 0, 1), text=(0, 0, 0, 1))
# Random edges.
for i in range(75):
node1 = choice(g.nodes)
node2 = choice(g.nodes)
g.add_edge(node1, node2, length=1.0, weight=random(), stroke=(0, 0, 0, 1))
for node in g.sorted()[:20]:
# More blue = more important.
node.fill = (0.6, 0.8, 1.0, 0.8 * node.weight)
# This node's label is different from its id.
# We'll also make it a link, see the href attribute at the bottom.
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:
export(
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")
class PatternGraph(ConceptGraph):
g = None
def __init__(self, homeDir=None):
super(self.__class__, self).__init__()
if homeDir is None:
homeDir = os.getcwd()
self.homeDir = homeDir + os.sep + u"html"
if not os.path.exists(self.homeDir):
os.makedirs(self.homeDir)
self.g = Graph()
def addNode(self, n):
self.g.add_node(n.name)
def addEdge(self, p, c):
self.g.add_edge(p.name, c.name, stroke=(0, 0, 0, 0.75)) # R,G,B,A
def exportGraph(self, title=u"Pattern Graph"):
logger.debug(u"exportGraph")
logger.info(u"Graph Size: %d" % self.g.__len__())
k = self.subGraph()
# Iterate through a list of unconnected subgraphs
if len(k) > 5:
klimit = 5
else:
klimit = len(k)
for i in range(0, klimit):
logger.debug(u"Graph[%d]=%d" % (i, len(k[i])))
newDir = self.homeDir + os.sep + u"graph" + str(i)
h = k[i]
h.export(newDir, overwrite=True, directed=True, weighted=0.5, title=title)
i += 1
def subGraph(self):
# Take the largest subgraph.
h = self.g.split()[0]
# Sort by Node.weight.i = 1
i = 0
newGraph = Graph()
for n in h.sorted()[:30]:
i += 1
n.fill = (0, 0.5, 1, 0.75 * n.weight)
logger.debug(u"i:%d=%s" % (i, n))
newGraph.add_node(n.id)
logger.debug(u"edges : %s" % n.edges)
for e in n.edges:
logger.debug(u"edge1 : %s, edge2 : %s" % (e.node1.id, e.node2.id))
if e.node1.id == n.id:
newGraph.add_node(e.node2.id)
else:
newGraph.add_node(e.node1.id)
newGraph.add_edge(e.node1.id, e.node2.id, stroke=(0, 0, 0, 0.75))
h = newGraph.split()
return h
# Python Data Science and Analytics.
# Data Science is a field in computer science that is dedicated to analyzing patterns in raw data using
# techniques like Artificial Intelligence (AI), Machine Learning (ML), mathematical functions, and
# statistical algorithms.
# Pattern is a web mining module for the Python programming language.
# It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural
# language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning
# (vector space model, clustering, SVM), network analysis and <canvas> visualization.
# Semantic network.
# The pattern.graph module has a Graph object from which we can start:
from pattern.graph import Graph
g = Graph()
g.add_edge('doll', 'toy', type='is-a') # doll is-a toy
g.add_edge('silent', 'doll', type='is-property-of')
g.add_edge('doll', 'girl', type='is-related-to')
node = g['doll']
print node.id
print node.links
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)
s = plaintext(r.description.lower())
print s
s = Sentence(parse(s))
print s
p = Pattern.fromstring('NP (VP) more important than NP')
for m in p.search(s):
a = m.constituents(p[+0])[-1] # Left NP.
b = m.constituents(p[-1])[+0] # Right NP.
a = (isinstance(a, Chunk) and a.head or a).string
b = (isinstance(b, Chunk) and b.head or b).string
if a and b:
if a not in g:
g.add_node(a, radius=5, stroke=(0,0,0,0.8))
if b not in g:
g.add_node(b, radius=5, stroke=(0,0,0,0.8))
g.add_edge(g[b], g[a], stroke=(0,0,0,0.6))
g = g.split()[0] # Largest subgraph.
for n in g.sorted()[:40]: # Sorted by Node.weight.
n.fill = (0.0, 0.5, 1.0, 0.7 * n.weight)
export(g, 'test', directed=True, weighted=0.6, distance=6, force=0.05, repulsion=150)
import os
os.system('ls -lR test/')
# Example of pattern: http://www.clips.ua.ac.be/pages/pattern
from pattern.web import Bing, plaintext
from pattern.en import Sentence, Chunk, parse
from builtins import str, bytes, dict, int
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)
# The visual tool for adding new triples is online at:
# http://nodebox.net/perception
# The data is bundled in Pattern as a .csv file.
from pattern.graph import MODULE # path to pattern/graph/commonsense.csv
data = pd(MODULE, "commonsense.csv")
data = Datasheet.load(data)
# Create the graph:
g = Graph()
for concept1, relation, concept2, context, weight in data:
g.add_node(concept1)
g.add_node(concept2)
g.add_edge(concept1, concept2, type=relation, weight=min(int(weight) * 0.1, 1.0))
# ------------------------------------------------------------------------------------
# The halo of a node is a semantical representation of a concept.
# The halo is made up of other concepts directly or indirectly related to this concept,
# defining it.
# For example:
#
# - Darth Vader is-a Sith
# - Darth Vader is-part-of Death Star
# - evil is-property of Darth Vader
# - black is-property-of Darth Vader
# - hoarse is property-of Darth Vader
# ...
