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 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
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
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)
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)
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 = 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
