def test1():
catalogs = Catalog.list()
print "Catalogs: " , catalogs
master = Catalog.create("master", "Computer/Home/Desktop")
AttributeType.create("Description", "String")
AttributeType.create("Status", "String")
AttributeType.create("Processor", "category_relationship")
print AttributeType.list()
computer = master.get_category("Computer")
computer.add_attribute("Status")
computer.add_attribute("Processor")
master.create_categories("Processors/Intel")
Product.create("Core Duo i5", dict(Description="i5 processor"), master, "Processors/Intel")
Product.create("Core Duo i7", dict(Description="i7 processor"), master, "Processors/Intel")
desktop = master.get_category("Computer/Home/Desktop")
desktop.add_attribute("Description")
print desktop.get_attributes()
Product.create("Multimedia Server 1", dict(Description="Multimedia Server for video editing", Status="Active", Processor="Processors/Intel"), master, "Computer/Home/Desktop")
Product.create("Multimedia Server 2", dict(Description="Multimedia Server for audio editing", Status="Inactive", Processor="Processors/Intel"), master, "Computer/Home/Desktop")
def __init__(self):
self.anchors_db = plyvel.DB('catalog/wikipedia-anchors')
self.catalog = Catalog("catalog")
self.cache = {}
class Disambiguator(object):
def __init__(self):
self.anchors_db = plyvel.DB('catalog/wikipedia-anchors')
self.catalog = Catalog("catalog")
self.cache = {}
def get_senses(self, spot):
# Here we convert a list of {'id': page_id, 'score': num_links}
# to this format [(page_id, score), ..]
senses = [
sense for sense in loads(self.anchors_db.get(spot.encode('utf8')))
]
# In this case we have a link_probability. Extract it
if isinstance(senses[0], int):
link_probability = senses.pop(0)
else:
link_probability = 1
# Then we take the sum of all the scores and extract the probability
total_score = float(sum(map(lambda x: x[1], senses)))
senses = sorted(map(lambda x: (x[0], x[1] / total_score), senses), key=lambda x: x[1], reverse=True)
#senses = map(lambda x: (x[0], 1 / float(len(senses))), senses)
print "LINKPROB", spot, link_probability, total_score, len(senses)
link_probability = max(link_probability, total_score, len(senses))
link_probability = total_score / float(link_probability)
return (link_probability, senses)
def relatedness(self, id_a, id_b):
a = self.catalog.get_entity('', id_a)
b = self.catalog.get_entity('', id_b)
score = a.related(b)
self.cache[(id_a, id_b)] = score
return score
def vote_for(self, p_a, pg_b, prg_b, scores={}):
"""
@param p_a the page_id of the sense to give to the spot a
@param pg_b all the possible page_id of the sense to give to the spot b
@param a list of floats containing the (p_b | b) probability
"""
#with profiled("Collaborative voting for " + str(p_a) + " in %s"):
relatedness = 0
probabilities = 0
for p_b, pr_b in zip(pg_b, prg_b):
if p_b < p_a:
relatedness += scores.get((p_b, p_a), 0)
else:
relatedness += scores.get((p_a, p_b), 0)
#relatedness += self.relatedness(p_b, p_a)
probabilities += pr_b
score = (relatedness * probabilities) / len(pg_b)
#print "Voting for", p_a, pg_b, score
return score
def precompute_related(self, senses):
"""
@param a set of ids
"""
with profiled("Precomputing scores in %s"):
scores = {}
operation = 0
maxoperation = len(senses) / 2
while operation < maxoperation:
spot_senses = senses.pop(0)
for other_senses in senses:
for source in spot_senses:
for dest in other_senses:
a, b = source, dest
if a > b:
a, b = b, a
if (a, b) in scores:
continue
scores[(a, b)] = self.relatedness(a, b)
senses.append(spot_senses)
operation += 1
return scores
def disambiguate(self, spots):
db = self.anchors_db
allsenses = []
with profiled("Disambiguation in %s"):
# First we get all the page connected to each spot
pages = defaultdict(list)
lp = {}
index = 0
while index < len(spots):
spot = spots[index]
link_prob, senses = self.get_senses(spot)
allsenses.append(map(lambda x: x[0], senses))
if link_prob > 0:
lp[spot], pages[spot] = link_prob, senses
#print "Link probability", spot, link_prob
index += 1
else:
del spots[index]
print "Removing spot", spot, link_prob
scores = self.precompute_related(allsenses)
# Then we remove each spot and append it to the end
# so we can apply the voting scheme
candidates = defaultdict(list)
for spot in spots:
sense_ids = pages.pop(spot)
#print "Voting for spot", spot
for p_a, pr_a in sense_ids:
score = 0
counter = 0
for counter, (p_b, pg_pr_b) in enumerate(pages.items()):
pg_b = map(lambda x: x[0], pg_pr_b)
prg_b = map(lambda x: x[1], pg_pr_b)
score += self.vote_for(p_a, pg_b, prg_b, scores)
score /= float(counter + 1)
#print "Vote to spot", spot, p_a, "is", score
print "Link probability", lp[spot]
rho = (score + lp[spot]) / 2.0
candidates[spot].append((pr_a, rho, p_a))
pages[spot] = sense_ids
winning = {}
for spot, champion_list in candidates.items():
print "Spot", spot, "has the following:"
for pr_a, score, p_a in sorted(champion_list, reverse=True, key=lambda x: x[1]):
#if score < 0.05:
# continue
print "\tProability:", pr_a, "Score:", score, "Page id:", p_a, "Title", self.catalog.get_title(p_a)
_, rho, wiki_id = sorted(champion_list, reverse=True, key=lambda x: x[1])[0]
print _, rho, wiki_id
#if rho > 0.05:
winning[spot] = {
"rho": rho,
"id": wiki_id,
"title": self.catalog.get_title(wiki_id),
"portals": self.catalog.get_portals(wiki_id),
}
return winning
import os
import json
import uuid
from flask import Flask, request, Response, jsonify, send_from_directory
from random import choice
from lib.logger import get_logger
logger = get_logger(LOG_NAME='noplp')
app = Flask(__name__)
app.rounds = {}
app.challenges = {}
datapath = os.path.join("/home/ec2-user/noplp/backend/data")
# datapath = os.path.join(os.getcwd(), "data")
app.catalog = Catalog(os.path.join(datapath, "list.csv"))
app.rounds = {}
app.current_round_id = None
def add_cors_headers(response):
response.headers['Access-Control-Allow-Origin'] = '*'
if request.method == 'OPTIONS':
response.headers[
'Access-Control-Allow-Methods'] = 'DELETE, GET, POST, PUT'
headers = request.headers.get('Access-Control-Request-Headers')
if headers:
response.headers['Access-Control-Allow-Headers'] = headers
return response
def __init__(self):
self.anchors_db = plyvel.DB('catalog/wikipedia-anchors')
self.catalog = Catalog("catalog")
self.cache = {}
class Disambiguator(object):
def __init__(self):
self.anchors_db = plyvel.DB('catalog/wikipedia-anchors')
self.catalog = Catalog("catalog")
self.cache = {}
def get_senses(self, spot):
# Here we convert a list of {'id': page_id, 'score': num_links}
# to this format [(page_id, score), ..]
senses = [
sense for sense in loads(self.anchors_db.get(spot.encode('utf8')))
]
# In this case we have a link_probability. Extract it
if isinstance(senses[0], int):
link_probability = senses.pop(0)
else:
link_probability = 1
# Then we take the sum of all the scores and extract the probability
total_score = float(sum(map(lambda x: x[1], senses)))
senses = sorted(map(lambda x: (x[0], x[1] / total_score), senses),
key=lambda x: x[1],
reverse=True)
#senses = map(lambda x: (x[0], 1 / float(len(senses))), senses)
print "LINKPROB", spot, link_probability, total_score, len(senses)
link_probability = max(link_probability, total_score, len(senses))
link_probability = total_score / float(link_probability)
return (link_probability, senses)
def relatedness(self, id_a, id_b):
a = self.catalog.get_entity('', id_a)
b = self.catalog.get_entity('', id_b)
score = a.related(b)
self.cache[(id_a, id_b)] = score
return score
def vote_for(self, p_a, pg_b, prg_b, scores={}):
"""
@param p_a the page_id of the sense to give to the spot a
@param pg_b all the possible page_id of the sense to give to the spot b
@param a list of floats containing the (p_b | b) probability
"""
#with profiled("Collaborative voting for " + str(p_a) + " in %s"):
relatedness = 0
probabilities = 0
for p_b, pr_b in zip(pg_b, prg_b):
if p_b < p_a:
relatedness += scores.get((p_b, p_a), 0)
else:
relatedness += scores.get((p_a, p_b), 0)
#relatedness += self.relatedness(p_b, p_a)
probabilities += pr_b
score = (relatedness * probabilities) / len(pg_b)
#print "Voting for", p_a, pg_b, score
return score
def precompute_related(self, senses):
"""
@param a set of ids
"""
with profiled("Precomputing scores in %s"):
scores = {}
operation = 0
maxoperation = len(senses) / 2
while operation < maxoperation:
spot_senses = senses.pop(0)
for other_senses in senses:
for source in spot_senses:
for dest in other_senses:
a, b = source, dest
if a > b:
a, b = b, a
if (a, b) in scores:
continue
scores[(a, b)] = self.relatedness(a, b)
senses.append(spot_senses)
operation += 1
return scores
def disambiguate(self, spots):
db = self.anchors_db
allsenses = []
with profiled("Disambiguation in %s"):
# First we get all the page connected to each spot
pages = defaultdict(list)
lp = {}
index = 0
while index < len(spots):
spot = spots[index]
link_prob, senses = self.get_senses(spot)
allsenses.append(map(lambda x: x[0], senses))
if link_prob > 0:
lp[spot], pages[spot] = link_prob, senses
#print "Link probability", spot, link_prob
index += 1
else:
del spots[index]
print "Removing spot", spot, link_prob
scores = self.precompute_related(allsenses)
# Then we remove each spot and append it to the end
# so we can apply the voting scheme
candidates = defaultdict(list)
for spot in spots:
sense_ids = pages.pop(spot)
#print "Voting for spot", spot
for p_a, pr_a in sense_ids:
score = 0
counter = 0
for counter, (p_b, pg_pr_b) in enumerate(pages.items()):
pg_b = map(lambda x: x[0], pg_pr_b)
prg_b = map(lambda x: x[1], pg_pr_b)
score += self.vote_for(p_a, pg_b, prg_b, scores)
score /= float(counter + 1)
#print "Vote to spot", spot, p_a, "is", score
print "Link probability", lp[spot]
rho = (score + lp[spot]) / 2.0
candidates[spot].append((pr_a, rho, p_a))
pages[spot] = sense_ids
winning = {}
for spot, champion_list in candidates.items():
print "Spot", spot, "has the following:"
for pr_a, score, p_a in sorted(champion_list,
reverse=True,
key=lambda x: x[1]):
#if score < 0.05:
# continue
print "\tProability:", pr_a, "Score:", score, "Page id:", p_a, "Title", self.catalog.get_title(
p_a)
_, rho, wiki_id = sorted(champion_list,
reverse=True,
key=lambda x: x[1])[0]
print _, rho, wiki_id
#if rho > 0.05:
winning[spot] = {
"rho": rho,
"id": wiki_id,
"title": self.catalog.get_title(wiki_id),
"portals": self.catalog.get_portals(wiki_id),
}
return winning
def GET(self, path="/"):
return Catalog.list(path)
def PUT(self, path):
return Catalog.create(path)
