def run(self):
global results
while True:
user = self.queue.get()
try:
results.append(EvalUser.load(user))
except Exception:
traceback.print_exc(sys.stderr)
self.queue.task_done()
def run(self):
global results
while True:
user = self.queue.get()
try:
results.append(EvalUser.load(user))
except Exception:
traceback.print_exc(sys.stderr)
self.queue.task_done()
def getUsersToFollow(keyword, count=10, currentFriends=[]):
twitterApi = APISingleton.getInstance()
users = twitterApi.search_users(q=keyword, per_page=random.randint(20, 200), page=random.randint(1,10))
results = []
goodResults = []
for u in users:
try:
eUser = EvalUser.load(u)
results.append(eUser)
results += eUser.BFS(100, random_walk=True)
goodResults = [u for u in results if u.getImpactFactor()*u.getRetweetFactor() > 0.05 and u.getUserObj().id not in currentFriends]
print "Keyword=",keyword, "GoodResults#=",len(goodResults)
if len(goodResults) > count:
return goodResults
except Exception:
traceback.print_exc(sys.stdout)
pass
return goodResults
tweets = twitterApi.search(q=sys.argv[1].lower(),
rpp=100,
page=1,
force=True)
tweets = [t.text for t in tweets]
tfidfArray = getTFIDFArray(tweets)
db = DBSingleton.getInstance()
cur = db.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute(
"select * from users where retweet_factor > 0.2 and impact_factor > 0.2 limit 1000;"
)
results = []
for row in cur:
try:
eUser = EvalUser.loadFromDBRow(row)
if eUser.getSim(tfidfArray) > 0:
results.append(eUser)
except Exception, e:
traceback.print_exc(sys.stdout)
pass
results.sort()
results.reverse()
jsonDic = []
for user in results[:40]:
jsonDic.append(user.getData())
print simplejson.dumps(jsonDic)
file = l[-1].replace("'", "").replace(".lock", "")
print file
os.remove(file) """
raise e
if __name__ == "__main__":
while 1:
try:
twitterApi = APISingleton.getInstance()
#ids = [random.randint(1, 100000000) for i in range(100)]
users = [i.user for i in twitterApi.public_timeline(nocache=True)]
friends = users[random.randint(0, len(users))].all_friend_ids()
ids = random.sample(friends, min(len(friends), 100))
us = handle_func(twitterApi.lookup_users, user_ids=ids)
print len(us)
for u in us:
EvalUser.load(u)
for fId in twitterApi.friends_ids(user_id=u.id)[0]:
print "fid: ", fId
try:
EvalUser.loadFromDB(fId)
except Exception, e:
handle_exception(e)
print e
except Exception, e:
handle_exception(e)
#print traceback.print_exc(sys.stderr)
print e
l = str(e).split()
file = l[-1].replace("'", "").replace(".lock", "")
print file
os.remove(file) """
raise e
if __name__ == "__main__":
while 1:
try:
twitterApi = APISingleton.getInstance()
#ids = [random.randint(1, 100000000) for i in range(100)]
users = [i.user for i in twitterApi.public_timeline(nocache=True)]
friends = users[random.randint(0, len(users))].all_friend_ids()
ids = random.sample(friends, min(len(friends), 100))
us = handle_func(twitterApi.lookup_users, user_ids=ids)
print len(us)
for u in us:
EvalUser.load(u)
for fId in twitterApi.friends_ids(user_id=u.id)[0]:
print "fid: ",fId
try:
EvalUser.loadFromDB(fId)
except Exception,e:
handle_exception(e)
print e
except Exception, e:
handle_exception(e)
#print traceback.print_exc(sys.stderr)
print e
import sys
import traceback
sys.path.append("../lib/")
import simplejson
from tweeapi import APISingleton
from tweeapi.utils import EvalUser
if __name__ == "__main__":
twitterApi = APISingleton.getInstance()
users = twitterApi.search_users(q=sys.argv[1], per_page=200)
results = []
for u in users[:]:
try:
eUser = EvalUser.load(u)
results.append(eUser)
results += eUser.BFS(10)
except Exception,e:
traceback.print_exc(sys.stdout)
pass
results.sort()
results.reverse()
jsonDic = []
for user in results:
jsonDic.append(user.getData())
print simplejson.dumps(jsonDic)
if __name__ == "__main__":
twitterApi = APISingleton.getInstance()
tweets = twitterApi.search(q=sys.argv[1].lower(), rpp=100, page=1, force=True)
tweets = [t.text for t in tweets]
tfidfArray = getTFIDFArray(tweets)
db = DBSingleton.getInstance()
cur = db.cursor(cursor_factory=psycopg2.extras.DictCursor)
cur.execute("select * from users where retweet_factor > 0.2 and impact_factor > 0.2 limit 1000;")
results = []
for row in cur:
try:
eUser = EvalUser.loadFromDBRow(row)
if eUser.getSim(tfidfArray) > 0:
results.append(eUser)
except Exception,e:
traceback.print_exc(sys.stdout)
pass
results.sort()
results.reverse()
jsonDic = []
for user in results[:40]:
jsonDic.append(user.getData())
print simplejson.dumps(jsonDic)
