def interactive_tests_CPPR():
while True:
queryid, user_id = map(int, raw_input("QueryID UserId?").split())
print "Computing CPPR(u,q)..."
pr = CPPR(user_id, queryid)
print "Done. Computing result..."
result = [0] * 100
i = 0
for _ in g.vertices():
# print "PR for vertex", i, "is\t", pr[_]
pp(result, (pr[_], i))
i += 1
print "Top 100 PR scores="
print nlargest(100, result)
def interactive_tests_CCPPR():
while True:
print "New CPPR computation..."
queryid, user_id = map(int, raw_input("QueryID UserId?").split())
print "Computing CCPPR(u,q)..."
t0 = time()
pr = CCPPR(user_id, queryid)
print "Done in", time()-t0
print "Computing top result..."
result = [0] * 100
i = 0
for _ in g.vertices():
# print "PR for vertex", i, "is\t", pr[_]
pp(result, (pr[_], i))
i += 1
print "Top 100 CPR scores="
print nlargest(100, result)
def compute_user_sim_batch(start):
"""
This function will be executed by the worker process. It computes a batch of user similarities, starting at
start-th user_id in the global variabl `users_set` and until the (start+BATCH_SIZE)-th user_id
For every user_id it will compute the similarity against _ALL_ the other users (except the same user) and
will keep the `TOP_N_SIMILAR_USERS`-th top ones in terms of similarity value.
It will then commit them directly to mongodb before terminating.
"""
from heapq import heappushpop as pp
print "Starting compute_user_sim_batch(", start, ")"
# Initializing heaps for every user we are going to deal with
results = {uid:([0] * TOP_N_SIMILAR_USERS) for uid in list(users_set)[start:start+BATCH_SIZE]}
t0 = time()
t1 = time()
i = 0
print_interval = int(BATCH_SIZE*len(users_set)/N_OF_PRINTS_PER_BATCH) # So that we approximately print the state X times
for u1, u2 in user_pair(start):
# print "Executing process", start, "for", u1, u2
try:
sim_res = us.sim(u1, u2)
if abs(sim_res - 1.0) < ZERO_FLOAT:
continue # Do not keep similarities too close to 1.0 (too similar users)
pp(results[u1], (sim_res, u2))
except KeyError as err:
print err
print "Huh? KeyError u1=", u1, "start=", start, "BATCH_SIZE=", BATCH_SIZE
i += 1
if i % print_interval is 0:
print i+1, \
"sim() calls\t in %5.1f" % (time()-t0), \
"\t\t avg %.4e" % ((time()-t0)/float(i+1)), \
"\t local_avg %.4e" % ((time()-t1)/float(i+1)), \
"\t(process", start, ")"
t1 = time()
print "Computation from", start, "to", start + BATCH_SIZE, "finished in", time()-t0,". Committing..."
t0 = time()
commit_user_sims(start, results)
print "Finished committing in", time()-t0
return True # Just to show we finished things properly
