def CCPPR(u, q):
"""
Returns the CCPPR vector for user u, combination of CPPR vectors of similar users.
This PPR computation is a two-level-collaborativeness PPR. It will be tested if we have time.
"""
sim_scores_and_users = load_sim_users(mdb().host, [u])[u]
result = {} # will map urlid -> cppr_score
original_index = g.vertex_properties['original_ids']
# For every similar user
for sim_u_s_u, u_s in sim_scores_and_users:
# First, compute the PPR vector
ppr = CPPR(u_s, q)
# Then, compute the combined score of every URL in the graph
for v in g.vertices():
orig_id = original_index[v]
result[orig_id] = sim_u_s_u * ppr[v] + result.setdefault(orig_id, 0)
# result = sum([sim_u_s_u * PPR(u_s, q) for sim_u_s_u, u_s in sim_scores_and_users])
return result
def main(argv, no_compute=False, store_results=True):
import sys
# from sys import argv
from time import time
argc = len(argv)
if argc <= len(CLI_ARGS):
print "Usage: %s" % argv[0], ' '.join(CLI_ARGS)
print "Currently missing parameters arguments:", ' '.join(CLI_ARGS[len(argv)-1:])
exit()
pairs_path = argv[1].strip()
mdb_host = argv[2].strip()
t_init = time()
with univ_open(pairs_path, 'r') as f:
pairs = [tuple([int(_) for _ in l.strip().split(" ")]) for l in f]
targeted_users = [p[1] for p in pairs]
sim_users = load_sim_users(mdb_host, targeted_users)
all_allowed_users = load_set_of_similar_users(mdb_host, targeted_users) | set(targeted_users)
# This will load everything needed to compute sim() function in-ram
similar_users(argv[1:], True, preset_set_of_users=all_allowed_users)
print "We loaded", len(us.users_clicks_list_indexed_by_uid), "users' clicks information"
# Init the sim_users array of the user_similiraty module
us.init_sim_users(sim_users)
if no_compute:
return
# We're ready to compute!
for q, u in pairs:
# Note: The collaborative score is not computed on the user's own clicked pages
# but using the clicks of the users similar to him
# so we have to compute scores for all pages clicked BY ITS SIMILAR USERS
for sim, u_sim in sim_users[u]:
try:
pages = us.users_clicks_list_indexed_by_uid[u_sim][q].keys()
except KeyError as err:
key = int(err.message)
if key == q and key != u_sim:
# Well, this similar user just does not have queried this query...
continue
print "For u_sim, q", u_sim, q
print "KeyError with value=", err
continue
if pages is None:
print "?!?! The user", u_sim, "user similar to", u, "has no pages information."
else:
print "User", u_sim, "user similar to", u, "has", len(pages), "pages informations for query", q
for page in pages:
# try:
sys.stdout.write("score(u=%s, q=%s, p=%s)=" % (u, q, page))
sys.stdout.flush()
sys.stdout.write("%.5e\n" % us.score(q, page, u))
# except KeyError as err:
# print "The KeyError is:", err
# print "For some reason, the user", u, "probably is not in the sim_users cache"
# print "Dump of the sim_users cache:"
# print sim_users
# raise err
# except Exception as err:
# print "Errors happen, this time it is:", err
if store_results:
print "Storing the computed scores in the DB"
print "We are going to use the cache of the us module"
print "The cache currently contains", len(us.scores), "entries"
t0 = time()
mdb = MongoClient(host=mdb_host)
scores_vectors = {}
for (q, p, u), score in us.scores.items():
if score == 0.0:
# Nil score is the same as no score
continue
try:
scores_vectors[(u, q)].append((p, score))
except KeyError:
scores_vectors[(u, q)]= [(p, score)]
print "Precomputation took", time()-t0
print "We have", len(scores_vectors), "'(u, q) -> score' entries to commit to the DB"
print "Committing new one..."
t0 = time()
scores_to_commit = []
for (user, q), scores in scores_vectors.items():
print "Dropping previous information..."
mdb.users.urls_perso_scores.remove({'uid': user, 'qid': q})
scores_to_commit.append(
{
'uid': user,
'qid': q,
'vector': scores
} \
)
mdb.users.urls_perso_scores.insert(scores_to_commit)
print "Done, committing took", time()-t0
print "Stats:"
for (user, q), scores in scores_vectors.items():
print "We have", len(scores), "scores for (q, u)=", (q, user)
print "Script execution took", time()-t_init