def get_nn_multi_query(self, song_id_list, nn_cf_scores, fanout):
print "*********************************************************"
print " GET NN MULTI QUERY "
print "*********************************************************"
st = time.time()
query_dicts = self.get_query_data_multi_query(song_id_list)
print "INFO: Step 1, Get query data and supervectors:", time.time(
) - st
st = time.time()
collab_song_infos = self.get_collab_info_multi_query(song_id_list)
print "INFO: Step 2, Get collaborative filtering results:", time.time(
) - st
total_nn_dict = {}
total_id_list = []
out_nn_cf_scores = []
st = time.time()
cf_data_query_time = 0
dist_comp_time = 0
for input_song in nn_cf_scores:
input_song_id = input_song[0]
input_song_cf_score = input_song[1]
if len(collab_song_infos[input_song_id].keys()) > 0:
t1 = time.time()
close_songs_dict = pardora_db_sqlite.get_cf_songs_data(
collab_song_infos[input_song_id], self.cursor)
cf_data_query_time += time.time() - t1
t2 = time.time()
nn_dict = self.get_nn_dict(query_dicts[input_song_id],\
close_songs_dict, fanout,\
parent_cf_score=input_song_cf_score)
dist_comp_time += time.time() - t2
else:
print "No collaborative filtering neighbors found."
nn_dict = None
total_nn_dict[input_song_id] = nn_dict
for k in nn_dict.keys():
total_id_list.append(k)
out_nn_cf_scores.append(
(k, collab_song_infos[input_song_id][k]))
print "INFO: Step 3, Compute closest songs:", time.time() - st,\
";\n \tCF data gather (", cf_data_query_time, "), Dist comp (", dist_comp_time, ")"
return total_nn_dict, out_nn_cf_scores, total_id_list
def get_nn_one_query(self, song_id_list, fanout):
print "*********************************************************"
print " GET NN COMPOUND QUERY "
print "*********************************************************"
st = time.time()
query_dict = self.get_query_data(song_id_list)
print "INFO: Step 1, Get query data and supervectors:", time.time(
) - st
st = time.time()
collab_song_info = self.get_collab_info(song_id_list)
print "INFO: Step 2, Get collaborative filtering results:", time.time(
) - st
st = time.time()
cf_data_query_time = 0
dist_comp_time = 0
nn_cf_scores = []
if len(collab_song_info.keys()) > 0:
t1 = time.time()
close_songs_dict = pardora_db_sqlite.get_cf_songs_data(
collab_song_info, self.cursor)
cf_data_query_time += time.time() - t1
t2 = time.time()
nn_dict = self.get_nn_dict(query_dict, close_songs_dict, fanout)
dist_comp_time += time.time() - t2
# keep track of cf score of the neighbors separately
for n in nn_dict.keys():
nn_cf_scores.append((n, collab_song_info[n]))
else:
print "No collaborative filtering neighbors found."
nn_dict = None
print "INFO: Step 3, Compute closest songs:", time.time() - st,\
";\n \tCF data gather (", cf_data_query_time, "), Dist comp (", dist_comp_time, ")"
return nn_dict, nn_cf_scores
