# path to the outpuut file kaggle_songs.txt
osfile = "output.txt"
print ("user_min: %d , user_max: %d"%(user_min,user_max))
sys.stdout.flush() #forces it to "flush" the buffer, meaning that it will write everything in the buffer to the terminal
# TRIPLETS
f_triplets_tr="train_triplets.txt" #48373586 triplets for training with exclusive users from kaggle_visible
f_triplets_tev="kaggle_visible_evaluation_triplets.txt" #1450933 triplets for recommendation evaluation, with exclusive new users users
print ('loading users in %s'%"kaggle_users.txt")
sys.stdout.flush()
users_v=list(MSD_util.load_users("kaggle_users.txt"))
print ('default ordering by popularity')
sys.stdout.flush()
songs_ordered=MSD_util.sort_dict_dec(MSD_util.song_to_count(f_triplets_tr)) # song_to_count creates a dictionary (song,count) and then it sorts the dict in decresing order
print ("loading unique users indexes")
uu = MSD_util.unique_users(f_triplets_tr) #unique_users returns a set of unique users in the train_triplets
u2i = {} # creates a dictionary (userId,index)
for i,u in enumerate(uu):
u2i[u]=i
print ('song to users on %s'%f_triplets_tr)
s2u_tr=MSD_util.song_to_users(f_triplets_tr) #creates dict with (song, set of users who have listened to this song)
print ("converting users to indexes") #converts the userIDs in s2u_tr to their index uu
for s in s2u_tr:
s_set = set()
for u in s2u_tr[s]:
s_set.add(u2i[u])
# triplets
f_triplets_tr = "kaggle_visible_evaluation_triplets.txt"
f_triplets_tev ="kaggle_visible_evaluation_triplets.txt"
print 'loading users in %s ' % "kaggle_users.txt"
sys.stdout.flush()
users_v = list(MSD_util.load_users("kaggle_users.txt"))
print ' default ordering by popularity'
sys.stdout.flush()
songs_ordered=MSD_util.sort_dict_dec(MSD_util.song_to_count(f_triplets_tr))
print 'loading unique users indexes'
uu = MSD_util.unique_users(f_triplets_tr)
u2i={}
for i,u in enumerate(uu):
u2i[u]=i
print ' song to users on %s ' % f_triplets_tr
s2u_tr = MSD_util.song_to_users(f_triplets_tr)
print ' converting users to indexes'
for s in s2u_tr:
s_set = set()
import sys
import MSD_util, MSD_rec
# paths to data
f_triplets_tr = "../data/train_data.txt"
f_triplets_vv = "../data/valid_visible.txt"
f_triplets_vp = "../data/valid_predict.txt"
# parameters
_tau = 500
print 'default ordering by popularity'
sys.stdout.flush()
songs_ordered = MSD_util.sort_dict_dec(
MSD_util.song_to_count(f_triplets_tr, binary=False))
print 'user to songs on %s' % f_triplets_vv
u2s_vv = MSD_util.user_to_songs(f_triplets_vv)
print 'user to songs on %s' % f_triplets_vp
u2s_vp = MSD_util.user_to_songs(f_triplets_vp)
# recommend top N most popular songs (extremely unpersonalized :|)
all_recs = []
for u in u2s_vv:
recs_500 = set(songs_ordered[:500]) - u2s_vv[u]
recs4u = list(recs_500)
if len(recs4u) < 500:
n_more = 500 - len(recs4u)
recs4u += songs_ordered[500:500 + n_more]
all_recs.append(recs4u)
print "user_min: %d , user_max: %d" % (user_min, user_max)
sys.stdout.flush()
# TRIPLETS
f_triplets_tr = "kaggle_visible_evaluation_triplets.txt"
f_triplets_tev = "year1_valid_triplets_visible.txt"
f_triplets_teh = "year1_valid_triplets_hidden.txt"
print 'loading users in %s' % "kaggle_users.txt"
sys.stdout.flush()
users_v = list(MSD_util.load_users("user_valid.txt"))
print 'default ordering by popularity'
sys.stdout.flush()
songs_ordered = MSD_util.sort_dict_dec(MSD_util.song_to_count(f_triplets_tr))
print "loading unique users indexes"
uu = MSD_util.unique_users(f_triplets_tr)
u2i = {}
for i, u in enumerate(uu):
u2i[u] = i
print 'song to users on %s' % f_triplets_tr
s2u_tr = MSD_util.song_to_users(f_triplets_tr)
print "converting users to indexes"
for s in s2u_tr:
s_set = set()
for u in s2u_tr[s]:
s_set.add(u2i[u])
def main(argv):
if len(argv) < 3:
print(
"Nee more arguments, Example:MSD_subm_rec.py user_min user_max resultFile.txt"
)
user_min = 1
user_max = 110000
osfile = "resultfull.txt"
#exit()
else:
user_min = argv[0]
user_max = argv[1]
osfile = argv[2]
user_min = int(user_min)
user_max = int(user_max)
print("user_min: %d , user_max: %d" % (user_min, user_max))
sys.stdout.flush()
# TRIPLETS
f_triplets_tr = "train_triplets.txt"
f_triplets_tev = "kaggle_visible_evaluation_triplets.txt"
print('loading users in %s' % "kaggle_users.txt")
sys.stdout.flush()
users_v = list(MSD_util.load_users("kaggle_users.txt"))
print('default ordering by popularity')
sys.stdout.flush()
songs_ordered = MSD_util.sort_dict_dec(
MSD_util.song_to_count(f_triplets_tr))
print("loading unique users indexes")
uu = MSD_util.unique_users(f_triplets_tr)
u2i = {}
for i, u in enumerate(uu):
u2i[u] = i
print('song to users on %s' % f_triplets_tr)
s2u_tr = MSD_util.song_to_users(f_triplets_tr)
print("converting users to indexes")
for s in s2u_tr:
s_set = set()
for u in s2u_tr[s]:
s_set.add(u2i[u])
s2u_tr[s] = s_set
del u2i
print('user to songs on %s' % f_triplets_tev)
u2s_v = MSD_util.user_to_songs(f_triplets_tev)
print('Creating predictor..')
_A = 0.15
_Q = 3
### calibrated
### pr=MSD_rec.PredSIc(s2u_tr, _A, _Q, "songs_scores.txt")
### uncalibrated
pr = MSD_rec.PredSI(s2u_tr, _A, _Q)
print('Creating recommender..')
cp = MSD_rec.SReco(songs_ordered)
cp.Add(pr)
cp.Gamma = [1.0]
r = cp.RecommendToUsers(users_v[user_min:user_max], u2s_v)
MSD_util.save_recommendations(r, "kaggle_songs.txt", osfile)
f_triplets_tr = "../data/train_data.txt"
f_triplets_vv = "../data/valid_visible.txt"
f_triplets_vp = "../data/valid_predict.txt"
# parameters
_A = 0
_Q = int(Q)
_tau = 200
_Gamma = [1.0]
_n_batch = 10
if mode == 1:
print 'default ordering by popularity'
sys.stdout.flush()
songs_ordered = MSD_util.sort_dict_dec(
MSD_util.song_to_count(f_triplets_tr))
print 'user to songs on %s' % f_triplets_tr
u2s_tr = MSD_util.user_to_songs(f_triplets_tr)
print 'user to songs on %s' % f_triplets_vv
u2s_vv = MSD_util.user_to_songs(f_triplets_vv)
print 'user to songs on %s' % f_triplets_vp
u2s_vp = MSD_util.user_to_songs(f_triplets_vp)
print 'Creating predictor...'
predictor = MSD_rec.PredSU(u2s_tr, _A, _Q)
print 'Creating recommender..'
recommender = MSD_rec.Reco(songs_ordered, _tau, _Gamma)
recommender.Add(predictor)
recommender.Valid(100,
u2s_vp.keys()[user_min:user_max], u2s_vv, u2s_vp,
_n_batch, suffix)
