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]) s2u_tr[s]=s_set del u2i
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() for u in s2u_tr[s]: s_set.add(u2i[u]) s2u_tr[s]=s_set
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)
# 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]) s2u_tr[s] = s_set del u2i
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.txt" #48373586 triplets for training with exclusive users from kaggle_visible f_triplets_tev="testV.txt" #1450933 triplets for recommendation evaluation, with exclusive new users users f_triplets_teh = "testH.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)) # 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]) s2u_tr[s]=s_set