def _generate_answers(self, song_meta_json, train, questions):
song_meta = {int(song["id"]): song for song in song_meta_json}
song_mp_counter, song_mp = most_popular(train, "songs", 200)
tag_mp_counter, tag_mp = most_popular(train, "tags", 100)
song_mp_per_genre = self._song_mp_per_genre(song_meta, song_mp_counter)
answers = []
for q in tqdm(questions):
genre_counter = Counter()
for sid in q["songs"]:
for genre in song_meta[sid]["song_gn_gnr_basket"]:
genre_counter.update({genre: 1})
top_genre = genre_counter.most_common(1)
if len(top_genre) != 0:
cur_songs = song_mp_per_genre[top_genre[0][0]]
else:
cur_songs = song_mp
answers.append({
"id": q["id"],
"songs": remove_seen(q["songs"], cur_songs)[:100],
"tags": remove_seen(q["tags"], tag_mp)[:10]
})
return answers
def _generate_answers(self, song_meta_json, train, questions):
# key를 song_id value를 해당 song_id에 대한 정보로 dictionary 생성
song_meta = {int(song["id"]): song for song in song_meta_json}
# 상위 200개 곡
song_mp_counter, song_mp = most_popular(train, "songs", 200)
# 상위 100개 태그
tag_mp_counter, tag_mp = most_popular(train, "tags", 100)
song_mp_per_genre = self._song_mp_per_genre(song_meta, song_mp_counter)
answers = []
for q in tqdm(questions):
genre_counter = Counter()
for sid in q["songs"]:
for genre in song_meta[sid]["song_gn_gnr_basket"]:
genre_counter.update({genre: 1})
top_genre = genre_counter.most_common(1)
# 가장 인기있는 장르가 존재하면
if len(top_genre) != 0:
# 해당 장르에서 가장 많이 등장한 song 추천
cur_songs = song_mp_per_genre[top_genre[0][0]]
else:
# 아니면 가장 많이 등장한 노래 추천
cur_songs = song_mp
answers.append({
"id": q["id"],
"songs": remove_seen(q["songs"], cur_songs)[:100],
"tags": remove_seen(q["tags"], tag_mp)[:10]
})
return answers
def fill_X(train, val):
# embedding load ( need to mk s2v model by ply_tag_embedding.py )
embed = PlyEmbedding(train)
embed.load_s2v(s2v_path)
# p2v for tag_by_song, title-vector for t2r
titles, vectors = embed.song_based(
mode='s2v', by='mean',
keyedvector=False) ## title, vectors not in KeydVectos
# T2R
t2r = Title2Rec()
# remove non alpha or hangul. tokenize, ( t : title, v : vectors, ID : plylst id )
t, v, ID = t2r.preprocess_clustering(titles,
vectors,
ID=True,
khaiii=True,
verbose=True)
# load cluster ( need to mk cluster pkl file )
t2r.load_cluster(cluster_path)
# sort by cluster & distance from center
data = t2r.pre_fasttext(t, v)
# fit fasttext & title2rec
t2r.fit_fasttext(data)
t2r.fit_title2rec(t, ID)
# most popular
_, pop_songs = most_popular(train, 'songs', 100)
_, pop_tags = most_popular(train, 'tags', 10)
for ply in tqdm(val):
ply['song_dirty'] = 0
ply['tag_dirty'] = 0
if ply['songs'] != []:
if ply['tags'] != []:
pass
else:
ply['tags'] = embed.tag_by_songs(ply, 10, 3.9)
if len(ply['tags']) < 10:
ply['tags'] = put_most_popular(ply['tags'], pop_tags)
ply['tag_dirty'] = 1
else:
songs, tags, song_sign, tag_sign = t2r.title2rec(
ply, 100, 10, song_const, tag_const)
if (song_sign) and (len(songs) == 0):
songs = put_most_popular(songs, pop_songs)
#raise RuntimeError("song length < 100")
if (tag_sign) and (len(tags) < 10):
tags = put_most_popular(tags, pop_tags)
ply['songs'] = songs
ply['tags'] = tags
ply['song_dirty'] = song_sign
ply['tag_dirty'] = tag_sign
return val
def _generate_answers(self, train, questions):
_, song_mp = most_popular(train, "songs", 200)
_, tag_mp = most_popular(train, "tags", 100)
answers = []
for q in tqdm(questions):
answers.append({
"id": q["id"],
"songs": remove_seen(q["songs"], song_mp)[:100],
"tags": remove_seen(q["tags"], tag_mp)[:10],
})
return answers
def generate_answers(train, questions):
_, song_mp = most_popular(train, "songs", 200)
_, tag_mp = most_popular(train, "tags", 100)
answers = []
for q in questions:
if len(q["songs"]) != 0 and len(q["tags"]) != 0:
answers.append({
"id": q["id"],
"songs": q["songs"],
"tags": q["tags"]
})
else:
answers.append({
"id": q["id"],
"songs": remove_seen(q["songs"], song_mp)[:100],
"tags": remove_seen(q["tags"], tag_mp)[:10]
})
return answers
def _generate_answers(self, song_meta_json, train,
questions): #train : arena_data/orig/train.json
song_meta = {int(song["id"]): song
for song in song_meta_json
} #song id를 키값으로 저장하고 그 song의 특징(tags, name 등) 저장
song_mp_counter, song_mp = most_popular(
train, "songs", 200
) #song_mp_counter : 딕셔너리값({... , 18273 : 1, ...}),song_mp : train에서 'songs'에 가장 많이 있는 200개 곡
tag_mp_counter, tag_mp = most_popular(
train, "tags", 100) #train에서 'tags'에 가장 많이 있는 100개 태그
song_mp_per_genre = self._song_mp_per_genre(
song_meta, song_mp_counter
) #song_mp_per_genre = res ex) res = { pop : ['hello' : 200 ... ], }
art_dic = self._artist_songs(
song_meta, song_mp_counter) #200넘는곡을 가진 가수의 이름과 곡 딕셔너리
tag_id = self._songs_most_tag(train)
answers = []
for q in tqdm(questions):
genre_counter = Counter()
art_c = Counter()
tag_c = Counter()
for sid in q["songs"]:
for genre in song_meta[sid]["artist_name_basket"]:
art_c.update({genre: 1})
artist_name = list(art_c.keys())
for sid in q["songs"]:
for genre in song_meta[sid]["song_gn_gnr_basket"]:
genre_counter.update({genre: 1})
top_genre = genre_counter.most_common(1)
if len(artist_name) == 1 and artist_name[0] in art_dic.keys():
cur_songs = list(art_dic[artist_name[0]])
elif len(top_genre) != 0:
cur_songs = song_mp_per_genre[top_genre[0][0]]
else:
cur_songs = song_mp
tag_list = []
if (q['songs'] != []):
for sid in q["songs"]:
if (sid in tag_id):
for a in tag_id[sid]:
tag_c.update({a: 1})
tag_list = [k for k, v in tag_c.most_common()]
if len(tag_list) > 10:
cur_tags = tag_list[:10]
else:
new_list = remove_seen(tag_list, tag_mp)[:10]
cur_tags = (tag_list + new_list)[:10]
else:
cur_tags = remove_seen(q["tags"], tag_mp)[:10]
answers.append({
"id": q["id"],
"songs": remove_seen(q["songs"], cur_songs)[:100],
"tags": cur_tags
})
return answers
def _generate_answers(self, song_meta_json, train, questions):
song_meta = {int(song["id"]): song for song in song_meta_json}
train_meta = {int(plylst["id"]): plylst for plylst in train}
song_mp_counter, song_mp = most_popular(train, "songs", 200)
tag_mp_counter, tag_mp = most_popular(train, "tags", 100)
song_mp_per_genre = self._song_mp_per_genre(song_meta, song_mp_counter)
tag_per_song = self._tag_per_song(train_meta)
## modified for song prediction
## pre-processing train set data
_, song_pop = most_popular(train, "songs", 200000)
#song_pop = set(song_pop)
voca_dict, voca_dict_t = self._build_vocadict(song_pop)
# filtering song list
num_users = len(train)
f_song_lst, f_usr_lst = self._const_filtered_lst(train,
voca_dict,
num_users,
to_idx=num_users,
val=False)
num_items = len(set(f_song_lst))
data_len = len(f_song_lst)
# re-setting index of filtered songs
item_ids = np.array([voca_dict[i] for i in f_song_lst])
data = np.ones(data_len)
rows, cols, data = zip(*set(zip(f_usr_lst, item_ids, data)))
print('train preproc done', num_items)
## pre-processing valid/test set data
v_num_users = len(questions)
f_song_lst_v, f_usr_lst_v = self._const_filtered_lst(
questions, voca_dict, num_users, to_idx=v_num_users, val=True)
data_len_v = len(f_song_lst_v)
v_item_ids = np.array([voca_dict[i] for i in f_song_lst_v])
v_data = np.ones(data_len_v)
v_rows, v_cols, v_data = zip(
*set(zip(f_usr_lst_v, v_item_ids, v_data)))
print('valid preproc done', num_items)
n_rows = rows + v_rows
n_cols = cols + v_cols
n_data = data + v_data
t_num_users = num_users + v_num_users
usr_item_mat = sp.csr_matrix((n_data, (n_rows, n_cols)),
shape=(t_num_users, num_items))
item_usr_mat = usr_item_mat.T
als_model = implicit.als.AlternatingLeastSquares(factors=100,
regularization=0.05,
iterations=50)
#als_model = implicit.bpr.BayesianPersonalizedRanking(factors=50) ### actually bpr
#als_model = implicit.lmf.LogisticMatrixFactorization(factors=50) ### actually Logistic MF
als_model.fit(item_usr_mat)
print("als model fitting done")
### for cold-start users (plylst containing no song)
title_to_tok, vocab = title_to_token(train)
v_title_to_tok, v_vocab = title_to_token(questions)
title_to_tok.extend(v_title_to_tok)
vocab.extend(v_vocab)
print("title to token converted", len(title_to_tok), len(vocab))
fin_vocab = get_fin_vocab(vocab)
print("final vocab size", len(fin_vocab))
title_to_idx = []
for plylst in title_to_tok:
res_idx = tok_to_idx(plylst, fin_vocab)
title_to_idx.append(res_idx)
user_lst, vocab_lst = preproc_for_csr(title_to_idx, 0)
cb_rows = np.array(user_lst)
cb_cols = np.array(vocab_lst)
cb_data = np.ones(len(user_lst))
plylst_tt_mat = sp.csr_matrix(
(cb_data, (cb_rows, cb_cols)),
shape=(len(title_to_tok), len(fin_vocab)))
print("csr matrix for tf-idf matrix made")
tfidf_mat = build_tfidf_mat(plylst_tt_mat)
####
answers = []
for idx, q in tqdm(enumerate(questions)):
genre_counter = Counter()
for sid in q["songs"]:
for genre in song_meta[sid]["song_gn_gnr_basket"]:
genre_counter.update({genre: 1})
top_genre = genre_counter.most_common(1)
if len(top_genre) != 0:
cur_songs = song_mp_per_genre[top_genre[0][0]]
else:
cur_songs = song_mp
## modified for tag prediction
tag_lst = self._tag_per_plylst(q, tag_per_song)
tag_res = remove_seen(q["tags"], tag_lst)[:10]
if len(tag_res) < 10:
tag_res = remove_seen(q["tags"], tag_mp)[:10]
## modified for song prediction
if len(q["songs"]) == 0:
n_idx = idx + len(train)
most_sim_lst = get_sim_plylst(tfidf_mat, given=n_idx, topn=30)
cands = gather_cand(train, questions, most_sim_lst)
song_res = remove_seen(q["songs"], cands)[:100]
#print(n_idx, song_res)
else:
song_lst = self._cal_alsmodel(idx, num_users, usr_item_mat,
als_model, voca_dict_t)
song_res = remove_seen(q["songs"], song_lst)[:100]
if len(song_res) < 100:
print('checked here', idx)
song_res = remove_seen(q["songs"], cur_songs)[:100]
answers.append({"id": q["id"], "songs": song_res, "tags": tag_res})
return answers
def Recommender(train,
questions,
n_msp,
n_mtp,
mode,
sim_measure,
song_meta,
freq_song,
save=False):
## 최종 추천리스트
rec_list = []
## 1단계: 전처리
# 1) 추천 결과가 없거나 모자란 경우를 위해 most_popular 생성
_, song_mp = most_popular(train, "songs", 200)
_, tag_mp = most_popular(train, "tags", 20)
# 2) 빠른 접근을 위한 Dictionary 생성
song_plylst_dic, song_tag_dic, plylst_song_dic, plylst_tag_dic, tag_plylst_dic, tag_song_dic, song_issue_dic, song_artist_dic = DicGenerator(
train, song_meta)
# 3) 미리 계산한 플레이리스트 유사도 불러오기
'''
sim_scores: 입력으로 들어온 questions과 train간 유사도 (Autoencoder 기반)
gnr_scores: 입력으로 들어온 questions과 train간 유사도 (genre 정보 추가)
title_scores: 입력으로 들어온 questions과 train간 유사도 (Word2vec 기반)
'''
sim_scores = np.load(f'scores/{mode}_scores_bias_{sim_measure}.npy',
allow_pickle=True).item()
gnr_scores = np.load(f'scores/{mode}_scores_bias_{sim_measure}_gnr.npy',
allow_pickle=True).item()
title_scores = np.load(
f'scores/{mode}_scores_title_{sim_measure}_24000.npy',
allow_pickle=True).item()
## 2단계: 함수 정의
# 1) Counter 객체에서 빈도수 기준 topk개 출력
def most_similar(cnt, topk):
cnt_topk = cnt.most_common(topk)
return [k for k, v in cnt_topk]
# 2) 미리 계산한 유사도 기준 topk개의 플레이리스트의 plylsts와 scores 출력
def most_similar_emb(q_id, topk, title=False, genre=False):
# title_scores 기준
if title:
plylsts = [t[0] for t in title_scores[q_id][:topk]]
scores = [t[1] for t in title_scores[q_id][:topk]]
# gnr_scores 기준
elif genre:
plylsts = [t[0] for t in gnr_scores[q_id][:topk]]
scores = [t[1] for t in gnr_scores[q_id][:topk]]
# sim_scores 기준
else:
plylsts = [t[0] for t in sim_scores[q_id][:topk]]
scores = [t[1] for t in sim_scores[q_id][:topk]]
return plylsts, scores
# 3) new_song_plylst_dict
def get_new_song_plylst_dict(plylst_ms):
new_song_plylst_dict = defaultdict(set)
for plylst in plylst_ms:
for _song in plylst_song_dic[plylst]:
new_song_plylst_dict[_song].add(plylst)
return new_song_plylst_dict
## 3단계: 입력으로 들어온 questions 플레이리스트에 대해 추천
for q in tqdm(questions):
# 1) question 플레이리스트의 정보
# 수록 song/tag
q_songs = q['songs']
q_tags = q['tags']
# 수록 song/tag와 함께 등장한 song/tag/plylst 빈도 수
song_plylst_C = Counter()
song_tag_C = Counter()
tag_plylst_C = Counter()
tag_song_C = Counter()
# 수록 song/tag가 둘 다 없거나 적을 때
no_songs_tags, few_songs_tags = False, False
if len(q_songs) == 0 and len(q_tags) == 0:
no_songs_tags = True
elif len(q_songs) <= 3:
few_songs_tags = True
# 2) 빈도수 기반 추천을 위해 카운트
# 수록 song에 대해
for q_s in q_songs:
song_plylst_C.update(song_plylst_dic[q_s])
song_tag_C.update(song_tag_dic[q_s])
# 수록 tag에 대해
for q_t in q_tags:
tag_plylst_C.update(tag_plylst_dic[q_t])
tag_song_C.update(tag_song_dic[q_t])
# 수록곡 수로 나눠서 비율로 계산
for i, j in list(song_plylst_C.items()):
if len(plylst_song_dic[i]) > 0:
song_plylst_C[i] = (j / len(plylst_song_dic[i]))
# 3) 유사도 기반 추천을 위해 점수 계산
plylst_song_scores = defaultdict(lambda: 0)
plylst_tag_scores = defaultdict(lambda: 0)
# Case 1: song과 tag가 둘 다 없는 경우
if no_songs_tags:
# plylst_ms / plylst_mt: title_scores 기준 유사한 플레이리스트 n_msp / n_mtp개
plylst_ms, song_scores = most_similar_emb(q['id'],
n_msp,
title=True)
plylst_mt, tag_scores = most_similar_emb(q['id'],
n_mtp,
title=True)
plylst_add, add_scores = most_similar_emb(q['id'], n_mtp)
# Case 2: song과 tag가 부족한 경우
elif few_songs_tags:
# plylst_ms / plylst_mt: sim_scores 기준 n_msp개 / title_scores 기준 n_mtp개
plylst_ms, song_scores = most_similar_emb(q['id'], n_msp)
plylst_mt, tag_scores = most_similar_emb(q['id'],
n_mtp,
title=True)
plylst_add, add_scores = most_similar_emb(q['id'],
n_mtp,
genre=True)
# Case 3: song과 tag가 충분한 경우
else:
# plylst_ms / plylst_mt: sim_scores 기준 유사한 플레이리스트 n_msp / n_mtp개
plylst_ms, song_scores = most_similar_emb(q['id'], n_msp)
plylst_mt, tag_scores = most_similar_emb(q['id'],
n_mtp,
genre=True)
plylst_add, add_scores = most_similar_emb(q['id'],
n_mtp,
title=True)
new_song_plylst_dict = get_new_song_plylst_dict(plylst_ms)
# 3-1. plylst_song_scores 계산
for idx, ms_p in enumerate(plylst_ms):
for song in plylst_song_dic[ms_p]:
song_score = 0
for q_s in q_songs:
try:
song_score += len(new_song_plylst_dict[q_s]
& new_song_plylst_dict[song]) / len(
new_song_plylst_dict[q_s])
except:
pass
if song in freq_song:
plylst_song_scores[song] += song_plylst_C[
ms_p] * song_score * song_scores[idx] * (n_msp -
idx) * 4
else:
plylst_song_scores[song] += song_plylst_C[
ms_p] * song_score * song_scores[idx] * (n_msp - idx)
for tag in plylst_tag_dic[ms_p]:
plylst_tag_scores[tag] += tag_scores[idx] * (n_msp - idx)
# 3-2. plylst_tag_scores 계산
for idx, mt_p in enumerate(plylst_mt):
for tag in plylst_tag_dic[mt_p]:
plylst_tag_scores[tag] += tag_scores[idx] * (n_mtp - idx)
for song in plylst_song_dic[mt_p]:
plylst_song_scores[song] += tag_scores[idx]
# 3-3. plylst_{song/tag}_scores 보정
for idx, mt_p in enumerate(plylst_add):
for tag in plylst_tag_dic[mt_p]:
plylst_tag_scores[tag] += add_scores[idx] * (n_mtp - idx)
# 4) song과 tag 둘 다 없거나 적은 경우 예측해서 채워넣기
if no_songs_tags:
# q_songs 새롭게 채워넣기 (원래는 song가 없지만 title_scores 기준 유사한 플레이리스트로부터 song 예측)
pre_songs = sorted(plylst_song_scores.items(),
key=lambda x: x[1],
reverse=True)
pre_songs = [scores[0] for scores in pre_songs][:200]
pre_songs = pre_songs + remove_seen(pre_songs, song_mp)
q_songs = pre_songs[:100]
# q_tags 새롭게 채워넣기 (원래는 tag가 없지만 title_scores 기준 유사한 플레이리스트로부터 tag 예측)
pre_tags = sorted(plylst_tag_scores.items(),
key=lambda x: x[1],
reverse=True)
pre_tags = [scores[0] for scores in pre_tags][:20]
pre_tags = pre_tags + remove_seen(pre_tags, tag_mp)
q_tags = pre_tags[:10]
# 5) questions 플레이리스트에 대해 추천
## song 추천
# song 있을 때
lt_song_art = []
if len(q_songs) > 0:
plylst_song_scores = sorted(plylst_song_scores.items(),
key=lambda x: x[1],
reverse=True)
lt_artist = []
for w_song in q_songs:
lt_artist.extend(song_artist_dic[w_song])
counter_artist = Counter(lt_artist)
counter_artist = sorted(counter_artist.items(),
key=lambda x: x[1],
reverse=True)
if few_songs_tags:
artist = [art[0] for art in counter_artist]
else:
artist = [x[0] for x in counter_artist if x[1] > 1]
cand_ms = [scores[0] for scores in plylst_song_scores
][(100 - len(artist)):1000]
for cand in cand_ms:
if artist == []:
break
if cand in q_songs:
break
for art in song_artist_dic[cand]:
if art in artist:
lt_song_art.append(cand)
artist.remove(art)
break
song_ms = [scores[0] for scores in plylst_song_scores][:200]
# song 없고, tag 있을 때
else:
song_ms = most_similar(tag_song_C, 200)
## tag 추천
# tag 있을 때
if len(q_tags) > 0:
plylst_tag_scores = sorted(plylst_tag_scores.items(),
key=lambda x: x[1],
reverse=True)
tag_ms = [scores[0] for scores in plylst_tag_scores][:20]
# tag 없고, song 있을 때
else:
plylst_tag_scores = sorted(plylst_tag_scores.items(),
key=lambda x: x[1],
reverse=True)
tag_ms = [scores[0] for scores in plylst_tag_scores][:20]
## issue date 늦은 song 제거
if q['updt_date']:
q_updt_date = q['updt_date'][:4] + q['updt_date'][5:7] + q[
'updt_date'][8:10]
song_ms = [x for x in song_ms if song_issue_dic[x] < q_updt_date]
## 중복 제거 및 부족하면 most_popular로 채워넣기
song_candidate = song_ms + remove_seen(song_ms, song_mp)
tag_candidate = tag_ms + remove_seen(tag_ms, tag_mp)
song_remove = q_songs
tag_remove = q_tags
song_candidate = song_candidate[:100] if no_songs_tags else remove_seen(
song_remove, song_candidate)[:100]
if len(lt_song_art) > 0:
lt_song_art = [x for x in lt_song_art if x not in song_candidate]
song_candidate[(100 - len(lt_song_art)):100] = lt_song_art
rec_list.append({
"id":
q["id"],
"songs":
song_candidate,
"tags":
tag_candidate[:10] if no_songs_tags else remove_seen(
tag_remove, tag_candidate)[:10]
})
# 6) results.json 파일 저장 여부
if save == True:
write_json(
rec_list, 'results/results_' +
dt.datetime.now().strftime("%y%m%d-%H%M%S") + '_' + mode + '.json')
return rec_list
