def restart():
print("RESTART RECOMMENDATION ENGINE", file=stderr)
Recommender.mat = generate_mat()
Recommender.tmat = Recommender.mat.transpose()
Recommender.model = NMSLibAlternatingLeastSquares()
Recommender.model.fit(Recommender.mat)
Recommender.last_restart = datetime.utcnow()
Recommender.count = Review.query.count()
def fromds(cls, ds):
DEFAULT = {
'factors': 40,
'regularization': 0.01,
'iterations': 10,
}
coo, users, movies = ds.model_input
attrs = {
'ds': ds,
'model': NMSLibAlternatingLeastSquares(**DEFAULT),
'config': hashlib.md5(str(DEFAULT).encode()).hexdigest(),
'_fit': False,
'data': coo,
'user_lookup': users,
'movie_lookup': movies,
'redis': Redis().redis
}
return cls(**attrs)
def _get_model(self):
return NMSLibAlternatingLeastSquares(factors=2,
regularization=0,
index_params={"post": 2},
random_state=23)
def _get_model(self):
return NMSLibAlternatingLeastSquares(factors=2, regularization=0,
index_params={'post': 2})
train_user_small.append(train_user[idx]); train_music_small.append(train_music[idx]); train_count_small.append(train_count[idx])
train_user[idx] = 0; train_music[idx] = 0; train_count[idx] = 0
'''
train = coo_matrix((train_count, (train_user, train_music))).tocsr()
#train_small = coo_matrix((train_count_small, (train_user_small, train_music_small))).tocsr()
test = coo_matrix((test_count, (test_user, test_music)))
ex_train_user = sorted(set(train_user))
ex_train_music = sorted(set(train_music))
#del ex_train_user[0]; del ex_train_music[0]
#ex_train_user_small = sorted(set(train_user_small))
#ex_train_music_small = sorted(set(train_music_small))
ex_test_user = sorted(set(test_user))
ex_test_music = sorted(set(test_music))
print("Status : Train data loaded. CV ready.")
# WMF 모델 생성
#model = wmf(factors=NO_COMPONENT, regularization=REGULARIZATION)
#model.fit(train.transpose() * ALPHA)
# NMSLIB 모델 생성
model_nmslib = NMSLIB()
model_nmslib.fit(train.transpose() * ALPHA)
print(
"Status : Model trained. (No. users = %d, No. component = %d, Regularization = %.2f, Top K = %d)"
% (len(ex_train_user), NO_COMPONENT, REGULARIZATION, TOP_K))
ndcg_score(model_nmslib, test, ex_test_user, ex_test_music, TOP_K)
#ndcg_score(model_nmslib, test, ex_train_user_small, ex_train_music_small, TOP_K)
def _get_model(self):
return NMSLibAlternatingLeastSquares(factors=2, regularization=0)