def train_embed(data_dir, params, model_name):
# ハイパラ読み込み
embedding_dim = params['embedding_dim']
batch_size = params['batch_size']
lr = params['lr']
weight_decay = params['weight_decay']
#warmup = params['warmup']
warmup = 350
#lr_decay_every = params['lr_decay_every']
lr_decay_every = 2
lr_decay_rate = params['lr_decay_rate']
if model_name == 'SparseTransE':
alpha = params['alpha']
# dataload
dataset = AmazonDataset(data_dir, model_name='TransE')
relation_size = len(set(list(dataset.triplet_df['relation'].values)))
entity_size = len(dataset.entity_list)
if model_name == 'TransE':
model = TransE(int(embedding_dim), relation_size, entity_size).to(device)
elif model_name == 'SparseTransE':
model = SparseTransE(int(embedding_dim), relation_size, entity_size, alpha=alpha).to(device)
iterater = TrainIterater(batch_size=int(batch_size), data_dir=data_dir, model_name=model_name)
#iterater.iterate_epoch(model, lr=lr, epoch=3000, weight_decay=weight_decay, warmup=warmup,
# lr_decay_rate=lr_decay_rate, lr_decay_every=lr_decay_every, eval_every=1e+5)
iterater.iterate_epoch(model, lr=lr, epoch=3000, weight_decay=weight_decay, warmup=warmup,
lr_decay_rate=lr_decay_rate, lr_decay_every=lr_decay_every, eval_every=1e+5,
early_stop=True)
return model
def objective(trial):
start = time.time()
# pagerank para
mu = trial.suggest_uniform('mu', 0, 1)
alpha = trial.suggest_uniform('beta', 0, 0.5)
kappa1 = trial.suggest_uniform('kappa1', 0, 1)
kappa2 = trial.suggest_uniform('kappa2', 0, 1)
kappa3 = trial.suggest_uniform('kappa3', 0, 1)
kappa = [kappa1, kappa2, kappa3]
# model para
embedding_dim = int(
trial.suggest_discrete_uniform('embedding_dim', 16, 128, 16))
#alpha = trial.suggest_loguniform('alpha', 1e-6, 1e-2) #SparseTransEの時だけ
# training para
lambda_ = trial.suggest_uniform('lambada_', 0, 1)
batch_size = trial.suggest_int('batch_size', 256, 512, 128)
lr = trial.suggest_loguniform('lr', 1e-4, 1e-2)
weight_decay = trial.suggest_loguniform('weight_decay', 1e-6, 1e-2)
warmup = trial.suggest_int('warmup', 10, 100)
#lr_decay_every = trial.suggest_int('lr_decay_every', 1, 10)
lr_decay_every = 2
lr_decay_rate = trial.suggest_uniform('lr_decay_rate', 0.5, 1)
data_dir = ['../data_luxury_5core/valid1', '../data_luxury_5core/valid2']
score_sum = 0
for i in range(len(data_dir)):
dataset = AmazonDataset(data_dir[i], model_name='TransE')
relation_size = len(set(list(dataset.triplet_df['relation'].values)))
entity_size = len(dataset.entity_list)
ppr_transe = PPR_TransE(embedding_dim, relation_size, entity_size,
data_dir[i], alpha, mu, kappa).to(device)
iterater = TrainIterater(batch_size=int(batch_size),
data_dir=data_dir[i],
model_name=model_name)
iterater.iterate_epoch(ppr_transe,
lr=lr,
epoch=2000,
weight_decay=weight_decay,
lambda_=lambda_,
warmup=warmup,
lr_decay_rate=lr_decay_rate,
lr_decay_every=lr_decay_every,
eval_every=1e+5)
# inference
inf = Inference(data_dir[i])
score = inf.get_score(ppr_transe, kappa, mu, alpha)
score_sum += score
mi, sec = time_since(time.time() - start)
print('{}m{}sec'.format(mi, sec))
return -1 * score_sum / 2
def objective(trial):
start = time.time()
import gc
gc.collect()
data_dir = [data_path + '/valid1', data_path + '/valid2']
score_sum = 0
embed_model = {'TransE': TransE, 'SparseTransE': SparseTransE}
# hyper para
embedding_dim = trial.suggest_discrete_uniform('embedding_dim', 16, 128,
16)
if model_name == 'SparseTransE':
alpha = trial.suggest_loguniform('alpha', 1e-6,
1e-2) #SparseTransEの時だけ
batch_size = trial.suggest_int('batch_size', 128, 512, 128)
lr = trial.suggest_loguniform('lr', 1e-4, 1e-2)
weight_decay = trial.suggest_loguniform('weight_decay', 1e-6, 1e-2)
#warmup = trial.suggest_int('warmup', 100, 500)
#warmup = trial.suggest_int('warmup', 10, 100)
warmup = 350
#lr_decay_every = trial.suggest_int('lr_decay_every', 1, 10)
lr_decay_every = 2
lr_decay_rate = trial.suggest_uniform('lr_decay_rate', 0.5, 1)
for dir_path in data_dir:
# データ読み込み
dataset = AmazonDataset(dir_path, model_name=model_name)
relation_size = len(set(list(dataset.triplet_df['relation'].values)))
entity_size = len(dataset.entity_list)
#model = TransE(int(embedding_dim), relation_size, entity_size).to(device)
model = embed_model[model_name](int(embedding_dim), relation_size,
entity_size).to(device)
iterater = TrainIterater(batch_size=int(batch_size),
data_dir=dir_path,
model_name=model_name)
score = iterater.iterate_epoch(model,
lr=lr,
epoch=3000,
weight_decay=weight_decay,
warmup=warmup,
lr_decay_rate=lr_decay_rate,
lr_decay_every=lr_decay_every,
eval_every=1e+5,
early_stop=True)
score_sum += score
torch.cuda.empty_cache()
mi, sec = time_since(time.time() - start)
print('{}m{}sec'.format(mi, sec))
return -1 * score_sum / 2
def objective(trial):
start = time.time()
import gc
gc.collect()
dataset = AmazonDataset('./data')
embedding_dim = trial.suggest_discrete_uniform('embedding_dim', 16, 64, 16)
bpr = BPR(int(embedding_dim), len(dataset.user_list),
len(dataset.item_list)).to(device)
batch_size = trial.suggest_discrete_uniform('batch_size', 64, 256, 64)
iterater = TrainIterater(batch_size=int(batch_size))
lr = trial.suggest_loguniform('lr', 1e-5, 1e-2)
weight_decay = trial.suggest_loguniform('weight_decay', 1e-6, 1e-2)
warmup = trial.suggest_int('warmup', 100, 500)
#warmup = trial.suggest_int('warmup', 1, 5)
lr_decay_every = trial.suggest_int('lr_decay_every', 1, 5)
lr_decay_rate = trial.suggest_uniform('lr_decay_rate', 0.5, 1)
score = iterater.iterate_epoch(bpr,
lr=lr,
epoch=3000,
weight_decay=weight_decay,
warmup=warmup,
lr_decay_rate=lr_decay_rate,
lr_decay_every=lr_decay_every,
eval_every=1e+5)
torch.cuda.empty_cache()
mi, sec = time_since(time.time() - start)
print('{}m{}sec'.format(mi, sec))
return -1 * score
# dataload
data_dir = '../' + data_path + '/test/'
dataset = AmazonDataset(data_dir, model_name='SparseTransE')
relation_size = len(set(list(dataset.triplet_df['relation'].values)))
entity_size = len(dataset.entity_list)
embedding_dim = params['embedding_dim']
alpha = params['alpha']
model = SparseTransE(int(embedding_dim),
relation_size,
entity_size,
alpha=alpha).to(device)
batch_size = params['batch_size']
iterater = TrainIterater(batch_size=int(batch_size),
data_dir=data_dir,
model_name=model_name)
lr = params['lr']
weight_decay = params['weight_decay']
warmup = 350
lr_decay_every = 2
lr_decay_rate = params['lr_decay_rate']
score = iterater.iterate_epoch(model,
lr=lr,
epoch=3000,
weight_decay=weight_decay,
warmup=warmup,
lr_decay_rate=lr_decay_rate,
if __name__ == '__main__':
params = load_param('./result_beauty')
embedding_dim = params['embedding_dim']
batch_size = params['batch_size']
lr = params['lr']
weight_decay = params['weight_decay']
warmup = params['warmup']
lr_decay_every = params['lr_decay_every']
lr_decay_rate = params['lr_decay_rate']
#data_dir = '../data_beauty_2core_es/test/bpr'
data_dir = '../data_beauty_2core_es/test/bpr'
dataset = AmazonDataset(data_dir)
bpr = BPR(int(embedding_dim), len(dataset.user_list),
len(dataset.item_list)).to(device)
iterater = TrainIterater(batch_size=int(batch_size), data_dir=data_dir)
score = iterater.iterate_epoch(bpr,
lr=lr,
epoch=3000,
weight_decay=weight_decay,
warmup=warmup,
lr_decay_rate=lr_decay_rate,
lr_decay_every=lr_decay_every,
eval_every=1e+5,
early_stop=True)
# test結果を記録
np.savetxt('./result_beauty/score.txt', np.array([score]))
