def objective(trial):
start = time.time()
data_dir = ['../data_luxury_5core/valid1/', '../data_luxury_5core/valid2/']
score_sum = 0
for data_path in data_dir:
# dataload
dataset = dataloader.AmazonDataset(data_path)
edges = [[r[0], r[1]] for r in dataset.triplet_df.values]
for r in dataset.triplet_df.values:
if r[2] == 0:
edges.append([r[1], r[0]])
#user_items_test_dict = pickle.load(open(data_path + 'user_items_test_dict.pickle', 'rb'))
# グラフを作る
G = nx.DiGraph()
G.add_nodes_from([i for i in range(len(dataset.entity_list))])
G.add_edges_from(edges)
# ハイパラ
alpha = trial.suggest_uniform('alpha', 0, 1)
ranking_mat = get_ranking_mat(G, alpha, dataset)
evaluater = evaluate.Evaluater(data_path)
score = evaluater.topn_map(ranking_mat)
score_sum += score
mi, sec = time_since(time.time() - start)
print('{}m{}s'.format(mi, sec))
return -1 * score_sum / 2
def objective(trial):
start = time.time()
import gc
gc.collect()
score_sum = 0
data_dirs = [
'../' + data_path + '/valid1/bpr', '../' + data_path + '/valid2/bpr'
]
for data_dir in data_dirs:
dataset = dataloader.AmazonDataset(data_dir)
embedding_dim = trial.suggest_discrete_uniform('embedding_dim', 16,
128, 16)
user_size = len(dataset.user_list)
item_size = len(dataset.item_list)
layer_size = trial.suggest_int('layer_size', 1, 3)
nfm = model.NFM(int(embedding_dim), user_size, item_size,
layer_size).to(device)
batch_size = int(
trial.suggest_discrete_uniform('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 = 350
lr_decay_every = 2
lr_decay_rate = trial.suggest_uniform('lr_decay_rate', 0.5, 1)
iterater = training.TrainIterater(batch_size=batch_size,
data_dir=data_dir)
score = iterater.iterate_epoch(nfm,
lr,
epoch=2000,
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)
torch.cuda.empty_cache()
score_sum += score
mi, sec = time_since(time.time() - start)
print('{}m{}sec'.format(mi, sec))
return -1 * score_sum / 2
def __init__(self, data_dir):
self.dataset = dataloader.AmazonDataset(data_dir)
def load_params():
return pickle.load(open('./result/best_param', 'rb'))
def time_since(runtime):
mi = int(runtime / 60)
sec = runtime - mi * 60
return (mi, sec)
if __name__ == '__main__':
data_dir = '../data_luxury_5core/bpr'
params = load_params()
dataset = dataloader.AmazonDataset(data_dir)
#print(dataset.user_items_test_dict)
embedding_dim = params['embedding_dim']
user_size = len(dataset.user_list)
item_size = len(dataset.item_list)
layer_size = int(params['layer_size'])
batch_size = int(params['batch_size'])
lr = params['lr']
weight_decay = params['weight_decay']
warmup = 350
lr_decay_every = 2
lr_decay_rate = params['lr_decay_rate']
nfm = model.NFM(int(embedding_dim), user_size, item_size,
with open(data_path + '/entity_list.txt', 'r') as f:
for l in f:
entity_list.append(l.replace('\n', ''))
with open(data_path + '/user_list.txt', 'r') as f:
for l in f:
user_list.append(l.replace('\n', ''))
with open(data_path + '/item_list.txt', 'r') as f:
for l in f:
item_list.append(l.replace('\n', ''))
user_idx = [entity_list.index(u) for u in user_list]
dataset = dataloader.AmazonDataset(data_path)
# グラフを作る
# user-itemとitem-userどちらの辺も追加
edges = [[r[0], r[1]] for r in dataset.triplet_df.values]
for r in dataset.triplet_df.values:
if r[2] == 0:
edges.append([r[1], r[0]])
# load network
G = nx.DiGraph()
G.add_nodes_from([i for i in range(len(dataset.entity_list))])
G.add_edges_from(edges)
alpha = 0.85
ranking_mat = get_ranking_mat(alpha)
evaluater = evaluate.Evaluater(data_path)
def __init__(self, batch_size, data_dir):
self.data_dir = data_dir
self.dataset = dataloader.AmazonDataset(data_dir)
self.batch_size = batch_size
