def main():
parser = argparse.ArgumentParser()
# Model filename
parser.add_argument('model_file', type=str)
# Size of the recommendation list
parser.add_argument('--k', default=20, type=int)
# parse the nn arguments
parser.add_argument('--hidden_size', default=100, type=int)
parser.add_argument('--num_layers', default=1, type=int)
parser.add_argument('--batch_size', default=50, type=int)
parser.add_argument('--dropout_input', default=0, type=float)
parser.add_argument('--dropout_hidden', default=.5, type=float)
# parse the optimizer arguments
parser.add_argument('--optimizer_type', default='Adagrad', type=str)
parser.add_argument('--lr', default=.01, type=float)
parser.add_argument('--weight_decay', default=0, type=float)
parser.add_argument('--momentum', default=0, type=float)
parser.add_argument('--eps', default=1e-6, type=float)
# parse the loss type
parser.add_argument('--loss_type', default='TOP1', type=str)
# etc
parser.add_argument('--n_epochs', default=10, type=int)
parser.add_argument('--time_sort', default=False, type=bool)
parser.add_argument('--n_samples', default=-1, type=int)
# Get the arguments
args = parser.parse_args()
PATH_DATA = Path('./data')
PATH_MODEL = Path('./models')
train = 'train.tsv'
test = 'test.tsv'
PATH_TRAIN = PATH_DATA / train
PATH_TEST = PATH_DATA / test
df_train = pd.read_csv(PATH_TRAIN,
sep='\t',
names=['SessionId', 'ItemId', 'TimeStamp'])
df_test = pd.read_csv(PATH_TEST,
sep='\t',
names=['SessionId', 'ItemId', 'TimeStamp'])
# sampling, if needed
n_samples = args.n_samples
if n_samples != -1:
df_train = df_train[:n_samples]
df_test = df_test[:n_samples]
session_key = 'SessionId'
item_key = 'ItemId'
time_key = 'TimeStamp'
use_cuda = True
input_size = df_train[item_key].nunique()
hidden_size = args.hidden_size
num_layers = args.num_layers
output_size = input_size
batch_size = args.batch_size
dropout_input = args.dropout_input
dropout_hidden = args.dropout_hidden
loss_type = args.loss_type
optimizer_type = args.optimizer_type
lr = args.lr
weight_decay = args.weight_decay
momentum = args.momentum
eps = args.eps
n_epochs = args.n_epochs
time_sort = args.time_sort
MODEL_FILE = PATH_MODEL / args.model_file
gru = GRU(input_size,
hidden_size,
output_size,
num_layers=num_layers,
dropout_input=dropout_input,
dropout_hidden=dropout_hidden,
batch_size=batch_size,
use_cuda=use_cuda)
gru.load_state_dict(torch.load(MODEL_FILE))
model = GRU4REC(input_size,
hidden_size,
output_size,
num_layers=num_layers,
dropout_input=dropout_input,
dropout_hidden=dropout_hidden,
batch_size=batch_size,
use_cuda=use_cuda,
loss_type=loss_type,
optimizer_type=optimizer_type,
lr=lr,
momentum=momentum,
time_sort=time_sort,
pretrained=gru)
model.init_data(df_train,
df_test,
session_key=session_key,
time_key=time_key,
item_key=item_key)
k = args.k
recall, mrr = model.test(k=k, batch_size=batch_size)
result = f'Recall@{k}:{recall:.7f},MRR@{k}:{mrr:.7f}'
print(result)
embedding_size,
hidden_size,
output_size,
num_layers=num_layers,
dropout_input=dropout_input,
dropout_hidden=dropout_hidden,
batch_size=batch_size,
use_cuda=use_cuda,
cuda_id=cuda_id)
print(loss_type + ':')
for i in range(1, 21):
model_name = 'GRU4REC_CrossEntropy_Adagrad_0.01_epoch%d' % i
print(model_name)
model_file = r'./models/' + model_name
gru.load_state_dict(torch.load(model_file))
model = GRU4REC(input_size,
if_embedding,
embedding_size,
hidden_size,
output_size,
num_layers=num_layers,
dropout_input=dropout_input,
dropout_hidden=dropout_hidden,
batch_size=batch_size,
optimizer_type=optimizer_type,
lr=lr,
weight_decay=weight_decay,
momentum=momentum,
eps=eps,