def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[5]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropit'
writer = SummaryWriter(log_path)
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
trainer = get_trainer(trainer_config, dataset, model)
trainer.train(verbose=True, writer=writer)
writer.close()
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
trainer = get_trainer(trainer_config, new_dataset, model)
results, _ = trainer.eval('test')
print('Previous interactions test result. {:s}'.format(results))
model.normalized_data_mat = model.get_data_mat(new_dataset)
results, _ = trainer.eval('test')
print('Updated interactions test result. {:s}'.format(results))
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[6]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropui'
writer = SummaryWriter(log_path)
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
trainer = get_trainer(trainer_config, dataset, model)
trainer.train(verbose=True, writer=writer)
writer.close()
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
model.n_users, model.n_items = new_dataset.n_users, new_dataset.n_items
model.feat_mat, _, _, model.row_sum = model.generate_feat(new_dataset,
is_updating=True)
model.update_feat_mat()
trainer = get_trainer(trainer_config, new_dataset, model)
trainer.inductive_eval(dataset.n_users, dataset.n_items)
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[3]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropit'
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
trainer = get_trainer(trainer_config, new_dataset, model)
results, _ = trainer.eval('test')
print('Previous interactions test result. {:s}'.format(results))
data_mat = sp.coo_matrix((np.ones((len(
new_dataset.train_array), )), np.array(new_dataset.train_array).T),
shape=(new_dataset.n_users, new_dataset.n_items),
dtype=np.float32).tocsr()
model.data_mat = data_mat
results, _ = trainer.eval('test')
print('Updated interactions test result. {:s}'.format(results))
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[3]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropui'
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
model.n_users, model.n_items = new_dataset.n_users, new_dataset.n_items
data_mat = sp.coo_matrix((np.ones((len(
new_dataset.train_array), )), np.array(new_dataset.train_array).T),
shape=(new_dataset.n_users, new_dataset.n_items),
dtype=np.float32).tocsr()
model.data_mat = data_mat
sim_mat = model.sim_mat.tocoo()
sim_mat = sp.coo_matrix((sim_mat.data, (sim_mat.row, sim_mat.col)),
shape=(new_dataset.n_items, new_dataset.n_items))
model.sim_mat = sim_mat.tocsr()
trainer = get_trainer(trainer_config, new_dataset, model)
trainer.inductive_eval(dataset.n_users, dataset.n_items)
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
param_grid = {'lr': [1.e-3], 'l2_reg': [0., 1.e-5], 'dropout': [0.3, 0.5, 0.7, 0.9]}
grid = ParameterGrid(param_grid)
max_ndcg = -np.inf
best_params = None
for params in grid:
ndcg = fitness(params['lr'], params['l2_reg'], params['dropout'])
print('NDCG: {:.3f}, Parameters: {:s}'.format(ndcg, str(params)))
if ndcg > max_ndcg:
max_ndcg = ndcg
best_params = params
print('Maximum NDCG!')
print('Maximum NDCG: {:.3f}, Best Parameters: {:s}'.format(max_ndcg, str(best_params)))
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
param_grid = {'k': [10, 50, 200, 1000]}
grid = ParameterGrid(param_grid)
max_ndcg = -np.inf
best_params = None
for params in grid:
ndcg = fitness(params['k'])
print('NDCG: {:.3f}, Parameters: {:s}'.format(ndcg, str(params)))
if ndcg > max_ndcg:
max_ndcg = ndcg
best_params = params
print('Maximum NDCG!')
print('Maximum NDCG: {:.3f}, Best Parameters: {:s}'.format(max_ndcg, str(best_params)))
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[2]
dataset_config['path'] = dataset_config['path'][:-4] + str(1)
writer = SummaryWriter(log_path)
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
trainer = get_trainer(trainer_config, dataset, model)
trainer.train(verbose=True, writer=writer)
writer.close()
results, _ = trainer.eval('test')
print('Test result. {:s}'.format(results))
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[2]
dataset_config['path'] = dataset_config['path'][:-4] + str(1)
dataset = get_dataset(dataset_config)
adj = generate_daj_mat(dataset)
part_adj = adj[:dataset.n_users, dataset.n_users:]
part_adj_tensor = get_sparse_tensor(part_adj, 'cpu')
with torch.no_grad():
u, s, v = torch.svd_lowrank(part_adj_tensor, 64)
sort_ranked_users, sort_ranked_items = graph_rank_nodes(dataset, 'sort')
degree_ranked_users, degree_ranked_items = graph_rank_nodes(
dataset, 'degree')
pr_ranked_users, pr_ranked_items = graph_rank_nodes(dataset, 'page_rank')
ranked_users = (sort_ranked_users, degree_ranked_users, pr_ranked_users)
ranked_items = (sort_ranked_items, degree_ranked_items, pr_ranked_items)
pdf = PdfPages('figure_5.pdf')
fig, ax = plt.subplots(nrows=1,
ncols=2,
constrained_layout=True,
figsize=(11, 4))
axes = ax.flatten()
plot_error(part_adj,
u.cpu().numpy(), ranked_users, axes[0], device, 'users')
plot_error(
part_adj.T,
v.cpu().numpy(),
ranked_items,
axes[1],
device,
'items',
)
pdf.savefig()
plt.close(fig)
pdf.close()
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[2]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropui'
writer = SummaryWriter(log_path)
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
trainer = get_trainer(trainer_config, dataset, model)
trainer.train(verbose=True, writer=writer)
writer.close()
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
model.n_users, model.n_items = new_dataset.n_users, new_dataset.n_items
model.norm_adj = model.generate_graph(new_dataset)
model.feat_mat, _, _, model.row_sum = model.generate_feat(new_dataset,
is_updating=True)
model.update_feat_mat()
trainer = get_trainer(trainer_config, new_dataset, model)
print('Inductive results.')
trainer.inductive_eval(dataset.n_users, dataset.n_items)
model = get_model(model_config, new_dataset)
model.load('checkpoints/...')
trainer = get_trainer(trainer_config, new_dataset, model)
print('Transductive model results.')
trainer.inductive_eval(dataset.n_users, dataset.n_items)
model_config['name'] = 'Popularity'
trainer_config['name'] = 'BasicTrainer'
model = get_model(model_config, new_dataset)
trainer = get_trainer(trainer_config, new_dataset, model)
print('Popularity model results.')
trainer.inductive_eval(dataset.n_users, dataset.n_items)
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[7]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropui'
writer = SummaryWriter(log_path)
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
trainer = get_trainer(trainer_config, dataset, model)
trainer.train(verbose=True, writer=writer)
writer.close()
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
model.n_users, model.n_items = new_dataset.n_users, new_dataset.n_items
model.norm_adj = model.generate_graph(new_dataset)
with torch.no_grad():
old_embedding = model.embedding.weight
model.embedding = torch.nn.Embedding(new_dataset.n_users +
new_dataset.n_items + 3,
model.embedding_size,
device=device)
model.embedding.weight[:, :] = old_embedding[:-3, :].mean(
dim=0)[None, :].expand(model.embedding.weight.shape)
model.embedding.weight[-3:, :] = old_embedding[-3:, :]
model.embedding.weight[:dataset.n_users, :] = old_embedding[:dataset.
n_users, :]
model.embedding.weight[new_dataset.n_users:new_dataset.n_users + dataset.n_items, :] = \
old_embedding[dataset.n_users:-3, :]
trainer = get_trainer(trainer_config, new_dataset, model)
trainer.inductive_eval(dataset.n_users, dataset.n_items)
def main():
log_path = __file__[:-3]
init_run(log_path, 2021)
device = torch.device('cuda')
config = get_gowalla_config(device)
dataset_config, model_config, trainer_config = config[5]
dataset_config['path'] = dataset_config['path'][:-4] + '0_dropui'
writer = SummaryWriter(log_path)
dataset = get_dataset(dataset_config)
model = get_model(model_config, dataset)
trainer = get_trainer(trainer_config, dataset, model)
trainer.train(verbose=True, writer=writer)
writer.close()
dataset_config['path'] = dataset_config['path'][:-7]
new_dataset = get_dataset(dataset_config)
model.config['dataset'] = new_dataset
model.n_users, model.n_items = new_dataset.n_users, new_dataset.n_items
data_mat = model.get_data_mat(new_dataset)[:, :dataset.n_items]
model.normalized_data_mat = normalize(data_mat, axis=1, norm='l2')
trainer = get_trainer(trainer_config, new_dataset, model)
trainer.inductive_eval(dataset.n_users, dataset.n_items)