def get_model_and_optimizer(training_method, dataset_name, features_dimension, device):
training_method_signature = 'BP' if training_method == 'bp' else 'ALT'
if training_method_signature == 'BP':
model = GAE(GraphEncoder(features_dimension, 16))
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
else:
model = GAE(DFAGraphEncoder(features_dimension, 16, training_method=training_method))
if dataset_name == 'cora':
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
elif dataset_name == 'citeseer':
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)
elif dataset_name == 'pubmed':
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
return model.to(device), optimizer
def fit_model_once(self):
#GCN
if self.model_type == "GCN":
encoder = EncoderGCN(in_channels=self.dataset.num_features,
out_channels=32)
#SAGE
if self.model_type == "SAGE":
encoder = EncoderSAGE(in_channels=self.dataset.num_features,
out_channels=32)
#GIN
if self.model_type == "GIN":
encoder = EncoderGIN(in_channels=self.dataset.num_features,
out_channels=32)
#GAT
if self.model_type == "GAT":
encoder = EncoderGAT(in_channels=self.dataset.num_features,
out_channels=16,
heads=8)
#AGNN
if self.model_type == "AGNN":
encoder = EncoderAGNN(in_channels=self.dataset.num_features,
out_channels=16)
#GraphUNet
if self.model_type == "GraphUNet":
encoder = EncoderGraphUNet(in_channels=self.dataset.num_features,
hidden_channels=32,
out_channels=16)
model = GAE(encoder=encoder, decoder=InnerProductDecoder())
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
trainer = TrainerGae(model,
self.device,
self.data,
writer_path='runs/{}/{}/'.format(
self.model_type, self.text_encoding) +
self.time_mark())
model = trainer.fit(optimizer,
patience=self.patience,
num_epochs=self.num_epochs)
auc, ap = trainer.evaluate(validation=False, test=True)
return model, auc, ap
def train(dataset, args, writer = None):
task = args.task
test_loader = loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
if task == 'link':
model = GAE(models.GNNStack(dataset.num_node_features, args.hidden_dim, int(dataset.num_classes),
args))
elif task == 'node':
model = models.GNNStack(dataset.num_node_features, args.hidden_dim, int(dataset.num_classes),
args)
else:
raise RuntimeError("Unknown task.")
metrics_for_labels = True if args.metrics_for_labels == 'True' else False
scheduler, opt = build_optimizer(args, model.parameters())
print("Training \nModel: {}, Data representation: {}. Dataset: {}, Task type: {}". format(args.model_name, args.graph_type, args.dataset, args.task))
metric_text = 'test accuracy' if task == 'node' else 'test precision'
for epoch in range(args.epochs):
total_loss = 0
model.train()
for batch in loader:
opt.zero_grad()
if task == 'node':
pred = model(batch)
label = batch.y
pred = pred[batch.train_mask]
label = label[batch.train_mask]
loss = model.loss(pred, label)
else:
train_pos_edge_index = batch.train_pos_edge_index
z = model.encode(batch)
loss = model.recon_loss(z, train_pos_edge_index)
loss.backward()
opt.step()
total_loss += loss.item() * batch.num_graphs
total_loss /= len(loader.dataset)
if writer == None:
print(total_loss)
else:
writer.add_scalar("loss", total_loss, epoch)
if epoch % 10 == 0:
test_metric, _ = test(loader, model, task = task)
if writer == None:
print(test_metric, metric_text)
else:
writer.add_scalar(metric_text, test_metric, epoch)
if metrics_for_labels == True and epoch == args.epochs -1:
_, labels_metrics = test(loader, model, task = task, metrics_for_labels=metrics_for_labels)
print('{} for labels:\n {}'.format(metric_text, labels_metrics))
def fit_model_once(self):
gcn_model = GAE(encoder=EncoderGCN(
in_channels=self.dataset.num_features, out_channels=32),
decoder=InnerProductDecoder())
optimizer = torch.optim.Adam(gcn_model.parameters(), lr=0.01)
trainer_gcn = TrainerGae(
gcn_model,
self.device,
self.data,
writer_path='runs/gae_gcn/{}/'.format(self.feature) +
self.time_mark())
gcn_model = trainer_gcn.fit(optimizer,
patience=self.patience,
num_epochs=self.num_epochs)
auc, ap = trainer_gcn.evaluate(validation=False, test=True)
return auc, ap
return self.conv_mu(x, edge_index), self.conv_logstd(x, edge_index)
in_channels, out_channels = dataset.num_features, 16
if not args.variational and not args.linear:
model = GAE(GCNEncoder(in_channels, out_channels))
elif not args.variational and args.linear:
model = GAE(LinearEncoder(in_channels, out_channels))
elif args.variational and not args.linear:
model = VGAE(VariationalGCNEncoder(in_channels, out_channels))
elif args.variational and args.linear:
model = VGAE(VariationalLinearEncoder(in_channels, out_channels))
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train():
model.train()
optimizer.zero_grad()
z = model.encode(train_data.x, train_data.edge_index)
loss = model.recon_loss(z, train_data.pos_edge_label_index)
if args.variational:
loss = loss + (1 / train_data.num_nodes) * model.kl_loss()
loss.backward()
optimizer.step()
return float(loss)
@torch.no_grad()
def run_experiment(args):
"""
Performing experiment for the given arguments
"""
dataset, data = load_data(args.dataset)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Define Model
encoder = create_encoder(args.model, dataset.num_features,
args.latent_dim).to(device)
decoder = create_decoder(args.decoder).to(device)
if args.model == 'GAE':
model = GAE(encoder=encoder, decoder=decoder).to(device)
else:
model = VGAE(encoder=encoder, decoder=decoder).to(device)
# Split edges of a torch_geometric.data.Data object into pos negative train/val/test edges
# default ratios of positive edges: val_ratio=0.05, test_ratio=0.1
print("Data.edge_index.size", data.edge_index.size(1))
data = model.split_edges(data)
node_features, train_pos_edge_index = data.x.to(
device), data.train_pos_edge_index.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train_epoch():
"""
Performing training over a single epoch and optimize over loss
:return: log - loss of training loss
"""
# Todo: Add logging of results
model.train()
optimizer.zero_grad()
# Compute latent embedding Z
latent_embeddings = model.encode(node_features, train_pos_edge_index)
# Calculate loss and
loss = model.recon_loss(latent_embeddings, train_pos_edge_index)
if args.model in ['VGAE']:
loss = loss + (1 / data.num_nodes) * model.kl_loss()
# Compute gradients
loss.backward()
# Perform optimization step
optimizer.step()
# print("Train-Epoch: {} Loss: {}".format(epoch, loss))
# ToDo: Add logging via Tensorboard
log = {'loss': loss}
return log
def test(pos_edge_index, neg_edge_index):
model.eval()
with torch.no_grad():
# compute latent var
z = model.encode(node_features, train_pos_edge_index)
# model.test return - AUC, AP
return model.test(z, pos_edge_index, neg_edge_index)
def test_naive_graph(z, sample_size=1000):
if args.sample_dense_evaluation:
graph_type = "sampled"
z_sample, index_mapping = sample_graph(z, sample_size)
t = time.time()
adjacency = model.decoder.forward_all(
z_sample, sigmoid=(args.decoder == 'dot'))
else:
graph_type = "full"
t = time.time()
adjacency = model.decoder.forward_all(
z, sigmoid=(args.decoder == 'dot'))
print(f"Computing {graph_type} graph took {time.time() - t} seconds.")
print(
f"Adjacency matrix takes {adjacency.element_size() * adjacency.nelement() / 10 ** 6} MB of memory."
)
if args.min_sim_absolute_value is None:
args.min_sim_absolute_value, _ = sample_percentile(
args.min_sim,
adjacency,
dist_measure=args.decoder,
sample_size=sample_size)
if args.sample_dense_evaluation:
precision, recall = sampled_dense_precision_recall(
data, adjacency, index_mapping, args.min_sim_absolute_value)
else:
precision, recall = dense_precision_recall(
data, adjacency, args.min_sim_absolute_value)
print("Predicted {} adjacency matrix has precision {} and recall {}!".
format(graph_type, precision, recall))
return precision, recall
def sample_graph(z, sample_size):
N, D = z.shape
sample_size = min(sample_size, N)
sample_ix = np.random.choice(np.arange(N),
size=sample_size,
replace=False)
# Returns the sampled embeddings, and a mapping from their indices to the originals
return z[sample_ix], {i: sample_ix[i] for i in np.arange(sample_size)}
def test_compare_lsh_naive_graphs(z, assure_correctness=True):
"""
:param z:
:param assure_correctness:
:return:
"""
# Naive Adjacency-Matrix (Non-LSH-Version)
t = time.time()
# Don't use sigmoid in order to directly compare thresholds with LSH
naive_adjacency = model.decoder.forward_all(
z, sigmoid=(args.decoder == 'dot'))
naive_time = time.time() - t
naive_size = naive_adjacency.element_size() * naive_adjacency.nelement(
) / 10**6
if args.min_sim_absolute_value is None:
args.min_sim_absolute_value, _ = sample_percentile(
args.min_sim, z, dist_measure=args.decoder)
print(
"______________________________Naive Graph Computation KPI____________________________________________"
)
print(f"Computing naive graph took {naive_time} seconds.")
print(f"Naive adjacency matrix takes {naive_size} MB of memory.")
# LSH-Adjacency-Matrix:
t = time.time()
lsh_adjacency = LSHDecoder(bands=args.lsh_bands,
rows=args.lsh_rows,
verbose=True,
assure_correctness=assure_correctness,
sim_thresh=args.min_sim_absolute_value)(z)
lsh_time = time.time() - t
lsh_size = lsh_adjacency.element_size() * lsh_adjacency._nnz() / 10**6
print(
"__________________________________LSH Graph Computation KPI__________________________________________"
)
print(f"Computing LSH graph took {lsh_time} seconds.")
print(f"Sparse adjacency matrix takes {lsh_size} MB of memory.")
print(
"________________________________________Precision-Recall_____________________________________________"
)
# 1) Evaluation: Both Adjacency matrices against ground truth graph
naive_precision, naive_recall = dense_precision_recall(
data, naive_adjacency, args.min_sim_absolute_value)
lsh_precision, lsh_recall = sparse_precision_recall(
data, lsh_adjacency)
print(
f"Naive-Precision {naive_precision}; Naive-Recall {naive_recall}")
print(f"LSH-Precision {lsh_precision}; LSH-Recall {lsh_recall}")
print(
"_____________________________Comparison Sparse vs Dense______________________________________________"
)
# 2) Evation: Compare both adjacency matrices against each other
compare_precision, compare_recall = sparse_v_dense_precision_recall(
naive_adjacency, lsh_adjacency, args.min_sim_absolute_value)
print(
f"LSH sparse matrix has {compare_precision} precision and {compare_recall} recall w.r.t. the naively generated dense matrix!"
)
return naive_precision, naive_recall, naive_time, naive_size, lsh_precision, lsh_recall, lsh_time, lsh_size, compare_precision, compare_recall
# Training routine
early_stopping = EarlyStopping(args.use_early_stopping,
patience=args.early_stopping_patience,
verbose=True)
logs = []
if args.load_model and os.path.isfile("checkpoint.pt"):
print("Loading model from savefile...")
model.load_state_dict(torch.load("checkpoint.pt"))
if not (args.load_model and args.early_stopping_patience == 0):
for epoch in range(1, args.epochs):
log = train_epoch()
logs.append(log)
# Validation metrics
val_auc, val_ap = test(data.val_pos_edge_index,
data.val_neg_edge_index)
print('Validation-Epoch: {:03d}, AUC: {:.4f}, AP: {:.4f}'.format(
epoch, val_auc, val_ap))
# Stop training if validation scores have not improved
early_stopping(val_ap, model)
if early_stopping.early_stop:
print("Applying early-stopping")
break
else:
epoch = 0
# Load best encoder
print("Load best model for evaluation.")
model.load_state_dict(torch.load('checkpoint.pt'))
print(
"__________________________________________________________________________"
)
# Training is finished, calculate test metrics
test_auc, test_ap = test(data.test_pos_edge_index,
data.test_neg_edge_index)
print('Test Results: {:03d}, AUC: {:.4f}, AP: {:.4f}'.format(
epoch, test_auc, test_ap))
# Check if early stopping was applied or not - if not: model might not be done with training
if args.epochs == epoch + 1:
print("Model might need more epochs - Increase number of Epochs!")
# Evaluate full graph
latent_embeddings = model.encode(node_features, train_pos_edge_index)
# Save embeddings to embeddings folder if flag is set
if args.save_embeddings:
embeddings_folder = osp.join(osp.dirname(osp.abspath(__file__)),
'embeddings')
if not osp.isdir(embeddings_folder):
os.makedirs(embeddings_folder)
torch.save(
latent_embeddings,
osp.join(embeddings_folder,
args.dataset + "_" + args.decoder + ".pt"))
if not args.lsh:
# Compute precision recall w.r.t the ground truth graph
graph_precision, graph_recall = test_naive_graph(latent_embeddings)
del model
del encoder
del decoder
torch.cuda.empty_cache()
else:
# Precision w.r.t. the generated graph
naive_precision, naive_recall, naive_time, naive_size, lsh_precision, \
lsh_recall, lsh_time, lsh_size, \
compare_precision, compare_recall = test_compare_lsh_naive_graphs(
latent_embeddings)
del model
del encoder
del decoder
torch.cuda.empty_cache()
return {
'args': args,
'test_auc': test_auc,
'test_ap': test_ap,
'naive_precision': naive_precision,
'naive_recall': naive_recall,
'naive_time': naive_time,
'naive_size': naive_size,
'lsh_precision': lsh_precision,
'lsh_recall': lsh_recall,
'lsh_time': lsh_time,
'lsh_size': lsh_size,
'compare_precision': compare_precision,
'compare_recall': compare_recall
}
class UnsGAE(object):
def __init__(self, data, embed_dim, **kwargs):
super(UnsGAE, self).__init__()
self.data = data
self.input_dim = self.data.dim
self.embed_dim = embed_dim
# for now, we only work with 2-layer encoders
self.hidden_dim = kwargs.get('hidden_dim', 2*embed_dim)
self.encoder = kwargs.get('encoder', batched_SAGEEncoder)
self.encoder = self.encoder(self.input_dim,
self.hidden_dim,
self.embed_dim)
self.model = GAE(self.encoder)
# preparing the device
device = kwargs.get('device', 'cuda')
if device=='gpu' and not(torch.cuda.is_available()):
print('CUDA is not available in PyTorch. the model ' +\
'will be initiated on CPU.')
device = 'cpu'
self.device = torch.device(device)
def init_model(self, sizes, weights_path=None):
self.model = self.model.to(self.device)
# sizes are directly used for initializing the model
# but it will be used for every feed-forward as the
# sampling size of the neighbors
assert len(sizes)==self.model.encoder.num_layers, \
'Number of sizes should be equal to the number of layers in the encoder.'
self.sizes = sizes
if not(hasattr(self.data, 'loader')):
self.data.get_neighbor_sampler(self.sizes)
if weights_path is not None:
self.model.load_state_dict(torch.load(weights_path, map_location=self.device))
def init_training(self, neg_num, optim='Adam', lr=1e-5, smooth_par=0.75):
if optim=='Adam':
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
elif optim=='SGD':
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=lr)
self.train_one_epoch = self._train_edge_batching
self.neg_num = neg_num
if not(hasattr(self.data, 'pos_pairs')):
assert 'pos_samples_path' in kwargs, 'The provided data does ' +\
'not come with positive pairs, and we need a path to the ' +\
'already selected positive samples. You can provide it through ' +\
'input pos_samples_path .'
include_nodes = kwargs.get('include_nodes', None)
self.data.load_positive_pairs(kwargs['pos_samples_path'], include_nodes)
if not(hasattr(self.data, 'neg_sampler')):
#smooth_par = kwargs.get('smooth_par', 0.75)
self.data.get_negative_sampler(smooth_par)
if not(hasattr(self.data, 'x_all')):
self.data._fetch_node_features()
def init_validation(self):
if not(hasattr(self.data, 'x_all')):
self.data._fetch_node_features()
def embed_some(self, sample_inds, b=100):
"""This will be used in the training, when the
embedding of a batch of samples are needed
"""
quot, rem = np.divmod(len(sample_inds), b)
Z = []
for i in range(quot+1):
if i<quot:
b_ids = sample_inds[i*b:(i+1)*b]
elif rem>0:
b_ids = sample_inds[i*b:]
# neighbor-sampling for each sample
_, n_id, adjs = self.data.train_loader.sample(b_ids)
adjs = [adj.to(self.device) for adj in adjs]
# get feature vectors through the neighbors sampled above
batch_X = torch.from_numpy(self.data.get_node_features(n_id))
batch_X = batch_X.to(torch.float).to(self.device)
# the encoder's output as the embedding
try:
batch_Z = self.model.encoder(batch_X, adjs)
except:
pdb.set_trace()
Z += [batch_Z]
Z = torch.cat(Z, dim=0)
return Z
def embed_all(self):
L = self.model.encoder.num_layers
pbar = tqdm(total=self.data.n_x * L, position=0, leave=True)
pbar.set_description('Evaluating')
self.model.encoder.eval()
# inference is used in the evaluation stage (not in training) when
# the embeddings for "all" nodes will be computed. It's written in a way
# that is faster than the foward-passing function which is mostly used
# for single batches in the training
with torch.no_grad():
for i in range(L):
xs = []
for batch_size, n_id, adj in self.data.test_loader:
edge_index, _, size = adj.to(self.device)
if i==0:
x = torch.from_numpy(self.data.get_node_features(n_id))
x = x.to(torch.float).to(self.device)
else:
x = x_all[n_id,:].to(self.device)
x_target = x[:size[1]]
x = self.model.encoder.convs[i]((x,x_target), edge_index)
if i != L-1:
x = F.relu(x)
xs.append(x[:batch_size,:].cpu())
pbar.update(batch_size)
x_all = torch.cat(xs, dim=0)
pbar.close()
return x_all
def _train_edge_batching(self, ep, batch_size=5000):
assert hasattr(self.data, 'pos_pairs'), 'Positive and negative ' + \
'samples must be generated before starting the training'
self.model.train()
neg_num = self.neg_num
torch.multiprocessing.set_sharing_strategy('file_system')
pbar = tqdm(total=self.data.pos_pairs.shape[1], position=0, leave=True)
pbar.set_description(f'Epoch {ep:02d}')
total_loss = 0
np.random.shuffle(self.data.pos_pairs.T)
quot, rem = np.divmod(self.data.pos_pairs.shape[1], batch_size)
for i in range(quot+1):
# positive mini-batch
# (#: batch size)
if i<quot:
batch_pos_pairs = self.data.pos_pairs[:,i*batch_size:(i+1)*batch_size]
else:
batch_pos_pairs = self.data.pos_pairs[:,i*batch_size:]
batch_pos_samples, pos_edge_index = np.unique(batch_pos_pairs,
return_inverse=True)
pos_edge_index = pos_edge_index.reshape(batch_pos_pairs.shape)
# negative mini-batch
# (#: batch_size * neg_num)
batch_neg_samples = self.data.neg_sampler.sample(
torch.Size([neg_num*batch_pos_pairs.shape[1]]))
neg_edge_index = np.array([np.repeat(pos_edge_index[0,:],neg_num),
np.arange(pos_edge_index.max()+1,
pos_edge_index.max()+len(batch_neg_samples)+1)])
# embeddings of the nodes involved in + and - edges
self.optimizer.zero_grad()
unodes = batch_pos_samples.tolist() + batch_neg_samples.tolist()
Z = self.embed_some(unodes)
# reconstruction loss
pos_edge_index = torch.from_numpy(pos_edge_index).to(self.device)
neg_edge_index = torch.from_numpy(neg_edge_index).to(self.device)
loss = self.model.recon_loss(Z, pos_edge_index, neg_edge_index)
loss.backward()
self.optimizer.step()
total_loss += float(loss)
pbar.update(batch_size)
pbar.close()
loss = total_loss / (quot+1)
return loss
def validate(self):
self.model.eval()
Z = self.embed_all()
ents_Z = Z[:-1,:][self.data.selected_inds[:-1]>=self.data.nA,:].detach().numpy()
prop_Z = Z[self.data.tags=='prop',:].detach().numpy()
scores = np.dot(ents_Z, prop_Z.T).squeeze()
sorted_ents = self.data.selected_ents[np.argsort(-scores)]
unstudied_sorted_ents = np.array([x for x in sorted_ents
if x not in self.data.studied_ents])
preds = unstudied_sorted_ents[:50]
prec = np.isin(preds,self.data.GT).sum() / len(preds)
return prec
def run_GAE(input_data, output_dir, epochs=1000, lr=0.01, weight_decay=0.0005):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device: '.ljust(32), device)
print('Model Name: '.ljust(32), 'GAE')
print('Model params:{:19} lr: {} weight_decay: {}'.format(
'', lr, weight_decay))
print('Total number of epochs to run: '.ljust(32), epochs)
print('*' * 70)
data = input_data.clone().to(device)
in_channels = data.num_features
out_channels = data.num_classes.item()
model = GAE(GAEncoder(in_channels, out_channels)).to(device)
data = input_data.clone().to(device)
split_data = model.split_edges(data)
x, train_pos_edge_index, edge_attr = split_data.x.to(
device), split_data.train_pos_edge_index.to(device), data.edge_attr.to(
device)
split_data.train_idx = split_data.test_idx = data.y = None
optimizer = torch.optim.Adam(model.parameters(),
lr=lr,
weight_decay=weight_decay)
train_losses, test_losses = [], []
aucs = []
aps = []
model.train()
for epoch in range(1, epochs + 1):
train_loss = 0
test_loss = 0
optimizer.zero_grad()
z = model.encode(x, train_pos_edge_index)
train_loss = model.recon_loss(z, train_pos_edge_index)
train_losses.append(train_loss)
train_loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
z = model.encode(x, train_pos_edge_index)
auc, ap = model.test(z, split_data.test_pos_edge_index,
split_data.test_neg_edge_index)
test_loss = model.recon_loss(z, data.test_pos_edge_index)
test_losses.append(test_loss.item())
aucs.append(auc)
aps.append(ap)
figname = os.path.join(
output_dir, "_".join((GAE.__name__, str(lr), str(weight_decay))))
makepath(output_dir)
if (epoch % int(epochs / 10) == 0):
print(
'Epoch: {} Train loss: {} Test loss: {} AUC: {} AP: {}'
.format(epoch, train_loss, test_loss, auc, ap))
if (epoch == epochs):
print(
'-' * 65,
'\nFinal epoch: {} Train loss: {} Test loss: {} AUC: {} AP: {}'
.format(epoch, train_loss, test_loss, auc, ap))
log = 'Final epoch: {} Train loss: {} Test loss: {} AUC: {} AP: {}'.format(
epoch, train_loss, test_loss, auc, ap)
write_log(log, figname)
print('-' * 65)
plot_linkpred(train_losses, test_losses, aucs, aps, output_dir, epochs,
figname)
return
graph_out_channels,
hidden_channels,
node_out_channels,
depth,
sum_res,
act,
)
# Hardware
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
node_ae = node_ae.to(device)
encoder, decoder = encoder.to(device), decoder.to(device)
# Optimizer
optimizer = torch.optim.Adam(
set(node_ae.parameters()) | set(encoder.parameters()) | set(decoder.parameters()),
lr=0.01,
)
def train():
node_ae.train()
encoder.train()
decoder.train()
train_loss = 0.0
for i, sample in enumerate(loader):
optimizer.zero_grad()
data = sample["X"]
data = data.to(device)
def perturb_edges(data,
name,
remove_pct,
add_pct,
hidden_channels=16,
epochs=400):
if remove_pct == 0 and add_pct == 0:
return
try:
cached = pickle.load(
open(f'{ROOT}/cache/edge/{name}_{remove_pct}_{add_pct}.pt', 'rb'))
print(f'Use cached edge augmentation for dataset {name}')
if data.setting == 'inductive':
data.train_edge_index = cached
else:
data.edge_index = cached
return
except FileNotFoundError:
try:
A_pred, adj_orig = pickle.load(
open(f'{ROOT}/cache/edge/{name}.pt', 'rb'))
A = sample_graph_det(adj_orig, A_pred, remove_pct, add_pct)
data.edge_index, _ = from_scipy_sparse_matrix(A)
pickle.dump(
data.edge_index,
open(f'{ROOT}/cache/edge/{name}_{remove_pct}_{add_pct}.pt',
'wb'))
return
except FileNotFoundError:
print(
f'cache/edge/{name}_{remove_pct}_{add_pct}.pt not found! Regenerating it now'
)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if data.setting == 'inductive':
train_data = Data(x=data.train_x,
ori_x=data.ori_x,
edge_index=data.train_edge_index,
y=data.train_y)
else:
train_data = deepcopy(data)
edge_index = deepcopy(train_data.edge_index)
train_data = train_test_split_edges(train_data,
val_ratio=0.1,
test_ratio=0)
num_features = train_data.ori_x.shape[1]
model = GAE(GCNEncoder(num_features, hidden_channels))
model = model.to(device)
x = train_data.ori_x.to(device)
train_pos_edge_index = train_data.train_pos_edge_index.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
best_val_auc = 0
best_z = None
for epoch in range(1, epochs + 1):
model.train()
optimizer.zero_grad()
z = model.encode(x, train_pos_edge_index)
loss = model.recon_loss(z, train_pos_edge_index)
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
z = model.encode(x, train_pos_edge_index)
auc, ap = model.test(z, train_data.val_pos_edge_index,
train_data.val_neg_edge_index)
print('Val | Epoch: {:03d}, AUC: {:.4f}, AP: {:.4f}'.format(
epoch, auc, ap))
if auc > best_val_auc:
best_val_auc = auc
best_z = deepcopy(z)
A_pred = torch.sigmoid(torch.mm(z, z.T)).cpu().numpy()
adj_orig = to_scipy_sparse_matrix(edge_index).asformat('csr')
adj_pred = sample_graph_det(adj_orig, A_pred, remove_pct, add_pct)
if data.setting == 'inductive':
data.train_edge_index, _ = from_scipy_sparse_matrix(adj_pred)
else:
data.edge_index, _ = from_scipy_sparse_matrix(adj_pred)
pickle.dump((A_pred, adj_orig), open(f'{ROOT}/cache/edge/{name}.pt', 'wb'))
if data.setting == 'inductive':
pickle.dump(
data.train_edge_index,
open(f'{ROOT}/cache/edge/{name}_{remove_pct}_{add_pct}.pt', 'wb'))
else:
pickle.dump(
data.edge_index,
open(f'{ROOT}/cache/edge/{name}_{remove_pct}_{add_pct}.pt', 'wb'))
def run_model(dataset, conf):
# ## 1) Build Table graph
# ### Tables tokenization
tokenized_tables, vocabulary, cell_dict, reversed_dictionary = corpus_tuple = create_corpus(
dataset, include_attr=conf["add_attr"])
if conf["shuffle_vocab"] == True:
shuffled_vocab = shuffle_vocabulary(vocabulary)
else:
shuffled_vocab = None
nodes = build_node_features(vocabulary)
row_edges_index, row_edges_weights = build_graph_edges(
tokenized_tables,
s_vocab=shuffled_vocab,
sample_frac=conf["row_edges_sample"],
columns=False)
col_edges_index, col_edges_weights = build_graph_edges(
tokenized_tables,
s_vocab=shuffled_vocab,
sample_frac=conf["column_edges_sample"],
columns=True)
edges = torch.cat((row_edges_index, col_edges_index), dim=1)
weights = torch.cat((row_edges_weights, col_edges_weights), dim=0)
graph_data = Data(x=nodes, edge_index=edges, edge_attr=weights)
# ## 2 ) Run Table Auto-Encoder Model:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
loader = DataLoader(torch.arange(graph_data.num_nodes),
batch_size=128,
shuffle=True)
graph_data = graph_data.to(device)
x, train_pos_edge_index = nodes, edges
class Encoder(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super(Encoder, self).__init__()
self.conv1 = GCNConv(in_channels, 2 * out_channels, cached=True)
self.conv_mu = GCNConv(2 * out_channels, out_channels, cached=True)
self.conv_logvar = GCNConv(2 * out_channels,
out_channels,
cached=True)
def forward(self, x, edge_index):
x = F.relu(self.conv1(x, edge_index))
return self.conv_mu(x, edge_index), self.conv_logvar(x, edge_index)
channels = conf["vector_size"]
enc = Encoder(graph_data.num_features, channels)
model = GAE(enc)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
def train(model, optimizer, x, train_pos_edge_index):
model.train()
optimizer.zero_grad()
z = model.encode(x, train_pos_edge_index)
loss = model.recon_loss(z, train_pos_edge_index)
#loss = model.kl_loss()
loss.backward()
optimizer.step()
return loss
losses = []
for epoch in range(conf["epoch_num"]):
loss = train(model, optimizer, x, train_pos_edge_index)
losses.append(loss)
print(epoch, loss)
losses.append(loss)
# ### 3) Extract the latent cell vectors, generate table vectors:
def get_cell_vectors(model, x, train_pos_edge_index):
model.eval()
with torch.no_grad():
z = model.encode(x, train_pos_edge_index)
cell_vectors = z.numpy()
return z, cell_vectors
z, cell_vectors = get_cell_vectors(model, x, train_pos_edge_index)
vec_list = generate_table_vectors(cell_vectors,
tokenized_tables,
s_vocab=shuffled_vocab)
# ## 3) Evaluate the model
result_score = evaluate_model(dataset, vec_list, k=5)
return cell_vectors, vec_list, losses, result_score
