count += output.size(0)
preds = output.max(1)[1].type_as(y_batch)
correct += torch.sum(preds.eq(y_batch).double())
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print('Epoch: {:04d}'.format(epoch+1),
'loss_train: {:.4f}'.format(train_loss / count),
'acc_train: {:.4f}'.format(correct / count),
'time: {:.4f}s'.format(time.time() - t))
print('Optimization finished!')
# Evaluates the model
model.eval()
test_loss = 0
correct = 0
count = 0
for i in range(0, N_test, batch_size):
adj_batch = list()
idx_batch = list()
y_batch = list()
############## Task 8
##################
# your code here #
##################
for j in range(i, min(N_test, i + batch_size)):
n = G_test[j].number_of_nodes()
class Trainer:
def __init__(self, params):
self.params = params
self.prj_path = Path(__file__).parent.resolve()
self.save_path = self.prj_path / 'pretrained' / f'{self.params.species}' / 'models'
if not self.save_path.exists():
self.save_path.mkdir(parents=True)
self.device = torch.device('cpu' if self.params.gpu ==
-1 else f'cuda:{params.gpu}')
self.num_cells, self.num_genes, self.num_labels, self.graph, self.train_ids, self.test_ids, self.labels = load_data_internal(
params)
self.labels = self.labels.to(self.device)
self.model = GNN(in_feats=self.params.dense_dim,
n_hidden=self.params.hidden_dim,
n_classes=self.num_labels,
n_layers=self.params.n_layers,
gene_num=self.num_genes,
activation=F.relu,
dropout=self.params.dropout).to(self.device)
self.optimizer = torch.optim.Adam(
self.model.parameters(),
lr=self.params.lr,
weight_decay=self.params.weight_decay)
self.loss_fn = nn.CrossEntropyLoss(reduction='sum')
if self.params.num_neighbors == 0:
self.num_neighbors = self.num_cells + self.num_genes
else:
self.num_neighbors = self.params.num_neighbors
print(
f"Train Number: {len(self.train_ids)}, Test Number: {len(self.test_ids)}"
)
def fit(self):
max_test_acc, _train_acc, _epoch = 0, 0, 0
for epoch in range(self.params.n_epochs):
loss = self.train()
train_correct, train_unsure = self.evaluate(
self.train_ids, 'train')
train_acc = train_correct / len(self.train_ids)
test_correct, test_unsure = self.evaluate(self.test_ids, 'test')
test_acc = test_correct / len(self.test_ids)
if max_test_acc <= test_acc:
final_test_correct_num = test_correct
final_test_unsure_num = test_unsure
_train_acc = train_acc
_epoch = epoch
max_test_acc = test_acc
self.save_model()
print(
f">>>>Epoch {epoch:04d}: Train Acc {train_acc:.4f}, Loss {loss / len(self.train_ids):.4f}, Test correct {test_correct}, "
f"Test unsure {test_unsure}, Test Acc {test_acc:.4f}")
if train_acc == 1:
break
print(
f"---{self.params.species} {self.params.tissue} Best test result:---"
)
print(
f"Epoch {_epoch:04d}, Train Acc {_train_acc:.4f}, Test Correct Num {final_test_correct_num}, Test Total Num {len(self.test_ids)}, Test Unsure Num {final_test_unsure_num}, Test Acc {final_test_correct_num / len(self.test_ids):.4f}"
)
def train(self):
self.model.train()
total_loss = 0
for batch, nf in enumerate(
NeighborSampler(g=self.graph,
batch_size=self.params.batch_size,
expand_factor=self.num_neighbors,
num_hops=self.params.n_layers,
neighbor_type='in',
shuffle=True,
num_workers=8,
seed_nodes=self.train_ids)):
nf.copy_from_parent(
) # Copy node/edge features from the parent graph.
logits = self.model(nf)
batch_nids = nf.layer_parent_nid(-1).type(
torch.long).to(device=self.device)
loss = self.loss_fn(logits, self.labels[batch_nids])
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
total_loss += loss.item()
return total_loss
def evaluate(self, ids, type='test'):
self.model.eval()
total_correct, total_unsure = 0, 0
for nf in NeighborSampler(g=self.graph,
batch_size=self.params.batch_size,
expand_factor=self.num_cells +
self.num_genes,
num_hops=params.n_layers,
neighbor_type='in',
shuffle=True,
num_workers=8,
seed_nodes=ids):
nf.copy_from_parent(
) # Copy node/edge features from the parent graph.
with torch.no_grad():
logits = self.model(nf).cpu()
batch_nids = nf.layer_parent_nid(-1).type(torch.long)
logits = nn.functional.softmax(logits, dim=1).numpy()
label_list = self.labels.cpu()[batch_nids]
for pred, label in zip(logits, label_list):
max_prob = pred.max().item()
if max_prob < self.params.unsure_rate / self.num_labels:
total_unsure += 1
elif pred.argmax().item() == label:
total_correct += 1
return total_correct, total_unsure
def save_model(self):
state = {
'model': self.model.state_dict(),
'optimizer': self.optimizer.state_dict()
}
torch.save(
state,
self.save_path / f"{self.params.species}-{self.params.tissue}.pt")
class Runner:
def __init__(self, params):
self.params = params
self.postfix = time.strftime('%d_%m_%Y') + '_' + time.strftime(
'%H:%M:%S')
self.prj_path = Path(__file__).parent.resolve()
self.device = torch.device('cpu' if self.params.gpu ==
-1 else f'cuda:{params.gpu}')
if self.params.evaluate:
self.total_cell, self.num_genes, self.num_classes, self.id2label, self.test_dict, self.map_dict, self.time = load_data(
params)
else:
self.total_cell, self.num_genes, self.num_classes, self.id2label, self.test_dict, self.time = load_data(
params)
"""
test_dict = {
'graph': test_graph_dict,
'nid': test_index_dict,
'mask': test_mask_dict
"""
self.model = GNN(in_feats=params.dense_dim,
n_hidden=params.hidden_dim,
n_classes=self.num_classes,
n_layers=1,
gene_num=self.num_genes,
activation=F.relu,
dropout=params.dropout)
self.load_model()
self.num_neighbors = self.total_cell + self.num_genes
self.model.to(self.device)
def run(self):
for num in self.params.test_dataset:
tic = time.time()
if self.params.evaluate:
correct, total, unsure, acc, pred = self.evaluate_test(num)
print(
f"{self.params.species}_{self.params.tissue} #{num} Test Acc: {acc:.4f} ({correct}/{total}), Number of Unsure Cells: {unsure}"
)
else:
pred = self.inference(num)
toc = time.time()
print(
f'{self.params.species}_{self.params.tissue} #{num} Time Consumed: {toc - tic + self.time:.2f} seconds.'
)
self.save_pred(num, pred)
def load_model(self):
model_path = self.prj_path / 'pretrained' / self.params.species / 'models' / f'{self.params.species}-{self.params.tissue}.pt'
state = torch.load(model_path, map_location=self.device)
self.model.load_state_dict(state['model'])
def inference(self, num):
self.model.eval()
new_logits = torch.zeros(
(self.test_dict['graph'][num].number_of_nodes(), self.num_classes))
for nf in NeighborSampler(g=self.test_dict['graph'][num],
batch_size=self.params.batch_size,
expand_factor=self.total_cell +
self.num_genes,
num_hops=1,
neighbor_type='in',
shuffle=False,
num_workers=8,
seed_nodes=self.test_dict['nid'][num]):
nf.copy_from_parent(
) # Copy node/edge features from the parent graph.
with torch.no_grad():
logits = self.model(nf).cpu()
batch_nids = nf.layer_parent_nid(-1).type(torch.long)
new_logits[batch_nids] = logits
new_logits = new_logits[self.test_dict['mask'][num]]
new_logits = nn.functional.softmax(new_logits, dim=1).numpy()
predict_label = []
for pred in new_logits:
pred_label = self.id2label[pred.argmax().item()]
if pred.max().item() < self.params.unsure_rate / self.num_classes:
# unsure
predict_label.append('unsure')
else:
predict_label.append(pred_label)
return predict_label
def evaluate_test(self, num):
self.model.eval()
new_logits = torch.zeros(
(self.test_dict['graph'][num].number_of_nodes(), self.num_classes))
for nf in NeighborSampler(g=self.test_dict['graph'][num],
batch_size=self.params.batch_size,
expand_factor=self.total_cell +
self.num_genes,
num_hops=1,
neighbor_type='in',
shuffle=False,
num_workers=8,
seed_nodes=self.test_dict['nid'][num]):
nf.copy_from_parent(
) # Copy node/edge features from the parent graph.
with torch.no_grad():
logits = self.model(nf).cpu()
batch_nids = nf.layer_parent_nid(-1).type(torch.long)
new_logits[batch_nids] = logits
new_logits = new_logits[self.test_dict['mask'][num]]
new_logits = nn.functional.softmax(new_logits, dim=1).numpy()
total = new_logits.shape[0]
unsure_num, correct = 0, 0
predict_label = []
for pred, t_label in zip(new_logits, self.test_dict['label'][num]):
pred_label = self.id2label[pred.argmax().item()]
if pred.max().item() < self.params.unsure_rate / self.num_classes:
# unsure
unsure_num += 1
predict_label.append('unsure')
else:
if pred_label in self.map_dict[num][t_label]:
correct += 1
predict_label.append(pred_label)
return correct, total, unsure_num, correct / total, predict_label
def save_pred(self, num, pred):
label_map = pd.read_excel(
'./map/celltype2subtype.xlsx',
sheet_name=self.params.species,
header=0,
names=['species', 'old_type', 'new_type', 'new_subtype'])
label_map = label_map.fillna('N/A', inplace=False)
oldtype2newtype = {}
oldtype2newsubtype = {}
for _, old_type, new_type, new_subtype in label_map.itertuples(
index=False):
oldtype2newtype[old_type] = new_type
oldtype2newsubtype[old_type] = new_subtype
save_path = self.prj_path / self.params.save_dir
if not save_path.exists():
save_path.mkdir()
if self.params.evaluate:
df = pd.DataFrame({
'index':
self.test_dict['origin_id'][num],
'original label':
self.test_dict['label'][num],
'cell_type': [oldtype2newtype.get(p, p) for p in pred],
'cell_subtype': [oldtype2newsubtype.get(p, p) for p in pred]
})
else:
df = pd.DataFrame({
'index':
self.test_dict['origin_id'][num],
'cell_type': [oldtype2newtype.get(p, p) for p in pred],
'cell_subtype': [oldtype2newsubtype.get(p, p) for p in pred]
})
df.to_csv(save_path /
(self.params.species + f"_{self.params.tissue}_{num}.csv"),
index=False)
print(
f"output has been stored in {self.params.species}_{self.params.tissue}_{num}.csv"
)
