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Fix Warnings in train.py #30

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Fix Warnings in train.py #30

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9d65ff9
Fixed PyTorch warning
hnaik Feb 11, 2021
3e62733
Fix sklearn metrics warning: Precision zero_division
hnaik Feb 11, 2021
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12 changes: 6 additions & 6 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,7 +194,7 @@ def train(
else:
loss = model.loss(ypred, label, adj, batch_num_nodes)
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.clip)
nn.utils.clip_grad_norm_(model.parameters(), args.clip)
optimizer.step()
iter += 1
avg_loss += loss
Expand Down Expand Up @@ -294,7 +294,7 @@ def train_node_classifier(G, labels, model, args, writer=None):
else:
loss = model.loss(ypred_train, labels_train)
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.clip)
nn.utils.clip_grad_norm_(model.parameters(), args.clip)

optimizer.step()
#for param_group in optimizer.param_groups:
Expand Down Expand Up @@ -424,7 +424,7 @@ def train_node_classifier_multigraph(G_list, labels, model, args, writer=None):
else:
loss = model.loss(ypred_train_cmp, labels_train)
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.clip)
nn.utils.clip_grad_norm_(model.parameters(), args.clip)

optimizer.step()
#for param_group in optimizer.param_groups:
Expand Down Expand Up @@ -516,7 +516,7 @@ def evaluate(dataset, model, args, name="Validation", max_num_examples=None):
preds = np.hstack(preds)

result = {
"prec": metrics.precision_score(labels, preds, average="macro"),
"prec": metrics.precision_score(labels, preds, average="macro", zero_division=0),
"recall": metrics.recall_score(labels, preds, average="macro"),
"acc": metrics.accuracy_score(labels, preds),
}
Expand All @@ -534,13 +534,13 @@ def evaluate_node(ypred, labels, train_idx, test_idx):
labels_test = np.ravel(labels[:, test_idx])

result_train = {
"prec": metrics.precision_score(labels_train, pred_train, average="macro"),
"prec": metrics.precision_score(labels_train, pred_train, average="macro", zero_division=0),
"recall": metrics.recall_score(labels_train, pred_train, average="macro"),
"acc": metrics.accuracy_score(labels_train, pred_train),
"conf_mat": metrics.confusion_matrix(labels_train, pred_train),
}
result_test = {
"prec": metrics.precision_score(labels_test, pred_test, average="macro"),
"prec": metrics.precision_score(labels_test, pred_test, average="macro", zero_division=0),
"recall": metrics.recall_score(labels_test, pred_test, average="macro"),
"acc": metrics.accuracy_score(labels_test, pred_test),
"conf_mat": metrics.confusion_matrix(labels_test, pred_test),
Expand Down