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inductive mode bug fixes #23

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inductive mode bug fixes #23

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17 changes: 14 additions & 3 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,23 +86,34 @@ def get_inductive_links(df, train_edge_end, val_edge_end):

if args.use_inductive:
test_df = df[val_edge_end:]
inductive_nodes = set(test_df.src.values).union(test_df.src.values)
train_df = df[:train_edge_end]
train_nodes = set(train_df.src.values).union(train_df.dst.values)
all_nodes = set(df.src.values).union(df.dst.values)
inductive_nodes = set(test_df.src.values).union(test_df.dst.values)
print("inductive nodes", len(inductive_nodes))
neg_link_sampler = NegLinkInductiveSampler(inductive_nodes)
neg_link_sampler_train = NegLinkInductiveSampler(train_nodes, seed=args.exp_seed)
neg_link_sampler_val = NegLinkInductiveSampler(all_nodes, seed=args.exp_seed)
neg_link_sampler_test = NegLinkInductiveSampler(inductive_nodes, seed=args.exp_seed)
else:
neg_link_sampler = NegLinkSampler(g['indptr'].shape[0] - 1)
neg_link_sampler_train = neg_link_sampler
neg_link_sampler_val = neg_link_sampler
neg_link_sampler_test = neg_link_sampler

def eval(mode='val'):
neg_samples = 1
model.eval()
aps = list()
aucs_mrrs = list()
if mode == 'val':
neg_link_sampler = neg_link_sampler_val
eval_df = df[train_edge_end:val_edge_end]
elif mode == 'test':
neg_link_sampler = neg_link_sampler_test
eval_df = df[val_edge_end:]
neg_samples = args.eval_neg_samples
elif mode == 'train':
neg_link_sampler = neg_link_sampler_train
eval_df = df[:train_edge_end]
with torch.no_grad():
total_loss = 0
Expand Down Expand Up @@ -189,7 +200,7 @@ def eval(mode='val'):
model.memory_updater.last_updated_nid = None
for _, rows in df[:train_edge_end].groupby(group_indexes[random.randint(0, len(group_indexes) - 1)]):
t_tot_s = time.time()
root_nodes = np.concatenate([rows.src.values, rows.dst.values, neg_link_sampler.sample(len(rows))]).astype(np.int32)
root_nodes = np.concatenate([rows.src.values, rows.dst.values, neg_link_sampler_train.sample(len(rows))]).astype(np.int32)
ts = np.concatenate([rows.time.values, rows.time.values, rows.time.values]).astype(np.float32)
if sampler is not None:
if 'no_neg' in sample_param and sample_param['no_neg']:
Expand Down