def prepare_data(self):
dataset = MAG240MDataset(self.data_dir)
log.info(dataset.num_authors)
log.info(dataset.num_institutions)
author_path = f'{dataset.dir}/author_feat_year.npy'
path = f'{dataset.dir}/institution_feat_year.npy'
t = time.perf_counter()
if not osp.exists(path):
log.info('get institution_feat...')
author_feat = np.memmap(author_path,
dtype=np.int32,
mode='r',
shape=(dataset.num_authors, ))
author_feat = author_feat[:]
author_feat = np.expand_dims(author_feat, axis=1)
# author
edge_index = dataset.edge_index('author', 'institution')
edge_index = edge_index.T
log.info(edge_index.shape)
institution_graph = Graph(edge_index,
num_nodes=dataset.num_institutions)
institution_graph.tensor()
log.info('finish institution graph')
institution_x = np.memmap(path,
dtype=np.int32,
mode='w+',
shape=(dataset.num_institutions, ))
degree = paddle.zeros(shape=[dataset.num_institutions, 1],
dtype='float32')
temp_one = paddle.ones(shape=[edge_index.shape[0], 1],
dtype='float32')
degree = scatter(degree,
overwrite=False,
index=institution_graph.edges[:, 1],
updates=temp_one)
log.info('finish degree')
inputs = author_feat
inputs = paddle.to_tensor(inputs, dtype='float32')
outputs = institution_graph.send_recv(inputs)
outputs = outputs / degree
outputs = outputs.astype('int32').numpy()
del inputs
save_col_slice(x_src=outputs,
x_dst=institution_x,
start_row_idx=0,
end_row_idx=dataset.num_institutions)
del outputs
institution_x.flush()
del institution_x
log.info(f'Done! [{time.perf_counter() - t:.2f}s]')
def prepare_data(self):
dataset = MAG240MDataset(self.data_dir)
log.info(dataset.num_authors)
log.info(dataset.num_papers)
path = f'{dataset.dir}/author_feat.npy'
t = time.perf_counter()
if not osp.exists(path):
log.info('get author_feat...')
paper_feat = dataset.paper_feat
# author
edge_index = dataset.edge_index('author', 'writes', 'paper')
edge_index = edge_index.T
row, col = edge_index[:, 0], edge_index[:, 1]
edge_index = np.stack([col, row], axis=1)
log.info(edge_index.shape)
author_graph = Graph(edge_index, num_nodes=dataset.num_authors)
author_graph.tensor()
log.info('finish author graph')
author_x = np.memmap(path,
dtype=np.float16,
mode='w+',
shape=(dataset.num_authors,
self.num_features))
dim_chunk_size = 64
degree = paddle.zeros(shape=[dataset.num_authors, 1],
dtype='float32')
degree += 1e-10
temp_one = paddle.ones(shape=[edge_index.shape[0], 1],
dtype='float32')
degree = scatter(degree,
author_graph.edges[:, 1],
temp_one,
overwrite=False)
log.info('finish degree')
for i in tqdm(range(0, self.num_features, dim_chunk_size)):
j = min(i + dim_chunk_size, self.num_features)
inputs = get_col_slice(paper_feat,
start_row_idx=0,
end_row_idx=dataset.num_papers,
start_col_idx=i,
end_col_idx=j)
inputs = paddle.to_tensor(inputs, dtype='float32')
outputs = author_graph.send_recv(inputs)
outputs = outputs / degree
outputs = outputs.astype('float16').numpy()
del inputs
save_col_slice(x_src=outputs,
x_dst=author_x,
start_row_idx=0,
end_row_idx=dataset.num_authors,
start_col_idx=i,
end_col_idx=j)
del outputs
author_x.flush()
del author_x
log.info(f'Done! [{time.perf_counter() - t:.2f}s]')
