def process(self):
with open(os.path.join(self.root, self.name + '_%s.pkl' % self.split),
'rb') as f:
self.dataset = pickle.load(f)
# self.n_samples = len(self.dataset)
print("preparing graphs for the %s set..." % (self.split.upper()))
print('Converting graphs into PyG objects...')
pyg_graph_list = []
for data in tqdm(self.dataset):
node_features = data.node_feat
edge_list = (data.W !=
0).nonzero() # converting adj matrix to edge_list
g = Data()
g.__num_nodes__ = node_features.size(0)
g.edge_index = edge_list.T
#g.edge_index = torch.from_numpy(edge_list)
g.x = node_features.long()
# adding edge features for Residual Gated ConvNet
edge_feat_dim = 1
g.edge_attr = torch.ones(g.num_edges, edge_feat_dim)
g.y = data.node_label.to(torch.float32)
pyg_graph_list.append(g)
del self.dataset
data, slices = self.collate(pyg_graph_list)
print('Saving...')
torch.save((data, slices), self.processed_paths[0])
def read_csv_graph_pyg(raw_dir, add_inverse_edge = True, additional_node_files = [], additional_edge_files = []):
graph_list = read_csv_graph_raw(raw_dir, add_inverse_edge, additional_node_files = additional_node_files, additional_edge_files = additional_edge_files)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph["num_nodes"]
g.edge_index = torch.tensor(graph["edge_index"])
if graph["edge_feat"] is not None:
g.edge_attr = torch.tensor(graph["edge_feat"])
if graph["node_feat"] is not None:
g.x = torch.tensor(graph["node_feat"])
for key in additional_node_files:
g[key] = torch.tensor(graph[key])
for key in additional_edge_files:
g[key] = torch.tensor(graph[key])
pyg_graph_list.append(g)
return pyg_graph_list
def read_graph_pyg(raw_dir,
add_inverse_edge=False,
additional_node_files=[],
additional_edge_files=[],
binary=False):
if binary:
# npz
graph_list = read_binary_graph_raw(raw_dir, add_inverse_edge)
else:
# csv
graph_list = read_csv_graph_raw(
raw_dir,
add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph['num_nodes']
g.edge_index = torch.from_numpy(graph['edge_index'])
del graph['num_nodes']
del graph['edge_index']
if graph['edge_feat'] is not None:
g.edge_attr = torch.from_numpy(graph['edge_feat'])
del graph['edge_feat']
if graph['node_feat'] is not None:
g.x = torch.from_numpy(graph['node_feat'])
del graph['node_feat']
for key in additional_node_files:
g[key] = torch.from_numpy(graph[key])
del graph[key]
for key in additional_edge_files:
g[key] = torch.from_numpy(graph[key])
del graph[key]
pyg_graph_list.append(g)
add_order_info_01(g) # DAGNN
# length of longest path
# layer ids start with 0 so max, gives actual path length and -1 is not necessary
g.len_longest_path = float(torch.max(g._bi_layer_idx0).item())
return pyg_graph_list
def __getitem__(self, idx):
'''Get datapoint with index'''
data = Data()
smiles, y = self.smiles_list[idx]
graph = self.smiles2graph(smiles)
data.__num_nodes__ = int(graph['num_nodes'])
data.edge_index = torch.from_numpy(graph['edge_index']).to(torch.int64)
data.edge_attr = torch.from_numpy(graph['edge_feat']).to(torch.int64)
data.x = torch.from_numpy(graph['node_feat']).to(torch.int64)
return data
def read_graph_pyg(raw_dir,
add_inverse_edge=False,
additional_node_files=[],
additional_edge_files=[],
binary=False):
if binary:
# npz
graph_list = read_binary_graph_raw(raw_dir, add_inverse_edge)
else:
# csv
graph_list = read_csv_graph_raw(
raw_dir,
add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph['num_nodes']
g.edge_index = torch.from_numpy(graph['edge_index'])
del graph['num_nodes']
del graph['edge_index']
if graph['edge_feat'] is not None:
g.edge_attr = torch.from_numpy(graph['edge_feat'])
del graph['edge_feat']
if graph['node_feat'] is not None:
g.x = torch.from_numpy(graph['node_feat'])
del graph['node_feat']
for key in additional_node_files:
g[key] = torch.from_numpy(graph[key])
del graph[key]
for key in additional_edge_files:
g[key] = torch.from_numpy(graph[key])
del graph[key]
pyg_graph_list.append(g)
return pyg_graph_list
def process(self):
data_df = pd.read_csv(osp.join(self.raw_dir, 'data.csv.gz'))
smiles_list = data_df['smiles']
homolumogap_list = data_df['homolumogap']
print('Converting SMILES strings into graphs...')
data_list = []
for i in tqdm(range(len(smiles_list))):
data = Data()
smiles = smiles_list[i]
homolumogap = homolumogap_list[i]
graph = self.smiles2graph(smiles)
assert (len(graph['edge_feat']) == graph['edge_index'].shape[1])
assert (len(graph['node_feat']) == graph['num_nodes'])
data.__num_nodes__ = int(graph['num_nodes'])
data.edge_index = torch.from_numpy(graph['edge_index']).to(
torch.int64)
data.edge_attr = torch.from_numpy(graph['edge_feat']).to(
torch.int64)
data.x = torch.from_numpy(graph['node_feat']).to(torch.int64)
data.y = torch.Tensor([homolumogap])
data_list.append(data)
# double-check prediction target
split_dict = self.get_idx_split()
assert (all(
[not torch.isnan(data_list[i].y)[0] for i in split_dict['train']]))
assert (all(
[not torch.isnan(data_list[i].y)[0] for i in split_dict['valid']]))
assert (all(
[torch.isnan(data_list[i].y)[0] for i in split_dict['test-dev']]))
assert (all([
torch.isnan(data_list[i].y)[0]
for i in split_dict['test-challenge']
]))
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
data, slices = self.collate(data_list)
print('Saving...')
torch.save((data, slices), self.processed_paths[0])
def read_csv_graph_pyg(raw_dir,
add_inverse_edge=True,
additional_node_files=[],
additional_edge_files=[]):
graph_list = read_csv_graph_raw(
raw_dir,
add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph["num_nodes"]
g.edge_index = torch.from_numpy(graph["edge_index"])
if graph["edge_feat"] is not None:
g.edge_attr = torch.from_numpy(graph["edge_feat"])
if graph["node_feat"] is not None:
g.x = torch.from_numpy(graph["node_feat"])
for key in additional_node_files:
if 'node_' not in key:
feat_name = 'node_' + key
else:
feat_name = key
g[feat_name] = torch.from_numpy(graph[feat_name])
for key in additional_edge_files:
if 'edge_' not in key:
feat_name = 'edge_' + key
else:
feat_name = key
g[feat_name] = torch.from_numpy(graph[feat_name])
add_order_info_01(g) # DAGNN
# length of longest path
# layer ids start with 0 so max, gives actual path length and -1 is not necessary
g.len_longest_path = float(torch.max(g._bi_layer_idx0).item())
pyg_graph_list.append(g)
return pyg_graph_list
def read_csv_graph_pyg(raw_dir, add_inverse_edge=False):
graph_list = read_csv_graph_raw(raw_dir, add_inverse_edge)
pyg_graph_list = []
for graph in graph_list:
g = Data()
g.__num_nodes__ = graph["num_nodes"]
g.edge_index = torch.tensor(graph["edge_index"])
if graph["edge_feat"] is not None:
g.edge_attr = torch.tensor(graph["edge_feat"])
if graph["node_feat"] is not None:
g.x = torch.tensor(graph["node_feat"])
pyg_graph_list.append(g)
return pyg_graph_list
def read_csv_heterograph_pyg(raw_dir, add_inverse_edge = False, additional_node_files = [], additional_edge_files = []):
graph_list = read_csv_heterograph_raw(raw_dir, add_inverse_edge, additional_node_files = additional_node_files, additional_edge_files = additional_edge_files)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph["num_nodes_dict"]
g.num_nodes_dict = graph["num_nodes_dict"]
# add edge connectivity
g.edge_index_dict = {}
for triplet, edge_index in graph["edge_index_dict"].items():
g.edge_index_dict[triplet] = torch.from_numpy(edge_index)
if graph["edge_feat_dict"] is not None:
g.edge_attr_dict = {}
for triplet in graph["edge_feat_dict"].keys():
g.edge_attr_dict[triplet] = torch.from_numpy(graph["edge_feat_dict"][triplet])
if graph["node_feat_dict"] is not None:
g.x_dict = {}
for nodetype in graph["node_feat_dict"].keys():
g.x_dict[nodetype] = torch.from_numpy(graph["node_feat_dict"][nodetype])
for key in additional_edge_files:
g[key + '_dict'] = {}
for triplet in graph[key].keys():
g[key + '_dict'][triplet] = torch.from_numpy(graph[key][triplet])
for key in additional_node_files:
g[key + '_dict'] = {}
for nodetype in graph[key].keys():
g[key + '_dict'][nodetype] = torch.from_numpy(graph[key][nodetype])
pyg_graph_list.append(g)
return pyg_graph_list
def read_csv_graph_pyg(raw_dir,
add_inverse_edge=True,
additional_node_files=[],
additional_edge_files=[]):
graph_list = read_csv_graph_raw(
raw_dir,
add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph["num_nodes"]
g.edge_index = torch.from_numpy(graph["edge_index"])
if graph["edge_feat"] is not None:
g.edge_attr = torch.from_numpy(graph["edge_feat"])
if graph["node_feat"] is not None:
g.x = torch.from_numpy(graph["node_feat"])
for key in additional_node_files:
if 'node_' not in key:
feat_name = 'node_' + key
else:
feat_name = key
g[feat_name] = torch.from_numpy(graph[feat_name])
for key in additional_edge_files:
if 'edge_' not in key:
feat_name = 'edge_' + key
else:
feat_name = key
g[feat_name] = torch.from_numpy(graph[feat_name])
pyg_graph_list.append(g)
return pyg_graph_list
def read_csv_graph_pyg(raw_dir, add_inverse_edge=False):
graph_list = read_csv_graph_raw(raw_dir, add_inverse_edge)
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph["num_nodes"]
g.edge_index = torch.tensor(graph["edge_index"])
if graph["edge_feat"] is not None:
g.edge_attr = torch.tensor(graph["edge_feat"])
if graph["node_feat"] is not None:
g.x = torch.tensor(graph["node_feat"])
pyg_graph_list.append(g)
return pyg_graph_list
def graphs_to_pyg(graph_list):
pyg_graph_list = []
print('Converting graphs into PyG objects...')
for graph in tqdm(graph_list):
g = Data()
g.__num_nodes__ = graph["num_nodes"]
g.edge_index = torch.tensor(graph["edge_index"])
if graph["edge_feat"] is not None:
g.edge_attr = torch.DoubleTensor(graph["edge_feat"])
if graph["node_feat"] is not None:
g.x = torch.DoubleTensor(graph["node_feat"])
if graph["target"] is not None:
tar = int(graph["target"])
tar = 0 if tar == -1 else tar
g.y = torch.LongTensor([tar])
pyg_graph_list.append(g)
return pyg_graph_list
