def process(self): add_inverse_edge = self.meta_info[ self.name]["add_inverse_edge"] == "True" if self.meta_info[self.name]["additional node files"] == 'None': additional_node_files = [] else: additional_node_files = self.meta_info[ self.name]["additional node files"].split(',') if self.meta_info[self.name]["additional edge files"] == 'None': additional_edge_files = [] else: additional_edge_files = self.meta_info[ self.name]["additional edge files"].split(',') if self.is_hetero: data = read_csv_heterograph_pyg( self.raw_dir, add_inverse_edge=add_inverse_edge, additional_node_files=additional_node_files, additional_edge_files=additional_edge_files)[0] else: data = read_csv_graph_pyg( self.raw_dir, add_inverse_edge=add_inverse_edge, additional_node_files=additional_node_files, additional_edge_files=additional_edge_files)[0] data = data if self.pre_transform is None else self.pre_transform(data) print('Saving...') torch.save(self.collate([data]), self.processed_paths[0])
def process(self): add_inverse_edge = self.meta_info[ self.name]["add_inverse_edge"] == "True" data = read_csv_graph_pyg(self.raw_dir, add_inverse_edge=add_inverse_edge)[0] data = data if self.pre_transform is None else self.pre_transform(data) torch.save(self.collate([data]), self.processed_paths[0])
def process(self): add_inverse_edge = self.meta_info[ self.name]["add_inverse_edge"] == "True" if self.meta_info[self.name]["additional node files"] == 'None': additional_node_files = [] else: additional_node_files = self.meta_info[ self.name]["additional node files"].split(',') if self.meta_info[self.name]["additional edge files"] == 'None': additional_edge_files = [] else: additional_edge_files = self.meta_info[ self.name]["additional edge files"].split(',') if self.is_hetero: data = read_csv_heterograph_pyg( self.raw_dir, add_inverse_edge=add_inverse_edge, additional_node_files=additional_node_files, additional_edge_files=additional_edge_files)[0] node_label_dict = read_node_label_hetero(self.raw_dir) data.y_dict = {} if "classification" in self.task_type: for nodetype, node_label in node_label_dict.items(): data.y_dict[nodetype] = torch.from_numpy(node_label).to( torch.long) else: for nodetype, node_label in node_label_dict.items(): data.y_dict[nodetype] = torch.from_numpy(node_label).to( torch.float32) else: data = read_csv_graph_pyg( self.raw_dir, add_inverse_edge=add_inverse_edge, additional_node_files=additional_node_files, additional_edge_files=additional_edge_files)[0] ### adding prediction target node_label = pd.read_csv(osp.join(self.raw_dir, 'node-label.csv.gz'), compression="gzip", header=None).values if "classification" in self.task_type: data.y = torch.from_numpy(node_label).to(torch.long) else: data.y = torch.from_numpy(node_label).to(torch.float32) data if self.pre_transform is None else self.pre_transform(data) print('Saving...') torch.save(self.collate([data]), self.processed_paths[0])
def process(self): ### read pyg graph list add_inverse_edge = self.meta_info[ self.name]["add_inverse_edge"] == "True" if self.meta_info[self.name]["additional node files"] == 'None': additional_node_files = [] else: additional_node_files = self.meta_info[ self.name]["additional node files"].split(',') if self.meta_info[self.name]["additional edge files"] == 'None': additional_edge_files = [] else: additional_edge_files = self.meta_info[ self.name]["additional edge files"].split(',') data_list = read_csv_graph_pyg( self.raw_dir, add_inverse_edge=add_inverse_edge, additional_node_files=additional_node_files, additional_edge_files=additional_edge_files) if self.task_type == "sequence prediction": graph_label_notparsed = pd.read_csv(osp.join( self.raw_dir, "graph-label.csv.gz"), compression="gzip", header=None).values graph_label = [ str(graph_label_notparsed[i][0]).split(' ') for i in range(len(graph_label_notparsed)) ] for i, g in enumerate(data_list): g.y = graph_label[i] else: graph_label = pd.read_csv(osp.join(self.raw_dir, "graph-label.csv.gz"), compression="gzip", header=None).values for i, g in enumerate(data_list): g.y = torch.tensor(graph_label[i]).view(1, -1) 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 process(self): ### read pyg graph list add_inverse_edge = self.meta_info[ self.name]["add_inverse_edge"] == "True" data_list = read_csv_graph_pyg(self.raw_dir, add_inverse_edge=add_inverse_edge) graph_label = pd.read_csv(osp.join(self.raw_dir, "graph-label.csv.gz"), compression="gzip", header=None).values ### add target labels for i, g in enumerate(data_list): g.y = torch.tensor(graph_label[i]).view(1, -1) data, slices = self.collate(data_list) torch.save((data, slices), self.processed_paths[0])
def process(self): add_inverse_edge = self.meta_info[ self.name]["add_inverse_edge"] == "True" data = read_csv_graph_pyg(self.raw_dir, add_inverse_edge=add_inverse_edge)[0] ### adding prediction target node_label = pd.read_csv(osp.join(self.raw_dir, 'node-label.csv.gz'), compression="gzip", header=None).values if "classification" in self.task_type: data.y = torch.tensor(node_label, dtype=torch.long) else: data.y = torch.tensor(node_label, dtype=torch.float32) data.__num_nodes__ = int(self.meta_info[self.name]["num nodes"]) data = data if self.pre_transform is None else self.pre_transform(data) torch.save(self.collate([data]), self.processed_paths[0])