def test_filter(self): pyg_dataset = TUDataset("./enzymes", "ENZYMES") ds = pyg_to_dicts(pyg_dataset) graphs = [Graph(**item) for item in ds] dataset = GraphDataset(graphs, task="graph") thresh = 90 orig_dataset_size = len(dataset) num_graphs_large = 0 for graph in dataset: if graph.num_nodes >= thresh: num_graphs_large += 1 dataset = dataset.filter(lambda graph: graph.num_nodes < thresh, deep_copy=False) filtered_dataset_size = len(dataset) self.assertEqual( orig_dataset_size - filtered_dataset_size, num_graphs_large, )
def test_dataset_split_custom(self): # transductive split with node task (self defined dataset) G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y = ( simple_networkx_graph()) Graph.add_edge_attr(G, "edge_feature", edge_x) Graph.add_edge_attr(G, "edge_label", edge_y) Graph.add_node_attr(G, "node_feature", x) Graph.add_node_attr(G, "node_label", y) Graph.add_graph_attr(G, "graph_feature", graph_x) Graph.add_graph_attr(G, "graph_label", graph_y) num_nodes = len(list(G.nodes)) nodes_train = torch.tensor(list(G.nodes)[:int(0.3 * num_nodes)]) nodes_val = torch.tensor( list(G.nodes)[int(0.3 * num_nodes):int(0.6 * num_nodes)]) nodes_test = torch.tensor(list(G.nodes)[int(0.6 * num_nodes):]) graph_train = Graph(node_feature=x, node_label=y, edge_index=edge_index, node_label_index=nodes_train, directed=True) graph_val = Graph(node_feature=x, node_label=y, edge_index=edge_index, node_label_index=nodes_val, directed=True) graph_test = Graph(node_feature=x, node_label=y, edge_index=edge_index, node_label_index=nodes_test, directed=True) graphs_train = [graph_train] graphs_val = [graph_val] graphs_test = [graph_test] dataset_train, dataset_val, dataset_test = (GraphDataset(graphs_train, task='node'), GraphDataset(graphs_val, task='node'), GraphDataset(graphs_test, task='node')) self.assertEqual(dataset_train[0].node_label_index.tolist(), list(range(int(0.3 * num_nodes)))) self.assertEqual( dataset_val[0].node_label_index.tolist(), list(range(int(0.3 * num_nodes), int(0.6 * num_nodes)))) self.assertEqual(dataset_test[0].node_label_index.tolist(), list(range(int(0.6 * num_nodes), num_nodes))) # transductive split with link_pred task (train/val split) edges = list(G.edges) num_edges = len(edges) edges_train = edges[:int(0.7 * num_edges)] edges_val = edges[int(0.7 * num_edges):] link_size_list = [len(edges_train), len(edges_val)] # generate pseudo pos and neg edges, they may overlap here train_pos = torch.LongTensor(edges_train).permute(1, 0) val_pos = torch.LongTensor(edges_val).permute(1, 0) val_neg = torch.randint(high=10, size=val_pos.shape, dtype=torch.int64) val_neg_double = torch.cat((val_neg, val_neg), dim=1) num_train = len(edges_train) num_val = len(edges_val) graph_train = Graph(node_feature=x, edge_index=edge_index, edge_feature=edge_x, directed=True, edge_label_index=train_pos) graph_val = Graph(node_feature=x, edge_index=edge_index, edge_feature=edge_x, directed=True, edge_label_index=val_pos, negative_edge=val_neg_double) graphs_train = [graph_train] graphs_val = [graph_val] dataset_train, dataset_val = (GraphDataset(graphs_train, task='link_pred', resample_negatives=True), GraphDataset( graphs_val, task='link_pred', edge_negative_sampling_ratio=2)) self.assertEqual(dataset_train[0].edge_label_index.shape[1], 2 * link_size_list[0]) self.assertEqual(dataset_train[0].edge_label.shape[0], 2 * link_size_list[0]) self.assertEqual(dataset_val[0].edge_label_index.shape[1], val_pos.shape[1] + val_neg_double.shape[1]) self.assertEqual(dataset_val[0].edge_label.shape[0], val_pos.shape[1] + val_neg_double.shape[1]) self.assertTrue( torch.equal(dataset_train[0].edge_label_index[:, :num_train], train_pos)) self.assertTrue( torch.equal(dataset_val[0].edge_label_index[:, :num_val], val_pos)) self.assertTrue( torch.equal(dataset_val[0].edge_label_index[:, num_val:], val_neg_double)) dataset_train.resample_negatives = False self.assertTrue( torch.equal(dataset_train[0].edge_label_index, dataset_train[0].edge_label_index)) # transductive split with link_pred task with edge label edge_label_train = torch.LongTensor([1, 2, 3, 2, 1, 1, 2, 3, 2, 0, 0]) edge_label_val = torch.LongTensor([1, 2, 3, 2, 1, 0]) graph_train = Graph(node_feature=x, edge_index=edge_index, directed=True, edge_label_index=train_pos, edge_label=edge_label_train) graph_val = Graph(node_feature=x, edge_index=edge_index, directed=True, edge_label_index=val_pos, negative_edge=val_neg, edge_label=edge_label_val) graphs_train = [graph_train] graphs_val = [graph_val] dataset_train, dataset_val = (GraphDataset(graphs_train, task='link_pred'), GraphDataset(graphs_val, task='link_pred')) self.assertTrue( torch.equal(dataset_train[0].edge_label_index, dataset_train[0].edge_label_index)) self.assertTrue( torch.equal(dataset_train[0].edge_label[:num_train], edge_label_train)) self.assertTrue( torch.equal(dataset_val[0].edge_label[:num_val], edge_label_val)) # Multiple graph tensor backend link prediction (inductive) pyg_dataset = Planetoid('./cora', 'Cora') x = pyg_dataset[0].x y = pyg_dataset[0].y edge_index = pyg_dataset[0].edge_index row, col = edge_index mask = row < col row, col = row[mask], col[mask] edge_index = torch.stack([row, col], dim=0) edge_index = torch.cat( [edge_index, torch.flip(edge_index, [0])], dim=1) graphs = [ Graph(node_feature=x, node_label=y, edge_index=edge_index, directed=False) ] graphs = [copy.deepcopy(graphs[0]) for _ in range(10)] edge_label_index = graphs[0].edge_label_index dataset = GraphDataset(graphs, task='link_pred', edge_message_ratio=0.6, edge_train_mode="all") datasets = {} datasets['train'], datasets['val'], datasets['test'] = dataset.split( transductive=False, split_ratio=[0.85, 0.05, 0.1]) edge_label_index_split = ( datasets['train'][0].edge_label_index[:, 0:edge_label_index.shape[1]]) self.assertTrue(torch.equal(edge_label_index, edge_label_index_split)) # transductive split with node task (pytorch geometric dataset) pyg_dataset = Planetoid("./cora", "Cora") ds = pyg_to_dicts(pyg_dataset, task="cora") graphs = [Graph(**item) for item in ds] split_ratio = [0.3, 0.3, 0.4] node_size_list = [0 for i in range(len(split_ratio))] for graph in graphs: custom_splits = [[] for i in range(len(split_ratio))] split_offset = 0 num_nodes = graph.num_nodes shuffled_node_indices = torch.randperm(graph.num_nodes) for i, split_ratio_i in enumerate(split_ratio): if i != len(split_ratio) - 1: num_split_i = int(split_ratio_i * num_nodes) nodes_split_i = ( shuffled_node_indices[split_offset:split_offset + num_split_i]) split_offset += num_split_i else: nodes_split_i = shuffled_node_indices[split_offset:] custom_splits[i] = nodes_split_i node_size_list[i] += len(nodes_split_i) graph.custom = {"general_splits": custom_splits} node_feature = graphs[0].node_feature edge_index = graphs[0].edge_index directed = graphs[0].directed graph_train = Graph( node_feature=node_feature, edge_index=edge_index, directed=directed, node_label_index=graphs[0].custom["general_splits"][0]) graph_val = Graph( node_feature=node_feature, edge_index=edge_index, directed=directed, node_label_index=graphs[0].custom["general_splits"][1]) graph_test = Graph( node_feature=node_feature, edge_index=edge_index, directed=directed, node_label_index=graphs[0].custom["general_splits"][2]) train_dataset = GraphDataset([graph_train], task="node") val_dataset = GraphDataset([graph_val], task="node") test_dataset = GraphDataset([graph_test], task="node") self.assertEqual(len(train_dataset[0].node_label_index), node_size_list[0]) self.assertEqual(len(val_dataset[0].node_label_index), node_size_list[1]) self.assertEqual(len(test_dataset[0].node_label_index), node_size_list[2]) # transductive split with edge task pyg_dataset = Planetoid("./cora", "Cora") graphs_g = GraphDataset.pyg_to_graphs(pyg_dataset) ds = pyg_to_dicts(pyg_dataset, task="cora") graphs = [Graph(**item) for item in ds] split_ratio = [0.3, 0.3, 0.4] edge_size_list = [0 for i in range(len(split_ratio))] for i, graph in enumerate(graphs): custom_splits = [[] for i in range(len(split_ratio))] split_offset = 0 edges = list(graphs_g[i].G.edges) num_edges = graph.num_edges random.shuffle(edges) for i, split_ratio_i in enumerate(split_ratio): if i != len(split_ratio) - 1: num_split_i = int(split_ratio_i * num_edges) edges_split_i = (edges[split_offset:split_offset + num_split_i]) split_offset += num_split_i else: edges_split_i = edges[split_offset:] custom_splits[i] = edges_split_i edge_size_list[i] += len(edges_split_i) graph.custom = {"general_splits": custom_splits} node_feature = graphs[0].node_feature edge_index = graphs[0].edge_index directed = graphs[0].directed train_index = torch.tensor( graphs[0].custom["general_splits"][0]).permute(1, 0) train_index = torch.cat((train_index, train_index), dim=1) val_index = torch.tensor( graphs[0].custom["general_splits"][1]).permute(1, 0) val_index = torch.cat((val_index, val_index), dim=1) test_index = torch.tensor( graphs[0].custom["general_splits"][2]).permute(1, 0) test_index = torch.cat((test_index, test_index), dim=1) graph_train = Graph(node_feature=node_feature, edge_index=edge_index, directed=directed, edge_label_index=train_index) graph_val = Graph(node_feature=node_feature, edge_index=edge_index, directed=directed, edge_label_index=val_index) graph_test = Graph(node_feature=node_feature, edge_index=edge_index, directed=directed, edge_label_index=test_index) train_dataset = GraphDataset([graph_train], task="edge") val_dataset = GraphDataset([graph_val], task="edge") test_dataset = GraphDataset([graph_test], task="edge") self.assertEqual(train_dataset[0].edge_label_index.shape[1], 2 * edge_size_list[0]) self.assertEqual(val_dataset[0].edge_label_index.shape[1], 2 * edge_size_list[1]) self.assertEqual(test_dataset[0].edge_label_index.shape[1], 2 * edge_size_list[2]) # inductive split with graph task pyg_dataset = TUDataset("./enzymes", "ENZYMES") ds = pyg_to_dicts(pyg_dataset) graphs = [Graph(**item) for item in ds] num_graphs = len(graphs) split_ratio = [0.3, 0.3, 0.4] graph_size_list = [] split_offset = 0 custom_split_graphs = [] for i, split_ratio_i in enumerate(split_ratio): if i != len(split_ratio) - 1: num_split_i = int(split_ratio_i * num_graphs) custom_split_graphs.append(graphs[split_offset:split_offset + num_split_i]) split_offset += num_split_i graph_size_list.append(num_split_i) else: custom_split_graphs.append(graphs[split_offset:]) graph_size_list.append(len(graphs[split_offset:])) dataset = GraphDataset(graphs, task="graph", custom_split_graphs=custom_split_graphs) split_res = dataset.split(transductive=False) self.assertEqual(graph_size_list[0], len(split_res[0])) self.assertEqual(graph_size_list[1], len(split_res[1])) self.assertEqual(graph_size_list[2], len(split_res[2]))
def test_dataset_split(self): # inductively split with graph task pyg_dataset = TUDataset("./enzymes", "ENZYMES") ds = pyg_to_dicts(pyg_dataset) graphs = [Graph(**item) for item in ds] dataset = GraphDataset(graphs, task="graph") split_res = dataset.split(transductive=False) num_graphs = len(dataset) num_train = int(0.8 * num_graphs) num_val = int(0.1 * num_graphs) num_test = num_graphs - num_train - num_val self.assertEqual(num_train, len(split_res[0])) self.assertEqual(num_val, len(split_res[1])) self.assertEqual(num_test, len(split_res[2])) # inductively split with link_pred task # and default (`all`) edge_train_mode pyg_dataset = TUDataset("./enzymes", "ENZYMES") ds = pyg_to_dicts(pyg_dataset) graphs = [Graph(**item) for item in ds] dataset = GraphDataset(graphs, task="link_pred") split_res = dataset.split(transductive=False) num_graphs = len(dataset) num_train = int(0.8 * num_graphs) num_val = int(0.1 * num_graphs) num_test = num_graphs - num_train - num_val self.assertEqual(num_train, len(split_res[0])) self.assertEqual(num_val, len(split_res[1])) self.assertEqual(num_test, len(split_res[2])) # inductively split with link_pred task and `disjoint` edge_train_mode pyg_dataset = TUDataset("./enzymes", "ENZYMES") ds = pyg_to_dicts(pyg_dataset) graphs = [Graph(**item) for item in ds] dataset = GraphDataset( graphs, task="link_pred", edge_train_mode="disjoint", ) split_res = dataset.split(transductive=False) num_graphs = len(dataset) num_train = int(0.8 * num_graphs) num_val = int(0.1 * num_graphs) num_test = num_graphs - num_train - num_val self.assertEqual(num_train, len(split_res[0])) self.assertEqual(num_val, len(split_res[1])) self.assertEqual(num_test, len(split_res[2])) # transductively split with node task pyg_dataset = Planetoid("./cora", "Cora") ds = pyg_to_dicts(pyg_dataset, task="cora") graphs = [Graph(**item) for item in ds] dataset = GraphDataset(graphs, task="node") num_nodes = dataset.num_nodes[0] num_edges = dataset.num_edges[0] node_0 = int(0.8 * num_nodes) node_1 = int(0.1 * num_nodes) node_2 = num_nodes - node_0 - node_1 split_res = dataset.split() self.assertEqual(len(split_res[0][0].node_label_index), node_0) self.assertEqual(len(split_res[1][0].node_label_index), node_1) self.assertEqual(len(split_res[2][0].node_label_index), node_2) # transductively split with link_pred task # and default (`all`) edge_train_mode dataset = GraphDataset(graphs, task="link_pred") edge_0 = 2 * 2 * int(0.8 * num_edges) edge_1 = 2 * 2 * int(0.1 * num_edges) edge_2 = 2 * 2 * (num_edges - int(0.8 * num_edges) - int(0.1 * num_edges)) split_res = dataset.split() self.assertEqual(split_res[0][0].edge_label_index.shape[1], edge_0) self.assertEqual(split_res[1][0].edge_label_index.shape[1], edge_1) self.assertEqual(split_res[2][0].edge_label_index.shape[1], edge_2) # transductively split with link_pred task, `split` edge_train_mode # and 0.5 edge_message_ratio dataset = GraphDataset( graphs, task="link_pred", edge_train_mode="disjoint", edge_message_ratio=0.5, ) split_res = dataset.split() edge_0 = 2 * int(0.8 * num_edges) edge_0 = 2 * (edge_0 - int(0.5 * edge_0)) edge_1 = 2 * 2 * int(0.1 * num_edges) edge_2 = 2 * 2 * (num_edges - int(0.8 * num_edges) - int(0.1 * num_edges)) self.assertEqual( split_res[0][0].edge_label_index.shape[1], edge_0, ) self.assertEqual(split_res[1][0].edge_label_index.shape[1], edge_1) self.assertEqual(split_res[2][0].edge_label_index.shape[1], edge_2) # transductively split with link_pred task # and specified edge_negative_sampling_ratio dataset = GraphDataset(graphs, task="link_pred", edge_negative_sampling_ratio=2) split_res = dataset.split() edge_0 = (2 + 1) * (2 * int(0.8 * num_edges)) edge_1 = (2 + 1) * (2 * int(0.1 * num_edges)) edge_2 = (2 + 1) * ( 2 * (num_edges - int(0.8 * num_edges) - int(0.1 * num_edges))) self.assertEqual(split_res[0][0].edge_label_index.shape[1], edge_0) self.assertEqual(split_res[1][0].edge_label_index.shape[1], edge_1) self.assertEqual(split_res[2][0].edge_label_index.shape[1], edge_2)