def __init__(self, path: str): data = _GTNDataSource(path, "gtn-imdb")[0] if _backend.DependentBackend.is_dgl(): super(GTNIMDBDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'feat': getattr(data, 'x'), 'label': getattr(data, 'y'), 'pos': getattr(data, 'pos'), 'train_mask': getattr(data, 'train_mask'), 'val_mask': getattr(data, 'val_mask'), 'test_mask': getattr(data, 'test_mask') }, getattr(data, 'edge_index')) ]) elif _backend.DependentBackend.is_pyg(): super(GTNIMDBDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': getattr(data, 'x'), 'y': getattr(data, 'y'), 'pos': getattr(data, 'pos'), 'train_mask': getattr(data, 'train_mask'), 'val_mask': getattr(data, 'val_mask'), 'test_mask': getattr(data, 'test_mask') }, getattr(data, 'edge_index')) ])
def __init__(self, path: str): filename: str = "POS" url = "http://snap.stanford.edu/node2vec/" data = _MATLABMatrix(path, filename, url)[0] if _backend.DependentBackend.is_dgl(): super(WIKIPEDIADataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {'label': data.y}, data.edge_index, {'attr': data.edge_attr}) ]) elif _backend.DependentBackend.is_pyg(): super(WIKIPEDIADataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {'y': data.y}, data.edge_index, {'attr': data.edge_attr}) ])
def __init__(self, path: str): filename: str = "BlogCatalog".lower() url: str = "http://leitang.net/code/social-dimension/data/" data = _MATLABMatrix(path, filename, url)[0] if _backend.DependentBackend.is_dgl(): super(BlogCatalogDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {'label': data.y}, data.edge_index, {'edge_attr': data.edge_attr}) ]) elif _backend.DependentBackend.is_pyg(): super(BlogCatalogDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {'y': data.y}, data.edge_index, {'edge_attr': data.edge_attr}) ])
def __init__(self, path: str): ogbl_dataset = GraphPropPredDataset("ogbg-molhiv", path) idx_split: _typing.Mapping[str, np.ndarray] = ogbl_dataset.get_idx_split() train_index: _typing.Any = idx_split['train'].tolist() test_index: _typing.Any = idx_split['test'].tolist() val_index: _typing.Any = idx_split['valid'].tolist() super(OGBGCode2Dataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph(({ "feat": torch.from_numpy(data['node_feat']), "node_is_attributed": torch.from_numpy(data["node_is_attributed"]), "node_dfs_order": torch.from_numpy(data["node_dfs_order"]), "node_depth": torch.from_numpy(data["node_depth"]) } if _backend.DependentBackend.is_dgl() else { "x": torch.from_numpy(data['node_feat']), "node_is_attributed": torch.from_numpy(data["node_is_attributed"]), "node_dfs_order": torch.from_numpy(data["node_dfs_order"]), "node_depth": torch.from_numpy(data["node_depth"]) }), torch.from_numpy(data['edge_index'])) for data, label in ogbl_dataset ], train_index, val_index, test_index)
def __init__(self, path: str): pyg_dataset = TUDataset(os.path.join(path, '_pyg'), "COLLAB") if hasattr(pyg_dataset, "__data_list__"): delattr(pyg_dataset, "__data_list__") if hasattr(pyg_dataset, "_data_list"): delattr(pyg_dataset, "_data_list") super(COLLABDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {}, pyg_data.edge_index, graph_data={'y': pyg_data.y}) for pyg_data in pyg_dataset ])
def __init__(self, path: str): train_dataset = PPI(os.path.join(path, '_pyg'), 'train') if hasattr(train_dataset, "__data_list__"): delattr(train_dataset, "__data_list__") if hasattr(train_dataset, "_data_list"): delattr(train_dataset, "_data_list") val_dataset = PPI(os.path.join(path, '_pyg'), 'val') if hasattr(val_dataset, "__data_list__"): delattr(val_dataset, "__data_list__") if hasattr(val_dataset, "_data_list"): delattr(val_dataset, "_data_list") test_dataset = PPI(os.path.join(path, '_pyg'), 'test') if hasattr(test_dataset, "__data_list__"): delattr(test_dataset, "__data_list__") if hasattr(test_dataset, "_data_list"): delattr(test_dataset, "_data_list") train_index = range(len(train_dataset)) val_index = range(len(train_dataset), len(train_dataset) + len(val_dataset)) test_index = range( len(train_dataset) + len(val_dataset), len(train_dataset) + len(val_dataset) + len(test_dataset)) super(PPIDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': data.x, 'y': data.y }, data.edge_index) for data in train_dataset ] + [ GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': data.x, 'y': data.y }, data.edge_index) for data in val_dataset ] + [ GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': data.x, 'y': data.y }, data.edge_index) for data in test_dataset ], train_index, val_index, test_index)
def __init__(self, path: str): pyg_dataset = Coauthor(os.path.join(path, '_pyg'), "CS") if hasattr(pyg_dataset, "__data_list__"): delattr(pyg_dataset, "__data_list__") if hasattr(pyg_dataset, "_data_list"): delattr(pyg_dataset, "_data_list") pyg_data = pyg_dataset[0] static_graph = GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': pyg_data.x, 'y': pyg_data.y }, pyg_data.edge_index) super(CoauthorCSDataset, self).__init__([static_graph])
def __init__(self, path: str): pyg_dataset = ModelNet( os.path.join(path, '_pyg'), '40', False, pre_transform=torch_geometric.transforms.FaceToEdge()) if hasattr(pyg_dataset, "__data_list__"): delattr(pyg_dataset, "__data_list__") if hasattr(pyg_dataset, "_data_list"): delattr(pyg_dataset, "_data_list") super(ModelNet40TestDataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {'pos': pyg_data.pos}, pyg_data.edge_index, graph_data={'y': pyg_data.y}) for pyg_data in pyg_dataset ])
def __init__(self, path: str): pyg_dataset = Reddit(os.path.join(path, '_pyg')) if hasattr(pyg_dataset, "__data_list__"): delattr(pyg_dataset, "__data_list__") if hasattr(pyg_dataset, "_data_list"): delattr(pyg_dataset, "_data_list") pyg_data = pyg_dataset[0] static_graph = GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': pyg_data.x, 'y': pyg_data.y, 'train_mask': getattr(pyg_data, 'train_mask'), 'val_mask': getattr(pyg_data, 'val_mask'), 'test_mask': getattr(pyg_data, 'test_mask') }, pyg_data.edge_index) super(RedditDataset, self).__init__([static_graph])
def ogbn_data_to_general_static_graph( cls, ogbn_data: _typing.Mapping[str, _typing.Union[np.ndarray, int]], nodes_label: np.ndarray = ..., nodes_label_key: str = ..., train_index: _typing.Optional[np.ndarray] = ..., val_index: _typing.Optional[np.ndarray] = ..., test_index: _typing.Optional[np.ndarray] = ..., nodes_data_key_mapping: _typing.Optional[_typing.Mapping[str, str]] = ..., edges_data_key_mapping: _typing.Optional[_typing.Mapping[str, str]] = ..., graph_data_key_mapping: _typing.Optional[_typing.Mapping[str, str]] = ... ) -> GeneralStaticGraph: homogeneous_static_graph: GeneralStaticGraph = ( GeneralStaticGraphGenerator.create_homogeneous_static_graph( dict([(target_key, torch.from_numpy(ogbn_data[source_key])) for source_key, target_key in nodes_data_key_mapping.items()]), torch.from_numpy(ogbn_data['edge_index']), dict([(target_key, torch.from_numpy(ogbn_data[source_key])) for source_key, target_key in edges_data_key_mapping.items()]) if isinstance( edges_data_key_mapping, _typing.Mapping) else ..., dict([(target_key, torch.from_numpy(ogbn_data[source_key])) for source_key, target_key in graph_data_key_mapping.items()]) if isinstance( graph_data_key_mapping, _typing.Mapping) else ...)) if isinstance(nodes_label, np.ndarray) and isinstance( nodes_label_key, str): if ' ' in nodes_label_key: raise ValueError("Illegal nodes label key") homogeneous_static_graph.nodes.data[nodes_label_key] = ( torch.from_numpy(nodes_label.squeeze()).squeeze()) if isinstance(train_index, np.ndarray): homogeneous_static_graph.nodes.data['train_mask'] = index_to_mask( torch.from_numpy(train_index), ogbn_data['num_nodes']) if isinstance(val_index, np.ndarray): homogeneous_static_graph.nodes.data['val_mask'] = index_to_mask( torch.from_numpy(val_index), ogbn_data['num_nodes']) if isinstance(test_index, np.ndarray): homogeneous_static_graph.nodes.data['test_mask'] = index_to_mask( torch.from_numpy(test_index), ogbn_data['num_nodes']) return homogeneous_static_graph
def __init__(self, path: str): ogbl_dataset = GraphPropPredDataset("ogbg-molhiv", path) idx_split: _typing.Mapping[str, np.ndarray] = ogbl_dataset.get_idx_split() train_index: _typing.Any = idx_split['train'].tolist() test_index: _typing.Any = idx_split['test'].tolist() val_index: _typing.Any = idx_split['valid'].tolist() super(OGBGPPADataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( {'_NID': torch.arange(data['num_nodes'])}, torch.from_numpy(data['edge_index']), {'edge_feat': torch.from_numpy(data['edge_feat'])}, ({ 'label': torch.from_numpy(label) } if _backend.DependentBackend.is_dgl() else { 'y': torch.from_numpy(label) })) for data, label in ogbl_dataset ], train_index, val_index, test_index)
def __init__(self, path: str): pyg_dataset = QM9(os.path.join(path, '_pyg')) if hasattr(pyg_dataset, "__data_list__"): delattr(pyg_dataset, "__data_list__") if hasattr(pyg_dataset, "_data_list"): delattr(pyg_dataset, "_data_list") super(QM9Dataset, self).__init__([ GeneralStaticGraphGenerator.create_homogeneous_static_graph( { 'x': data.x, 'pos': data.pos, 'z': data.z }, data.edge_index, edges_data={'edge_attr': data.edge_attr}, graph_data={ 'idx': data.idx, 'y': data.y }) for data in pyg_dataset ])