图级别数据读取函数示例

def _load_graph_kernel_dataset(dataset):

    dataset.download()

    def _load_from_txt_file(filename, names=None, dtype=None, index_increment=None):
        df = pd.read_csv(
            dataset._resolve_path(filename=f"{dataset.name}_{filename}.txt"),
            header=None,
            index_col=False,
            dtype=dtype,
            names=names,
        )
        # We optional increment the index by 1 because indexing, e.g. node IDs, for this dataset starts
        # at 1 whereas the Pandas DataFrame implicit index starts at 0 potentially causing confusion selecting
        # rows later on.
        if index_increment:
            df.index = df.index + index_increment
        return df

    # edge information:
    df_graph = _load_from_txt_file(filename="A", names=["source", "target"])

    if dataset._edge_labels_as_weights:
        # there's some edge labels, that can be used as edge weights
        df_edge_labels = _load_from_txt_file(
            filename="edge_labels", names=["weight"], dtype=int
        )
        df_graph = pd.concat([df_graph, df_edge_labels], axis=1)

    # node information:
    df_graph_ids = _load_from_txt_file(
        filename="graph_indicator", names=["graph_id"], index_increment=1
    )

    df_node_labels = _load_from_txt_file(
        filename="node_labels", dtype="category", index_increment=1
    )
    # One-hot encode the node labels because these are used as node features in graph classification
    # tasks.
    df_node_features = pd.get_dummies(df_node_labels)

    if dataset._node_attributes:
        # there's some actual node attributes
        df_node_attributes = _load_from_txt_file(
            filename="node_attributes", dtype=np.float32, index_increment=1
        )

        df_node_features = pd.concat([df_node_features, df_node_attributes], axis=1)

    # graph information:
    df_graph_labels = _load_from_txt_file(
        filename="graph_labels", dtype="category", names=["label"], index_increment=1
    )

    # split the data into each of the graphs, based on the nodes in each one
    def graph_for_nodes(nodes):
        # each graph is disconnected, so the source is enough to identify the graph for an edge
        edges = df_graph[df_graph["source"].isin(nodes.index)]
        return StellarGraph(nodes, edges)

    groups = df_node_features.groupby(df_graph_ids["graph_id"])
    graphs = [graph_for_nodes(nodes) for _, nodes in groups]

    return graphs, df_graph_labels["label"]