def forward()

in source/sagemaker/sagemaker_graph_fraud_detection/dgl_fraud_detection/model/mxnet.py [0:0]

• 9 lines of code
• 9 McCabe index (conditional complexity)

    def forward(self, G, feat_dict):
        # The input is a dictionary of node features for each type
        funcs = {}
        for srctype, etype, dsttype in G.canonical_etypes:
            # Compute W_r * h
            if srctype in feat_dict:
                Wh = self.weight[etype](feat_dict[srctype])
                # Save it in graph for message passing
                G.srcnodes[srctype].data['Wh_%s' % etype] = Wh
                # Specify per-relation message passing functions: (message_func, reduce_func).
                # Note that the results are saved to the same destination feature 'h', which
                # hints the type wise reducer for aggregation.
                funcs[etype] = (fn.copy_u('Wh_%s' % etype, 'm'), fn.mean('m', 'h'))
        # Trigger message passing of multiple types.
        # The first argument is the message passing functions for each relation.
        # The second one is the type wise reducer, could be "sum", "max",
        # "min", "mean", "stack"
        G.multi_update_all(funcs, 'sum')
        # return the updated node feature dictionary
        return {ntype: G.dstnodes[ntype].data['h'] for ntype in G.ntypes if 'h' in G.dstnodes[ntype].data}