def __init__()

in seal_link_pred.py [0:0]

• 35 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, root, data, split_edge, num_hops, percent=100, split='train', 
                 use_coalesce=False, node_label='drnl', ratio_per_hop=1.0, 
                 max_nodes_per_hop=None, directed=False, **kwargs):
        self.data = data
        self.split_edge = split_edge
        self.num_hops = num_hops
        self.percent = percent
        self.use_coalesce = use_coalesce
        self.node_label = node_label
        self.ratio_per_hop = ratio_per_hop
        self.max_nodes_per_hop = max_nodes_per_hop
        self.directed = directed
        super(SEALDynamicDataset, self).__init__(root)

        pos_edge, neg_edge = get_pos_neg_edges(split, self.split_edge, 
                                               self.data.edge_index, 
                                               self.data.num_nodes, 
                                               self.percent)
        self.links = torch.cat([pos_edge, neg_edge], 1).t().tolist()
        self.labels = [1] * pos_edge.size(1) + [0] * neg_edge.size(1)

        if self.use_coalesce:  # compress mutli-edge into edge with weight
            self.data.edge_index, self.data.edge_weight = coalesce(
                self.data.edge_index, self.data.edge_weight, 
                self.data.num_nodes, self.data.num_nodes)

        if 'edge_weight' in self.data:
            edge_weight = self.data.edge_weight.view(-1)
        else:
            edge_weight = torch.ones(self.data.edge_index.size(1), dtype=int)
        self.A = ssp.csr_matrix(
            (edge_weight, (self.data.edge_index[0], self.data.edge_index[1])), 
            shape=(self.data.num_nodes, self.data.num_nodes)
        )
        if self.directed:
            self.A_csc = self.A.tocsc()
        else:
            self.A_csc = None