model.py

import torch as th


class EGES(th.nn.Module):
    def __init__(self, dim, num_nodes, num_brands, num_shops, num_cates):
        super(EGES, self).__init__()
        self.dim = dim
        # embeddings for nodes
        base_embeds = th.nn.Embedding(num_nodes, dim)
        brand_embeds = th.nn.Embedding(num_brands, dim)
        shop_embeds = th.nn.Embedding(num_shops, dim)
        cate_embeds = th.nn.Embedding(num_cates, dim)
        self.embeds = [base_embeds, brand_embeds, shop_embeds, cate_embeds]
        # weights for each node's side information
        self.side_info_weights = th.nn.Embedding(num_nodes, 4)

    def forward(self, srcs, dsts):
        # srcs: sku_id, brand_id, shop_id, cate_id
        srcs = self.query_node_embed(srcs)
        dsts = self.query_node_embed(dsts)

        return srcs, dsts

    def query_node_embed(self, nodes):
        """
        @nodes: tensor of shape (batch_size, num_side_info)
        """
        batch_size = nodes.shape[0]
        # query side info weights, (batch_size, 4)
        side_info_weights = th.exp(self.side_info_weights(nodes[:, 0]))
        # merge all embeddings
        side_info_weighted_embeds_sum = []
        side_info_weights_sum = []
        for i in range(4):
            # weights for i-th side info, (batch_size, ) -> (batch_size, 1)
            i_th_side_info_weights = side_info_weights[:, i].view(
                (batch_size, 1)
            )
            # batch of i-th side info embedding * its weight, (batch_size, dim)
            side_info_weighted_embeds_sum.append(
                i_th_side_info_weights * self.embeds[i](nodes[:, i])
            )
            side_info_weights_sum.append(i_th_side_info_weights)
        # stack: (batch_size, 4, dim), sum: (batch_size, dim)
        side_info_weighted_embeds_sum = th.sum(
            th.stack(side_info_weighted_embeds_sum, axis=1), axis=1
        )
        # stack: (batch_size, 4), sum: (batch_size, )
        side_info_weights_sum = th.sum(
            th.stack(side_info_weights_sum, axis=1), axis=1
        )
        # (batch_size, dim)
        H = side_info_weighted_embeds_sum / side_info_weights_sum

        return H

    def loss(self, srcs, dsts, labels):
        dots = th.sigmoid(th.sum(srcs * dsts, axis=1))
        dots = th.clamp(dots, min=1e-7, max=1 - 1e-7)

        return th.mean(
            -(labels * th.log(dots) + (1 - labels) * th.log(1 - dots))
        )