lightgcn.py

"""
Created on Mar 1, 2020
Pytorch Implementation of LightGCN in
Xiangnan He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

@author: Jianbai Ye (gusye@mail.ustc.edu.cn)

Define models here
"""
import torch
from torch import nn
import numpy as np


class BasicModel(nn.Module):
    def __init__(self):
        super(BasicModel, self).__init__()

    def getUsersRating(self, users):
        raise NotImplementedError


class PairWiseModel(BasicModel):
    def __init__(self):
        super(PairWiseModel, self).__init__()

    def bpr_loss(self, users, pos, neg):
        """
        Parameters:
            users: users list
            pos: positive items for corresponding users
            neg: negative items for corresponding users
        Return:
            (log-loss, l2-loss)
        """
        raise NotImplementedError

class LightGCN(BasicModel):
    def __init__(self, user_num, item_num,
                 norm_adj,
                 latent_dim=64,
                 n_layers=3,
                 keep_prob=0.6,
                 A_split=False,
                 dropout=0,
                 pretrain=0,
                 device='cuda'
                 ):
        super(LightGCN, self).__init__()
        self.latent_dim = latent_dim
        self.n_layers = n_layers
        self.keep_prob = keep_prob
        self.A_split = A_split
        self.num_users = user_num
        self.num_items = item_num
        self.dropout = dropout
        self.pretrain = pretrain

        self.Graph = self._convert_sp_mat_to_sp_tensor(norm_adj)
        self.Graph = self.Graph.coalesce().to(device)
        self._init_weight_()

    def _init_weight_(self):
        self.embedding_user = torch.nn.Embedding(
            num_embeddings=self.num_users, embedding_dim=self.latent_dim)
        self.embedding_item = torch.nn.Embedding(
            num_embeddings=self.num_items, embedding_dim=self.latent_dim)
        if self.pretrain == 0:
            #             nn.init.xavier_uniform_(self.embedding_user.weight, gain=1)
            #             nn.init.xavier_uniform_(self.embedding_item.weight, gain=1)
            #             print('use xavier initilizer')
            # random normal init seems to be a better choice when lightGCN actually don't use any non-linear activation function
            nn.init.normal_(self.embedding_user.weight, std=0.1)
            nn.init.normal_(self.embedding_item.weight, std=0.1)
            # world.cprint('use NORMAL distribution initilizer')
        else:
            self.embedding_user.weight.data.copy_(torch.from_numpy(self.config['user_emb']))
            self.embedding_item.weight.data.copy_(torch.from_numpy(self.config['item_emb']))
            print('use pretarined data')
        self.f = nn.Sigmoid()
        print(f"lgn is ready to go(dropout:{self.dropout})")

        # print("save_txt")

    def _convert_sp_mat_to_sp_tensor(self, X):
        coo = X.tocoo().astype(np.float32)
        row = torch.Tensor(coo.row).long()
        col = torch.Tensor(coo.col).long()
        index = torch.stack([row, col])
        data = torch.FloatTensor(coo.data)
        return torch.sparse.FloatTensor(index, data, torch.Size(coo.shape))

    def __dropout_x(self, x, keep_prob):
        size = x.size()
        index = x.indices().t()
        values = x.values()
        random_index = torch.rand(len(values)) + keep_prob
        random_index = random_index.int().bool()
        index = index[random_index]
        values = values[random_index] / keep_prob
        g = torch.sparse.FloatTensor(index.t(), values, size)
        return g

    def __dropout(self, keep_prob):
        if self.A_split:
            graph = []
            for g in self.Graph:
                graph.append(self.__dropout_x(g, keep_prob))
        else:
            graph = self.__dropout_x(self.Graph, keep_prob)
        return graph

    def computer(self):
        """
        propagate methods for lightGCN
        """
        users_emb = self.embedding_user.weight
        items_emb = self.embedding_item.weight
        all_emb = torch.cat([users_emb, items_emb])
        #   torch.split(all_emb , [self.num_users, self.num_items])
        embs = [all_emb]
        if self.dropout:
            if self.training:
                print("droping")
                g_droped = self.__dropout(self.keep_prob)
            else:
                g_droped = self.Graph
        else:
            g_droped = self.Graph

        for layer in range(self.n_layers):
            if self.A_split:
                temp_emb = []
                for f in range(len(g_droped)):
                    temp_emb.append(torch.sparse.mm(g_droped[f], all_emb))
                side_emb = torch.cat(temp_emb, dim=0)
                all_emb = side_emb
            else:
                all_emb = torch.sparse.mm(g_droped, all_emb)
            embs.append(all_emb)
        embs = torch.stack(embs, dim=1)
        # print(embs.size())
        light_out = torch.mean(embs, dim=1)
        users, items = torch.split(light_out, [self.num_users, self.num_items])
        return users, items

    def getUsersRating(self, users):
        all_users, all_items = self.computer()
        users_emb = all_users[users.long()]
        items_emb = all_items
        rating = self.f(torch.matmul(users_emb, items_emb.t()))
        return rating

    def getEmbedding(self, users, pos_items, neg_items):
        all_users, all_items = self.computer()
        users_emb = all_users[users]
        pos_emb = all_items[pos_items]
        neg_emb = all_items[neg_items]
        users_emb_ego = self.embedding_user(users)
        pos_emb_ego = self.embedding_item(pos_items)
        neg_emb_ego = self.embedding_item(neg_items)
        return users_emb, pos_emb, neg_emb, users_emb_ego, pos_emb_ego, neg_emb_ego

    def bpr_loss(self, users, pos, neg):
        (users_emb, pos_emb, neg_emb,
         userEmb0, posEmb0, negEmb0) = self.getEmbedding(users.long(), pos.long(), neg.long())
        reg_loss = (1 / 2) * (userEmb0.norm(2).pow(2) +
                              posEmb0.norm(2).pow(2) +
                              negEmb0.norm(2).pow(2)) / float(len(users))
        pos_scores = torch.mul(users_emb, pos_emb)
        pos_scores = torch.sum(pos_scores, dim=1)
        neg_scores = torch.mul(users_emb, neg_emb)
        neg_scores = torch.sum(neg_scores, dim=1)

        loss = torch.mean(torch.nn.functional.softplus(neg_scores - pos_scores))

        return loss, reg_loss


    def reg_loss(self, users, pos, neg):
        (users_emb, pos_emb, neg_emb,
         userEmb0, posEmb0, negEmb0) = self.getEmbedding(users.long(), pos.long(), neg.long())
        reg_loss = (1 / 2) * (userEmb0.norm(2).pow(2) +
                              posEmb0.norm(2).pow(2) +
                              negEmb0.norm(2).pow(2)) / float(len(users))
        return reg_loss

    def forward(self, users, items):
        # compute embedding
        all_users, all_items = self.computer()
        # print('forward')
        # all_users, all_items = self.computer()
        users_emb = all_users[users]
        items_emb = all_items[items]
        inner_pro = torch.mul(users_emb, items_emb)
        gamma = torch.sum(inner_pro, dim=1)
        # return torch.sigmoid(gamma)
        return gamma