pfcn_biasedmf.py

# _*_ coding: utf-8 _*_
# @Time   : 2022/3/24
# @Author : Jiakai Tang
# @Email  : whut_tangjiakai@qq.com


import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

from recbole.model.layers import MLPLayers
from recbole.model.abstract_recommender import FairRecommender
from recbole.model.loss import BPRLoss
from recbole.utils import InputType

r"""
PFCN
################################################
Reference:
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2021. Towards Personalized Fairness based on Causal Notion. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.
"""

class PFCN_BiasedMF(FairRecommender):
    r""" PFCN is a framework for achieving counterfactually fair recommendations through adversary learning by generating feature-independent user embeddings for recommendation.

    PFCN_BiasedMF's base model is Biased MF
    """

    input_type = InputType.PAIRWISE

    def __init__(self, config, dataset):
        super(PFCN_BiasedMF, self).__init__(config, dataset)

        # load dataset info
        self.embedding_size = config['embedding_size']
        self.sst_attrs = config['sst_attr_list']
        self.filter_mode = config['filter_mode'].lower()
        try:
            assert self.filter_mode in ('cm','sm','none')
        except AssertionError:
            raise AssertionError('filter_mode must be cm, sm or none')
        self.filter_num, self.sst_dict = self._get_filter_info()
        self.sst_size = self._get_sst_size(dataset.get_user_feature())

        if self.filter_mode != 'none':
            self.dis_drop_out = config['dis_dropout']
            self.dis_weight = config['dis_weight']
            self.dis_hidden_size_list = config['dis_hidden_size_list']
        self.activation = config['activation']

        # define layers and loss
        self.user_embedding_layer = nn.Embedding(self.n_users, self.embedding_size)
        self.user_bias = nn.Embedding(self.n_users, 1)
        self.item_embedding_layer = nn.Embedding(self.n_items, self.embedding_size)
        self.item_bias = nn.Embedding(self.n_items, 1)
        self.global_bias = nn.Parameter(torch.tensor(0.1))
        self.loss_fun = BPRLoss()
        self.sigmoid = nn.Sigmoid()

        if self.filter_mode != 'none':
            self.filter_layer = self.init_filter()
            self.dis_layer_dict = self.init_dis_layer()
            self.multi_dis_fun = nn.CrossEntropyLoss()
            self.bin_dis_fun = nn.BCELoss()

    def _get_filter_info(self):
        if self.filter_mode == 'cm':
            filter_num = len(self.sst_attrs)
            sst_dict = {}
            for i, sst in enumerate(self.sst_attrs):
                sst_dict[sst] = i + 1
        elif self.filter_mode == 'sm':
            filter_num = 2 ** len(self.sst_attrs) - 1
            sst_dict = {}
            for i, sst in zip(2 ** np.array(range(len(self.sst_attrs))), self.sst_attrs):
                sst_dict[sst] = i
        else:
            filter_num = 0
            sst_dict = {}

        return filter_num, sst_dict

    def _get_sst_size(self, user_feature):
        r""" calculate size of each sensitive attribute for discriminator construction

        Args:
            user_feature(Interaction): contain user's features, such as gender, age, etc.

        Returns:
            dict: every sensitive attribute and its number
        """
        sst_size = {}
        for sst in self.sst_attrs:
            try:
                assert sst in user_feature.columns
            except AssertionError:
                raise ValueError(f'{sst} sensitive attribute not in user feature')

            sst_size[sst] = len(user_feature[sst][1:].unique())

        return sst_size

    def init_filter(self):
        r""" according selection of filter mode, build corresponding filter layer

        Returns:
            list: filter layer
        """
        filter_layer = {}
        embedding_size = self.embedding_size
        for i in range(self.filter_num):
            filter_model = MLPLayers([embedding_size, embedding_size*2, embedding_size],
                               activation=self.activation,
                               bn=True,
                               init_method='norm')
            filter_layer[i+1] = filter_model.to(self.device)

        return filter_layer

    def init_dis_layer(self):
        r""" build discriminator for each sensitive attribute

        Return:
            dict: sensitive attribute and its discriminator
        """
        embedding_size = self.embedding_size
        sst_size = self.sst_size
        sst_attrs = self.sst_attrs
        dis_hidden_size_list = self.dis_hidden_size_list
        dis_layer_dict = {}
        for sst in sst_attrs:
            output_dim = sst_size[sst]
            if output_dim == 2:
                output_dim = 1
            dis_layer_dict[sst] = MLPLayers([embedding_size] + dis_hidden_size_list + [output_dim],
                                   dropout=self.dis_drop_out,
                                   activation=self.activation,
                                   bn=True,
                                   init_method='norm').to(self.device)

        return dis_layer_dict

    def forward(self, user, item=None, sst_list=None):
        user_embed = self.user_embedding_layer(user)
        item_embed = None
        if item is not None:
            item_embed = self.item_embedding_layer(item)
        if self.filter_mode == 'none':
            return user_embed, item_embed
        elif self.filter_mode == 'sm':
            idx = 0
            for sst in sst_list:
                idx += self.sst_dict[sst]

            user_embed = self.filter_layer[idx](user_embed)
        else:
            user_temp = None
            for sst in sst_list:
                idx = self.sst_dict[sst]
                embed = self.filter_layer[idx](user_embed)
                user_temp = embed if user_temp is None else user_temp + embed

            user_embed = user_temp / len(self.filter_layer)

        return user_embed, item_embed

    def predict(self, interaction, sst_list=None):
        user = interaction[self.USER_ID]
        item = interaction[self.ITEM_ID]
        user_embeddings, item_embeddings = self.forward(user, item, sst_list)
        user_bias = self.user_bias(user)
        item_bias = self.item_bias(item)
        global_bias = self.global_bias

        pred_scores = torch.mul(user_embeddings, item_embeddings).sum(dim=-1, keepdim=True) + user_bias + item_bias + global_bias

        return self.sigmoid(pred_scores)

    def calculate_loss(self, interaction, sst_list):
        user = interaction[self.USER_ID]
        pos_item = interaction[self.POS_ITEM_ID]
        neg_item = interaction[self.NEG_ITEM_ID]

        user_embed, pos_item_embed = self.forward(user, pos_item, sst_list)
        neg_item_embed = self.item_embedding_layer(neg_item)
        user_bias = self.user_bias(user)
        pos_item_bias = self.item_bias(pos_item)
        neg_item_bias = self.item_bias(neg_item)
        global_bias = self.global_bias

        pos_scores = torch.mul(user_embed, pos_item_embed).sum(dim=-1) + user_bias + pos_item_bias + global_bias
        neg_scores = torch.mul(user_embed, neg_item_embed).sum(dim=-1) + user_bias + neg_item_bias + global_bias

        bpr_loss = self.loss_fun(pos_scores, neg_scores)
        if self.filter_mode != 'none':
            dis_loss = self.calculate_dis_loss(interaction,sst_list)
            return bpr_loss - self.dis_weight * dis_loss

        return bpr_loss

    def calculate_dis_loss(self, interaction, sst_list):
        user = interaction[self.USER_ID]
        sst_label_dict = {}
        for sst in sst_list:
            sst_label_dict[sst] = interaction[sst]
        dis_loss = .0

        user_embed, _ = self.forward(user,None,sst_list)
        for sst in sst_list:
            dis_layer = self.dis_layer_dict[sst]
            if self.sst_size[sst] == 2:
                logits = nn.Sigmoid()(dis_layer(user_embed))
                dis_loss += self.bin_dis_fun(logits, sst_label_dict[sst].float().unsqueeze(1))
            else:
                dis_loss += self.multi_dis_fun(dis_layer(user_embed), sst_label_dict[sst].long())

        return dis_loss

    def full_sort_predict(self, interaction, sst_list=None):
        user = interaction[self.USER_ID]

        user_embed = self.forward(user,None,sst_list)
        all_item_embed = self.item_embedding_layer.weight
        user_bias = self.user_bias(user)
        item_bias = self.item_bias.weight
        global_bias = self.global_bias
        # dot with all item embedding to accelerate
        pred_scores = torch.mm(user_embed, all_item_embed.t()) + user_bias + item_bias + global_bias

        return self.sigmoid(pred_scores.view(-1))

    def get_sst_embed(self, user_data, sst_list=None):
        ret_dict = {}
        user_indices = torch.arange(1,self.n_users)
        sst_list = self.sst_attrs if self.filter_mode == 'none' else sst_list
        for sst in sst_list:
            ret_dict[sst] = user_data[sst][user_indices-1]
        user_embeddings, _ = self.forward(user_indices.to(self.device),None,sst_list)
        ret_dict['embedding'] = user_embeddings

        return ret_dict