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# _*_ coding: utf-8 _*_
# @Time : 2022/3/8
# @Author : Jiakai Tang
# @Email : whut_tangjiakai@qq.com
r"""
FOCF
################################################
Reference:
Yao, S. and B. Huang, "Beyond parity: fairness objectives for collaborative filtering." in NIPS. 2017
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from recbole.model.init import xavier_normal_initialization
from recbole.model.abstract_recommender import FairRecommender
from recbole.utils import InputType
class FOCF(FairRecommender):
r""" FOCF is fair-aware recommendation model by adding fairness regulation
Base recommendation model is MF
"""
input_type = InputType.POINTWISE
def __init__(self, config, dataset):
super(FOCF, self).__init__(config, dataset)
# load dataset info
self.embedding_size = config['embedding_size']
self.RATING = config['RATING_FIELD']
self.SST_FIELD = config['sst_attr_list'][0]
self.fair_weight = config['fair_weight']
self.max_rating = dataset.inter_feat[self.RATING].max()
# define layers and loss
self.user_embedding_layer = nn.Embedding(self.n_users, self.embedding_size)
self.item_embedding_layer = nn.Embedding(self.n_items, self.embedding_size)
self.rating_loss_fun = nn.MSELoss()
# self.sigmoid = nn.Sigmoid()
# self.rec_loss_fun = nn.BCELoss()
self.fair_loss_fun = self.get_loss_fun(config['fair_objective'])
self.apply(xavier_normal_initialization)
def get_loss_fun(self, fair_objective):
fair_objective = fair_objective.strip().lower()
if fair_objective == 'none':
return None
elif fair_objective == 'value':
return self.value_unfairness
elif fair_objective == 'absolute':
return self.absolute_unfairness
elif fair_objective == 'under':
return self.under_unfairness
elif fair_objective == 'over':
return self.over_unfairness
elif fair_objective == 'nonparity':
return self.nonparity_unfairness
else:
raise ValueError("you must set config['fair_objective'] be one of (none,"
"value,absolute,under,over,nonparity)")
def get_average_score(self, scores):
res_score = 0.
if len(scores) > 0:
res_score = scores.mean()
return res_score
def get_item_ratings(self, pred_scores, interaction):
sst_unique_value, sst_inverse = torch.unique(interaction[self.SST_FIELD], return_inverse=True)
iid_unique_value, iid_inverse = torch.unique(interaction[self.ITEM_ID], return_inverse=True)
iid_unique_len = len(iid_unique_value)
interaction_len = len(pred_scores)
avg_pred_list = torch.zeros((iid_unique_len,2), device=self.device)
sst_num = torch.zeros((iid_unique_len,2), device=self.device)
avg_true_list = torch.zeros((iid_unique_len,2), device=self.device)
# for i, true_score in enumerate(interaction[self.RATING]):
# pred_score = pred_scores[i]
# avg_pred_list[iid_inverse[i]][sst_inverse[i]] += pred_score
# avg_true_list[iid_inverse[i]][sst_inverse[i]] += true_score
# sst_num[iid_inverse[i]][sst_inverse[i]] += 1
index = (iid_inverse, sst_inverse)
avg_pred_list.index_put_(index, pred_scores, accumulate=True)
avg_true_list.index_put_(index, interaction[self.RATING], accumulate=True)
sst_num.index_put_(index, torch.ones(interaction_len, device=self.device), accumulate=True)
sst_num += 1e-5
return avg_pred_list/sst_num, avg_true_list/sst_num
def value_unfairness(self, pred_scores, interaction):
avg_pred_list, avg_true_list = self.get_item_ratings(pred_scores, interaction)
diff = avg_pred_list - avg_true_list
loss_input = torch.abs(diff[:,0]- diff[:,1])
loss_target = torch.zeros_like(loss_input, device=self.device)
return F.smooth_l1_loss(loss_input, loss_target)
def absolute_unfairness(self, pred_scores, interaction):
avg_pred_list, avg_true_list = self.get_item_ratings(pred_scores, interaction)
diff = torch.abs(avg_pred_list - avg_true_list)
loss_input = torch.abs(diff[:,0]- diff[:,1])
loss_target = torch.zeros_like(loss_input, device=self.device)
return F.smooth_l1_loss(loss_input, loss_target)
def under_unfairness(self, pred_scores, interaction):
avg_pred_list, avg_true_list = self.get_item_ratings(pred_scores, interaction)
zero_tensor = torch.tensor(0., dtype=torch.float32, device=self.device)
diff = torch.where((avg_true_list - avg_pred_list)>zero_tensor, avg_true_list - avg_pred_list, zero_tensor)
loss_input = torch.abs(diff[:,0]- diff[:,1])
loss_target = torch.zeros_like(loss_input, device=self.device)
return F.smooth_l1_loss(loss_input, loss_target)
def over_unfairness(self, pred_scores, interaction):
avg_pred_list, avg_true_list = self.get_item_ratings(pred_scores, interaction)
zero_tensor = torch.tensor(0., dtype=torch.float32, device=self.device)
diff = torch.where((avg_pred_list - avg_true_list)>zero_tensor, avg_pred_list - avg_true_list, zero_tensor)
loss_input = torch.abs(diff[:,0]- diff[:,1])
loss_target = torch.zeros_like(loss_input, device=self.device)
return F.smooth_l1_loss(loss_input, loss_target)
def nonparity_unfairness(self, pred_scores, interaction):
sst_unique_value = torch.unique(interaction[self.SST_FIELD])
sst1 = sst_unique_value[0]
sst2 = sst_unique_value[1]
avg_score_1 = pred_scores[interaction[self.SST_FIELD] == sst1].mean()
avg_score_2 = pred_scores[interaction[self.SST_FIELD] == sst2].mean()
return F.smooth_l1_loss(avg_score_1, avg_score_2)
def forward(self, user, item):
user_embedding = self.user_embedding_layer(user)
item_embedding = self.item_embedding_layer(item)
pred_scores = torch.mul(user_embedding, item_embedding).sum(dim=-1)
# pred_scores = self.sigmoid(torch.mul(user_embedding, item_embedding).sum(dim=-1))
return pred_scores, user_embedding, item_embedding
def predict(self, interaction):
user = interaction[self.USER_ID]
item = interaction[self.ITEM_ID]
pred_scores, _, _ = self.forward(user, item)
return torch.clamp(pred_scores, min=0., max=self.max_rating) / self.max_rating
def calculate_loss(self, interaction):
users = interaction[self.USER_ID]
items = interaction[self.ITEM_ID]
scores = interaction[self.RATING]
pred_scores, user_embeddings, item_embeddings = self.forward(users, items)
rating_loss = self.rating_loss_fun(pred_scores, scores)
# rec_loss = self.rec_loss_fun(pred_scores, scores)
fair_loss = 0.
if self.fair_loss_fun:
fair_loss = self.fair_loss_fun(pred_scores, interaction)
# rating loss + fair objective loss
loss = rating_loss + self.fair_weight * fair_loss
# loss = rec_loss + self.fair_weight * fair_loss
return loss
def full_sort_predict(self, interaction):
user = interaction[self.USER_ID]
user_embed = self.user_embedding_layer(user)
all_item_embed = self.item_embedding_layer.weight
pred_scores = torch.mm(user_embed, all_item_embed.t()).view(-1)
return torch.clamp(pred_scores, min=0., max=self.max_rating) / self.max_rating