RecSys-Extraction-Attack/utils.py at main · Yueeeeeeee/RecSys-Extraction-Attack · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
import numpy as np
import random
import torch

from datasets import DATASETS
from model import *
import argparse


def fix_random_seed_as(random_seed):
    random.seed(random_seed)
    np.random.seed(random_seed)
    torch.manual_seed(random_seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


def set_template(args):
    args.min_uc = 5
    args.min_sc = 5
    args.split = 'leave_one_out'
    dataset_code = {'1': 'ml-1m', '20': 'ml-20m', 'b': 'beauty', 'bd': 'beauty_dense' , 'g': 'games', 's': 'steam', 'y': 'yoochoose'}
    args.dataset_code = dataset_code[input('Input 1 / 20 for movielens, b for beauty, bd for dense beauty, g for games, s for steam and y for yoochoose: ')]
    if args.dataset_code == 'ml-1m':
        args.sliding_window_size = 0.5
        args.bert_hidden_units = 64
        args.bert_dropout = 0.1
        args.bert_attn_dropout = 0.1
        args.bert_max_len = 200
        args.bert_mask_prob = 0.2
        args.bert_max_predictions = 40
    elif args.dataset_code == 'ml-20m':
        args.sliding_window_size = 0.5
        args.bert_hidden_units = 64
        args.bert_dropout = 0.1
        args.bert_attn_dropout = 0.1
        args.bert_max_len = 200
        args.bert_mask_prob = 0.2
        args.bert_max_predictions = 20
    elif args.dataset_code in ['beauty', 'beauty_dense']:
        args.sliding_window_size = 0.5
        args.bert_hidden_units = 64
        args.bert_dropout = 0.5
        args.bert_attn_dropout = 0.2
        args.bert_max_len = 50
        args.bert_mask_prob = 0.6
        args.bert_max_predictions = 30
    elif args.dataset_code == 'games':
        args.sliding_window_size = 0.5
        args.bert_hidden_units = 64
        args.bert_dropout = 0.5
        args.bert_attn_dropout = 0.5
        args.bert_max_len = 50
        args.bert_mask_prob = 0.5
        args.bert_max_predictions = 25
    elif args.dataset_code == 'steam':
        args.sliding_window_size = 0.5
        args.bert_hidden_units = 64
        args.bert_dropout = 0.2
        args.bert_attn_dropout = 0.2
        args.bert_max_len = 50
        args.bert_mask_prob = 0.4
        args.bert_max_predictions = 20
    elif args.dataset_code == 'yoochoose':
        args.sliding_window_size = 0.5
        args.bert_hidden_units = 256
        args.bert_dropout = 0.2
        args.bert_attn_dropout = 0.2
        args.bert_max_len = 50
        args.bert_mask_prob = 0.4
        args.bert_max_predictions = 20

    batch = 128
    args.train_batch_size = batch
    args.val_batch_size = batch
    args.test_batch_size = batch
    args.train_negative_sampler_code = 'random'
    args.train_negative_sample_size = 0
    args.train_negative_sampling_seed = 0
    args.test_negative_sampler_code = 'random'
    args.test_negative_sample_size = 100
    args.test_negative_sampling_seed = 98765

    model_codes = {'b': 'bert', 's':'sas', 'n':'narm'}
    args.model_code = model_codes[input('Input model code, b for BERT, s for SASRec and n for NARM: ')]

    if torch.cuda.is_available():
        args.device = 'cuda:' + input('Input GPU ID: ')
    else:
        args.device = 'cpu'
    args.optimizer = 'AdamW'
    args.lr = 0.001
    args.weight_decay = 0.01
    args.enable_lr_schedule = True
    args.decay_step = 10000
    args.gamma = 1.
    args.enable_lr_warmup = False
    args.warmup_steps = 100
    args.num_epochs = 1000

    args.metric_ks = [1, 5, 10]
    args.best_metric = 'NDCG@10'
    args.model_init_seed = 98765
    args.bert_num_blocks = 2
    args.bert_num_heads = 2
    args.bert_head_size = None


parser = argparse.ArgumentParser()

################
# Dataset
################
parser.add_argument('--dataset_code', type=str, default='ml-1m', choices=DATASETS.keys())
parser.add_argument('--min_rating', type=int, default=0)
parser.add_argument('--min_uc', type=int, default=5)
parser.add_argument('--min_sc', type=int, default=5)
parser.add_argument('--split', type=str, default='leave_one_out')
parser.add_argument('--dataset_split_seed', type=int, default=0)

################
# Dataloader
################
parser.add_argument('--dataloader_random_seed', type=float, default=0)
parser.add_argument('--train_batch_size', type=int, default=64)
parser.add_argument('--val_batch_size', type=int, default=64)
parser.add_argument('--test_batch_size', type=int, default=64)
parser.add_argument('--sliding_window_size', type=float, default=0.5)

################
# NegativeSampler
################
parser.add_argument('--train_negative_sampler_code', type=str, default='random', choices=['popular', 'random'])
parser.add_argument('--train_negative_sample_size', type=int, default=0)
parser.add_argument('--train_negative_sampling_seed', type=int, default=0)
parser.add_argument('--test_negative_sampler_code', type=str, default='random', choices=['popular', 'random'])
parser.add_argument('--test_negative_sample_size', type=int, default=100)
parser.add_argument('--test_negative_sampling_seed', type=int, default=0)

################
# Trainer
################
# device #
parser.add_argument('--device', type=str, default='cpu', choices=['cpu', 'cuda'])
parser.add_argument('--num_gpu', type=int, default=1)
# optimizer & lr#
parser.add_argument('--optimizer', type=str, default='AdamW', choices=['AdamW', 'Adam', 'SGD'])
parser.add_argument('--weight_decay', type=float, default=0)
parser.add_argument('--adam_epsilon', type=float, default=1e-9)
parser.add_argument('--momentum', type=float, default=None)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--enable_lr_schedule', type=bool, default=True)
parser.add_argument('--decay_step', type=int, default=100)
parser.add_argument('--gamma', type=float, default=1)
parser.add_argument('--enable_lr_warmup', type=bool, default=True)
parser.add_argument('--warmup_steps', type=int, default=100)
# epochs #
parser.add_argument('--num_epochs', type=int, default=100)
# logger #
parser.add_argument('--log_period_as_iter', type=int, default=12800)
# evaluation #
parser.add_argument('--metric_ks', nargs='+', type=int, default=[1, 5, 10, 20])
parser.add_argument('--best_metric', type=str, default='NDCG@10')

################
# Model
################
parser.add_argument('--model_code', type=str, default='bert', choices=['bert', 'sas', 'narm'])
# BERT specs, used for SASRec and NARM as well #
parser.add_argument('--bert_max_len', type=int, default=None)
parser.add_argument('--bert_hidden_units', type=int, default=64)
parser.add_argument('--bert_num_blocks', type=int, default=2)
parser.add_argument('--bert_num_heads', type=int, default=2)
parser.add_argument('--bert_head_size', type=int, default=32)
parser.add_argument('--bert_dropout', type=float, default=0.1)
parser.add_argument('--bert_attn_dropout', type=float, default=0.1)
parser.add_argument('--bert_mask_prob', type=float, default=0.2)

################
# Distillation & Retraining
################
parser.add_argument('--num_generated_seqs', type=int, default=3000)
parser.add_argument('--num_original_seqs', type=int, default=0)
parser.add_argument('--num_poisoned_seqs', type=int, default=100)
parser.add_argument('--num_alter_items', type=int, default=10)

################


args = parser.parse_args()