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main.py
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main.py
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import argparse
import torch
import numpy as np
import pickle
from util.loader import DataLoader
from util.utils import set_seed
from config.model_param import model_specific_param
from model_imports import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default='citeulike')
parser.add_argument('--model', default='MF')
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--layers', type=int, default=2)
parser.add_argument('--topN', default='10,20')
parser.add_argument('--bs', type=int, default=2048, help='training batch size')
parser.add_argument('--emb_size', type=int, default=64)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--reg', type=float, default=0.0001)
parser.add_argument('--runs', type=int, default=1, help='model runs')
parser.add_argument('--seed', type=int, default=2024)
parser.add_argument('--use_gpu', default=True, help='Whether to use CUDA')
parser.add_argument('--save_emb', default=True, help='Whether to save the user/item embeddings')
parser.add_argument('--gpu_id', type=int, default=0, help='CUDA id')
parser.add_argument('--cold_object', default='item', type=str, choices=['user', 'item'])
parser.add_argument('--backbone', default='MF')
args, _ = parser.parse_known_args()
parser = model_specific_param(args.model, parser)
args = parser.parse_args()
print(args)
device = torch.device("cuda:%d" % (args.gpu_id) if (torch.cuda.is_available() and args.use_gpu) else "cpu")
# data loader
training_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/warm_train.csv')
# following the widely used setting in previous works, the 'all' set is used for validation.
all_valid_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/overall_val.csv')
warm_valid_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/warm_val.csv')
cold_valid_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/cold_{args.cold_object}_val.csv')
all_test_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/overall_test.csv')
warm_test_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/warm_test.csv')
cold_test_data = DataLoader.load_data_set(f'./data/{args.dataset}/cold_{args.cold_object}/cold_{args.cold_object}_test.csv')
# dataset information
data_info_dict = pickle.load(open(f'./data/{args.dataset}/cold_{args.cold_object}/info_dict.pkl', 'rb'))
user_num = data_info_dict['user_num']
item_num = data_info_dict['item_num']
warm_user_idx = data_info_dict['warm_user']
warm_item_idx = data_info_dict['warm_item']
cold_user_idx = data_info_dict['cold_user']
cold_item_idx = data_info_dict['cold_item']
print(f"Dataset: {args.dataset}, User num: {user_num}, Item num: {item_num}.")
# content obtaining
user_content, item_content = None, None
if args.cold_object == 'user':
user_content = np.load(f'./data/{args.dataset}/{args.dataset}_{args.cold_object}_content.npy')
print(f'user content shape: {user_content.shape}')
if args.cold_object == 'item':
item_content = np.load(f'./data/{args.dataset}/{args.dataset}_{args.cold_object}_content.npy')
print(f'item content shape: {item_content.shape}')
top_Ns = args.topN.split(',')
all_hit_results = [[] for _ in top_Ns]
all_precision_results = [[] for _ in top_Ns]
all_recall_results = [[] for _ in top_Ns]
all_ndcg_results = [[] for _ in top_Ns]
cold_hit_results = [[] for _ in top_Ns]
cold_precision_results = [[] for _ in top_Ns]
cold_recall_results = [[] for _ in top_Ns]
cold_ndcg_results = [[] for _ in top_Ns]
warm_hit_results = [[] for _ in top_Ns]
warm_precision_results = [[] for _ in top_Ns]
warm_recall_results = [[] for _ in top_Ns]
warm_ndcg_results = [[] for _ in top_Ns]
time_results = []
for round in range(args.runs):
print(f"Start round {round} running!")
if args.runs == 1:
set_seed(args.seed, args.use_gpu)
else:
set_seed(round, args.use_gpu)
# model register
available_models = ['MF', 'LightGCN', 'SimGCL', 'XSimGCL', 'NCL', 'KNN', 'DUIF', 'DeepMusic', 'MTPR',
'VBPR', 'AMR', 'GAR', 'ALDI', 'CLCRec', 'LARA', 'CCFCRec', 'DropoutNet', 'Heater',
'MetaEmbedding', 'GoRec']
if args.model in available_models:
if args.model == 'MF' or args.model == 'LightGCN' or args.model == 'SimGCL' \
or args.model == 'XSimGCL' or args.model == 'NCL':
# recommender backbone training
model = eval(args.model)(args, training_data, warm_valid_data, cold_valid_data, all_valid_data,
warm_test_data, cold_test_data, all_test_data, user_num, item_num,
warm_user_idx, warm_item_idx, cold_user_idx, cold_item_idx, device)
else:
# cold-start model training
model = eval(args.model)(args, training_data, warm_valid_data, cold_valid_data, all_valid_data,
warm_test_data, cold_test_data, all_test_data, user_num, item_num,
warm_user_idx, warm_item_idx, cold_user_idx, cold_item_idx, device,
user_content=user_content, item_content=item_content)
print(f"Registered model: {args.model}.")
else:
raise ValueError(f"Invalid model name: {args.model}!")
# model running
model.run()
# results recording
for i in range(len(top_Ns)):
all_hit_results[i].append(model.overall_test_results[i][0])
all_precision_results[i].append(model.overall_test_results[i][1])
all_recall_results[i].append(model.overall_test_results[i][2])
all_ndcg_results[i].append(model.overall_test_results[i][3])
cold_hit_results[i].append(model.cold_test_results[i][0])
cold_precision_results[i].append(model.cold_test_results[i][1])
cold_recall_results[i].append(model.cold_test_results[i][2])
cold_ndcg_results[i].append(model.cold_test_results[i][3])
warm_hit_results[i].append(model.warm_test_results[i][0])
warm_precision_results[i].append(model.warm_test_results[i][1])
warm_recall_results[i].append(model.warm_test_results[i][2])
warm_ndcg_results[i].append(model.warm_test_results[i][3])
time_results.append((model.train_end_time - model.train_start_time)/args.epochs)
for i in range(len(top_Ns)):
print("*" * 80)
print(f"Top-{top_Ns[i]} Overall Test Performance:")
mean_all_hit, std_all_hit = np.mean(all_hit_results[i]), np.std(all_hit_results[i])
mean_all_precision, std_all_precision = np.mean(all_precision_results[i]), np.std(all_precision_results[i])
mean_all_recall, std_all_recall = np.mean(all_recall_results[i]), np.std(all_recall_results[i])
mean_all_ndcg, std_all_ndcg = np.mean(all_ndcg_results[i]), np.std(all_ndcg_results[i])
print(f"Hit@{top_Ns[i]}: {mean_all_hit:.4f}±{std_all_hit:.4f}, "
f"Precision@{top_Ns[i]}: {mean_all_precision:.4f}±{std_all_precision:.4f}, "
f"Recall@{top_Ns[i]}: {mean_all_recall:.4f}±{std_all_recall:.4f}, "
f"NDCG@{top_Ns[i]}: {mean_all_ndcg:.4f}±{std_all_ndcg:.4f}")
print(f"Top-{top_Ns[i]} Cold-Start Test Performance:")
mean_cold_hit, std_cold_hit = np.mean(cold_hit_results[i]), np.std(cold_hit_results[i])
mean_cold_precision, std_cold_precision = np.mean(cold_precision_results[i]), np.std(cold_precision_results[i])
mean_cold_recall, std_cold_recall = np.mean(cold_recall_results[i]), np.std(cold_recall_results[i])
mean_cold_ndcg, std_cold_ndcg = np.mean(cold_ndcg_results[i]), np.std(cold_ndcg_results[i])
print(f"Hit@{top_Ns[i]}: {mean_cold_hit:.4f}±{std_cold_hit:.4f}, "
f"Precision@{top_Ns[i]}: {mean_cold_precision:.4f}±{std_cold_precision:.4f}, "
f"Recall@{top_Ns[i]}: {mean_cold_recall:.4f}±{std_cold_recall:.4f}, "
f"NDCG@{top_Ns[i]}: {mean_cold_ndcg:.4f}±{std_cold_ndcg:.4f}")
print(f"Top-{top_Ns[i]} Warm-Start Test Performance:")
mean_warm_hit, std_warm_hit = np.mean(warm_hit_results[i]), np.std(warm_hit_results[i])
mean_warm_precision, std_warm_precision = np.mean(warm_precision_results[i]), np.std(warm_precision_results[i])
mean_warm_recall, std_warm_recall = np.mean(warm_recall_results[i]), np.std(warm_recall_results[i])
mean_warm_ndcg, std_warm_ndcg = np.mean(warm_ndcg_results[i]), np.std(warm_ndcg_results[i])
print(f"Hit@{top_Ns[i]}: {mean_warm_hit:.4f}±{std_warm_hit:.4f}, "
f"Precision@{top_Ns[i]}: {mean_warm_precision:.4f}±{std_warm_precision:.4f}, "
f"Recall@{top_Ns[i]}: {mean_warm_recall:.4f}±{std_warm_recall:.4f}, "
f"NDCG@{top_Ns[i]}: {mean_warm_ndcg:.4f}±{std_warm_ndcg:.4f}")
print(f"Efficiency Performance:")
mean_time, std_time = np.mean(time_results), np.std(time_results)
print(f"Time: {mean_time:.4f}±{std_time:.4f} seconds per epoch.")