main.py

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import torch.optim as optim
import torch.nn.functional as F
from sklearn.metrics import log_loss, roc_auc_score

import warnings
warnings.filterwarnings('ignore')
from time import time
import random

from model import bpr
from model.bpr import BPR
from util.loaddata import Data
import multiprocessing
import heapq
import util.metrics as metrics


if __name__ == '__main__':

    seed = 1024
    torch.manual_seed(seed)  # 为CPU设置随机种子
    torch.cuda.manual_seed(seed)  # 为当前GPU设置随机种子
    torch.cuda.manual_seed_all(seed)  # 为所有GPU设置随机种子
    np.random.seed(seed)
    random.seed(seed)

    device = 'cpu'
    use_cuda = True
    if use_cuda and torch.cuda.is_available():
        print('cuda ready...')
        device = 'cuda:0'

    filepath = 'amazon-book'
    data_generator = Data(path=filepath)
    bpr.data_generator = data_generator   # batch_size=args.batch_size

    model = BPR(data_generator.n_users, data_generator.n_items, embedding_size = 10, l2_reg_embedding=0.00001, device=device)
    model.fit(learning_rate=0.001, batch_size=2000, epochs=50, verbose=5, early_stop=False)