import numpy as np
import matplotlib.pyplot as plt
from load_data import load_data,load_content_data,split_rating_dat
from cvae import CVAE
P = 5
NUM_ITEM = 16980
ITEM_EMB = 8000
if __name__ == "__main__":
data_dir = "CVAE\\data\\citeulike-a\\"
train_users,test_users,train_items = split_rating_dat(load_data(data_dir + "users.dat"),P)
side_info = load_content_data(data_dir + "papers.dat",NUM_ITEM,ITEM_EMB)
model = CVAE(num_features=50, max_epoch=50 , max_iter=5, a=1, b=0.01, lambda_u=0.1, lambda_v=10, lambda_r=10,vae_pre_training="CVAE\\data\\vae_0920.pt")
model.fit(train_users,test_users,train_items, side_info)
# VAE 经过逐层预训练(单独用每一层的小网络训练)，得到预训练参数 vae_pre_training.pt
# 选择 p=5, 在citeulike-a上实验，在经过约 10 个epoch 后，recall@50 达到0.2，复现了论文中的实验结果数值
import torch
import cv2
from cvae import Data_Manager,CVAE
# --------------------------------------------------------------------------------
if __name__ == '__main__':
device_id = '0'
device = torch.device('cpu')
if torch.cuda.is_available():
device = torch.device('cuda:{}'.format(device_id))
# Data Manager
data_manager = Data_Manager()
# C-VAE
model = CVAE(data_manager, device, epochs=10)
model.fit()
# Single Sample
model.load_model('cvae_state.pt')
sample = model.sample(idx=5)
cv2.imwrite('sample_test.png', sample)