Upgrade Packages and Adjust Code for Compatibility with Python 3.11

main.py

@@ -1,35 +1,18 @@
-#!/usr/bin/env python37
-# -*- coding: utf-8 -*-
-"""
-Created on 30 Sep, 2019
-
-@author: wangshuo
-"""
-
 import os
 import time
+import sys
 import argparse
 import pickle
 import numpy as np
-import random
-from tqdm import tqdm
-from os.path import join
-
 import torch
-from torch import nn
 from torch.utils.data import DataLoader
-import torch.nn.functional as F
-import torch.optim as optim
-from torch.optim.lr_scheduler import StepLR
-from torch.autograd import Variable
-from torch.backends import cudnn
-
+from tqdm import tqdm
 from utils import collate_fn
 from model import GraphRec
 from dataloader import GRDataset
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--dataset_path', default='dataset/Ciao/', help='dataset directory path: datasets/Ciao/Epinions')
+parser.add_argument('--dataset_path', default='./datasets/Ciao/', help='dataset directory path: ./datasets/Ciao or ./datasets/Epinions')
 parser.add_argument('--batch_size', type=int, default=256, help='input batch size')
 parser.add_argument('--embed_dim', type=int, default=64, help='the dimension of embedding')
 parser.add_argument('--epoch', type=int, default=30, help='the number of epochs to train for')
@@ -44,48 +27,70 @@
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 def main():
-    print('Loading data...')
-    with open(args.dataset_path + 'dataset.pkl', 'rb') as f:
-        train_set = pickle.load(f)
-        valid_set = pickle.load(f)
-        test_set = pickle.load(f)
-
-    with open(args.dataset_path + 'list.pkl', 'rb') as f:
-        u_items_list = pickle.load(f)
-        u_users_list = pickle.load(f)
-        u_users_items_list = pickle.load(f)
-        i_users_list = pickle.load(f)
-        (user_count, item_count, rate_count) = pickle.load(f)
+    dataset_path = os.path.abspath(args.dataset_path)
+    print(f"Loading data from {dataset_path}")
+    if not os.path.exists(dataset_path):
+        print(f"Error: Dataset path {dataset_path} does not exist.")
+        sys.exit(1)
 
+    try:
+        dataset_file = os.path.join(dataset_path, 'dataset.pkl')
+        print(f"Trying to open {dataset_file}")
+        with open(dataset_file, 'rb') as f:
+            train_set = pickle.load(f)
+            valid_set = pickle.load(f)
+            test_set = pickle.load(f)
+            print("Successfully loaded dataset.pkl")
+    except FileNotFoundError as e:
+        print(f"Error: {e}")
+        return
+    except Exception as e:
+        print(f"Unexpected error while loading dataset.pkl: {e}")
+        return
+
+    try:
+        list_file = os.path.join(dataset_path, 'list.pkl')
+        print(f"Trying to open {list_file}")
+        with open(list_file, 'rb') as f:
+            u_items_list = pickle.load(f)
+            u_users_list = pickle.load(f)
+            u_users_items_list = pickle.load(f)
+            i_users_list = pickle.load(f)
+            (user_count, item_count, rate_count) = pickle.load(f)
+            print("Successfully loaded list.pkl")
+    except FileNotFoundError as e:
+        print(f"Error: {e}")
+        return
+    except Exception as e:
+        print(f"Unexpected error while loading list.pkl: {e}")
+        return
+
     train_data = GRDataset(train_set, u_items_list, u_users_list, u_users_items_list, i_users_list)
     valid_data = GRDataset(valid_set, u_items_list, u_users_list, u_users_items_list, i_users_list)
     test_data = GRDataset(test_set, u_items_list, u_users_list, u_users_items_list, i_users_list)
-    train_loader = DataLoader(train_data, batch_size = args.batch_size, shuffle = True, collate_fn = collate_fn)
-    valid_loader = DataLoader(valid_data, batch_size = args.batch_size, shuffle = False, collate_fn = collate_fn)
-    test_loader = DataLoader(test_data, batch_size = args.batch_size, shuffle = False, collate_fn = collate_fn)
+    train_loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn)
+    valid_loader = DataLoader(valid_data, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn)
+    test_loader = DataLoader(test_data, batch_size=args.batch_size, shuffle=False, collate_fn=collate_fn)
 
     model = GraphRec(user_count+1, item_count+1, rate_count+1, args.embed_dim).to(device)
 
     if args.test:
         print('Load checkpoint and testing...')
-        ckpt = torch.load('best_checkpoint.pth.tar')
+        ckpt = torch.load('best_checkpoint.pth.tar', map_location=device)
         model.load_state_dict(ckpt['state_dict'])
         mae, rmse = validate(test_loader, model)
         print("Test: MAE: {:.4f}, RMSE: {:.4f}".format(mae, rmse))
         return
 
-    optimizer = optim.RMSprop(model.parameters(), args.lr)
-    criterion = nn.MSELoss()
-    scheduler = StepLR(optimizer, step_size = args.lr_dc_step, gamma = args.lr_dc)
+    optimizer = torch.optim.RMSprop(model.parameters(), args.lr)
+    criterion = torch.nn.MSELoss()
+    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_dc_step, gamma=args.lr_dc)
 
     for epoch in tqdm(range(args.epoch)):
-        # train for one epoch
-        scheduler.step(epoch = epoch)
-        trainForEpoch(train_loader, model, optimizer, epoch, args.epoch, criterion, log_aggr = 100)
-
+        scheduler.step(epoch=epoch)
+        trainForEpoch(train_loader, model, optimizer, epoch, args.epoch, criterion, log_aggr=100)
         mae, rmse = validate(valid_loader, model)
-
-        # store best loss and save a model checkpoint
+
         ckpt_dict = {
             'epoch': epoch + 1,
             'state_dict': model.state_dict(),
@@ -102,12 +107,9 @@ def main():
 
         print('Epoch {} validation: MAE: {:.4f}, RMSE: {:.4f}, Best MAE: {:.4f}'.format(epoch, mae, rmse, best_mae))
 
-
 def trainForEpoch(train_loader, model, optimizer, epoch, num_epochs, criterion, log_aggr=1):
     model.train()
-
     sum_epoch_loss = 0
-
     start = time.time()
     for i, (uids, iids, labels, u_items, u_users, u_users_items, i_users) in tqdm(enumerate(train_loader), total=len(train_loader)):
         uids = uids.to(device)
@@ -120,24 +122,20 @@ def trainForEpoch(train_loader, model, optimizer, epoch, num_epochs, criterion,
 
         optimizer.zero_grad()
         outputs = model(uids, iids, u_items, u_users, u_users_items, i_users)
-
         loss = criterion(outputs, labels.unsqueeze(1))
         loss.backward()
-        optimizer.step() 
-
+        optimizer.step()
         loss_val = loss.item()
         sum_epoch_loss += loss_val
 
         iter_num = epoch * len(train_loader) + i + 1
 
         if i % log_aggr == 0:
             print('[TRAIN] epoch %d/%d batch loss: %.4f (avg %.4f) (%.2f im/s)'
-                % (epoch + 1, num_epochs, loss_val, sum_epoch_loss / (i + 1),
-                  len(uids) / (time.time() - start)))
+                % (epoch + 1, num_epochs, loss_val, sum_epoch_loss / (i + 1), len(uids) / (time.time() - start)))
 
         start = time.time()
 
-
 def validate(valid_loader, model):
     model.eval()
     errors = []
@@ -158,6 +156,5 @@ def validate(valid_loader, model):
     rmse = np.sqrt(np.mean(np.power(errors, 2)))
     return mae, rmse
 
-
 if __name__ == '__main__':
     main()

requirements.txt

@@ -1,99 +1,17 @@
-alabaster==0.7.12
-asn1crypto==0.24.0
-astroid==2.3.1
-attrs==19.2.0
-Babel==2.7.0
-backcall==0.1.0
-bleach==3.1.0
-certifi==2019.9.11
-cffi==1.12.3
-chardet==3.0.4
-cloudpickle==1.2.2
-cryptography==2.7
-decorator==4.4.0
-defusedxml==0.6.0
-docutils==0.15.2
-entrypoints==0.3
-idna==2.8
-imagesize==1.1.0
-ipykernel==5.1.2
-ipython==7.8.0
-ipython-genutils==0.2.0
-ipywidgets==7.5.1
-isort==4.3.21
-jedi==0.15.1
-jeepney==0.4.1
-Jinja2==2.10.1
-json5==0.8.5
-jsonschema==3.0.2
-jupyter==1.0.0
-jupyter-client==5.3.3
-jupyter-console==6.0.0
-jupyter-core==4.5.0
-jupyterlab==1.1.4
-jupyterlab-server==1.0.6
-keyring==18.0.0
-lazy-object-proxy==1.4.2
-MarkupSafe==1.1.1
-mccabe==0.6.1
-mistune==0.8.4
-nbconvert==5.6.0
-nbformat==4.4.0
-notebook==6.0.1
-numpy==1.17.2
-numpydoc==0.9.1
-packaging==19.2
-pandas==0.25.1
-pandocfilters==1.4.2
-parso==0.5.1
-pexpect==4.7.0
-pickleshare==0.7.5
-Pillow==6.2.0
-prometheus-client==0.7.1
-prompt-toolkit==2.0.10
-psutil==5.6.3
-ptyprocess==0.6.0
-pycodestyle==2.5.0
-pycparser==2.19
-pyflakes==2.1.1
-Pygments==2.4.2
-pylint==2.4.2
-pyOpenSSL==19.0.0
-pyparsing==2.4.2
-pyrsistent==0.15.4
-PySocks==1.7.1
-python-dateutil==2.8.0
-pytz==2019.2
-pyzmq==18.1.0
-QtAwesome==0.6.0
-qtconsole==4.5.5
-QtPy==1.9.0
-requests==2.22.0
-rope==0.14.0
-scipy==1.3.1
-SecretStorage==3.1.1
-Send2Trash==1.5.0
-six==1.12.0
-snowballstemmer==1.9.1
-Sphinx==2.2.0
-sphinxcontrib-applehelp==1.0.1
-sphinxcontrib-devhelp==1.0.1
-sphinxcontrib-htmlhelp==1.0.2
-sphinxcontrib-jsmath==1.0.1
-sphinxcontrib-qthelp==1.0.2
-sphinxcontrib-serializinghtml==1.1.3
-spyder==3.3.6
-spyder-kernels==0.5.2
-terminado==0.8.2
-testpath==0.4.2
-torch==1.1.0
-torchvision==0.3.0
-tornado==6.0.3
-tqdm==4.36.1
-traitlets==4.3.3
-urllib3==1.24.2
-wcwidth==0.1.7
-webencodings==0.5.1
-widgetsnbextension==3.5.1
-wrapt==1.11.2
-wurlitzer==1.0.3
+filelock==3.15.4
+fsspec==2024.6.1
+Jinja2==3.1.4
+MarkupSafe==2.1.5
+mpmath==1.3.0
+networkx==3.3
+numpy==1.26.4
+pandas==2.2.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+scipy==1.14.0
+six==1.16.0
+sympy==1.12.1
+torch==2.2.2
+tqdm==4.66.4
+typing_extensions==4.12.2
+tzdata==2024.1