update 2.3.0

README.md

@@ -3,7 +3,7 @@
 </p>
 
 ![PyPI - Python Version](https://img.shields.io/badge/pyhton-3.5%2B-blue) 
-[![Version](https://img.shields.io/badge/version-2.2.0-orange)](https://github.com/AmazingDD/daisyRec) 
+[![Version](https://img.shields.io/badge/version-2.3.0-orange)](https://github.com/AmazingDD/daisyRec) 
 ![GitHub repo size](https://img.shields.io/github/repo-size/AmazingDD/daisyRec) 
 ![GitHub](https://img.shields.io/github/license/AmazingDD/daisyRec)
 [![arXiv](https://img.shields.io/badge/arXiv-daisyRec-%23B21B1B)](https://arxiv.org/abs/2206.10848)

daisy/__init__.py

@@ -1 +1 @@
-__version__ = 'v2.2.0'
+__version__ = 'v2.3.0'

daisy/assets/basic.yaml

@@ -4,9 +4,9 @@ reproducibility: True
 state: ~
 
 optimization_metric: 'ndcg'
-hyperopt_trail: 20
+hyperopt_trail: 30
 tune_testset: False
-hyperopt_pack: '{}'
+tune_pack: '{}'
 
 algo_name: 'mf'
 val_method: 'tsbr'

daisy/model/EASERecommender.py

@@ -47,7 +47,7 @@ def fit(self, train_set):
         self.interaction_matrix = X # user_num * item_num
 
     def predict(self, u, i):
-        self.interaction_matrix[u, :].multiply(self.item_similarity[:, i].T).sum(axis=1).getA1()
+        return self.interaction_matrix[u, :].multiply(self.item_similarity[:, i].T).sum(axis=1).getA1()[0]
 
     def rank(self, test_loader):
         rec_ids = None

daisy/model/FMRecommender.py

@@ -97,7 +97,7 @@ def calc_loss(self, batch):
     def predict(self, u, i):
         u = torch.tensor(u, device=self.device)
         i = torch.tensor(i, device=self.device)
-        pred = self.forward(u, i).cpu()
+        pred = self.forward(u, i).cpu().item()
 
         return pred
 
@@ -122,11 +122,11 @@ def rank(self, test_loader):
         return rec_ids.cpu().numpy()
 
     def full_rank(self, u):
-        u = torch.tensor(u, self.device)
+        u = torch.tensor(u, device=self.device)
 
         user_emb = self.embed_user(u)
         items_emb = self.embed_item.weight 
         scores = torch.matmul(user_emb, items_emb.transpose(1, 0)) #  (item_num,)
-        scores += self.u_bias(u) + self.i_bias.weight + self.bias_
+        scores += self.u_bias(u) + self.i_bias.weight.squeeze() + self.bias_
 
         return torch.argsort(scores, descending=True)[:self.topk].cpu().numpy()

daisy/model/Item2VecRecommender.py

@@ -79,7 +79,7 @@ def predict(self, u, i):
         item_emb = self.shared_embedding(i)
         pred = (user_emb * item_emb).sum(dim=-1)
 
-        return pred.cpu()
+        return pred.cpu().item()
 
     def rank(self, test_loader):
         rec_ids = torch.tensor([], device=self.device)
@@ -101,7 +101,7 @@ def rank(self, test_loader):
         return rec_ids.cpu().numpy()
 
     def full_rank(self, u):
-        u = torch.tensor(u, self.device)
+        u = torch.tensor(u, device=self.device)
 
         user_emb = self.user_embedding(u)
         items_emb = self.shared_embedding.weight 

daisy/model/LightGCNRecommender.py

@@ -176,7 +176,7 @@ def predict(self, u, i):
         i_embedding = self.restore_item_e[i]
         pred = torch.matmul(u_embedding, i_embedding.t())
 
-        return pred.cpu()
+        return pred.cpu().item()
 
     def rank(self, test_loader):
         if self.restore_user_e is None or self.restore_item_e is None:

daisy/model/MFRecommender.py

@@ -99,7 +99,7 @@ def calc_loss(self, batch):
     def predict(self, u, i):
         u = torch.tensor(u, device=self.device)
         i = torch.tensor(i, device=self.device)
-        pred = self.forward(u, i).cpu()
+        pred = self.forward(u, i).cpu().item()
 
         return pred
 
@@ -124,7 +124,7 @@ def rank(self, test_loader):
 
 
     def full_rank(self, u):
-        u = torch.tensor(u, self.device)
+        u = torch.tensor(u, device=self.device)
 
         user_emb = self.embed_user(u)
         items_emb = self.embed_item.weight 

daisy/model/NFMRecommender.py

@@ -151,7 +151,7 @@ def calc_loss(self, batch):
     def predict(self, u, i):
         u = torch.tensor(u, device=self.device)
         i = torch.tensor(i, device=self.device)
-        pred = self.forward(u, i).cpu()
+        pred = self.forward(u, i).cpu().item()
 
         return pred
 
@@ -192,7 +192,7 @@ def rank(self, test_loader):
         return rec_ids.cpu().numpy()
 
     def full_rank(self, u):
-        u = torch.tensor(u, self.device)
+        u = torch.tensor(u, device=self.device)
 
         user_emb = self.embed_user(u)  # factor
         items_emb = self.embed_item.weight  # item_num * factor
@@ -202,7 +202,7 @@ def full_rank(self, u):
         if self.num_layers:
             fm = self.deep_layers(fm) # item_num * factor
         fm += self.u_bias(u) + self.i_bias.weight + self.bias_
-        scores = self.prediction(fm) # item_num
+        scores = self.prediction(fm).squeeze() # item_num
 
         return torch.argsort(scores, descending=True)[:self.topk].cpu().numpy()
 

daisy/model/NGCFRecommender.py

@@ -217,7 +217,7 @@ def predict(self, u, i):
         i_embedding = self.restore_item_e[i]
         pred = torch.matmul(u_embedding, i_embedding.t())
 
-        return pred.cpu()
+        return pred.cpu().item()
 
     def rank(self, test_loader):
         if self.restore_user_e is None or self.restore_item_e is None:

daisy/model/NeuMFRecommender.py

@@ -171,7 +171,7 @@ def calc_loss(self, batch):
     def predict(self, u, i):
         u = torch.tensor(u, device=self.device)
         i = torch.tensor(i, device=self.device)
-        pred = self.forward(u, i).cpu()
+        pred = self.forward(u, i).cpu().item()
 
         return pred
 
@@ -206,10 +206,10 @@ def rank(self, test_loader):
 
             rec_ids = torch.cat((rec_ids, rank_list), 0)
 
-        return rec_ids
+        return rec_ids.cpu().numpy()
 
     def full_rank(self, u):
-        u = torch.tensor(u, self.device)
+        u = torch.tensor(u, device=self.device)
 
         if not self.model == 'MLP':
             embed_user_GMF = self.embed_user_GMF(u) # factor

daisy/model/VAECFRecommender.py

@@ -7,6 +7,7 @@
   year={2018}
 }
 '''
+import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -81,7 +82,7 @@ def forward(self, rating_matrix):
         h = self.encoder(h)
 
         mu = h[:, :int(self.lat_dim / 2)]
-        logvar = h[:, int(self.lat_dim / 2):]
+        logvar = h[:, math.ceil(self.lat_dim / 2):]
 
         z = self.reparameterize(mu, logvar)
         z = self.decoder(z)
@@ -109,13 +110,13 @@ def calc_loss(self, batch):
         return loss
 
     def predict(self, u, i):
-        u = torch.tensor(u, device=self.device)
-        i = torch.tensor(i, device=self.device)
+        u = torch.tensor([u], device=self.device)
+        i = torch.tensor([i], device=self.device)
 
         rating_matrix = self.get_user_rating_matrix(u)
         scores, _, _ = self.forward(rating_matrix)
 
-        return scores[[torch.arange(len(i)).to(self.device), i]].cpu()
+        return scores[[torch.arange(len(i)).to(self.device), i]].cpu().item()
 
     def rank(self, test_loader):
         rec_ids = torch.tensor([], device=self.device)
@@ -137,7 +138,7 @@ def rank(self, test_loader):
         return rec_ids.cpu().numpy()
 
     def full_rank(self, u):
-        u = torch.tensor(u, device=self.device)
+        u = torch.tensor([u], device=self.device)
         rating_matrix = self.get_user_rating_matrix(u)
         scores, _, _ = self.forward(rating_matrix)
 

daisy/utils/parser.py

@@ -9,9 +9,8 @@ def parse_args():
                         help='the metric to be optimized for hyper-parameter tuning via HyperOpt')
     parser.add_argument('--hyperopt_trail', 
                         type=int, 
-                        default=30, 
                         help='the number of trails of HyperOpt')
-    parser.add_argument('--hyperopt_pack', 
+    parser.add_argument('--tune_pack', 
                         type=str, 
                         help='record the searching space of hyper-parameters for HyperOpt')
     # common settings

run_examples/tune.py

@@ -1,3 +1,4 @@
+from ast import Global
 import json
 import optuna
 import numpy as np
@@ -102,7 +103,7 @@
     config['logger'] = logger
 
     ''' unpack hyperparameters to tune '''
-    param_dict = json.loads(config['hyperopt_pack'])
+    param_dict = json.loads(config['tune_pack'])
     algo_name = config['algo_name']
     kpi_name = config['optimization_metric']
     tune_param_names = tune_params_config[algo_name]
@@ -133,6 +134,7 @@
 
     ''' define optimization target function '''
     def objective(trial):
+        global TRIAL_CNT
         for param in tune_param_names:
             if param not in param_dict.keys(): continue
 
@@ -145,7 +147,7 @@ def objective(trial):
                         param, param_dict[param]['min'], param_dict[param]['max'], 1 if step is None else step)
                 elif param_type_config[param] == 'float':
                     config[param] = trial.suggest_float(
-                        param, param_dict[param]['min'], param_dict[param]['max'], param_dict[param]['step'])
+                        param, param_dict[param]['min'], param_dict[param]['max'], step=param_dict[param]['step'])
                 else:
                     raise ValueError(f'Invalid parameter type for {param}...')
             else:
@@ -224,7 +226,7 @@ def objective(trial):
 
     ''' record the best choices '''
     logger.info(f'Trial {study.best_trial.number} get the best {kpi_name}({study.best_trial.value}) with params: {study.best_trial.params}')
-    line = ','.join([study.best_params[param] for param in tune_param_names]) + f',{study.best_value:.4f}\n'
+    line = ','.join([str(study.best_params[param]) for param in tune_param_names]) + f',{study.best_value:.4f}\n'
     f.write(line)
     f.flush()
     f.close()

setup.py

@@ -44,14 +44,14 @@
     # package_dir={"": "daisy"},
     package_data={"": ["*.yaml"]},
     # packages = find_packages(exclude=['tests*']),
-    version='v2.2.0',  # Ideally should be same as your GitHub release tag varsion
+    version='v2.3.0',  # Ideally should be same as your GitHub release tag varsion
     description=('An easy-to-use library for recommender systems.'),
     long_description=long_description,
     # long_description_content_type="text/markdown",
     author='Yu Di',
     author_email='[email protected]',
     url='https://github.com/AmazingDD/daisyRec',
-    download_url='https://github.com/AmazingDD/daisyRec/archive/refs/tags/v2.2.0.tar.gz',
+    download_url='https://github.com/AmazingDD/daisyRec/archive/refs/tags/v2.3.0.tar.gz',
     keywords=['ranking', 'recommendation'],
     # include_package_data=True,
     install_requires=install_requires,