fixed device and added models

cornac/models/globallocalkernel/recom_globallocalkernel.py

@@ -206,7 +206,7 @@ def __init__(
         self.verbose = verbose
 
         # Device
-        if torch.cuda.is_available() and (self.device != 'cpu'):
+        if torch.cuda.is_available():
             self.device = torch.device("cuda")
         else:
             self.device = torch.device("cpu")

experiment_log_1.txt

@@ -0,0 +1,86 @@
+
+========================================
+Experiment conducted on: 2024-12-14 22:16:07.572951
+
+Hyperparameters:
+name: GlobalLocalKernel
+trainable: True
+verbose: False
+is_fitted: False
+ignored_attrs: ['train_set', 'val_set', 'test_set']
+num_users: None
+num_items: 1656
+uid_map: None
+iid_map: None
+max_rating: 4.0
+min_rating: 1.0
+global_mean: None
+_Recommender__user_ids: None
+_Recommender__item_ids: None
+n_hid: 10
+n_dim: 2
+n_layers: 2
+lambda_s: 0.006
+lambda_2: 0.001
+gk_size: 3
+dot_scale: 1
+max_epoch_p: 500
+max_epoch_f: 500
+tol_p: 0.0001
+tol_f: 1e-05
+patience_p: 10
+patience_f: 10
+lr_p: 0.1
+lr_f: 0.01
+device: cuda
+model: CompleteNet(
+  (local_kernel_net): KernelNet(
+    (layers): ModuleList(
+      (0-1): 2 x KernelLayer(
+        (activation): Sigmoid()
+      )
+      (2): KernelLayer(
+        (activation): Identity()
+      )
+    )
+    (dropout): Dropout(p=0.33, inplace=False)
+  )
+)
+train_r_local: [[4.18834   3.834389  4.190889  ... 3.719673  4.9786563 4.025386 ]
+ [4.1866603 3.833935  4.1901083 ... 3.7193959 4.9786563 4.0248137]
+ [3.643859  3.643947  3.9684842 ... 3.4783204 4.9786563 3.7472897]
+ ...
+ [3.0270095 3.4925659 3.7013142 ... 3.32962   4.9786563 3.496717 ]
+ [3.030718  3.493905  3.7029653 ... 3.3311675 4.9786563 3.4985132]
+ [3.030718  3.493905  3.7029653 ... 3.3311675 4.9786563 3.4985132]]
+_train_r: [[4. 0. 0. ... 0. 0. 0.]
+ [0. 5. 0. ... 0. 0. 0.]
+ [0. 0. 5. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+_train_mask: [[1. 0. 0. ... 0. 0. 0.]
+ [0. 1. 0. ... 0. 0. 0.]
+ [0. 0. 1. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+
+Test Results:
+Early stopping fine-tuning at epoch: 380
+Early stopping fine-tuning at epoch: 381
+Early stopping fine-tuning at epoch: 391
+Early stopping fine-tuning at epoch: 401
+Early stopping fine-tuning at epoch: 411
+Early stopping fine-tuning at epoch: 432
+
+TEST:
+...
+                  |    MAE |   RMSE |    AUC |    MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s)
+----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + --------
+GlobalLocalKernel | 0.8034 | 0.9727 | 0.5107 | 0.0189 |  0.0217 |       0.0192 |    0.0133 |   28.9218 | 190.2286
+
+
+========================================

experiment_log_2.txt

@@ -0,0 +1,83 @@
+
+========================================
+Experiment conducted on: 2024-12-14 22:22:56.926776
+
+Hyperparameters:
+name: GlobalLocalKernel
+trainable: True
+verbose: False
+is_fitted: False
+ignored_attrs: ['train_set', 'val_set', 'test_set']
+num_users: None
+num_items: 1656
+uid_map: None
+iid_map: None
+max_rating: 4.0
+min_rating: 1.0
+global_mean: None
+_Recommender__user_ids: None
+_Recommender__item_ids: None
+n_hid: 10
+n_dim: 2
+n_layers: 2
+lambda_s: 0.006
+lambda_2: 0.001
+gk_size: 3
+dot_scale: 1
+max_epoch_p: 500
+max_epoch_f: 500
+tol_p: 0.0001
+tol_f: 1e-05
+patience_p: 10
+patience_f: 10
+lr_p: 0.1
+lr_f: 0.01
+device: cuda
+model: CompleteNet(
+  (local_kernel_net): KernelNet(
+    (layers): ModuleList(
+      (0-1): 2 x KernelLayer(
+        (activation): Sigmoid()
+      )
+      (2): KernelLayer(
+        (activation): Identity()
+      )
+    )
+    (dropout): Dropout(p=0.33, inplace=False)
+  )
+)
+train_r_local: [[4.074561  3.7919703 4.1602736 ... 3.5502026 4.9746857 3.943836 ]
+ [4.0745606 3.79197   4.1602736 ... 3.5502021 4.9746857 3.9438357]
+ [3.7859447 3.7108083 4.024741  ... 3.356383  4.9746857 3.8038127]
+ ...
+ [3.1270785 3.4874184 3.683483  ... 3.1386068 4.9746857 3.4953837]
+ [3.1272159 3.4874575 3.6835485 ... 3.1387024 4.9746857 3.4954498]
+ [3.1272159 3.4874575 3.6835485 ... 3.1387024 4.9746857 3.4954498]]
+_train_r: [[4. 0. 0. ... 0. 0. 0.]
+ [0. 5. 0. ... 0. 0. 0.]
+ [0. 0. 5. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+_train_mask: [[1. 0. 0. ... 0. 0. 0.]
+ [0. 1. 0. ... 0. 0. 0.]
+ [0. 0. 1. ... 0. 0. 0.]
+ ...
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]
+ [0. 0. 0. ... 0. 0. 0.]]
+
+Test Results:
+
+TEST:
+...
+                  |    MAE |   RMSE |    AUC |    MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s)
+----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + --------
+GlobalLocalKernel | 0.8051 | 0.9771 | 0.5428 | 0.0232 |  0.0261 |       0.0254 |    0.0169 |   30.8466 | 191.1777
+MF                | 0.7430 | 0.8998 | 0.7445 | 0.0548 |  0.0761 |       0.0675 |    0.0463 |    0.0290 |   0.7707
+PMF               | 0.7534 | 0.9138 | 0.7744 | 0.0671 |  0.0969 |       0.0813 |    0.0639 |    1.4337 |   1.1157
+BPR               | 2.0143 | 2.2267 | 0.8695 | 0.1042 |  0.1500 |       0.1110 |    0.1195 |    1.2896 |   0.7294
+
+
+========================================

try.py

@@ -5,6 +5,7 @@
 from cornac.models import GlobalLocalKernel
 from cornac.eval_methods import RatioSplit
 from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP
+from cornac.models import MF, PMF, BPR
 
 # Function to find the next available log file name
 def get_next_log_file(base_name="experiment_log", ext=".txt"):
@@ -49,13 +50,17 @@ def log_results(log_file, test_results, model_instance):
     # Example hyperparameters
     n_hid=10, 
     n_dim=2, 
-    max_epoch_p=30, 
-    max_epoch_f=100,
+    max_epoch_p=500, 
+    max_epoch_f=500,
     lr_p=0.1,
     lr_f=0.01, 
     verbose=False
 )
 
+mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)
+pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123)
+bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)
+
 # Define some basic metrics
 metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]
 
@@ -68,7 +73,7 @@ def log_results(log_file, test_results, model_instance):
 sys.stdout = StringIO()  # Redirect stdout to capture results
 
 # Run the experiment on the smaller subset
-cornac.Experiment(eval_method=rs, models=[my_model], metrics=metrics, user_based=True).run()
+cornac.Experiment(eval_method=rs, models=[my_model, mf, pmf, bpr], metrics=metrics, user_based=True).run()
 
 # Retrieve experiment results
 experiment_results = sys.stdout.getvalue()