[ENH] add log in examples

examples/hed/main.py

@@ -46,13 +46,20 @@ def main():
     )
 
     args = parser.parse_args()
+    
+    # Build logger
+    print_log("Abductive Learning on the HED example.", logger="current")
 
     ### Working with Data
+    print_log("Working with Data.", logger="current")
+    
     total_train_data = get_dataset(train=True)
     train_data, val_data = split_equation(total_train_data, 3, 1)
     test_data = get_dataset(train=False)
 
     ### Building the Learning Part
+    print_log("Building the Learning Part.", logger="current")
+    
     # Build necessary components for BasicNN
     cls = SymbolNet(num_classes=4)
     loss_fn = nn.CrossEntropyLoss()
@@ -75,6 +82,8 @@ def main():
     model = ABLModel(base_model)
 
     ### Building the Reasoning Part
+    print_log("Building the Reasoning Part.", logger="current")
+    
     # Build knowledge base
     kb = HedKB()
 
@@ -82,14 +91,13 @@ def main():
     reasoner = HedReasoner(kb, dist_func="hamming", use_zoopt=True, max_revision=args.max_revision)
 
     ### Building Evaluation Metrics
+    print_log("Building Evaluation Metrics.", logger="current")
     metric_list = [ConsistencyMetric(kb=kb)]
 
     ### Bridge Learning and Reasoning
+    print_log("Bridge Learning and Reasoning.", logger="current")
     bridge = HedBridge(model, reasoner, metric_list)
 
-    # Build logger
-    print_log("Abductive Learning on the HED example.", logger="current")
-
     # Retrieve the directory of the Log file and define the directory for saving the model weights.
     log_dir = ABLLogger.get_current_instance().log_dir
     weights_dir = osp.join(log_dir, "weights")

examples/hwf/main.py

@@ -113,12 +113,19 @@ def main():
     )
 
     args = parser.parse_args()
+    
+    # Build logger
+    print_log("Abductive Learning on the HWF example.", logger="current")
 
     ### Working with Data
+    print_log("Working with Data.", logger="current")
+    
     train_data = get_dataset(train=True, get_pseudo_label=True)
     test_data = get_dataset(train=False, get_pseudo_label=True)
 
     ### Building the Learning Part
+    print_log("Building the Learning Part.", logger="current")
+    
     # Build necessary components for BasicNN
     cls = SymbolNet(num_classes=13, image_size=(45, 45, 1))
     loss_fn = nn.CrossEntropyLoss()
@@ -140,6 +147,8 @@ def main():
     model = ABLModel(base_model)
 
     ### Building the Reasoning Part
+    print_log("Building the Reasoning Part.", logger="current")
+    
     # Build knowledge base
     if args.ground:
         kb = HwfGroundKB()
@@ -152,14 +161,13 @@ def main():
     )
 
     ### Building Evaluation Metrics
+    print_log("Building Evaluation Metrics.", logger="current")
     metric_list = [SymbolAccuracy(prefix="hwf"), ReasoningMetric(kb=kb, prefix="hwf")]
 
     ### Bridge Learning and Reasoning
+    print_log("Bridge Learning and Reasoning.", logger="current")
     bridge = SimpleBridge(model, reasoner, metric_list)
 
-    # Build logger
-    print_log("Abductive Learning on the HWF example.", logger="current")
-
     # Retrieve the directory of the Log file and define the directory for saving the model weights.
     log_dir = ABLLogger.get_current_instance().log_dir
     weights_dir = osp.join(log_dir, "weights")

examples/mnist_add/main.py

@@ -78,11 +78,17 @@ def main():
 
     args = parser.parse_args()
 
+    # Build logger
+    print_log("Abductive Learning on the MNIST Addition example.", logger="current")
+
     ### Working with Data
+    print_log("Working with Data.", logger="current")
     train_data = get_dataset(train=True, get_pseudo_label=True)
     test_data = get_dataset(train=False, get_pseudo_label=True)
 
     ### Building the Learning Part
+    print_log("Building the Learning Part.", logger="current")
+    
     # Build necessary components for BasicNN
     cls = LeNet5(num_classes=10)
     loss_fn = nn.CrossEntropyLoss(label_smoothing=0.2)
@@ -112,6 +118,8 @@ def main():
     model = ABLModel(base_model)
 
     ### Building the Reasoning Part
+    print_log("Building the Reasoning Part.", logger="current")
+    
     # Build knowledge base
     if args.prolog:
         kb = PrologKB(pseudo_label_list=list(range(10)), pl_file="add.pl")
@@ -126,14 +134,13 @@ def main():
     )
 
     ### Building Evaluation Metrics
+    print_log("Building Evaluation Metrics.", logger="current")
     metric_list = [SymbolAccuracy(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
 
     ### Bridge Learning and Reasoning
+    print_log("Bridge Learning and Reasoning.", logger="current")
     bridge = SimpleBridge(model, reasoner, metric_list)
 
-    # Build logger
-    print_log("Abductive Learning on the MNIST Addition example.", logger="current")
-
     # Retrieve the directory of the Log file and define the directory for saving the model weights.
     log_dir = ABLLogger.get_current_instance().log_dir
     weights_dir = osp.join(log_dir, "weights")

examples/zoo/main.py

@@ -30,8 +30,13 @@ def main():
         "--loops", type=int, default=3, help="number of loop iterations (default : 3)"
     )
     args = parser.parse_args()
+    
+    # Build logger
+    print_log("Abductive Learning on the ZOO example.", logger="current")
 
     ### Working with Data
+    print_log("Working with Data.", logger="current")
+    
     X, y = load_and_preprocess_dataset(dataset_id=62)
     X_label, y_label, X_unlabel, y_unlabel, X_test, y_test = split_dataset(X, y, test_size=0.3)
     label_data = transform_tab_data(X_label, y_label)
@@ -39,12 +44,17 @@ def main():
     train_data = transform_tab_data(X_unlabel, y_unlabel)
 
     ### Building the Learning Part
+    print_log("Building the Learning Part.", logger="current")
+    
+    # Build base model
     base_model = RandomForestClassifier()
 
     # Build ABLModel
     model = ABLModel(base_model)
 
     ### Building the Reasoning Part
+    print_log("Building the Reasoning Part.", logger="current")
+    
     # Build knowledge base
     kb = ZooKB()
     
@@ -52,16 +62,17 @@ def main():
     reasoner = Reasoner(kb, dist_func=consitency)
 
     ### Building Evaluation Metrics
+    print_log("Building Evaluation Metrics.", logger="current")
     metric_list = [SymbolAccuracy(prefix="zoo"), ReasoningMetric(kb=kb, prefix="zoo")]
     
-    # Build logger
-    print_log("Abductive Learning on the ZOO example.", logger="current")
-    log_dir = ABLLogger.get_current_instance().log_dir
-    weights_dir = osp.join(log_dir, "weights")
-    
     ### Bridging learning and reasoning
+    print_log("Bridge Learning and Reasoning.", logger="current")
     bridge = SimpleBridge(model, reasoner, metric_list)
     
+    # Retrieve the directory of the Log file and define the directory for saving the model weights.
+    log_dir = ABLLogger.get_current_instance().log_dir
+    weights_dir = osp.join(log_dir, "weights")
+    
     # Performing training and testing
     print_log("------- Use labeled data to pretrain the model -----------", logger="current")
     base_model.fit(X_label, y_label)