Merge branch 'Dev' of https://github.com/AbductiveLearning/ABL-Package into Dev

README.md

@@ -53,7 +53,7 @@ For Linux users:
 $ sudo apt-get install swi-prolog
 ```
 
-For Windows and Mac users, please refer to the [Swi-Prolog Download Page](https://www.swi-prolog.org/Download.html).
+For Windows and Mac users, please refer to the [Swi-Prolog Install Guide](https://github.com/yuce/pyswip/blob/master/INSTALL.md).
 
 ## Examples
 

abl/learning/basic_nn.py

@@ -66,9 +66,9 @@ class BasicNN:
         num_workers: int = 0,
         save_interval: Optional[int] = None,
         save_dir: Optional[str] = None,
-        train_transform: Callable[..., Any] = None,
-        test_transform: Callable[..., Any] = None,
-        collate_fn: Callable[[List[Any]], Any] = None,
+        train_transform: Optional[Callable[..., Any]] = None,
+        test_transform: Optional[Callable[..., Any]] = None,
+        collate_fn: Optional[Callable[[List[Any]], Any]] = None,
     ) -> None:
         if not isinstance(model, torch.nn.Module):
             raise TypeError("model must be an instance of torch.nn.Module")

abl/reasoning/kb.py

@@ -471,7 +471,7 @@ class PrologKB(KBBase):
         except (IndexError, ImportError):
             print("A Prolog-based knowledge base is in use. Please install Swi-Prolog \
                    using the command 'sudo apt-get install swi-prolog' for Linux users, \
-                   or download it from https://www.swi-prolog.org/Download.html for Windows and Mac users.")
+                   or download it following the guide in https://github.com/yuce/pyswip/blob/master/INSTALL.md for Windows and Mac users.")
         
         self.prolog = pyswip.Prolog()
         self.pl_file = pl_file

abl/utils/__init__.py

@@ -1,6 +1,6 @@
 from .cache import Cache, abl_cache
 from .logger import ABLLogger, print_log
-from .utils import confidence_dist, flatten, hamming_dist, reform_list, to_hashable
+from .utils import confidence_dist, flatten, hamming_dist, reform_list, to_hashable, tab_data_to_tuple
 
 __all__ = [
     "Cache",
@@ -12,4 +12,5 @@ __all__ = [
     "reform_list",
     "to_hashable",
     "abl_cache",
+    "tab_data_to_tuple",
 ]

abl/utils/utils.py

@@ -154,4 +154,13 @@ def restore_from_hashable(x):
         return [restore_from_hashable(item) for item in x]
     return x
 
-
+def tab_data_to_tuple(X, y, reasoning_result = 0):
+    '''
+    Convert a tabular data to a tuple by adding a dimension to each element of X and y. The tuple contains three elements: data, label, and reasoning result.
+    If X is None, return None.
+    '''
+    if X is None:
+        return None
+    if len(X) != len(y):
+        raise ValueError("The length of X and y should be the same, but got {} and {}.".format(len(X), len(y)))
+    return ([[x] for x in X], [[y_item] for y_item in y], [reasoning_result] * len(y))

docs/Overview/Installation.rst

@@ -31,4 +31,4 @@ For Linux users:
 
     $ sudo apt-get install swi-prolog
 
-For Windows and Mac users, please refer to the `Swi-Prolog Download Page <https://www.swi-prolog.org/Download.html>`_.
+For Windows and Mac users, please refer to the `Swi-Prolog Install Guide <https://github.com/yuce/pyswip/blob/master/INSTALL.md>`_.

docs/README.rst

@@ -51,7 +51,7 @@ For Linux users:
 
     $ sudo apt-get install swi-prolog
 
-For Windows and Mac users, please refer to the `Swi-Prolog Download Page <https://www.swi-prolog.org/Download.html>`_.
+For Windows and Mac users, please refer to the `Swi-Prolog Install Guide <https://github.com/yuce/pyswip/blob/master/INSTALL.md>`_.
 
 References
 ----------

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

@@ -8,14 +8,12 @@ from abl.bridge import SimpleBridge
 from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.learning import ABLModel
 from abl.reasoning import Reasoner
-from abl.utils import ABLLogger, confidence_dist, print_log
+from abl.utils import ABLLogger, confidence_dist, print_log, tab_data_to_tuple
 
 from get_dataset import load_and_preprocess_dataset, split_dataset
 from kb import ZooKB
 
 
-def transform_tab_data(X, y):
-    return ([[x] for x in X], [[y_item] for y_item in y], [0] * len(y))
 
 def consitency(data_example, candidates, candidate_idxs, reasoning_results):
     pred_prob = data_example.pred_prob
@@ -30,21 +28,31 @@ 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)
-    test_data = transform_tab_data(X_test, y_test)
-    train_data = transform_tab_data(X_unlabel, y_unlabel)
+    label_data = tab_data_to_tuple(X_label, y_label)
+    test_data = tab_data_to_tuple(X_test, y_test)
+    train_data = tab_data_to_tuple(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 +60,17 @@ 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)

examples/zoo/zoo.ipynb

@@ -27,7 +27,7 @@
     "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.learning import ABLModel\n",
     "from abl.reasoning import Reasoner\n",
-    "from abl.utils import ABLLogger, confidence_dist, print_log\n",
+    "from abl.utils import ABLLogger, confidence_dist, print_log, tab_data_to_tuple\n",
     "\n",
     "from get_dataset import load_and_preprocess_dataset, split_dataset\n",
     "from kb import ZooKB"
@@ -106,11 +106,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def transform_tab_data(X, y):\n",
-    "    return ([[x] for x in X], [[y_item] for y_item in y], [0] * len(y))\n",
-    "label_data = transform_tab_data(X_label, y_label)\n",
-    "test_data = transform_tab_data(X_test, y_test)\n",
-    "train_data = transform_tab_data(X_unlabel, y_unlabel)"
+    "label_data = tab_data_to_tuple(X_label, y_label, reasoning_result = 0)\n",
+    "test_data = tab_data_to_tuple(X_test, y_test, reasoning_result = 0)\n",
+    "train_data = tab_data_to_tuple(X_unlabel, y_unlabel, reasoning_result = 0)"
    ]
   },
   {

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 name = "abl"
 version = "0.1.4"
 authors = [
-  { name="LAMDA 2023" },
+  { name="LAMDA 2024" },
 ]
 description = "Abductive learning package project"
 readme = "README.md"
@@ -25,12 +25,12 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
 ]
 dependencies = [
-    "numpy",
-    "pyswip==0.2.9",
-    "torch",
-    "torchvision",
-    "zoopt",
-    "termcolor"
+    "numpy>=1.15.0",
+    "pyswip>=0.2.9",
+    "torch>=1.11.0",
+    "torchvision>=0.12.0",
+    "zoopt>=0.3.0",
+    "termcolor>=2.3.0"
 ]
 
 [project.urls]

requirements.txt

@@ -1,6 +1,6 @@
-numpy
-pyswip==0.2.9
-torch
-torchvision
-zoopt
-termcolor
+numpy>=1.15.0,
+pyswip>=0.2.9,
+torch>=1.11.0,
+torchvision>=0.12.0,
+zoopt>=0.3.0,
+termcolor>=2.3.0