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

abl/reasoning/kb.py

@@ -26,8 +26,8 @@ class KBBase(ABC):
         list so that each aligns with its corresponding index in the base model: the first with
         the 0th index, the second with the 1st, and so forth.
     max_err : float, optional
-        The upper tolerance limit when comparing the similarity between a pseudo-label example's
-        reasoning result and the ground truth. This is only applicable when the reasoning
+        The upper tolerance limit when comparing the similarity between the reasoning result of 
+        pseudo-labels and the ground truth. This is only applicable when the reasoning
         result is of a numerical type. This is particularly relevant for regression problems where
         exact matches might not be feasible. Defaults to 1e-10.
     use_cache : bool, optional
@@ -81,16 +81,16 @@ class KBBase(ABC):
     @abstractmethod
     def logic_forward(self, pseudo_label: List[Any], x: Optional[List[Any]] = None) -> Any:
         """
-        How to perform (deductive) logical reasoning, i.e. matching each pseudo-label example to
+        How to perform (deductive) logical reasoning, i.e. matching pseudo-labels to
         their reasoning result. Users are required to provide this.
 
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example.
+            Pseudo-labels of an example.
         x : Optional[List[Any]]
-            The corresponding input example. If deductive logical reasoning does not require any 
-            information from the input, the overridden function provided by the user can omit 
+            The example. If deductive logical reasoning does not require any 
+            information from the example, the overridden function provided by the user can omit 
             this parameter.
         
         Returns
@@ -113,11 +113,11 @@ class KBBase(ABC):
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example (to be revised by abductive reasoning).
+            Pseudo-labels of an example (to be revised by abductive reasoning).
         y : Any
             Ground truth of the reasoning result for the example.
         x : List[Any]
-            The corresponding input example. If the information from the input 
+            The example. If the information from the example
             is not required in the reasoning process, then this parameter will not have 
             any effect.
         max_revision_num : int
@@ -129,7 +129,7 @@ class KBBase(ABC):
         -------
         Tuple[List[List[Any]], List[Any]]
             A tuple of two element. The first element is a list of candidate revisions, i.e. revised
-            pseudo-label examples that are compatible with the knowledge base. The second element is 
+            pseudo-labels of the example. that are compatible with the knowledge base. The second element is 
             a list of reasoning results corresponding to each candidate, i.e., the outcome of the 
             logic_forward function.
         """
@@ -161,26 +161,26 @@ class KBBase(ABC):
         revision_idx: List[int],
     ) -> List[List[Any]]:
         """
-        Revise the pseudo-label example at specified index positions.
+        Revise the pseudo-labels at specified index positions.
 
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example (to be revised).
+            Pseudo-labels of an example (to be revised).
         y : Any
             Ground truth of the reasoning result for the example.
         x : List[Any]
-            The corresponding input example. If the information from the input 
+            The example. If the information from the example
             is not required in the reasoning process, then this parameter will not have 
             any effect.
         revision_idx : List[int]
-            A list specifying indices of where revisions should be made to the pseudo-label example.
+            A list specifying indices of where revisions should be made to the pseudo-labels.
 
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
             A tuple of two element. The first element is a list of candidate revisions, i.e. revised
-            pseudo-label examples that are compatible with the knowledge base. The second element is 
+            pseudo-labels of the example that are compatible with the knowledge base. The second element is 
             a list of reasoning results corresponding to each candidate, i.e., the outcome of the 
             logic_forward function.
         """
@@ -203,7 +203,7 @@ class KBBase(ABC):
         x: List[Any],
     ) -> List[List[Any]]:
         """
-        For a specified number of labels in a pseudo-label example to revise, iterate through
+        For a specified number of labels in a pseudo-labels to revise, iterate through
         all possible indices to find any candidates that are compatible with the knowledge base.
         """
         new_candidates, new_reasoning_results = [], []
@@ -224,31 +224,31 @@ class KBBase(ABC):
     ) -> List[List[Any]]:
         """
         Perform abductive reasoning by exhastive search. Specifically, begin with 0 and
-        continuously increase the number of labels in a pseudo-label example to revise, until
+        continuously increase the number of labels to revise, until
         candidates that are compatible with the knowledge base are found.
 
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example (to be revised).
+            Pseudo-labels of an example (to be revised).
         y : Any
             Ground truth of the reasoning result for the example.
         x : List[Any]
-            The corresponding input example. If the information from the input 
+            The example. If the information from the example
             is not required in the reasoning process, then this parameter will not have 
             any effect.
         max_revision_num : int
             The upper limit on the number of revisions.
         require_more_revision : int
             If larger than 0, then after having found any candidates compatible with the
-            knowledge base, continue to increase the number of labels in a pseudo-label example to
+            knowledge base, continue to increase the number of labels to
             revise to get more possible compatible candidates.
 
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
             A tuple of two element. The first element is a list of candidate revisions, i.e. revised
-            pseudo-label examples that are compatible with the knowledge base. The second element is 
+            pseudo-labels of the example that are compatible with the knowledge base. The second element is 
             a list of reasoning results corresponding to each candidate, i.e., the outcome of the 
             logic_forward function.
         """
@@ -291,7 +291,7 @@ class GroundKB(KBBase):
     pseudo_label_list : list
         Refer to class ``KBBase``.
     GKB_len_list : list
-        List of possible lengths for a pseudo-label example.
+        List of possible lengths for pseudo-labels of an example.
     max_err : float, optional
         Refer to class ``KBBase``.
 
@@ -364,11 +364,11 @@ class GroundKB(KBBase):
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example (to be revised by abductive reasoning).
+            Pseudo-labels of an example (to be revised by abductive reasoning).
         y : Any
             Ground truth of the reasoning result for the example.
         x : List[Any]
-            The corresponding input example (unused in GroundKB).
+            The example (unused in GroundKB).
         max_revision_num : int
             The upper limit on the number of revised labels for each example.
         require_more_revision : int
@@ -378,7 +378,7 @@ class GroundKB(KBBase):
         -------
         Tuple[List[List[Any]], List[Any]]
             A tuple of two element. The first element is a list of candidate revisions, i.e. revised
-            pseudo-label examples that are compatible with the knowledge base. The second element is 
+            pseudo-labels of THE example that are compatible with the knowledge base. The second element is 
             a list of reasoning results corresponding to each candidate, i.e., the outcome of the 
             logic_forward function.
         """
@@ -467,9 +467,14 @@ class PrologKB(KBBase):
         import pyswip
         
         super().__init__(pseudo_label_list)
-        self.pl_file = pl_file
+        
+        try:
+            import pyswip
+        except IndexError:
+            print("A Prolog-based knowledge base is used. You need to install Swi-Prolog from https://www.swi-prolog.org/Download.html")
+        
         self.prolog = pyswip.Prolog()
-
+        self.pl_file = pl_file
         if not os.path.exists(self.pl_file):
             raise FileNotFoundError(f"The Prolog file {self.pl_file} does not exist.")
         self.prolog.consult(self.pl_file)
@@ -484,7 +489,7 @@ class PrologKB(KBBase):
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example.
+            Pseudo-labels of an example.
         """
         result = list(self.prolog.query("logic_forward(%s, Res)." % pseudo_label))[0]["Res"]
         if result == "true":
@@ -526,7 +531,7 @@ class PrologKB(KBBase):
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example (to be revised by abductive reasoning).
+            Pseudo-labels of an example (to be revised by abductive reasoning).
         y : Any
             Ground truth of the reasoning result for the example.
         x : List[Any]
@@ -534,7 +539,7 @@ class PrologKB(KBBase):
             is not required in the reasoning process, then this parameter will not have 
             any effect.
         revision_idx : List[int]
-            A list specifying indices of where revisions should be made to the pseudo-label example.
+            A list specifying indices of where revisions should be made to the pseudo-labels.
 
         Returns
         -------
@@ -555,12 +560,12 @@ class PrologKB(KBBase):
         revision_idx: List[int],
     ) -> List[List[Any]]:
         """
-        Revise the pseudo-label example at specified index positions by querying Prolog.
+        Revise the pseudo-labels at specified index positions by querying Prolog.
 
         Parameters
         ----------
         pseudo_label : List[Any]
-            Pseudo-label example (to be revised).
+            Pseudo-labels of an example (to be revised).
         y : Any
             Ground truth of the reasoning result for the example.
         x : List[Any]
@@ -568,15 +573,15 @@ class PrologKB(KBBase):
             is not required in the reasoning process, then this parameter will not have 
             any effect.
         revision_idx : List[int]
-            A list specifying indices of where revisions should be made to the pseudo-label example.
+            A list specifying indices of where revisions should be made to the pseudo-labels.
 
         Returns
         -------
         Tuple[List[List[Any]], List[Any]]
-            A list of candidates, i.e. revised pseudo-label examples that are compatible with the
+            A list of candidates, i.e. revised pseudo-labels of the example that are compatible with the
             knowledge base.
             A tuple of two element. The first element is a list of candidate revisions, i.e. revised
-            pseudo-label examples that are compatible with the knowledge base. The second element is 
+            pseudo-labels of the example that are compatible with the knowledge base. The second element is 
             a list of reasoning results corresponding to each candidate, i.e., the outcome of the 
             logic_forward function.
         """

abl/reasoning/reasoner.py

@@ -283,7 +283,7 @@ class Reasoner:
         Returns
         -------
         List[Any]
-            A revised pseudo-label example through abductive reasoning, which is compatible
+            A revised pseudo-labels of the example through abductive reasoning, which is compatible
             with the knowledge base.
         """
         symbol_num = data_example.elements_num("pred_pseudo_label")

docs/Examples/HWF.rst

@@ -169,8 +169,8 @@ sklearn-style interface.
 
 .. code:: ipython3
 
-    # class of symbol may be one of ['0', '1', ..., '9', '+', '-', '*', '/'], total of 14 classes
-    cls = SymbolNet(num_classes=14, image_size=(45, 45, 1))
+    # class of symbol may be one of ['1', ..., '9', '+', '-', '*', '/'], total of 14 classes
+    cls = SymbolNet(num_classes=13, image_size=(45, 45, 1))
     loss_fn = nn.CrossEntropyLoss()
     optimizer = torch.optim.Adam(cls.parameters(), lr=0.001, betas=(0.9, 0.99))
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -297,16 +297,16 @@ reasoning in the knowledge base <kb-abd>` for details of abductive reasoning.
 
 .. code:: ipython3
 
-    pseudo_label_example = ["1", "-", "2", "*", "5"]
-    reasoning_result = kb.logic_forward(pseudo_label_example)
-    print(f"Reasoning result of pseudo-label example {pseudo_label_example} is {reasoning_result}.")
+    pseudo_labels = ["1", "-", "2", "*", "5"]
+    reasoning_result = kb.logic_forward(pseudo_labels)
+    print(f"Reasoning result of pseudo-labels {pseudo_labels} is {reasoning_result}.")
 
 
 Out:
     .. code:: none
         :class: code-out
 
-        Reasoning result of pseudo-label example ['1', '-', '2', '*', '5'] is -9.
+        Reasoning result of pseudo-labels ['1', '-', '2', '*', '5'] is -9.
     
 
 .. note::
@@ -319,8 +319,8 @@ Out:
     Also, when building the knowledge base, we can also set the
     ``max_err`` parameter during initialization, which is shown in the
     ``examples/hwf/main.py`` file. This parameter specifies the upper tolerance limit
-    when comparing the similarity between a pseudo-label example’s reasoning
-    result and the ground truth during abductive reasoning, with a default
+    when comparing the similarity between the reasoning result of pseudo-labels and 
+    the ground truth during abductive reasoning, with a default
     value of 1e-10.
 
 Then, we create a reasoner by instantiating the class ``Reasoner``. Due

docs/Examples/MNISTAdd.rst

@@ -254,16 +254,16 @@ reasoning in the knowledge base <kb-abd>` for details of abductive reasoning.
 
 .. code:: ipython3
 
-    pseudo_label_example = [1, 2]
-    reasoning_result = kb.logic_forward(pseudo_label_example)
-    print(f"Reasoning result of pseudo-label example {pseudo_label_example} is {reasoning_result}.")
+    pseudo_labels = [1, 2]
+    reasoning_result = kb.logic_forward(pseudo_labels)
+    print(f"Reasoning result of pseudo-labels {pseudo_labels} is {reasoning_result}.")
 
 
 Out:
     .. code:: none
         :class: code-out
 
-        Reasoning result of pseudo-label example [1, 2] is 3.
+        Reasoning result of pseudo-labels [1, 2] is 3.
     
 
 .. note::

docs/Examples/Zoo.rst

@@ -227,22 +227,22 @@ Out:
     .. code:: none
         :class: code-out
 
-        abl - INFO - Abductive Learning on the Zoo example.
+        abl - INFO - Abductive Learning on the ZOO example.
         abl - INFO - ------- Use labeled data to pretrain the model -----------
         abl - INFO - ------- Test the initial model -----------
-        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.935 zoo/reasoning_accuracy: 0.935 
+        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.903 zoo/reasoning_accuracy: 0.903 
         abl - INFO - ------- Use ABL to train the model -----------
         abl - INFO - loop(train) [1/3] segment(train) [1/1] 
         abl - INFO - Evaluation start: loop(val) [1]
-        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.984 zoo/reasoning_accuracy: 1.000 
+        abl - INFO - Evaluation ended, zoo/character_accuracy: 1.000 zoo/reasoning_accuracy: 1.000 
         abl - INFO - loop(train) [2/3] segment(train) [1/1] 
         abl - INFO - Evaluation start: loop(val) [2]
-        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.984 zoo/reasoning_accuracy: 1.000 
+        abl - INFO - Evaluation ended, zoo/character_accuracy: 1.000 zoo/reasoning_accuracy: 1.000 
         abl - INFO - loop(train) [3/3] segment(train) [1/1] 
         abl - INFO - Evaluation start: loop(val) [3]
-        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.984 zoo/reasoning_accuracy: 1.000 
+        abl - INFO - Evaluation ended, zoo/character_accuracy: 1.000 zoo/reasoning_accuracy: 1.000 
         abl - INFO - ------- Test the final model -----------
-        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.903 zoo/reasoning_accuracy: 0.935 
+        abl - INFO - Evaluation ended, zoo/character_accuracy: 0.968 zoo/reasoning_accuracy: 0.968 
         
 
 We may see from the results, after undergoing training with ABL, the

docs/Intro/Basics.rst

@@ -80,7 +80,7 @@ To implement this process, the following five steps are necessary:
 3. :green:`Build the` :green-bold:`reasoning` :green:`part`
 
     Define a knowledge base by building a subclass of ``KBBase``, specifying how to 
-    process pseudo-label examples to reasoning results.
+    process pseudo-labels to reasoning results.
     Also, create a ``Reasoner`` for minimizing inconsistencies 
     between the knowledge base and data.
 

docs/Intro/Datasets.rst

@@ -28,7 +28,7 @@ ABL-Package requires user data to be either structured as a tuple ``(X, gt_pseud
 
 - ``gt_pseudo_label``: List[List[Any]], optional
     
-    A list of sublists with each sublist representing a ground-truth pseudo-label example. Each example consists of ground-truth pseudo-labels for each **instance** within a example of ``X``. 
+    A list of sublists with each sublist representing ground-truth pseudo-labels of an example. Each pseudo-label in the sublist serves as ground-truth for each **instance** within the example. 
     
     .. note::
 

docs/Intro/Reasoning.rst

@@ -42,15 +42,15 @@ and override the ``logic_forward`` function:
 -  ``pseudo_label_list`` is the list of possible pseudo-labels (also,
    the output of the machine learning model).
 -  ``logic_forward`` defines how to perform (deductive) reasoning,
-   i.e. matching each pseudo-label example to its reasoning result. 
+   i.e. matching each pseudo-labels to its reasoning result. 
 
 .. note::
 
    Generally, the overridden function ``logic_forward`` provided by the user accepts 
-   only one parameter, ``pseudo_label`` (a pseudo-label example). However, for certain 
+   only one parameter, ``pseudo_label`` (pseudo-labels of an example). However, for certain 
    scenarios, deductive reasoning in the knowledge base may necessitate information 
    from the input. In these scenarios, ``logic_forward`` can also accept two parameters: 
-   ``pseudo_label`` and ``x``.
+   ``pseudo_label`` and ``x``. See examples in `Zoo <../Examples/Zoo.html>`_.
 
 After that, other operations, including how to perform abductive
 reasoning, will be **automatically** set up.
@@ -60,7 +60,7 @@ MNIST Addition example
 
 As an example, the ``pseudo_label_list`` passed in MNIST Addition is all the
 possible digits, namely, ``[0,1,2,...,9]``, and the ``logic_forward``
-should be: “Add the two labels in the pseudo-label example to get the result.”. Therefore, the
+should be: “Add the two pseudo-labels to get the result.”. Therefore, the
 construction of the KB (``add_kb``) for MNIST Addition would be:
 
 .. code:: python
@@ -78,15 +78,15 @@ and (deductive) reasoning in ``add_kb`` would be:
 
 .. code:: python
 
-   pseudo_label_example = [1, 2]
-   reasoning_result = add_kb.logic_forward(pseudo_label_example)
-   print(f"Reasoning result of pseudo-label example {pseudo_label_example} is {reasoning_result}.")
+   pseudo_labels = [1, 2]
+   reasoning_result = add_kb.logic_forward(pseudo_labels)
+   print(f"Reasoning result of pseudo-labels {pseudo_labels} is {reasoning_result}.")
 
 Out:
    .. code:: none
       :class: code-out
 
-      Reasoning result of pseudo-label example [1, 2] is 3
+      Reasoning result of pseudo-labels [1, 2] is 3
 
 .. _other-par:
 
@@ -97,19 +97,21 @@ We can also pass the following parameters in the ``__init__`` function when buil
 knowledge base:
 
 -  ``max_err`` (float, optional), specifying the upper tolerance limit
-   when comparing the similarity between a pseudo-label example's reasoning result
+   when comparing the similarity between the reasoning result of pseudo-labels 
    and the ground truth during abductive reasoning. This is only
    applicable when the reasoning result is of a numerical type. This is
    particularly relevant for regression problems where exact matches
    might not be feasible. Defaults to 1e-10. See :ref:`an example <kb-abd-2>`.
 -  ``use_cache`` (bool, optional), indicating whether to use cache to store
-   previous candidates (pseudo-label examples generated from abductive reasoning) 
+   previous candidates (pseudo-labels generated from abductive reasoning) 
    to speed up subsequent abductive reasoning operations. Defaults to True. 
    For more information of abductive reasoning, please refer to :ref:`this <kb-abd>`.
 -  ``cache_size`` (int, optional), specifying the maximum cache
    size. This is only operational when ``use_cache`` is set to True.
    Defaults to 4096.
 
+.. _prolog:
+
 Building a knowledge base from Prolog file
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -179,7 +181,7 @@ override the ``logic_forward`` function, and are allowed to pass other
 :ref:`optional parameters <other-par>`. Additionally, we are required pass the
 ``GKB_len_list`` parameter in the ``__init__`` function.
 
--  ``GKB_len_list`` is the list of possible lengths for a pseudo-label example.
+-  ``GKB_len_list`` is the list of possible lengths for pseudo-labels of an example.
 
 After that, other operations, including auto-construction of GKB, and
 how to perform abductive reasoning, will be **automatically** set up.
@@ -212,18 +214,18 @@ Performing abductive reasoning in the knowledge base
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 As mentioned in :ref:`What is Abductive Reasoning? <abd>`, abductive reasoning
-enables the inference of candidates (which are pseudo-label examples) as potential
+enables the inference of candidates (i.e., possible pseudo-labels) as potential
 explanations for the reasoning result. Also, in Abductive Learning where
-an observation (a pseudo-label example predicted by the learning part) is
+an observation (pseudo-labels of an example predicted by the learning part) is
 available, we aim to let the candidate do not largely revise the
-previously identified pseudo-label example.
+previously identified pseudo-labels.
 
 ``KBBase`` (also, ``GroundKB`` and ``PrologKB``) implement the method
 ``abduce_candidates(pseudo_label, y, x, max_revision_num, require_more_revision)``
 for performing abductive reasoning, where the parameters are:
 
--  ``pseudo_label``, the pseudo-label example to be revised by abductive
-   reasoning, usually generated by the learning part.
+-  ``pseudo_label``, pseudo-labels of an example, usually generated by the learning 
+   part. They are to be revised by abductive reasoning.
 -  ``y``, the ground truth of the reasoning result for the example. The
    returned candidates should be compatible with it.
 - ``x``, the corresponding input example. If the information from the input 
@@ -237,8 +239,8 @@ for performing abductive reasoning, where the parameters are:
    method will only output candidates with the minimum possible
    revisions.)
 
-And it return a list of candidates (i.e., revised pseudo-label examples) that
-are all compatible with ``y``.
+And it return a list of candidates (i.e., revised pseudo-labels of the example) 
+that are all compatible with ``y``.
 
 MNIST Addition example (cont.)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -320,6 +322,9 @@ specify:
    derived from the predicted probability in the data example. For
    “hamming”, it directly calculates the Hamming distance between the
    predicted pseudo-label in the data example and candidate.
+- ``idx_to_label`` (dict, optional), a mapping from index in the base model to label. 
+   If not provided, a default order-based index to label mapping is created. 
+   Defaults to None.
 
 The main method implemented by ``Reasoner`` is
 ``abduce(data_example)``, which obtains the most consistent candidate 

docs/Overview/Installation.rst

@@ -22,3 +22,7 @@ sequentially run following commands in your terminal/command line.
     $ git clone https://github.com/AbductiveLearning/ABL-Package.git
     $ cd ABL-Package
     $ pip install -v -e .
+
+(Optional) If the use of a `Prolog-based knowledge base <prolog>`_ is necessary, you will also need to install Swi-Prolog.
+
+`http://www.swi-prolog.org/build/unix.html <http://www.swi-prolog.org/build/unix.html>`_

examples/hwf/hwf.ipynb

@@ -211,8 +211,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# class of symbol may be one of ['0', '1', ..., '9', '+', '-', '*', '/'], total of 14 classes\n",
-    "cls = SymbolNet(num_classes=14, image_size=(45, 45, 1))\n",
+    "# class of symbol may be one of ['1', ..., '9', '+', '-', '*', '/'], total of 13 classes\n",
+    "cls = SymbolNet(num_classes=13, image_size=(45, 45, 1))\n",
     "loss_fn = nn.CrossEntropyLoss()\n",
     "optimizer = torch.optim.Adam(cls.parameters(), lr=0.001)\n",
     "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
@@ -244,7 +244,7 @@
      "output_type": "stream",
      "text": [
       "Predicted class index for a batch of 32 instances: ndarray with shape (32,)\n",
-      "Predicted class probabilities for a batch of 32 instances: ndarray with shape (32, 14)\n"
+      "Predicted class probabilities for a batch of 32 instances: ndarray with shape (32, 13)\n"
      ]
     }
    ],
@@ -294,7 +294,7 @@
       "the first element is a ndarray of shape (3,), and the second element is a ndarray of shape (5,).\n",
       "\n",
       "Predicted class probabilities for the 100 data examples: a list of length 2, \n",
-      "the first element is a ndarray of shape (3, 14), and the second element is a ndarray of shape (5, 14).\n"
+      "the first element is a ndarray of shape (3, 13), and the second element is a ndarray of shape (5, 13).\n"
      ]
     }
    ],
@@ -378,14 +378,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reasoning result of pseudo-label example ['1', '-', '2', '*', '5'] is -9.\n"
+      "Reasoning result of pseudo-labels ['1', '-', '2', '*', '5'] is -9.\n"
      ]
     }
    ],
    "source": [
-    "pseudo_label_example = [\"1\", \"-\", \"2\", \"*\", \"5\"]\n",
-    "reasoning_result = kb.logic_forward(pseudo_label_example)\n",
-    "print(f\"Reasoning result of pseudo-label example {pseudo_label_example} is {reasoning_result}.\")"
+    "pseudo_labels = [\"1\", \"-\", \"2\", \"*\", \"5\"]\n",
+    "reasoning_result = kb.logic_forward(pseudo_labels)\n",
+    "print(f\"Reasoning result of pseudo-labels {pseudo_labels} is {reasoning_result}.\")"
    ]
   },
   {
@@ -394,7 +394,7 @@
    "source": [
     "Note: In addition to building a knowledge base based on `KBBase`, we can also establish a knowledge base with a ground KB using `GroundKB`. The corresponding code can be found in the `main.py` file. Those interested are encouraged to examine it for further insights.\n",
     "\n",
-    "Note: Also, when building the knowledge base, we can also set the `max_err` parameter during initialization, which is shown in the `main.py` file. This parameter specifies the upper tolerance limit when comparing the similarity between a pseudo-label example’s reasoning result and the ground truth during abductive reasoning, with a default value of 1e-10."
+    "Note: Also, when building the knowledge base, we can also set the `max_err` parameter during initialization, which is shown in the `main.py` file. This parameter specifies the upper tolerance limit when comparing the similarity between the reasoning result of pseudo-labels and the ground truth during abductive reasoning, with a default value of 1e-10."
    ]
   },
   {

examples/hwf/main.py

@@ -120,7 +120,7 @@ def main():
 
     ### Building the Learning Part
     # Build necessary components for BasicNN
-    cls = SymbolNet(num_classes=14, image_size=(45, 45, 1))
+    cls = SymbolNet(num_classes=13, image_size=(45, 45, 1))
     loss_fn = nn.CrossEntropyLoss()
     optimizer = torch.optim.Adam(cls.parameters(), lr=args.lr)
     use_cuda = not args.no_cuda and torch.cuda.is_available()

examples/mnist_add/mnist_add.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -125,7 +125,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,21 +324,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Reasoning result of pseudo-label example [1, 2] is 3.\n"
+      "Reasoning result of pseudo-labels [1, 2] is 3.\n"
      ]
     }
    ],
    "source": [
-    "pseudo_label_example = [1, 2]\n",
-    "reasoning_result = kb.logic_forward(pseudo_label_example)\n",
-    "print(f\"Reasoning result of pseudo-label example {pseudo_label_example} is {reasoning_result}.\")"
+    "pseudo_labels = [1, 2]\n",
+    "reasoning_result = kb.logic_forward(pseudo_labels)\n",
+    "print(f\"Reasoning result of pseudo-labels {pseudo_labels} is {reasoning_result}.\")"
    ]
   },
   {
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [

examples/zoo/zoo.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ZOO\n",
+    "# Zoo\n",
     "\n",
     "This notebook shows an implementation of [Zoo](https://archive.ics.uci.edu/dataset/111/zoo). In this task, attributes of animals (such as presence of hair, eggs, etc.) and their targets (the animal class they belong to) are given, along with a knowledge base which contain information about the relations between attributes and targets, e.g., Implies(milk == 1, mammal == 1). \n",
     "\n",
@@ -328,7 +328,7 @@
    ],
    "source": [
     "# Build logger\n",
-    "print_log(\"Abductive Learning on the ZOO example.\", logger=\"current\")\n",
+    "print_log(\"Abductive Learning on the Zoo example.\", logger=\"current\")\n",
     "log_dir = ABLLogger.get_current_instance().log_dir\n",
     "weights_dir = osp.join(log_dir, \"weights\")\n",
     "\n",