[MNT] resolve comments in evaluation folder

abl/evaluation/__init__.py

@@ -1,5 +1,5 @@
 from .base_metric import BaseMetric
-from .semantics_metric import SemanticsMetric
+from .reasoning_metric import ReasoningMetric
 from .symbol_metric import SymbolMetric
 
-__all__ = ["BaseMetric", "SemanticsMetric", "SymbolMetric"]
+__all__ = ["BaseMetric", "ReasoningMetric", "SymbolMetric"]

abl/evaluation/semantics_metric.py → abl/evaluation/reasoning_metric.py

@@ -5,7 +5,7 @@ from ..structures import ListData
 from .base_metric import BaseMetric
 
 
-class SemanticsMetric(BaseMetric):
+class ReasoningMetric(BaseMetric):
     def __init__(self, kb: KBBase = None, prefix: Optional[str] = None) -> None:
         super().__init__(prefix)
         self.kb = kb
@@ -15,7 +15,9 @@ class SemanticsMetric(BaseMetric):
         y_list = data_samples.Y
         x_list = data_samples.X
         for pred_pseudo_label, y, x in zip(pred_pseudo_label_list, y_list, x_list):
-            if self.kb._check_equal(self.kb.logic_forward(pred_pseudo_label, *(x,) if self.kb._num_args == 2 else ()), y):
+            if self.kb._check_equal(
+                self.kb.logic_forward(pred_pseudo_label, *(x,) if self.kb._num_args == 2 else ()), y
+            ):
                 self.results.append(1)
             else:
                 self.results.append(0)
@@ -23,5 +25,5 @@ class SemanticsMetric(BaseMetric):
     def compute_metrics(self) -> dict:
         results = self.results
         metrics = dict()
-        metrics["semantics_accuracy"] = sum(results) / len(results)
+        metrics["reasoning_accuracy"] = sum(results) / len(results)
         return metrics

abl/evaluation/symbol_metric.py

@@ -1,5 +1,7 @@
 from typing import Optional
 
+import numpy as np
+
 from ..structures import ListData
 from .base_metric import BaseMetric
 
@@ -15,10 +17,8 @@ class SymbolMetric(BaseMetric):
         if not len(pred_pseudo_label_list) == len(gt_pseudo_label_list):
             raise ValueError("lengthes of pred_pseudo_label and gt_pseudo_label should be equal")
 
-        correct_num = 0
-        for pred_pseudo_label, gt_pseudo_label in zip(pred_pseudo_label_list, gt_pseudo_label_list):
-            if pred_pseudo_label == gt_pseudo_label:
-                correct_num += 1
+        correct_num = np.sum(np.array(pred_pseudo_label_list) == np.array(gt_pseudo_label_list))
+
         self.results.append((correct_num, len(pred_pseudo_label_list)))
 
     def compute_metrics(self) -> dict:

docs/API/abl.evaluation.rst

@@ -13,4 +13,4 @@ Metrics
 
    BaseMetric
    SymbolMetric
-   SemanticsMetric
+   ReasoningMetric

docs/Intro/Basics.rst

@@ -26,7 +26,7 @@ It first features class ``ListData`` (inherited from base class
 ``BaseDataElement``), which defines the data structures used in
 Abductive Learning, and comprises common data operations like insertion,
 deletion, retrieval, slicing, etc. Additionally, a series of Evaluation
-Metrics, including class ``SymbolMetric`` and ``SemanticsMetric`` (both
+Metrics, including class ``SymbolMetric`` and ``ReasoningMetric`` (both
 specialized metrics derived from base class ``BaseMetric``), outline
 methods for evaluating model quality from a data perspective.
 

docs/Intro/Evaluation.rst

@@ -18,7 +18,7 @@ To customize our own metrics, we need to inherit from ``BaseMetric`` and impleme
 - The ``compute_metrics`` method uses all the information saved in ``self.results`` to calculate and return a dict that holds the evaluation results. 
 
 Besides, we can assign a ``str`` to the ``prefix`` argument of the ``__init__`` method. This string is automatically prefixed to the output metric names. For example, if we set ``prefix="mnist_add"``, the output metric name will be ``character_accuracy``.
-We provide two basic metrics, namely ``SymbolMetric`` and ``SemanticsMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively. Using ``SymbolMetric`` as an example, the following code shows how to implement a custom metrics.
+We provide two basic metrics, namely ``SymbolMetric`` and ``ReasoningMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively. Using ``SymbolMetric`` as an example, the following code shows how to implement a custom metrics.
 
 .. code:: python
 

docs/Intro/Quick-Start.rst

@@ -162,13 +162,13 @@ Read more about `building the reasoning part <Reasoning.html>`_.
 Building Evaluation Metrics
 ---------------------------
 
-ABL-Package provides two basic metrics, namely ``SymbolMetric`` and ``SemanticsMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively.
+ABL-Package provides two basic metrics, namely ``SymbolMetric`` and ``ReasoningMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively.
 
 .. code:: python
 
-   from abl.evaluation import SemanticsMetric, SymbolMetric
+   from abl.evaluation import ReasoningMetric, SymbolMetric
 
-   metric_list = [SymbolMetric(prefix="mnist_add"), SemanticsMetric(kb=kb, prefix="mnist_add")]
+   metric_list = [SymbolMetric(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
 
 Read more about `building evaluation metrics <Evaluation.html>`_
 
@@ -203,7 +203,7 @@ Training log would be similar to this:
    abl - INFO - loop(train) [1/5] segment(train) [3/3] 
    abl - INFO - model loss: 1.33183
    abl - INFO - Evaluation start: loop(val) [1]
-   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.450 mnist_add/semantics_accuracy: 0.237 
+   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.450 mnist_add/reasoning_accuracy: 0.237 
    abl - INFO - Saving model: loop(save) [1]
    abl - INFO - Checkpoints will be saved to results/work_dir/weights/model_checkpoint_loop_1.pth
    abl - INFO - loop(train) [2/5] segment(train) [1/3] 
@@ -213,7 +213,7 @@ Training log would be similar to this:
    abl - INFO - loop(train) [2/5] segment(train) [3/3] 
    abl - INFO - model loss: 0.11282
    abl - INFO - Evaluation start: loop(val) [2]
-   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.976 mnist_add/semantics_accuracy: 0.954 
+   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.976 mnist_add/reasoning_accuracy: 0.954 
    abl - INFO - Saving model: loop(save) [2]
    abl - INFO - Checkpoints will be saved to results/work_dir/weights/model_checkpoint_loop_2.pth
    ...
@@ -224,9 +224,9 @@ Training log would be similar to this:
    abl - INFO - loop(train) [5/5] segment(train) [3/3] 
    abl - INFO - model loss: 0.03423
    abl - INFO - Evaluation start: loop(val) [5]
-   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.992 mnist_add/semantics_accuracy: 0.984 
+   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.992 mnist_add/reasoning_accuracy: 0.984 
    abl - INFO - Saving model: loop(save) [5]
    abl - INFO - Checkpoints will be saved to results/work_dir/weights/model_checkpoint_loop_5.pth
-   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.987 mnist_add/semantics_accuracy: 0.975 
+   abl - INFO - Evaluation ended, mnist_add/character_accuracy: 0.987 mnist_add/reasoning_accuracy: 0.975 
 
 Read more about `bridging machine learning and reasoning <Bridge.html>`_.

examples/hed/hed_example.ipynb

@@ -12,7 +12,7 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "\n",
-    "from abl.evaluation import SemanticsMetric, SymbolMetric\n",
+    "from abl.evaluation import ReasoningMetric, SymbolMetric\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from abl.reasoning import PrologKB, Reasoner\n",
     "from abl.utils import ABLLogger, print_log, reform_list\n",
@@ -210,7 +210,7 @@
    "outputs": [],
    "source": [
     "# Set up metrics\n",
-    "metric_list = [SymbolMetric(prefix=\"hed\"), SemanticsMetric(prefix=\"hed\")]"
+    "metric_list = [SymbolMetric(prefix=\"hed\"), ReasoningMetric(prefix=\"hed\")]"
    ]
   },
   {

examples/hwf/hwf_example.ipynb

@@ -14,7 +14,7 @@
     "from abl.reasoning import Reasoner, KBBase\n",
     "from abl.learning import BasicNN, ABLModel\n",
     "from abl.bridge import SimpleBridge\n",
-    "from abl.evaluation import SymbolMetric, SemanticsMetric\n",
+    "from abl.evaluation import SymbolMetric, ReasoningMetric\n",
     "from abl.utils import ABLLogger, print_log\n",
     "\n",
     "from examples.models.nn import SymbolNet\n",
@@ -146,7 +146,7 @@
    "outputs": [],
    "source": [
     "# Add metric\n",
-    "metric_list = [SymbolMetric(prefix=\"hwf\"), SemanticsMetric(kb=kb, prefix=\"hwf\")]"
+    "metric_list = [SymbolMetric(prefix=\"hwf\"), ReasoningMetric(kb=kb, prefix=\"hwf\")]"
    ]
   },
   {

examples/mnist_add/mnist_add_example.ipynb

@@ -12,7 +12,7 @@
     "import torch.nn as nn\n",
     "\n",
     "from abl.bridge import SimpleBridge\n",
-    "from abl.evaluation import SemanticsMetric, SymbolMetric\n",
+    "from abl.evaluation import ReasoningMetric, SymbolMetric\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from abl.reasoning import KBBase, Reasoner\n",
     "from abl.utils import ABLLogger, print_log\n",
@@ -138,7 +138,7 @@
    "outputs": [],
    "source": [
     "# Set up metrics\n",
-    "metric_list = [SymbolMetric(prefix=\"mnist_add\"), SemanticsMetric(kb=kb, prefix=\"mnist_add\")]"
+    "metric_list = [SymbolMetric(prefix=\"mnist_add\"), ReasoningMetric(kb=kb, prefix=\"mnist_add\")]"
    ]
   },
   {