[MNT] change SymbolMetric to SymbolAccuracy

abl/data/evaluation/__init__.py

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

abl/data/evaluation/symbol_metric.py → abl/data/evaluation/symbol_accuracy.py

@@ -6,7 +6,7 @@ from ..structures import ListData
 from .base_metric import BaseMetric
 
 
-class SymbolMetric(BaseMetric):
+class SymbolAccuracy(BaseMetric):
     """
     A metrics class for evaluating symbol-level accuracy.
 

docs/Examples/HED.rst

@@ -32,7 +32,7 @@ model.
     from examples.models.nn import SymbolNet
     from abl.learning import ABLModel, BasicNN
     from examples.hed.reasoning import HedKB, HedReasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolMetric
+    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
     from examples.hed.bridge import HedBridge
 
@@ -259,7 +259,7 @@ Building Evaluation Metrics
 
 Next, we set up evaluation metrics. These metrics will be used to
 evaluate the model performance during training and testing.
-Specifically, we use ``SymbolMetric`` and ``ReasoningMetric``, which are
+Specifically, we use ``SymbolAccuracy`` and ``ReasoningMetric``, which are
 used to evaluate the accuracy of the machine learning model’s
 predictions and the accuracy of the final reasoning results,
 respectively.
@@ -267,7 +267,7 @@ respectively.
 .. code:: ipython3
 
     # Set up metrics
-    metric_list = [SymbolMetric(prefix="hed"), ReasoningMetric(kb=kb, prefix="hed")]
+    metric_list = [SymbolAccuracy(prefix="hed"), ReasoningMetric(kb=kb, prefix="hed")]
 
 Bridge Learning and Reasoning
 -----------------------------

docs/Examples/HWF.rst

@@ -30,7 +30,7 @@ machine learning model.
     from examples.models.nn import SymbolNet
     from abl.learning import ABLModel, BasicNN
     from abl.reasoning import KBBase, Reasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolMetric
+    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
     from abl.bridge import SimpleBridge
 
@@ -353,14 +353,14 @@ Building Evaluation Metrics
 
 Next, we set up evaluation metrics. These metrics will be used to
 evaluate the model performance during training and testing.
-Specifically, we use ``SymbolMetric`` and ``ReasoningMetric``, which are
+Specifically, we use ``SymbolAccuracy`` and ``ReasoningMetric``, which are
 used to evaluate the accuracy of the machine learning model’s
 predictions and the accuracy of the final reasoning results,
 respectively.
 
 .. code:: ipython3
 
-    metric_list = [SymbolMetric(prefix="hwf"), ReasoningMetric(kb=kb, prefix="hwf")]
+    metric_list = [SymbolAccuracy(prefix="hwf"), ReasoningMetric(kb=kb, prefix="hwf")]
 
 Bridge Learning and Reasoning
 -----------------------------

docs/Examples/MNISTAdd.rst

@@ -28,7 +28,7 @@ machine learning model.
     from examples.models.nn import LeNet5
     from abl.learning import ABLModel, BasicNN
     from abl.reasoning import KBBase, Reasoner
-    from abl.data.evaluation import ReasoningMetric, SymbolMetric
+    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
     from abl.utils import ABLLogger, print_log
     from abl.bridge import SimpleBridge
 
@@ -300,14 +300,14 @@ Building Evaluation Metrics
 
 Next, we set up evaluation metrics. These metrics will be used to
 evaluate the model performance during training and testing.
-Specifically, we use ``SymbolMetric`` and ``ReasoningMetric``, which are
+Specifically, we use ``SymbolAccuracy`` and ``ReasoningMetric``, which are
 used to evaluate the accuracy of the machine learning model’s
 predictions and the accuracy of the final reasoning results,
 respectively.
 
 .. code:: ipython3
 
-    metric_list = [SymbolMetric(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
+    metric_list = [SymbolAccuracy(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
 
 Bridge Learning and Reasoning
 -----------------------------

docs/Intro/Basics.rst

@@ -26,7 +26,7 @@ It first features class ``ListData`` (derived 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 ``ReasoningMetric`` (both
+Metrics, including class ``SymbolAccuracy`` and ``ReasoningMetric`` (both
 specialized metrics derived from base class ``BaseMetric``), outline
 methods for evaluating model quality from a data perspective.
 

docs/Intro/Evaluation.rst

@@ -15,7 +15,7 @@ In this section, we will look at how to build evaluation metrics.
 .. code:: python
 
     # Import necessary modules
-    from abl.data.evaluation import BaseMetric, SymbolMetric, ReasoningMetric
+    from abl.data.evaluation import BaseMetric, SymbolAccuracy, ReasoningMetric
 
 ABL-Package seperates the evaluation process from model training and testing as an independent class, ``BaseMetric``. The training and testing processes are implemented in the ``BaseBridge`` class, so metrics are used by this class and its sub-classes. After building a ``bridge`` with a list of ``BaseMetric`` instances, these metrics will be used by the ``bridge.valid`` method to evaluate the model performance during training and testing. 
 
@@ -25,11 +25,11 @@ 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__`` function. 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 ``ReasoningMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the final reasoning results, respectively. Using ``SymbolMetric`` as an example, the following code shows how to implement a custom metrics.
+We provide two basic metrics, namely ``SymbolAccuracy`` and ``ReasoningMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the final reasoning results, respectively. Using ``SymbolAccuracy`` as an example, the following code shows how to implement a custom metrics.
 
 .. code:: python
 
-    class SymbolMetric(BaseMetric):
+    class SymbolAccuracy(BaseMetric):
         def __init__(self, prefix: Optional[str] = None) -> None:
             # prefix is used to distinguish different metrics
             super().__init__(prefix)

docs/Intro/Quick-Start.rst

@@ -107,13 +107,13 @@ Read more about `building the reasoning part <Reasoning.html>`_.
 Building Evaluation Metrics
 ---------------------------
 
-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.
+ABL-Package provides two basic metrics, namely ``SymbolAccuracy`` 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.data.evaluation import ReasoningMetric, SymbolMetric
+   from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 
-   metric_list = [SymbolMetric(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
+   metric_list = [SymbolAccuracy(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
 
 Read more about `building evaluation metrics <Evaluation.html>`_
 

examples/hed/hed.ipynb

@@ -26,7 +26,7 @@
     "from examples.models.nn import SymbolNet\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from examples.hed.reasoning import HedKB, HedReasoner\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolMetric\n",
+    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log\n",
     "from examples.hed.bridge import HedBridge"
    ]
@@ -335,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolMetric` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
+    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolAccuracy` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
    ]
   },
   {
@@ -345,7 +345,7 @@
    "outputs": [],
    "source": [
     "# Set up metrics\n",
-    "metric_list = [SymbolMetric(prefix=\"hed\"), ReasoningMetric(kb=kb, prefix=\"hed\")]"
+    "metric_list = [SymbolAccuracy(prefix=\"hed\"), ReasoningMetric(kb=kb, prefix=\"hed\")]"
    ]
   },
   {

examples/hwf/hwf.ipynb

@@ -27,7 +27,7 @@
     "from examples.models.nn import SymbolNet\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from abl.reasoning import KBBase, Reasoner\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolMetric\n",
+    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log\n",
     "from abl.bridge import SimpleBridge"
    ]
@@ -434,7 +434,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolMetric` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
+    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolAccuracy` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
    ]
   },
   {
@@ -443,7 +443,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_list = [SymbolMetric(prefix=\"hwf\"), ReasoningMetric(kb=kb, prefix=\"hwf\")]"
+    "metric_list = [SymbolAccuracy(prefix=\"hwf\"), ReasoningMetric(kb=kb, prefix=\"hwf\")]"
    ]
   },
   {

examples/hwf/main.py

@@ -10,7 +10,7 @@ from examples.hwf.datasets import get_dataset
 from examples.models.nn import SymbolNet
 from abl.learning import ABLModel, BasicNN
 from abl.reasoning import KBBase, GroundKB, Reasoner
-from abl.data.evaluation import ReasoningMetric, SymbolMetric
+from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.utils import ABLLogger, print_log
 from abl.bridge import SimpleBridge
 
@@ -152,7 +152,7 @@ def main():
     )
 
     ### Building Evaluation Metrics
-    metric_list = [SymbolMetric(prefix="hwf"), ReasoningMetric(kb=kb, prefix="hwf")]
+    metric_list = [SymbolAccuracy(prefix="hwf"), ReasoningMetric(kb=kb, prefix="hwf")]
 
     ### Bridge Learning and Reasoning
     bridge = SimpleBridge(model, reasoner, metric_list)

examples/mnist_add/main.py

@@ -9,7 +9,7 @@ from examples.mnist_add.datasets import get_dataset
 from examples.models.nn import LeNet5
 from abl.learning import ABLModel, BasicNN
 from abl.reasoning import KBBase, GroundKB, PrologKB, Reasoner
-from abl.data.evaluation import ReasoningMetric, SymbolMetric
+from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.utils import ABLLogger, print_log
 from abl.bridge import SimpleBridge
 
@@ -117,7 +117,7 @@ def main():
     )
 
     ### Building Evaluation Metrics
-    metric_list = [SymbolMetric(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
+    metric_list = [SymbolAccuracy(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
 
     ### Bridge Learning and Reasoning
     bridge = SimpleBridge(model, reasoner, metric_list)

examples/mnist_add/mnist_add.ipynb

@@ -26,7 +26,7 @@
     "from examples.models.nn import LeNet5\n",
     "from abl.learning import ABLModel, BasicNN\n",
     "from abl.reasoning import KBBase, Reasoner\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolMetric\n",
+    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log\n",
     "from abl.bridge import SimpleBridge"
    ]
@@ -122,7 +122,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAD1CAYAAADNj/Z6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAKUklEQVR4nO3df6zVdR3H8XPuDy4iCHcRUnKRQIFkOFHzV+Z0maVhM5dZyzY1JWCUbmk/tkwrXWXRQB0xayVUarOcthhmFJpL5GflYgTiryyV3wgIXrjnnv6x/kh4fy+de++58H48/n2d+z3ff+7hyXe7n1OuVqvVEgCQVkO9bwAAqC8xAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAguaauvvADDZf15H0AXfC7zgfqfQsHzWcH1F/RZ4cnAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBIrqneNwBAbg2DBoV7+Z1H99KdHNiLlw4L9/ahneHetCv+v/foezeGe2Xt+nCvlScDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHLOGehGjUMGh/tzd48M99XvnRfu7dWOcD971afDvVyuxvtDbwv3hko412zob9aFe2Xzlp69AaAunr9hYrj//do5vXQn9XP/J1rD/Sfjju3R9/dkAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEjOOQPdqLJjV7hfevxfw72zFJ8D0FxuDPelp9wb7g2lcvz+J8fv39OmTD033Ddc/PZwr2za1I13A3RV07Ft4d7x4kvh3m9HfP3jfj4t3C86b0V8gW6w8A+nhnv/zfHn67CV7eHe/Hj870OpFJ8zUytPBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJOfQoe7UWQnnXzx2Vrh//eN/7s67OeTc3fZYuH/oxCnh3vR7hw5BPTTP3xvu65aeGe6jv/hkTe+/tqaf7prRpSU9ev36HvnmyQAApCcGACA5MQAAyYkBAEhODABAcmIAAJITAwCQnHMGetG4b8R/DTtx54yarj/0tA3hfuaw58O9s1oO9wdXnhLuyy+cFe6DG/qHO9A3bfh8fEbK0uNmh/v4Z6d15+3QAzwZAIDkxAAAJCcGACA5MQAAyYkBAEhODABAcmIAAJJzzkAvqmzbFu7Hfq1nvy/76cJXxN+o/e4JO8L9tQ/GPz+4ID2/s2VCuPdbsibcO+PLAwfQ2Noa7rd8bn64b620h/vYufFO/XkyAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACTnnAG6bM+sN8J9ZNMRNV1/0ZffF+4tu5fXdH1g/9bMHBPulxy5ONzHzrsx3N+1rGfPUKF2ngwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyTlngP+qnHdyuP903B0FV4jPGViwe3C4H7lmY7h3FLw7sH8v33hWuD9+/u0FVxgYrsfNXBfulYKrU3+eDABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDkxAADJOWcgkYb+/cP9nnnxOQJDG+NzBHZX94b7HdMuD/fm51eGO7B/uy47PdxXXD873FvK8TkCReauejjcZ286J9yX3faecB/w4NKDvicOjicDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHLOGUhkz6+Hh3vROQJFzllxdbgPX+QcAfh/NI04Jtx/9r2ZBVdoCdevbpwY7le1LomvXo7ffeY7VoX7rG9uDfdH/zgm3Cubt8Q3QCFPBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDknDNwGNk446xwXzHhrnDvLLj+svb4j4lHTI3/Vrij4PrAAXTEvz1T1n8y3F9e1BbuI771ZLgvL50d7o1HDwv3zzzxVLhf3/pCuP/gugvDfdRN8TkIFPNkAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyYkBAEjOOQOHkPKkCeG+4ivxOQKN5YL2q8YnDVz74xnh3vZq/LfKcCja+PD4cB82cFfhNRo/9nq4V7ZtC/eOVzeEe8P74/cfUXopfkGNqrv3hPuWysCCK+wI136vxWecUDtPBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDknDPQl5xxYjjf98DccO8stcTXLzhH4PhfTg/3cd//S8H7w+Hn6EE7w/2R8QsKr3HC9Ph3q+22vn1GR7m5X7ivvTU+A2XK4CfC/Ve7jgr3th+tDvdKuNIVngwAQHJiAACSEwMAkJwYAIDkxAAAJCcGACA5MQAAyTlnoBc1DBgQ7ttvjr/zfGBDwTkCBS5/7oJwH3vDqnDv3Le3pveHQ9G61SPiF4wvvsa3r7wn3L/UdGW4D3kmPsVj4D/eCPddI/uHe0dLOdxPnx5/NjxyTHwGyu7O+LPj1juuCPdh2/v2OQyHA08GACA5MQAAyYkBAEhODABAcmIAAJITAwCQnBgAgOScM9CLXrnmpHBffuKdNV3/6b3xt3q/flF7uFedIwBvMX7O1nBfffGewmt85MiC/bNzDuaW3mJzJT6jZGhjwQ3UaFn7vnC/dvb14T78LucI1JsnAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByzhnoRo0njA33R2/8bsEV4u8c31eNzxGYfvN14T5k55KC9wf+V2XNM+E+efGMwmusv+CH4d5Yru3/ZT19jsD9O1vDff4l54f78DXOEejrPBkAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAknPOwEFoGDAg3J+9OT4noLUh3ouc+tTV4d423zkC0NvGXrWy8DWTHvpUuD992n3ddTv7dcUL54b7qgUnhPuoBzaEe2VdfBYDfZ8nAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAknPo0EF45ZqTwv1vZ99Z0/X/9EZzuI+aVnDwR03vDvSUEVe+HO7nT4oPFHvhw/3CvW1R/Nvf77cr4p+vPhnuPlsOf54MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMk5Z+A/TptY+JKFN9xe8IojwvWfHXvC/ZYZU8O9ZdPygvcH+qLK9tfCvXHxqnAfs7g77wbeypMBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDnnDLxp31Hx94WXSqXS0Mb4HIEiN/1rcri3LHSOAAC9z5MBAEhODABAcmIAAJITAwCQnBgAgOTEAAAkJwYAIDnnDHSjL7xyRrhv/WjxWQYA0Ns8GQCA5MQAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSc87Am5oXrSx8zeRjTil4xb6CfWOX7wcAeosnAwCQnBgAgOTEAAAkJwYAIDkxAADJiQEASE4MAEByYgAAkhMDAJCcGACA5MQAACQnBgAgOTEAAMmJAQBITgwAQHLlarVarfdNAAD148kAACQnBgAgOTEAAMmJAQBITgwAQHJiAACSEwMAkJwYAIDkxAAAJPdvx8SA9zSwVzUAAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -380,7 +380,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolMetric` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
+    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolAccuracy` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
    ]
   },
   {
@@ -389,7 +389,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_list = [SymbolMetric(prefix=\"mnist_add\"), ReasoningMetric(kb=kb, prefix=\"mnist_add\")]"
+    "metric_list = [SymbolAccuracy(prefix=\"mnist_add\"), ReasoningMetric(kb=kb, prefix=\"mnist_add\")]"
    ]
   },
   {
@@ -420,7 +420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,13 +432,6 @@
     "bridge.train(train_data, loops=5, segment_size=1/3, save_interval=1, save_dir=weights_dir)\n",
     "bridge.test(test_data)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

examples/zoo/main.py

@@ -8,7 +8,7 @@ import openml
 
 from abl.learning import ABLModel
 from abl.reasoning import KBBase, Reasoner
-from abl.data.evaluation import ReasoningMetric, SymbolMetric
+from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 from abl.bridge import SimpleBridge
 from abl.utils.utils import confidence_dist
 from abl.utils import ABLLogger, print_log
@@ -182,7 +182,7 @@ train_data = transform_tab_data(X_unlabel, y_unlabel)
 
 # %%
 # Set up metrics
-metric_list = [SymbolMetric(prefix="zoo"), ReasoningMetric(kb=kb, prefix="zoo")]
+metric_list = [SymbolAccuracy(prefix="zoo"), ReasoningMetric(kb=kb, prefix="zoo")]
 
 # %% [markdown]
 # ### Bridge Machine Learning and Logic Reasoning

examples/zoo/zoo.ipynb

@@ -25,7 +25,7 @@
     "from abl.learning import ABLModel\n",
     "from examples.zoo.kb import ZooKB\n",
     "from abl.reasoning import Reasoner\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolMetric\n",
+    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
     "from abl.utils import ABLLogger, print_log, confidence_dist\n",
     "from abl.bridge import SimpleBridge"
    ]
@@ -276,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolMetric` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
+    "Next, we set up evaluation metrics. These metrics will be used to evaluate the model performance during training and testing. Specifically, we use `SymbolAccuracy` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model’s predictions and the accuracy of the final reasoning results, respectively."
    ]
   },
   {
@@ -285,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_list = [SymbolMetric(prefix=\"zoo\"), ReasoningMetric(kb=kb, prefix=\"zoo\")]"
+    "metric_list = [SymbolAccuracy(prefix=\"zoo\"), ReasoningMetric(kb=kb, prefix=\"zoo\")]"
    ]
   },
   {