[MNT] change abducer to reasoner

abl/bridge/base_bridge.py

@@ -9,22 +9,22 @@ DataSet = Tuple[List[List[Any]], Optional[List[List[Any]]], List[List[Any]]]
 
 
 class BaseBridge(metaclass=ABCMeta):
-    def __init__(self, model: ABLModel, abducer: ReasonerBase) -> None:
+    def __init__(self, model: ABLModel, reasoner: ReasonerBase) -> None:
         if not isinstance(model, ABLModel):
             raise TypeError(
                 "Expected an instance of ABLModel, but received type: {}".format(
                     type(model)
                 )
             )
-        if not isinstance(abducer, ReasonerBase):
+        if not isinstance(reasoner, ReasonerBase):
             raise TypeError(
                 "Expected an instance of ReasonerBase, but received type: {}".format(
-                    type(abducer)
+                    type(reasoner)
                 )
             )
 
         self.model = model
-        self.abducer = abducer
+        self.reasoner = reasoner
 
     @abstractmethod
     def predict(

abl/bridge/simple_bridge.py

@@ -15,13 +15,13 @@ class SimpleBridge(BaseBridge):
     def __init__(
         self,
         model: ABLModel,
-        abducer: ReasonerBase,
+        reasoner: ReasonerBase,
         metric_list: List[BaseMetric],
     ) -> None:
-        super().__init__(model, abducer)
+        super().__init__(model, reasoner)
         self.metric_list = metric_list
 
-    # TODO: add abducer.mapping to the property of SimpleBridge
+    # TODO: add reasoner.mapping to the property of SimpleBridge
 
     def predict(self, data_samples: ListData) -> Tuple[List[ndarray], List[ndarray]]:
         self.model.predict(data_samples)
@@ -33,14 +33,14 @@ class SimpleBridge(BaseBridge):
         max_revision: int = -1,
         require_more_revision: int = 0,
     ) -> List[List[Any]]:
-        self.abducer.batch_abduce(data_samples, max_revision, require_more_revision)
+        self.reasoner.batch_abduce(data_samples, max_revision, require_more_revision)
         return data_samples.abduced_pseudo_label
 
     def idx_to_pseudo_label(
         self, data_samples: ListData, mapping: Optional[Dict] = None
     ) -> List[List[Any]]:
         if mapping is None:
-            mapping = self.abducer.mapping
+            mapping = self.reasoner.mapping
         pred_idx = data_samples.pred_idx
         data_samples.pred_pseudo_label = [
             [mapping[_idx] for _idx in sub_list] for sub_list in pred_idx
@@ -51,7 +51,7 @@ class SimpleBridge(BaseBridge):
         self, data_samples: ListData, mapping: Optional[Dict] = None
     ) -> List[List[Any]]:
         if mapping is None:
-            mapping = self.abducer.remapping
+            mapping = self.reasoner.remapping
         abduced_idx = [
             [mapping[_abduced_pseudo_label] for _abduced_pseudo_label in sub_list]
             for sub_list in data_samples.abduced_pseudo_label

abl/learning/basic_nn.py

@@ -92,7 +92,7 @@ class BasicNN:
             )
             self.test_transform = self.train_transform
 
-    def _fit(self, data_loader) -> float:
+    def _fit(self, data_loader: DataLoader) -> float:
         """
         Internal method to fit the model on data for n epochs, with early stopping.
 
@@ -180,7 +180,7 @@ class BasicNN:
 
         return total_loss / total_num
 
-    def _predict(self, data_loader) -> torch.Tensor:
+    def _predict(self, data_loader: DataLoader) -> torch.Tensor:
         """
         Internal method to predict the outputs given a DataLoader.
 
@@ -262,7 +262,7 @@ class BasicNN:
             )
         return self._predict(data_loader).softmax(axis=1).cpu().numpy()
 
-    def _score(self, data_loader) -> Tuple[float, float]:
+    def _score(self, data_loader: DataLoader) -> Tuple[float, float]:
         """
         Internal method to compute loss and accuracy for the data provided through a DataLoader.
 
@@ -334,12 +334,7 @@ class BasicNN:
         print_log(f"mean loss: {mean_loss:.3f}, accuray: {accuracy:.3f}", logger="current")
         return accuracy
 
-    def _data_loader(
-        self,
-        X: List[Any],
-        y: List[int] = None,
-        shuffle: bool = True,
-    ) -> DataLoader:
+    def _data_loader(self, X: List[Any], y: List[int] = None, shuffle: bool = True) -> DataLoader:
         """
         Generate a DataLoader for user-provided input and target data.
 

examples/hed/hed_bridge.py

@@ -19,17 +19,17 @@ class HEDBridge(SimpleBridge):
     def __init__(
         self,
         model: ABLModel,
-        abducer: ReasonerBase,
+        reasoner: ReasonerBase,
         metric_list: BaseMetric,
     ) -> None:
-        super().__init__(model, abducer, metric_list)
+        super().__init__(model, reasoner, metric_list)
 
     def pretrain(self, weights_dir):
         if not os.path.exists(os.path.join(weights_dir, "pretrain_weights.pth")):
             print_log("Pretrain Start", logger="current")
 
             cls_autoencoder = SymbolNetAutoencoder(
-                num_classes=len(self.abducer.kb.pseudo_label_list)
+                num_classes=len(self.reasoner.kb.pseudo_label_list)
             )
             device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
             criterion = torch.nn.MSELoss()
@@ -74,7 +74,7 @@ class HEDBridge(SimpleBridge):
         max_revision=-1,
         require_more_revision=0,
     ):
-        return self.abducer.abduce(
+        return self.reasoner.abduce(
             (pred_label, pred_prob, pseudo_label, Y),
             max_revision,
             require_more_revision,
@@ -86,8 +86,8 @@ class HEDBridge(SimpleBridge):
         pred_pseudo_label_list = []
         abduced_pseudo_label_list = []
         for _mapping in candidate_mappings:
-            self.abducer.mapping = _mapping
-            self.abducer.set_remapping()
+            self.reasoner.mapping = _mapping
+            self.reasoner.set_remapping()
             pred_pseudo_label = self.label_to_pseudo_label(pred_label)
             abduced_pseudo_label = self.abduce_pseudo_label(
                 pred_label, pred_prob, pred_pseudo_label, Y, 20
@@ -100,8 +100,8 @@ class HEDBridge(SimpleBridge):
 
         max_revisible_instances = max(mapping_score)
         return_idx = mapping_score.index(max_revisible_instances)
-        self.abducer.mapping = candidate_mappings[return_idx]
-        self.abducer.set_remapping()
+        self.reasoner.mapping = candidate_mappings[return_idx]
+        self.reasoner.set_remapping()
         return abduced_pseudo_label_list[return_idx]
 
     def check_training_impact(self, filtered_X, filtered_abduced_label, X):
@@ -137,7 +137,7 @@ class HEDBridge(SimpleBridge):
         pred_pseudo_label = self.label_to_pseudo_label(pred_label)
         consistent_num = sum(
             [
-                self.abducer.kb.consist_rule(instance, rule)
+                self.reasoner.kb.consist_rule(instance, rule)
                 for instance in pred_pseudo_label
             ]
         )
@@ -159,11 +159,11 @@ class HEDBridge(SimpleBridge):
                 pred_pseudo_label = self.label_to_pseudo_label(pred_label)
                 consistent_instance = []
                 for instance in pred_pseudo_label:
-                    if self.abducer.kb.logic_forward([instance]):
+                    if self.reasoner.kb.logic_forward([instance]):
                         consistent_instance.append(instance)
 
                 if len(consistent_instance) != 0:
-                    rule = self.abducer.abduce_rules(consistent_instance)
+                    rule = self.reasoner.abduce_rules(consistent_instance)
                     if rule != None:
                         rules.append(rule)
                         break
@@ -280,7 +280,7 @@ class HEDBridge(SimpleBridge):
                     else:
                         if equation_len == min_len:
                             print_log(
-                                "Learned mapping is: " + str(self.abducer.mapping),
+                                "Learned mapping is: " + str(self.reasoner.mapping),
                                 logger="current",
                             )
                             self.model.load(load_path="./weights/pretrain_weights.pth")

examples/hwf/hwf_example.ipynb

@@ -2,10 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import sys\n",
+    "\n",
+    "sys.setrecursionlimit(10000)\n",
+    "\n",
     "import torch\n",
     "import numpy as np\n",
     "import torch.nn as nn\n",
@@ -23,9 +27,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/16 20:43:38 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Abductive Learning on the HWF example.\n"
+     ]
+    }
+   ],
    "source": [
     "# Initialize logger and print basic information\n",
     "print_log(\"Abductive Learning on the HWF example.\", logger=\"current\")\n",
@@ -45,21 +57,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize knowledge base and abducer\n",
+    "# Initialize knowledge base and reasoner\n",
     "class HWF_KB(KBBase):\n",
-    "    def __init__(\n",
-    "        self, \n",
-    "        pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], \n",
-    "        prebuild_GKB=False,\n",
-    "        GKB_len_list=[1, 3, 5, 7],\n",
-    "        max_err=1e-3,\n",
-    "        use_cache=True\n",
-    "    ):\n",
-    "        super().__init__(pseudo_label_list, prebuild_GKB, GKB_len_list, max_err, use_cache)\n",
     "\n",
     "    def _valid_candidate(self, formula):\n",
     "        if len(formula) % 2 == 0:\n",
@@ -79,8 +82,8 @@
     "        formula = [mapping[f] for f in formula]\n",
     "        return eval(''.join(formula))\n",
     "\n",
-    "kb = HWF_KB(prebuild_GKB=True)\n",
-    "abducer = ReasonerBase(kb, dist_func='confidence')"
+    "kb = HWF_KB(pseudo_label_list=['1', '2', '3', '4', '5', '6', '7', '8', '9', '+', '-', 'times', 'div'], max_err=1e-10, use_cache=False)\n",
+    "reasoner = ReasonerBase(kb, dist_func='confidence')"
    ]
   },
   {
@@ -93,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,12 +149,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Add metric\n",
-    "metric_list = [SymbolMetric(prefix=\"hwf\"), SemanticsMetric(prefix=\"hwf\")]"
+    "metric_list = [SymbolMetric(prefix=\"hwf\"), SemanticsMetric(kb=kb, prefix=\"hwf\")]"
    ]
   },
   {
@@ -164,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,11 +186,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "bridge = SimpleBridge(model=model, abducer=abducer, metric_list=metric_list)"
+    "bridge = SimpleBridge(model=model, reasoner=reasoner, metric_list=metric_list)"
    ]
   },
   {
@@ -200,11 +203,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11/16 20:44:02 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:02 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:02 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:02 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [1/10] model loss is 0.16911\n",
+      "11/16 20:44:03 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:03 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:03 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:03 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [2/10] model loss is 0.17734\n",
+      "11/16 20:44:03 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:03 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:04 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:04 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [3/10] model loss is 0.01907\n",
+      "11/16 20:44:04 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:04 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:04 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:04 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [4/10] model loss is 0.01403\n",
+      "11/16 20:44:05 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:05 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:05 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:05 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [5/10] model loss is 0.00509\n",
+      "11/16 20:44:06 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:06 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:06 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:06 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [6/10] model loss is 0.00713\n",
+      "11/16 20:44:06 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:07 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:07 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:07 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [7/10] model loss is 0.00455\n",
+      "11/16 20:44:07 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:07 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:08 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:08 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [8/10] model loss is 0.00946\n",
+      "11/16 20:44:08 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:08 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:08 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [9/10] model loss is 0.00957\n",
+      "11/16 20:44:09 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:09 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:09 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:09 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/3] segment(train) [10/10] model loss is 0.00323\n",
+      "11/16 20:44:09 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [1]\n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, hwf/character_accuracy: 0.997 hwf/semantics_accuracy: 0.985 \n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [1]\n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_loop_1.pth\n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [1/10] model loss is 0.00666\n",
+      "11/16 20:44:10 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_3.pth\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [2/10] model loss is 0.01438\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [3/10] model loss is 0.00450\n",
+      "11/16 20:44:11 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:12 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:12 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [4/10] model loss is 0.00764\n",
+      "11/16 20:44:12 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:12 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:12 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [5/10] model loss is 0.00644\n",
+      "11/16 20:44:13 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:13 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:13 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [6/10] model loss is 0.00189\n",
+      "11/16 20:44:13 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:13 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:13 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [7/10] model loss is 0.00397\n",
+      "11/16 20:44:14 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:14 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [8/10] model loss is 0.00936\n",
+      "11/16 20:44:14 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:14 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:14 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [9/10] model loss is 0.00960\n",
+      "11/16 20:44:15 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:15 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:15 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/3] segment(train) [10/10] model loss is 0.00572\n",
+      "11/16 20:44:15 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [2]\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, hwf/character_accuracy: 0.999 hwf/semantics_accuracy: 0.995 \n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [2]\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_loop_2.pth\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [1/10] model loss is 0.00180\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [2/10] model loss is 0.00615\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:16 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [3/10] model loss is 0.01000\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_2.pth\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [4/10] model loss is 0.00415\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [5/10] model loss is 0.00960\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:17 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [6/10] model loss is 0.00697\n",
+      "11/16 20:44:18 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:18 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [7/10] model loss is 0.00977\n",
+      "11/16 20:44:18 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:18 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [8/10] model loss is 0.00734\n",
+      "11/16 20:44:18 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:18 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [9/10] model loss is 0.00922\n",
+      "11/16 20:44:19 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_epoch_1.pth\n",
+      "11/16 20:44:19 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/3] segment(train) [10/10] model loss is 0.00982\n",
+      "11/16 20:44:19 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [3]\n",
+      "11/16 20:44:20 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, hwf/character_accuracy: 0.998 hwf/semantics_accuracy: 0.986 \n",
+      "11/16 20:44:20 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [3]\n",
+      "11/16 20:44:20 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231116_20_43_38/weights/model_checkpoint_loop_3.pth\n",
+      "11/16 20:44:20 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, hwf/character_accuracy: 0.994 hwf/semantics_accuracy: 0.970 \n"
+     ]
+    }
+   ],
    "source": [
-    "bridge.train(train_data, epochs=3, batch_size=1000)\n",
+    "bridge.train(train_data, loops=3, segment_size=1000, save_interval=1, save_dir=weights_dir)\n",
     "bridge.test(test_data)"
    ]
   }

examples/mnist_add/mnist_add_example.ipynb

@@ -50,7 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize knowledge base and abducer\n",
+    "# Initialize knowledge base and reasoner\n",
     "class add_KB(KBBase):\n",
     "    def logic_forward(self, nums):\n",
     "        return sum(nums)\n",
@@ -58,7 +58,7 @@
     "kb = add_KB(pseudo_label_list=list(range(10)))\n",
     "\n",
     "# kb = prolog_KB(pseudo_label_list=list(range(10)), pl_file='datasets/mnist_add/add.pl')\n",
-    "abducer = ReasonerBase(kb, dist_func=\"confidence\")"
+    "reasoner = ReasonerBase(kb, dist_func=\"confidence\")"
    ]
   },
   {
@@ -171,7 +171,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bridge = SimpleBridge(model, abducer, metric)"
+    "bridge = SimpleBridge(model, reasoner, metric)"
    ]
   },
   {