[MNT] change 'ReasonerBase' to 'Reasoner'

examples/hed/hed_bridge.py

@@ -7,7 +7,7 @@ from abl.bridge import SimpleBridge
 from abl.dataset import RegressionDataset
 from abl.evaluation import BaseMetric
 from abl.learning import ABLModel, BasicNN
-from abl.reasoning import ReasonerBase
+from abl.reasoning import Reasoner
 from abl.structures import ListData
 from abl.utils import print_log
 from examples.hed.datasets.get_hed import get_pretrain_data
@@ -19,7 +19,7 @@ class HEDBridge(SimpleBridge):
     def __init__(
         self,
         model: ABLModel,
-        reasoner: ReasonerBase,
+        reasoner: Reasoner,
         metric_list: BaseMetric,
     ) -> None:
         super().__init__(model, reasoner, metric_list)
@@ -92,11 +92,11 @@ class HEDBridge(SimpleBridge):
     def check_training_impact(self, filtered_data_samples, data_samples):
         character_accuracy = self.model.valid(filtered_data_samples)
         revisible_ratio = len(filtered_data_samples.X) / len(data_samples.X)
-        print_log(
-            f"Revisible ratio is {revisible_ratio:.3f}, Character \
-                accuracy is {character_accuracy:.3f}",
-            logger="current",
+        log_string = (
+            f"Revisible ratio is {revisible_ratio:.3f}, Character "
+            f"accuracy is {character_accuracy:.3f}"
         )
+        print_log(log_string, logger="current")
 
         if character_accuracy >= 0.9 and revisible_ratio >= 0.9:
             return True
@@ -109,11 +109,11 @@ class HEDBridge(SimpleBridge):
         true_ratio = self.calc_consistent_ratio(val_X_true, rule)
         false_ratio = self.calc_consistent_ratio(val_X_false, rule)
 
-        print_log(
-            f"True consistent ratio is {true_ratio:.3f}, False inconsistent ratio \
-                is {1 - false_ratio:.3f}",
-            logger="current",
+        log_string = (
+            f"True consistent ratio is {true_ratio:.3f}, False inconsistent ratio "
+            f"is {1 - false_ratio:.3f}"
         )
+        print_log(log_string, logger="current")
 
         if true_ratio > 0.95 and false_ratio < 0.1:
             return True

examples/hed/hed_example.ipynb

@@ -14,12 +14,11 @@
     "\n",
     "from abl.evaluation import SemanticsMetric, SymbolMetric\n",
     "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.reasoning import PrologKB, ReasonerBase\n",
+    "from abl.reasoning import PrologKB, Reasoner\n",
     "from abl.utils import ABLLogger, print_log, reform_list\n",
     "from examples.hed.datasets.get_hed import get_hed, split_equation\n",
     "from examples.hed.hed_bridge import HEDBridge\n",
-    "from examples.models.nn import SymbolNet\n",
-    "from zoopt import Dimension, Objective, Parameter, Opt"
+    "from examples.models.nn import SymbolNet"
    ]
   },
   {
@@ -68,7 +67,7 @@
     "        return rules\n",
     "\n",
     "\n",
-    "class HedReasoner(ReasonerBase):\n",
+    "class HedReasoner(Reasoner):\n",
     "    def revise_at_idx(self, data_sample):\n",
     "        revision_idx = np.where(np.array(data_sample.flatten(\"revision_flag\")) != 0)[0]\n",
     "        candidate = self.kb.revise_at_idx(\n",

examples/hwf/hwf_example.ipynb

@@ -11,7 +11,7 @@
     "import torch.nn as nn\n",
     "import os.path as osp\n",
     "\n",
-    "from abl.reasoning import ReasonerBase, KBBase\n",
+    "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",
@@ -75,7 +75,7 @@
     "    max_err=1e-10,\n",
     "    use_cache=False,\n",
     ")\n",
-    "reasoner = ReasonerBase(kb, dist_func=\"confidence\")"
+    "reasoner = Reasoner(kb, dist_func=\"confidence\")"
    ]
   },
   {
@@ -220,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.8.18"
   },
   "orig_nbformat": 4,
   "vscode": {

examples/mnist_add/mnist_add_example.ipynb

@@ -14,7 +14,7 @@
     "from abl.bridge import SimpleBridge\n",
     "from abl.evaluation import SemanticsMetric, SymbolMetric\n",
     "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.reasoning import KBBase, ReasonerBase\n",
+    "from abl.reasoning import KBBase, Reasoner\n",
     "from abl.utils import ABLLogger, print_log\n",
     "from examples.mnist_add.datasets.get_mnist_add import get_mnist_add\n",
     "from examples.models.nn import LeNet5"
@@ -109,7 +109,7 @@
     "\n",
     "\n",
     "kb = AddKB(pseudo_label_list=list(range(10)))\n",
-    "reasoner = ReasonerBase(kb, dist_func=\"confidence\")"
+    "reasoner = Reasoner(kb, dist_func=\"confidence\")"
    ]
   },
   {