[ENH] run hwf example successfully after reformating the code

abl/bridge/simple_bridge.py

@@ -75,17 +75,16 @@ class SimpleBridge(BaseBridge):
                 abduced_label = self.pseudo_label_to_label(abduced_pseudo_label)
                 min_loss = self.model.train(X, abduced_label)
 
-                print_log(f"Epoch(train) [{epoch}] [{seg_idx:3}/{len(data_loader)}] minimal_loss is {min_loss:.5f}", logger="current")
+                print_log(
+                    f"Epoch(train) [{epoch + 1}] [{seg_idx:3}/{len(data_loader)}] minimal_loss is {min_loss:.5f}",
+                    logger="current",
+                )
 
             if (epoch + 1) % eval_interval == 0 or epoch == epochs - 1:
                 print_log(f"Evaluation start: Epoch(val) [{epoch}]", logger="current")
                 self.valid(train_data)
 
-    def test(self, test_data):
-        return super().test(test_data)
-
     def _valid(self, data_loader):
-        res = dict()
         for X, Z, Y in data_loader:
             pred_label, pred_prob = self.predict(X)
             pred_pseudo_label = self.label_to_pseudo_label(pred_label)
@@ -97,10 +96,12 @@ class SimpleBridge(BaseBridge):
                 Y=Y,
                 logic_forward=self.abducer.kb.logic_forward,
             )
-            
             for metric in self.metric_list:
                 metric.process(data_samples)
-                res.update(metric.evaluate())
+
+        res = dict()
+        for metric in self.metric_list:
+            res.update(metric.evaluate())
         msg = "Evaluation ended, "
         for k, v in res.items():
             msg += k + f": {v:.3f} "
@@ -115,4 +116,5 @@ class SimpleBridge(BaseBridge):
         )
         self._valid(data_loader)
 
-        return super().valid(valid_data)
+    def test(self, test_data, batch_size=1000):
+        self.valid(test_data, batch_size)

examples/hwf/hwf_example.ipynb

@@ -10,16 +10,14 @@
     "import torch.nn as nn\n",
     "import torch\n",
     "\n",
-    "from abl.reasoning.reasoner import ReasonerBase\n",
-    "from abl.reasoning.kb import KBBase\n",
+    "from abl.reasoning import ReasonerBase, KBBase\n",
+    "from abl.learning import BasicNN, ABLModel\n",
+    "from abl.bridge import SimpleBridge\n",
+    "from abl.evaluation import SymbolMetric, ABLMetric\n",
+    "from abl.utils import ABLLogger\n",
     "\n",
-    "from abl.utils.plog import logger\n",
-    "from abl.learning.basic_nn import BasicNN\n",
-    "from abl.learning.abl_model import ABLModel\n",
-    "\n",
-    "from models.nn import SymbolNet\n",
-    "from datasets.get_hwf import get_hwf\n",
-    "from abl import framework"
+    "from examples.models.nn import SymbolNet\n",
+    "from datasets.get_hwf import get_hwf"
    ]
   },
   {
@@ -29,7 +27,7 @@
    "outputs": [],
    "source": [
     "# Initialize logger\n",
-    "recorder = logger()"
+    "logger = ABLLogger.get_instance(\"abl\")"
    ]
   },
   {
@@ -115,19 +113,30 @@
     "    optimizer,\n",
     "    device,\n",
     "    save_interval=1,\n",
-    "    save_dir=recorder.save_dir,\n",
+    "    save_dir=logger.save_dir,\n",
     "    batch_size=128,\n",
-    "    num_epochs=10,\n",
-    "    recorder=recorder,\n",
+    "    num_epochs=3,\n",
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize ABL model\n",
+    "# The main function of the ABL model is to serialize data and \n",
+    "# provide a unified interface for different machine learning models\n",
+    "model = ABLModel(base_model)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Use ABL model to join two parts"
+    "### Metric"
    ]
   },
   {
@@ -136,10 +145,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize ABL model\n",
-    "# The main function of the ABL model is to serialize data and \n",
-    "# provide a unified interface for different machine learning models\n",
-    "model = ABLModel(base_model)"
+    "# Add metric\n",
+    "metric = [SymbolMetric(prefix=\"hwf\"), ABLMetric(prefix=\"hwf\")]"
    ]
   },
   {
@@ -166,7 +173,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Train and save"
+    "### Bridge Machine Learning and Logic Reasoning"
    ]
   },
   {
@@ -175,13 +182,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Train model\n",
-    "framework.train(\n",
-    "    model, abducer, train_data, epochs=15, sample=500, verbose=1\n",
-    ")\n",
-    "\n",
-    "# Save results\n",
-    "recorder.dump()"
+    "bridge = SimpleBridge(model, abducer, metric)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train and Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bridge.train(train_data, epochs=3, batch_size=1000)\n",
+    "bridge.test(test_data)"
    ]
   }
  ],