[MNT] resolve all comments in hwf_example.ipynb

abl/utils/logger.py

@@ -198,11 +198,11 @@ class ABLLogger(Logger, ManagerMixin):
             import time
             local_time = time.strftime("%Y%m%d_%H_%M_%S", time.localtime())
 
-            save_dir = os.path.join("results", local_time)
-            self.save_dir = save_dir
-            if not os.path.exists(save_dir):
-                os.makedirs(save_dir)
-            log_file = osp.join(save_dir, local_time + ".log")
+            _log_dir = os.path.join("results", local_time)
+            self._log_dir = _log_dir
+            if not os.path.exists(_log_dir):
+                os.makedirs(_log_dir)
+            log_file = osp.join(_log_dir, local_time + ".log")
 
         file_handler = logging.FileHandler(log_file, file_mode)
         file_handler.setFormatter(
@@ -216,6 +216,10 @@ class ABLLogger(Logger, ManagerMixin):
     @property
     def log_file(self):
         return self._log_file
+    
+    @property
+    def log_dir(self):
+        return self._log_dir
 
     @classmethod
     def get_current_instance(cls) -> "ABLLogger":

examples/hwf/hwf_example.ipynb

@@ -6,15 +6,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import torch\n",
     "import numpy as np\n",
     "import torch.nn as nn\n",
-    "import torch\n",
+    "import os.path as osp\n",
     "\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",
+    "from abl.evaluation import SymbolMetric, SemanticsMetric\n",
+    "from abl.utils import ABLLogger, print_log\n",
     "\n",
     "from examples.models.nn import SymbolNet\n",
     "from datasets.get_hwf import get_hwf"
@@ -26,8 +27,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Initialize logger\n",
-    "logger = ABLLogger.get_instance(\"abl\")"
+    "# Initialize logger and print basic information\n",
+    "print_log(\"Abductive Learning on the HWF example.\", logger=\"current\")\n",
+    "\n",
+    "# Retrieve the directory of the Log file and define the directory for saving the model weights.\n",
+    "log_dir = ABLLogger.get_current_instance().log_dir\n",
+    "weights_dir = osp.join(log_dir, \"weights\")"
    ]
   },
   {
@@ -108,12 +113,12 @@
     "# Initialize BasicNN\n",
     "# The function of BasicNN is to wrap NN models into the form of an sklearn estimator\n",
     "base_model = BasicNN(\n",
-    "    cls,\n",
-    "    criterion,\n",
-    "    optimizer,\n",
-    "    device,\n",
+    "    model=cls,\n",
+    "    criterion=criterion,\n",
+    "    optimizer=optimizer,\n",
+    "    device=device,\n",
     "    save_interval=1,\n",
-    "    save_dir=logger.save_dir,\n",
+    "    save_dir=weights_dir,\n",
     "    batch_size=128,\n",
     "    num_epochs=3,\n",
     ")"
@@ -146,7 +151,7 @@
    "outputs": [],
    "source": [
     "# Add metric\n",
-    "metric = [SymbolMetric(prefix=\"hwf\"), ABLMetric(prefix=\"hwf\")]"
+    "metric_list = [SymbolMetric(prefix=\"hwf\"), SemanticsMetric(prefix=\"hwf\")]"
    ]
   },
   {
@@ -164,8 +169,8 @@
    "outputs": [],
    "source": [
     "# Get training and testing data\n",
-    "train_data = get_hwf(train=True, get_pseudo_label=True)\n",
-    "test_data = get_hwf(train=False, get_pseudo_label=True)"
+    "train_data = get_hwf(train=True, get_gt_pseudo_label=True)\n",
+    "test_data = get_hwf(train=False, get_gt_pseudo_label=True)"
    ]
   },
   {
@@ -182,7 +187,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bridge = SimpleBridge(model, abducer, metric)"
+    "bridge = SimpleBridge(model=model, abducer=abducer, metric_list=metric_list)"
    ]
   },
   {
@@ -220,7 +225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.16"
   },
   "orig_nbformat": 4,
   "vscode": {