[MNT] unify index of ListData, remove seg of valid

abl/bridge/simple_bridge.py

@@ -25,7 +25,7 @@ class SimpleBridge(BaseBridge):
 
     def predict(self, data_samples: ListData) -> Tuple[List[ndarray], List[ndarray]]:
         self.model.predict(data_samples)
-        return data_samples["pred_idx"], data_samples.get("pred_prob", None)
+        return data_samples.pred_idx, data_samples.get("pred_prob", None)
 
     def abduce_pseudo_label(
         self,
@@ -34,7 +34,7 @@ class SimpleBridge(BaseBridge):
         require_more_revision: int = 0,
     ) -> List[List[Any]]:
         self.abducer.batch_abduce(data_samples, max_revision, require_more_revision)
-        return data_samples["abduced_pseudo_label"]
+        return data_samples.abduced_pseudo_label
 
     def idx_to_pseudo_label(
         self, data_samples: ListData, mapping: Optional[Dict] = None
@@ -45,7 +45,7 @@ class SimpleBridge(BaseBridge):
         data_samples.pred_pseudo_label = [
             [mapping[_idx] for _idx in sub_list] for sub_list in pred_idx
         ]
-        return data_samples["pred_pseudo_label"]
+        return data_samples.pred_pseudo_label
 
     def pseudo_label_to_idx(
         self, data_samples: ListData, mapping: Optional[Dict] = None
@@ -57,7 +57,7 @@ class SimpleBridge(BaseBridge):
             for sub_list in data_samples.abduced_pseudo_label
         ]
         data_samples.abduced_idx = abduced_idx
-        return data_samples["abduced_idx"]
+        return data_samples.abduced_idx
 
     def data_preprocess(self, X: List[Any], gt_pseudo_label: List[Any], Y: List[Any]) -> ListData:
         data_samples = ListData()
@@ -104,16 +104,16 @@ class SimpleBridge(BaseBridge):
 
             if save_interval is not None and ((loop + 1) % save_interval == 0 or loop == loops - 1):
                 print_log(f"Saving model: loop(save) [{loop + 1}]", logger="current")
-                self.model.save(save_path=osp.join(save_dir, f"model_checkpoint_loop_{loop + 1}.pth"))
+                self.model.save(
+                    save_path=osp.join(save_dir, f"model_checkpoint_loop_{loop + 1}.pth")
+                )
 
-    def _valid(self, data_samples: ListData, batch_size: int = 128) -> None:
-        for seg_idx in range((len(data_samples) - 1) // batch_size + 1):
-            sub_data_samples = data_samples[seg_idx * batch_size : (seg_idx + 1) * batch_size]
-            self.predict(sub_data_samples)
-            self.idx_to_pseudo_label(sub_data_samples)
+    def _valid(self, data_samples: ListData) -> None:
+        self.predict(data_samples)
+        self.idx_to_pseudo_label(data_samples)
 
-            for metric in self.metric_list:
-                metric.process(sub_data_samples)
+        for metric in self.metric_list:
+            metric.process(data_samples)
 
         res = dict()
         for metric in self.metric_list:
@@ -123,12 +123,12 @@ class SimpleBridge(BaseBridge):
             msg += k + f": {v:.3f} "
         print_log(msg, logger="current")
 
-    def valid(self, valid_data: Union[ListData, DataSet], batch_size: int = 128) -> None:
+    def valid(self, valid_data: Union[ListData, DataSet]) -> None:
         if not isinstance(valid_data, ListData):
             data_samples = self.data_preprocess(*valid_data)
         else:
             data_samples = valid_data
-        self._valid(data_samples, batch_size=batch_size)
+        self._valid(data_samples)
 
-    def test(self, test_data: Union[ListData, DataSet], batch_size: int = 128) -> None:
-        self.valid(test_data, batch_size=batch_size)
+    def test(self, test_data: Union[ListData, DataSet]) -> None:
+        self.valid(test_data)

abl/evaluation/semantics_metric.py

@@ -11,7 +11,7 @@ class SemanticsMetric(BaseMetric):
 
     def process(self, data_samples: Sequence[dict]) -> None:
         for data_sample in data_samples:
-            if self.kb.check_equal(data_sample, data_sample["Y"][0]):
+            if self.kb.check_equal(data_sample, data_sample.Y[0]):
                 self.results.append(1)
             else:
                 self.results.append(0)

abl/evaluation/symbol_metric.py

@@ -8,9 +8,9 @@ class SymbolMetric(BaseMetric):
         super().__init__(prefix)
 
     def process(self, data_samples: Sequence[dict]) -> None:
-        pred_pseudo_label = data_samples["pred_pseudo_label"]
+        pred_pseudo_label = data_samples.pred_pseudo_label
 
-        gt_pseudo_label = data_samples["gt_pseudo_label"]
+        gt_pseudo_label = data_samples.gt_pseudo_label
 
         if not len(pred_pseudo_label) == len(gt_pseudo_label):
             raise ValueError("lengthes of pred_pseudo_label and gt_pseudo_label should be equal")

abl/learning/abl_model.py

@@ -69,11 +69,11 @@ class ABLModel:
         if hasattr(model, "predict_proba"):
             prob = model.predict_proba(X=data_X)
             label = prob.argmax(axis=1)
-            prob = reform_idx(prob, data_samples["X"])
+            prob = reform_idx(prob, data_samples.X)
         else:
             prob = None
             label = model.predict(X=data_X)
-        label = reform_idx(label, data_samples["X"])
+        label = reform_idx(label, data_samples.X)
 
         data_samples.pred_idx = label
         if prob is not None:

abl/utils/cache.py

@@ -102,7 +102,7 @@ class Cache(Generic[K, T]):
                 log_dir = ABLLogger.get_current_instance().log_dir
                 cache_dir = osp.join(log_dir, "cache")
                 os.makedirs(cache_dir, exist_ok=True)
-                cache_path = osp.join(cache_dir, "cache.pth")
+                cache_path = osp.join(cache_dir, "abduce_by_search_cache_res.pth")
                 with open(cache_path, "wb") as file:
                     pickle.dump(self.cache_dict, file, protocol=pickle.HIGHEST_PROTOCOL)
                 print_log(f"Cache will be saved to {cache_path}", logger="current")

examples/mnist_add/mnist_add_example.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,17 +24,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11/15 21:35:55 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Abductive Learning on the MNIST Add example.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize logger\n",
     "print_log(\"Abductive Learning on the MNIST Add example.\", logger=\"current\")\n",
@@ -54,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,53 +184,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11/15 21:36:18 - abl - \u001b[5m\u001b[4m\u001b[33mWARNING\u001b[0m - Transform used in the training phase will be used in prediction.\n",
-      "11/15 21:36:21 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/5] segment(train) [1/3] model loss is 1.80390\n",
-      "11/15 21:36:24 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/5] segment(train) [2/3] model loss is 1.41898\n",
-      "11/15 21:36:26 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [1/5] segment(train) [3/3] model loss is 1.08221\n",
-      "11/15 21:36:26 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [1]\n",
-      "11/15 21:36:27 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, mnist_add/character_accuracy: 0.590 \n",
-      "11/15 21:36:27 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [1]\n",
-      "11/15 21:36:27 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231115_21_35_55/weights/model_checkpoint_loop_1.pth\n",
-      "11/15 21:36:29 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/5] segment(train) [1/3] model loss is 0.65210\n",
-      "11/15 21:36:31 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/5] segment(train) [2/3] model loss is 0.13546\n",
-      "11/15 21:36:32 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [2/5] segment(train) [3/3] model loss is 0.08060\n",
-      "11/15 21:36:32 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [2]\n",
-      "11/15 21:36:34 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, mnist_add/character_accuracy: 0.982 \n",
-      "11/15 21:36:34 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [2]\n",
-      "11/15 21:36:34 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231115_21_35_55/weights/model_checkpoint_loop_2.pth\n",
-      "11/15 21:36:35 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/5] segment(train) [1/3] model loss is 0.06446\n",
-      "11/15 21:36:37 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/5] segment(train) [2/3] model loss is 0.05224\n",
-      "11/15 21:36:39 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [3/5] segment(train) [3/3] model loss is 0.05119\n",
-      "11/15 21:36:39 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [3]\n",
-      "11/15 21:36:40 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, mnist_add/character_accuracy: 0.989 \n",
-      "11/15 21:36:40 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [3]\n",
-      "11/15 21:36:40 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231115_21_35_55/weights/model_checkpoint_loop_3.pth\n",
-      "11/15 21:36:42 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [4/5] segment(train) [1/3] model loss is 0.04667\n",
-      "11/15 21:36:44 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [4/5] segment(train) [2/3] model loss is 0.04027\n",
-      "11/15 21:36:45 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [4/5] segment(train) [3/3] model loss is 0.03672\n",
-      "11/15 21:36:45 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [4]\n",
-      "11/15 21:36:46 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, mnist_add/character_accuracy: 0.990 \n",
-      "11/15 21:36:46 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [4]\n",
-      "11/15 21:36:46 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231115_21_35_55/weights/model_checkpoint_loop_4.pth\n",
-      "11/15 21:36:48 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [5/5] segment(train) [1/3] model loss is 0.03381\n",
-      "11/15 21:36:50 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [5/5] segment(train) [2/3] model loss is 0.03333\n",
-      "11/15 21:36:52 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - loop(train) [5/5] segment(train) [3/3] model loss is 0.03195\n",
-      "11/15 21:36:52 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation start: loop(val) [5]\n",
-      "11/15 21:36:53 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, mnist_add/character_accuracy: 0.992 \n",
-      "11/15 21:36:53 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Saving model: loop(save) [5]\n",
-      "11/15 21:36:53 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Checkpoints will be saved to results/20231115_21_35_55/weights/model_checkpoint_loop_5.pth\n",
-      "11/15 21:36:53 - abl - \u001b[4m\u001b[37mINFO\u001b[0m - Evaluation ended, mnist_add/character_accuracy: 0.988 \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "bridge.train(train_data, loops=5, segment_size=10000, save_interval=1, save_dir=weights_dir)\n",
     "bridge.test(test_data)"