[FIX] grammar check in docstring

abl/bridge/simple_bridge.py

@@ -233,10 +233,10 @@ class SimpleBridge(BaseBridge):
             Data will be split into segments of this size and data in each segment
             will be used together to train the model, by default 1.0.
         eval_interval : int
-            The model will be evaluated every ``eval_interval`` loops during training,
+            The model will be evaluated every ``eval_interval`` loop during training,
             by default 1.
         save_interval : int, optional
-            The model will be saved every ``eval_interval`` loops during training, by
+            The model will be saved every ``eval_interval`` loop during training, by
             default None.
         save_dir : str, optional
             Directory to save the model, by default None.

abl/data/evaluation/base_metric.py

@@ -52,7 +52,7 @@ class BaseMetric(metaclass=ABCMeta):
         -------
         dict
             The computed metrics. The keys are the names of the metrics,
-            and the values are corresponding results.
+            and the values are the corresponding results.
         """
 
     def evaluate(self) -> dict:
@@ -64,7 +64,7 @@ class BaseMetric(metaclass=ABCMeta):
         -------
         dict
             Evaluation metrics dict on the val dataset. The keys are the
-            names of the metrics, and the values are corresponding results.
+            names of the metrics, and the values are the corresponding results.
         """
         if len(self.results) == 0:
             print_log(

abl/data/evaluation/symbol_accuracy.py

@@ -11,7 +11,7 @@ class SymbolAccuracy(BaseMetric):
     A metrics class for evaluating symbol-level accuracy.
 
     This class is designed to assess the accuracy of symbol prediction. Symbol accuracy
-    are calculated by comparing predicted presudo labels and their ground truth.
+    is calculated by comparing predicted presudo labels and their ground truth.
 
     Parameters
     ----------

abl/learning/abl_model.py

@@ -86,7 +86,7 @@ class ABLModel:
         Returns
         -------
         float
-            The accuracy the trained model.
+            The accuracy of the trained model.
         """
         data_X = data_examples.flatten("X")
         data_y = data_examples.flatten("abduced_idx")

abl/learning/basic_nn.py

@@ -40,7 +40,7 @@ class BasicNN:
     num_workers : int
         The number of workers used for loading data, by default 0.
     save_interval : int, optional
-        The model will be saved every ``save_interval`` epochs during training, by default None.
+        The model will be saved every ``save_interval`` epoch during training, by default None.
     save_dir : str, optional
         The directory in which to save the model during training, by default None.
     train_transform : Callable[..., Any], optional

abl/learning/model_converter.py

@@ -63,7 +63,7 @@ class ModelConverter:
         num_workers : int
             The number of workers used for loading data, by default 0.
         save_interval : int, optional
-            The model will be saved every ``save_interval`` epochs during training, by default None.
+            The model will be saved every ``save_interval`` epoch during training, by default None.
         save_dir : str, optional
             The directory in which to save the model during training, by default None.
         train_transform : Callable[..., Any], optional
@@ -153,7 +153,7 @@ class ModelConverter:
         num_workers : int
             The number of workers used for loading data, by default 0.
         save_interval : int, optional
-            The model will be saved every ``save_interval`` epochs during training, by default None.
+            The model will be saved every ``save_interval`` epoch during training, by default None.
         save_dir : str, optional
             The directory in which to save the model during training, by default None.
         train_transform : Callable[..., Any], optional

abl/utils/utils.py

@@ -94,7 +94,7 @@ def confidence_dist(pred_prob: List[np.ndarray], candidates_idxs: List[List[Any]
     Parameters
     ----------
     pred_prob : List[np.ndarray]
-        Prediction probability distributions, each element is an ndarray
+        Prediction probability distributions, each element is an array
         representing the probability distribution of a particular prediction.
     candidates_idxs : List[List[Any]]
         Multiple possible candidates' indices.