[FIX] unify docstring

ablkit/bridge/simple_bridge.py

@@ -233,19 +233,19 @@ class SimpleBridge(BaseBridge):
             ``self.metric_list``. If ``val_data`` is None, ``train_data`` will be used to validate
             the model during training time. Defaults to None.
         loops : int
-            Machine Learning part and Reasoning part will be iteratively optimized
-            for ``loops`` times, by default 50.
+            Learning part and Reasoning part will be iteratively optimized
+            for ``loops`` times. Defaults to 50.
         segment_size : Union[int, float]
             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.
+            will be used together to train the model. Defaults to 1.0.
         eval_interval : int
             The model will be evaluated every ``eval_interval`` loop during training,
-            by default 1.
+            Defaults to 1.
         save_interval : int, optional
-            The model will be saved every ``eval_interval`` loop during training, by
-            default None.
+            The model will be saved every ``eval_interval`` loop during training.
+            Defaults to None.
         save_dir : str, optional
-            Directory to save the model, by default None.
+            Directory to save the model. Defaults to None.
         """
         data_examples = self.data_preprocess("train", train_data)
 

ablkit/data/evaluation/base_metric.py

@@ -24,7 +24,7 @@ class BaseMetric(metaclass=ABCMeta):
     prefix : str, optional
         The prefix that will be added in the metrics names to disambiguate homonymous
         metrics of different tasks. If prefix is not provided in the argument,
-        self.default_prefix will be used instead. Default to None.
+        self.default_prefix will be used instead. Defaults to None.
 
     """
 

ablkit/data/evaluation/reasoning_metric.py

@@ -25,10 +25,10 @@ class ReasoningMetric(BaseMetric):
     ----------
     kb : KBBase
         An instance of a knowledge base, used for logical reasoning and validation.
-        If not provided, reasoning checks are not performed. Default to None.
+        If not provided, reasoning checks are not performed. Defaults to None.
     prefix : str, optional
         The prefix that will be added to the metrics names to disambiguate homonymous
-        metrics of different tasks. Inherits from BaseMetric. Default to None.
+        metrics of different tasks. Inherits from BaseMetric. Defaults to None.
 
     Notes
     -----

ablkit/data/evaluation/symbol_accuracy.py

@@ -21,7 +21,7 @@ class SymbolAccuracy(BaseMetric):
     ----------
     prefix : str, optional
         The prefix that will be added to the metrics names to disambiguate homonymous
-        metrics of different tasks. Inherits from BaseMetric. Default to None.
+        metrics of different tasks. Inherits from BaseMetric. Defaults to None.
     """
 
     def process(self, data_examples: ListData) -> None:

ablkit/learning/basic_nn.py

@@ -33,30 +33,30 @@ class BasicNN:
     scheduler : Callable[..., Any], optional
         The learning rate scheduler used for training, which will be called
         at the end of each run of the ``fit`` method. It should implement the
-        ``step`` method, by default None.
+        ``step`` method. Defaults to None.
     device : Union[torch.device, str]
         The device on which the model will be trained or used for prediction,
-        by default torch.device("cpu").
+        Defaults to torch.device("cpu").
     batch_size : int, optional
-        The batch size used for training, by default 32.
+        The batch size used for training. Defaults to 32.
     num_epochs : int, optional
-        The number of epochs used for training, by default 1.
+        The number of epochs used for training. Defaults to 1.
     stop_loss : float, optional
-        The loss value at which to stop training, by default 0.0001.
+        The loss value at which to stop training. Defaults to 0.0001.
     num_workers : int
-        The number of workers used for loading data, by default 0.
+        The number of workers used for loading data. Defaults to 0.
     save_interval : int, optional
-        The model will be saved every ``save_interval`` epoch during training, by default None.
+        The model will be saved every ``save_interval`` epoch during training. Defaults to None.
     save_dir : str, optional
-        The directory in which to save the model during training, by default None.
+        The directory in which to save the model during training. Defaults to None.
     train_transform : Callable[..., Any], optional
         A function/transform that takes an object and returns a transformed version used
-        in the ``fit`` and ``train_epoch`` methods, by default None.
+        in the ``fit`` and ``train_epoch`` methods. Defaults to None.
     test_transform : Callable[..., Any], optional
         A function/transform that takes an object and returns a transformed version in the
-        ``predict``, ``predict_proba`` and ``score`` methods, , by default None.
+        ``predict``, ``predict_proba`` and ``score`` methods, . Defaults to None.
     collate_fn : Callable[[List[T]], Any], optional
-        The function used to collate data, by default None.
+        The function used to collate data. Defaults to None.
     """
 
     def __init__(
@@ -184,11 +184,11 @@ class BasicNN:
         Parameters
         ----------
         data_loader : DataLoader, optional
-            The data loader used for training, by default None.
+            The data loader used for training. Defaults to None.
         X : List[Any], optional
-            The input data, by default None.
+            The input data. Defaults to None.
         y : List[int], optional
-            The target data, by default None.
+            The target data. Defaults to None.
 
         Returns
         -------
@@ -291,9 +291,9 @@ class BasicNN:
         Parameters
         ----------
         data_loader : DataLoader, optional
-            The data loader used for prediction, by default None.
+            The data loader used for prediction. Defaults to None.
         X : List[Any], optional
-            The input data, by default None.
+            The input data. Defaults to None.
 
         Returns
         -------
@@ -333,9 +333,9 @@ class BasicNN:
         Parameters
         ----------
         data_loader : DataLoader, optional
-            The data loader used for prediction, by default None.
+            The data loader used for prediction. Defaults to None.
         X : List[Any], optional
-            The input data, by default None.
+            The input data. Defaults to None.
 
         Returns
         -------
@@ -423,11 +423,11 @@ class BasicNN:
         Parameters
         ----------
         data_loader : DataLoader, optional
-            The data loader used for scoring, by default None.
+            The data loader used for scoring. Defaults to None.
         X : List[Any], optional
-            The input data, by default None.
+            The input data. Defaults to None.
         y : List[int], optional
-            The target data, by default None.
+            The target data. Defaults to None.
 
         Returns
         -------
@@ -466,9 +466,9 @@ class BasicNN:
         X : List[Any]
             Input samples.
         y : List[int], optional
-            Target labels. If None, dummy labels are created, by default None.
+            Target labels. If None, dummy labels are created. Defaults to None.
         shuffle : bool, optional
-            Whether to shuffle the data, by default True.
+            Whether to shuffle the data. Defaults to True.
 
         Returns
         -------
@@ -507,7 +507,7 @@ class BasicNN:
         epoch_id : int
             The epoch id.
         save_path : str, optional
-            The path to save the model, by default None.
+            The path to save the model. Defaults to None.
         """
         if self.save_dir is None and save_path is None:
             raise ValueError("'save_dir' and 'save_path' should not be None simultaneously.")
@@ -536,7 +536,7 @@ class BasicNN:
         Parameters
         ----------
         load_path : str
-            The directory to load the model, by default "".
+            The directory to load the model. Defaults to "".
         """
 
         if load_path is None:

ablkit/learning/model_converter.py

@@ -50,30 +50,30 @@ class ModelConverter:
             The dict contains necessary parameters to construct a learning rate scheduler used
             for training, which will be called at the end of each run of the ``fit`` method.
             The scheduler class is specified by the ``scheduler`` key. It should implement the
-            ``step`` method, by default None.
+            ``step`` method. Defaults to None.
         device : torch.device, optional
             The device on which the model will be trained or used for prediction,
-            by default torch.device("cpu").
+            Defaults to torch.device("cpu").
         batch_size : int, optional
-            The batch size used for training, by default 32.
+            The batch size used for training. Defaults to 32.
         num_epochs : int, optional
-            The number of epochs used for training, by default 1.
+            The number of epochs used for training. Defaults to 1.
         stop_loss : float, optional
-            The loss value at which to stop training, by default 0.0001.
+            The loss value at which to stop training. Defaults to 0.0001.
         num_workers : int
-            The number of workers used for loading data, by default 0.
+            The number of workers used for loading data. Defaults to 0.
         save_interval : int, optional
-            The model will be saved every ``save_interval`` epoch during training, by default None.
+            The model will be saved every ``save_interval`` epoch during training. Defaults to None.
         save_dir : str, optional
-            The directory in which to save the model during training, by default None.
+            The directory in which to save the model during training. Defaults to None.
         train_transform : Callable[..., Any], optional
             A function/transform that takes an object and returns a transformed version used
-            in the `fit` and `train_epoch` methods, by default None.
+            in the `fit` and `train_epoch` methods. Defaults to None.
         test_transform : Callable[..., Any], optional
             A function/transform that takes an object and returns a transformed version in the
-            `predict`, `predict_proba` and `score` methods, , by default None.
+            `predict`, `predict_proba` and `score` methods, . Defaults to None.
         collate_fn : Callable[[List[T]], Any], optional
-            The function used to collate data, by default None.
+            The function used to collate data. Defaults to None.
 
         Returns
         -------
@@ -140,30 +140,30 @@ class ModelConverter:
             The dict contains necessary parameters to construct a learning rate scheduler used
             for training, which will be called at the end of each run of the ``fit`` method.
             The scheduler class is specified by the ``scheduler`` key. It should implement the
-            ``step`` method, by default None.
+            ``step`` method. Defaults to None.
         device : torch.device, optional
             The device on which the model will be trained or used for prediction,
-            by default torch.device("cpu").
+            Defaults to torch.device("cpu").
         batch_size : int, optional
-            The batch size used for training, by default 32.
+            The batch size used for training. Defaults to 32.
         num_epochs : int, optional
-            The number of epochs used for training, by default 1.
+            The number of epochs used for training. Defaults to 1.
         stop_loss : float, optional
-            The loss value at which to stop training, by default 0.0001.
+            The loss value at which to stop training. Defaults to 0.0001.
         num_workers : int
-            The number of workers used for loading data, by default 0.
+            The number of workers used for loading data. Defaults to 0.
         save_interval : int, optional
-            The model will be saved every ``save_interval`` epoch during training, by default None.
+            The model will be saved every ``save_interval`` epoch during training. Defaults to None.
         save_dir : str, optional
-            The directory in which to save the model during training, by default None.
+            The directory in which to save the model during training. Defaults to None.
         train_transform : Callable[..., Any], optional
             A function/transform that takes an object and returns a transformed version used
-            in the `fit` and `train_epoch` methods, by default None.
+            in the `fit` and `train_epoch` methods. Defaults to None.
         test_transform : Callable[..., Any], optional
             A function/transform that takes an object and returns a transformed version in the
-            `predict`, `predict_proba` and `score` methods, , by default None.
+            `predict`, `predict_proba` and `score` methods, . Defaults to None.
         collate_fn : Callable[[List[T]], Any], optional
-            The function used to collate data, by default None.
+            The function used to collate data. Defaults to None.
 
         Returns
         -------

ablkit/utils/logger.py

@@ -26,7 +26,7 @@ class FilterDuplicateWarning(logging.Filter):
     Parameters
     ----------
     name : str, optional
-        The name of the filter, by default "abl".
+        The name of the filter. Defaults to "abl".
     """
 
     def __init__(self, name: Optional[str] = "abl"):

ablkit/utils/utils.py

@@ -193,7 +193,7 @@ def tab_data_to_tuple(
     y : Union[List[Any], Any]
         The label.
     reasoning_result : Any, optional
-        The reasoning result, by default 0.
+        The reasoning result. Defaults to 0.
 
     Returns
     -------