[FIX] fix optional in docstring

abl/bridge/simple_bridge.py

@@ -170,7 +170,7 @@ class SimpleBridge(BaseBridge):
         ----------
         unlabel_data_examples : ListData
             Unlabeled data examples to concatenate.
-        label_data_examples : Optional[ListData]
+        label_data_examples : ListData, optional
             Labeled data examples to concatenate, if available.
 
         Returns
@@ -215,11 +215,11 @@ class SimpleBridge(BaseBridge):
             - ``gt_pseudo_label`` is only used to evaluate the performance of the ``ABLModel`` but not
             to train. ``gt_pseudo_label`` can be ``None``.
             - ``Y`` is a list representing the ground truth reasoning result for each sublist in ``X``.
-        label_data : Optional[Union[ListData, Tuple[List[List[Any]], List[List[Any]], List[Any]]]]
+        label_data : Union[ListData, Tuple[List[List[Any]], List[List[Any]], List[Any]]], optional
             Labeled data should be in the same format as ``train_data``. The only difference is
             that the ``gt_pseudo_label`` in ``label_data`` should not be ``None`` and will be
             utilized to train the model. Defaults to None.
-        val_data : Optional[Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]]]
+        val_data : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]], optional
             Validation data should be in the same format as ``train_data``. Both ``gt_pseudo_label``
             and ``Y`` can be either None or not, which depends on the evaluation metircs in
             ``self.metric_list``. If ``val_data`` is None, ``train_data`` will be used to validate the
@@ -233,10 +233,10 @@ class SimpleBridge(BaseBridge):
         eval_interval : int
             The model will be evaluated every ``eval_interval`` loops during training,
             by default 1.
-        save_interval : Optional[int]
+        save_interval : int, optional
             The model will be saved every ``eval_interval`` loops during training, by
             default None.
-        save_dir : Optional[str]
+        save_dir : str, optional
             Directory to save the model, by default None.
         """
         data_examples = self.data_preprocess("train", train_data)

abl/learning/basic_nn.py

@@ -163,7 +163,10 @@ class BasicNN:
         return self
 
     def fit(
-        self, data_loader: DataLoader = None, X: List[Any] = None, y: List[int] = None
+        self, 
+        data_loader: Optional[DataLoader] = None, 
+        X: Optional[List[Any]] = None, 
+        y: Optional[List[int]] = None,
     ) -> BasicNN:
         """
         Train the model for self.num_epochs times or until the average loss on one epoch
@@ -267,7 +270,11 @@ class BasicNN:
 
         return torch.cat(results, axis=0)
 
-    def predict(self, data_loader: DataLoader = None, X: List[Any] = None) -> numpy.ndarray:
+    def predict(
+        self, 
+        data_loader: Optional[DataLoader] = None, 
+        X: Optional[List[Any]] = None,
+    ) -> numpy.ndarray:
         """
         Predict the class of the input data. This method supports prediction with either
         a DataLoader object (data_loader) or a list of input data (X). If both data_loader
@@ -304,7 +311,11 @@ class BasicNN:
             )
         return self._predict(data_loader).argmax(axis=1).cpu().numpy()
 
-    def predict_proba(self, data_loader: DataLoader = None, X: List[Any] = None) -> numpy.ndarray:
+    def predict_proba(
+        self, 
+        data_loader: Optional[DataLoader] = None, 
+        X: Optional[List[Any]] = None,
+    ) -> numpy.ndarray:
         """
         Predict the probability of each class for the input data. This method supports
         prediction with either a DataLoader object (data_loader) or a list of input data (X).
@@ -392,7 +403,10 @@ class BasicNN:
         return mean_loss, accuracy
 
     def score(
-        self, data_loader: DataLoader = None, X: List[Any] = None, y: List[int] = None
+        self, 
+        data_loader: Optional[DataLoader] = None, 
+        X: Optional[List[Any]] = None, 
+        y: Optional[List[int]] = None,
     ) -> float:
         """
         Validate the model. It supports validation with either a DataLoader object (data_loader)
@@ -431,7 +445,12 @@ class BasicNN:
         print_log(f"mean loss: {mean_loss:.3f}, accuray: {accuracy:.3f}", logger="current")
         return accuracy
 
-    def _data_loader(self, X: List[Any], y: List[int] = None, shuffle: bool = True) -> DataLoader:
+    def _data_loader(
+        self,
+        X: Optional[List[Any]], 
+        y: Optional[List[int]] = None, 
+        shuffle: Optional[bool] = True,
+    ) -> DataLoader:
         """
         Generate a DataLoader for user-provided input data and target labels.
 
@@ -467,7 +486,7 @@ class BasicNN:
         )
         return data_loader
 
-    def save(self, epoch_id: int = 0, save_path: str = None) -> None:
+    def save(self, epoch_id: int = 0, save_path: Optional[str] = None) -> None:
         """
         Save the model and the optimizer. User can either provide a save_path or specify
         the epoch_id at which the model and optimizer is saved. if both save_path and

abl/learning/readme.md

@@ -1,136 +0,0 @@
-# `basic_model.py`
-
-可以使用`basic_model.py`中实现的`BasicModel`类将`pytorch`神经网络模型包装成`sklearn`模型的形式.
-
-## BasicModel 类提供的接口
-
-|  方法   | 功能  |
-|  ----  | ----  |
-| fit(X, y) | 训练神经网络 |
-| predict(X) | 预测 X 的类别 |
-| predict_proba(X) | 预测 X 的类别概率 |
-| score(X, y) | 计算模型在测试数据上的准确率 |
-| save() | 保存模型 |
-| load() | 加载模型 |
-
-
-## BasicModel 类的参数
-
-**model : torch.nn.Module** 
-+ The PyTorch model to be trained or used for prediction.
-
-**batch_size : int**
-+ The batch size used for training.
-
-**num_epochs : int**
-+ The number of epochs used for training.
-
-**stop_loss : Optional[float]**
-+ The loss value at which to stop training.
-
-**num_workers : int**
-+ The number of workers used for loading data.
-
-**loss_fn : torch.nn.Module**
-+ The loss function used for training.
-
-**optimizer : torch.nn.Module**
-+ The optimizer used for training.
-
-**transform : Callable[..., Any]**
-+ The transformation function used for data augmentation.
-
-**device : torch.device**
-+ The device on which the model will be trained or used for prediction.
-
-**recorder : Any**
-+ The recorder used to record training progress.
-
-**save_interval : Optional[int]**
-+ The interval at which to save the model during training.
-
-**save_dir : Optional[str]**
-+ The directory in which to save the model during training.
-
-**collate_fn : Callable[[List[T]], Any]**
-+ The function used to collate data.
-
-## 例子
->
-> ```python
-> # Three necessary component
-> cls = LeNet5()
-> loss_fn = nn.CrossEntropyLoss()
-> optimizer = torch.optim.Adam(cls.parameters())
-> 
-> # Initialize base_model
-> base_model = BasicModel(
->     cls,
->     loss_fn,
->     optimizer,
->     torch.device("cuda:0"),
->     batch_size=32,
->     num_epochs=10,
-> )
->
-> # Prepare data
-> train_X, train_y = get_train_data()
-> test_X, test_y = get_test_data()
-> 
-> # Train model
-> base_model.fit(train_X, train_y)
->
-> # Predict
-> base_model.predict(test_X)
->
-> # Validation
-> base_model.score(test_X, test_y)
-> ```
-
-# `wabl_models.py`
-
-`wabl_models.py`中实现的`WABLBasicModel`能够序列化数据并为不同的机器学习模型提供统一的接口.
-
-## WABLBasicModel 类提供的接口
-
-|  方法   | 功能  |
-|  ----  | ----  |
-| train(X, Y) | 利用训练数据训练机器学习模型（不涉及反绎） |
-| predict(X) | 预测 X 的类别和概率 |
-| valid(X, Y) | 计算模型在测试数据上的准确率 |
-
-## WABLBasicModel 类的参数
-**base_model : Machine Learning Model**
-+ The base model to use for training and prediction.
-
-**pseudo_label_list : List[Any]**
-+ A list of pseudo-labels to use for training.
-
-## 序列化数据
-考虑到训练数据可能多种组织形式，比如：\
-`X: List[List[img]], Y: List[List[label]]`\
-`X: List[List[img]], Y: List[label]`\
-`X: List[img], Y: List[label]`
-... \
-不便于训练. 因此先将形式统一为：`X: List[img], Y: List[label]`，也就是所谓的序列化数据.
-
-## 例子
->
-> ```python
-> # Three necessary component
-> # 'ml_model' is no longer limited to NN models
-> model = WABLBasicModel(ml_model, kb.pseudo_label_list)
->
-> # Prepare data
-> train_X, train_y = get_train_data()
-> test_X, test_y = get_test_data()
-> 
-> # Train model
-> model.train(train_X, train_y)
->
-> # Predict
-> model.predict(test_X)
->
-> # Validation
-> model.valid(test_X, test_y)
-> ```

abl/learning/torch_dataset/classification_dataset.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, List, Tuple
+from typing import Any, Callable, List, Tuple, Optional
 
 import torch
 from torch.utils.data import Dataset
@@ -19,7 +19,7 @@ class ClassificationDataset(Dataset):
         Defaults to None.
     """
 
-    def __init__(self, X: List[Any], Y: List[int], transform: Callable[..., Any] = None):
+    def __init__(self, X: List[Any], Y: List[int], transform: Optional[Callable[..., Any]] = None):
         if (not isinstance(X, list)) or (not isinstance(Y, list)):
             raise ValueError("X and Y should be of type list.")
         if len(X) != len(Y):

abl/learning/torch_dataset/prediction_dataset.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable, List, Tuple
+from typing import Any, Callable, List, Tuple, Optional
 
 import torch
 from torch.utils.data import Dataset
@@ -17,7 +17,7 @@ class PredictionDataset(Dataset):
         Defaults to None.
     """
 
-    def __init__(self, X: List[Any], transform: Callable[..., Any] = None):
+    def __init__(self, X: List[Any], transform: Optional[Callable[..., Any]] = None):
         if not isinstance(X, list):
             raise ValueError("X should be of type list.")
 

abl/reasoning/kb.py

@@ -21,7 +21,7 @@ class KBBase(ABC):
 
     Parameters
     ----------
-    pseudo_label_list : list
+    pseudo_label_list : List[Any]
         List of possible pseudo-labels. It's recommended to arrange the pseudo-labels in this
         list so that each aligns with its corresponding index in the base model: the first with
         the 0th index, the second with the 1st, and so forth.
@@ -51,11 +51,11 @@ class KBBase(ABC):
 
     def __init__(
         self,
-        pseudo_label_list: list,
-        max_err: float = 1e-10,
-        use_cache: bool = True,
-        key_func: Callable = to_hashable,
-        cache_size: int = 4096,
+        pseudo_label_list: List[Any],
+        max_err: Optional[float] = 1e-10,
+        use_cache: Optional[bool] = True,
+        key_func: Optional[Callable] = to_hashable,
+        cache_size: Optional[int] = 4096,
     ):
         if not isinstance(pseudo_label_list, list):
             raise TypeError(f"pseudo_label_list should be list, got {type(pseudo_label_list)}")
@@ -88,7 +88,7 @@ class KBBase(ABC):
         ----------
         pseudo_label : List[Any]
             Pseudo-labels of an example.
-        x : Optional[List[Any]]
+        x : List[Any], optional
             The example. If deductive logical reasoning does not require any 
             information from the example, the overridden function provided by the user can omit 
             this parameter.
@@ -288,9 +288,9 @@ class GroundKB(KBBase):
 
     Parameters
     ----------
-    pseudo_label_list : list
+    pseudo_label_list : List[Any]
         Refer to class ``KBBase``.
-    GKB_len_list : list
+    GKB_len_list : List[int]
         List of possible lengths for pseudo-labels of an example.
     max_err : float, optional
         Refer to class ``KBBase``.
@@ -304,7 +304,12 @@ class GroundKB(KBBase):
     abductive reasoning) will be automatically set up.
     """
 
-    def __init__(self, pseudo_label_list, GKB_len_list, max_err=1e-10):
+    def __init__(
+        self, 
+        pseudo_label_list: List[Any], 
+        GKB_len_list: List[int], 
+        max_err: Optional[float] = 1e-10,
+    ):
         super().__init__(pseudo_label_list, max_err)
         if not isinstance(GKB_len_list, list):
             raise TypeError("GKB_len_list should be list, but got {type(GKB_len_list)}")
@@ -445,12 +450,10 @@ class PrologKB(KBBase):
 
     Parameters
     ----------
-    pseudo_label_list : list
+    pseudo_label_list : List[Any]
         Refer to class ``KBBase``.
-    pl_file :
+    pl_file : str
         Prolog file containing the KB.
-    max_err : float, optional
-        Refer to class ``KBBase``.
 
     Notes
     -----

abl/utils/logger.py

@@ -24,7 +24,7 @@ class FilterDuplicateWarning(logging.Filter):
         The name of the filter, by default "abl".
     """
 
-    def __init__(self, name: str = "abl"):
+    def __init__(self, name: Optional[str] = "abl"):
         super().__init__(name)
         self.seen: set = set()
 
@@ -85,7 +85,7 @@ class ABLFormatter(logging.Formatter):
         self.info_format = f"%(asctime)s - %(name)s - {info_prefix} - %(" "message)s"
         self.debug_format = f"%(asctime)s - %(name)s - {debug_prefix} - %(" "message)s"
 
-    def _get_prefix(self, level: str, color: bool, blink=False) -> str:
+    def _get_prefix(self, level: str, color: bool, blink: Optional[bool] = False) -> str:
         """
         Get the prefix of the target log level.
 
@@ -192,8 +192,8 @@ class ABLLogger(Logger, ManagerMixin):
         name: str,
         logger_name="abl",
         log_file: Optional[str] = None,
-        log_level: Union[int, str] = "INFO",
-        file_mode: str = "w",
+        log_level: Optional[Union[int, str]] = "INFO",
+        file_mode: Optional[str] = "w",
     ):
         Logger.__init__(self, logger_name)
         ManagerMixin.__init__(self, name)
@@ -286,7 +286,11 @@ class ABLLogger(Logger, ManagerMixin):
         _release_lock()
 
 
-def print_log(msg, logger: Optional[Union[Logger, str]] = None, level=logging.INFO) -> None:
+def print_log(
+    msg, 
+    logger: Optional[Union[Logger, str]] = None, 
+    level: Optional[int] = logging.INFO,
+) -> None:
     """
     Print a log message using the specified logger or a default method.
 
@@ -297,7 +301,7 @@ def print_log(msg, logger: Optional[Union[Logger, str]] = None, level=logging.IN
     ----------
     msg : str
         The message to be logged.
-    logger : Optional[Union[Logger, str]], optional
+    logger : Union[Logger, str], optional
         The logger to use for logging the message. It can be a `logging.Logger` instance, a string
         specifying the logger name, 'silent', 'current', or None. If None, the `print`
         method is used.

abl/utils/utils.py

@@ -1,4 +1,4 @@
-from typing import List, Any, Union, Tuple
+from typing import List, Any, Union, Tuple, Optional
 
 import numpy as np
 
@@ -62,7 +62,7 @@ def reform_list(
     return reformed_list
 
 
-def hamming_dist(pred_pseudo_label, candidates):
+def hamming_dist(pred_pseudo_label: List[Any], candidates: List[List[Any]]) -> np.ndarray:
     """
     Compute the Hamming distance between two arrays.
 
@@ -87,7 +87,7 @@ def hamming_dist(pred_pseudo_label, candidates):
     return np.sum(pred_pseudo_label != candidates, axis=1)
 
 
-def confidence_dist(pred_prob, candidates_idxs):
+def confidence_dist(pred_prob: List[np.ndarray], candidates_idxs: List[List[Any]]) -> np.ndarray:
     """
     Compute the confidence distance between prediction probabilities and candidates.
 
@@ -109,7 +109,7 @@ def confidence_dist(pred_prob, candidates_idxs):
     return 1 - np.prod(pred_prob[cols, candidates_idxs], axis=1)
 
 
-def to_hashable(x):
+def to_hashable(x: Union[List[Any], Any]) -> Union[Tuple[Any, ...], Any]:
     """
     Convert a nested list to a nested tuple so it is hashable.
 
@@ -148,7 +148,11 @@ def restore_from_hashable(x):
         return [restore_from_hashable(item) for item in x]
     return x
 
-def tab_data_to_tuple(X, y, reasoning_result = 0):
+def tab_data_to_tuple(
+    X: Union[List[Any], Any], 
+    y: Union[List[Any], Any], 
+    reasoning_result: Optional[Any] = 0
+) -> Tuple[List[List[Any]], List[List[Any]], List[Any]]:
     '''
     Convert a tabular data to a tuple by adding a dimension to each element of 
     X and y. The tuple contains three elements: data, label, and reasoning result.