Merge branch 'Dev' into main

README.md

@@ -14,7 +14,7 @@
 
 </div>
 
-# Abductive Learning (ABL) Kit
+# ABL Kit: A Python Toolkit for Abductive Learning
 
 **ABL Kit** is an efficient Python toolkit for **Abductive Learning (ABL)**.
 ABL is a novel paradigm that integrates machine learning and 

ablkit/bridge/base_bridge.py

@@ -1,3 +1,9 @@
+"""
+This module contains the base class for the Bridge part.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 from abc import ABCMeta, abstractmethod
 from typing import Any, List, Optional, Tuple, Union
 
@@ -31,11 +37,11 @@ class BaseBridge(metaclass=ABCMeta):
     def __init__(self, model: ABLModel, reasoner: Reasoner) -> None:
         if not isinstance(model, ABLModel):
             raise TypeError(
-                "Expected an instance of ABLModel, but received type: {}".format(type(model))
+                f"Expected an instance of ABLModel, but received type: {type(model)}"
             )
         if not isinstance(reasoner, Reasoner):
             raise TypeError(
-                "Expected an instance of Reasoner, but received type: {}".format(type(reasoner))
+                f"Expected an instance of Reasoner, but received type: {type(reasoner)}"
             )
 
         self.model = model

ablkit/bridge/simple_bridge.py

@@ -1,3 +1,9 @@
+"""
+This module contains a simple implementation of the Bridge part.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 import os.path as osp
 from typing import Any, List, Optional, Tuple, Union
 
@@ -221,7 +227,7 @@ def train(
             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 : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]], optional # noqa: E501
+        val_data : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]], optional # noqa: E501 pylint: disable=line-too-long
             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
@@ -327,10 +333,11 @@ def valid(
 
         Parameters
         ----------
-        val_data : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]] # noqa: E501
-            Validation data should be in the form of ``(X, gt_pseudo_label, Y)`` or a ``ListData`` object
-            with ``X``, ``gt_pseudo_label`` and ``Y`` attributes. Both ``gt_pseudo_label`` and ``Y`` can be
-            either None or not, which depends on the evaluation metircs in ``self.metric_list``.
+        val_data : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]] # noqa: E501 pylint: disable=line-too-long
+            Validation data should be in the form of ``(X, gt_pseudo_label, Y)`` or a ``ListData``
+            object with ``X``, ``gt_pseudo_label`` and ``Y`` attributes. Both ``gt_pseudo_label``
+            and ``Y`` can be either None or not, which depends on the evaluation metircs in
+            ``self.metric_list``.
         """
         val_data_examples = self.data_preprocess("val", val_data)
         self._valid(val_data_examples)
@@ -346,10 +353,11 @@ def test(
 
         Parameters
         ----------
-        test_data : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]] # noqa: E501
+        test_data : Union[ListData, Tuple[List[List[Any]], Optional[List[List[Any]]], Optional[List[Any]]]] # noqa: E501 pylint: disable=line-too-long
             Test data should be in the form of ``(X, gt_pseudo_label, Y)`` or a ``ListData`` object
             with ``X``, ``gt_pseudo_label`` and ``Y`` attributes. Both ``gt_pseudo_label`` and ``Y``
-            can be either None or not, which depends on the evaluation metircs in ``self.metric_list``.
+            can be either None or not, which depends on the evaluation metircs in
+            ``self.metric_list``.
         """
         print_log("Test start:", logger="current")
         test_data_examples = self.data_preprocess("test", test_data)

ablkit/data/evaluation/base_metric.py

@@ -1,3 +1,9 @@
+"""
+This module contains the base class used for evaluation.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 import logging
 from abc import ABCMeta, abstractmethod
 from typing import Any, List, Optional

ablkit/data/evaluation/reasoning_metric.py

@@ -1,3 +1,10 @@
+"""
+This module contains the ReasoningMetric, which is used for evaluating the model performance
+on tasks that need reasoning.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 from typing import Optional
 
 from ...reasoning import KBBase
@@ -7,7 +14,7 @@
 
 class ReasoningMetric(BaseMetric):
     """
-    A metrics class for evaluating the model performance on tasks need reasoning.
+    A metrics class for evaluating the model performance on tasks that need reasoning.
 
     This class is designed to calculate the accuracy of the reasoing results. Reasoning
     results are generated by first using the learning part to predict pseudo-labels
@@ -34,6 +41,7 @@ def __init__(self, kb: KBBase, prefix: Optional[str] = None) -> None:
         super().__init__(prefix)
         self.kb = kb
 
+    # pylint: disable=protected-access
     def process(self, data_examples: ListData) -> None:
         """
         Process a batch of data examples.

ablkit/data/evaluation/symbol_accuracy.py

@@ -1,4 +1,8 @@
-from typing import Optional
+"""
+This module contains the class SymbolAccuracy, which is used for evaluating symbol-level accuracy.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
 
 import numpy as np
 
@@ -20,9 +24,6 @@ class SymbolAccuracy(BaseMetric):
         metrics of different tasks. Inherits from BaseMetric. Default to None.
     """
 
-    def __init__(self, prefix: Optional[str] = None) -> None:
-        super().__init__(prefix)
-
     def process(self, data_examples: ListData) -> None:
         """
         Processes a batch of data examples.

ablkit/data/structures/base_data_element.py

@@ -1,6 +1,8 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# Modified from
-# https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py
+"""
+Copyright (c) OpenMMLab. All rights reserved.
+Modified from
+https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py # noqa: E501 pylint: disable=line-too-long
+"""
 
 import copy
 from typing import Any, Iterator, Optional, Tuple, Type, Union

ablkit/data/structures/list_data.py

@@ -1,6 +1,8 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# Modified from
-# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_structures/instance_data.py # noqa
+"""
+Copyright (c) OpenMMLab. All rights reserved.
+Modified from
+https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/data_structures/instance_data.py # noqa: E501 pylint: disable=line-too-long
+"""
 
 from typing import List, Union
 
@@ -54,7 +56,7 @@ class ListData(BaseDataElement):
     ``torch.Tensor``, ``numpy.ndarray``, ``list``, ``str`` and ``tuple``.
 
     This design is inspired by and extends the functionalities of the ``BaseDataElement``
-    class implemented in `MMEngine <https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py>`_. # noqa: E501
+    class implemented in `MMEngine <https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py>`_. # noqa: E501 pylint: disable=line-too-long
 
     Examples:
         >>> from ablkit.data.structures import ListData
@@ -72,7 +74,7 @@ class implemented in `MMEngine <https://github.com/open-mmlab/mmengine/blob/main
             DATA FIELDS
             Y: [1, 2, 3]
             gt_pseudo_label: [[1, 2], [3, 4], [5, 6]]
-            X: [[tensor(1.1949), tensor(-0.9378)], [tensor(0.7414), tensor(0.7603)], [tensor(1.0587), tensor(1.9697)]] # noqa: E501
+            X: [[tensor(1.1949), tensor(-0.9378)], [tensor(0.7414), tensor(0.7603)], [tensor(1.0587), tensor(1.9697)]] # noqa: E501 pylint: disable=line-too-long
         ) at 0x7f3bbf1991c0>
         >>> print(data_examples[:1])
         <ListData(

ablkit/learning/abl_model.py

@@ -1,3 +1,10 @@
+"""
+This module contains the class ABLModel, which provides a unified interface for different
+machine learning models.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 import pickle
 from typing import Any, Dict
 
@@ -99,21 +106,20 @@ def _model_operation(self, operation: str, *args, **kwargs):
             method = getattr(model, operation)
             method(*args, **kwargs)
         else:
-            if f"{operation}_path" not in kwargs.keys():
+            if f"{operation}_path" not in kwargs:
                 raise ValueError(f"'{operation}_path' should not be None")
-            else:
-                try:
-                    if operation == "save":
-                        with open(kwargs["save_path"], "wb") as file:
-                            pickle.dump(model, file, protocol=pickle.HIGHEST_PROTOCOL)
-                    elif operation == "load":
-                        with open(kwargs["load_path"], "rb") as file:
-                            self.base_model = pickle.load(file)
-                except (OSError, pickle.PickleError):
-                    raise NotImplementedError(
-                        f"{type(model).__name__} object doesn't have the {operation} method \
-                            and the default pickle-based {operation} method failed."
-                    )
+            try:
+                if operation == "save":
+                    with open(kwargs["save_path"], "wb") as file:
+                        pickle.dump(model, file, protocol=pickle.HIGHEST_PROTOCOL)
+                elif operation == "load":
+                    with open(kwargs["load_path"], "rb") as file:
+                        self.base_model = pickle.load(file)
+            except (OSError, pickle.PickleError) as exc:
+                raise NotImplementedError(
+                    f"{type(model).__name__} object doesn't have the {operation} method \
+                        and the default pickle-based {operation} method failed."
+                ) from exc
 
     def save(self, *args, **kwargs) -> None:
         """

ablkit/learning/basic_nn.py

@@ -1,3 +1,9 @@
+"""
+This module contains the class BasicNN, which servers as a wrapper for PyTorch NN models.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 from __future__ import annotations
 
 import logging
@@ -474,7 +480,7 @@ def _data_loader(
             raise ValueError("X should not be None.")
         if y is None:
             y = [0] * len(X)
-        if not (len(y) == len(X)):
+        if not len(y) == len(X):
             raise ValueError("X and y should have equal length.")
 
         dataset = ClassificationDataset(X, y, transform=self.train_transform)

ablkit/learning/torch_dataset/classification_dataset.py

@@ -1,3 +1,9 @@
+"""
+Implementation of PyTorch dataset class used for classification.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 from typing import Any, Callable, List, Tuple, Optional
 
 import torch

ablkit/learning/torch_dataset/prediction_dataset.py

@@ -1,3 +1,9 @@
+"""
+Implementation of PyTorch dataset class used for Prediction.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 from typing import Any, Callable, List, Tuple, Optional
 
 import torch

ablkit/learning/torch_dataset/regression_dataset.py

@@ -1,3 +1,9 @@
+"""
+Implementation of PyTorch dataset class used for regression.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 from typing import Any, List, Tuple
 
 from torch.utils.data import Dataset

ablkit/reasoning/kb.py

@@ -1,3 +1,10 @@
+"""
+This module contains the classes KBBase, GroundKB, and PrologKB, which provide wrappers
+for different kinds of knowledge bases.
+
+Copyright (c) 2024 LAMDA.  All rights reserved.
+"""
+
 import bisect
 import inspect
 import logging
@@ -394,7 +401,7 @@ def abduce_candidates(
             base. The second element is a list of reasoning results corresponding to each
             candidate, i.e., the outcome of the ``logic_forward`` function.
         """
-        if self.GKB == {} or len(pseudo_label) not in self.GKB_len_list:
+        if not self.GKB or len(pseudo_label) not in self.GKB_len_list:
             return [], []
 
         all_candidates, all_reasoning_results = self._find_candidate_GKB(pseudo_label, y)
@@ -478,7 +485,7 @@ def __init__(self, pseudo_label_list: List[Any], pl_file: str):
         super().__init__(pseudo_label_list)
 
         try:
-            import pyswip
+            import pyswip  # pylint: disable=import-outside-toplevel
         except (IndexError, ImportError):
             print(
                 "A Prolog-based knowledge base is in use. Please install SWI-Prolog using the"
@@ -493,7 +500,7 @@ def __init__(self, pseudo_label_list: List[Any], pl_file: str):
             raise FileNotFoundError(f"The Prolog file {self.pl_file} does not exist.")
         self.prolog.consult(self.pl_file)
 
-    def logic_forward(self, pseudo_label: List[Any]) -> Any:
+    def logic_forward(self, pseudo_label: List[Any], x: Optional[List[Any]] = None) -> Any:
         """
         Consult prolog with the query ``logic_forward(pseudo_labels, Res).``, and set the
         returned ``Res`` as the reasoning results. To use this default function, there must be
@@ -504,11 +511,15 @@ def logic_forward(self, pseudo_label: List[Any]) -> Any:
         ----------
         pseudo_label : List[Any]
             Pseudo-labels of an example.
+        x : List[Any]
+            The corresponding input example. If the information from the input
+            is not required in the reasoning process, then this parameter will not have
+            any effect.
         """
-        result = list(self.prolog.query("logic_forward(%s, Res)." % pseudo_label))[0]["Res"]
+        result = list(self.prolog.query(f"logic_forward({pseudo_label}, Res)."))[0]["Res"]
         if result == "true":
             return True
-        elif result == "false":
+        if result == "false":
             return False
         return result
 
@@ -517,7 +528,7 @@ def _revision_pseudo_label(
         pseudo_label: List[Any],
         revision_idx: List[int],
     ) -> List[Any]:
-        import re
+        import re  # pylint: disable=import-outside-toplevel
 
         revision_pseudo_label = pseudo_label.copy()
         revision_pseudo_label = flatten(revision_pseudo_label)
@@ -533,7 +544,7 @@ def get_query_string(
         self,
         pseudo_label: List[Any],
         y: Any,
-        x: List[Any],
+        x: List[Any],  # pylint: disable=unused-argument
         revision_idx: List[int],
     ) -> str:
         """
@@ -563,7 +574,7 @@ def get_query_string(
         query_string = "logic_forward("
         query_string += self._revision_pseudo_label(pseudo_label, revision_idx)
         key_is_none_flag = y is None or (isinstance(y, list) and y[0] is None)
-        query_string += ",%s)." % y if not key_is_none_flag else ")."
+        query_string += f",{y})." if not key_is_none_flag else ")."
         return query_string
 
     def revise_at_idx(