diff --git a/README.md b/README.md index 35a41fd..bc0648a 100644 --- a/README.md +++ b/README.md @@ -19,9 +19,9 @@ -# ABL kit: A Python Toolkit for Abductive Learning +# ABLkit: A Python Toolkit for Abductive Learning -**ABL kit** is an efficient Python toolkit for **Abductive Learning (ABL)**. +**ABLkit** is an efficient Python toolkit for **Abductive Learning (ABL)**. ABL is a novel paradigm that integrates machine learning and logical reasoning in a unified framework. It is suitable for tasks where both data and (logical) domain knowledge are available. @@ -30,25 +30,25 @@ where both data and (logical) domain knowledge are available. Abductive Learning

-Key Features of ABL kit: +Key Features of ABLkit: - **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components. - **User-Friendly**: Provide data, model, and KB, and get started with just a few lines of code. - **High-Performance**: Optimization for high accuracy and fast training speed. -ABL kit encapsulates advanced ABL techniques, providing users with +ABLkit encapsulates advanced ABL techniques, providing users with an efficient and convenient toolkit to develop dual-driven ABL systems, which leverage the power of both data and knowledge.

-ABL kit +ABLkit

## Installation ### Install from PyPI -The easiest way to install ABL kit is using ``pip``: +The easiest way to install ABLkit is using ``pip``: ```bash pip install ablkit @@ -84,7 +84,7 @@ We use the MNIST Addition task as a quick start example. In this task, pairs of Working with Data
-ABL kit requires data in the format of `(X, gt_pseudo_label, Y)` where `X` is a list of input examples containing instances, `gt_pseudo_label` is the ground-truth label of each example in `X` and `Y` is the ground-truth reasoning result of each example in `X`. Note that `gt_pseudo_label` is only used to evaluate the machine learning model's performance but not to train it. +ABLkit requires data in the format of `(X, gt_pseudo_label, Y)` where `X` is a list of input examples containing instances, `gt_pseudo_label` is the ground-truth label of each example in `X` and `Y` is the ground-truth reasoning result of each example in `X`. Note that `gt_pseudo_label` is only used to evaluate the machine learning model's performance but not to train it. In the MNIST Addition task, the data loading looks like: @@ -103,7 +103,7 @@ test_data = get_dataset(train=False) Building the Learning Part
-Learning part is constructed by first defining a base model for machine learning. ABL kit offers considerable flexibility, supporting any base model that conforms to the scikit-learn style (which requires the implementation of fit and predict methods), or a PyTorch-based neural network (which has defined the architecture and implemented forward method). In this example, we build a simple LeNet5 network as the base model. +Learning part is constructed by first defining a base model for machine learning. ABLkit offers considerable flexibility, supporting any base model that conforms to the scikit-learn style (which requires the implementation of fit and predict methods), or a PyTorch-based neural network (which has defined the architecture and implemented forward method). In this example, we build a simple LeNet5 network as the base model. ```python # The 'models' module below is located in 'examples/mnist_add/' @@ -112,7 +112,7 @@ from models.nn import LeNet5 cls = LeNet5(num_classes=10) ``` -To facilitate uniform processing, ABL kit provides the `BasicNN` class to convert a PyTorch-based neural network into a format compatible with scikit-learn models. To construct a `BasicNN` instance, aside from the network itself, we also need to define a loss function, an optimizer, and the computing device. +To facilitate uniform processing, ABLkit provides the `BasicNN` class to convert a PyTorch-based neural network into a format compatible with scikit-learn models. To construct a `BasicNN` instance, aside from the network itself, we also need to define a loss function, an optimizer, and the computing device. ```python ​import torch @@ -167,7 +167,7 @@ reasoner = Reasoner(kb) Building Evaluation Metrics
-ABL kit provides two basic metrics, namely `SymbolAccuracy` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the `logic_forward` results, respectively. +ABLkit provides two basic metrics, namely `SymbolAccuracy` and `ReasoningMetric`, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the `logic_forward` results, respectively. ```python from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy diff --git a/ablkit/data/data_converter.py b/ablkit/data/data_converter.py index 023975c..6841ffe 100644 --- a/ablkit/data/data_converter.py +++ b/ablkit/data/data_converter.py @@ -7,7 +7,7 @@ from lambdaLearn.Base.TabularMixin import TabularMixin class DataConverter: """ - This class provides functionality to convert LambdaLearn data to ABL kit data. + This class provides functionality to convert LambdaLearn data to ABLkit data. """ def __init__(self) -> None: diff --git a/ablkit/data/structures/list_data.py b/ablkit/data/structures/list_data.py index 49366cb..256deda 100644 --- a/ablkit/data/structures/list_data.py +++ b/ablkit/data/structures/list_data.py @@ -21,9 +21,9 @@ IndexType = Union[str, slice, int, list, LongTypeTensor, BoolTypeTensor, np.ndar class ListData(BaseDataElement): """ - Abstract Data Interface used throughout the ABL kit. + Abstract Data Interface used throughout the ABLkit. - ``ListData`` is the underlying data structure used in the ABL kit, + ``ListData`` is the underlying data structure used in the ABLkit, designed to manage diverse forms of data dynamically generated throughout the Abductive Learning (ABL) framework. This includes handling raw data, predicted pseudo-labels, abduced pseudo-labels, pseudo-label indices, etc. diff --git a/ablkit/learning/model_converter.py b/ablkit/learning/model_converter.py index 56824b9..13b36f1 100644 --- a/ablkit/learning/model_converter.py +++ b/ablkit/learning/model_converter.py @@ -9,7 +9,7 @@ from lambdaLearn.Base.DeepModelMixin import DeepModelMixin class ModelConverter: """ - This class provides functionality to convert LambdaLearn models to ABL kit models. + This class provides functionality to convert LambdaLearn models to ABLkit models. """ def __init__(self) -> None: diff --git a/docs/Examples/HWF.rst b/docs/Examples/HWF.rst index 75616c5..ae3b00b 100644 --- a/docs/Examples/HWF.rst +++ b/docs/Examples/HWF.rst @@ -234,7 +234,7 @@ examples. .. code:: python from ablkit.data.structures import ListData - # ListData is a data structure provided by ABL kit that can be used to organize data examples + # ListData is a data structure provided by ABLkit that can be used to organize data examples data_examples = ListData() # We use the first 1001st and 3001st data examples in the training set as an illustration data_examples.X = [X_1000, X_3000] diff --git a/docs/Examples/MNISTAdd.rst b/docs/Examples/MNISTAdd.rst index 3127139..70d92ca 100644 --- a/docs/Examples/MNISTAdd.rst +++ b/docs/Examples/MNISTAdd.rst @@ -203,7 +203,7 @@ examples. .. code:: python from ablkit.data.structures import ListData - # ListData is a data structure provided by ABL kit that can be used to organize data examples + # ListData is a data structure provided by ABLkit that can be used to organize data examples data_examples = ListData() # We use the first 100 data examples in the training set as an illustration data_examples.X = train_X[:100] diff --git a/docs/Examples/Zoo.rst b/docs/Examples/Zoo.rst index 33b54ec..388dd94 100644 --- a/docs/Examples/Zoo.rst +++ b/docs/Examples/Zoo.rst @@ -84,7 +84,7 @@ Out: Next, we transform the tabular data to the format required by -ABL kit, which is a tuple of (X, gt_pseudo_label, Y). In this task, +ABLkit, which is a tuple of (X, gt_pseudo_label, Y). In this task, we treat the attributes as X and the targets as gt_pseudo_label (ground truth pseudo-labels). Y (reasoning results) are expected to be 0, indicating no rules are violated. diff --git a/docs/Intro/Basics.rst b/docs/Intro/Basics.rst index 008e286..c898b03 100644 --- a/docs/Intro/Basics.rst +++ b/docs/Intro/Basics.rst @@ -9,14 +9,14 @@ Learn the Basics ================ -Modules in ABL kit +Modules in ABLkit ---------------------- -ABL kit is an efficient toolkit for `Abductive Learning <../Overview/Abductive-Learning.html>`_ (ABL), +ABLkit is an efficient toolkit for `Abductive Learning <../Overview/Abductive-Learning.html>`_ (ABL), a paradigm which integrates machine learning and logical reasoning in a balanced-loop. -ABL kit comprises three primary parts: **Data**, **Learning**, and +ABLkit comprises three primary parts: **Data**, **Learning**, and **Reasoning**, corresponding to the three pivotal components of current -AI: data, models, and knowledge. Below is an overview of the ABL kit. +AI: data, models, and knowledge. Below is an overview of the ABLkit. .. image:: ../_static/img/ABLkit.png @@ -50,7 +50,7 @@ from the ``BaseBridge`` class). The Bridge part synthesizes data, learning, and reasoning, facilitating the training and testing of the entire ABL framework. -Use ABL kit Step by Step +Use ABLkit Step by Step ---------------------------- In a typical ABL process, as illustrated below, diff --git a/docs/Intro/Bridge.rst b/docs/Intro/Bridge.rst index 3619b6e..b61ec66 100644 --- a/docs/Intro/Bridge.rst +++ b/docs/Intro/Bridge.rst @@ -10,7 +10,7 @@ Bridge ====== -In this section, we will look at how to bridge learning and reasoning parts to train the model, which is the fundamental idea of Abductive Learning. ABL kit implements a set of bridge classes to achieve this. +In this section, we will look at how to bridge learning and reasoning parts to train the model, which is the fundamental idea of Abductive Learning. ABLkit implements a set of bridge classes to achieve this. .. code:: python @@ -42,7 +42,7 @@ In this section, we will look at how to bridge learning and reasoning parts to t | ``test(test_data)`` | Test the model. | +---------------------------------------+----------------------------------------------------+ -where ``train_data`` and ``test_data`` are both in the form of a tuple or a `ListData <../API/ablkit.data.html#structures.ListData>`_. Regardless of the form, they all need to include three components: ``X``, ``gt_pseudo_label`` and ``Y``. Since ``ListData`` is the underlying data structure used throughout the ABL kit, tuple-formed data will be firstly transformed into ``ListData`` in the ``train`` and ``test`` methods, and such ``ListData`` instances are referred to as ``data_examples``. More details can be found in `preparing datasets `_. +where ``train_data`` and ``test_data`` are both in the form of a tuple or a `ListData <../API/ablkit.data.html#structures.ListData>`_. Regardless of the form, they all need to include three components: ``X``, ``gt_pseudo_label`` and ``Y``. Since ``ListData`` is the underlying data structure used throughout the ABLkit, tuple-formed data will be firstly transformed into ``ListData`` in the ``train`` and ``test`` methods, and such ``ListData`` instances are referred to as ``data_examples``. More details can be found in `preparing datasets `_. ``SimpleBridge`` inherits from ``BaseBridge`` and provides a basic implementation. Besides the ``model`` and ``reasoner``, ``SimpleBridge`` has an extra initialization argument, ``metric_list``, which will be used to evaluate model performance. Its training process involves several Abductive Learning loops and each loop consists of the following five steps: diff --git a/docs/Intro/Datasets.rst b/docs/Intro/Datasets.rst index daf7476..99ce77d 100644 --- a/docs/Intro/Datasets.rst +++ b/docs/Intro/Datasets.rst @@ -10,7 +10,7 @@ Dataset & Data Structure ======================== -In this section, we will look at the dataset and data structure in ABL kit. +In this section, we will look at the dataset and data structure in ABLkit. .. code:: python @@ -20,7 +20,7 @@ In this section, we will look at the dataset and data structure in ABL kit. Dataset ------- -ABL kit requires user data to be either structured as a tuple ``(X, gt_pseudo_label, Y)`` or a ``ListData`` (the underlying data structure utilized in ABL kit, cf. the next section) object with ``X``, ``gt_pseudo_label`` and ``Y`` attributes. Regardless of the chosen format, the data should encompass three essential components: +ABLkit requires user data to be either structured as a tuple ``(X, gt_pseudo_label, Y)`` or a ``ListData`` (the underlying data structure utilized in ABLkit, cf. the next section) object with ``X``, ``gt_pseudo_label`` and ``Y`` attributes. Regardless of the chosen format, the data should encompass three essential components: - ``X``: List[List[Any]] @@ -62,11 +62,11 @@ where each sublist in ``X``, e.g., |data_example|, is a data example and each im Data Structure -------------- -Besides the user-provided dataset, various forms of data are utilized and dynamicly generated throughout the training and testing process of ABL framework. Examples include raw data, predicted pseudo-label, abduced pseudo-label, pseudo-label indices, etc. To manage this diversity and ensure a stable, versatile interface, ABL kit employs `abstract data interfaces <../API/ablkit.data.html#structures>`_ to encapsulate different forms of data that will be used in the total learning process. +Besides the user-provided dataset, various forms of data are utilized and dynamicly generated throughout the training and testing process of ABL framework. Examples include raw data, predicted pseudo-label, abduced pseudo-label, pseudo-label indices, etc. To manage this diversity and ensure a stable, versatile interface, ABLkit employs `abstract data interfaces <../API/ablkit.data.html#structures>`_ to encapsulate different forms of data that will be used in the total learning process. -``ListData`` is the underlying abstract data interface utilized in ABL kit. As the fundamental data structure, ``ListData`` implements commonly used data manipulation methods and is responsible for transferring data between various components of ABL, ensuring that stages such as prediction, abductive reasoning, and training can utilize ``ListData`` as a unified input format. Before proceeding to other stages, user-provided datasets will be firstly converted into ``ListData``. +``ListData`` is the underlying abstract data interface utilized in ABLkit. As the fundamental data structure, ``ListData`` implements commonly used data manipulation methods and is responsible for transferring data between various components of ABL, ensuring that stages such as prediction, abductive reasoning, and training can utilize ``ListData`` as a unified input format. Before proceeding to other stages, user-provided datasets will be firstly converted into ``ListData``. -Besides providing a tuple of ``(X, gt_pseudo_label, Y)``, ABL kit also allows users to directly supply data in ``ListData`` format, which similarly requires the inclusion of these three attributes. The following code shows the basic usage of ``ListData``. More information can be found in the `API documentation <../API/ablkit.data.html#structures>`_. +Besides providing a tuple of ``(X, gt_pseudo_label, Y)``, ABLkit also allows users to directly supply data in ``ListData`` format, which similarly requires the inclusion of these three attributes. The following code shows the basic usage of ``ListData``. More information can be found in the `API documentation <../API/ablkit.data.html#structures>`_. .. code-block:: python diff --git a/docs/Intro/Evaluation.rst b/docs/Intro/Evaluation.rst index 8ebac20..09f43fd 100644 --- a/docs/Intro/Evaluation.rst +++ b/docs/Intro/Evaluation.rst @@ -16,7 +16,7 @@ In this section, we will look at how to build evaluation metrics. from ablkit.data.evaluation import BaseMetric, SymbolAccuracy, ReasoningMetric -ABL kit seperates the evaluation process from model training and testing as an independent class, ``BaseMetric``. The training and testing processes are implemented in the ``BaseBridge`` class, so metrics are used by this class and its sub-classes. After building a ``bridge`` with a list of ``BaseMetric`` instances, these metrics will be used by the ``bridge.valid`` method to evaluate the model performance during training and testing. +ABLkit seperates the evaluation process from model training and testing as an independent class, ``BaseMetric``. The training and testing processes are implemented in the ``BaseBridge`` class, so metrics are used by this class and its sub-classes. After building a ``bridge`` with a list of ``BaseMetric`` instances, these metrics will be used by the ``bridge.valid`` method to evaluate the model performance during training and testing. To customize our own metrics, we need to inherit from ``BaseMetric`` and implement the ``process`` and ``compute_metrics`` methods. diff --git a/docs/Intro/Learning.rst b/docs/Intro/Learning.rst index 615f379..efb1e94 100644 --- a/docs/Intro/Learning.rst +++ b/docs/Intro/Learning.rst @@ -12,7 +12,7 @@ Learning Part In this section, we will look at how to build the learning part. -In ABL kit, building the learning part involves two steps: +In ABLkit, building the learning part involves two steps: 1. Build a machine learning base model used to make predictions on instance-level data. 2. Instantiate an ``ABLModel`` with the base model, which enables the learning part to process example-level data. @@ -76,7 +76,7 @@ Besides the necessary methods required to instantiate an ``ABLModel``, i.e., ``f Instantiating an ABLModel ------------------------- -Typically, base model is trained to make predictions on instance-level data, and can not directly process example-level data, which is not suitable for most neural-symbolic tasks. ABL kit provides the ``ABLModel`` to solve this problem. This class serves as a unified wrapper for all base models, which enables the learning part to train, test, and predict on example-level data. +Typically, base model is trained to make predictions on instance-level data, and can not directly process example-level data, which is not suitable for most neural-symbolic tasks. ABLkit provides the ``ABLModel`` to solve this problem. This class serves as a unified wrapper for all base models, which enables the learning part to train, test, and predict on example-level data. Generally, we can simply instantiate an ``ABLModel`` by: diff --git a/docs/Intro/Quick-Start.rst b/docs/Intro/Quick-Start.rst index 19e9eb4..4563673 100644 --- a/docs/Intro/Quick-Start.rst +++ b/docs/Intro/Quick-Start.rst @@ -14,7 +14,7 @@ We use the MNIST Addition task as a quick start example. In this task, pairs of Working with Data ----------------- -ABL kit requires data in the format of ``(X, gt_pseudo_label, Y)`` where ``X`` is a list of input examples containing instances, +ABLkit requires data in the format of ``(X, gt_pseudo_label, Y)`` where ``X`` is a list of input examples containing instances, ``gt_pseudo_label`` is the ground-truth label of each example in ``X`` and ``Y`` is the ground-truth reasoning result of each example in ``X``. Note that ``gt_pseudo_label`` is only used to evaluate the machine learning model's performance but not to train it. In the MNIST Addition task, the data loading looks like @@ -33,7 +33,7 @@ Read more about `preparing datasets `_. Building the Learning Part -------------------------- -Learning part is constructed by first defining a base model for machine learning. ABL kit offers considerable flexibility, supporting any base model that conforms to the scikit-learn style (which requires the implementation of ``fit`` and ``predict`` methods), or a PyTorch-based neural network (which has defined the architecture and implemented ``forward`` method). +Learning part is constructed by first defining a base model for machine learning. ABLkit offers considerable flexibility, supporting any base model that conforms to the scikit-learn style (which requires the implementation of ``fit`` and ``predict`` methods), or a PyTorch-based neural network (which has defined the architecture and implemented ``forward`` method). In this example, we build a simple LeNet5 network as the base model. .. code:: python @@ -43,7 +43,7 @@ In this example, we build a simple LeNet5 network as the base model. cls = LeNet5(num_classes=10) -To facilitate uniform processing, ABL kit provides the ``BasicNN`` class to convert a PyTorch-based neural network into a format compatible with scikit-learn models. To construct a ``BasicNN`` instance, aside from the network itself, we also need to define a loss function, an optimizer, and the computing device. +To facilitate uniform processing, ABLkit provides the ``BasicNN`` class to convert a PyTorch-based neural network into a format compatible with scikit-learn models. To construct a ``BasicNN`` instance, aside from the network itself, we also need to define a loss function, an optimizer, and the computing device. .. code:: python @@ -98,7 +98,7 @@ Read more about `building the reasoning part `_. Building Evaluation Metrics --------------------------- -ABL kit provides two basic metrics, namely ``SymbolAccuracy`` and ``ReasoningMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively. +ABLkit provides two basic metrics, namely ``SymbolAccuracy`` and ``ReasoningMetric``, which are used to evaluate the accuracy of the machine learning model's predictions and the accuracy of the ``logic_forward`` results, respectively. .. code:: python diff --git a/docs/Intro/Reasoning.rst b/docs/Intro/Reasoning.rst index 3f1db67..9a071f8 100644 --- a/docs/Intro/Reasoning.rst +++ b/docs/Intro/Reasoning.rst @@ -12,7 +12,7 @@ Reasoning part In this section, we will look at how to build the reasoning part, which leverages domain knowledge and performs deductive or abductive reasoning. -In ABL kit, building the reasoning part involves two steps: +In ABLkit, building the reasoning part involves two steps: 1. Build a knowledge base by creating a subclass of ``KBBase``, which specifies how to process pseudo-label of an example to the reasoning result. @@ -28,7 +28,7 @@ Building a knowledge base ------------------------- Generally, we can create a subclass derived from ``KBBase`` to build our own -knowledge base. In addition, ABL kit also offers several predefined +knowledge base. In addition, ABLkit also offers several predefined subclasses of ``KBBase`` (e.g., ``PrologKB`` and ``GroundKB``), which we can utilize to build our knowledge base more conveniently. diff --git a/docs/Makefile b/docs/Makefile index 7f624dc..35b8b01 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -4,7 +4,7 @@ # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build -SPHINXPROJ = ABL kit +SPHINXPROJ = ABLkit SOURCEDIR = . BUILDDIR = build diff --git a/docs/Overview/Installation.rst b/docs/Overview/Installation.rst index 123e1f7..c8ae51d 100644 --- a/docs/Overview/Installation.rst +++ b/docs/Overview/Installation.rst @@ -4,7 +4,7 @@ Installation Install from PyPI ^^^^^^^^^^^^^^^^^ -The easiest way to install ABL kit is using ``pip``: +The easiest way to install ABLkit is using ``pip``: .. code:: bash diff --git a/docs/README.rst b/docs/README.rst index 103885e..b3f9ed3 100644 --- a/docs/README.rst +++ b/docs/README.rst @@ -1,20 +1,20 @@ -ABL kit +ABLkit ======= -**ABL kit** is an efficient Python toolkit for **Abductive Learning (ABL)**. +**ABLkit** is an efficient Python toolkit for **Abductive Learning (ABL)**. ABL is a novel paradigm that integrates machine learning and logical reasoning in a unified framework. It is suitable for tasks where both data and (logical) domain knowledge are available. .. image:: _static/img/ABL.png -Key Features of ABL kit: +Key Features of ABLkit: - **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components. - **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code. - **High-Performance**: Optimization for high accuracy and fast training speed. -ABL kit encapsulates advanced ABL techniques, providing users with +ABLkit encapsulates advanced ABL techniques, providing users with an efficient and convenient toolkit to develop dual-driven ABL systems, which leverage the power of both data and knowledge. @@ -26,7 +26,7 @@ Installation Install from PyPI ^^^^^^^^^^^^^^^^^ -The easiest way to install ABL kit is using ``pip``: +The easiest way to install ABLkit is using ``pip``: .. code:: bash diff --git a/docs/conf.py b/docs/conf.py index 5fcc6f8..4da02e3 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -14,7 +14,7 @@ import ablkit # noqa: E402,F401 # -- Project information ----------------------------------------------------- -project = "ABL kit" +project = "ABLkit" copyright = "LAMDA, 2024" # -- General configuration --------------------------------------------------- diff --git a/docs/index.rst b/docs/index.rst index a62be39..63af4e5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -9,7 +9,7 @@ .. toctree:: :maxdepth: 1 - :caption: Introduction to ABL kit + :caption: Introduction to ABLkit Intro/Basics Intro/Quick-Start diff --git a/examples/hwf/hwf.ipynb b/examples/hwf/hwf.ipynb index 730a433..baf161d 100644 --- a/examples/hwf/hwf.ipynb +++ b/examples/hwf/hwf.ipynb @@ -237,7 +237,7 @@ "source": [ "from ablkit.data.structures import ListData\n", "\n", - "# ListData is a data structure provided by ABL kit that can be used to organize data examples\n", + "# ListData is a data structure provided by ABLkit that can be used to organize data examples\n", "data_examples = ListData()\n", "# We use the first 1001st and 3001st data examples in the training set as an illustration\n", "data_examples.X = [X_1000, X_3000]\n", diff --git a/examples/mnist_add/mnist_add.ipynb b/examples/mnist_add/mnist_add.ipynb index 3ec8e4d..502dd50 100644 --- a/examples/mnist_add/mnist_add.ipynb +++ b/examples/mnist_add/mnist_add.ipynb @@ -282,7 +282,7 @@ "source": [ "from ablkit.data.structures import ListData\n", "\n", - "# ListData is a data structure provided by ABL kit that can be used to organize data examples\n", + "# ListData is a data structure provided by ABLkit that can be used to organize data examples\n", "data_examples = ListData()\n", "# We use the first 100 data examples in the training set as an illustration\n", "data_examples.X = train_X[:100]\n", @@ -504,7 +504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.18" + "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { diff --git a/examples/zoo/zoo.ipynb b/examples/zoo/zoo.ipynb index bb05625..94508ed 100644 --- a/examples/zoo/zoo.ipynb +++ b/examples/zoo/zoo.ipynb @@ -97,7 +97,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we transform the tabular data to the format required by ABL kit, which is a tuple of (X, gt_pseudo_label, Y). In this task, we treat the attributes as X and the targets as gt_pseudo_label (ground truth pseudo-labels). Y (reasoning results) are expected to be 0, indicating no rules are violated." + "Next, we transform the tabular data to the format required by ABLkit, which is a tuple of (X, gt_pseudo_label, Y). In this task, we treat the attributes as X and the targets as gt_pseudo_label (ground truth pseudo-labels). Y (reasoning results) are expected to be 0, indicating no rules are violated." ] }, {