[ENH] change code from abl to ablkit

README.md

@@ -113,7 +113,7 @@ To facilitate uniform processing, ABL Kit provides the `BasicNN` class to conver
 
 ```python
 ​import torch
-​from abl.learning import BasicNN
+​from ablkit.learning import BasicNN
 ​    
 ​loss_fn = torch.nn.CrossEntropyLoss()
 ​optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001, alpha=0.9)
@@ -124,7 +124,7 @@ To facilitate uniform processing, ABL Kit provides the `BasicNN` class to conver
 The base model built above is trained to make predictions on instance-level data (e.g., a single image), while ABL deals with example-level data. To bridge this gap, we wrap the base_model into an instance of `ABLModel`. This class serves as a unified wrapper for base models, facilitating the learning part to train, test, and predict on example-level data, (e.g., images that comprise an equation).
 
 ```python
-from abl.learning import ABLModel
+from ablkit.learning import ABLModel
 ​    
 ​model = ABLModel(base_model)
 ```
@@ -138,7 +138,7 @@ from abl.learning import ABLModel
 To build the reasoning part, we first define a knowledge base by creating a subclass of `KBBase`. In the subclass, we initialize the `pseudo_label_list` parameter and override the `logic_forward` method, which specifies how to perform (deductive) reasoning that processes pseudo-labels of an example to the corresponding reasoning result. Specifically, for the MNIST Addition task, this `logic_forward` method is tailored to execute the sum operation.
 
 ```python
-from abl.reasoning import KBBase
+from ablkit.reasoning import KBBase
 ​    
 class AddKB(KBBase):
     def __init__(self, pseudo_label_list=list(range(10))):
@@ -153,7 +153,7 @@ kb = AddKB()
 Next, we create a reasoner by instantiating the class `Reasoner`, passing the knowledge base as a parameter. Due to the indeterminism of abductive reasoning, there could be multiple candidate pseudo-labels compatible to the knowledge base. In such scenarios, the reasoner can minimize inconsistency and return the pseudo-label with the highest consistency.
 
 ```python
-from abl.reasoning import Reasoner
+from ablkit.reasoning import Reasoner
 ​    
 reasoner = Reasoner(kb)
 ```
@@ -167,7 +167,7 @@ reasoner = Reasoner(kb)
 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.
 
 ```python
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
+from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
 ​    
 metric_list = [SymbolAccuracy(), ReasoningMetric(kb=kb)]
 ```
@@ -182,7 +182,7 @@ Now, we use `SimpleBridge` to combine learning and reasoning in a
 unified ABL framework.
 
 ```python
-from abl.bridge import SimpleBridge
+from ablkit.bridge import SimpleBridge
 ​    
 bridge = SimpleBridge(model, reasoner, metric_list)
 ```

abl/data/data_converter.py → ablkit/data/data_converter.py

@@ -1,6 +1,6 @@
 from typing import Any, Tuple
 
-from abl.utils import tab_data_to_tuple
+from ablkit.utils import tab_data_to_tuple
 from .structures.list_data import ListData
 from lambdaLearn.Base.TabularMixin import TabularMixin
 

abl/data/structures/list_data.py → ablkit/data/structures/list_data.py

@@ -57,7 +57,7 @@ class ListData(BaseDataElement):
     class implemented in `MMEngine <https://github.com/open-mmlab/mmengine/blob/main/mmengine/structures/base_data_element.py>`_. # noqa: E501
 
     Examples:
-        >>> from abl.data.structures import ListData
+        >>> from ablkit.data.structures import ListData
         >>> import numpy as np
         >>> import torch
         >>> data_examples = ListData()

docs/API/abl.bridge.rst

@@ -1,7 +1,7 @@
-abl.bridge
+ablkit.bridge
 ==================
 
-.. automodule:: abl.bridge
+.. automodule:: ablkit.bridge
    :members:
    :undoc-members:
    :show-inheritance:

docs/API/abl.data.rst

@@ -1,10 +1,10 @@
-abl.data
+ablkit.data
 ===================
 
 ``structures``
 --------------
 
-.. autoclass:: abl.data.structures.ListData
+.. autoclass:: ablkit.data.structures.ListData
    :members:
    :undoc-members:
    :show-inheritance:
@@ -12,7 +12,7 @@ abl.data
 ``evaluation``
 --------------
 
-.. automodule:: abl.data.evaluation
+.. automodule:: ablkit.data.evaluation
    :members:
    :undoc-members:
    :show-inheritance:

docs/API/abl.learning.rst

@@ -1,12 +1,12 @@
-abl.learning
+ablkit.learning
 ==================
 
-.. autoclass:: abl.learning.ABLModel
+.. autoclass:: ablkit.learning.ABLModel
    :members:
    :undoc-members:
    :show-inheritance:
 
-.. autoclass:: abl.learning.BasicNN
+.. autoclass:: ablkit.learning.BasicNN
    :members:
    :undoc-members:
    :show-inheritance:
@@ -14,7 +14,7 @@ abl.learning
 ``torch_dataset``
 -----------------
 
-.. automodule:: abl.learning.torch_dataset
+.. automodule:: ablkit.learning.torch_dataset
    :members:
    :undoc-members:
    :show-inheritance:

docs/API/abl.reasoning.rst

@@ -1,7 +1,7 @@
-abl.reasoning
+ablkit.reasoning
 ==================
 
-.. automodule:: abl.reasoning
+.. automodule:: ablkit.reasoning
    :members:
    :undoc-members:
    :show-inheritance:

docs/API/abl.utils.rst

@@ -1,7 +1,7 @@
-abl.utils
+ablkit.utils
 ==================
 
-.. automodule:: abl.utils
+.. automodule:: ablkit.utils
    :members:
    :undoc-members:
    :show-inheritance:

docs/Examples/HED.rst

@@ -34,8 +34,8 @@ model.
     import torch
     import torch.nn as nn
 
-    from abl.learning import ABLModel, BasicNN
-    from abl.utils import ABLLogger, print_log
+    from ablkit.learning import ABLModel, BasicNN
+    from ablkit.utils import ABLLogger, print_log
 
     from bridge import HedBridge
     from consistency_metric import ConsistencyMetric

docs/Examples/HWF.rst

@@ -32,11 +32,11 @@ machine learning model.
     import torch
     import torch.nn as nn
 
-    from abl.bridge import SimpleBridge
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-    from abl.learning import ABLModel, BasicNN
-    from abl.reasoning import KBBase, Reasoner
-    from abl.utils import ABLLogger, print_log
+    from ablkit.bridge import SimpleBridge
+    from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
+    from ablkit.learning import ABLModel, BasicNN
+    from ablkit.reasoning import KBBase, Reasoner
+    from ablkit.utils import ABLLogger, print_log
 
     from datasets import get_dataset
     from models.nn import SymbolNet
@@ -232,7 +232,7 @@ examples.
 
 .. code:: ipython3
 
-    from abl.data.structures import ListData
+    from ablkit.data.structures import ListData
     # ListData is a data structure provided by ABL Kit 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

docs/Examples/MNISTAdd.rst

@@ -31,11 +31,11 @@ machine learning model.
     import torch.nn as nn
     from torch.optim import RMSprop, lr_scheduler
 
-    from abl.bridge import SimpleBridge
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-    from abl.learning import ABLModel, BasicNN
-    from abl.reasoning import KBBase, Reasoner
-    from abl.utils import ABLLogger, print_log
+    from ablkit.bridge import SimpleBridge
+    from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
+    from ablkit.learning import ABLModel, BasicNN
+    from ablkit.reasoning import KBBase, Reasoner
+    from ablkit.utils import ABLLogger, print_log
 
     from datasets import get_dataset
     from models.nn import LeNet5
@@ -202,7 +202,7 @@ examples.
 
 .. code:: ipython3
 
-    from abl.data.structures import ListData
+    from ablkit.data.structures import ListData
     # ListData is a data structure provided by ABL Kit 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

docs/Examples/Zoo.rst

@@ -28,11 +28,11 @@ further update the learning model.
     import numpy as np
     from sklearn.ensemble import RandomForestClassifier
 
-    from abl.bridge import SimpleBridge
-    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-    from abl.learning import ABLModel
-    from abl.reasoning import Reasoner
-    from abl.utils import ABLLogger, confidence_dist, print_log, tab_data_to_tuple
+    from ablkit.bridge import SimpleBridge
+    from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
+    from ablkit.learning import ABLModel
+    from ablkit.reasoning import Reasoner
+    from ablkit.utils import ABLLogger, confidence_dist, print_log, tab_data_to_tuple
 
     from get_dataset import load_and_preprocess_dataset, split_dataset
     from kb import ZooKB

docs/Intro/Bridge.rst

@@ -14,7 +14,7 @@ In this section, we will look at how to bridge learning and reasoning parts to t
 
 .. code:: python
 
-    from abl.bridge import BaseBridge, SimpleBridge
+    from ablkit.bridge import BaseBridge, SimpleBridge
 
 ``BaseBridge`` is an abstract class with the following initialization parameters:
 
@@ -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/abl.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 <Datasets.html>`_.
+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 <Datasets.html>`_.
 
 ``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:
 

docs/Intro/Datasets.rst

@@ -15,7 +15,7 @@ In this section, we will look at the dataset and data structure in ABL Kit.
 .. code:: python
 
     import torch
-    from abl.data.structures import ListData
+    from ablkit.data.structures import ListData
 
 Dataset
 -------
@@ -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/abl.data.html#structure>`_ 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, ABL Kit employs `abstract data interfaces <../API/ablkit.data.html#structure>`_ 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``.
 
-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/abl.data.html#structure>`_.
+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#structure>`_.
 
 .. code-block:: python
 

docs/Intro/Evaluation.rst

@@ -14,7 +14,7 @@ In this section, we will look at how to build evaluation metrics.
 
 .. code:: python
 
-    from abl.data.evaluation import BaseMetric, SymbolAccuracy, ReasoningMetric
+    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. 
 

docs/Intro/Learning.rst

@@ -21,7 +21,7 @@ In ABL Kit, building the learning part involves two steps:
 
     import sklearn
     import torchvision
-    from abl.learning import BasicNN, ABLModel
+    from ablkit.learning import BasicNN, ABLModel
 
 Building a base model
 ---------------------

docs/Intro/Quick-Start.rst

@@ -48,7 +48,7 @@ To facilitate uniform processing, ABL Kit provides the ``BasicNN`` class to conv
 .. code:: python
 
    import torch
-   from abl.learning import BasicNN
+   from ablkit.learning import BasicNN
 
    loss_fn = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.RMSprop(cls.parameters(), lr=0.001)
@@ -59,7 +59,7 @@ The base model built above is trained to make predictions on instance-level data
 
 .. code:: python
 
-   from abl.learning import ABLModel
+   from ablkit.learning import ABLModel
 
    model = ABLModel(base_model)
 
@@ -72,7 +72,7 @@ To build the reasoning part, we first define a knowledge base by creating a subc
 
 .. code:: python
 
-   from abl.reasoning import KBBase
+   from ablkit.reasoning import KBBase
 
    class AddKB(KBBase):
       def __init__(self, pseudo_label_list=list(range(10))):
@@ -89,7 +89,7 @@ In such scenarios, the reasoner can minimize inconsistency and return the pseudo
 
 .. code:: python
 
-   from abl.reasoning import Reasoner
+   from ablkit.reasoning import Reasoner
    
    reasoner = Reasoner(kb)
 
@@ -102,7 +102,7 @@ ABL Kit provides two basic metrics, namely ``SymbolAccuracy`` and ``ReasoningMet
 
 .. code:: python
 
-   from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
+   from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
 
    metric_list = [SymbolAccuracy(), ReasoningMetric(kb=kb)]
 
@@ -115,7 +115,7 @@ Now, we use ``SimpleBridge`` to combine learning and reasoning in a unified ABL
 
 .. code:: python
 
-   from abl.bridge import SimpleBridge
+   from ablkit.bridge import SimpleBridge
 
    bridge = SimpleBridge(model, reasoner, metric_list)
 

docs/Intro/Reasoning.rst

@@ -22,7 +22,7 @@ In ABL Kit, building the reasoning part involves two steps:
 
 .. code:: python
 
-   from abl.reasoning import KBBase, GroundKB, PrologKB, Reasoner
+   from ablkit.reasoning import KBBase, GroundKB, PrologKB, Reasoner
 
 Building a knowledge base
 -------------------------

docs/index.rst

@@ -32,11 +32,11 @@
    :maxdepth: 1
    :caption: API
 
-   API/abl.data
-   API/abl.learning
-   API/abl.reasoning
-   API/abl.bridge
-   API/abl.utils
+   API/ablkit.data
+   API/ablkit.learning
+   API/ablkit.reasoning
+   API/ablkit.bridge
+   API/ablkit.utils
 
 .. toctree::
    :maxdepth: 1

examples/hed/bridge.py

@@ -4,13 +4,13 @@ from typing import Any, List, Optional, Tuple, Union
 
 import torch
 
-from abl.bridge import SimpleBridge
-from abl.data.evaluation import BaseMetric
-from abl.data.structures import ListData
-from abl.learning import ABLModel, BasicNN
-from abl.learning.torch_dataset import RegressionDataset
-from abl.reasoning import Reasoner
-from abl.utils import print_log
+from ablkit.bridge import SimpleBridge
+from ablkit.data.evaluation import BaseMetric
+from ablkit.data.structures import ListData
+from ablkit.learning import ABLModel, BasicNN
+from ablkit.learning.torch_dataset import RegressionDataset
+from ablkit.reasoning import Reasoner
+from ablkit.utils import print_log
 
 from datasets import get_pretrain_data
 from models.nn import SymbolNetAutoencoder

examples/hed/consistency_metric.py

@@ -1,8 +1,8 @@
 from typing import Optional
 
-from abl.data.evaluation.base_metric import BaseMetric
-from abl.data.structures import ListData
-from abl.reasoning import KBBase
+from ablkit.data.evaluation.base_metric import BaseMetric
+from ablkit.data.structures import ListData
+from ablkit.reasoning import KBBase
 
 
 class ConsistencyMetric(BaseMetric):

examples/hed/hed.ipynb

@@ -24,8 +24,8 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "\n",
-    "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.utils import ABLLogger, print_log\n",
+    "from ablkit.learning import ABLModel, BasicNN\n",
+    "from ablkit.utils import ABLLogger, print_log\n",
     "\n",
     "from bridge import HedBridge\n",
     "from consistency_metric import ConsistencyMetric\n",

examples/hed/main.py

@@ -4,8 +4,8 @@ import os.path as osp
 import torch
 import torch.nn as nn
 
-from abl.learning import ABLModel, BasicNN
-from abl.utils import ABLLogger, print_log
+from ablkit.learning import ABLModel, BasicNN
+from ablkit.utils import ABLLogger, print_log
 
 from bridge import HedBridge
 from consistency_metric import ConsistencyMetric

examples/hed/reasoning/reasoning.py

@@ -3,8 +3,8 @@ import os
 
 import numpy as np
 
-from abl.reasoning import PrologKB, Reasoner
-from abl.utils import reform_list
+from ablkit.reasoning import PrologKB, Reasoner
+from ablkit.utils import reform_list
 
 CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
 

examples/hwf/hwf.ipynb

@@ -25,11 +25,11 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "\n",
-    "from abl.bridge import SimpleBridge\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
-    "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.reasoning import KBBase, Reasoner\n",
-    "from abl.utils import ABLLogger, print_log\n",
+    "from ablkit.bridge import SimpleBridge\n",
+    "from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
+    "from ablkit.learning import ABLModel, BasicNN\n",
+    "from ablkit.reasoning import KBBase, Reasoner\n",
+    "from ablkit.utils import ABLLogger, print_log\n",
     "\n",
     "from datasets import get_dataset\n",
     "from models.nn import SymbolNet"
@@ -235,7 +235,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from abl.data.structures import ListData\n",
+    "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",
     "data_examples = ListData()\n",

examples/hwf/main.py

@@ -5,11 +5,11 @@ import numpy as np
 import torch
 from torch import nn
 
-from abl.bridge import SimpleBridge
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-from abl.learning import ABLModel, BasicNN
-from abl.reasoning import GroundKB, KBBase, Reasoner
-from abl.utils import ABLLogger, print_log
+from ablkit.bridge import SimpleBridge
+from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
+from ablkit.learning import ABLModel, BasicNN
+from ablkit.reasoning import GroundKB, KBBase, Reasoner
+from ablkit.utils import ABLLogger, print_log
 
 from datasets import get_dataset
 from models.nn import SymbolNet

examples/mnist_add/main.py

@@ -5,11 +5,11 @@ import torch
 from torch import nn
 from torch.optim import RMSprop, lr_scheduler
 
-from abl.bridge import SimpleBridge
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-from abl.learning import ABLModel, BasicNN
-from abl.reasoning import GroundKB, KBBase, PrologKB, Reasoner
-from abl.utils import ABLLogger, print_log
+from ablkit.bridge import SimpleBridge
+from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
+from ablkit.learning import ABLModel, BasicNN
+from ablkit.reasoning import GroundKB, KBBase, PrologKB, Reasoner
+from ablkit.utils import ABLLogger, print_log
 
 from datasets import get_dataset
 from models.nn import LeNet5

examples/mnist_add/mnist_add.ipynb

@@ -25,11 +25,11 @@
     "import torch.nn as nn\n",
     "from torch.optim import RMSprop, lr_scheduler\n",
     "\n",
-    "from abl.bridge import SimpleBridge\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
-    "from abl.learning import ABLModel, BasicNN\n",
-    "from abl.reasoning import KBBase, Reasoner\n",
-    "from abl.utils import ABLLogger, print_log\n",
+    "from ablkit.bridge import SimpleBridge\n",
+    "from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
+    "from ablkit.learning import ABLModel, BasicNN\n",
+    "from ablkit.reasoning import KBBase, Reasoner\n",
+    "from ablkit.utils import ABLLogger, print_log\n",
     "\n",
     "from datasets import get_dataset\n",
     "from models.nn import LeNet5"
@@ -280,7 +280,7 @@
     }
    ],
    "source": [
-    "from abl.data.structures import ListData\n",
+    "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",
     "data_examples = ListData()\n",

examples/zoo/kb.py

@@ -1,7 +1,7 @@
 import openml
 from z3 import If, Implies, Int, Not, Solver, Sum, sat  # noqa: F401
 
-from abl.reasoning import KBBase
+from ablkit.reasoning import KBBase
 
 
 class ZooKB(KBBase):

examples/zoo/main.py

@@ -4,11 +4,11 @@ import os.path as osp
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 
-from abl.bridge import SimpleBridge
-from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
-from abl.learning import ABLModel
-from abl.reasoning import Reasoner
-from abl.utils import ABLLogger, avg_confidence_dist, print_log, tab_data_to_tuple
+from ablkit.bridge import SimpleBridge
+from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy
+from ablkit.learning import ABLModel
+from ablkit.reasoning import Reasoner
+from ablkit.utils import ABLLogger, avg_confidence_dist, print_log, tab_data_to_tuple
 
 from get_dataset import load_and_preprocess_dataset, split_dataset
 from kb import ZooKB

examples/zoo/zoo.ipynb

@@ -23,11 +23,11 @@
     "import numpy as np\n",
     "from sklearn.ensemble import RandomForestClassifier\n",
     "\n",
-    "from abl.bridge import SimpleBridge\n",
-    "from abl.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
-    "from abl.learning import ABLModel\n",
-    "from abl.reasoning import Reasoner\n",
-    "from abl.utils import ABLLogger, avg_confidence_dist, print_log, tab_data_to_tuple\n",
+    "from ablkit.bridge import SimpleBridge\n",
+    "from ablkit.data.evaluation import ReasoningMetric, SymbolAccuracy\n",
+    "from ablkit.learning import ABLModel\n",
+    "from ablkit.reasoning import Reasoner\n",
+    "from ablkit.utils import ABLLogger, avg_confidence_dist, print_log, tab_data_to_tuple\n",
     "\n",
     "from get_dataset import load_and_preprocess_dataset, split_dataset\n",
     "from kb import ZooKB"

tests/conftest.py

@@ -5,9 +5,9 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 
-from abl.data.structures import ListData
-from abl.learning import BasicNN
-from abl.reasoning import GroundKB, KBBase, PrologKB, Reasoner
+from ablkit.data.structures import ListData
+from ablkit.learning import BasicNN
+from ablkit.reasoning import GroundKB, KBBase, PrologKB, Reasoner
 
 
 class LeNet5(nn.Module):

tests/test_abl_model.py

@@ -3,7 +3,7 @@ from unittest.mock import Mock, create_autospec
 import numpy as np
 import pytest
 
-from abl.learning import ABLModel
+from ablkit.learning import ABLModel
 
 
 class TestABLModel(object):

tests/test_reasoning.py

@@ -2,7 +2,7 @@ import numpy as np
 import platform
 import pytest
 
-from abl.reasoning import PrologKB, Reasoner
+from ablkit.reasoning import PrologKB, Reasoner
 
 
 class TestKBBase(object):