Merge branch 'Dev' of https://github.com/AbductiveLearning/ABL-Package into Dev

abl/learning/basic_nn.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Any, Callable, List, Optional, Tuple
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import numpy
 import torch
@@ -28,7 +28,7 @@ class BasicNN:
         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.
-    device : torch.device, optional
+    device : Union[torch.device, str]
         The device on which the model will be trained or used for prediction,
         by default torch.device("cpu").
     batch_size : int, optional
@@ -59,7 +59,7 @@ class BasicNN:
         loss_fn: torch.nn.Module,
         optimizer: torch.optim.Optimizer,
         scheduler: Optional[Callable[..., Any]] = None,
-        device: torch.device = torch.device("cpu"),
+        device: Union[torch.device, str] = torch.device("cpu"),
         batch_size: int = 32,
         num_epochs: int = 1,
         stop_loss: Optional[float] = 0.0001,
@@ -79,7 +79,12 @@ class BasicNN:
         if scheduler is not None and not hasattr(scheduler, "step"):
             raise NotImplementedError("scheduler should implement the ``step`` method")
         if not isinstance(device, torch.device):
-            raise TypeError("device must be an instance of torch.device")
+            if not isinstance(device, str):
+                raise TypeError(
+                    "device must be an instance of torch.device or a str indicates the target device"
+                )
+            else:
+                device = torch.device(device)
         if not isinstance(batch_size, int):
             raise TypeError("batch_size must be an integer")
         if not isinstance(num_epochs, int):

docs/Examples/HED.rst

@@ -188,7 +188,7 @@ sklearn-style interface.
         cls,
         loss_fn,
         optimizer,
-        device,
+        device=device,
         batch_size=32,
         num_epochs=1,
         stop_loss=None,

docs/Examples/Zoo.rst

@@ -32,7 +32,7 @@ further update the learning model.
     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
+    from abl.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
@@ -91,11 +91,9 @@ indicating no rules are violated.
 
 .. code:: ipython3
 
-    def transform_tab_data(X, y):
-        return ([[x] for x in X], [[y_item] for y_item in y], [0] * len(y))
-    label_data = transform_tab_data(X_label, y_label)
-    test_data = transform_tab_data(X_test, y_test)
-    train_data = transform_tab_data(X_unlabel, y_unlabel)
+    label_data = tab_data_to_tuple(X_label, y_label, reasoning_result = 0)
+    data = tab_data_to_tuple(X_test, y_test, reasoning_result = 0)
+    train_data = tab_data_to_tuple(X_unlabel, y_unlabel, reasoning_result = 0)
 
 Building the Learning Part
 --------------------------

pyproject.toml

@@ -25,7 +25,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
 ]
 dependencies = [
-    "numpy>=1.21.6",
+    "numpy>=1.15.0",
     "pyswip>=0.2.9",
     "torch>=1.11.0",
     "torchvision>=0.12.0",

requirements.txt

@@ -1,4 +1,4 @@
-numpy>=1.21.6,
+numpy>=1.15.0,
 pyswip>=0.2.9,
 torch>=1.11.0,
 torchvision>=0.12.0,