[ENH] simplify quick start

README.md

@@ -80,9 +80,8 @@ In the MNIST Addition task, the data loading looks like:
 from datasets import get_dataset
     
 # train_data and test_data are tuples in the format (X, gt_pseudo_label, Y)
-# If get_pseudo_label is set to False, the gt_pseudo_label in each tuple will be None.
-train_data = get_dataset(train=True, get_pseudo_label=True)
-test_data = get_dataset(train=False, get_pseudo_label=True)
+train_data = get_dataset(train=True)
+test_data = get_dataset(train=False)
 ```
 
 ### Building the Learning Part
@@ -148,7 +147,7 @@ ABL-Package provides two basic metrics, namely `SymbolAccuracy` and `ReasoningMe
 ```python
 from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 ​    
-metric_list = [SymbolAccuracy(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
+metric_list = [SymbolAccuracy(), ReasoningMetric(kb=kb)]
 ```
 
 ### Bridging Learning and Reasoning

docs/Intro/Quick-Start.rst

@@ -25,9 +25,8 @@ In the MNIST Addition task, the data loading looks like
    from datasets import get_dataset
    
    # train_data and test_data are tuples in the format (X, gt_pseudo_label, Y)
-   # If get_pseudo_label is set to False, the gt_pseudo_label in each tuple will be None.
-   train_data = get_dataset(train=True, get_pseudo_label=True)
-   test_data = get_dataset(train=False, get_pseudo_label=True)
+   train_data = get_dataset(train=True)
+   test_data = get_dataset(train=False)
 
 Read more about `preparing datasets <Datasets.html>`_.
 
@@ -105,7 +104,7 @@ ABL-Package provides two basic metrics, namely ``SymbolAccuracy`` and ``Reasonin
 
    from abl.data.evaluation import ReasoningMetric, SymbolAccuracy
 
-   metric_list = [SymbolAccuracy(prefix="mnist_add"), ReasoningMetric(kb=kb, prefix="mnist_add")]
+   metric_list = [SymbolAccuracy(), ReasoningMetric(kb=kb)]
 
 Read more about `building evaluation metrics <Evaluation.html>`_
 

examples/mnist_add/datasets/get_dataset.py

@@ -5,7 +5,7 @@ from torchvision.transforms import transforms
 
 CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
 
-def get_dataset(train=True, get_pseudo_label=False):
+def get_dataset(train=True, get_pseudo_label=True):
     transform = transforms.Compose(
         [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
     )