Fix doc format.

docs/source/conf.py

@@ -21,7 +21,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'rLLM'
-copyright = '2024, Rllm Team'
+copyright = '2024, rLLM Team'
 author = 'Zheng Wang, Weichen Li, Xiaotong Huang, Enze Zhang'
 version = '1.0'
 # The full version, including alpha/beta/rc tags

docs/source/introduce/table_data_handle.rst

@@ -4,12 +4,12 @@ Table Data Handle
 Data Handling of Tables
 -----------------------
 
-A table contains many different columns with many different types. Each column type in Rllm is described by a certain semantic type, i.e., ColType. Rllm supports two basic column types so far:
+A table contains many different columns with many different types. Each column type in rLLM is described by a certain semantic type, i.e., ColType. rLLM supports two basic column types so far:
 
 - :obj:`ColType.CATEGORICAL`: represent categorical or discrete data, such as grade levels in a student dataset and diabetes types in a diabetes dataset.
 - :obj:`ColType.NUMERICAL`: represent numerical or continuous data, such as such as temperature in a weather dataset and income in a salary dataset.
 
-A table in Rllm is described by an instance of :class:`~rllm.data.table_data.TableData` with many default attributes:
+A table in rLLM is described by an instance of :class:`~rllm.data.table_data.TableData` with many default attributes:
 
 - :obj:`df`: A `pandas.DataFrame`_ stores raw tabular data.
 - :obj:`col_types`: A dictionary indicating :class:`~rllm.types.ColType` of each column.
@@ -73,10 +73,10 @@ A table in Rllm is described by an instance of :class:`~rllm.data.table_data.Tab
     dataset.y
     >>> tensor([0, 1, 1,  ..., 0, 1, 0])
 
-    dataset.stats_dict[ColType.CATEGORICAL][0] 
+    dataset.stats_dict[ColType.CATEGORICAL][0]
     >>> {<StatType.COUNT: 'COUNT'>: 3, <StatType.MOST_FREQUENT: 'MOST_FREQUENT'>: 2, <StatType.COLNAME: 'COLNAME'>: 'Pclass'}
 
-    dataset.stats_dict[ColType.NUMERICAL][0]   
+    dataset.stats_dict[ColType.NUMERICAL][0]
     >>> {<StatType.MEAN: 'MEAN'>: 29.69911766052246, <StatType.MAX: 'MAX'>: 80.0, <StatType.MIN: 'MIN'>: 0.41999998688697815, <StatType.STD: 'STD'>: 14.526496887207031, <StatType.QUANTILES: 'QUANTILES'>: [0.41999998688697815, 20.125, 28.0, 38.0, 80.0], <StatType.COLNAME: 'COLNAME'>: 'Age'}
 
 Also, an instance of :class:`~rllm.data.table_data.TableData` contains many basic properties:
@@ -105,9 +105,9 @@ We support transferring the data in a :class:`~rllm.data.table_data.TableData` t
 Common Benchmark Datasets (Table Part)
 ---------------------------------------
 
-Rllm contains a large number of common benchmark datasets. The list of all datasets are available in :mod:`~rllm.datasets`. Our dataset includes graph datasets and tabular datasets. We use tabular data for the demonstration.
+rLLM contains a large number of common benchmark datasets. The list of all datasets are available in :mod:`~rllm.datasets`. Our dataset includes graph datasets and tabular datasets. We use tabular data for the demonstration.
 
-Initializing tabular datasets is straightforward in Rllm. An initialization of a dataset will automatically download its raw files and process its columns.
+Initializing tabular datasets is straightforward in rLLM. An initialization of a dataset will automatically download its raw files and process its columns.
 
 In the below example, we will use one of the pre-loaded datasets, containing the Titanic passengers.
 
@@ -138,7 +138,7 @@ In the below example, we will use one of the pre-loaded datasets, containing the
 
         [5 rows x 11 columns]
 
-Rllm also supports a custom dataset, so that you can use Rllm for your own problem. Assume you prepare your `pandas.DataFrame`_ as :obj:`df` with five columns: :obj:`cat1`, :obj:`cat2`, :obj:`num1`, :obj:`num2`, and :obj:`y`. Creating :class:`~rllm.data.table_data.TableData` object is very easy.
+rLLM also supports a custom dataset, so that you can use rLLM for your own problem. Assume you prepare your `pandas.DataFrame`_ as :obj:`df` with five columns: :obj:`cat1`, :obj:`cat2`, :obj:`num1`, :obj:`num2`, and :obj:`y`. Creating :class:`~rllm.data.table_data.TableData` object is very easy.
 
 .. _pandas.DataFrame: http://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.html#pandas.DataFrame
 

docs/source/tutorial/gnns.rst

@@ -1,9 +1,9 @@
 Design of GNNs
 ===============
 
-What is a GNN?
+What is GNN?
 ----------------
-In machine learning, **Graph Neural Networks (GNNs)** are a class of neural networks specifically designed to process graph-structured data. In a GNN, the input is represented as a graph, where nodes (vertices) correspond to entities and edges represent the relationships or interactions between these entities. A typical GNN architecture consists of an initial Transform followed by multiple Convolution layers, as detailed in *Understanding Transform* and *Understanding Convolution*.
+In machine learning, **Graph Neural Networks (GNNs)** are a class of neural networks specifically designed to process graph-structured data. In a GNN, the input is represented as a graph, where nodes (vertices) correspond to entities and edges represent the relationships or interactions between these entities. A typical GNN architecture consists of an initial Transform followed by multiple Convolution layers, as detailed in :doc:`Understanding Transforms <transforms>` and :doc:`Understanding Convolutions <convolutions>`.
 
 
 Construct a GCN 
@@ -91,4 +91,4 @@ Finally, we need to implement a :obj:`train()` function and a :obj:`test()` func
         acc = correct / int(data.test_mask.sum())
 
     print(f"Accuracy: {acc:.4f}")
-    >>> 0.8150
+    >>> 0.8150

docs/source/tutorial/rtls.rst

@@ -3,7 +3,7 @@ Design of RTLs
 
 What is RTL?
 ----------------
-In machine learning, **Relational Table Learnings (RTLs)** typically refers to the learning of relational table data, which consists of multiple interconnected tables with significant heterogeneity. In an RTL, the input comprises multiple table signals that are interrelated.  A typical RTL architecture consists of one or more Transforms followed by multiple Convolution layers, as detailed in **Understanding Transforms** and **Understanding Convolutions**.
+In machine learning, **Relational Table Learnings (RTLs)** typically refers to the learning of relational table data, which consists of multiple interconnected tables with significant heterogeneity. In an RTL, the input comprises multiple table signals that are interrelated.  A typical RTL architecture consists of one or more Transforms followed by multiple Convolution layers, as detailed in :doc:`Understanding Transforms <transforms>` and :doc:`Understanding Convolutions <convolutions>`.
 
 
 Construct a BRIDGE
@@ -132,5 +132,6 @@ Finally, we jointly train the model and evaluate the results on the test set.
         )
         preds = logits.argmax(dim=1)
         acc = (preds[test_mask] == y[test_mask]).sum(dim=0) / test_mask.sum()
+
     print(f'Accuracy: {acc:.4f}')
-    >>> 0.3860
+    >>> 0.3860

docs/source/tutorial/tnns.rst

@@ -2,7 +2,8 @@ Design of TNNs
 ===============
 What is TNN?
 ----------------
-In machine learning, **Table/Tabular Neural Networks (TNNs)** are recently emerging neural networks specifically designed to process tabular data. In a TNN, the input is structured tabular data, usually organized in rows and columns. A typical TNN architecture consists of an initial Transform followed by multiple Convolution layers, as detailed in *Understanding Transforms* and *Understanding Convolutions*.
+In machine learning, **Table/Tabular Neural Networks (TNNs)** are recently emerging neural networks specifically designed to process tabular data. In a TNN, the input is structured tabular data, usually organized in rows and columns. A typical TNN architecture consists of an initial Transform followed by multiple Convolution layers, as detailed in :doc:`Understanding Transforms <transforms>` and :doc:`Understanding Convolutions <convolutions>`.
+
 
 
 Construct a TabTransformer
@@ -108,5 +109,6 @@ Finally, we train our model and get the classification results on the test set.
             pred_class = pred.argmax(dim=-1)
             correct += (y == pred_class).sum()
         acc = int(correct) / len(test_dataset)
+
     print(f'Accuracy: {acc:.4f}')
     >>> 0.8082