Merge pull request #1373 from Sherry-XLL/1.1.x

Fix warnings in docs and modify configuration
RUCAIBox · Aug 2, 2022 · 411a45a · 411a45a
2 parents 8c1a151 + 33ec0ec
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diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -20,11 +20,11 @@
 # -- Project information -----------------------------------------------------
 
 project = "RecBole"
-copyright = "2020, RecBole Contributors"
+copyright = "2022, RecBole Contributors"
 author = "AIBox RecBole group"
 
 # The full version, including alpha/beta/rc tags
-release = "0.2.0"
+release = "1.0.1"
 
 
 # -- General configuration ---------------------------------------------------

diff --git a/docs/source/developer_guide/customize_trainers.rst b/docs/source/developer_guide/customize_trainers.rst
@@ -20,7 +20,7 @@ and revise :meth:`~recbole.trainer.trainer.Trainer.evaluate` or :meth:`~recbole.
 Example
 ----------------
 1. Alternative Optimization
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 Here we present a simple Trainer example, which is used for alternative optimization.
 We revise the :meth:`~recbole.trainer.trainer.Trainer._train_epoch` method.
 To begin with, we need to create a new class for
@@ -105,7 +105,7 @@ Complete Code
             return total_loss
 
 2. Mixed precision training
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 Here we present a simple Trainer example, which is used for mixed
 precision training. Mixed precision training offers significant
 computational speedup by performing operations in half-precision
@@ -164,7 +164,7 @@ Complete Code
             scaler.update()        
 
 3. Layer-specific learning rate
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 Here we present a simple Trainer example, which is used for setting
 layer-specific learning rate. For pretrained model, layers closer to
 the input layer are more likely to have learned more general

diff --git a/docs/source/get_started/distributed_training.rst b/docs/source/get_started/distributed_training.rst
@@ -1,5 +1,5 @@
 Distributed Training
-===============
+=======================
 Now we support distributed training and evaluation. Here is a distributed training example for using RecBole. 
 We will show you how to train and test **BPR** model on the **ml-100k** dataset in the distributed environment.
 

diff --git a/docs/source/get_started/started/context-aware.rst b/docs/source/get_started/started/context-aware.rst
@@ -1,16 +1,12 @@
 Quick Start: Context-aware Recommendation
-===============
+============================================
 For context-aware recommendation, we choose **LR** model to show you how to
 train and test it on the **ml-100k** dataset from both **API** and
 **source code**.
 
-.. _header-n3:
-
 Quick-start From API
 ---------------------
 
-.. _header-n4:
-
 1. Prepare your data:
 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
@@ -43,8 +39,6 @@ we set ``rating=4`` as threshold to label the interaction. For more
 information about label setting, please read the `Label of
 data <https://recbole.io/docs/user_guide/data/label_of_data.html>`__.
 
-.. _header-n9:
-
 2. Choose a model:
 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
@@ -66,10 +60,8 @@ If you want to run different models, you can read `Running Different
 Models <https://recbole.io/docs/user_guide/usage/running_different_models.html>`__
 for more information.
 
-.. _header-n14:
-
 3. Set training and evaluation config:
->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 In RecBole, we support multiple training and evaluation methods. You can
 choose how to train and test model by simply setting the config.
@@ -108,7 +100,7 @@ Settings <https://recbole.io/docs/user_guide/config/evaluation_settings.html>`__
 .. _header-n19:
 
 4. Run the model and collect the result
->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 Now you have finished all the preparations, it’s time to run the model!
 
@@ -199,7 +191,7 @@ can read other docs for depth usage.
 .. _header-n29:
 
 Quick-start From Source
----------------------
+-------------------------
 
 Besides using API, you can also directly run the source code of
 `RecBole <https://github.com/RUCAIBox/RecBole>`__. The whole process is

diff --git a/docs/source/get_started/started/general.rst b/docs/source/get_started/started/general.rst
@@ -1,16 +1,12 @@
 Quick Start: General Recommendation
-===============
+=====================================
 For general recommendation, we choose **BPR** model to show you how to train
 and test it on the **ml-100k** dataset from both **API** and **source
 code**.
 
-.. _header-n3:
-
 Quick-start From API
 --------------------------
 
-.. _header-n4:
-
 1. Prepare your data:
 >>>>>>>>>>>>>>>>>>>>>>>>>
 
@@ -36,8 +32,6 @@ General recommendation models utilize the historical interactions
 between users and items to make recommendations, so it needs to specify
 and load the user and item columns of the dataset.
 
-.. _header-n9:
-
 2. Choose a model:
 >>>>>>>>>>>>>>>>>>>>>>>>>
 
@@ -59,10 +53,8 @@ If you want to run different models, you can read `Running Different
 Models <https://recbole.io/docs/user_guide/usage/running_different_models.html>`__
 for more information.
 
-.. _header-n14:
-
 3. Set training and evaluation config:
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 In RecBole, we support multiple training and evaluation methods. You can
 choose how to train and test model by simply setting the config.
@@ -101,7 +93,7 @@ Settings <https://recbole.io/docs/user_guide/config/evaluation_settings.html>`__
 .. _header-n40:
 
 4. Run the model and collect the result
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 Now you have finished all the preparations, it’s time to run the model!
 

diff --git a/docs/source/get_started/started/knowledge-based.rst b/docs/source/get_started/started/knowledge-based.rst
@@ -1,5 +1,5 @@
 Quick Start: Knowledge-based Recommendation
-===============
+=============================================
 For Knowledge-based Recommendation, we choose **CKE** model to show you how
 to train and test it on the **ml-100k** dataset from both **API** and
 **source code**.
@@ -90,7 +90,7 @@ for more information.
 .. _header-n164:
 
 3. Set training and evaluation config:
->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 In RecBole, we support multiple training and evaluation methods. You can
 choose how to train and test model by simply setting the config.
@@ -123,7 +123,7 @@ Settings <https://recbole.io/docs/user_guide/config/evaluation_settings.html>`__
 .. _header-n269:
 
 4. Run the model and collect the result
->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 Now you have finished all the preparations, it’s time to run the model!
 

diff --git a/docs/source/get_started/started/sequential.rst b/docs/source/get_started/started/sequential.rst
@@ -1,11 +1,9 @@
 Quick Start: Sequential Recommendation
-===============
+========================================
 For sequential recommendation, we choose **GRU4Rec** model to show you how
 to train and test it on the **ml-100k** dataset from both **API** and
 **source code**.
 
-.. _header-n4:
-
 Quick-start From API
 ---------------------
 
@@ -97,7 +95,7 @@ for more information.
 .. _header-n16:
 
 3. Set training and evaluation config:
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 In RecBole, we support multiple training and evaluation methods. You can
 choose how to train and test model by simply setting the config.
@@ -134,7 +132,7 @@ Settings <https://recbole.io/docs/user_guide/config/evaluation_settings.html>`__
 .. _header-n21:
 
 4. Run the model and collect the result
->>>>>>>>>>>>>>>>>>>>>>>>>
+>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
 
 Now you have finished all the preparations, it’s time to run the model!
 

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -89,6 +89,7 @@ Features:
    recbole/recbole.utils.logger
    recbole/recbole.utils.utils
 
+
 The Team
 ------------------
 RecBole is developed and maintained by `RUC, BUPT, ECNU  <https://www.recbole.io/about.html>`_.
@@ -99,7 +100,7 @@ Here is the list of our lead developers in each development phase. They are the
 Time                     Version 	        Lead Developers
 ======================   ===============   =============================================
 June 2020 ~ Nov. 2020    v0.1.1            `Shanlei Mu <https://github.com/ShanleiMu>`_, `Yupeng Hou <https://github.com/hyp1231>`_, `Zihan Lin <https://github.com/linzihan-backforward>`_, `Kaiyuan Li <https://github.com/tsotfsk>`_
-Nov. 2020 ~ Now          v0.1.2 ~ v1.0.0   `Yushuo Chen <https://github.com/chenyushuo>`_, `Xingyu Pan <https://github.com/2017pxy>`_
+Nov. 2020 ~ Now          v0.1.2 ~ v1.0.1   `Yushuo Chen <https://github.com/chenyushuo>`_, `Xingyu Pan <https://github.com/2017pxy>`_
 ======================   ===============   =============================================
 
 License