From c91af20500f33d909d985beb168c8f2bf3db11b0 Mon Sep 17 00:00:00 2001 From: ZW-ZHANG Date: Fri, 31 Dec 2021 12:23:28 +0800 Subject: [PATCH] update doc --- README.md | 2 +- autogl/module/feature/_generators/_eigen.py | 2 +- docs/index.rst | 28 +++++++++++++-------- docs/requirements.txt | 6 ----- setup.py | 2 +- 5 files changed, 20 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index bebfd55..a41bee9 100644 --- a/README.md +++ b/README.md @@ -86,7 +86,7 @@ Please make sure you meet the following requirements before installing AutoGL. 3. Graph Library Backend -You will need either PyTorch Geometric (PyG) or Deep Graph Library (DGL) as the backend. You can select a backend following [here](TODO) if you install both. + You will need either PyTorch Geometric (PyG) or Deep Graph Library (DGL) as the backend. You can select a backend following [here](TODO) if you install both. 3.1 PyTorch Geometric (>=1.7.0) diff --git a/autogl/module/feature/_generators/_eigen.py b/autogl/module/feature/_generators/_eigen.py index c6295d3..5127940 100644 --- a/autogl/module/feature/_generators/_eigen.py +++ b/autogl/module/feature/_generators/_eigen.py @@ -60,7 +60,7 @@ class EigenFeatureGenerator(BaseFeatureGenerator): References ---------- .. [#] Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu: - Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. CoRR abs/2006.04330 (2020) + Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. TKDE (2021) https://arxiv.org/abs/2006.04330 diff --git a/docs/index.rst b/docs/index.rst index 8371f29..b630300 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,7 +13,7 @@ The workflow below shows the overall framework of AutoGL. .. image:: ../resources/workflow.svg :align: center -AutoGL uses ``AutoGL Dataset`` to maintain datasets for graph-based machine learning, which is based on the dataset in PyTorch Geometric with some support added to corporate with the auto solver framework. +AutoGL uses ``AutoGL Dataset`` to maintain datasets for graph-based machine learning, which is based on the dataset in PyTorch Geometric or Deep Graph Library with some support added to corporate with the auto solver framework. Different graph-based machine learning tasks are solved by different ``AutoGL Solvers`` , which make use of four main modules to automatically solve given tasks, namely ``Auto Feature Engineer``, ``Auto Model``, ``Neural Architecture Search``, ``HyperParameter Optimization``, and ``Auto Ensemble``. @@ -31,17 +31,17 @@ Please make sure you meet the following requirements before installing AutoGL. see `PyTorch `_ for installation. -If you want to use Pytorch Geometric backend, you need to install: +3. Graph Library Backend -3. PyTorch Geometric (>=1.7.0) + You will need either PyTorch Geometric (PyG) or Deep Graph Library (DGL) as the backend. - see `PyTorch Geometric `_ for installation. +3.1 PyTorch Geometric (>=1.7.0) -If you want to use Deep Graph Library backend, you need to install: + see for installation. -3. Deep Graph Library (>=0.7) +3.2 Deep Graph Library (>=0.7.0) - see `Deep Graph Library `_ for installation. + see for installation. Installation ~~~~~~~~~~~~ @@ -97,8 +97,11 @@ In AutoGL, the tasks are solved by corresponding solvers, which in general do th docfile/tutorial/t_hetero_node_clf docfile/tutorial/t_homo_graph_classification_gin docfile/tutorial/t_backend - docfile/tutorial/t_dataset - docfile/tutorial/t_fe + + .. + docfile/tutorial/t_dataset + docfile/tutorial/t_fe + docfile/tutorial/t_model docfile/tutorial/t_trainer docfile/tutorial/t_hpo @@ -111,8 +114,11 @@ In AutoGL, the tasks are solved by corresponding solvers, which in general do th :caption: Documentation docfile/documentation/data - docfile/documentation/dataset - docfile/documentation/feature + + .. + docfile/documentation/dataset + docfile/documentation/feature + docfile/documentation/model docfile/documentation/train docfile/documentation/hpo diff --git a/docs/requirements.txt b/docs/requirements.txt index 9216223..91f92e2 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -13,11 +13,5 @@ requests scikit-learn scipy tabulate -# https://download.pytorch.org/whl/lts/1.8/cpu/torch-1.8.1%2Bcpu-cp36-cp36m-linux_x86_64.whl -# https://pytorch-geometric.com/whl/torch-1.8.0+cpu/torch_cluster-1.5.9-cp36-cp36m-linux_x86_64.whl -# https://pytorch-geometric.com/whl/torch-1.8.0+cpu/torch_scatter-2.0.6-cp36-cp36m-linux_x86_64.whl -# https://pytorch-geometric.com/whl/torch-1.8.0+cpu/torch_sparse-0.6.10-cp36-cp36m-linux_x86_64.whl -# https://pytorch-geometric.com/whl/torch-1.8.0+cpu/torch_spline_conv-1.2.1-cp36-cp36m-linux_x86_64.whl -# torch-geometric tqdm nni \ No newline at end of file diff --git a/setup.py b/setup.py index e78c790..afaa49d 100644 --- a/setup.py +++ b/setup.py @@ -16,7 +16,7 @@ with open("README.md", 'r') as fh: ''' https://setuptools.readthedocs.io/en/latest/ ''' setup( name='autogl', - version='0.2.0-pre', + version='0.3.0-pre', author='THUMNLab/aglteam', maintainer='THUMNLab/aglteam', author_email='autogl@tsinghua.edu.cn',