gat_pool

.github/ISSUE_TEMPLATE/bug_report.md

@@ -3,7 +3,7 @@ name: Bug report
 about: Create a report to help us improve
 title: "[BUG]"
 labels: bug
-assignees: Frozenmad
+assignees: general502570
 
 ---
 

.github/ISSUE_TEMPLATE/feature_request.md

@@ -3,7 +3,7 @@ name: Feature request
 about: Suggest an idea for this project
 title: "[FEATURE]"
 labels: enhancement
-assignees: Frozenmad
+assignees: general502570
 
 ---
 

README.md

@@ -1,5 +1,7 @@
 # Auto Graph Learning
 
+[Chinese Introduction](README_cn.md)
+
 An autoML framework & toolkit for machine learning on graphs.
 
 *Actively under development by @THUMNLab*
@@ -12,6 +14,7 @@ Feel free to open <a href="https://github.com/THUMNLab/AutoGL/issues">issues</a>
 
 ## News!
 
+- 2022.4.19 New version v0.3.1！We have released Chinese tutorial for the first time!
 - 2021.12.31 New Version! v0.3.0-pre is here!
     - AutoGL now support [__Deep Graph Library (DGL)__](https://www.dgl.ai/) backend to be interface-friendly for DGL users! All the homogeneous node classification task, link prediction task, and graph classification task are currently supported under DGL backend. AutoGL is also compatible with PyG 2.0 now.
     - The __heterogeneous__ node classification tasks are now supported! See [hetero tutorial](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_hetero_node_clf.html) for more details.

README_cn.md

@@ -0,0 +1,178 @@
+# 智图 (AutoGL)
+[English Introduction](../..)
+
+用于图数据的自动机器学习框架和工具包。
+
+*由清华大学媒体与网络实验室进行开发与维护*
+
+若有任何意见或建议，欢迎通过<a href="https://github.com/THUMNLab/AutoGL/issues">issues</a> 或邮件<a href="mailto:autogl@tsinghua.edu.cn">autogl@tsinghua.edu.cn</a>与我们联系。
+
+<!--
+ [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+% [![Documentation Status](http://mn.cs.tsinghua.edu.cn/autogl/documentation/?badge=latest)](http://mn.cs.tsinghua.edu.cn/autogl/documentation/?badge=latest)-->
+
+## 最新消息
+
+- 2022.4.19 v0.3.1版本更新！首次更新中文教程！
+- 2021.12.31 v0.3.0-pre版本更新!
+    - 智图目前支持[__Deep Graph Library (DGL)__](https://www.dgl.ai/)作为后端，以方便DGL的用户使用。目前在DGL后端已经支持同构图的节点分类、链接预测以及图分类等任务。智图现在也可兼容PyG 2.0版本。
+    - 智图可以支持__异构图__节点分类任务!详情请参考[异构图教程](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_hetero_node_clf.html)。
+    - 为了使智图算法更灵活，`model`模块目前支持__解耦__为两个子模块，即编码器`encoder`和解码器`decoder`。在__解耦__设计中，一个`encoder`可以被用来处理不同任务，以减少重复开发的负担，
+    - 我们扩展了支持的[神经架构搜索算法](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html)，例如[AutoAttend](https://proceedings.mlr.press/v139/guan21a.html)，[GASSO](https://proceedings.neurips.cc/paper/2021/hash/8c9f32e03aeb2e3000825c8c875c4edd-Abstract.html)， [硬件感知算法](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/documentation/nas.html#autogl.module.nas.estimator.OneShotEstimator_HardwareAware)等。 
+- 2021.07.11 智图更新v0.2.0-pre版本! 在新版本中，智图支持[神经架构搜索(NAS)](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_nas.html)，可以对给定的数据集和架构定制化神经网络架构。智图也支持了[采样](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_trainer.html#node-classification-with-sampling)功能以处理大规模图数据集，包括节点采样、层采样和子图采样。链接预测任务也已经支持。详情请参考我们的[教程](http://mn.cs.tsinghua.edu.cn/autogl/documentation/index.html).
+- 2021.04.16 我们关于图自动机器学习的综述文章已经被IJCAI 2021接受! 详情见[这里](http://arxiv.org/abs/2103.00742)。
+- 2021.04.10 我们的论文[__AutoGL: A Library for Automated Graph Learning__](https://arxiv.org/abs/2104.04987)已经被 _ICLR 2021 Workshop on Geometrical and Topological Representation Learning_ 接受! 
+
+## 介绍
+
+智图的设计目标是可以简单、快速地对图数据集和任务进行自动机器学习，可供研究者和开发者使用。更多详细信息，可以参阅我们的文档。
+
+下图是智图的整体框架。
+
+<img src="./resources/workflow.svg">
+
+智图通过 `datasets` 类以支持图数据集，其基于 PyTorch Geometric 和 Deep Graph Library 的数据集，并添加了一些函数以支持自动机器学习框架。
+
+智图通过 `AutoGL solvers` 以处理不同的图机器学习任务，利用五个主要模块自动解决给定的任务，即自动特征工程 `auto feature engineer`，神经架构搜索 `neural architecture search`，自动模型 `auto model`，超参数优化 `hyperparameter optimization`，和自动模型集成 `auto ensemble`。
+
+目前，智图支持以下算法：
+
+<table>
+    <tbody>
+    <tr valign="top">
+        <td>特征工程</td>
+        <td>图模型</td>
+        <td>神经架构搜索</td>
+        <td>超参数优化</td>
+        <td>模型集成</td>
+    </tr>
+    <tr valign="top">
+        <td><b>生成器</b><br>Graphlets <br> EigenGNN <br> <a href="http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_fe.html">更多 ...</a><br><br><b>选择器</b><br> SeFilterConstant<br> gbdt <br> <br><b>全图特征</b><br> Netlsd<br> NxAverageClustering<br> <a href="http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_fe.html">更多 ...</a></td>
+        <td><b>同构图编码器</b><br> GCNEncoder <br> GATEncoder <br> SAGEEncoder <br> GINEncoder <br> <br><b>解码器</b><br>LogSoftmaxDecoder <br> DotProductDecoder <br> SumPoolMLPDecoder <br> JKSumPoolDecoder </td>
+        <td>
+        <b>搜索算法</b><br>
+        Random<br>
+        RL<br>
+        Evolution<br>
+        GASSO<br>
+        <a href='http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/documentation/nas.html'>更多 ...</a><br><br>
+        <b>搜索空间</b><br>
+        SinglePath<br>
+        GraphNas<br>
+        AutoAttend<br>
+        <a href='http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/documentation/nas.html'>更多 ...</a><br><br>
+        <b>模型评估</b><br>
+        Oneshot<br>
+        Scratch<br>
+        </td>
+        <td> Grid <br> Random <br> Anneal <br> Bayes <br> CAMES <br> MOCAMES <br> Quasi random <br> TPE <br> AutoNE </td>
+        <td> Voting <br> Stacking </td>
+    </tr>
+    </tbody>
+</table>
+
+此工具包还可作为一个框架供用户实现和测试自己的自动机器学习或图机器学习模型。
+
+## 安装
+
+### 依赖
+
+在安装智图之前，请首先安装以下依赖项。
+
+1. Python >= 3.6.0
+
+2. PyTorch (>=1.6.0)
+
+    详细信息请参考<https://pytorch.org/>。
+
+3. 图机器学习工具包
+
+    智图需要 PyTorch Geometric（PyG）或 Deep Graph Library（DGL）作为后端。若两者均安装，可在运行时选择任一后端，参考[这里](http://mn.cs.tsinghua.edu.cn/autogl/documentation/docfile/tutorial/t_backend.html)。
+
+    3.1 PyTorch Geometric (>=1.7.0)
+
+    详细信息请参考<https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html/>。
+
+    3.2 Deep Graph Library (>=0.7.0)
+
+    详细信息请参考<https://dgl.ai/>。
+
+
+### 安装
+
+#### 通过pip进行安装
+
+运行以下命令以通过`pip`安装智图。
+
+```
+pip install autogl
+```
+
+#### 从源代码安装
+
+运行以下命令以从源安装智图。
+
+```
+git clone https://github.com/THUMNLab/AutoGL.git
+cd AutoGL
+python setup.py install
+```
+
+#### 开发者安装
+
+如果您想以开发者方式安装智图，请运行以下命令以创建软链接，然后即可修改本地程序后而无需重复安装。
+```
+pip install -e .
+```
+
+## 文档
+
+您可参考<a href="http://mn.cs.tsinghua.edu.cn/autogl/documentation/">文档页面</a> 以参阅我们的详细文档。
+
+文档也可以进行本地编译。首先，请安装 sphinx 和 sphinx-rtd-theme:
+```
+pip install -U Sphinx
+pip install sphinx-rtd-theme
+```
+
+然后，通过以下方式创建 html 文档：
+```
+cd docs
+make clean && make html
+```
+文档将在如下路径自动生成：`docs/_build/html`
+
+## 引用
+
+如果您使用了智图代码，请按如下方式引用我们的[论文](https://openreview.net/forum?id=0yHwpLeInDn):
+```
+@inproceedings{guan2021autogl,
+  title={Auto{GL}: A Library for Automated Graph Learning},
+  author={Chaoyu Guan and Ziwei Zhang and Haoyang Li and Heng Chang and Zeyang Zhang and Yijian Qin and Jiyan Jiang and Xin Wang and Wenwu Zhu},
+  booktitle={ICLR 2021 Workshop on Geometrical and Topological Representation Learning},
+  year={2021},
+  url={https://openreview.net/forum?id=0yHwpLeInDn}
+}
+```
+
+或许您也会发现我们的[综述](http://arxiv.org/abs/2103.00742)有帮助:
+```
+@article{zhang2021automated,
+  title={Automated Machine Learning on Graphs: A Survey},
+  author={Zhang, Ziwei and Wang, Xin and Zhu, Wenwu},
+  booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}},
+  year={2021},
+  note={Survey track}
+}
+```
+
+## 版权相关
+从v0.2版本开始，智图的所有代码采用[Apache license](LICENSE)。
+
+## 用户交流微信群
+![weixin](https://user-images.githubusercontent.com/17705534/163801846-2aa1bdf8-919e-4636-b7d9-e5346f352c15.jpg)
+
+
+
+
+

autogl/__init__.py

@@ -16,4 +16,4 @@
     train,
 )
 
-__version__ = "0.3.0-pre"
+__version__ = "0.3.1"

autogl/module/feature/__init__.py

@@ -33,6 +33,9 @@
 from ._selectors import (
     FilterConstant, GBDTFeatureSelector
 )
+from ._auto_feature import (
+    IdentityFeature, OnlyConstFeature, AutoFeatureEngineer
+)
 
 __all__ = [
     "BaseFeatureEngineer",
@@ -61,5 +64,8 @@
     "NXGlobalEfficiency",
     "NXIsEulerian",
     "FilterConstant",
-    "GBDTFeatureSelector"
+    "GBDTFeatureSelector",
+    "IdentityFeature",
+    "OnlyConstFeature",
+    "AutoFeatureEngineer"
 ]

autogl/module/feature/_auto_feature.py

@@ -166,7 +166,7 @@ def __init__(
             verbosity: int = 0,
             *args, **kwargs
     ):
-        super(AutoFeatureEngineer, self).__init__(multi_graph=False)
+        super(AutoFeatureEngineer, self).__init__()
         self._ops = [op_sum, op_mean, op_max, op_min]
         self._sim = cosine_similarity
         self._fixlen = fix_length
@@ -208,7 +208,7 @@ def _fit(self, homogeneous_static_graph: autogl.data.graph.GeneralStaticGraph):
         for u, v in homogeneous_static_graph.edges.connections.t().numpy():
             neighbours[u].append(v)
         self.__neighbours: _typing.Sequence[np.ndarray] = tuple(
-            [np.ndarray(v) for v in neighbours]
+            [np.array(v) for v in neighbours]
         )
 
         x: np.ndarray = _original_features.numpy()

autogl/module/feature/_base_feature_engineer/_base_feature_engineer.py

@@ -29,7 +29,11 @@ def transform(self, dataset, inplace: bool = True):
         return dataset
 
     def fit_transform(self, dataset, inplace: bool = True):
-        return self.fit_transform(dataset, inplace)
+        for fe in self.fe_components:
+            dataset = fe.fit(dataset)
+        for fe in self.fe_components:
+            dataset = fe.transform(dataset)
+        return dataset
 
     def __init__(self, feature_engineers: _typing.Iterable[_AbstractBaseFeatureEngineer]):
         self.__fe_components: _typing.List[_AbstractBaseFeatureEngineer] = []

autogl/module/feature/_base_feature_engineer/_base_feature_engineer_dgl.py

@@ -40,7 +40,7 @@ def fit(self, dataset):
         with torch.no_grad():
             for i, data in enumerate(dataset):
                 dataset[i] = self.__postprocess(
-                    self._postprocess(self._transform(self._fit(self._preprocess(self.__preprocess(data)))))
+                    self._postprocess(self._fit(self._preprocess(self.__preprocess(data))))
                 )
         return dataset
 

autogl/module/feature/_base_feature_engineer/_base_feature_engineer_pyg.py

@@ -25,7 +25,7 @@ def fit(self, dataset):
         with torch.no_grad():
             for i, data in enumerate(dataset):
                 dataset[i] = self.__postprocess(
-                    self._postprocess(self._transform(self._fit(self._preprocess(self.__preprocess(data)))))
+                    self._postprocess(self._fit(self._preprocess(self.__preprocess(data))))
                 )
         return dataset
 

autogl/module/feature/_selectors/_basic.py

@@ -16,16 +16,12 @@ def __transform_homogeneous_static_graph(
     ) -> GeneralStaticGraph:
         if (
                 'x' in static_graph.nodes.data and
-                self._selection not in (Ellipsis, None) and
-                isinstance(self._selection, torch.Tensor) and
-                torch.is_tensor(self._selection) and self._selection.dim() == 1
+                isinstance(self._selection, (torch.Tensor, np.ndarray))
         ):
             static_graph.nodes.data['x'] = static_graph.nodes.data['x'][:, self._selection]
         if (
                 'feat' in static_graph.nodes.data and
-                self._selection not in (Ellipsis, None) and
-                isinstance(self._selection, torch.Tensor) and
-                torch.is_tensor(self._selection) and self._selection.dim() == 1
+                isinstance(self._selection, (torch.Tensor, np.ndarray))
         ):
             static_graph.nodes.data['feat'] = static_graph.nodes.data['feat'][:, self._selection]
         return static_graph

autogl/module/feature/_selectors/_gbdt.py

@@ -41,6 +41,7 @@ def _gbdt_generator(
     )
 
     num_classes: int = torch.max(data.y).item() + 1
+    parameters["num_class"] = num_classes
     __optimizer_parameters = {
         "num_boost_round": 100,
         "early_stopping_rounds": 5,
@@ -78,7 +79,7 @@ def _gbdt_generator(
         train_x = pd.DataFrame(x, columns=feature_index, index=None)
         dtrain = lightgbm.Dataset(train_x, label=label)
         clf = lightgbm.train(
-            train_set=dtrain, params=params,
+            train_set=dtrain, params=parameters,
             **__optimizer_parameters
         )
 

autogl/module/model/pyg/__init__.py

@@ -8,6 +8,7 @@
 from .gcn import AutoGCN
 from .gat import AutoGAT
 from .gin import AutoGIN
+from .gin_gat_glf import AutoGATPooling
 
 __all__ = [
     "ModelUniversalRegistry",
@@ -19,4 +20,5 @@
     "AutoGCN",
     "AutoGAT",
     "AutoGIN",
+    "AutoGATPooling"
 ]