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# Paddle Graph Learning (PGL) <img src="./docs/source/_static/logo.png" alt="The logo of Paddle Graph Learning (PGL)" width="320">
[DOC](https://pgl.readthedocs.io/en/latest/) | [Quick Start](https://pgl.readthedocs.io/en/latest/instruction.html) | [中文](./README.zh.md) [DOC](https://pgl.readthedocs.io/en/latest/) | [Quick Start](https://pgl.readthedocs.io/en/latest/instruction.html) | [中文](./README.zh.md)
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle). Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle).
<img src="./docs/source/_static/framework_of_pgl.png" alt="The Framework of Paddle Graph Learning (PGL)" width="800"> <img src="./docs/source/_static/framework_of_pgl.png" alt="The Framework of Paddle Graph Learning (PGL)" width="800">
We provide python interfaces for storing/reading/querying graph structured data and two fundamental computational interfaces, which are walk based paradigm and message-passing based paradigm as shown in the above framework of PGL, for building cutting-edge graph learning algorithms. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications. The newly released PGL supports heterogeneous graph learning on both walk based paradigm and message-passing based paradigm by providing MetaPath sampling and Message Passing mechanism on heterogeneous graph. Furthermor, The newly released PGL also support distributed graph storage and some distributed training algorithms, such as distributed deep walk and distributed graphsage. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications.
## Highlight: Efficient and Flexible Message Passing Paradigm ## Highlight: Efficiency - Support Scatter-Gather and LodTensor Message Passing
One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. At PGL we adopt **Message Passing Paradigm** similar to [DGL](https://github.com/dmlc/dgl) to help to build a customize graph neural network easily. Users only need to write ```send``` and ```recv``` functions to easily implement a simple GCN. As shown in the following figure, for the first step the send function is defined on the edges of the graph, and the user can customize the send function ![](http://latex.codecogs.com/gif.latex?\\phi^e}) to send the message from the source to the target node. For the second step, the recv function ![](http://latex.codecogs.com/gif.latex?\\phi^v}) is responsible for aggregating ![](http://latex.codecogs.com/gif.latex?\\oplus}) messages together from different sources. One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. At PGL we adopt **Message Passing Paradigm** similar to [DGL](https://github.com/dmlc/dgl) to help to build a customize graph neural network easily. Users only need to write ```send``` and ```recv``` functions to easily implement a simple GCN. As shown in the following figure, for the first step the send function is defined on the edges of the graph, and the user can customize the send function ![](http://latex.codecogs.com/gif.latex?\\phi^e}) to send the message from the source to the target node. For the second step, the recv function ![](http://latex.codecogs.com/gif.latex?\\phi^v}) is responsible for aggregating ![](http://latex.codecogs.com/gif.latex?\\oplus}) messages together from different sources.
...@@ -19,7 +19,7 @@ One of the most important benefits of graph neural networks compared to other mo ...@@ -19,7 +19,7 @@ One of the most important benefits of graph neural networks compared to other mo
<img src="./docs/source/_static/message_passing_paradigm.png" alt="The basic idea of message passing paradigm" width="800"> <img src="./docs/source/_static/message_passing_paradigm.png" alt="The basic idea of message passing paradigm" width="800">
As shown in the left of the following figure, to adapt general user-defined message aggregate functions, DGL uses the degree bucketing method to combine nodes with the same degree into a batch and then apply an aggregate function ![](http://latex.codecogs.com/gif.latex?\\oplus}) on each batch serially. For our PGL UDF aggregate function, we organize the message as a [LodTensor](http://www.paddlepaddle.org/documentation/docs/en/1.4/user_guides/howto/basic_concept/lod_tensor_en.html) in [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) taking the message as variable length sequences. And we **utilize the features of LodTensor in Paddle to obtain fast parallel aggregation**. As shown in the left of the following figure, to adapt general user-defined message aggregate functions, DGL uses the degree bucketing method to combine nodes with the same degree into a batch and then apply an aggregate function ![](http://latex.codecogs.com/gif.latex?\\oplus}) on each batch serially. For our PGL UDF aggregate function, we organize the message as a [LodTensor](http://www.paddlepaddle.org/documentation/docs/en/1.4/user_guides/howto/basic_concept/lod_tensor_en.html) in [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) taking the message as variable length sequences. And we **utilize the features of LodTensor in Paddle to obtain fast parallel aggregation**.
<img src="./docs/source/_static/parallel_degree_bucketing.png" alt="The parallel degree bucketing of PGL" width="800"> <img src="./docs/source/_static/parallel_degree_bucketing.png" alt="The parallel degree bucketing of PGL" width="800">
...@@ -35,10 +35,10 @@ Users only need to call the ```sequence_ops``` functions provided by Paddle to e ...@@ -35,10 +35,10 @@ Users only need to call the ```sequence_ops``` functions provided by Paddle to e
Although DGL does some kernel fusion optimization for general sum, max and other aggregate functions with scatter-gather. For **complex user-defined functions** with degree bucketing algorithm, the serial execution for each degree bucket cannot take full advantage of the performance improvement provided by GPU. However, operations on the PGL LodTensor-based message is performed in parallel, which can fully utilize GPU parallel optimization. In our experiments, PGL can reach up to 13 times the speed of DGL with complex user-defined functions. Even without scatter-gather optimization, PGL still has excellent performance. Of course, we still provide build-in scatter-optimized message aggregation functions. Although DGL does some kernel fusion optimization for general sum, max and other aggregate functions with scatter-gather. For **complex user-defined functions** with degree bucketing algorithm, the serial execution for each degree bucket cannot take full advantage of the performance improvement provided by GPU. However, operations on the PGL LodTensor-based message is performed in parallel, which can fully utilize GPU parallel optimization. In our experiments, PGL can reach up to 13 times the speed of DGL with complex user-defined functions. Even without scatter-gather optimization, PGL still has excellent performance. Of course, we still provide build-in scatter-optimized message aggregation functions.
## Performance ### Performance
We test all the GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs to get average speeds. And we report the accuracy on test dataset without early stoppping. We test all the following GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs to get average speeds. And we report the accuracy on test dataset without early stoppping.
| Dataset | Model | PGL Accuracy | PGL speed (epoch time) | DGL 0.3.0 speed (epoch time) | | Dataset | Model | PGL Accuracy | PGL speed (epoch time) | DGL 0.3.0 speed (epoch time) |
| -------- | ----- | ----------------- | ------------ | ------------------------------------ | | -------- | ----- | ----------------- | ------------ | ------------------------------------ |
...@@ -49,6 +49,7 @@ We test all the GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs t ...@@ -49,6 +49,7 @@ We test all the GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs t
| Citeseer | GCN |70.2%| **0.0045** |0.0046s| | Citeseer | GCN |70.2%| **0.0045** |0.0046s|
| Citeseer | GAT |68.8%| **0.0124s** |0.0139s| | Citeseer | GAT |68.8%| **0.0124s** |0.0139s|
If we use complex user-defined aggregation like [GraphSAGE-LSTM](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) that aggregates neighbor features with LSTM ignoring the order of recieved messages, the optimized message-passing in DGL will be forced to degenerate into degree bucketing scheme. The speed performance will be much slower than the one implemented in PGL. Performances may be various with different scale of the graph, in our experiments, PGL can reach up to 13 times the speed of DGL. If we use complex user-defined aggregation like [GraphSAGE-LSTM](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) that aggregates neighbor features with LSTM ignoring the order of recieved messages, the optimized message-passing in DGL will be forced to degenerate into degree bucketing scheme. The speed performance will be much slower than the one implemented in PGL. Performances may be various with different scale of the graph, in our experiments, PGL can reach up to 13 times the speed of DGL.
| Dataset | PGL speed (epoch time) | DGL 0.3.0 speed (epoch time) | Speed up| | Dataset | PGL speed (epoch time) | DGL 0.3.0 speed (epoch time) | Speed up|
...@@ -57,7 +58,49 @@ If we use complex user-defined aggregation like [GraphSAGE-LSTM](https://cs.stan ...@@ -57,7 +58,49 @@ If we use complex user-defined aggregation like [GraphSAGE-LSTM](https://cs.stan
| Pubmed | **0.0388s** |0.5275s | 13.59x| | Pubmed | **0.0388s** |0.5275s | 13.59x|
| Citeseer | **0.0150s** | 0.1278s | 8.52x | | Citeseer | **0.0150s** | 0.1278s | 8.52x |
## Highlight: Flexibility - Natively Support Heterogeneous Graph Learning
Graph can conveniently represent the relation between things in the real world, but the categories of things and the relation between things are various. Therefore, in the heterogeneous graph, we need to distinguish the node types and edge types in the graph network. PGL models heterogeneous graphs that contain multiple node types and multiple edge types, and can describe complex connections between different types.
### Support meta path walk sampling on heterogeneous graph
<img src="./docs/source/_static/metapath_sampling.png" alt="The metapath sampling in heterogeneous graph" width="800">
The left side of the figure above describes a shopping social network. The nodes above have two categories of users and goods, and the relations between users and users, users and goods, and goods and goods. The right of the above figure is a simple sampling process of MetaPath. When you input any MetaPath as UPU (user-product-user), you will find the following results
<img src="./docs/source/_static/metapath_result.png" alt="The metapath result" width="320">
Then on this basis, and introducing word2vec and other methods to support learning metapath2vec and other algorithms of heterogeneous graph representation.
### Support Message Passing mechanism on heterogeneous graph
<img src="./docs/source/_static/him_message_passing.png" alt="The message passing mechanism on heterogeneous graph" width="800">
Because of the different node types on the heterogeneous graph, the message delivery is also different. As shown on the left, it has five neighbors, belonging to two different node types. As shown on the right of the figure above, nodes belonging to different types need to be aggregated separately during message delivery, and then merged into the final message to update the target node. On this basis, PGL supports heterogeneous graph algorithms based on message passing, such as GATNE and other algorithms.
## Large-Scale: Support distributed graph storage and distributed training algorithms
In most cases of large-scale graph learning, we need distributed graph storage and distributed training support. As shown in the following figure, PGL provided a general solution of large-scale training, we adopted [PaddleFleet](https://github.com/PaddlePaddle/Fleet) as our distributed parameter servers, which supports large scale distributed embeddings and a lightweighted distributed storage engine so tcan easily set up a large scale distributed training algorithm with MPI clusters.
<img src="./docs/source/_static/distributed_frame.png" alt="The distributed frame of PGL" width="800">
## Highlight: Tons of Models
The following are 13 graph learning models that have been implemented in the framework. See the details [here](https://pgl.readthedocs.io/en/latest/introduction.html#highlight-tons-of-models)
|Model | feature |
|---|---|
| GCN | Graph Convolutional Neural Networks |
| GAT | Graph Attention Network |
| GraphSage |Large-scale graph convolution network based on neighborhood sampling|
| unSup-GraphSage | Unsupervised GraphSAGE |
| LINE | Representation learning based on first-order and second-order neighbors |
| DeepWalk | Representation learning by DFS random walk |
| MetaPath2Vec | Representation learning based on metapath |
| Node2Vec | The representation learning Combined with DFS and BFS |
| Struct2Vec | Representation learning based on structural similarity |
| SGC | Simplified graph convolution neural network |
| GES | The graph represents learning method with node features |
| DGI | Unsupervised representation learning based on graph convolution network |
| GATNE | Representation Learning of Heterogeneous Graph based on MessagePassing |
The above models consists of three parts, namely, graph representation learning, graph neural network and heterogeneous graph learning, which are also divided into graph representation learning and graph neural network.
## System requirements ## System requirements
...@@ -72,7 +115,7 @@ PGL supports both Python 2 & 3 ...@@ -72,7 +115,7 @@ PGL supports both Python 2 & 3
## Installation ## Installation
The current version of PGL is 1.0.0. You can simply install it via pip. You can simply install it via pip.
```sh ```sh
pip install pgl pip install pgl
......
README.zh.md
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# Paddle Graph Learning (PGL) <img src="./docs/source/_static/logo.png" alt="The logo of Paddle Graph Learning (PGL)" width="320">
[文档](https://pgl.readthedocs.io/en/latest/) | [快速开始](https://pgl.readthedocs.io/en/latest/instruction.html) | [English](./README.md) [文档](https://pgl.readthedocs.io/en/latest/) | [快速开始](https://pgl.readthedocs.io/en/latest/instruction.html) | [English](./README.md)
...@@ -6,10 +6,10 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd ...@@ -6,10 +6,10 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd
<img src="./docs/source/_static/framework_of_pgl.png" alt="The Framework of Paddle Graph Learning (PGL)" width="800"> <img src="./docs/source/_static/framework_of_pgl.png" alt="The Framework of Paddle Graph Learning (PGL)" width="800">
我们提供一系列的Python接口用于存储/读取/查询图数据结构，并且提供基于游走（Walk Based）以及消息传递（Message Passing）两种计算范式的计算接口（见上图）。利用这些接口，我们就能够轻松的搭建最前沿的图学习算法。结合PaddlePaddle深度学习框架，我们的框架基本能够覆盖大部分的图网络应用，包括图表示学习以及图神经网络。 在最新发布的PGL中引入了异构图的支持，新增MetaPath采样支持异构图表示学习，新增异构图Message Passing机制支持基于消息传递的异构图算法，利用新增的异构图接口，能轻松搭建前沿的异构图学习算法。而且，在最新发布的PGL中，同时也增加了分布式图存储以及一些分布式图学习训练算法，例如，分布式deep walk和分布式graphsage。结合PaddlePaddle深度学习框架，我们的框架基本能够覆盖大部分的图网络应用，包括图表示学习以及图神经网络。
## 特色：高效灵活的消息传递范式 # 特色：高效性——支持Scatter-Gather及LodTensor消息传递
对比于一般的模型，图神经网络模型最大的优势在于它利用了节点与节点之间连接的信息。但是，如何通过代码来实现建模这些节点连接十分的麻烦。PGL采用与[DGL](https://github.com/dmlc/dgl)相似的**消息传递范式**用于作为构建图神经网络的接口。用于只需要简单的编写```send```还有```recv```函数就能够轻松的实现一个简单的GCN网络。如下图所示，首先，send函数被定义在节点之间的边上，用户自定义send函数![](http://latex.codecogs.com/gif.latex?\\phi^e})会把消息从源点发送到目标节点。然后，recv函数![](http://latex.codecogs.com/gif.latex?\\phi^v})负责将这些消息用汇聚函数 ![](http://latex.codecogs.com/gif.latex?\\oplus}) 汇聚起来。 对比于一般的模型，图神经网络模型最大的优势在于它利用了节点与节点之间连接的信息。但是，如何通过代码来实现建模这些节点连接十分的麻烦。PGL采用与[DGL](https://github.com/dmlc/dgl)相似的**消息传递范式**用于作为构建图神经网络的接口。用于只需要简单的编写```send```还有```recv```函数就能够轻松的实现一个简单的GCN网络。如下图所示，首先，send函数被定义在节点之间的边上，用户自定义send函数![](http://latex.codecogs.com/gif.latex?\\phi^e})会把消息从源点发送到目标节点。然后，recv函数![](http://latex.codecogs.com/gif.latex?\\phi^v})负责将这些消息用汇聚函数 ![](http://latex.codecogs.com/gif.latex?\\oplus}) 汇聚起来。
...@@ -31,9 +31,7 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd ...@@ -31,9 +31,7 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd
尽管DGL用了一些内核融合（kernel fusion）的方法来将常用的sum，max等聚合函数用scatter-gather进行优化。但是对于**复杂的用户定义函数**，他们使用的Degree Bucketing算法，仅仅使用串行的方案来处理不同的分块，并不同充分利用GPU进行加速。然而，在PGL中我们使用基于LodTensor的消息传递能够充分地利用GPU的并行优化，在复杂的用户定义函数下，PGL的速度在我们的实验中甚至能够达到DGL的13倍。即使不使用scatter-gather的优化，PGL仍然有高效的性能表现。当然，我们也是提供了scatter优化的聚合函数。 尽管DGL用了一些内核融合（kernel fusion）的方法来将常用的sum，max等聚合函数用scatter-gather进行优化。但是对于**复杂的用户定义函数**，他们使用的Degree Bucketing算法，仅仅使用串行的方案来处理不同的分块，并不同充分利用GPU进行加速。然而，在PGL中我们使用基于LodTensor的消息传递能够充分地利用GPU的并行优化，在复杂的用户定义函数下，PGL的速度在我们的实验中甚至能够达到DGL的13倍。即使不使用scatter-gather的优化，PGL仍然有高效的性能表现。当然，我们也是提供了scatter优化的聚合函数。
## 性能测试 ### 性能测试
我们用Tesla V100-SXM2-16G测试了下列所有的GNN算法，每一个算法跑了200个Epoch来计算平均速度。准确率是在测试集上计算出来的，并且我们没有使用Early-stopping策略。 我们用Tesla V100-SXM2-16G测试了下列所有的GNN算法，每一个算法跑了200个Epoch来计算平均速度。准确率是在测试集上计算出来的，并且我们没有使用Early-stopping策略。
| 数据集 | 模型 | PGL准确率 | PGL速度 (epoch) | DGL 0.3.0 速度 (epoch) | | 数据集 | 模型 | PGL准确率 | PGL速度 (epoch) | DGL 0.3.0 速度 (epoch) |
...@@ -54,6 +52,52 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd ...@@ -54,6 +52,52 @@ Paddle Graph Learning (PGL)是一个基于[PaddlePaddle](https://github.com/Padd
| Citeseer | **0.0150s** | 0.1278s | 8.52x | | Citeseer | **0.0150s** | 0.1278s | 8.52x |
## 特色：易用性——原生支持异构图
图可以很方便的表示真实世界中事物之间的联系，但是事物的类别以及事物之间的联系多种多样，因此，在异构图中，我们需要对图网络中的节点类型以及边类型进行区分。PGL针对异构图包含多种节点类型和多种边类型的特点进行建模，可以描述不同类型之间的复杂联系。
### 支持异构图MetaPath walk采样
<img src="./docs/source/_static/metapath_sampling.png" alt="The metapath sampling in heterogeneous graph" width="800">
上图左边描述的是一个购物的社交网络，上面的节点有用户和商品两大类，关系有用户和用户之间的关系，用户和商品之间的关系以及商品和商品之间的关系。上图的右边是一个简单的MetaPath采样过程，输入metapath为UPU（user-product-user），采出结果为
<img src="./docs/source/_static/metapath_result.png" alt="The metapath result" width="320">
然后在此基础上引入word2vec等方法，支持异构图表示学习metapath2vec等算法。
### 支持异构图Message Passing机制
<img src="./docs/source/_static/him_message_passing.png" alt="The message passing mechanism on heterogeneous graph" width="800">
在异构图上由于节点类型不同，消息传递也方式也有所不同。如上图左边，它有五个邻居节点，属于两种不同的节点类型。如上图右边，在消息传递的时候需要把属于不同类型的节点分开聚合，然后在合并成最终的消息，从而更新目标节点。在此基础上PGL支持基于消息传递的异构图算法，如GATNE等算法。
## 特色：规模性——支持分布式图存储以及分布式学习算法
在大规模的图网络学习中，通常需要多机图存储以及多机分布式训练。如下图所示，PGL提供一套大规模训练的解决方案，我们利用[PaddleFleet](https://github.com/PaddlePaddle/Fleet)(支持大规模分布式Embedding学习)作为我们参数服务器模块以及一套简易的分布式存储方案，可以轻松在MPI集群上搭建分布式大规模图学习方法。
<img src="./docs/source/_static/distributed_frame.png" alt="The distributed frame of PGL" width="800">
## 特色：丰富性——覆盖业界大部分图学习网络
下列是框架中已经自带实现的十三种图网络学习模型
| 模型 | 特点 |
|---|---|--- |
| GCN | 图卷积网络 |
| GAT | 基于Attention的图卷积网络 |
| GraphSage | 基于邻居采样的大规模图卷积网络 |
| unSup-GraphSage | 无监督学习的GraphSAGE |
| LINE | 基于一阶、二阶邻居的表示学习 |
| DeepWalk | DFS随机游走的表示学习 |
| MetaPath2Vec | 基于metapath的表示学习 |
| Node2Vec | 结合DFS及BFS的表示学习 |
| Struct2Vec | 基于结构相似的表示学习 |
| SGC | 简化的图卷积网络 |
| GES | 加入节点特征的图表示学习方法 |
| DGI | 基于图卷积网络的无监督表示学习 |
| GATNE | 基于MessagePassing的异构图表示学习 |
上述模型包含图表示学习，图神经网络以及异构图三部分，而异构图里面也分图表示学习和图神经网络。
## 依赖 ## 依赖
PGL依赖于: PGL依赖于:
...@@ -67,7 +111,7 @@ PGL支持Python 2和3。 ...@@ -67,7 +111,7 @@ PGL支持Python 2和3。
## 安装 ## 安装
当前，PGL的版本是1.0.0。你可以简单的用pip进行安装。 你可以简单的用pip进行安装。
```sh ```sh
pip install pgl pip install pgl
......
docs/requirements.txt
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...@@ -3,6 +3,5 @@ mistune ...@@ -3,6 +3,5 @@ mistune
sphinx_rtd_theme sphinx_rtd_theme
numpy >= 1.16.4 numpy >= 1.16.4
cython >= 0.25.2 cython >= 0.25.2
networkx
paddlepaddle paddlepaddle
pgl pgl
docs/source/api/pgl.contrib.heter_graph.rst 0 → 100644
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pgl.contrib.heter\_graph module: Heterogenous Graph Storage
===============================
.. automodule:: pgl.contrib.heter_graph
:members:
:undoc-members:
:show-inheritance:
docs/source/api/pgl.contrib.heter_graph_wrapper.rst 0 → 100644
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pgl.contrib.heter\_graph\_wrapper module: Heterogenous Graph data holders for Paddle GNN.
=========================
.. automodule:: pgl.contrib.heter_graph_wrapper
:members:
:undoc-members:
:show-inheritance:
docs/source/api/pgl.rst
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pgl.data_loader pgl.data_loader
pgl.utils.paddle_helper pgl.utils.paddle_helper
pgl.utils.mp_reader pgl.utils.mp_reader
pgl.contrib.heter_graph
pgl.contrib.heter_graph_wrapper
docs/source/conf.py
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author = 'PaddlePaddle' author = 'PaddlePaddle'
# The full version, including alpha/beta/rc tags # The full version, including alpha/beta/rc tags
release = '1.0.0' release = '1.0.1'
# -- General configuration --------------------------------------------------- # -- General configuration ---------------------------------------------------
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html_theme = "sphinx_rtd_theme" html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
html_show_sourcelink = False html_show_sourcelink = False
#html_logo = 'pgl_logo.png' html_logo = '_static/logo.png'
# Add any paths that contain custom static files (such as style sheets) here, # Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files, # relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css". # so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static'] html_static_path = ['_static']
'''
html_theme_options = { html_theme_options = {
'canonical_url': '', 'canonical_url': '',
'analytics_id': 'UA-XXXXXXX-1', # Provided by Google in your dashboard 'analytics_id': 'UA-XXXXXXX-1', # Provided by Google in your dashboard
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'includehidden': True, 'includehidden': True,
'titles_only': False 'titles_only': False
} }
'''
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docs/source/examples/gat_examples.rst
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View the Code
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examples/gat/train.py
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docs/source/examples/gcn_examples.rst
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View the Code
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examples/gcn/train.py
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docs/source/examples/graphsage_examples.rst
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View the Code
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examples/graphsage/train.py
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examples/graphsage/reader.py
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examples/graphsage/model.py
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# Building Graph Attention Networks
[Graph Attention Networks \(GAT\)](https://arxiv.org/abs/1710.10903) is a novel architectures that operate on graph-structured data, which leverages masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. Based on PGL, we reproduce GAT algorithms and reach the same level of indicators as the paper in citation network benchmarks.
### Simple example to build single head GAT
To build a gat layer, one can use our pre-defined ```pgl.layers.gat``` or just write a gat layer with message passing interface.
```python
import paddle.fluid as fluid
def gat_layer(graph_wrapper, node_feature, hidden_size):
def send_func(src_feat, dst_feat, edge_feat):
logits = src_feat["a1"] + dst_feat["a2"]
logits = fluid.layers.leaky_relu(logits, alpha=0.2)
return {"logits": logits, "h": src_feat }
def recv_func(msg):
norm = fluid.layers.sequence_softmax(msg["logits"])
output = msg["h"] * norm
return output
h = fluid.layers.fc(node_feature, hidden_size, bias_attr=False, name="hidden")
a1 = fluid.layers.fc(node_feature, 1, name="a1_weight")
a2 = fluid.layers.fc(node_feature, 1, name="a2_weight")
message = graph_wrapper.send(send_func,
nfeat_list=[("h", h), ("a1", a1), ("a2", a2)])
output = graph_wrapper.recv(recv_func, message)
return output
```
### Datasets
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the [paper](https://arxiv.org/abs/1609.02907).
### Dependencies
- paddlepaddle>=1.6
- pgl
### Performance
We train our models for 200 epochs and report the accuracy on the test dataset.
| Dataset | Accuracy |
| --- | --- |
| Cora | ~83% |
| Pubmed | ~78% |
| Citeseer | ~70% |
### How to run
For examples, use gpu to train gat on cora dataset.
```
python train.py --dataset cora --use_cuda
```
#### Hyperparameters
- dataset: The citation dataset "cora", "citeseer", "pubmed".
- use_cuda: Use gpu if assign use_cuda.
### View the Code
See the code [here](gat_examples_code.html)
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# Building Graph Convolutional Network
[Graph Convolutional Network \(GCN\)](https://arxiv.org/abs/1609.02907) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks.
### Simple example to build GCN
To build a gcn layer, one can use our pre-defined ```pgl.layers.gcn``` or just write a gcn layer with message passing interface.
```python
import paddle.fluid as fluid
def gcn_layer(graph_wrapper, node_feature, hidden_size, act):
def send_func(src_feat, dst_feat, edge_feat):
return src_feat["h"]
def recv_func(msg):
return fluid.layers.sequence_pool(msg, "sum")
message = graph_wrapper.send(send_func, nfeat_list=[("h", node_feature)])
output = graph_wrapper.recv(recv_func, message)
output = fluid.layers.fc(output, size=hidden_size, act=act)
return output
```
### Datasets
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the [paper](https://arxiv.org/abs/1609.02907).
### Dependencies
- paddlepaddle>=1.6
- pgl
### Performance
We train our models for 200 epochs and report the accuracy on the test dataset.
| Dataset | Accuracy |
| --- | --- |
| Cora | ~81% |
| Pubmed | ~79% |
| Citeseer | ~71% |
### How to run
For examples, use gpu to train gcn on cora dataset.
```
python train.py --dataset cora --use_cuda
```
#### Hyperparameters
- dataset: The citation dataset "cora", "citeseer", "pubmed".
- use_cuda: Use gpu if assign use_cuda.
### View the Code
See the code [here](gcn_examples_code.html)
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# Graph Representation Learning: Node2vec
[Node2vec](https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce node2vec algorithms and reach the same level of indicators as the paper.
## Datasets
The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3) and [Arxiv](http://snap.stanford.edu/data/ca-AstroPh.html).
## Dependencies
- paddlepaddle>=1.6
- pgl
## How to run
For examples, use gpu to train gcn on cora dataset.
```sh
# multiclass task example
python node2vec.py --use_cuda --dataset BlogCatalog --save_path ./tmp/node2vec_BlogCatalog/ --offline_learning --epoch 400
python multi_class.py --use_cuda --ckpt_path ./tmp/node2vec_BlogCatalog/paddle_model --epoch 1000
# link prediction task example
python node2vec.py --use_cuda --dataset ArXiv --save_path
./tmp/node2vec_ArXiv --offline_learning --epoch 10
python link_predict.py --use_cuda --ckpt_path ./tmp/node2vec_ArXiv/paddle_model --epoch 400
```
## Hyperparameters
- dataset: The citation dataset "BlogCatalog" and "ArXiv".
- use_cuda: Use gpu if assign use_cuda.
### Experiment results
Dataset|model|Task|Metric|PGL Result|Reported Result
--|--|--|--|--|--
BlogCatalog|deepwalk|multi-label classification|MacroF1|0.250|0.211
BlogCatalog|node2vec|multi-label classification|MacroF1|0.262|0.258
ArXiv|deepwalk|link prediction|AUC|0.9538|0.9340
ArXiv|node2vec|link prediction|AUC|0.9541|0.9366
## View the Code
See the code [here](node2vec_examples_code.html)
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# StaticGraphWrapper for GAT Speed Optimization
[Graph Attention Networks \(GAT\)](https://arxiv.org/abs/1710.10903) is a novel architectures that operate on graph-structured data, which leverages masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. Based on PGL, we reproduce GAT algorithms and reach the same level of indicators as the paper in citation network benchmarks.
However, different from the reproduction in **examples/gat**, we use `pgl.graph_wrapper.StaticGraphWrapper` to preload the graph data into gpu or cpu memories which achieves better performance on speed.
### Datasets
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the [paper](https://arxiv.org/abs/1609.02907).
### Dependencies
- paddlepaddle>=1.4 (The speed can be faster in 1.5.)
- pgl
### Performance
We train our models for 200 epochs and report the accuracy on the test dataset.
| Dataset | Accuracy | epoch time | examples/gat | Improvement |
| --- | --- | --- | --- | --- |
| Cora | ~83% | 0.0119s | 0.0175s | 1.47x |
| Pubmed | ~78% | 0.0193s |0.0295s | 1.53x |
| Citeseer | ~70% | 0.0124s |0.0253s | 2.04x |
### How to run
For examples, use gpu to train gat on cora dataset.
```sh
python train.py --dataset cora --use_cuda
```
#### Hyperparameters
- dataset: The citation dataset "cora", "citeseer", "pubmed".
- use_cuda: Use gpu if assign use_cuda.
### View the Code
See the code [here](static_gat_examples_code.html)
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# StaticGraphWrapper for GCN Speed Optimization
[Graph Convolutional Network \(GCN\)](https://arxiv.org/abs/1609.02907) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks.
However, different from the reproduction in **examples/gcn**, we use `pgl.graph_wrapper.StaticGraphWrapper` to preload the graph data into gpu or cpu memories which achieves better performance on speed.
### Datasets
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the [paper](https://arxiv.org/abs/1609.02907).
### Dependencies
- paddlepaddle>=1.6
- pgl
### Performance
We train our models for 200 epochs and report the accuracy on the test dataset.
| Dataset | Accuracy | epoch time | examples/gcn | Improvement |
| --- | --- | --- |---| --- | --- |
| Cora | ~81% | 0.0047s | 0.0104s | 2.21x |
| Pubmed | ~79% | 0.0049s |0.0154s | 3.14x |
| Citeseer | ~71% | 0.0045s |0.0177s | 3.93x |
### How to run
For examples, use gpu to train gcn on cora dataset.
```sh
python train.py --dataset cora --use_cuda
```
#### Hyperparameters
- dataset: The citation dataset "cora", "citeseer", "pubmed".
- use_cuda: Use gpu if assign use_cuda.
### View the Code
See the code [here](static_gcn_examples_code.html)
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View the Code
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examples/node2vec/node2vec.py
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examples/node2vec/multi_class.py
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examples/node2vec/link_predict.py
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examples/static_gat/train.py
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docs/source/index.rst
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.. toctree:: .. toctree::
:maxdepth: 1 :maxdepth: 1
:caption: Quick Start :caption: Quick Start
:hidden:
instruction.rst
See instruction_ for quick start.
.. _instruction: instruction.html
quick_start/instruction.rst
quick_start/introduction_for_hetergraph.rst
.. toctree:: .. toctree::
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examples/static_graph_wrapper.rst examples/static_graph_wrapper.rst
examples/node2vec_examples.rst examples/node2vec_examples.rst
examples/graphsage_examples.rst examples/graphsage_examples.rst
examples/dgi_examples.rst
examples/distribute_deepwalk_examples.rst
examples/distribute_graphsage_examples.rst
examples/ges_examples.rst
examples/line_examples.rst
examples/sgc_examples.rst
examples/strucvec_examples.rst
examples/gatne_examples.rst
examples/metapath2vec_examples.rst
examples/unsup_graphsage_examples.rst
.. toctree:: .. toctree::
:maxdepth: 2 :maxdepth: 2
......
docs/source/md/introduction.md
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# Paddle Graph Learning (PGL) # Paddle Graph Learning (PGL)
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle).
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle).
<div /> <div />
...@@ -9,17 +8,18 @@ Paddle Graph Learning (PGL) is an efficient and flexible graph learning framewor ...@@ -9,17 +8,18 @@ Paddle Graph Learning (PGL) is an efficient and flexible graph learning framewor
<center>The Framework of Paddle Graph Learning (PGL)</center> <center>The Framework of Paddle Graph Learning (PGL)</center>
<div /> <div />
We provide python interfaces for storing/reading/querying graph structured data and two fundamental computational interfaces, which are walk based paradigm and message-passing based paradigm as shown in the above framework of PGL, for building cutting-edge graph learning algorithms. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications. The newly released PGL supports heterogeneous graph learning on both walk based paradigm and message-passing based paradigm by providing MetaPath sampling and Message Passing mechanism on heterogeneous graph. Furthermor, The newly released PGL also support distributed graph storage and some distributed training algorithms, such as distributed deep walk and distributed graphsage. Combined with the PaddlePaddle deep learning framework, we are able to support both graph representation learning models and graph neural networks, and thus our framework has a wide range of graph-based applications.
## Highlight: Efficiency - Support Scatter-Gather and LodTensor Message Passing
## Highlight: Efficient and Flexible <br/> Message Passing Paradigm One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. At PGL we adopt **Message Passing Paradigm** similar to DGL to help to build a customize graph neural network easily. Users only need to write ``send`` and ``recv`` functions to easily implement a simple GCN. As shown in the following figure, for the first step the send function is defined on the edges of the graph, and the user can customize the send function $\phi^e$ to send the message from the source to the target node. For the second step, the recv function $\phi^v$ is responsible for aggregating $\oplus$ messages together from different sources.
<div /> <div />
<div align=center><img src="_static/message_passing_paradigm.png" width="700"></div> <div align=center><img src="_static/message_passing_paradigm.png" width="700"></div>
<center>The basic idea of message passing paradigm</center> <center>The basic idea of message passing paradigm</center>
<div /> <div />
As shown in the left of the following figure, to adapt general user-defined message aggregate functions, DGL uses the degree bucketing method to combine nodes with the same degree into a batch and then apply an aggregate function $\oplus$ on each batch serially. For our PGL UDF aggregate function, we organize the message as a [LodTensor](http://www.paddlepaddle.org/documentation/docs/en/1.4/user_guides/howto/basic_concept/lod_tensor_en.html) in [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) taking the message as variable length sequences. And we **utilize the features of LodTensor in Paddle to obtain fast parallel aggregation**.
As shown in the left of the following figure, to adapt general user-defined message aggregate functions, DGL uses the degree bucketing method to combine nodes with the same degree into a batch and then apply an aggregate function $\oplus$ on each batch serially. For our PGL UDF aggregate function, we organize the message as a [LodTensor](http://www.paddlepaddle.org/documentation/docs/en/1.4/user_guides/howto/basic_concept/lod_tensor_en.html) in [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) taking the message as variable length sequences. And we **utilize the features of LodTensor in Paddle to obtain fast parallel aggregation**.
<div/> <div/>
...@@ -28,21 +28,22 @@ As shown in the left of the following figure, to adapt general user-defined mess ...@@ -28,21 +28,22 @@ As shown in the left of the following figure, to adapt general user-defined mess
<div/> <div/>
Users only need to call the ```sequence_ops``` functions provided by Paddle to easily implement efficient message aggregation. For examples, using ```sequence_pool``` to sum the neighbor message. Users only need to call the ``sequence_ops`` functions provided by Paddle to easily implement efficient message aggregation. For examples, using ``sequence_pool`` to sum the neighbor message.
```python ```python
import paddle.fluid as fluid import paddle.fluid as fluid
def recv(msg): def recv(msg):
return fluid.layers.sequence_pool(msg, "sum") return fluid.layers.sequence_pool(msg, "sum")
``` ```
Although DGL does some kernel fusion optimization for general sum, max and other aggregate functions with scatter-gather. For **complex user-defined functions** with degree bucketing algorithm, the serial execution for each degree bucket cannot take full advantage of the performance improvement provided by GPU. However, operations on the PGL LodTensor-based message is performed in parallel, which can fully utilize GPU parallel optimization. Even without scatter-gather optimization, PGL still has excellent performance. Of course, we still provide build-in scatter-optimized message aggregation functions.
Although DGL does some kernel fusion optimization for general sum, max and other aggregate functions with scatter-gather. For **complex user-defined functions** with degree bucketing algorithm, the serial execution for each degree bucket cannot take full advantage of the performance improvement provided by GPU. However, operations on the PGL LodTensor-based message is performed in parallel, which can fully utilize GPU parallel optimization. In our experiments, PGL can reach up to 13 times the speed of DGL with complex user-defined functions. Even without scatter-gather optimization, PGL still has excellent performance. Of course, we still provide build-in scatter-optimized message aggregation functions.
## Performance ### Performance
We test all the GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs to get average speeds. And we report the accuracy on test dataset without early stoppping.
| Dataset | Model | PGL Accuracy | PGL speed (epoch time) | DGL speed (epoch time) | We test all the following GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs to get average speeds. And we report the accuracy on test dataset without early stoppping.
| Dataset | Model | PGL Accuracy | PGL speed (epoch time) | DGL 0.3.0 speed (epoch time) |
| -------- | ----- | ----------------- | ------------ | ------------------------------------ | | -------- | ----- | ----------------- | ------------ | ------------------------------------ |
| Cora | GCN |81.75% | 0.0047s | **0.0045s** | | Cora | GCN |81.75% | 0.0047s | **0.0045s** |
| Cora | GAT | 83.5% | **0.0119s** | 0.0141s | | Cora | GAT | 83.5% | **0.0119s** | 0.0141s |
...@@ -50,3 +51,95 @@ We test all the GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs t ...@@ -50,3 +51,95 @@ We test all the GNN algorithms with Tesla V100-SXM2-16G running for 200 epochs t
| Pubmed | GAT | 77% |0.0193s|**0.0144s**| | Pubmed | GAT | 77% |0.0193s|**0.0144s**|
| Citeseer | GCN |70.2%| **0.0045** |0.0046s| | Citeseer | GCN |70.2%| **0.0045** |0.0046s|
| Citeseer | GAT |68.8%| **0.0124s** |0.0139s| | Citeseer | GAT |68.8%| **0.0124s** |0.0139s|
If we use complex user-defined aggregation like [GraphSAGE-LSTM](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) that aggregates neighbor features with LSTM ignoring the order of recieved messages, the optimized message-passing in DGL will be forced to degenerate into degree bucketing scheme. The speed performance will be much slower than the one implemented in PGL. Performances may be various with different scale of the graph, in our experiments, PGL can reach up to 13 times the speed of DGL.
| Dataset | PGL speed (epoch time) | DGL 0.3.0 speed (epoch time) | Speed up|
| -------- | ------------ | ------------------------------------ |----|
| Cora | **0.0186s** | 0.1638s | 8.80x|
| Pubmed | **0.0388s** |0.5275s | 13.59x|
| Citeseer | **0.0150s** | 0.1278s | 8.52x |
## Highlight: Flexibility - Natively Support Heterogeneous Graph Learning
Graph can conveniently represent the relation between things in the real world, but the categories of things and the relation between things are various. Therefore, in the heterogeneous graph, we need to distinguish the node types and edge types in the graph network. PGL models heterogeneous graphs that contain multiple node types and multiple edge types, and can describe complex connections between different types.
### Support meta path walk sampling on heterogeneous graph
<div/>
<div align=center><img src="_static/metapath_sampling.png" width="750"></div>
<center>The metapath sampling in heterogeneous graph</center>
<div/>
The left side of the figure above describes a shopping social network. The nodes above have two categories of users and goods, and the relations between users and users, users and goods, and goods and goods. The right of the above figure is a simple sampling process of MetaPath. When you input any MetaPath as UPU (user-product-user), you will find the following results
<div/>
<div align=center><img src="_static/metapath_result.png" width="300"></div>
<center>The metapath result</center>
<div/>
Then on this basis, and introducing word2vec and other methods to support learning metapath2vec and other algorithms of heterogeneous graph representation.
### Support Message Passing mechanism on heterogeneous graph
<div/>
<div align=center><img src="_static/him_message_passing.png" width="750"></div>
<center>The message passing mechanism on heterogeneous graph</center>
<div/>
Because of the different node types on the heterogeneous graph, the message delivery is also different. As shown on the left, it has five neighbors, belonging to two different node types. As shown on the right of the figure above, nodes belonging to different types need to be aggregated separately during message delivery, and then merged into the final message to update the target node. On this basis, PGL supports heterogeneous graph algorithms based on message passing, such as GATNE and other algorithms.
## Large-Scale: Support distributed graph storage and distributed training algorithms
In most cases of large-scale graph learning, we need distributed graph storage and distributed training support. As shown in the following figure, PGL provided a general solution of large-scale training, we adopted [PaddleFleet](https://github.com/PaddlePaddle/Fleet) as our distributed parameter servers, which supports large scale distributed embeddings and a lightweighted distributed storage engine so tcan easily set up a large scale distributed training algorithm with MPI clusters.
<div/>
<div align=center><img src="_static/distributed_frame.png" width="750"></div>
<center>The distributed frame of PGL</center>
<div/>
## Highlight: Tons of Models
The following are 13 graph learning models that have been implemented in the framework.
|Model | feature |
|---|---|
| [GCN](examples/gcn_examples.html)| Graph Convolutional Neural Networks |
| [GAT](examples/gat_examples.html)| Graph Attention Network |
| [GraphSage](examples/graphsage_examples.html)|Large-scale graph convolution network based on neighborhood sampling|
| [unSup-GraphSage](examples/unsup_graphsage_examples.html) | Unsupervised GraphSAGE |
| [LINE](examples/line_examples.html)| Representation learning based on first-order and second-order neighbors |
| [DeepWalk](examples/distribute_deepwalk_examples.html)| Representation learning by DFS random walk |
| [MetaPath2Vec](examples/metapath2vec_examples.html)| Representation learning based on metapath |
| [Node2Vec](examples/node2vec_examples.html)| The representation learning Combined with DFS and BFS |
| [Struct2Vec](examples/strucvec_examples.html)| Representation learning based on structural similarity |
| [SGC](examples/sgc_examples.html)| Simplified graph convolution neural network |
| [GES](examples/ges_examples.html)| The graph represents learning method with node features |
| [DGI](examples/dgi_examples.html)| Unsupervised representation learning based on graph convolution network |
| [GATNE](examples/gatne_examples.html)| Representation Learning of Heterogeneous Graph based on MessagePassing |
The above models consists of three parts, namely, graph representation learning, graph neural network and heterogeneous graph learning, which are also divided into graph representation learning and graph neural network.
## System requirements
PGL requires:
* paddle >= 1.6
* cython
PGL supports both Python 2 & 3
## Installation
You can simply install it via pip.
```sh
pip install pgl
```
## The Team
PGL is developed and maintained by NLP and Paddle Teams at Baidu
## License
PGL uses Apache License 2.0.
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Quick Start with Heterogenous Graph
========================
Install PGL
-----------
To install Paddle Graph Learning, we need the following packages.
.. code-block:: sh
paddlepaddle >= 1.6
cython
We can simply install pgl by pip.
.. code-block:: sh
pip install pgl
.. mdinclude:: md/quick_start_for_heterGraph.md
docs/source/md/quick_start.md → docs/source/quick_start/md/quick_start.md
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## Step 1: using PGL to create a graph ## Step 1: using PGL to create a graph
Suppose we have a graph with 10 nodes and 14 edges as shown in the following figure: Suppose we have a graph with 10 nodes and 14 edges as shown in the following figure:
![A simple graph](_static/quick_start_graph.png) ![A simple graph](images/quick_start_graph.png)
Our purpose is to train a graph neural network to classify yellow and green nodes. So we can create this graph in such way: Our purpose is to train a graph neural network to classify yellow and green nodes. So we can create this graph in such way:
```python ```python
......
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## Introduction
In real world, there exists many graphs contain multiple types of nodes and edges, which we call them Heterogeneous Graphs. Obviously, heterogenous graphs are more complex than homogeneous graphs.
To deal with such heterogeneous graphs, PGL develops a graph framework to support graph neural network computations and meta-path-based sampling on heterogenous graph.
The goal of this tutorial:
* example of heterogenous graph data;
* Understand how PGL supports computations in heterogenous graph;
* Using PGL to implement a simple heterogenous graph neural network model to classfiy a particular type of node in a heterogenous graph network.
## Example of heterogenous graph
There are a lot of graph data that consists of edges and nodes of multiple types. For example, **e-commerce network** is very common heterogenous graph in real world. It contains at least two types of nodes (user and item) and two types of edges (buy and click).
The following figure depicts several users click or buy some items. This graph has two types of nodes corresponding to "user" and "item". It also contain two types of edge "buy" and "click".
![A simple heterogenous e-commerce graph](images/heter_graph_introduction.png)
## Creating a heterogenous graph with PGL
In heterogenous graph, there exists multiple edges, so we should distinguish them. In PGL, the edges are built in below format:
```python
edges = {
'click': [(0, 4), (0, 7), (1, 6), (2, 5), (3, 6)],
'buy': [(0, 5), (1, 4), (1, 6), (2, 7), (3, 5)],
}
```
In heterogenous graph, nodes are also of different types. Therefore, you need to mark the type of each node, the format of the node type is as follows:
```python
node_types = [(0, 'user'), (1, 'user'), (2, 'user'), (3, 'user'), (4, 'item'),
(5, 'item'),(6, 'item'), (7, 'item')]
```
Because of the different types of edges, edge features also need to be separated by different types.
```python
import numpy as np
num_nodes = len(node_types)
node_features = {'features': np.random.randn(num_nodes, 8).astype("float32")}
edge_num_list = []
for edge_type in edges:
edge_num_list.append(len(edges[edge_type]))
edge_features = {
'click': {'h': np.random.randn(edge_num_list[0], 4)},
'buy': {'h':np.random.randn(edge_num_list[1], 4)},
}
```
Now, we can build a heterogenous graph by using PGL.
```python
import paddle.fluid as fluid
import paddle.fluid.layers as fl
import pgl
from pgl.contrib import heter_graph
from pgl.contrib import heter_graph_wrapper
g = heter_graph.HeterGraph(num_nodes=num_nodes,
edges=edges,
node_types=node_types,
node_feat=node_features,
edge_feat=edge_features)
```
In PGL, we need to use graph_wrapper as a container for graph data, so here we need to create a graph_wrapper for each type of edge graph.
```python
place = fluid.CPUPlace()
# create a GraphWrapper as a container for graph data
gw = heter_graph_wrapper.HeterGraphWrapper(name='heter_graph',
place = place,
edge_types = g.edge_types_info(),
node_feat=g.node_feat_info(),
edge_feat=g.edge_feat_info())
```
## MessagePassing
After building the heterogeneous graph, we can easily carry out the message passing mode. In this case, we have two different types of edges, so we can write a function in such way:
```python
def message_passing(gw, edge_types, features, name=''):
def __message(src_feat, dst_feat, edge_feat):
return src_feat['h']
def __reduce(feat):
return fluid.layers.sequence_pool(feat, pool_type='sum')
assert len(edge_types) == len(features)
output = []
for i in range(len(edge_types)):
msg = gw[edge_types[i]].send(__message, nfeat_list=[('h', features[i])])
out = gw[edge_types[i]].recv(msg, __reduce)
output.append(out)
# list of matrix
return output
```
```python
edge_types = ['click', 'buy']
features = []
for edge_type in edge_types:
features.append(gw[edge_type].node_feat['features'])
output = message_passing(gw, edge_types, features)
output = fl.concat(input=output, axis=1)
output = fluid.layers.fc(output, size=4, bias_attr=False, act='relu', name='fc1')
logits = fluid.layers.fc(output, size=1, bias_attr=False, act=None, name='fc2')
```
## data preprocessing
In this case, we implement a simple node classifier, we can use 0,1 to represent two classes.
```python
y = [0,1,0,1,0,1,1,0]
label = np.array(y, dtype="float32").reshape(-1,1)
```
## Setting up the training program
The training process of the heterogeneous graph node classification model is the same as the training of other paddlepaddle-based models.
* First we build the loss function;
* Second, creating a optimizer;
* Finally, creating a executor and execute the training program.
```python
node_label = fluid.layers.data("node_label", shape=[None, 1], dtype="float32", append_batch_size=False)
loss = fluid.layers.sigmoid_cross_entropy_with_logits(x=logits, label=node_label)
loss = fluid.layers.mean(loss)
adam = fluid.optimizer.Adam(learning_rate=0.01)
adam.minimize(loss)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
feed_dict = gw.to_feed(g)
for epoch in range(30):
feed_dict['node_label'] = label
train_loss = exe.run(fluid.default_main_program(), feed=feed_dict, fetch_list=[loss], return_numpy=True)
print('Epoch %d | Loss: %f'%(epoch, train_loss[0]))
```
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file loads and preprocesses the dataset for GATNE model.
"""
import sys
import os
import tqdm
import numpy as np
import logging
import random
from pgl.contrib import heter_graph
import pickle as pkl
class Dataset(object):
"""Implementation of Dataset class
This is a simple implementation of loading and processing dataset for GATNE model.
Args:
config: dict, some configure parameters.
"""
def __init__(self, config):
self.train_edges_file = config['data_path'] + 'train.txt'
self.valid_edges_file = config['data_path'] + 'valid.txt'
self.test_edges_file = config['data_path'] + 'test.txt'
self.nodes_file = config['data_path'] + 'nodes.txt'
self.config = config
self.word2index = self.load_word2index()
self.build_graph()
self.valid_data = self.load_test_data(self.valid_edges_file)
self.test_data = self.load_test_data(self.test_edges_file)
def build_graph(self):
"""Build pgl heterogeneous graph.
"""
edge_data_by_type, all_edges, all_nodes = self.load_training_data(
self.train_edges_file,
slf_loop=self.config['slf_loop'],
symmetry_edge=self.config['symmetry_edge'])
num_nodes = len(all_nodes)
node_features = {
'index': np.array(
[i for i in range(num_nodes)], dtype=np.int64).reshape(-1, 1)
}
self.graph = heter_graph.HeterGraph(
num_nodes=num_nodes,
edges=edge_data_by_type,
node_types=None,
node_feat=node_features)
self.edge_types = sorted(self.graph.edge_types_info())
logging.info('total %d nodes are loaded' % (self.graph.num_nodes))
def load_training_data(self, file_, slf_loop=True, symmetry_edge=True):
"""Load train data from file and preprocess them.
Args:
file_: str, file name for loading data
slf_loop: bool, if true, add self loop edge for every node
symmetry_edge: bool, if true, add symmetry edge for every edge
"""
logging.info('loading data from %s' % file_)
edge_data_by_type = dict()
all_edges = list()
all_nodes = list()
with open(file_, 'r') as reader:
for line in reader:
words = line.strip().split(' ')
if words[0] not in edge_data_by_type:
edge_data_by_type[words[0]] = []
src, dst = words[1], words[2]
edge_data_by_type[words[0]].append((src, dst))
all_edges.append((src, dst))
all_nodes.append(src)
all_nodes.append(dst)
if symmetry_edge:
edge_data_by_type[words[0]].append((dst, src))
all_edges.append((dst, src))
all_nodes = list(set(all_nodes))
all_edges = list(set(all_edges))
# edge_data_by_type['Base'] = all_edges
if slf_loop:
for e_type in edge_data_by_type.keys():
for n in all_nodes:
edge_data_by_type[e_type].append((n, n))
# remapping to index
edges_by_type = {}
for edge_type, edges in edge_data_by_type.items():
res_edges = []
for edge in edges:
res_edges.append(
(self.word2index[edge[0]], self.word2index[edge[1]]))
edges_by_type[edge_type] = res_edges
return edges_by_type, all_edges, all_nodes
def load_test_data(self, file_):
"""Load testing data from file.
"""
logging.info('loading data from %s' % file_)
true_edge_data_by_type = {}
fake_edge_data_by_type = {}
with open(file_, 'r') as reader:
for line in reader:
words = line.strip().split(' ')
src, dst = self.word2index[words[1]], self.word2index[words[2]]
e_type = words[0]
if int(words[3]) == 1: # true edges
if e_type not in true_edge_data_by_type:
true_edge_data_by_type[e_type] = list()
true_edge_data_by_type[e_type].append((src, dst))
else: # fake edges
if e_type not in fake_edge_data_by_type:
fake_edge_data_by_type[e_type] = list()
fake_edge_data_by_type[e_type].append((src, dst))
return (true_edge_data_by_type, fake_edge_data_by_type)
def load_word2index(self):
"""Load words(nodes) from file and map to index.
"""
word2index = {}
with open(self.nodes_file, 'r') as reader:
for index, line in enumerate(reader):
node = line.strip()
word2index[node] = index
return word2index
def generate_walks(self):
"""Generate random walks for every edge type.
"""
all_walks = {}
for e_type in self.edge_types:
layer_walks = self.simulate_walks(
edge_type=e_type,
num_walks=self.config['num_walks'],
walk_length=self.config['walk_length'])
all_walks[e_type] = layer_walks
return all_walks
def simulate_walks(self, edge_type, num_walks, walk_length, schema=None):
"""Generate random walks in specified edge type.
"""
walks = []
nodes = list(range(0, self.graph[edge_type].num_nodes))
for walk_iter in tqdm.tqdm(range(num_walks)):
random.shuffle(nodes)
for node in nodes:
walk = self.graph[edge_type].random_walk(
[node], max_depth=walk_length - 1)
for i in range(len(walk)):
walks.append(walk[i])
return walks
def generate_pairs(self, all_walks):
"""Generate word pairs for training.
"""
logging.info(['edge_types before generate pairs', self.edge_types])
pairs = []
skip_window = self.config['win_size'] // 2
for layer_id, e_type in enumerate(self.edge_types):
walks = all_walks[e_type]
for walk in tqdm.tqdm(walks):
for i in range(len(walk)):
for j in range(1, skip_window + 1):
if i - j >= 0 and walk[i] != walk[i - j]:
neg_nodes = self.graph[e_type].sample_nodes(
self.config['neg_num'])
pairs.append(
(walk[i], walk[i - j], *neg_nodes, layer_id))
if i + j < len(walk) and walk[i] != walk[i + j]:
neg_nodes = self.graph[e_type].sample_nodes(
self.config['neg_num'])
pairs.append(
(walk[i], walk[i + j], *neg_nodes, layer_id))
return pairs
def fetch_batch(self, pairs, batch_size, for_test=False):
"""Produce batch pairs data for training.
"""
np.random.shuffle(pairs)
n_batches = (len(pairs) + (batch_size - 1)) // batch_size
neg_num = len(pairs[0]) - 3
result = []
for i in range(1, n_batches):
batch_pairs = np.array(
pairs[batch_size * (i - 1):batch_size * i], dtype=np.int64)
x = batch_pairs[:, 0].reshape(-1, ).astype(np.int64)
y = batch_pairs[:, 1].reshape(-1, 1, 1).astype(np.int64)
neg = batch_pairs[:, 2:2 + neg_num].reshape(-1, neg_num,
1).astype(np.int64)
t = batch_pairs[:, -1].reshape(-1, 1).astype(np.int64)
result.append((x, y, neg, t))
return result
if __name__ == "__main__":
config = {
'data_path': './data/youtube/',
'train_pairs_file': 'train_pairs.pkl',
'slf_loop': True,
'symmetry_edge': True,
'num_walks': 20,
'walk_length': 10,
'win_size': 5,
'neg_num': 5,
}
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(level='INFO', format=log_format)
dataset = Dataset(config)
logging.info('generating walks')
all_walks = dataset.generate_walks()
logging.info('finishing generate walks')
logging.info(['length of all walks: ', all_walks.keys()])
train_pairs = dataset.generate_pairs(all_walks)
pkl.dump(train_pairs,
open(config['data_path'] + config['train_pairs_file'], 'wb'))
logging.info('finishing generate train_pairs')
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# GATNE: General Attributed Multiplex HeTerogeneous Network Embedding
[GATNE](https://arxiv.org/pdf/1905.01669.pdf) is a algorithms framework for embedding large-scale Attributed Multiplex Heterogeneous Networks(AMHN). Given a heterogeneous graph, which consists of nodes and edges of multiple types, it can learn continuous feature representations for every node. Based on PGL, we reproduce GATNE algorithm.
## Datasets
YouTube dataset contains 2000 nodes, 1310617 edges and 5 edge types. And we use YouTube dataset for example.
You can dowload YouTube datasets from [here](https://github.com/THUDM/GATNE/tree/master/data)
After downloading the data, put them, let's say, in ./data/ . Note that the current directory is the root directory of GATNE model. Then in ./data/youtube/ directory, there are three files:
* train.txt
* valid.txt
* test.txt
Then you can run the below command to preprocess the data.
```sh
python data_process.py --input_file ./data/youtube/train.txt --output_file ./data/youtube/nodes.txt
```
## Dependencies
- paddlepaddle>=1.6
- pgl>=1.0.0
## Hyperparameters
All the hyper parameters are saved in config.yaml file. So before training GATNE model, you can open the config.yaml to modify the hyper parameters as you like.
for example, you can change the \"use_cuda\" to \"True \" in order to use GPU for training or modify \"data_path\" to use different dataset.
Some important hyper parameters in config.yaml:
- use_cuda: use GPU to train model
- data_path: the directory of dataset
- lr: learning rate
- neg_num: number of negatie samples.
- num_walks: number of walks started from each node
- walk_length: walk length
## How to run
Then run the below command:
```sh
python main.py -c config.yaml
```
### Experiment results
| | PGL result | Reported result |
|:---:|------------|-----------------|
| AUC | 84.83 | 84.61 |
| PR | 82.77 | 81.93 |
| F1 | 76.98 | 76.83 |
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task_name: train.gatne
use_cuda: True
log_level: info
seed: 1667
optimizer:
type:
args:
lr: 0.005
trainer:
type: trainer
args:
epochs: 2
log_dir: logs/
save_dir: checkpoints/
output_dir: outputs/
data_loader:
type: Dataset
args:
data_path: ./data/youtube/
train_pairs_file: train_pairs.pkl
batch_size: 256
num_walks: 20
walk_length: 10
win_size: 5
neg_num: 5
slf_loop: True
symmetry_edge: True
model:
type: GATNE
args:
dimensions: 200
edge_dim: 32
att_dim: 32
att_head: 1
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file preprocess the data before training.
"""
import sys
import argparse
def gen_nodes_file(file_, result_file):
"""calculate the total number of nodes and save them for latter processing.
"""
nodes = []
with open(file_, 'r') as reader:
for line in reader:
tokens = line.strip().split(' ')
nodes.append(tokens[1])
nodes.append(tokens[2])
nodes = list(set(nodes))
nodes.sort(key=int)
print('total number of nodes: %d' % len(nodes))
print('saving nodes file in %s' % (result_file))
with open(result_file, 'w') as writer:
for n in nodes:
writer.write(n + '\n')
print('finished')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='GATNE')
parser.add_argument(
'--input_file',
default='./data/youtube/train.txt',
type=str,
help='input file')
parser.add_argument(
'--output_file',
default='./data/youtube/nodes.txt',
type=str,
help='output file')
args = parser.parse_args()
print('generating nodes file')
gen_nodes_file(args.input_file, args.output_file)
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement the training process of GATNE model.
"""
import os
import argparse
import time
import numpy as np
import logging
import pickle as pkl
import pgl
from pgl.utils import paddle_helper
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as fl
from utils import *
import Dataset
import model as Model
from sklearn.metrics import (auc, f1_score, precision_recall_curve,
roc_auc_score)
def set_seed(seed):
"""Set random seed.
"""
random.seed(seed)
np.random.seed(seed)
def produce_model(exe, program, dataset, model, feed_dict):
"""Output the learned model parameters for testing.
"""
edge_types = dataset.edge_types
num_nodes = dataset.graph[edge_types[0]].num_nodes
edge_types_count = len(edge_types)
neg_num = dataset.config['neg_num']
final_model = {}
feed_dict['train_inputs'] = np.array(
[n for n in range(num_nodes)], dtype=np.int64).reshape(-1, )
feed_dict['train_labels'] = np.array(
[n for n in range(num_nodes)], dtype=np.int64).reshape(-1, 1, 1)
feed_dict['train_negs'] = np.tile(feed_dict['train_labels'],
(1, neg_num)).reshape(-1, neg_num, 1)
for i in range(edge_types_count):
feed_dict['train_types'] = np.array(
[i for _ in range(num_nodes)], dtype=np.int64).reshape(-1, 1)
edge_node_embed = exe.run(program,
feed=feed_dict,
fetch_list=[model.last_node_embed],
return_numpy=True)[0]
final_model[edge_types[i]] = edge_node_embed
return final_model
def evaluate(final_model, edge_types, data):
"""Calculate the AUC score, F1 score and PR score of the final model
"""
edge_types_count = len(edge_types)
AUC, F1, PR = [], [], []
true_edge_data_by_type = data[0]
fake_edge_data_by_type = data[1]
for i in range(edge_types_count):
try:
local_model = final_model[edge_types[i]]
true_edges = true_edge_data_by_type[edge_types[i]]
fake_edges = fake_edge_data_by_type[edge_types[i]]
except Exception as e:
logging.warn('edge type not exists. %s' % str(e))
continue
tmp_auc, tmp_f1, tmp_pr = calculate_score(local_model, true_edges,
fake_edges)
AUC.append(tmp_auc)
F1.append(tmp_f1)
PR.append(tmp_pr)
return {'AUC': np.mean(AUC), 'F1': np.mean(F1), 'PR': np.mean(PR)}
def calculate_score(model, true_edges, fake_edges):
"""Calculate the AUC score, F1 score and PR score of specified edge type
"""
true_list = list()
prediction_list = list()
true_num = 0
for edge in true_edges:
tmp_score = get_score(model, edge)
if tmp_score is not None:
true_list.append(1)
prediction_list.append(tmp_score)
true_num += 1
for edge in fake_edges:
tmp_score = get_score(model, edge)
if tmp_score is not None:
true_list.append(0)
prediction_list.append(tmp_score)
sorted_pred = prediction_list[:]
sorted_pred.sort()
threshold = sorted_pred[-true_num]
y_pred = np.zeros(len(prediction_list), dtype=np.int32)
for i in range(len(prediction_list)):
if prediction_list[i] >= threshold:
y_pred[i] = 1
y_true = np.array(true_list)
y_scores = np.array(prediction_list)
ps, rs, _ = precision_recall_curve(y_true, y_scores)
return roc_auc_score(y_true, y_scores), f1_score(y_true, y_pred), auc(rs,
ps)
def get_score(local_model, edge):
"""Calculate the cosine similarity score between two nodes.
"""
try:
vector1 = local_model[edge[0]]
vector2 = local_model[edge[1]]
return np.dot(vector1, vector2) / (np.linalg.norm(vector1) *
np.linalg.norm(vector2))
except Exception as e:
logging.warn('get_score warning: %s' % str(e))
return None
pass
def run_epoch(epoch,
config,
dataset,
data,
train_prog,
test_prog,
model,
feed_dict,
exe,
for_test=False):
"""Run training process of every epoch.
"""
total_loss = []
for idx, batch_data in enumerate(data):
feed_dict['train_inputs'] = batch_data[0]
feed_dict['train_labels'] = batch_data[1]
feed_dict['train_negs'] = batch_data[2]
feed_dict['train_types'] = batch_data[3]
loss, lr = exe.run(train_prog,
feed=feed_dict,
fetch_list=[model.loss, model.lr],
return_numpy=True)
total_loss.append(loss[0])
if (idx + 1) % 500 == 0:
avg_loss = np.mean(total_loss)
logging.info("epoch %d | step %d | lr %.4f | train_loss %f " %
(epoch, idx + 1, lr, avg_loss))
total_loss = []
return avg_loss
def save_model(program, exe, dataset, model, feed_dict, filename):
"""Save model.
"""
final_model = produce_model(exe, program, dataset, model, feed_dict)
logging.info('saving model in %s' % (filename))
pkl.dump(final_model, open(filename, 'wb'))
def test(program, exe, dataset, model, feed_dict):
"""Testing and validating.
"""
final_model = produce_model(exe, program, dataset, model, feed_dict)
valid_result = evaluate(final_model, dataset.edge_types,
dataset.valid_data)
test_result = evaluate(final_model, dataset.edge_types, dataset.test_data)
logging.info("valid_AUC %.4f | valid_PR %.4f | valid_F1 %.4f" %
(valid_result['AUC'], valid_result['PR'], valid_result['F1']))
logging.info("test_AUC %.4f | test_PR %.4f | test_F1 %.4f" %
(test_result['AUC'], test_result['PR'], test_result['F1']))
return test_result
def main(config):
"""main function for training GATNE model.
"""
logging.info(config)
set_seed(config['seed'])
dataset = getattr(
Dataset, config['data_loader']['type'])(config['data_loader']['args'])
edge_types = dataset.graph.edge_types_info()
logging.info(['total edge types: ', edge_types])
# train_pairs is a list of tuple: [(src1, dst1, neg, e1), (src2, dst2, neg, e2)]
# e(int), edge num count, for select which edge embedding
train_pairs_file = config['data_loader']['args']['data_path'] + \
config['data_loader']['args']['train_pairs_file']
if os.path.exists(train_pairs_file):
logging.info('loading train pairs from pkl file %s' % train_pairs_file)
train_pairs = pkl.load(open(train_pairs_file, 'rb'))
else:
logging.info('generating walks')
all_walks = dataset.generate_walks()
logging.info('generating train pairs')
train_pairs = dataset.generate_pairs(all_walks)
logging.info('dumping train pairs to %s' % (train_pairs_file))
pkl.dump(train_pairs, open(train_pairs_file, 'wb'))
logging.info('total train pairs: %d' % (len(train_pairs)))
data = dataset.fetch_batch(train_pairs,
config['data_loader']['args']['batch_size'])
place = fluid.CUDAPlace(0) if config['use_cuda'] else fluid.CPUPlace()
train_program = fluid.Program()
startup_program = fluid.Program()
test_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
model = getattr(Model, config['model']['type'])(
config['model']['args'], dataset, place)
test_program = train_program.clone(for_test=True)
with fluid.program_guard(train_program, startup_program):
global_steps = len(data) * config['trainer']['args']['epochs']
model.backward(global_steps, config['optimizer']['args'])
# train
exe = fluid.Executor(place)
exe.run(startup_program)
feed_dict = model.gw.to_feed(dataset.graph)
logging.info('test before training...')
test(test_program, exe, dataset, model, feed_dict)
logging.info('training...')
for epoch in range(1, 1 + config['trainer']['args']['epochs']):
train_result = run_epoch(epoch, config['trainer']['args'], dataset,
data, train_program, test_program, model,
feed_dict, exe)
logging.info('validating and testing...')
test_result = test(test_program, exe, dataset, model, feed_dict)
filename = os.path.join(config['trainer']['args']['save_dir'],
'dict_embed_model_epoch_%d.pkl' % (epoch))
save_model(test_program, exe, dataset, model, feed_dict, filename)
logging.info(
"final_test_AUC %.4f | final_test_PR %.4f | fianl_test_F1 %.4f" % (
test_result['AUC'], test_result['PR'], test_result['F1']))
logging.info('training finished')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='GATNE')
parser.add_argument(
'-c',
'--config',
default=None,
type=str,
help='config file path (default: None)')
parser.add_argument(
'-n',
'--taskname',
default=None,
type=str,
help='task name(default: None)')
args = parser.parse_args()
if args.config:
# load config file
config = Config(args.config, isCreate=True, isSave=True)
config = config()
else:
raise AssertionError(
"Configuration file need to be specified. Add '-c config.yaml', for example."
)
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(
level=getattr(logging, config['log_level'].upper()), format=log_format)
main(config)
examples/GATNE/model.py 0 → 100644
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement the GATNE model.
"""
import numpy as np
import math
import logging
import paddle.fluid as fluid
import paddle.fluid.layers as fl
from pgl.contrib import heter_graph_wrapper
class GATNE(object):
"""Implementation of GATNE model.
Args:
config: dict, some configure parameters.
dataset: instance of Dataset class
place: GPU or CPU place
"""
def __init__(self, config, dataset, place):
logging.info(['model is: ', self.__class__.__name__])
self.config = config
self.graph = dataset.graph
self.placce = place
self.edge_types = sorted(self.graph.edge_types_info())
logging.info('edge_types in model: %s' % str(self.edge_types))
neg_num = dataset.config['neg_num']
# hyper parameters
self.num_nodes = self.graph.num_nodes
self.embedding_size = self.config['dimensions']
self.embedding_u_size = self.config['edge_dim']
self.dim_a = self.config['att_dim']
self.att_head = self.config['att_head']
self.edge_type_count = len(self.edge_types)
self.u_num = self.edge_type_count
self.gw = heter_graph_wrapper.HeterGraphWrapper(
name="heter_graph",
place=place,
edge_types=self.graph.edge_types_info(),
node_feat=self.graph.node_feat_info(),
edge_feat=self.graph.edge_feat_info())
self.train_inputs = fl.data(
'train_inputs', shape=[None], dtype='int64')
self.train_labels = fl.data(
'train_labels', shape=[None, 1, 1], dtype='int64')
self.train_types = fl.data(
'train_types', shape=[None, 1], dtype='int64')
self.train_negs = fl.data(
'train_negs', shape=[None, neg_num, 1], dtype='int64')
self.forward()
def forward(self):
"""Build the GATNE net.
"""
param_attr_init = fluid.initializer.Uniform(
low=-1.0, high=1.0, seed=np.random.randint(100))
embed_param_attrs = fluid.ParamAttr(
name='Base_node_embed', initializer=param_attr_init)
# node_embeddings
base_node_embed = fl.embedding(
input=fl.reshape(
self.train_inputs, shape=[-1, 1]),
size=[self.num_nodes, self.embedding_size],
param_attr=embed_param_attrs)
node_features = []
for edge_type in self.edge_types:
param_attr_init = fluid.initializer.Uniform(
low=-1.0, high=1.0, seed=np.random.randint(100))
embed_param_attrs = fluid.ParamAttr(
name='%s_node_embed' % edge_type, initializer=param_attr_init)
features = fl.embedding(
input=self.gw[edge_type].node_feat['index'],
size=[self.num_nodes, self.embedding_u_size],
param_attr=embed_param_attrs)
node_features.append(features)
# mp_output: list of embedding(self.num_nodes, dim)
mp_output = self.message_passing(self.gw, self.edge_types,
node_features)
# U : (num_type[m], num_nodes, dim[s])
node_type_embed = fl.stack(mp_output, axis=0)
# U : (num_nodes, num_type[m], dim[s])
node_type_embed = fl.transpose(node_type_embed, perm=[1, 0, 2])
#gather node_type_embed from train_inputs
node_type_embed = fl.gather(node_type_embed, self.train_inputs)
# M_r
trans_weights = fl.create_parameter(
shape=[
self.edge_type_count, self.embedding_u_size,
self.embedding_size // self.att_head
],
attr=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=1.0 / math.sqrt(self.embedding_size)),
dtype='float32',
name='trans_w')
# W_r
trans_weights_s1 = fl.create_parameter(
shape=[self.edge_type_count, self.embedding_u_size, self.dim_a],
attr=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=1.0 / math.sqrt(self.embedding_size)),
dtype='float32',
name='trans_w_s1')
# w_r
trans_weights_s2 = fl.create_parameter(
shape=[self.edge_type_count, self.dim_a, self.att_head],
attr=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=1.0 / math.sqrt(self.embedding_size)),
dtype='float32',
name='trans_w_s2')
trans_w = fl.gather(trans_weights, self.train_types)
trans_w_s1 = fl.gather(trans_weights_s1, self.train_types)
trans_w_s2 = fl.gather(trans_weights_s2, self.train_types)
attention = self.attention(node_type_embed, trans_w_s1, trans_w_s2)
node_type_embed = fl.matmul(attention, node_type_embed)
node_embed = base_node_embed + fl.reshape(
fl.matmul(node_type_embed, trans_w), [-1, self.embedding_size])
self.last_node_embed = fl.l2_normalize(node_embed, axis=1)
nce_weight_initializer = fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=1.0 / math.sqrt(self.embedding_size))
nce_weight_attrs = fluid.ParamAttr(
name='nce_weight', initializer=nce_weight_initializer)
weight_pos = fl.embedding(
input=self.train_labels,
size=[self.num_nodes, self.embedding_size],
param_attr=nce_weight_attrs)
weight_neg = fl.embedding(
input=self.train_negs,
size=[self.num_nodes, self.embedding_size],
param_attr=nce_weight_attrs)
tmp_node_embed = fl.unsqueeze(self.last_node_embed, axes=[1])
pos_logits = fl.matmul(
tmp_node_embed, weight_pos, transpose_y=True) # [B, 1, 1]
neg_logits = fl.matmul(
tmp_node_embed, weight_neg, transpose_y=True) # [B, 1, neg_num]
pos_score = fl.squeeze(pos_logits, axes=[1])
pos_score = fl.clip(pos_score, min=-10, max=10)
pos_score = -1.0 * fl.logsigmoid(pos_score)
neg_score = fl.squeeze(neg_logits, axes=[1])
neg_score = fl.clip(neg_score, min=-10, max=10)
neg_score = -1.0 * fl.logsigmoid(-1.0 * neg_score)
neg_score = fl.reduce_sum(neg_score, dim=1, keep_dim=True)
self.loss = fl.reduce_mean(pos_score + neg_score)
def attention(self, node_type_embed, trans_w_s1, trans_w_s2):
"""Calculate attention weights.
"""
attention = fl.tanh(fl.matmul(node_type_embed, trans_w_s1))
attention = fl.matmul(attention, trans_w_s2)
attention = fl.reshape(attention, [-1, self.u_num])
attention = fl.softmax(attention)
attention = fl.reshape(attention, [-1, self.att_head, self.u_num])
return attention
def message_passing(self, gw, edge_types, features, name=''):
"""Message passing from source nodes to dstination nodes
"""
def __message(src_feat, dst_feat, edge_feat):
"""send function
"""
return src_feat['h']
def __reduce(feat):
"""recv function
"""
return fluid.layers.sequence_pool(feat, pool_type='average')
if not isinstance(edge_types, list):
edge_types = [edge_types]
if not isinstance(features, list):
features = [features]
assert len(edge_types) == len(features)
output = []
for i in range(len(edge_types)):
msg = gw[edge_types[i]].send(
__message, nfeat_list=[('h', features[i])])
neigh_feat = gw[edge_types[i]].recv(msg, __reduce)
neigh_feat = fl.fc(neigh_feat,
size=neigh_feat.shape[-1],
name='neigh_fc_%d' % (i),
act='sigmoid')
slf_feat = fl.fc(features[i],
size=neigh_feat.shape[-1],
name='slf_fc_%d' % (i),
act='sigmoid')
out = fluid.layers.concat([slf_feat, neigh_feat], axis=1)
out = fl.fc(out, size=neigh_feat.shape[-1], name='fc', act=None)
out = fluid.layers.l2_normalize(out, axis=1)
output.append(out)
# list of matrix
return output
def backward(self, global_steps, opt_config):
"""Build the optimizer.
"""
self.lr = fl.polynomial_decay(opt_config['lr'], global_steps, 0.001)
adam = fluid.optimizer.Adam(learning_rate=self.lr)
adam.minimize(self.loss)
examples/GATNE/utils.py 0 → 100644
浏览文件 @ 36febd60
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement a class for model configure.
"""
import datetime
import os
import yaml
import random
import shutil
class Config(object):
"""Implementation of Config class for model configure.
Args:
config_file(str): configure filename, which is a yaml file.
isCreate(bool): if true, create some neccessary directories to save models, log file and other outputs.
isSave(bool): if true, save config_file in order to record the configure message.
"""
def __init__(self, config_file, isCreate=False, isSave=False):
self.config_file = config_file
self.config = self.get_config_from_yaml(config_file)
if isCreate:
self.create_necessary_dirs()
if isSave:
self.save_config_file()
def get_config_from_yaml(self, yaml_file):
"""Get the configure hyperparameters from yaml file.
"""
try:
with open(yaml_file, 'r') as f:
config = yaml.load(f)
except Exception:
raise IOError("Error in parsing config file '%s'" % yaml_file)
return config
def create_necessary_dirs(self):
"""Create some necessary directories to save some important files.
"""
time_stamp = datetime.datetime.now().strftime('%m%d_%H%M')
self.config['trainer']['args']['log_dir'] = ''.join(
(self.config['trainer']['args']['log_dir'],
self.config['task_name'], '/')) # , '.%s/' % (time_stamp)))
self.config['trainer']['args']['save_dir'] = ''.join(
(self.config['trainer']['args']['save_dir'],
self.config['task_name'], '/')) # , '.%s/' % (time_stamp)))
self.config['trainer']['args']['output_dir'] = ''.join(
(self.config['trainer']['args']['output_dir'],
self.config['task_name'], '/')) # , '.%s/' % (time_stamp)))
# if os.path.exists(self.config['trainer']['args']['save_dir']):
# input('save_dir is existed, do you really want to continue?')
self.make_dir(self.config['trainer']['args']['log_dir'])
self.make_dir(self.config['trainer']['args']['save_dir'])
self.make_dir(self.config['trainer']['args']['output_dir'])
def save_config_file(self):
"""Save config file so that we can know the config when we look back
"""
filename = self.config_file.split('/')[-1]
targetpath = self.config['trainer']['args']['save_dir']
shutil.copyfile(self.config_file, targetpath + filename)
def make_dir(self, path):
"""Build directory"""
if not os.path.exists(path):
os.makedirs(path)
def __getitem__(self, key):
"""Return the configure dict"""
return self.config[key]
def __call__(self):
"""__call__"""
return self.config
examples/dgi/README.md
浏览文件 @ 36febd60
# PGL Examples for DGI # DGI: Deep Graph Infomax
[Deep Graph Infomax \(DGI\)](https://arxiv.org/abs/1809.10341) is a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. [Deep Graph Infomax \(DGI\)](https://arxiv.org/abs/1809.10341) is a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures.
......
examples/distribute_deepwalk/README.md
浏览文件 @ 36febd60
# PGL Examples for distributed deepwalk # Distributed Deepwalk in PGL
[Deepwalk](https://arxiv.org/pdf/1403.6652.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce distributed deepwalk algorithms and reach the same level of indicators as the paper. [Deepwalk](https://arxiv.org/pdf/1403.6652.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce distributed deepwalk algorithms and reach the same level of indicators as the paper.
## Datasets ## Datasets
The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3). The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3).
## Dependencies ## Dependencies
...@@ -8,7 +9,9 @@ The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/ ...@@ -8,7 +9,9 @@ The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/
## How to run ## How to run
For examples, train deepwalk in distributed mode on cora dataset. We adopt [PaddlePaddle Fleet](https://github.com/PaddlePaddle/Fleet) as our distributed training frameworks ```pgl_deepwalk.cfg``` is config file for deepwalk hyperparameter and ```local_config``` is a config file for parameter servers. By default, we have 2 pservers and 2 trainers. We can use ```cloud_run.sh``` to help you startup the parameter servers and model trainers.
For examples, train deepwalk in distributed mode on BlogCataLog dataset.
```sh ```sh
# train deepwalk in distributed mode. # train deepwalk in distributed mode.
sh cloud_run.sh sh cloud_run.sh
......
examples/distribute_graphsage/README.md
浏览文件 @ 36febd60
...@@ -55,3 +55,5 @@ python train.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --sample_w ...@@ -55,3 +55,5 @@ python train.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --sample_w
- samples_1: The max neighbors for the first hop neighbor sampling. (default: 25) - samples_1: The max neighbors for the first hop neighbor sampling. (default: 25)
- samples_2: The max neighbors for the second hop neighbor sampling. (default: 10) - samples_2: The max neighbors for the second hop neighbor sampling. (default: 10)
- hidden_size: The hidden size of the GraphSAGE models. - hidden_size: The hidden size of the GraphSAGE models.
examples/distribute_graphsage/reader.py
浏览文件 @ 36febd60
...@@ -89,8 +89,7 @@ def worker(batch_info, graph_wrapper, samples): ...@@ -89,8 +89,7 @@ def worker(batch_info, graph_wrapper, samples):
if len(start_nodes) == 0: if len(start_nodes) == 0:
break break
subgraph = graph.subgraph( subgraph = graph.subgraph(nodes=nodes, eid=eids, edges=[ eid2edges[e] for e in eids])
nodes=nodes, eid=eids, edges=[eid2edges[e] for e in eids])
sub_node_index = subgraph.reindex_from_parrent_nodes( sub_node_index = subgraph.reindex_from_parrent_nodes(
batch_train_samples) batch_train_samples)
feed_dict = graph_wrapper.to_feed(subgraph) feed_dict = graph_wrapper.to_feed(subgraph)
...@@ -103,7 +102,8 @@ def worker(batch_info, graph_wrapper, samples): ...@@ -103,7 +102,8 @@ def worker(batch_info, graph_wrapper, samples):
return work return work
def multiprocess_graph_reader(graph_wrapper, def multiprocess_graph_reader(
graph_wrapper,
samples, samples,
node_index, node_index,
batch_size, batch_size,
...@@ -138,7 +138,7 @@ def multiprocess_graph_reader(graph_wrapper, ...@@ -138,7 +138,7 @@ def multiprocess_graph_reader(graph_wrapper,
reader_pool = [] reader_pool = []
for i in range(num_workers): for i in range(num_workers):
reader_pool.append( reader_pool.append(
worker(batch_info[block_size * i:block_size * (i + 1)], worker(batch_info[block_size * i:block_size * (i + 1)],
graph_wrapper, samples)) graph_wrapper, samples))
multi_process_sample = mp_reader.multiprocess_reader( multi_process_sample = mp_reader.multiprocess_reader(
reader_pool, use_pipe=True, queue_size=1000) reader_pool, use_pipe=True, queue_size=1000)
...@@ -146,3 +146,4 @@ def multiprocess_graph_reader(graph_wrapper, ...@@ -146,3 +146,4 @@ def multiprocess_graph_reader(graph_wrapper,
return paddle.reader.buffered(r, 1000) return paddle.reader.buffered(r, 1000)
return reader() return reader()
examples/distribute_graphsage/requirements.txt
浏览文件 @ 36febd60
scipy scipy
redis==2.10.6 redis==2.10.6
redis-py-cluster==1.3.6 redis-py-cluster==1.3.6
examples/distribute_graphsage/train.py
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...@@ -170,9 +170,7 @@ def main(args): ...@@ -170,9 +170,7 @@ def main(args):
with fluid.program_guard(train_program, startup_program): with fluid.program_guard(train_program, startup_program):
graph_wrapper = pgl.graph_wrapper.GraphWrapper( graph_wrapper = pgl.graph_wrapper.GraphWrapper(
"sub_graph", "sub_graph", fluid.CPUPlace(), node_feat=[('feats', [None, 602], np.dtype('float32'))])
fluid.CPUPlace(),
node_feat=[('feats', [None, 602], np.dtype('float32'))])
model_loss, model_acc = build_graph_model( model_loss, model_acc = build_graph_model(
graph_wrapper, graph_wrapper,
num_class=data["num_class"], num_class=data["num_class"],
......
examples/gat/README.md
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# PGL Examples for GAT # GAT: Graph Attention Networks
[Graph Attention Networks \(GAT\)](https://arxiv.org/abs/1710.10903) is a novel architectures that operate on graph-structured data, which leverages masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. Based on PGL, we reproduce GAT algorithms and reach the same level of indicators as the paper in citation network benchmarks. [Graph Attention Networks \(GAT\)](https://arxiv.org/abs/1710.10903) is a novel architectures that operate on graph-structured data, which leverages masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. Based on PGL, we reproduce GAT algorithms and reach the same level of indicators as the paper in citation network benchmarks.
### Simple example to build single head GAT ### Simple example to build single head GAT
......
examples/gcn/README.md
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# PGL Examples for GCN # GCN: Graph Convolutional Networks
[Graph Convolutional Network \(GCN\)](https://arxiv.org/abs/1609.02907) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks. [Graph Convolutional Network \(GCN\)](https://arxiv.org/abs/1609.02907) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks.
......
examples/ges/README.md
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# PGL Examples for GES # GES: Graph Embedding with Side Information
[Graph Embedding with Side Information](https://arxiv.org/pdf/1803.02349.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce ges algorithms. [Graph Embedding with Side Information](https://arxiv.org/pdf/1803.02349.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce ges algorithms.
## Datasets ## Datasets
The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3). The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3).
......
examples/graphsage/README.md
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# GraphSAGE in PGL # GraphSAGE: Inductive Representation Learning on Large Graphs
[GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) is a general inductive framework that leverages node feature [GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) is a general inductive framework that leverages node feature
information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learns a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Based on PGL, we reproduce GraphSAGE algorithm and reach the same level of indicators as the paper in Reddit Dataset. Besides, this is an example of subgraph sampling and training in PGL. information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learns a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Based on PGL, we reproduce GraphSAGE algorithm and reach the same level of indicators as the paper in Reddit Dataset. Besides, this is an example of subgraph sampling and training in PGL.
......
examples/line/README.md
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# PGL Examples for LINE # LINE: Large-scale Information Network Embedding
[LINE](http://www.www2015.it/documents/proceedings/proceedings/p1067.pdf) is an algorithmic framework for embedding very large-scale information networks. It is suitable to a variety of networks including directed, undirected, binary or weighted edges. Based on PGL, we reproduce LINE algorithms and reach the same level of indicators as the paper. [LINE](http://www.www2015.it/documents/proceedings/proceedings/p1067.pdf) is an algorithmic framework for embedding very large-scale information networks. It is suitable to a variety of networks including directed, undirected, binary or weighted edges. Based on PGL, we reproduce LINE algorithms and reach the same level of indicators as the paper.
## Datasets ## Datasets
...@@ -36,7 +36,7 @@ For examples, use gpu to train LINE on Flickr dataset. ...@@ -36,7 +36,7 @@ For examples, use gpu to train LINE on Flickr dataset.
# multiclass task example # multiclass task example
python line.py --use_cuda --order first_order --data_path ./data/flickr/ --save_dir ./checkpoints/model/ python line.py --use_cuda --order first_order --data_path ./data/flickr/ --save_dir ./checkpoints/model/
python multi_class.py --ckpt_path ./checkpoints/model/model_eopch_20 --percent 0.5 python multi_class.py --ckpt_path ./checkpoints/model/model_epoch_20 --percent 0.5
``` ```
......
examples/line/line.py
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...@@ -42,6 +42,16 @@ def make_dir(path): ...@@ -42,6 +42,16 @@ def make_dir(path):
raise raise
def save_param(dirname, var_name_list):
"""save_param"""
if not os.path.exists(dirname):
os.makedirs(dirname)
for var_name in var_name_list:
var = fluid.global_scope().find_var(var_name)
var_tensor = var.get_tensor()
np.save(os.path.join(dirname, var_name + '.npy'), np.array(var_tensor))
def set_seed(seed): def set_seed(seed):
"""Set global random seed. """Set global random seed.
""" """
...@@ -153,9 +163,9 @@ def main(args): ...@@ -153,9 +163,9 @@ def main(args):
# save parameters in every epoch # save parameters in every epoch
log.info("saving persistables parameters...") log.info("saving persistables parameters...")
fluid.io.save_persistables(exe, cur_save_path = os.path.join(args.save_dir,
os.path.join(args.save_dir, "model_epoch_%d" "model_epoch_%d" % (epoch + 1))
% (epoch + 1)), main_program) save_param(cur_save_path, ['shared_w'])
if __name__ == '__main__': if __name__ == '__main__':
......
examples/line/multi_class.py
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...@@ -33,6 +33,15 @@ from pgl.utils.logger import log ...@@ -33,6 +33,15 @@ from pgl.utils.logger import log
from data_loader import FlickrDataset from data_loader import FlickrDataset
def load_param(dirname, var_name_list):
"""load_param"""
for var_name in var_name_list:
var = fluid.global_scope().find_var(var_name)
var_tensor = var.get_tensor()
var_tmp = np.load(os.path.join(dirname, var_name + '.npy'))
var_tensor.set(var_tmp, fluid.CPUPlace())
def set_seed(seed): def set_seed(seed):
"""Set global random seed. """Set global random seed.
""" """
...@@ -200,12 +209,15 @@ def main(args): ...@@ -200,12 +209,15 @@ def main(args):
return False return False
return os.path.exists(os.path.join(args.ckpt_path, var.name)) return os.path.exists(os.path.join(args.ckpt_path, var.name))
fluid.io.load_vars( log.info('loading pretrained parameters from npy')
exe, args.ckpt_path, main_program=train_prog, predicate=existed_params) load_param(args.ckpt_path, ['shared_w'])
step = 0 step = 0
prev_time = time.time() prev_time = time.time()
train_model['pyreader'].start() train_model['pyreader'].start()
final_macro_f1 = 0.0
final_micro_f1 = 0.0
while 1: while 1:
try: try:
train_loss_val, train_probs_val, train_labels_val, train_topk_val = exe.run( train_loss_val, train_probs_val, train_labels_val, train_topk_val = exe.run(
...@@ -257,8 +269,13 @@ def main(args): ...@@ -257,8 +269,13 @@ def main(args):
log.info("\t\tStep %d " % step + "Test Loss: %f " % log.info("\t\tStep %d " % step + "Test Loss: %f " %
test_loss_val + "Test Macro F1: %f " % test_macro_f1 + test_loss_val + "Test Macro F1: %f " % test_macro_f1 +
"Test Micro F1: %f " % test_micro_f1) "Test Micro F1: %f " % test_micro_f1)
final_macro_f1 = max(test_macro_f1, final_macro_f1)
final_micro_f1 = max(test_micro_f1, final_micro_f1)
break break
log.info("\nFinal test Macro F1: %f " % final_macro_f1 +
"Final test Micro F1: %f " % final_micro_f1)
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LINE') parser = argparse.ArgumentParser(description='LINE')
...@@ -268,7 +285,7 @@ if __name__ == '__main__': ...@@ -268,7 +285,7 @@ if __name__ == '__main__':
default='./data/flickr/', default='./data/flickr/',
help='dataset for training') help='dataset for training')
parser.add_argument("--use_cuda", action='store_true', help="use_cuda") parser.add_argument("--use_cuda", action='store_true', help="use_cuda")
parser.add_argument("--epochs", type=int, default=10) parser.add_argument("--epochs", type=int, default=5)
parser.add_argument("--seed", type=int, default=1667) parser.add_argument("--seed", type=int, default=1667)
parser.add_argument( parser.add_argument(
"--lr", type=float, default=0.025, help='learning rate') "--lr", type=float, default=0.025, help='learning rate')
......
examples/metapath2vec/Dataset.py 0 → 100644
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file loads and preprocesses the dataset for metapath2vec model.
"""
import sys
import os
import glob
import numpy as np
import tqdm
import time
import logging
import random
from pgl.contrib import heter_graph
import pickle as pkl
class Dataset(object):
"""Implementation of Dataset class
This is a simple implementation of loading and processing dataset for metapath2vec model.
Args:
config: dict, some configure parameters.
"""
NEGATIVE_TABLE_SIZE = 1e8
def __init__(self, config):
self.config = config
self.walk_files = config['input_path'] + config['walk_path']
self.word2id_file = config['input_path'] + config['word2id_file']
self.word2freq = {}
self.word2id = {}
self.id2word = {}
self.sentences_count = 0
self.token_count = 0
self.negatives = []
self.discards = []
logging.info('reading sentences')
self.read_words()
logging.info('initializing discards')
self.initDiscards()
logging.info('initializing negatives')
self.initNegatives()
def read_words(self):
"""Read words(nodes) from walk files which are produced by sampler.
"""
word_freq = dict()
for walk_file in glob.glob(self.walk_files):
with open(walk_file, 'r') as reader:
for walk in reader:
walk = walk.strip().split(' ')
if len(walk) > 1:
self.sentences_count += 1
for word in walk:
self.token_count += 1
word_freq[word] = word_freq.get(word, 0) + 1
wid = 0
logging.info('Read %d sentences.' % self.sentences_count)
logging.info('Read %d words.' % self.token_count)
logging.info('%d words have been sampled.' % len(word_freq))
for w, c in word_freq.items():
if c < self.config['min_count']:
continue
self.word2id[w] = wid
self.id2word[wid] = w
self.word2freq[wid] = c
wid += 1
self.word_count = len(self.word2id)
logging.info(
'%d words displayed less than %d(min_count) have been discarded.' %
(len(word_freq) - len(self.word2id), self.config['min_count']))
pkl.dump(self.word2id, open(self.word2id_file, 'wb'))
def initDiscards(self):
"""Get a frequency table for sub-sampling.
"""
t = 0.0001
f = np.array(list(self.word2freq.values())) / self.token_count
self.discards = np.sqrt(t / f) + (t / f)
def initNegatives(self):
"""Get a table for negative sampling
"""
pow_freq = np.array(list(self.word2freq.values()))**0.75
words_pow = sum(pow_freq)
ratio = pow_freq / words_pow
count = np.round(ratio * Dataset.NEGATIVE_TABLE_SIZE)
for wid, c in enumerate(count):
self.negatives += [wid] * int(c)
self.negatives = np.array(self.negatives)
np.random.shuffle(self.negatives)
self.sampling_prob = ratio
def getNegatives(self, size):
"""Get negative samples from negative samling table.
"""
return np.random.choice(self.negatives, size)
def walk_from_files(self, walkpath_files):
"""Generate walks from files.
"""
bucket = []
for filename in walkpath_files:
with open(filename) as reader:
for line in reader:
words = line.strip().split(' ')
if len(words) > 1:
word_ids = [
self.word2id[w] for w in words if w in self.word2id
]
bucket.append(word_ids)
if len(bucket) == self.config['batch_size']:
yield bucket
bucket = []
if len(bucket):
yield bucket
def pairs_generator(self, walkpath_files):
"""Generate train pairs(src, pos, negs) for training model.
"""
def wrapper():
"""wrapper for multiprocess calling.
"""
for walks in self.walk_from_files(walkpath_files):
res = self.gen_pairs(walks)
yield res
return wrapper
def gen_pairs(self, walks):
"""Generate train pairs data for training model.
"""
src = []
pos = []
negs = []
skip_window = self.config['win_size'] // 2
for walk in walks:
for i in range(len(walk)):
for j in range(1, skip_window + 1):
if i - j >= 0:
src.append(walk[i])
pos.append(walk[i - j])
negs.append(
self.getNegatives(size=self.config['neg_num']))
if i + j < len(walk):
src.append(walk[i])
pos.append(walk[i + j])
negs.append(
self.getNegatives(size=self.config['neg_num']))
src = np.array(src, dtype=np.int64).reshape(-1, 1, 1)
pos = np.array(pos, dtype=np.int64).reshape(-1, 1, 1)
negs = np.expand_dims(np.array(negs, dtype=np.int64), -1)
return {"src": src, "pos": pos, "negs": negs}
if __name__ == "__main__":
config = {
'input_path': './data/out_aminer_CPAPC/',
'walk_path': 'aminer_walks_CPAPC_500num_100len/*',
'author_label_file': 'author_label.txt',
'venue_label_file': 'venue_label.txt',
'remapping_author_label_file': 'multi_class_author_label.txt',
'remapping_venue_label_file': 'multi_class_venue_label.txt',
'word2id_file': 'word2id.pkl',
'win_size': 7,
'neg_num': 5,
'min_count': 2,
'batch_size': 1,
}
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(level=getattr(logging, 'INFO'), format=log_format)
dataset = Dataset(config)
examples/metapath2vec/README.md 0 → 100644
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# metapath2vec: Scalable Representation Learning for Heterogeneous Networks
[metapath2vec](https://ericdongyx.github.io/papers/KDD17-dong-chawla-swami-metapath2vec.pdf) is a algorithm framework for representation learning in heterogeneous networks which contains multiple types of nodes and links. Given a heterogeneous graph, metapath2vec algorithm first generates meta-path-based random walks and then use skipgram model to train a language model. Based on PGL, we reproduce metapath2vec algorithm.
## Datasets
You can dowload datasets from [here](https://ericdongyx.github.io/metapath2vec/m2v.html)
We use the "aminer" data for example. After downloading the aminer data, put them, let's say, in ./data/net_aminer/ . We also need to put "label/" directory in ./data/.
## Dependencies
- paddlepaddle>=1.6
- pgl>=1.0.0
## Hyperparameters
All the hyper parameters are saved in config.yaml file. So before training, you can open the config.yaml to modify the hyper parameters as you like.
for example, you can change the \"use_cuda\" to \"True \" in order to use GPU for training or modify \"data_path\" to specify the data you want.
Some important hyper parameters in config.yaml:
- **use_cuda**: use GPU to train model
- **data_path**: the directory of dataset that you want to load
- **lr**: learning rate
- **neg_num**: number of negative samples.
- **num_walks**: number of walks started from each node
- **walk_length**: walk length
- **metapath**: meta path scheme
## Metapath randomwalk sampling
Before training, we should generate some metapath random walks to train skipgram model. we can run the below command to produce metapath randomwalk data.
```sh
python sample.py -c config.yaml
```
## Training and Testing
After finishing metapath randomwalk sampling, you can run the below command to train and test the model.
```sh
python main.py -c config.yaml
python multi_class.py --dataset ./data/out_aminer_CPAPC/author_label.txt --word2id ./checkpoints/train.metapath2vec/word2id.pkl --ckpt_path ./checkpoints/train.metapath2vec/model_epoch5/
```
## Experiment results
| train_percent | Metric | PGL Result | Reported Result |
|---------------|----------|------------|-----------------|
| 50% | macro-F1 | 0.9249 | 0.9314 |
| 50% | micro-F1 | 0.9283 | 0.9365 |
examples/metapath2vec/config.yaml 0 → 100644
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task_name: train.metapath2vec
use_cuda: True
log_level: info
seed: 1667
sampler:
type:
args:
data_path: ./data/net_aminer/
author_label_file: ./data/label/googlescholar.8area.author.label.txt
venue_label_file: ./data/label/googlescholar.8area.venue.label.txt
output_path: ./data/out_aminer_CPAPC/
new_author_label_file: author_label.txt
new_venue_label_file: venue_label.txt
walk_saved_path: walks/
num_walks: 1000
walk_length: 100
metapath: conf-paper-author-paper-conf
optimizer:
type: Adam
args:
lr: 0.005
end_lr: 0.0001
trainer:
type: trainer
args:
epochs: 5
log_dir: logs/
save_dir: checkpoints/
output_dir: outputs/
num_sample_workers: 8
data_loader:
type: Dataset
args:
input_path: ./data/out_aminer_CPAPC/ # same path as output_path in sampler
walk_path: walks/*
word2id_file: word2id.pkl
batch_size: 32
win_size: 7 # default: 7
neg_num: 5
min_count: 10
model:
type: SkipgramModel
args:
embed_dim: 128
examples/metapath2vec/main.py 0 → 100644
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement the training process of metapath2vec model.
"""
import os
import sys
import argparse
import time
import numpy as np
import logging
import pickle as pkl
import shutil
import glob
import pgl
from pgl.utils import paddle_helper
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as fl
from utils import *
import Dataset
import model as Models
from pgl.utils import mp_reader
from sklearn.metrics import (auc, f1_score, precision_recall_curve,
roc_auc_score)
def set_seed(seed):
"""Set global random seed."""
random.seed(seed)
np.random.seed(seed)
def save_param(dirname, var_name_list):
"""save_param"""
if not os.path.exists(dirname):
os.makedirs(dirname)
for var_name in var_name_list:
var = fluid.global_scope().find_var(var_name)
var_tensor = var.get_tensor()
np.save(os.path.join(dirname, var_name + '.npy'), np.array(var_tensor))
def multiprocess_data_generator(config, dataset):
"""Using multiprocess to generate training data.
"""
num_sample_workers = config['trainer']['args']['num_sample_workers']
walkpath_files = [[] for i in range(num_sample_workers)]
for idx, f in enumerate(glob.glob(dataset.walk_files)):
walkpath_files[idx % num_sample_workers].append(f)
gen_data_pool = [
dataset.pairs_generator(files) for files in walkpath_files
]
if num_sample_workers == 1:
gen_data_func = gen_data_pool[0]
else:
gen_data_func = mp_reader.multiprocess_reader(
gen_data_pool, use_pipe=True, queue_size=100)
return gen_data_func
def run_epoch(epoch,
config,
data_generator,
train_prog,
model,
feed_dict,
exe,
for_test=False):
"""Run training process of every epoch.
"""
total_loss = []
for idx, batch_data in enumerate(data_generator()):
feed_dict['train_inputs'] = batch_data['src']
feed_dict['train_labels'] = batch_data['pos']
feed_dict['train_negs'] = batch_data['negs']
loss, lr = exe.run(train_prog,
feed=feed_dict,
fetch_list=[model.loss, model.lr],
return_numpy=True)
total_loss.append(loss[0])
if (idx + 1) % 500 == 0:
avg_loss = np.mean(total_loss)
logging.info("epoch %d | step %d | lr %.4f | train_loss %f " %
(epoch, idx + 1, lr, avg_loss))
total_loss = []
def main(config):
"""main function for training metapath2vec model.
"""
logging.info(config)
set_seed(config['seed'])
dataset = getattr(
Dataset, config['data_loader']['type'])(config['data_loader']['args'])
data_generator = multiprocess_data_generator(config, dataset)
# move word2id file to checkpoints directory
src_word2id_file = dataset.word2id_file
dst_wor2id_file = config['trainer']['args']['save_dir'] + config[
'data_loader']['args']['word2id_file']
logging.info('backup word2id file to %s' % dst_wor2id_file)
shutil.move(src_word2id_file, dst_wor2id_file)
place = fluid.CUDAPlace(0) if config['use_cuda'] else fluid.CPUPlace()
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
model = getattr(Models, config['model']['type'])(
dataset=dataset, config=config['model']['args'], place=place)
with fluid.program_guard(train_program, startup_program):
global_steps = int(dataset.sentences_count *
config['trainer']['args']['epochs'] /
config['data_loader']['args']['batch_size'])
model.backward(global_steps, config['optimizer']['args'])
# train
exe = fluid.Executor(place)
exe.run(startup_program)
feed_dict = {}
logging.info('training...')
for epoch in range(1, 1 + config['trainer']['args']['epochs']):
run_epoch(epoch, config['trainer']['args'], data_generator,
train_program, model, feed_dict, exe)
logging.info('saving model...')
cur_save_path = os.path.join(config['trainer']['args']['save_dir'],
"model_epoch%d" % (epoch))
save_param(cur_save_path, ['content'])
logging.info('finishing training')
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='metapath2vec')
parser.add_argument(
'-c',
'--config',
default=None,
type=str,
help='config file path (default: None)')
parser.add_argument(
'-n',
'--taskname',
default=None,
type=str,
help='task name(default: None)')
args = parser.parse_args()
if args.config:
# load config file
config = Config(args.config, isCreate=True, isSave=True)
config = config()
else:
raise AssertionError(
"Configuration file need to be specified. Add '-c config.yaml', for example."
)
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(
level=getattr(logging, config['log_level'].upper()), format=log_format)
main(config)
examples/metapath2vec/model.py 0 → 100644
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement the skipgram model for training metapath2vec.
"""
import argparse
import time
import math
import os
import io
from multiprocessing import Pool
import logging
import numpy as np
import glob
import pgl
from pgl import data_loader
from pgl.utils import op
from pgl.utils.logger import log
import paddle.fluid as fluid
import paddle.fluid.layers as fl
class SkipgramModel(object):
"""Implemetation of skipgram model.
Args:
config: dict, some configure parameters.
dataset: instance of Dataset class
place: GPU or CPU place
"""
def __init__(self, config, dataset, place):
self.config = config
self.dataset = dataset
self.place = place
self.neg_num = self.dataset.config['neg_num']
self.num_nodes = len(dataset.word2id)
self.train_inputs = fl.data(
'train_inputs', shape=[None, 1, 1], dtype='int64')
self.train_labels = fl.data(
'train_labels', shape=[None, 1, 1], dtype='int64')
self.train_negs = fl.data(
'train_negs', shape=[None, self.neg_num, 1], dtype='int64')
self.forward()
def backward(self, global_steps, opt_config):
"""Build the optimizer.
"""
self.lr = fl.polynomial_decay(opt_config['lr'], global_steps,
opt_config['end_lr'])
adam = fluid.optimizer.Adam(learning_rate=self.lr)
adam.minimize(self.loss)
def forward(self):
"""Build the skipgram model.
"""
initrange = 1.0 / self.config['embed_dim']
embed_init = fluid.initializer.UniformInitializer(
low=-initrange, high=initrange)
weight_init = fluid.initializer.TruncatedNormal(
scale=1.0 / math.sqrt(self.config['embed_dim']))
embed_src = fl.embedding(
input=self.train_inputs,
size=[self.num_nodes, self.config['embed_dim']],
param_attr=fluid.ParamAttr(
name='content', initializer=embed_init))
weight_pos = fl.embedding(
input=self.train_labels,
size=[self.num_nodes, self.config['embed_dim']],
param_attr=fluid.ParamAttr(
name='weight', initializer=weight_init))
weight_negs = fl.embedding(
input=self.train_negs,
size=[self.num_nodes, self.config['embed_dim']],
param_attr=fluid.ParamAttr(
name='weight', initializer=weight_init))
pos_logits = fl.matmul(
embed_src, weight_pos, transpose_y=True) # [batch_size, 1, 1]
pos_score = fl.squeeze(pos_logits, axes=[1])
pos_score = fl.clip(pos_score, min=-10, max=10)
pos_score = -1.0 * fl.logsigmoid(pos_score)
neg_logits = fl.matmul(
embed_src, weight_negs,
transpose_y=True) # [batch_size, 1, neg_num]
neg_score = fl.squeeze(neg_logits, axes=[1])
neg_score = fl.clip(neg_score, min=-10, max=10)
neg_score = -1.0 * fl.logsigmoid(-1.0 * neg_score)
neg_score = fl.reduce_sum(neg_score, dim=1, keep_dim=True)
self.loss = fl.reduce_mean(pos_score + neg_score)
examples/metapath2vec/multi_class.py 0 → 100644
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file provides the multi class task for testing the embedding learned by metapath2vec model.
"""
import argparse
import sys
import os
import tqdm
import time
import math
import logging
import random
import pickle as pkl
import numpy as np
import sklearn.metrics
from sklearn.metrics import f1_score
import pgl
import paddle.fluid as fluid
import paddle.fluid.layers as fl
import Dataset
from utils import *
def load_param(dirname, var_name_list):
"""load_param"""
for var_name in var_name_list:
var = fluid.global_scope().find_var(var_name)
var_tensor = var.get_tensor()
var_tmp = np.load(os.path.join(dirname, var_name + '.npy'))
var_tensor.set(var_tmp, fluid.CPUPlace())
def load_data(file_, word2id):
"""Load data for node classification.
"""
words_label = []
line_count = 0
with open(file_, 'r') as reader:
for line in reader:
line_count += 1
tokens = line.strip().split(' ')
word, label = tokens[0], int(tokens[1]) - 1
if word in word2id:
words_label.append((word2id[word], label))
words_label = np.array(words_label, dtype=np.int64)
np.random.shuffle(words_label)
logging.info('%d/%d word_label pairs have been loaded' %
(len(words_label), line_count))
return words_label
def node_classify_model(word2id, num_labels, embed_dim=16):
"""Build node classify model.
Args:
word2id(dict): map word(node) to its corresponding index
num_labels: The number of labels.
embed_dim: The dimension of embedding.
"""
nodes = fl.data('nodes', shape=[None, 1], dtype='int64')
labels = fl.data('labels', shape=[None, 1], dtype='int64')
embed_nodes = fl.embedding(
input=nodes,
size=[len(word2id), embed_dim],
param_attr=fluid.ParamAttr(name='content'))
embed_nodes.stop_gradient = True
probs = fl.fc(input=embed_nodes, size=num_labels, act='softmax')
predict = fl.argmax(probs, axis=-1)
loss = fl.cross_entropy(input=probs, label=labels)
loss = fl.reduce_mean(loss)
return {
'loss': loss,
'probs': probs,
'predict': predict,
'labels': labels,
}
def run_epoch(exe, prog, model, feed_dict, lr):
"""Run training process of every epoch.
"""
if lr is None:
loss, predict = exe.run(prog,
feed=feed_dict,
fetch_list=[model['loss'], model['predict']],
return_numpy=True)
lr_ = 0
else:
loss, predict, lr_ = exe.run(
prog,
feed=feed_dict,
fetch_list=[model['loss'], model['predict'], lr],
return_numpy=True)
macro_f1 = f1_score(feed_dict['labels'], predict, average="macro")
micro_f1 = f1_score(feed_dict['labels'], predict, average="micro")
return {
'loss': loss,
'pred': predict,
'lr': lr_,
'macro_f1': macro_f1,
'micro_f1': micro_f1
}
def main(args):
"""main function for training node classification task.
"""
word2id = pkl.load(open(args.word2id, 'rb'))
words_label = load_data(args.dataset, word2id)
# split data for training and testing
split_position = int(words_label.shape[0] * args.train_percent)
train_words_label = words_label[0:split_position, :]
test_words_label = words_label[split_position:, :]
place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = node_classify_model(
word2id, args.num_labels, embed_dim=args.embed_dim)
test_prog = train_prog.clone(for_test=True)
with fluid.program_guard(train_prog, startup_prog):
lr = fl.polynomial_decay(args.lr, 1000, 0.001)
adam = fluid.optimizer.Adam(lr)
adam.minimize(model['loss'])
exe = fluid.Executor(place)
exe.run(startup_prog)
load_param(args.ckpt_path, ['content'])
feed_dict = {}
X = train_words_label[:, 0].reshape(-1, 1)
labels = train_words_label[:, 1].reshape(-1, 1)
logging.info('%d/%d data to train' %
(labels.shape[0], words_label.shape[0]))
test_feed_dict = {}
test_X = test_words_label[:, 0].reshape(-1, 1)
test_labels = test_words_label[:, 1].reshape(-1, 1)
logging.info('%d/%d data to test' %
(test_labels.shape[0], words_label.shape[0]))
for epoch in range(args.epochs):
feed_dict['nodes'] = X
feed_dict['labels'] = labels
train_result = run_epoch(exe, train_prog, model, feed_dict, lr)
test_feed_dict['nodes'] = test_X
test_feed_dict['labels'] = test_labels
test_result = run_epoch(exe, test_prog, model, test_feed_dict, lr=None)
logging.info(
'epoch %d | lr %.4f | train_loss %.5f | train_macro_F1 %.4f | train_micro_F1 %.4f | test_loss %.5f | test_macro_F1 %.4f | test_micro_F1 %.4f'
% (epoch, train_result['lr'], train_result['loss'],
train_result['macro_f1'], train_result['micro_f1'],
test_result['loss'], test_result['macro_f1'],
test_result['micro_f1']))
logging.info(
'final_test_macro_f1 score: %.4f | final_test_micro_f1 score: %.4f' %
(test_result['macro_f1'], test_result['micro_f1']))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='multi_class')
parser.add_argument(
'--dataset',
default=None,
type=str,
help='training and testing data file(default: None)')
parser.add_argument(
'--word2id',
default=None,
type=str,
help='word2id file (default: None)')
parser.add_argument(
'--ckpt_path', default=None, type=str, help='task name(default: None)')
parser.add_argument("--use_cuda", action='store_true', help="use_cuda")
parser.add_argument(
'--train_percent',
default=0.5,
type=float,
help='train_percent(default: 0.5)')
parser.add_argument(
'--num_labels',
default=8,
type=int,
help='number of labels(default: 8)')
parser.add_argument(
'--epochs',
default=100,
type=int,
help='number of epochs for training(default: 10)')
parser.add_argument(
'--lr',
default=0.025,
type=float,
help='learning rate(default: 0.025)')
parser.add_argument(
'--embed_dim',
default=128,
type=int,
help='dimension of embedding(default: 128)')
args = parser.parse_args()
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(level='INFO', format=log_format)
main(args)
examples/metapath2vec/sample.py 0 → 100644
浏览文件 @ 36febd60
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement the sampler to sample metapath random walk sequence for
training metapath2vec model.
"""
import multiprocessing
from multiprocessing import Pool
import argparse
import sys
import os
import numpy as np
import pickle as pkl
import tqdm
import time
import logging
import random
from pgl.contrib import heter_graph
from pgl.sample import metapath_randomwalk
from utils import *
class Sampler(object):
"""Implemetation of sampler in order to sample metapath random walk.
Args:
config: dict, some configure parameters.
"""
def __init__(self, config):
self.config = config
self.build_graph()
def build_graph(self):
"""Build pgl heterogeneous graph.
"""
self.conf_id2index, self.conf_name2index, conf_node_type = self.remapping_id(
self.config['data_path'] + 'id_conf.txt',
start_index=0,
node_type='conf')
logging.info('%d venues have been loaded.' % (len(self.conf_id2index)))
self.author_id2index, self.author_name2index, author_node_type = self.remapping_id(
self.config['data_path'] + 'id_author.txt',
start_index=len(self.conf_id2index),
node_type='author')
logging.info('%d authors have been loaded.' %
(len(self.author_id2index)))
self.paper_id2index, self.paper_name2index, paper_node_type = self.remapping_id(
self.config['data_path'] + 'paper.txt',
start_index=(len(self.conf_id2index) + len(self.author_id2index)),
node_type='paper',
separator='\t')
logging.info('%d papers have been loaded.' %
(len(self.paper_id2index)))
node_types = conf_node_type + author_node_type + paper_node_type
num_nodes = len(node_types)
edges_by_types = {}
paper_author_edges = self.load_edges(
self.config['data_path'] + 'paper_author.txt', self.paper_id2index,
self.author_id2index)
paper_conf_edges = self.load_edges(
self.config['data_path'] + 'paper_conf.txt', self.paper_id2index,
self.conf_id2index)
edges_by_types['edge'] = paper_author_edges + paper_conf_edges
logging.info('%d edges have been loaded.' %
(len(edges_by_types['edge'])))
node_features = {
'index': np.array([i for i in range(num_nodes)]).reshape(
-1, 1).astype(np.int64)
}
self.graph = heter_graph.HeterGraph(
num_nodes=num_nodes,
edges=edges_by_types,
node_types=node_types,
node_feat=node_features)
def remapping_id(self, file_, start_index, node_type, separator='\t'):
"""Mapp the ID and name of nodes to index.
"""
node_types = []
id2index = {}
name2index = {}
index = start_index
with open(file_, encoding="ISO-8859-1") as reader:
for line in reader:
tokens = line.strip().split(separator)
id2index[tokens[0]] = index
if len(tokens) == 2:
name2index[tokens[1]] = index
node_types.append((index, node_type))
index += 1
return id2index, name2index, node_types
def load_edges(self, file_, src2index, dst2index, symmetry=True):
"""Load edges from file.
"""
edges = []
with open(file_, 'r') as reader:
for line in reader:
items = line.strip().split()
src, dst = src2index[items[0]], dst2index[items[1]]
edges.append((src, dst))
if symmetry:
edges.append((dst, src))
edges = list(set(edges))
return edges
def generate_multi_class_data(self, name_label_file):
"""Mapp the data that will be used in multi class task to index.
"""
if 'author' in name_label_file:
name2index = self.author_name2index
else:
name2index = self.conf_name2index
index_label_list = []
with open(name_label_file, encoding="ISO-8859-1") as reader:
for line in reader:
tokens = line.strip().split(' ')
name, label = tokens[0], int(tokens[1])
index = name2index[name]
index_label_list.append((index, label))
return index_label_list
def generate_walks(args):
"""Generate metapath random walk and save to file.
"""
g, meta_path, filename, walk_length = args
walks = []
node_types = g._node_types
first_type = meta_path.split('-')[0]
nodes = np.where(node_types == first_type)[0]
if len(nodes) > 4000:
nodes = np.random.choice(nodes, 4000, replace=False)
logging.info('%d number of start nodes' % (len(nodes)))
logging.info('save walks in file: %s' % (filename))
with open(filename, 'w') as writer:
for start_node in nodes:
walk = metapath_randomwalk(g, start_node, meta_path, walk_length)
walk = [str(walk[i]) for i in range(0, len(walk), 2)] # skip paper
writer.write(' '.join(walk) + '\n')
def multiprocess_generate_walks(sampler, edge_type, meta_path, num_walks,
walk_length, saved_path):
"""Use multiprocess to generate metapath random walk.
"""
args = []
for i in range(num_walks):
filename = saved_path + '%04d' % (i)
args.append(
(sampler.graph[edge_type], meta_path, filename, walk_length))
pool = Pool(16)
pool.map(generate_walks, args)
pool.close()
pool.join()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='metapath2vec')
parser.add_argument(
'-c',
'--config',
default=None,
type=str,
help='config file path (default: None)')
args = parser.parse_args()
if args.config:
# load config file
config = Config(args.config, isCreate=False, isSave=False)
config = config()
config = config['sampler']['args']
else:
raise AssertionError(
"Configuration file need to be specified. Add '-c config.yaml', for example."
)
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(level="INFO", format=log_format)
logging.info(config)
log_format = '%(asctime)s-%(levelname)s-%(name)s: %(message)s'
logging.basicConfig(level=getattr(logging, 'INFO'), format=log_format)
if not os.path.exists(config['output_path']):
os.makedirs(config['output_path'])
config['walk_saved_path'] = config['output_path'] + config[
'walk_saved_path']
if not os.path.exists(config['walk_saved_path']):
os.makedirs(config['walk_saved_path'])
sampler = Sampler(config)
begin = time.time()
logging.info('multi process sampling')
multiprocess_generate_walks(
sampler=sampler,
edge_type='edge',
meta_path=config['metapath'],
num_walks=config['num_walks'],
walk_length=config['walk_length'],
saved_path=config['walk_saved_path'])
logging.info('total time: %.4f' % (time.time() - begin))
logging.info('generating multi class data')
word_label_list = sampler.generate_multi_class_data(config[
'author_label_file'])
with open(config['output_path'] + config['new_author_label_file'],
'w') as writer:
for line in word_label_list:
line = [str(i) for i in line]
writer.write(' '.join(line) + '\n')
word_label_list = sampler.generate_multi_class_data(config[
'venue_label_file'])
with open(config['output_path'] + config['new_venue_label_file'],
'w') as writer:
for line in word_label_list:
line = [str(i) for i in line]
writer.write(' '.join(line) + '\n')
logging.info('finished')
examples/metapath2vec/utils.py 0 → 100644
浏览文件 @ 36febd60
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This file implement a class for model configure.
"""
import datetime
import os
import yaml
import random
import shutil
class Config(object):
"""Implementation of Config class for model configure.
Args:
config_file(str): configure filename, which is a yaml file.
isCreate(bool): if true, create some neccessary directories to save models, log file and other outputs.
isSave(bool): if true, save config_file in order to record the configure message.
"""
def __init__(self, config_file, isCreate=False, isSave=False):
self.config_file = config_file
self.config = self.get_config_from_yaml(config_file)
if isCreate:
self.create_necessary_dirs()
if isSave:
self.save_config_file()
def get_config_from_yaml(self, yaml_file):
"""Get the configure hyperparameters from yaml file.
"""
try:
with open(yaml_file, 'r') as f:
config = yaml.load(f)
except Exception:
raise IOError("Error in parsing config file '%s'" % yaml_file)
return config
def create_necessary_dirs(self):
"""Create some necessary directories to save some important files.
"""
time_stamp = datetime.datetime.now().strftime('%m%d_%H%M')
self.config['trainer']['args']['log_dir'] = ''.join(
(self.config['trainer']['args']['log_dir'],
self.config['task_name'], '/')) # , '.%s/' % (time_stamp)))
self.config['trainer']['args']['save_dir'] = ''.join(
(self.config['trainer']['args']['save_dir'],
self.config['task_name'], '/')) # , '.%s/' % (time_stamp)))
self.config['trainer']['args']['output_dir'] = ''.join(
(self.config['trainer']['args']['output_dir'],
self.config['task_name'], '/')) # , '.%s/' % (time_stamp)))
# if os.path.exists(self.config['trainer']['args']['save_dir']):
# input('save_dir is existed, do you really want to continue?')
self.make_dir(self.config['trainer']['args']['log_dir'])
self.make_dir(self.config['trainer']['args']['save_dir'])
self.make_dir(self.config['trainer']['args']['output_dir'])
def save_config_file(self):
"""Save config file so that we can know the config when we look back
"""
filename = self.config_file.split('/')[-1]
targetpath = self.config['trainer']['args']['save_dir']
shutil.copyfile(self.config_file, targetpath + filename)
def make_dir(self, path):
"""Build directory"""
if not os.path.exists(path):
os.makedirs(path)
def __getitem__(self, key):
"""Return the configure dict"""
return self.config[key]
def __call__(self):
"""__call__"""
return self.config
examples/node2vec/README.md
浏览文件 @ 36febd60
# PGL Examples for node2vec # node2vec: Scalable Feature Learning for Networks
[Node2vec](https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce node2vec algorithms and reach the same level of indicators as the paper. [Node2vec](https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf) is an algorithmic framework for representational learning on graphs. Given any graph, it can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks. Based on PGL, we reproduce node2vec algorithms and reach the same level of indicators as the paper.
## Datasets ## Datasets
The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3) and [Arxiv](http://snap.stanford.edu/data/ca-AstroPh.html). The datasets contain two networks: [BlogCatalog](http://socialcomputing.asu.edu/datasets/BlogCatalog3) and [Arxiv](http://snap.stanford.edu/data/ca-AstroPh.html).
......
examples/sgc/README.md
浏览文件 @ 36febd60
# PGL Examples for SGC # SGC: Simplifying Graph Convolutional Networks
[Simplifying Graph Convolutional Networks \(SGC\)](https://arxiv.org/pdf/1902.07153.pdf) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce SGC algorithms and reach the same level of indicators as the paper in citation network benchmarks. [Simplifying Graph Convolutional Networks \(SGC\)](https://arxiv.org/pdf/1902.07153.pdf) is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce SGC algorithms and reach the same level of indicators as the paper in citation network benchmarks.
......
examples/strucvec/README.md
浏览文件 @ 36febd60
## PGL Examples For Struc2Vec # struc2vec: Learning Node Representations from Structural Identity
[Struc2vec](https://arxiv.org/abs/1704.03165) is is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. A novel and flexible framework for learning latent representations is proposed in the paper of struc2vec. We reproduce Struc2vec algorithm in the PGL. [Struc2vec](https://arxiv.org/abs/1704.03165) is is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. A novel and flexible framework for learning latent representations is proposed in the paper of struc2vec. We reproduce Struc2vec algorithm in the PGL.
## DataSet ### DataSet
The paper of use air-traffic network to valid algorithm of Struc2vec. The paper of use air-traffic network to valid algorithm of Struc2vec.
The each edge in the dataset indicate that having one flight between the airports. Using the the connection between the airports to predict the level of activity. The following dataset will be used to valid the algorithm accuracy.Data collected from the Bureau of Transportation Statistics2 from January to October, 2016. The network has 1,190 nodes, 13,599 edges (diameter is 8). [Link](https://www.transtats.bts.gov/) The each edge in the dataset indicate that having one flight between the airports. Using the the connection between the airports to predict the level of activity. The following dataset will be used to valid the algorithm accuracy.Data collected from the Bureau of Transportation Statistics2 from January to October, 2016. The network has 1,190 nodes, 13,599 edges (diameter is 8). [Link](https://www.transtats.bts.gov/)
- usa-airports.edgelist - usa-airports.edgelist
- labels-usa-airports.txt - labels-usa-airports.txt
## Dependencies ### Dependencies
If use want to use the struc2vec model in pgl, please install the gensim, pathos, fastdtw additional. If use want to use the struc2vec model in pgl, please install the gensim, pathos, fastdtw additional.
- paddlepaddle>=1.6 - paddlepaddle>=1.6
- pgl - pgl
...@@ -15,11 +15,11 @@ If use want to use the struc2vec model in pgl, please install the gensim, pathos ...@@ -15,11 +15,11 @@ If use want to use the struc2vec model in pgl, please install the gensim, pathos
- pathos - pathos
- fastdtw - fastdtw
## How to use ### How to use
For examples, we want to train and valid the Struc2vec model on American airpot dataset For examples, we want to train and valid the Struc2vec model on American airpot dataset
> python struc2vec.py --edge_file data/usa-airports.edgelist --label_file data/labels-usa-airports.txt --train True --valid True --opt2 True > python struc2vec.py --edge_file data/usa-airports.edgelist --label_file data/labels-usa-airports.txt --train True --valid True --opt2 True
## Hyperparameters ### Hyperparameters
| Args| Meaning| | Args| Meaning|
| ------------- | ------------- | | ------------- | ------------- |
| edge_file | input file name for edges| | edge_file | input file name for edges|
...@@ -35,7 +35,7 @@ For examples, we want to train and valid the Struc2vec model on American airpot ...@@ -35,7 +35,7 @@ For examples, we want to train and valid the Struc2vec model on American airpot
| valid| The flag to use the w2v embedding to valid the classification result| | valid| The flag to use the w2v embedding to valid the classification result|
| num_class| The num of class in classification model to be trained| | num_class| The num of class in classification model to be trained|
## Experiment results ### Experiment results
| Dataset | Model | Metric | PGL Result | Paper repo Result | | Dataset | Model | Metric | PGL Result | Paper repo Result |
| ------------- | ------------- |------------- |------------- |------------- | | ------------- | ------------- |------------- |------------- |------------- |
| American airport dataset | Struc2vec without time cost optimization| ACC |0.6483|0.6340| | American airport dataset | Struc2vec without time cost optimization| ACC |0.6483|0.6340|
......
examples/strucvec/classify.py
浏览文件 @ 36febd60
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. """
classify.py
"""
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -11,7 +14,6 @@ ...@@ -11,7 +14,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import numpy as np import numpy as np
import paddle import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
......
examples/strucvec/sklearn_classify.py
浏览文件 @ 36febd60
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. """
sklearn_classify.py
"""
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. # you may not use this file except in compliance with the License.
...@@ -36,7 +39,7 @@ def train_lr_l2_model(args, data): ...@@ -36,7 +39,7 @@ def train_lr_l2_model(args, data):
test_size=0.2, test_size=0.2,
random_state=random_num + random_seed) random_state=random_num + random_seed)
# use the one vs rest to train the lr model with l2 # use the one vs rest to train the lr model with l2
pred_test = [] pred_test = []
for i in range(0, args.num_class): for i in range(0, args.num_class):
y_train_relabel = np.where(y_train == i, 1, 0) y_train_relabel = np.where(y_train == i, 1, 0)
......
docs/source/examples/md/graphsage_examples.md → examples/unsup_graphsage/README.md
浏览文件 @ 36febd60
# GraphSage for Large Scale Networks # Unsupervised GraphSAGE in PGL
[GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) is a general inductive framework that leverages node feature [GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf) is a general inductive framework that leverages node feature
information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learns a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Based on PGL, we reproduce GraphSAGE algorithm and reach the same level of indicators as the paper in Reddit Dataset. Besides, this is an example of subgraph sampling and training in PGL. information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, GraphSAGE learns a function that generates embeddings by sampling and aggregating features from a node’s local neighborhood. Based on PGL, we reproduce GraphSAGE algorithm and reach the same level of indicators as the paper in Reddit Dataset. Besides, this is an example of subgraph sampling and training in PGL.
For purpose of unsupervised learning, we use graph edges as positive samples for graphsage training.
### Datasets ### Datasets(Quickstart)
The reddit dataset should be downloaded from the following links and placed in directory ```./data```. The details for Reddit Dataset can be found [here](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf). The dataset `./sample.txt` is handcrafted bigraph for quick demo purpose, which format is `src \t dst`.
- reddit.npz https://drive.google.com/open?id=19SphVl_Oe8SJ1r87Hr5a6znx3nJu1F2J
- reddit_adj.npz: https://drive.google.com/open?id=174vb0Ws7Vxk_QTUtxqTgDHSQ4El4qDHt
### Dependencies ### Dependencies
```txt
- paddlepaddle>=1.6 - paddlepaddle>=1.6
- pgl - pgl
### How to run
To train a GraphSAGE model on Reddit Dataset, you can just run
```
python train.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --normalize --symmetry
``` ```
### How to run
If you want to train a GraphSAGE model with multiple GPUs, you can just run #### 1. Training
```sh
python train.py --data_path ./sample.txt --num_nodes 2000 --phase train
``` ```
CUDA_VISIBLE_DEVICES=0,1 python train_multi.py --use_cuda --epoch 10 --graphsage_type graphsage_mean --normalize --symmetry --num_trainer 2 #### 2. Predicting
```sh
python train.py --data_path ./sample.txt --num_nodes 2000 --phase predict
``` ```
The resulted node embedding is stored in `emb.npy` file, which latter can be loaded using `np.load`.
#### Hyperparameters #### Hyperparameters
- epoch: Number of epochs default (1)
- epoch: Number of epochs default (10)
- use_cuda: Use gpu if assign use_cuda. - use_cuda: Use gpu if assign use_cuda.
- graphsage_type: We support 4 aggregator types including "graphsage_mean", "graphsage_maxpool", "graphsage_meanpool" and "graphsage_lstm". - layer_type: We support 4 aggregator types including "graphsage_mean", "graphsage_maxpool", "graphsage_meanpool" and "graphsage_lstm".
- normalize: Normalize the input feature if assign normalize.
- sample_workers: The number of workers for multiprocessing subgraph sample. - sample_workers: The number of workers for multiprocessing subgraph sample.
- lr: Learning rate. - lr: Learning rate.
- symmetry: Make the edges symmetric if assign symmetry.
- batch_size: Batch size. - batch_size: Batch size.
- samples_1: The max neighbors for the first hop neighbor sampling. (default: 25) - samples: The max neighbors sampling rate for each hop. (default: [10, 10])
- samples_2: The max neighbors for the second hop neighbor sampling. (default: 10) - num_layers: The number of layer for graph sampling. (default: 2)
- hidden_size: The hidden size of the GraphSAGE models. - hidden_size: The hidden size of the GraphSAGE models.
- checkpoint. Path for model checkpoint at each epoch. (default: 'model_ckpt')
### Performance
We train our models for 200 epochs and report the accuracy on the test dataset.
| Aggregator | Accuracy | Reported in paper |
| --- | --- | --- |
| Mean | 95.70% | 95.0% |
| Meanpool | 95.60% | 94.8% |
| Maxpool | 94.95% | 94.8% |
| LSTM | 95.13% | 95.4% |
### View the Code
See the code [here](graphsage_examples_code.html).
examples/unsup_graphsage/train.py
浏览文件 @ 36febd60
...@@ -318,7 +318,7 @@ def main(args): ...@@ -318,7 +318,7 @@ def main(args):
model_dict=ret_dict, model_dict=ret_dict,
epoch=epoch, epoch=epoch,
batch_size=args.batch_size, batch_size=args.batch_size,
log_per_step=10) log_per_step=1)
epoch_end = time.time() epoch_end = time.time()
print("Epoch: {0}, Train total expend: {1} ".format( print("Epoch: {0}, Train total expend: {1} ".format(
epoch, epoch_end - epoch_start)) epoch, epoch_end - epoch_start))
...@@ -326,7 +326,7 @@ def main(args): ...@@ -326,7 +326,7 @@ def main(args):
log.info("Run Epoch Error %s" % e) log.info("Run Epoch Error %s" % e)
fluid.io.save_params( fluid.io.save_params(
exe, exe,
dirname=args.checkpoint + '_%s' % epoch, dirname=args.checkpoint + '_%s' % (epoch + 1),
main_program=train_program) main_program=train_program)
log.info("EPOCH END") log.info("EPOCH END")
...@@ -343,7 +343,7 @@ def main(args): ...@@ -343,7 +343,7 @@ def main(args):
args.num_layers, args.num_layers,
ret_dict.graph_wrappers, ret_dict.graph_wrappers,
batch_size=args.batch_size, batch_size=args.batch_size,
data=(test_src, test_src, test_src, test_src), data=(test_src, test_src, test_src),
samples=args.samples, samples=args.samples,
num_workers=args.sample_workers, num_workers=args.sample_workers,
feed_name_list=feed_name_list, feed_name_list=feed_name_list,
......
pgl/__init__.py
浏览文件 @ 36febd60
...@@ -13,7 +13,7 @@ ...@@ -13,7 +13,7 @@
# limitations under the License. # limitations under the License.
"""Generate pgl apis """Generate pgl apis
""" """
__version__ = "1.0.0" __version__ = "1.0.1"
from pgl import layers from pgl import layers
from pgl import graph_wrapper from pgl import graph_wrapper
from pgl import graph from pgl import graph
......
pgl/contrib/heter_graph.py
浏览文件 @ 36febd60
...@@ -14,11 +14,12 @@ ...@@ -14,11 +14,12 @@
""" """
This package implement Heterogeneous Graph structure for handling Heterogeneous graph data. This package implement Heterogeneous Graph structure for handling Heterogeneous graph data.
""" """
import time
import numpy as np import numpy as np
import pickle as pkl import pickle as pkl
import time import time
import pgl.graph_kernel as graph_kernel import pgl.graph_kernel as graph_kernel
from pgl import graph from pgl.graph import Graph
__all__ = ['HeterGraph'] __all__ = ['HeterGraph']
...@@ -31,123 +32,111 @@ def _hide_num_nodes(shape): ...@@ -31,123 +32,111 @@ def _hide_num_nodes(shape):
return shape return shape
class HeterGraph(object): class NodeGraph(Graph):
"""Implementation of graph structure in pgl """Implementation of a graph that has multple node types.
This is a simple implementation of heterogeneous graph structure in pgl
Args: Args:
num_nodes_every_type: dict, number of nodes for every node type num_nodes: number of nodes in the graph
edges: list of (u, v) tuples
node_types (optional): list of (u, node_type) tuples to specify the node type of every node
node_feat (optional): a dict of numpy array as node features
edge_feat (optional): a dict of numpy array as edge features
"""
def __init__(self,
num_nodes,
edges,
node_types=None,
node_feat=None,
edge_feat=None):
super(NodeGraph, self).__init__(num_nodes, edges, node_feat, edge_feat)
if isinstance(node_types, list):
self._node_types = np.array(node_types, dtype=object)[:, 1]
else:
self._node_types = node_types
class HeterGraph(object):
"""Implementation of heterogeneous graph structure in pgl
edges_every_type: dict, every element is a list of (u, v) tuples. This is a simple implementation of heterogeneous graph structure in pgl.
node_feat_every_type: features for every node type. Args:
num_nodes: number of nodes in a heterogeneous graph
edges: dict, every element in dict is a list of (u, v) tuples.
node_types (optional): list of (u, node_type) tuples to specify the node type of every node
node_feat (optional): a dict of numpy array as node features
edge_feat (optional): a dict of dict as edge features for every edge type
Examples: Examples:
.. code-block:: python .. code-block:: python
import numpy as np import numpy as np
num_nodes_every_type = {'type1':3,'type2':4, 'type3':2} num_nodes = 4
edges_every_type = { node_types = [(0, 'user'), (1, 'item'), (2, 'item'), (3, 'user')]
('type1','type2', 'edges_type1'): [(0,1), (1,2)], edges = {
('type1', 'type3', 'edges_type2'): [(1,2), (3,1)], 'edges_type1': [(0,1), (3,2)],
} 'edges_type2': [(1,2), (3,1)],
node_feat_every_type = {
'type1': {'features1': np.random.randn(3, 4),
'features2': np.random.randn(3, 4)},
'type2': {'features3': np.random.randn(4, 4)},
'type3': {'features1': np.random.randn(2, 4),
'features2': np.random.randn(2, 4)}
} }
edges_feat_every_type = { node_feat = {'feature': np.random.randn(4, 16)}
('type1','type2','edges_type1'): {'h': np.random.randn(2, 4)}, edges_feat = {
('type1', 'type3', 'edges_type2'): {'h':np.random.randn(2, 4)}, 'edges_type1': {'h': np.random.randn(2, 16)},
'edges_type2': {'h': np.random.randn(2, 16)},
} }
g = heter_graph.HeterGraph( g = heter_graph.HeterGraph(
num_nodes_every_type=num_nodes_every_type, num_nodes=num_nodes,
edges_every_type=edges_every_type, edges=edges,
node_feat_every_type=node_feat_every_type, node_types=node_types,
edge_feat_every_type=edges_feat_every_type) node_feat=node_feat,
edge_feat=edges_feat)
""" """
def __init__(self, def __init__(self,
num_nodes_every_type, num_nodes,
edges_every_type, edges,
node_feat_every_type=None, node_types=None,
edge_feat_every_type=None): node_feat=None,
edge_feat=None):
self._num_nodes_dict = num_nodes_every_type self._num_nodes = num_nodes
self._edges_dict = edges_every_type self._edges_dict = edges
if node_feat_every_type is not None:
self._node_feat = node_feat_every_type if node_feat is not None:
self._node_feat = node_feat
else: else:
self._node_feat = {} self._node_feat = {}
if edge_feat_every_type is not None: if edge_feat is not None:
self._edge_feat = edge_feat_every_type self._edge_feat = edge_feat
else: else:
self._edge_feat = {} self._edge_feat = {}
self._multi_graph = {} self._multi_graph = {}
for key, value in self._edges_dict.items(): for key, value in self._edges_dict.items():
if not self._node_feat:
node_feat = None
else:
node_feat = self._node_feat[key[0]]
if not self._edge_feat: if not self._edge_feat:
edge_feat = None edge_feat = None
else: else:
edge_feat = self._edge_feat[key] edge_feat = self._edge_feat[key]
self._multi_graph[key] = graph.Graph( self._multi_graph[key] = NodeGraph(
num_nodes=self._num_nodes_dict[key[1]], num_nodes=self._num_nodes,
edges=value, edges=value,
node_feat=node_feat, node_types=node_types,
node_feat=self._node_feat,
edge_feat=edge_feat) edge_feat=edge_feat)
@property
def num_nodes(self):
"""Return the number of nodes.
"""
return self._num_nodes
def __getitem__(self, edge_type): def __getitem__(self, edge_type):
"""__getitem__ """__getitem__
""" """
return self._multi_graph[edge_type] return self._multi_graph[edge_type]
def meta_path_random_walk(self, start_node, edge_types, meta_path,
max_depth):
"""Meta path random walk sampling.
Args:
start_nodes: int, node to begin random walk.
edge_types: list, the edge types to be sampled.
meta_path: 'user-item-user'
max_depth: the max length of every walk.
"""
edges_type_list = []
node_type_list = meta_path.split('-')
for i in range(1, len(node_type_list)):
edges_type_list.append(
(node_type_list[i - 1], node_type_list[i], edge_types[i - 1]))
no_neighbors_flag = False
walk = [start_node]
for i in range(max_depth):
for e_type in edges_type_list:
cur_node = [walk[-1]]
nxt_node = self._multi_graph[e_type].sample_successor(
cur_node, max_degree=1) # list of np.array
nxt_node = nxt_node[0]
if len(nxt_node) == 0:
no_neighbors_flag = True
break
else:
walk.append(nxt_node.tolist()[0])
if no_neighbors_flag:
break
return walk
def node_feat_info(self): def node_feat_info(self):
"""Return the information of node feature for HeterGraphWrapper. """Return the information of node feature for HeterGraphWrapper.
...@@ -155,17 +144,13 @@ class HeterGraph(object): ...@@ -155,17 +144,13 @@ class HeterGraph(object):
function is used to help constructing HeterGraphWrapper function is used to help constructing HeterGraphWrapper
Return: Return:
A dict of list of tuple (name, shape, dtype) for all given node feature. A list of tuple (name, shape, dtype) for all given node feature.
""" """
node_feat_info = {} node_feat_info = []
for node_type_name, feat_dict in self._node_feat.items(): for feat_name, feat in self._node_feat.items():
tmp_node_feat_info = [] node_feat_info.append(
for feat_name, feat in feat_dict.items(): (feat_name, _hide_num_nodes(feat.shape), feat.dtype))
full_name = feat_name
tmp_node_feat_info.append(
(full_name, _hide_num_nodes(feat.shape), feat.dtype))
node_feat_info[node_type_name] = tmp_node_feat_info
return node_feat_info return node_feat_info
...@@ -193,7 +178,7 @@ class HeterGraph(object): ...@@ -193,7 +178,7 @@ class HeterGraph(object):
"""Return the information of all edge types. """Return the information of all edge types.
Return: Return:
A list of tuple ('srctype','dsttype', 'edges_type') for all edge types. A list of all edge types.
""" """
edge_types_info = [] edge_types_info = []
......
pgl/contrib/heter_graph_wrapper.py
浏览文件 @ 36febd60
...@@ -26,6 +26,7 @@ from pgl.utils.logger import log ...@@ -26,6 +26,7 @@ from pgl.utils.logger import log
from pgl.graph_wrapper import GraphWrapper from pgl.graph_wrapper import GraphWrapper
ALL = "__ALL__" ALL = "__ALL__"
__all__ = ["HeterGraphWrapper"]
def is_all(arg): def is_all(arg):
...@@ -34,89 +35,6 @@ def is_all(arg): ...@@ -34,89 +35,6 @@ def is_all(arg):
return isinstance(arg, str) and arg == ALL return isinstance(arg, str) and arg == ALL
class BipartiteGraphWrapper(GraphWrapper):
"""Implement a bipartite graph wrapper that creates a graph data holders.
"""
def __init__(self, name, place, node_feat=[], edge_feat=[]):
super(BipartiteGraphWrapper, self).__init__(name, place, node_feat,
edge_feat)
def send(self,
message_func,
src_nfeat_list=None,
dst_nfeat_list=None,
efeat_list=None):
"""Send message from all src nodes to dst nodes.
The UDF message function should has the following format.
.. code-block:: python
def message_func(src_feat, dst_feat, edge_feat):
'''
Args:
src_feat: the node feat dict attached to the src nodes.
dst_feat: the node feat dict attached to the dst nodes.
edge_feat: the edge feat dict attached to the
corresponding (src, dst) edges.
Return:
It should return a tensor or a dictionary of tensor. And each tensor
should have a shape of (num_edges, dims).
'''
pass
Args:
message_func: UDF function.
src_nfeat_list: a list of tuple (name, tensor) for src nodes
dst_nfeat_list: a list of tuple (name, tensor) for dst nodes
efeat_list: a list of names or tuple (name, tensor)
Return:
A dictionary of tensor representing the message. Each of the values
in the dictionary has a shape (num_edges, dim) which should be collected
by :code:`recv` function.
"""
if efeat_list is None:
efeat_list = {}
if src_nfeat_list is None:
src_nfeat_list = {}
if dst_nfeat_list is None:
dst_nfeat_list = {}
src, dst = self.edges
src_feat = {}
for feat in src_nfeat_list:
if isinstance(feat, str):
src_feat[feat] = self.node_feat[feat]
else:
name, tensor = feat
src_feat[name] = tensor
dst_feat = {}
for feat in dst_nfeat_list:
if isinstance(feat, str):
dst_feat[feat] = self.node_feat[feat]
else:
name, tensor = feat
dst_feat[name] = tensor
efeat = {}
for feat in efeat_list:
if isinstance(feat, str):
efeat[feat] = self.edge_feat[feat]
else:
name, tensor = feat
efeat[name] = tensor
src_feat = op.read_rows(src_feat, src)
dst_feat = op.read_rows(dst_feat, dst)
msg = message_func(src_feat, dst_feat, efeat)
return msg
class HeterGraphWrapper(object): class HeterGraphWrapper(object):
"""Implement a heterogeneous graph wrapper that creates a graph data holders """Implement a heterogeneous graph wrapper that creates a graph data holders
that attributes and features in the heterogeneous graph. that attributes and features in the heterogeneous graph.
...@@ -141,43 +59,40 @@ class HeterGraphWrapper(object): ...@@ -141,43 +59,40 @@ class HeterGraphWrapper(object):
(-1 or None) or we can easily use :code:`HeterGraph.edge_feat_info()` (-1 or None) or we can easily use :code:`HeterGraph.edge_feat_info()`
to get the edge_feat settings. to get the edge_feat settings.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle.fluid as fluid
import numpy as np import numpy as np
num_nodes_every_type = {'type1':3,'type2':4, 'type3':2} from pgl.contrib import heter_graph
edges_every_type = { from pgl.contrib import heter_graph_wrapper
('type1','type2', 'edges_type1'): [(0,1), (1,2)], num_nodes = 4
('type1', 'type3', 'edges_type2'): [(1,2), (3,1)], node_types = [(0, 'user'), (1, 'item'), (2, 'item'), (3, 'user')]
} edges = {
node_feat_every_type = { 'edges_type1': [(0,1), (3,2)],
'type1': {'features1': np.random.randn(3, 4), 'edges_type2': [(1,2), (3,1)],
'features2': np.random.randn(3, 4)}, }
'type2': {'features3': np.random.randn(4, 4)}, node_feat = {'feature': np.random.randn(4, 16)}
'type3': {'features1': np.random.randn(2, 4), edges_feat = {
'features2': np.random.randn(2, 4)} 'edges_type1': {'h': np.random.randn(2, 16)},
} 'edges_type2': {'h': np.random.randn(2, 16)},
edges_feat_every_type = { }
('type1','type2','edges_type1'): {'h': np.random.randn(2, 4)},
('type1', 'type3', 'edges_type2'): {'h':np.random.randn(2, 4)}, g = heter_graph.HeterGraph(
} num_nodes=num_nodes,
edges=edges,
g = heter_graph.HeterGraph( node_types=node_types,
num_nodes_every_type=num_nodes_every_type, node_feat=node_feat,
edges_every_type=edges_every_type, edge_feat=edges_feat)
node_feat_every_type=node_feat_every_type,
edge_feat_every_type=edges_feat_every_type) place = fluid.CPUPlace()
gw = heter_graph_wrapper.HeterGraphWrapper(
place = fluid.CPUPlace() name='heter_graph',
place = place,
gw = pgl.heter_graph_wrapper.HeterGraphWrapper( edge_types = g.edge_types_info(),
name='heter_graph', node_feat=g.node_feat_info(),
place = place, edge_feat=g.edge_feat_info())
edge_types = g.edge_types_info(),
node_feat=g.node_feat_info(),
edge_feat=g.edge_feat_info())
""" """
def __init__(self, name, place, edge_types, node_feat={}, edge_feat={}): def __init__(self, name, place, edge_types, node_feat={}, edge_feat={}):
...@@ -186,10 +101,9 @@ class HeterGraphWrapper(object): ...@@ -186,10 +101,9 @@ class HeterGraphWrapper(object):
self._edge_types = edge_types self._edge_types = edge_types
self._multi_gw = {} self._multi_gw = {}
for edge_type in self._edge_types: for edge_type in self._edge_types:
type_name = self.__data_name_prefix + '/' + edge_type[ type_name = self.__data_name_prefix + '/' + edge_type
0] + '_' + edge_type[1]
if node_feat: if node_feat:
n_feat = node_feat[edge_type[0]] n_feat = node_feat
else: else:
n_feat = {} n_feat = {}
...@@ -198,7 +112,7 @@ class HeterGraphWrapper(object): ...@@ -198,7 +112,7 @@ class HeterGraphWrapper(object):
else: else:
e_feat = {} e_feat = {}
self._multi_gw[edge_type] = BipartiteGraphWrapper( self._multi_gw[edge_type] = GraphWrapper(
name=type_name, name=type_name,
place=self._place, place=self._place,
node_feat=n_feat, node_feat=n_feat,
......
pgl/graph_wrapper.py
浏览文件 @ 36febd60
...@@ -596,8 +596,7 @@ class GraphWrapper(BaseGraphWrapper): ...@@ -596,8 +596,7 @@ class GraphWrapper(BaseGraphWrapper):
feed_dict[self.__data_name_prefix + '/edges_src'] = src feed_dict[self.__data_name_prefix + '/edges_src'] = src
feed_dict[self.__data_name_prefix + '/edges_dst'] = dst feed_dict[self.__data_name_prefix + '/edges_dst'] = dst
feed_dict[self.__data_name_prefix + '/num_nodes'] = np.array( feed_dict[self.__data_name_prefix + '/num_nodes'] = np.array(graph.num_nodes)
graph.num_nodes)
feed_dict[self.__data_name_prefix + '/uniq_dst'] = uniq_dst feed_dict[self.__data_name_prefix + '/uniq_dst'] = uniq_dst
feed_dict[self.__data_name_prefix + '/uniq_dst_count'] = uniq_dst_count feed_dict[self.__data_name_prefix + '/uniq_dst_count'] = uniq_dst_count
feed_dict[self.__data_name_prefix + '/node_ids'] = graph.nodes feed_dict[self.__data_name_prefix + '/node_ids'] = graph.nodes
......
pgl/layers/__init__.py
浏览文件 @ 36febd60
...@@ -16,6 +16,9 @@ ...@@ -16,6 +16,9 @@
from pgl.layers import conv from pgl.layers import conv
from pgl.layers.conv import * from pgl.layers.conv import *
from pgl.layers import set2set
from pgl.layers.set2set import *
__all__ = [] __all__ = []
__all__ += conv.__all__ __all__ += conv.__all__
__all__ += set2set.__all__
pgl/layers/set2set.py
浏览文件 @ 36febd60
...@@ -23,6 +23,8 @@ import paddle.fluid.layers as L ...@@ -23,6 +23,8 @@ import paddle.fluid.layers as L
import pgl import pgl
__all__ = ['Set2Set']
class Set2Set(object): class Set2Set(object):
"""Implementation of set2set pooling operator. """Implementation of set2set pooling operator.
......
pgl/sample.py
浏览文件 @ 36febd60
...@@ -22,7 +22,10 @@ import pgl ...@@ -22,7 +22,10 @@ import pgl
from pgl.utils.logger import log from pgl.utils.logger import log
from pgl import graph_kernel from pgl import graph_kernel
__all__ = ['graphsage_sample', 'node2vec_sample', 'deepwalk_sample'] __all__ = [
'graphsage_sample', 'node2vec_sample', 'deepwalk_sample',
'metapath_randomwalk'
]
def edge_hash(src, dst): def edge_hash(src, dst):
...@@ -251,3 +254,45 @@ def node2vec_sample(graph, nodes, max_depth, p=1.0, q=1.0): ...@@ -251,3 +254,45 @@ def node2vec_sample(graph, nodes, max_depth, p=1.0, q=1.0):
prev_nodes, prev_succs = cur_nodes, cur_succs prev_nodes, prev_succs = cur_nodes, cur_succs
cur_nodes = nxt_nodes cur_nodes = nxt_nodes
return walk return walk
def metapath_randomwalk(graph, start_node, metapath, walk_length):
"""Implementation of metapath random walk in heterogeneous graph.
Args:
graph: instance of pgl heterogeneous graph
start_node: start node to generate walk
metapath: meta path for sample nodes.
e.g: "user-item-user"
walk_length: the walk length
Return:
a list of metapath walk, each element is a node id.
"""
np.random.seed()
walk = []
metapath = metapath.split('-')
assert metapath[0] == metapath[
-1], "The last meta path item should be the same as the first one"
mp_len = len(metapath) - 1
walk.append(start_node)
for i in range(1, walk_length):
cur_node = walk[-1]
succs = graph.successor(cur_node)
if succs.size > 0:
succs_node_types = graph._node_types[succs]
else:
# no successor of current node
break
succs_nodes = succs[np.where(succs_node_types == metapath[i % mp_len])[
0]]
if succs_nodes.size > 0:
walk.append(np.random.choice(succs_nodes))
else:
# no successor of such node type
break
return walk
pgl/utils/paddle_helper.py
浏览文件 @ 36febd60
...@@ -223,7 +223,7 @@ def scatter_add(input, index, updates): ...@@ -223,7 +223,7 @@ def scatter_add(input, index, updates):
Same type and shape as input. Same type and shape as input.
""" """
output = fluid.layers.scatter(input, index, updates, mode='add') output = fluid.layers.scatter(input, index, updates, overwrite=False)
return output return output
......
requirements.txt
浏览文件 @ 36febd60
...@@ -3,4 +3,4 @@ cython >= 0.25.2 ...@@ -3,4 +3,4 @@ cython >= 0.25.2
#paddlepaddle #paddlepaddle
redis-py-cluster == 1.3.6 redis-py-cluster
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