add heter graph

PaddlePaddle · Oct 23, 2019 · c26ccbc · c26ccbc
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diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -15,14 +15,9 @@ Quick Start
 .. toctree::
     :maxdepth: 1
     :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::

diff --git a/docs/source/quick_start/images/heter_graph_introduction.png b/docs/source/quick_start/images/heter_graph_introduction.png
diff --git a/docs/source/_static/quick_start_graph.png → .../quick_start/images/quick_start_graph.png b/docs/source/_static/quick_start_graph.png → .../quick_start/images/quick_start_graph.png
diff --git a/docs/source/instruction.rst → docs/source/quick_start/instruction.rst b/docs/source/instruction.rst → docs/source/quick_start/instruction.rst
diff --git a/docs/source/quick_start/introduction_for_hetergraph.rst b/docs/source/quick_start/introduction_for_hetergraph.rst
@@ -0,0 +1,22 @@
+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
+
+
diff --git a/docs/source/md/quick_start.md → docs/source/quick_start/md/quick_start.md b/docs/source/md/quick_start.md → docs/source/quick_start/md/quick_start.md
@@ -1,6 +1,6 @@
 ## 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:
-![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:
 ```python

diff --git a/docs/source/quick_start/md/quick_start_for_heterGraph.md b/docs/source/quick_start/md/quick_start_for_heterGraph.md
@@ -0,0 +1,168 @@
+## 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]))
+```
+
+
+
+
+