Fixes the docstring rendering for GAE apps (#2131)

Signed-off-by: Tao He <sighingnow@gmail.com>

coordinator/setup.cfg

@@ -22,7 +22,7 @@ max-complexity = 18
 # for flake8-quotes
 inline-quotes = double
 # for flake8-comprehensions
-extend-ignore = E203,E402,F401,F401,F403,C4
+extend-ignore = E203,E402,F401,F401,F403,C4,N802,N806
 extend-exclude =
     *_pb2.py
     *_pb2_grpc.py

docs/analytics_engine.rst

@@ -46,27 +46,30 @@ property graph or not is described in its docstring.
 
 .. autosummary::
 
+- :func:`attribute_assortativity_coefficient`
+- :func:`numeric_assortativity_coefficient`
 - :func:`average_degree_connectivity`
 - :func:`average_shortest_path_length`
-- :func:`attribute_assortativity_coefficient`
 - :func:`bfs`
 - :func:`avg_clustering`
 - :func:`clustering`
-- :func:`degree_centrality`
 - :func:`degree_assortativity_coefficient`
+- :func:`degree_centrality`
 - :func:`eigenvector_centrality`
 - :func:`hits`
 - :func:`is_simple_path`
 - :func:`k_core`
 - :func:`k_shell`
 - :func:`katz_centrality`
 - :func:`louvain`
+- :func:`cdlp`
 - :func:`lpa`
-- :func:`numeric_assortativity_coefficient`
+- :func:`lpa_u2i`
 - :func:`pagerank`
 - :func:`pagerank_nx`
 - :func:`sssp`
 - :func:`triangles`
+- :func:`voterank`
 - :func:`wcc`
 
 The list is continuously growing.

docs/developer_guide.rst

@@ -115,13 +115,13 @@ First, you need to ``git clone`` the latest version of code from our `repo <http
 and run the command:
 
 .. code:: bash
+
     cd GraphScope
     ./scripts/install_deps.sh --dev
 
     # With argument --cn to speed up the download if you are in China.
     ./scripts/install_deps.sh --dev --cn
 
-
 The `install_deps.sh` does not install the grape-jdk dependency `fastFFI` by default.
 If you don't care about `grape jdk <https://github.com/alibaba/GraphScope/blob/main/analytical_engine/java/README.md>`,
 you can ignore this phase. But if you want to use the grape jdk of GraphScope,

docs/interactive_engine.rst

@@ -362,7 +362,9 @@ Currently GIE supports the following Gremlin steps:
     g.V().values("age").sum()
 
 - Match step, e.g.,
+
 .. code:: java
+
     g.V().match(
         __.as('a').out().as('b'),
         __.as('b').out().as('c')

docs/reference/app.rst

@@ -45,18 +45,28 @@ Functions
 BuiltIn apps
 ------------
 
+.. autofunction:: graphscope.attribute_assortativity_coefficient
+.. autofunction:: graphscope.numeric_assortativity_coefficient
+.. autofunction:: graphscope.average_degree_connectivity
+.. autofunction:: graphscope.average_shortest_path_length
 .. autofunction:: graphscope.bfs
-.. autofunction:: graphscope.pagerank
-.. autofunction:: graphscope.sssp
-.. autofunction:: graphscope.wcc
 .. autofunction:: graphscope.avg_clustering
 .. autofunction:: graphscope.clustering
+.. autofunction:: graphscope.degree_assortativity_coefficient
 .. autofunction:: graphscope.degree_centrality
 .. autofunction:: graphscope.eigenvector_centrality
 .. autofunction:: graphscope.hits
+.. autofunction:: graphscope.is_simple_path
 .. autofunction:: graphscope.k_core
+.. autofunction:: graphscope.k_shell
 .. autofunction:: graphscope.katz_centrality
+.. autofunction:: graphscope.louvain
+.. autofunction:: graphscope.cdlp
 .. autofunction:: graphscope.lpa
+.. autofunction:: graphscope.lpa_u2i
+.. autofunction:: graphscope.pagerank
+.. autofunction:: graphscope.pagerank_nx
+.. autofunction:: graphscope.sssp
 .. autofunction:: graphscope.triangles
-.. autofunction:: graphscope.louvain
-
+.. autofunction:: graphscope.voterank
+.. autofunction:: graphscope.wcc

docs/reference/gnn_engine/data_loader_en.rst

@@ -174,8 +174,6 @@ orders** as specified in the above tables.
 2 Data source type
 ==================
 
-.. container::
-
 The system abstracts the data access layer to allow easy connection to
 multiple types of data sources. Currently, it supports LocalFileSystem.
 If it is used on the Alibaba Cloud PAI platform, you can directly read

docs/reference/gnn_engine/gsl_en.rst

@@ -284,8 +284,6 @@ For example, in the heterogeneous graph shown below, start from a ‘user’
 vertex, sample its one-hop neighbors along the ‘u2i’ edge, and then
 sample its two-hop neighbors along the ‘i2i’ edge.
 
-.. container::
-
 .. code:: python
 
    g.V("user").batch(64)                       # (1) randomly get 64 'user' vertices
@@ -297,8 +295,6 @@ general, this kind of sampling is often used for unsupervised learning,
 which uses the edges as positive samples, and then samples the source
 vertices of the edges as negative samples.
 
-.. container::
-
 .. code:: python
 
    g.E("u2i").batch(64)                     # (1) randomly get 64 'u2i' edges
@@ -309,8 +305,6 @@ When the edge is undirected, one can conduct circular sampling by
 ``outV()`` and ``inV()``. The source and destination vertices are
 neighbors of each other.
 
-.. container::
-
 .. code:: python
 
    g.V("user").batch(64)                   # (1) randomly get 64 'user' vertex
@@ -345,8 +339,6 @@ one-hop neighbors (aggregating neighborhood information to the central
 node). The item vertex is encoded in a similar way. An example is shown
 below.
 
-.. container::
-
 .. code:: python
 
    g.E("u2i").shuffle().batch(512)                            # (1) randomly get 512 u2i edegs

docs/reference/networkx/transformation.rst

@@ -1,7 +1,7 @@
 .. _transformation:
 
 Graph Transformation
-^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^
 
 In GraphScope, :class:`nx.Graph` or :class:`nx.DiGraph` can be created from GraphScope graph object.
 We implement a copy-on-write transformation strategy for creating from GraphScope graph.
@@ -40,5 +40,4 @@ Note: :class:`nx.Graph` and :class:`nx.DiGraph` not support to be created from a
 
 
 .. autofunction:: graphscope.nx.Graph.__init__
-.. autofunction:: graphscope.nx.Graph._init_with_arrow_property_graph
 .. autofunction:: graphscope.nx.Graph._convert_arrow_to_dynamic

docs/reference/operation.rst

@@ -17,7 +17,6 @@ Operation object
    Operation.key
    Operation.signature
    Operation.eval
-   Operation.set_output
    Operation.evaluated
 
 BuiltIn operations
@@ -28,21 +27,43 @@ BuiltIn operations
    :toctree: generated/
 
    create_app
+   bind_app
+   run_app
    create_graph
+   create_loader
+   add_labels_to_graph
    dynamic_to_arrow
    arrow_to_dynamic
    modify_edges
    modify_vertices
-   run_app
    report_graph
    project_arrow_property_graph
-   project_arrow_property_graph_to_simple
-   project_dynamic_property_graph
+   project_to_simple
+   copy_graph
+   to_directed
+   to_undirected
+   create_graph_view
+   clear_graph
+   clear_edges
+   create_subgraph
+   create_unload_op
    unload_app
    unload_graph
+   unload_context
    context_to_numpy
    context_to_dataframe
    to_vineyard_tensor
+   to_vineyard_dataframe
+   to_data_sink
+   output
+   get_context_data
    add_column
    graph_to_numpy
    graph_to_dataframe
+   create_interactive_query
+   create_learning_instance
+   close_interactive_query
+   close_learning_instance
+   gremlin_query
+   gremlin_to_subgraph
+   fetch_gremlin_result

docs/zh/analytics_engine.rst

@@ -36,27 +36,30 @@ GraphScope 图分析引擎内置了许多常用的图分析算法，包括连通
 
 .. autosummary::
 
+- :func:`attribute_assortativity_coefficient`
+- :func:`numeric_assortativity_coefficient`
 - :func:`average_degree_connectivity`
 - :func:`average_shortest_path_length`
-- :func:`attribute_assortativity_coefficient`
 - :func:`bfs`
 - :func:`avg_clustering`
 - :func:`clustering`
-- :func:`degree_centrality`
 - :func:`degree_assortativity_coefficient`
+- :func:`degree_centrality`
 - :func:`eigenvector_centrality`
 - :func:`hits`
 - :func:`is_simple_path`
 - :func:`k_core`
 - :func:`k_shell`
 - :func:`katz_centrality`
 - :func:`louvain`
+- :func:`cdlp`
 - :func:`lpa`
-- :func:`numeric_assortativity_coefficient`
+- :func:`lpa_u2i`
 - :func:`pagerank`
 - :func:`pagerank_nx`
 - :func:`sssp`
 - :func:`triangles`
+- :func:`voterank`
 - :func:`wcc`
 
 算法的支持列表会随着不断增加持续更新中。

docs/zh/deployment.rst

@@ -54,7 +54,7 @@ Coordinator 作为 GraphScope 后端服务的入口，通过 grpc 接收来自 P
 
 
 使用Helm部署GraphScope
---------------------
+---------------------
 
 获取并添加仓库信息
 
@@ -84,7 +84,8 @@ Coordinator 作为 GraphScope 后端服务的入口，通过 grpc 接收来自 P
 
 
 在AWS/阿里云上部署集群
-------------------------
+-------------------
+
 我们提供了一个可在AWS或阿里云上创建 Kubernetes 集群的交互式脚本。这一脚本可以帮助用户使用已有的集群或创建新的 Kubernetes 集群，然后输出集群的配置文件。
 用法如下。你也可以通过 `./scripts/launch_cluster.py --help` 获得更详细的帮助信息。
 
@@ -102,7 +103,8 @@ Coordinator 作为 GraphScope 后端服务的入口，通过 grpc 接收来自 P
 
 
 本地部署GraphScope
-----------------------
+-----------------
+
 我们提供了一个可在本地安装GraphScope相关依赖的脚本，该脚本可以运行在 Ubuntu 20.04+ 或 MacOS 11.2+ 平台上, 主要的用法如下：
 你可以通过 `./scripts/install_deps.sh -h` 获取更详细的帮助信息。
 

python/graphscope/analytical/app/__init__.py

@@ -16,7 +16,6 @@
 # limitations under the License.
 #
 
-
 # fmt: off
 from graphscope.analytical.app.attribute_assortativity import \
     attribute_assortativity_coefficient

python/graphscope/framework/app.py

@@ -477,15 +477,16 @@ def __init__(self, session, op):
 
 def load_app(gar=None, algo=None, context=None, **kwargs):
     """Load an app from gar.
-    bytes or the resource of the specified path or bytes.
 
     Args:
         algo: str
           Algo name inside resource. None will extract name from gar resource
           if there is only one app in it.
         gar: bytes or BytesIO or str
-          str represent the path of resource.
-          for java apps, gar can be none to indicate we should find the app in
+          str represent the path of resource, bytes or the resource of the
+          specified path or bytes.
+
+          For java apps, gar can be none to indicate we should find the app in
           previouse added libs.
 
     Returns:

python/graphscope/framework/context.py

@@ -61,7 +61,7 @@ class BaseContextDAGNode(DAGNode):
     """Base class of concrete context DAG node.
 
     In GraphScope, it will return a instance of concrete class `ContextDAGNode`
-    after evaluating an app, that will be automatically executed by :method:`sess.run`
+    after evaluating an app, that will be automatically executed by :meth:`Session.run`
     in eager mode and return a instance of :class:`graphscope.framework.context.Context`
 
     We can further use the handle to retrieve data:

python/graphscope/framework/graph.py

@@ -195,7 +195,7 @@ class GraphDAGNode(DAGNode, GraphInterface):
 
     In GraphScope, all operations that generate a new graph will return
     a instance of :class:`GraphDAGNode`, which will be automatically
-    executed by :method:`sess.run` in `eager` mode.
+    executed by :meth:`Session.run` in `eager` mode.
 
     The following example demonstrates its usage:
 
@@ -233,10 +233,12 @@ def __init__(
             session (:class:`Session`): A graphscope session instance.
             incoming_data: Graph can be initialized through various type of sources,
                 which can be one of:
-                    - :class:`graphscope.framework.operation.Operation`
-                    - :class:`graphscope.nx.Graph`
-                    - :class:`graphscope.Graph`
-                    - :class:`vineyard.Object`, :class:`vineyard.ObjectId` or :class:`vineyard.ObjectName`
+
+                - :class:`graphscope.framework.operation.Operation`
+                - :class:`graphscope.nx.Graph`
+                - :class:`graphscope.Graph`
+                - :class:`vineyard.Object`, :class:`vineyard.ObjectId` or :class:`vineyard.ObjectName`
+
             oid_type: (str, optional): Type of vertex original id. Defaults to "int64".
             directed: (bool, optional): Directed graph or not. Defaults to True.
             generate_eid: (bool, optional): Generate id for each edge when setted True. Defaults to True.
@@ -450,13 +452,20 @@ def add_edges(
     ):
         """Add edges to the graph, and return a new graph.
         Here the src_label and dst_label must be both specified or both unspecified,
-            i.   src_label and dst_label both unspecified and current graph has no vertex label.
-                 We deduce vertex label from edge table, and set vertex label name to '_'.
-            ii.  src_label and dst_label both unspecified and current graph has one vertex label.
-                 We set src_label and dst label to this single vertex label.
-            ii.  src_label and dst_label both specified and existed in current graph's vertex labels.
-            iii. src_label and dst_label both specified and some are not existed in current graph's vertex labels.
-                 we deduce missing vertex labels from edge tables.
+
+        i. src_label and dst_label both unspecified and current graph has no vertex label.
+
+            We deduce vertex label from edge table, and set vertex label name to '_'.
+
+        ii. src_label and dst_label both unspecified and current graph has one vertex label.
+
+            We set src_label and dst label to this single vertex label.
+
+        ii. src_label and dst_label both specified and existed in current graph's vertex labels.
+
+        iii. src_label and dst_label both specified and some are not existed in current graph's vertex labels.
+
+            We deduce missing vertex labels from edge tables.
 
 
         Args:

python/graphscope/learning/graph.py

@@ -260,7 +260,7 @@ def V(self, t, feed=None):
 
         Args:
         t (string): The type of node which is the entry of query or the type
-            of edge when node is from edge source or dst.
+                    of edge when node is from edge source or dst.
         feed (None| numpy.ndarray | types.GeneratorType | `Nodes`): When `feed`
             is not `None`, the `type` should be a node type, which means query the
             attributes of the specified node ids.

python/graphscope/nx/classes/graph.py

@@ -1046,7 +1046,7 @@ def set_edge_data(self, u, v, data):
         -----
             the method is called when to set_items in AdjEdgeAttr
 
-        Examples:
+        Examples
         --------
         >>> G = nx.Graph()  # or DiGraph
         >>> G.add_edge(1, 2)
@@ -1082,7 +1082,7 @@ def set_node_data(self, n, data):
         -----
             the method is called when to set_items in NodeAttr
 
-        Examples:
+        Examples
         --------
         >>> G = nx.Graph()  # or DiGraph
         >>> G.add_node(1)