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Merge pull request #383 from adamnsch/ref-docs-server-ep-coverage-test
Add testing for server endpoint coverage in ref docs
2 parents 4805384 + 034f2bc commit 20ac789

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‎doc/sphinx/source/algorithms.rst‎

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‎doc/sphinx/source/graph.rst‎

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@@ -13,115 +13,119 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g
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Gets a graph object representing a graph in the graph catalog.
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.. py:function:: gds.alpha.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) -> "Series[Any]"
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.. py:function:: gds.alpha.graph.graphProperty.drop(G: Graph, graph_property: str, **config: Any) -> Series[Any]
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Removes a graph property from a projected graph.
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.. py:function:: gds.alpha.graph.graphProperty.stream(G: Graph, graph_property: str, **config: Any) -> DataFrame
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Streams the given graph property.
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.. py:function:: gds.alpha.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) -> "Series[Any]"
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.. py:function:: gds.alpha.graph.nodeLabel.mutate(G: Graph, node_label: str, **config: Any) -> Series[Any]
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Mutates the in-memory graph with the given node Label.
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.. py:function:: gds.alpha.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) -> "Series[Any]"
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.. py:function:: gds.alpha.graph.nodeLabel.write(G: Graph, node_label: str, **config: Any) -> Series[Any]
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Writes the given node Label to an online Neo4j database.
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.. py:function:: gds.beta.graph.export.csv(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graph.export.csv(G: Graph, **config: Any) -> Series[Any]
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Exports a named graph to CSV files.
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.. py:function:: gds.beta.graph.export.csv.estimate(G: Graph, **config: Any) -> "Series[Any]
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Estimate the required disk space for exporting a named graph to CSV files.
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.. py:function:: gds.beta.graph.generate(graph_name: str, node_count: int, average_degree: int, **config: Any) -> Tuple[Graph, "Series[Any]"]
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.. py:function:: gds.beta.graph.generate(graph_name: str, node_count: int, average_degree: int, **config: Any) -> Tuple[Graph, Series[Any]]
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Computes a random graph, which will be stored in the graph catalog.
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.. py:function:: gds.beta.graph.project.subgraph(graph_name: str,from_G: Graph,node_filter: str,relationship_filter: str,**config: Any,) -> Tuple[Graph, "Series[Any]"]
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.. py:function:: gds.beta.graph.project.subgraph(graph_name: str,from_G: Graph,node_filter: str,relationship_filter: str,**config: Any,) -> Tuple[Graph, Series[Any]]
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Creates a named graph in the catalog for use by algorithms.
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.. py:function:: gds.beta.graph.relationships.stream(G: Graph, relationship_types: List[str] = ["*"], **config: Any) -> TopologyDataFrame
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Streams the given relationship source/target pairs
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.. py:function:: gds.beta.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graph.relationships.toUndirected(G: Graph, query: str, relationship_type: str, mutate_relationship_type: str, **config: Any) -> Series[Any]
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The ToUndirected procedure converts directed relationships to undirected relationships
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.. py:function:: gds.beta.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graph.relationships.toUndirected.estimate(G: Graph, relationship_type: str, mutate_relationship_type: str, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.graph.deleteRelationships(G: Graph, relationship_type: str) -> "Series[Any]"
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.. py:function:: gds.graph.deleteRelationships(G: Graph, relationship_type: str) -> Series[Any]
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Delete the relationship type for a given graph stored in the graph-catalog.
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.. py:function:: gds.graph.drop(G: Graph,failIfMissing: bool = False,dbName: str = "",username: Optional[str] = None,) -> Optional["Series[Any]"]
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.. py:function:: gds.graph.drop(G: Graph,failIfMissing: bool = False,dbName: str = "",username: Optional[str] = None,) -> Optional[Series[Any]]
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Drops a named graph from the catalog and frees up the resources it occupies.
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.. py:function:: gds.graph.exists(graph_name: str) -> "Series[Any]"
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.. py:function:: gds.graph.exists(graph_name: str) -> Series[Any]
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Checks if a graph exists in the catalog.
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.. py:function:: gds.graph.export(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.export(G: Graph, **config: Any) -> Series[Any]
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Exports a named graph into a new offline Neo4j database.
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.. py:function:: gds.graph.list(G: Optional[Graph] = None) -> DataFrame
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Lists information about named graphs stored in the catalog.
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.. py:function:: gds.graph.nodeProperties.drop(G: Graph, node_properties: List[str], **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.nodeProperties.drop(G: Graph, node_properties: List[str], **config: Any) -> Series[Any]
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Removes node properties from a projected graph.
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.. py:function:: gds.graph.nodeProperties.stream(G: Graph,relationship_properties: List[str],relationship_types: Strings = ["*"],separate_property_columns: bool = False,**config: Any,) -> DataFrame
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Streams the given node properties.
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.. py:function:: gds.graph.nodeProperties.write(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.nodeProperties.write(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> Series[Any]
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Writes the given node properties to an online Neo4j database.
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.. py:function:: gds.graph.nodeProperty.stream(G: Graph, node_properties: str, node_labels: Strings = ["*"], **config: Any) -> DataFrame
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Streams the given node property.
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.. py:function:: gds.graph.project(graph_name: str, node_spec: Any, relationship_spec: Any, **config: Any) -> Tuple[Graph, "Series[Any]"]
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.. py:function:: gds.graph.project(graph_name: str, node_spec: Any, relationship_spec: Any, **config: Any) -> Tuple[Graph, Series[Any]]
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Creates a named graph in the catalog for use by algorithms.
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.. py:function:: gds.graph.project.cypher(graph_name: str, node_spec: Any, relationship_spec: Any, **config: Any) -> Tuple[Graph, "Series[Any]"]
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.. py:function:: gds.graph.project.cypher(graph_name: str, node_spec: Any, relationship_spec: Any, **config: Any) -> Tuple[Graph, Series[Any]]
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Creates a named graph in the catalog for use by algorithms.
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.. py:function:: gds.graph.project.cypher.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.project.cypher.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.graph.project.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.project.estimate(node_projection: Any, relationship_projection: Any, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.graph.relationship.write(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.relationship.write(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> Series[Any]
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Writes the given relationship and an optional relationship property to an online Neo4j database.
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.. py:function:: gds.graph.relationshipProperties.stream(G: Graph,relationship_properties: List[str],relationship_types: Strings = ["*"],separate_property_columns: bool = False,**config: Any,) -> DataFrame
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.. py:function:: gds.graph.relationshipProperties.stream(G: Graph, relationship_properties: List[str],relationship_types: Union[str, List[str]] = ["*"],separate_property_columns: bool = False,**config: Any,) -> DataFrame
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Streams the given relationship properties.
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.. py:function:: gds.graph.relationshipProperty.stream(G: Graph, node_properties: str, node_labels: Strings = ["*"], **config: Any) -> DataFrame
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.. py:function:: gds.graph.relationshipProperties.write(G: Graph, relationship_type: str, relationship_properties: List[str], **config: Any,) -> DataFrame
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Write the given relationship properties back to the database.
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.. py:function:: gds.graph.relationshipProperty.stream(G: Graph, node_properties: str, node_labels: Union[str, List[str]] = ["*"], **config: Any) -> DataFrame
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Streams the given relationship property.
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.. py:function:: gds.graph.relationships.drop(G: Graph, relationship_type: str,) -> "Series[Any]"
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.. py:function:: gds.graph.relationships.drop(G: Graph, relationship_type: str,) -> Series[Any]
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Delete the relationship type for a given graph stored in the graph-catalog.
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@@ -145,11 +149,11 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g
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Streams the given relationship property.
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.. py:function:: gds.graph.writeNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.writeNodeProperties(G: Graph, node_properties: List[str], node_labels: Strings = ["*"], **config: Any) -> Series[Any]
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Writes the given node properties to an online Neo4j database.
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.. py:function:: gds.graph.writeRelationship(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> "Series[Any]"
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.. py:function:: gds.graph.writeRelationship(G: Graph, relationship_type: str, relationship_property: str = "", **config: Any) -> Series[Any]
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Writes the given relationship and an optional relationship property to an online Neo4j database.
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.. py:function:: gds.find_node_id(labels: List[str] = [], properties: Dict[str, Any] = {}) -> int
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Finds a node id by its labels and properties.
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Finds a node id by its labels and properties.

‎doc/sphinx/source/graphdatascience.rst‎

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.. autoclass:: graphdatascience.GraphDataScience
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:members:
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:inherited-members:
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:inherited-members:
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:exclude-members: __init__

‎doc/sphinx/source/misc.rst‎

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@@ -21,7 +21,7 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g
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Set a limit; global by, default, but also optionally for a specific user
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.. py:function:: gds.alpha.systemMonitor() -> "Series[Any]"
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.. py:function:: gds.alpha.systemMonitor() -> Series[Any]
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Get an overview of the system's workload and available resources
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List progress events for currently running tasks.
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.. py:function:: gds.debug.sysInfo() -> "Series[Any]"
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.. py:function:: gds.debug.sysInfo() -> Series[Any]
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Returns details about the status of the system
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‎doc/sphinx/source/ml.rst‎

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Get a pipeline object representing a pipeline in the Pipeline Catalog.
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.. py:function:: gds.alpha.ml.splitRelationships.mutate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.alpha.ml.splitRelationships.mutate(G: Graph, **config: Any) -> Series[Any]
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Splits a graph into holdout and remaining relationship types and adds them to the graph.
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.. py:function:: gds.alpha.scaleProperties.mutate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.alpha.pipeline.nodeRegression.create(name: str) -> Tuple[NRTrainingPipeline, Series[Any]]
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Scale node properties
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Creates a node regression training pipeline in the pipeline catalog.
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.. py:function:: gds.alpha.scaleProperties.stream(G: Graph, **config: Any) -> DataFrame
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Scale node properties
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.. py:function:: gds.beta.graphSage.mutate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graphSage.mutate(G: Graph, **config: Any) -> Series[Any]
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The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods.
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.. py:function:: gds.beta.graphSage.mutate.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graphSage.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
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The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods.
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The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods.
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.. py:function:: gds.beta.graphSage.stream.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graphSage.stream.estimate(G: Graph, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.beta.graphSage.train(G: Graph, **config: Any) -> Tuple[MODEL_TYPE, "Series[Any]"]
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.. py:function:: gds.beta.graphSage.train(G: Graph, **config: Any) -> Tuple[MODEL_TYPE, Series[Any]]
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The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods.
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.. py:function:: gds.beta.graphSage.train.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graphSage.train.estimate(G: Graph, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.beta.graphSage.write(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graphSage.write(G: Graph, **config: Any) -> Series[Any]
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The GraphSage algorithm inductively computes embeddings for nodes based on a their features and neighborhoods.
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.. py:function:: gds.beta.graphSage.write.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.graphSage.write.estimate(G: Graph, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.beta.hashgnn.mutate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.hashgnn.mutate(G: Graph, **config: Any) -> Series[Any]
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.beta.hashgnn.mutate.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.hashgnn.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.beta.hashgnn.stream(G: Graph, **config: Any) -> DataFrame
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.beta.hashgnn.stream.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.hashgnn.stream.estimate(G: Graph, **config: Any) -> Series[Any]
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.beta.node2vec.mutate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.node2vec.mutate(G: Graph, **config: Any) -> Series[Any]
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The Node2Vec algorithm computes embeddings for nodes based on random walks.
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.. py:function:: gds.beta.node2vec.mutate.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.node2vec.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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The Node2Vec algorithm computes embeddings for nodes based on random walks.
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.. py:function:: gds.beta.node2vec.stream.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.node2vec.stream.estimate(G: Graph, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.beta.node2vec.write(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.node2vec.write(G: Graph, **config: Any) -> Series[Any]
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The Node2Vec algorithm computes embeddings for nodes based on random walks.
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.. py:function:: gds.beta.node2vec.write.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.beta.node2vec.write.estimate(G: Graph, **config: Any) -> Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.beta.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> "Series[Any]"
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.. py:function:: gds.beta.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> Series[Any]
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Drops a pipeline and frees up the resources it occupies.
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.. py:function:: gds.beta.pipeline.exists(pipeline_name: str) -> "Series[Any]"
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.. py:function:: gds.beta.pipeline.exists(pipeline_name: str) -> Series[Any]
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Checks if a given pipeline exists in the pipeline catalog.
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.. py:function:: gds.beta.pipeline.linkPrediction.create(name: str) -> Tuple[LPTrainingPipeline, "Series[Any]"]
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.. py:function:: gds.beta.pipeline.linkPrediction.create(name: str) -> Tuple[LPTrainingPipeline, Series[Any]]
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Creates a link prediction pipeline in the pipeline catalog.
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.. py:function:: gds.beta.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> DataFrame
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Lists all pipelines contained in the pipeline catalog.
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.. py:function:: gds.beta.pipeline.nodeClassification.create(name: str) -> Tuple[NCTrainingPipeline, "Series[Any]"]
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.. py:function:: gds.beta.pipeline.nodeClassification.create(name: str) -> Tuple[NCTrainingPipeline, Series[Any]]
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Creates a node classification training pipeline in the pipeline catalog.
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.. py:function:: gds.fastRP.mutate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.mutate(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.mutate.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.stats(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.stats(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.stats.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.stats.estimate(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.stream(G: Graph, **config: Any) -> DataFrame
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.stream.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.stream.estimate(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.write(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.write(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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.. py:function:: gds.fastRP.write.estimate(G: Graph, **config: Any) -> "Series[Any]"
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.. py:function:: gds.fastRP.write.estimate(G: Graph, **config: Any) -> Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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