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Link to pandas and nx classes in typed-out ref docs
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‎doc/sphinx/algorithms.json‎

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‎doc/sphinx/source/algorithms.rst‎

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‎doc/sphinx/source/conf.py‎

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@@ -69,7 +69,6 @@ def setup(app): # type: ignore
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"pandas": ("https://pandas.pydata.org/docs/", None),
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"pyarrow": ("https://arrow.apache.org/docs/", None),
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"networkx": ("https://networkx.org/documentation/stable/", None),
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"nx": ("https://networkx.org/documentation/stable/", None),
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}
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rst_epilog = """

‎doc/sphinx/source/graph.rst‎

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‎doc/sphinx/source/misc.rst‎

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@@ -5,7 +5,7 @@ This includes procedures for configuring the library.
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These all assume that an object of :class:`.GraphDataScience` is available as `gds`.
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.. py:function:: gds.alpha.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame
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.. py:function:: gds.alpha.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame
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List defaults; global by default, but also optionally for a specific user and/ or key
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.. deprecated:: 2.5.0
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.. deprecated:: 2.5.0
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Since GDS server version 2.5.0 you should use the endpoint :func:`gds.config.defaults.set` instead.
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.. py:function:: gds.alpha.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame
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.. py:function:: gds.alpha.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame
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List limits; global by default, but also optionally for a specific user and/ or key
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.. deprecated:: 2.5.0
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Set a limit; global by, default, but also optionally for a specific user
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.. deprecated:: 2.5.0
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Since GDS server version 2.5.0 you should use the endpoint :func:`gds.config.limits.set` instead.
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.. py:function:: gds.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame
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.. py:function:: gds.config.defaults.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame
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List defaults; global by default, but also optionally for a specific user and/ or key
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.. py:function:: gds.config.defaults.set(key: str, value: Any, username: Optional[str] = None) -> None
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Set a default; global by, default, but also optionally for a specific user
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.. py:function:: gds.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> DataFrame
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.. py:function:: gds.config.limits.list(key: Optional[str] = None, username: Optional[str] = None) -> pandas.DataFrame
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List limits; global by default, but also optionally for a specific user and/ or key
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.. deprecated:: 2.5.0
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Since GDS server version 2.5.0 you should use the endpoint :func:`gds.systemMonitor` instead.
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.. py:function:: gds.alpha.userLog() -> DataFrame
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.. py:function:: gds.alpha.userLog() -> pandas.DataFrame
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Log warnings and hints for currently running tasks.
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.. deprecated:: 2.5.0
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Since GDS server version 2.5.0 you should use the endpoint :func:`gds.userLog` instead.
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.. py:function:: gds.beta.listProgress(job_id: Optional[str] = None) -> DataFrame
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.. py:function:: gds.beta.listProgress(job_id: Optional[str] = None) -> pandas.DataFrame
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List progress events for currently running tasks.
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.. deprecated:: 2.5.0
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Since GDS server version 2.5.0 you should use the endpoint :func:`gds.listProgress` instead.
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Since GDS server version 2.5.0 you should use the endpoint :func:`gds.listProgress` instead.
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.. py:function:: gds.systemMonitor() -> Series[Any]
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Get an overview of the system's workload and available resources
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.. py:function:: gds.listProgress(job_id: Optional[str] = None) -> DataFrame
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.. py:function:: gds.listProgress(job_id: Optional[str] = None) -> pandas.DataFrame
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List progress events for currently running tasks.
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Return True if the graph data science library is licensed.
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.. py:function:: gds.userLog() -> DataFrame
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.. py:function:: gds.userLog() -> pandas.DataFrame
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Log warnings and hints for currently running tasks.

‎doc/sphinx/source/ml.rst‎

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@@ -9,241 +9,241 @@ These all assume that an object of :class:`.GraphDataScience` is available as `g
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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) -> pandas.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.ml.splitRelationships.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
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.. py:function:: gds.alpha.ml.splitRelationships.mutate.estimate(G: Graph, **config: Any) -> pandas.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.pipeline.nodeRegression.create(name: str) -> Tuple[NRTrainingPipeline, Series[Any]]
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.. py:function:: gds.alpha.pipeline.nodeRegression.create(name: str) -> Tuple[NRTrainingPipeline, pandas.Series[Any]]
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Creates a node regression training pipeline in the pipeline catalog.
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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) -> pandas.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) -> pandas.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.stream(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.beta.graphSage.stream(G: Graph, **config: Any) -> pandas.DataFrame
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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) -> pandas.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, pandas.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) -> pandas.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) -> pandas.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) -> pandas.Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. deprecated:: 2.5.0
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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) -> pandas.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) -> pandas.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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.. py:function:: gds.beta.hashgnn.stream(G: Graph, **config: Any) -> pandas.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) -> pandas.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) -> pandas.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) -> pandas.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.stream(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.beta.node2vec.stream(G: Graph, **config: Any) -> pandas.DataFrame
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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) -> pandas.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) -> pandas.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) -> pandas.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]) -> pandas.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) -> pandas.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.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> DataFrame
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.. py:function:: gds.beta.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> pandas.DataFrame
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Lists all pipelines contained in the pipeline catalog.
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.. py:function:: gds.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> Series[Any]
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.. py:function:: gds.pipeline.drop(pipeline: TrainingPipeline[PipelineModel]) -> pandas.Series[Any]
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Drops a pipeline and frees up the resources it occupies.
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.. py:function:: gds.pipeline.exists(pipeline_name: str) -> pandas.Series[Any]
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Checks if a given pipeline exists in the pipeline catalog.
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.. py:function:: gds.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> DataFrame
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.. py:function:: gds.pipeline.list(pipeline: Optional[TrainingPipeline[PipelineModel]] = None) -> pandas.DataFrame
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Lists all pipelines contained 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, pandas.Series[Any]]
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Creates a link prediction pipeline 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, pandas.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) -> pandas.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) -> pandas.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) -> pandas.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) -> pandas.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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.. py:function:: gds.fastRP.stream(G: Graph, **config: Any) -> pandas.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) -> pandas.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) -> pandas.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) -> pandas.Series[Any]
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Random Projection produces node embeddings via the fastrp algorithm
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Return a list of selected values in a one hot encoding format.
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.. py:function:: gds.hashgnn.mutate(G: Graph, **config: Any) -> pandas.Series[Any]
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.hashgnn.mutate.estimate(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.hashgnn.mutate.estimate(G: Graph, **config: Any) -> pandas.DataFrame
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.hashgnn.stream(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.hashgnn.stream(G: Graph, **config: Any) -> pandas.DataFrame
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.hashgnn.stream.estimate(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.hashgnn.stream.estimate(G: Graph, **config: Any) -> pandas.DataFrame
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.. py:function:: gds.hashgnn.write(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.hashgnn.write(G: Graph, **config: Any) -> pandas.DataFrame
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HashGNN creates node embeddings by hashing and message passing.
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.. py:function:: gds.hashgnn.write.estimate(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.hashgnn.write.estimate(G: Graph, **config: Any) -> pandas.DataFrame
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.node2vec.mutate(G: Graph, **config: Any) -> Series[Any]
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.. py:function:: gds.node2vec.mutate(G: Graph, **config: Any) -> pandas.Series[Any]
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The Node2Vec algorithm computes embeddings for nodes based on random walks.
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.. py:function:: gds.node2vec.mutate.estimate(G: Graph, **config: Any) -> Series[Any]
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.. py:function:: gds.node2vec.mutate.estimate(G: Graph, **config: Any) -> pandas.Series[Any]
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.. py:function:: gds.node2vec.stream(G: Graph, **config: Any) -> DataFrame
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.. py:function:: gds.node2vec.stream(G: Graph, **config: Any) -> pandas.DataFrame
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The Node2Vec algorithm computes embeddings for nodes based on random walks.
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.. py:function:: gds.node2vec.stream.estimate(G: Graph, **config: Any) -> Series[Any]
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.. py:function:: gds.node2vec.stream.estimate(G: Graph, **config: Any) -> pandas.Series[Any]
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Returns an estimation of the memory consumption for that procedure.
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.. py:function:: gds.node2vec.write(G: Graph, **config: Any) -> Series[Any]
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.. py:function:: gds.node2vec.write(G: Graph, **config: Any) -> pandas.Series[Any]
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The Node2Vec algorithm computes embeddings for nodes based on random walks.
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.. py:function:: gds.node2vec.write.estimate(G: Graph, **config: Any) -> Series[Any]
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.. py:function:: gds.node2vec.write.estimate(G: Graph, **config: Any) -> pandas.Series[Any]
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Returns an estimation of the memory consumption for that procedure.

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