Node Regression Training Pipeline

class graphdatascience.pipeline.nr_training_pipeline.NRTrainingPipeline

Represents a node regression training pipeline. Construct an instance of this class using graphdatascience.GraphDataScience.nr_pipe().

addLinearRegression(**config: Any) → Series[Any]

Add a linear regression model candidate to the pipeline.

Parameters:

**config (Any) – The configuration for the linear regression model.

Returns:

The result of the query.

Return type:

Series[Any]

addNodeProperty(procedure_name: str, **config: Any) → Series[Any]

Add a node property step to the pipeline.

Parameters:

• procedure_name (str) – The name of the procedure to use.

• **config (Any) – The configuration for the node property.

Returns:

The result of the query.

Return type:

Series[Any]

addRandomForest(**config: Any) → Series[Any]

Add a random forest regressor candidate to the pipeline.

Parameters:

**config (Any) – The configuration for the random forest regressor.

Returns:

The result of the query.

Return type:

Series[Any]

auto_tuning_config() → Series[Any]

Get the auto-tuning configuration of the pipeline.

Returns:

A Series containing the auto-tuning configuration.

Return type:

Series[Any]

configureAutoTuning(**config: Any) → Series[Any]

Configure auto-tuning for the pipeline.

Parameters:

**config (Any) – The configuration for auto-tuning.

Returns:

The result of the query.

Return type:

Series[Any]

configureSplit(**config: Any) → Series[Any]

Configure the splits for training the pipeline.

Parameters:

**config (Any) – The configuration for the splits.

Returns:

The result of the query.

Return type:

Series[Any]

creation_time() → Any

Get the creation time of the pipeline.

Returns:

The creation time of the pipeline.

Return type:

Any

drop(failIfMissing: bool = False) → Series[Any]

Drop the pipeline.

Parameters:

failIfMissing (bool) – If True, an error will be thrown if the pipeline does not exist.

Returns:

The result of the query.

Return type:

Series[Any]

exists() → bool

Check if the pipeline exists.

Returns:

True if the pipeline exists, False otherwise.

Return type:

bool

feature_properties() → Series[Any]

Get the feature properties of the pipeline.

Returns:

A Series containing the feature properties of the pipeline.

Return type:

Series[Any]

name() → str

Get the name of the pipeline.

Returns:

The name of the pipeline.

Return type:

str

node_property_steps() → DataFrame

Get the node property steps of the pipeline.

Returns:

A DataFrame containing the node property steps.

Return type:

DataFrame

parameter_space() → Series[Any]

Get the parameter space of the pipeline.

Returns:

A Series containing the parameter space.

Return type:

Series[Any]

selectFeatures(node_properties: str | list[str]) → Series[Any]

Select the node properties to use for training.

Parameters:

node_properties (str | list[str]) – The node properties to use for training.

Returns:

The result of the query.

Return type:

Series[Any]

split_config() → Series[float]

Get the split configuration of the pipeline.

Returns:

A Series containing the split configuration.

Return type:

Series[float]

train(G: Graph, **config: Any) → tuple[MODEL_TYPE, Series[Any]]

Train a model on a given graph using the pipeline.

Parameters:

• G (Graph) – The graph to train on.

• **config (Any) – The configuration for training.

Returns:

A tuple containing the trained model and the result of the query.

Return type:

tuple[MODEL_TYPE, Series[Any]]

train_estimate(G: Graph, **config: Any) → Series[Any]

Estimate the training time for a given graph and configuration.

Parameters:

• G (Graph) – The graph to train on.

• **config (Any) – The configuration for training.

Returns:

The result of the query.

Return type:

Series[Any]

type() → str

Get the type of the pipeline.

Returns:

The type of the pipeline. It will be one of NodeClassificationPipeline, LinkPredictionPipeline, or NodeRegressionPipeline.

Return type:

str