Link Prediction Training Pipeline¶

class graphdatascience.pipeline.lp_training_pipeline.LPTrainingPipeline¶

Represents a link prediction training pipeline. Construct an instance of this class using graphdatascience.GraphDataScience.lp_pipe().

addFeature(feature_type: str, **config: Any) → Series[Any]¶

Add a link feature to the pipeline.

Parameters:

feature_type (str) – The type of feature to add.
**config (Any) – The configuration for the feature, this includes the node properties to use.

Returns:

The result of the query.

Return type:

Series[Any]

addLogisticRegression(**config: Any) → Series[Any]¶

Add a logistic regression model candidate to the pipeline.

Parameters:: **config (Any) – The configuration for the logistic regression model.
Returns:: The result of the query.
Return type:: Series[Any]

addMLP(**config: Any) → Series[Any]¶

Add a multi-layer perceptron model candidate to the pipeline.

Parameters:: **config (Any) – The configuration for the multi-layer perceptron 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 model candidate to the pipeline.

Parameters:: **config (Any) – The configuration for the random forest model.
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_steps() → DataFrame¶

Get the feature steps of the pipeline.

Returns:: A DataFrame containing the feature steps of the pipeline.
Return type:: DataFrame

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]

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